The intellectual property of deep generative networks (GNets) can be protected using a cascaded hiding network (HNet) which embeds watermarks (or marks) into GNet outputs, known as box-free watermarking. Although both GNet and HNet are encapsulated in a black box (called operation network, or ONet), with only the generated and marked outputs from HNet being released to end users and deemed secure, in this paper, we reveal an overlooked vulnerability in such systems. Specifically, we show that the hidden GNet outputs can still be reliably estimated via query-based reverse engineering, leaking the generated and unmarked images, despite the attacker's limited knowledge of the system. Our first attempt is to reverse-engineer an inverse model for HNet under the stringent black-box condition, for which we propose to exploit the query process with specially curated input images. While effective, this method yields unsatisfactory image quality. To improve this, we subsequently propose an alternative method leveraging the equivalent additive property of box-free model watermarking and reverse-engineering a forward surrogate model of HNet, with better image quality preservation. Extensive experimental results on image processing and image generation tasks demonstrate that both attacks achieve impressive watermark removal success rates (100%) while also maintaining excellent image quality (reaching the highest PSNR of 34.69 dB), substantially outperforming existing attacks, highlighting the urgent need for robust defensive strategies to mitigate the identified vulnerability in box-free model watermarking.

Multi-server federated learning (FL) has emerged as a promising paradigm to alleviate the communication bottlenecks of single-server FL by exploiting edge-level aggregation. In realistic dense deployments, the coverage regions of neighboring edge servers (ESs) often overlap, enabling some clients to communicate with multiple ESs. However, existing multi-server FL schemes fail to fully leverage such overlapping clients for efficient inter-server collaboration, leading to excessive reliance on cloud aggregation and slow convergence under heterogeneous data distributions. To address this challenge, we propose FedOC, a novel multi-server FL framework that explicitly exploits overlapping clients to accelerate training. In FedOC, overlapping clients can serve as relay overlapping clients (ROCs) to enable real-time edge-to-edge model relaying, or as normal overlapping clients (NOCs) that dynamically select edge models for local training based on delivery latency, thereby facilitating indirect data fusion across ESs. By integrating decentralized inter-ES aggregation with latency-aware client training, FedOC significantly reduces the need for frequent cloud aggregation while improving training efficiency. Extensive experiments on MNIST, Fashion-MNIST and CIFAR-10 demonstrate that FedOC achieves substantially faster convergence and higher final accuracy than other baselines. In particular, under strong data heterogeneity and cloud-free aggregation, FedOC improves the final test accuracy by approximately 3%–25% on these datasets compared with other baselines, while significantly reducing the latency required to reach a target accuracy.

Multi-server Federated Learning (FL) has emerged as a promising approach to alleviate the communication bottle-necks of traditional single-server FL. In practical deployments, the coverage areas of different edge servers (ESs) may overlap, allowing clients in overlapping regions to access models from multiple ESs. Leveraging this observation, we enable overlapping clients (OCs) to relay edge models between neighboring ESs and dynamically select suitable models for local training, facilitating multi-hop model propagation across ESs without relying on frequent cloud aggregation. This design significantly reduces communication latency while improving training efficiency. We derive a convergence upper bound for the above OCs-based FL framework, which explicitly quantifies the impact of inter-server propagation on convergence error. Guided by this theoretical result, we formulate an optimization problem that aims to maximize dissemination range of each ES model among all ESs by OCs within a limited latency. To solve this problem, we develop a conflict-graph-based local search algorithm optimizing the routing strategy and scheduling the transmission times of individual ESs to its neighboring ESs. By integrating decentralized inter-ES aggregation with latency-aware client training, our proposed algorithm significantly reduces the need for frequent cloud aggregation while improving training efficiency. Extensive experimental results show remarkable performance gains of our scheme compared to existing state-of-the-art methods.

Integrated sensing and communication (ISAC) technology endows users with environmental awareness capabilities, which will play a crucial role in future mobile edge computing (MEC) systems. In this paper, we consider the design of sensingassisted beam alignment and investigate resource management for a task-oriented mmWave integrated sensing, communication, and computing (ISCC) system, which can be formulated as a preference-agnostic multi-objective optimization problem. To solve this problem, we first introduce a multi-objective Markov decision process (MOMDP) to reformulate the original problem and innovatively propose a preference-agnostic multi-objective soft actor-critic (PA-MOSAC) algorithm. To demonstrate the effectiveness of our proposed system architecture and resource management algorithm, we also introduce a traditional mmWave MEC (T-MEC) system based on the same set of system parameters as a benchmark. The proximal policy optimization (PPO) algorithm, known for its robustness, is used to address resource management in the T-MEC system. Through a comprehensive comparative analysis of the two systems and algorithms, we discover that our proposed sensing-assisted beam alignment can reduce task execution delay by 25% with only 1% increase in energy consumption. We also verify the convergence of our proposed PA-MOSAC algorithm and demonstrate its superior performance over the benchmark scheme.

Information security is a non-negligible issue for wireless transmission. Covert communication provides high security by concealing the transmitted signals within environmental noise. However, existing solutions suffer from low transmission efficiency. Ambient backscatter, concealing data within ubiquitous ambient signals, provides a promising way to achieve high-efficiency covert communication. In this paper, we propose CoScatter, an efficient covert transmission system based on OFDM WiFi backscatter. Current studies rely on redundant modulation, resulting in low throughput. This paper is to increase throughput and shorten transmission time, thereby reducing exposure risk. This is the first work to realize single-sample level demodulation, efficiently eliminating the redundancy, increasing the throughput, and reducing the transmission time. We discover that the main obstacles are the additional phase offsets introduced by three independent wireless channels in backscatter systems. Based on this, we design a new backscatter channel equalization procedure to remove the channel influences while preserving all the covert information embedded by the tag, realizing an efficient covert transmission. Evaluation results show that Coscatter achieves a throughput exceeding 15.7 Mbps, which is around 64x of that of RapidRider, and 16x of that of Tscatter. Consequently, the exposure risk of CoScatter is reduced to 1/64 of that of RapidRider and 1/16 of that of Tscatter.

Box-free model watermarking is an emerging technique to safeguard the intellectual property of deep learning models, particularly those for low-level image processing tasks. Existing works have verified and improved its effectiveness in several aspects. However, in this paper, we systematically investigate the vulnerability and demonstrate that box-free model watermarking is prone to removal attacks, even under the real-world threat model such that the protected model and the watermark extractor are in black boxes. Under this setting, we carry out three studies. 1) We develop an extractor-gradient-guided (EGG) remover and show its effectiveness when the extractor uses ReLU activation only. 2) More generally, for an unknown extractor, we leverage adversarial attacks and design the EGG remover based on the estimated gradients. 3) Under the most stringent condition that the extractor is inaccessible, we design a transferable remover based on a set of private proxy models. In all cases, the proposed removers can successfully remove embedded watermarks while preserving the quality of the processed images, and we also demonstrate that the EGG remover can even replace the watermarks. Extensive experimental results verify the effectiveness and generalizability of the proposed attacks, revealing the vulnerabilities of the existing box-free methods and calling for further research.

Box-free model watermarking has gained significant attention in deep neural network (DNN) intellectual property protection due to its model-agnostic nature and its ability to flexibly manage high-entropy image outputs from generative models. Typically operating in a black-box manner, it employs an encoder-decoder framework for watermark embedding and extraction. While existing research has focused primarily on the encoders for the robustness to resist various attacks, the decoders have been largely overlooked, leading to attacks against the watermark. In this paper, we identify one such attack against the decoder, where query responses are utilized to obtain backpropagated gradients to train a watermark remover. To address this issue, we propose Decoder Gradient Shields (DGSs), a family of defense mechanisms, including DGS at the output (DGS-O), at the input (DGS-I), and in the layers (DGS-L) of the decoder, with a closed-form solution for DGS-O and provable performance for all DGS. Leveraging the joint design of reorienting and rescaling of the gradients from watermark channel gradient leaking queries, the proposed DGSs effectively prevent the watermark remover from achieving training convergence to the desired low-loss value, while preserving image quality of the decoder output. We demonstrate the effectiveness of our proposed DGSs in diverse application scenarios. Our experimental results on deraining and image generation tasks with the state-of-the-art box-free watermarking show that our DGSs achieve a defense success rate of 100% under all settings.

Deep learning technologies have seen widespread adoption in WiFi-based wireless sensing systems. However, they are inherently vulnerable to adversarial perturbation attacks, which has received little attention within the WiFi sensing community. To more comprehensively understand the potential threats posed by perturbation attacks, we present a novel attack method, named WiIntruder, distinguishing itself with universality, robustness, and stealthiness. This paper intends to provide a catalyst that promotes the assessment of security in existing WiFi-based sensing systems. We achieve the three aforementioned salient features in WiIntruder through the following three steps: (1) Maximizing transferability by differentiating user-state-specific feature spaces across sensing models, thereby enabling a universal perturbation attack vector applicable to a wide range of applications; (2) Mitigating the impact of perturbation signal distortion by optimizing key factors of device synchronization and wireless propagation through a heuristic particle swarm algorithm; and (3) Enhancing the diversity and stealthiness of attack patterns by randomly switching among perturbation surrogates generated by a generative adversarial network. Experimental results confirm the threat posed by WiIntruder to four common WiFi-based services, with the average accuracy decrease by 72.9% under black-box attack scenarios.

Collaborative perception significantly enhances autonomous driving safety by extending each vehicle's perception range through message sharing among connected and autonomous vehicles. Unfortunately, it is also vulnerable to adversarial message attacks from malicious agents, resulting in severe performance degradation. While existing defenses employ hypothesis-and-verification frameworks to detect malicious agents based on single-shot outliers, they overlook temporal message correlations, which can be circumvented by subtle yet harmful perturbations in model input and output spaces. This paper reveals a novel blind area confusion (BAC) attack that compromises existing single-shot outlier-based detection methods. As a countermeasure, we propose GCP, a Guarded Collaborative Perception framework based on spatial-temporal aware malicious agent detection, which maintains single-shot spatial consistency through a confidence-scaled spatial concordance loss, while simultaneously examining temporal anomalies by reconstructing historical bird's eye view motion flows in low-confidence regions. Wealso employ a joint spatial-temporal Benjamini-Hochberg test to synthesize dual-domain anomaly results for reliable malicious agent detection. Extensive experiments demonstrate GCP's superior performance under diverse attack scenarios, achieving up to 34.69% improvements in [email protected] compared to the state-of-the art CP defense strategies under BAC attacks, while maintaining consistent 5-8% improvements under other typical attacks. Code will be released at https://github.com/yihangtao/GCP.git.

Robot vacuum cleaners (RVCs) with autonomous navigation and decision-making capabilities have become an integral part of modern homes. During their operations, these devices may inadvertently enter privacy-sensitive areas, leading to potential privacy breaches. However, existing defense methods risk exposing the location of private areas, require root privileges, or are designed for infrared sensors that are ineffective for camera-based RVCs. To overcome these limitations, we propose a novel solution, a ground-view adversarial patch named GPatch, preventing RVCs from entering privacy-sensitive areas. Users only need to place GPatch at the entrance of restricted areas to prevent an RVC's unauthorized access, while also providing a warning to unauthorized individuals. We evaluate GPatch in realworld environments with an average success rate of 87.27%, and experimental results demonstrate its effectiveness, robustness, and transferability, making it a practical, user-friendly, and reliable solution for safeguarding privacy in home environments.

This paper presents an innovative framework that boosts computing power by utilizing ubiquitous computing power distribution and enabling higher computing node accessibility via adaptive UAV positioning, establishing a UAV-enabled Computing Power Network (UAV-CPN). In a UAV-CPN, a UAV functions as a dynamic relay, outsourcing computing tasks from the request zone to an expanded service zone with diverse computing nodes, including vehicle onboard units, edge servers, and dedicated powerful nodes. This approach has the potential to alleviate communication bottlenecks and overcome the "island effect" observed in multi-access edge computing. A significant challenge is to quantify computing power performance under complex dynamics of communication and computing. To address this challenge, we introduce task completion probability to capture the capability of UAV-CPNs for task computing. We further enhance UAV-CPN performance under a hybrid energy architecture by jointly optimizing UAV altitude and transmit power, where fuel cells and batteries collectively power both UAV propulsion and communication systems. Extensive evaluations show significant performance gains, highlighting the importance of balancing communication and computing capabilities, especially under dual-energy constraints. These findings underscore the potential of UAV-CPNs to significantly boost computing power.

Recently, there has been a surge in the development of advanced intelligent generative content (AIGC), especially large language models (LLMs). For many downstream tasks, it is necessary to fine-tune LLMs using private data. While federated learning offers a promising privacy-preserving solution to LLM fine-tuning, the substantial size of an LLM, combined with high computational and communication demands, makes it hard to apply to handle downstream tasks. More importantly, private edge servers often possess varying computing and network resources in real-world scenarios, introducing additional complexities to LLM fine-tuning. To tackle these problems, we design and implement an automated federated pipeline, named, to fine-tune LLMs on heterogeneous edge servers with minimal training cost and no additional inference latency. firstly identifies the weights to be fine-tuned based on their contributions to the LLM training. It then configures a low-rank adapter for each selected weight within the resource constraints of the edge server and aggregates these local adapters from all edge servers to fine-tune the whole LLM. Finally, it appropriately quantizes the parameters of LLM to reduce memory consumption according to the requirements of edge servers. Extensive experiments demonstrate that expedites model training and achieves higher accuracy than the state-of-the-art benchmarks.

Collaborative Perception (CP) has been shown to be a promising technique for multi-agent autonomous driving and multi-agent robotic systems, where multiple agents share their perception information to enhance the overall perception performance and expand the perception range. However, in CP, an ego agent needs to receive messages from its collaborators, which makes it vulnerable to attacks from malicious agents. To address this critical issue, we propose a unified, probability-agnostic, and adaptive framework, namely, CP-uniGuard, which is a tailored defense mechanism for CP deployed by each agent to accurately detect and eliminate malicious agents in its collaboration network. Our key idea is to enable CP to reach a consensus rather than a conflict against an ego agent's perception results. Based on this idea, we first develop a probability-agnostic sample consensus (PASAC) method to effectively sample a subset of the collaborators and verify the consensus without prior probabilities of malicious agents. Furthermore, we define collaborative consistency loss (CCLoss) for object detection task and bird's eye view (BEV) segmentation task to capture the discrepancy between an ego agent and its collaborators, which is used as a verification criterion for consensus. In addition, we propose online adaptive threshold via dual sliding windows to dynamically adjust the threshold for consensus verification and ensure the reliability of the systems in dynamic environments. Finally, we conduct extensive experiments and demonstrate the effectiveness of our framework.

Sporadic ambient radio frequency signals can offer opportunistic spectrum and energy sources for backscatter communications, but they also induce unpredictable transmission interruptions in ambient backscatter wireless networks (AmBWNs). Integrating self-carrier-generative active transmissions with backscatter communications could significantly enhance transmission stability but require frequent mode switching to accommodate the ever-changing ambient radio frequency signals. However, this will result in frequent changes in network topology and link capacity, posing significant challenges in solving the network maximum flow problem in hybrid AmBWNs. To address this problem, we design a link-aware online maximum flow (OnMAXFlow) scheme to tackle agile and adaptive flow scheduling and communication mode selection. Specifically, we first employ an online learning framework to dynamically track changes in ambient signal strength and channel states, enabling real-time evaluation of link capacity. We then model the network maximum flow problem as a stochastic multi-armed bandit (MAB) problem and solve it with a Kullback-Leibler upper confidence bound (KL-UCB) algorithm. Our experimental evaluation results reveal that our OnMAXFlow scheme exhibits rapid convergence and superior adaptability against the varying network states, while maintaining spectrum efficiency and latency performance comparable to the Oracle scheme, which always selects the optimal transmission modes and paths.

The demand for ultra-low-power wireless connectivity motivates the study of backscatter communication technology. There are already some related products on the market based on excitation signals from commercial radios. However, their modulation techniques, which are the key to backscatter communications, mainly focus on the phase or frequency domain while amplitude modulation is largely ignored. Most research works either deploy one finely tuned RF impedance port for every needed reflection state or connect a nonlinear device to antenna and tune the reflection amplitude by adjusting its biasing voltage, where the former is too complex and the latter is unstable under variable incident signal power, limiting the backscatter applications. For this reason, we introduce AMscatter to leverage the duty ratio of the frequency-shift wave (FS-wave) to design a novel amplitude modulation. Both theoretical analysis and experimental results show that the reflection amplitude approximates a sinusoidal function of the duty ratio. This method requires only two fixed RF impedances, making AMscatter simple and stable. Moreover, we show how to use AMscatter to design quadrature amplitude modulation (QAM) and pulse shaping to improve backscatter communication performance. Extensive experimental results show that the throughput can be as high as 3.9 Mbps, the supported operational range can reach 20 m with 16-QAM modulation, and the out-of-band interference can be suppressed by 15 dB without negatively affecting the communication performance through our pulse shaping.

Multiple-input multiple-output wireless sensing systems achieve high-resolution detection through spatial diversity. However, they suffer from reliability degradation under low signal-to-noise ratio (SNR) conditions. Conventional matrix-based methods may discard critical target information embedded in inter-dimensional signal correlations due to dimension-reduction flattening operations. To overcome these limitations, this paper proposes a tensorial target detection (TTD) framework combining noise reduction and enhanced detection specifically designed for high-dimensional processing. Firstly, we propose a two-stage tensorial noise reduction (TNR) method based on the minimum mean square error criterion and the alternating least square iteration strategy to remove noise in high-order signal space. We further identify the sub-optimal performance caused by inter-dimensional noise coupling at the first stage of TNR, and resolve the issue via rank-constrained optimization for noise-target subspace separation at the second stage of TNR. Then, we develop an augmented tensorial detector based on cross-shaped spatial partitioning (CSP) to enhance detection performance, which jointly optimizes detection thresholds by adaptively refining noise estimation. Finally, field measurements confirm the TTD framework's operational validity, while in simulation the TNR method achieves 5 dB SNR improvement over 2D method, and the CSP-based detector delivers a 21% enhancement in detection probability over conventional approaches.

WiFi-based human activity recognition (HAR) has demonstrated significant potential in diverse intelligent applications. However, sensitive to environmental factors, cross-domain WiFi channel state information (CSI) poses significant challenges in the generalization of HAR models across different environments. In this paper, the structural causal model (SCM) is introduced to model causal relationships among activities, latent variables, and CSI data, laying a solid foundation toward developing a domain-invariant model for WiFi-based HAR tasks. In this paper, we propose CausalFi, a novel framework that leverages causal inference to mitigate the confounding effects of latent variables, significantly improving generalization performance. Integrating novel feature selection and importance sampling algorithms as the condition and intervention operations, CausalFi can effectively identify the stable action-relevant features from WiFi CSI for activity recognition. Furthermore, a novel counterfactual style augmentation approach is proposed to increase the stylistic diversity of the training data, reducing the risk of biased data distributions even with limited training samples in source domains. We implement a prototype of CausalFi using commercial ASUS RT-AC86U WiFi devices and conduct extensive cross-domain experiments to validate the effectiveness of the proposed approach. With an average recognition accuracy of 92.6%, CausalFi significantly outperforms state-of-the-art baselines in complex cross-domain environments, confirming the practicality of our framework for real-world WiFi-based HAR applications. © 2026 IEEE.

Throughput is a critical performance metric in backscatter communication systems. Existing approaches either suffer from limited throughput or necessitate modifications to transmitters or receivers, leading to incompatibility with commodity radios. In this paper, we introduce SubScatter, a system that achieves both high throughput and excellent compatibility. It employs a single Complementary Code Keying (CCK)-modulated 802.11b WiFi symbol to transmit eight tag bits by manipulating the phase of the backscattered signal across eight discrete time slots within the symbol, thereby enhancing throughput. To ensure compatibility with commercial-off-the-shelf (COTS) radios, SubScatter exclusively utilizes the physical service data unit (PSDU) for recovering the backscatter modulation that conveys the tag bits. Additionally, SubScatter employs real-time Hamming distance calculations to synchronize the binary envelope from the synchronization circuit with a reference sequence, facilitating the sub-symbol backscatter modulation. In addition, we emphasize SubScatter’s versatility in adapting its modulation scheme to optimize performance across various channel conditions. Extensive experiments conducted with our prototype validate its effectiveness, achieving a throughput approximately 11 times higher than that of leading backscatter systems compatible with COTS radios. Our Hamming-distance-based synchronization method outperforms conventional designs that rely solely on signal power detection, successfully reducing the bit error rate (BER) from over 10% to below 1%. Moreover, SubScatter’s throughput can be flexibly adjusted from 11 Mbps to 1.57 Mbps, while the BER improves from 0.29% to 0.04%.

With the proliferation of software-defined radio technology, malicious jamming attacks against wireless communications have become more aggressive and flexible, which could easily create a complex and highly dynamic jamming environment by varying both the jamming parameters and the jamming policies. Such a complex jamming environment makes it challenging for most of deep reinforcement learning (DRL) based anti-jamming schemes in rapidly identifying effective strategies. In this paper, we have developed a dual-tier policy-oriented anti-jamming (DPA) scheme based on DRL to facilitate swift adaptation to the complex jamming environment. Unlike existing works, an upper-tier jamming pattern recognition (JPR) network is introduced to extract underlying jamming policy-related information which serves as a guidance for the lower-tier deep recurrent Q-network on anti-jamming decision-making. The output of the JPR network can enable the sharing of experiences among various jamming patterns originated from the same jamming policy and facilitate more efficient and targeted anti-jamming strategic learning. Extensive experimental results demonstrate that the superiority of our DPA scheme over other DRL-based benchmark schemes in terms of both anti-jamming performance and convergence speed.

The implementation of deep learning-based automatic modulation recognition (AMR) on resource-constrained edge devices calls for efficient networks. Unfortunately, existing AMR networks fail to balance parameter scale, inference speed, and recognition accuracy, which hinders their edge applications. In view of this, we propose an expert-assistant network for efficient AMR with small parameter scale and inference time. To exploit the advantages of both convolutional neural networks (CNNs) and recurrent neural networks (RNNs) for parameter scale reduction, we build a lightweight AMR network with a CNN-RNN hybrid architecture. Given the speed-and-accuracy dilemma faced by existing CNN-RNN hybrid networks, we introduce an small-scale plug-in, called the assistants, as well as a temporal shuffling scheme to enable fast and accurate AMR with small parameter scales. Besides, techniques like non-recurrent dropout for the gated recurrent unit (GRU) layer, parameter estimator and transformer (PET) and model pruning are applied to further enhance the performance of the networks. Extensive experimental results demonstrate that the proposed Expert-Assistant (E-A) network achieves the best comprehensive performance on lightweight, computational efficiency and recognition accuracy. Our model performs especially well with extremely low parameters, which achieves an average accuracy near 60% and a highest accuracy near 90% with just 3.5K non-zero parameters on RML2016.10a.

Improving transmission efficiency is a key objective in OFDM WiFi backscatter systems. A promising direction is sub-symbol-level tag modulation, which embeds more tag data within each OFDM symbol. However, we observe that fine-grained tag modulation is coupled with channel variation, which distorts the cascade structure between the two channels, transmitter-to-tag and tag-to-receiver, making the conventional channel estimation method in WiFi ineffective. Although recent systems have explored new channel estimation methods, their accuracy is limited and the modulation redundancy remains necessary. To address this problem, we present Fascatter, a high-throughput OFDM WiFi backscatter system that enables single-sample-level tag modulation without modulation redundancy. The key enabler is a new channel estimation method that independently estimates the two channels at per-subcarrier granularity. We construct channel observations from the LTF fields and reference symbols, and accurately solve the two channels through matrix decomposition. We further introduce polynomial smoothing and multi-symbol fine-tuning modules to improve estimation robustness. Experimental results demonstrate that the channel estimation results are close to the actual channel responses, and our method shows robust performance under a variety of complex channel conditions. In particular, Fascatter achieves a throughput of up to 15.9 Mbps, which is at least 3.2× over state-of-the-art systems. © 2026 IEEE.

Due to the innovative application of metamaterials, holographic multiple-input multiple-output (H-MIMO) is expected to achieve a higher spatial diversity gain with lower hardware complexity. Together with the aid of a circular antenna arrangement in H-MIMO, integrated data and energy multicast (IDEM) can fully exploit the near-field channel to realize wider range of energy focusing and higher achievable rate. In this paper, we focus on beamforming design and investigate the IDEM systems that maximize the minimum rate of data users (DUs) while meeting the energy harvesting requirements for energy users (EUs). Specifically, we first derive the closed-form near-field resolution function in 3D space and show the asymptotic spatial orthogonality of near-field channel for circular antenna arrays. Then, we design an asymptotically optimal fully-digital beamformer based on the spatial orthogonality. After that, we apply the alternating optimization to develop H-MIMO beamforming scheme, where the digital beamformer is given in closed form while the analog beamformers of three different control modes are obtained numerically, respectively. Scaling schemes are also investigated to further improve the IDEM performance. Numerical results verify the correctness of the resolution function and asymptotic orthogonality and demonstrate that the proposed beamforming schemes outperform benchmark schemes, with very low complexity.

Given the limited computing capabilities on autonomous vehicles, onboard processing of large volumes of latency-sensitive tasks presents significant challenges. While vehicular edge computing (VEC) has emerged as a solution, offloading data-intensive tasks to roadside servers or other vehicles faces communication-computing bottleneck, such as signal blockage from other large vehicles and limited computing resources of roadside servers. To address these challenges, Reconfigurable Intelligent Surface (RIS) can be leveraged to create line-of-sight channels, mitigate interference on the ground, and extend connectivity to more edge servers by elevating RIS adaptively. To this end, we propose RAISE, an optimization framework for RIS placement in multi-server VEC systems. Specifically, RAISE optimizes RIS altitude and tilt angle together with the optimal task assignment to maximize task throughput under deadline constraints. To find a solution, a two-layer optimization approach is proposed, where the inner layer exploits the unimodularity of the task assignment problem to derive the efficient optimal strategy while the outer layer develops a near-optimal hill climbing (HC) algorithm for RIS placement with low complexity. Extensive experiments demonstrate that the proposed RAISE framework consistently outperforms existing benchmarks.

Collaborative Perception (CP) has shown a promising technique for autonomous driving, where multiple connected and autonomous vehicles (CAVs) share their perception information to enhance the overall perception performance and expand the perception range. However, in CP, ego CAV needs to receive messages from the collaborators, which makes it easy to be attacked by malicious agents. For example, a malicious agent can send harmful information to the ego CAV to mislead it. To address this critical issue, we propose a novel method, **CP-Guard**, a tailored defense mechanism for CP that can be deployed by each agent to accurately detect and eliminate malicious agents in its collaboration network. Our key idea is that CP will lead to a consensus rather than a conflict against the ego CAV's perception results. Based on this idea, we first develop a probability-agnostic sample consensus (PASAC) method that can effectively sample a subset of the collaborators and verify the consensus without prior probabilities of malicious agents. Furthermore, we design a collaborative consistency loss (CCLoss) to calculate the discrepancy between the ego CAV and the collaborators, which is used as a verification criterion for consensus. Finally, we conduct extensive experiments in collaborative bird's eye view (BEV) tasks and the results demonstrate the effectiveness of our CP-Guard.

The intellectual property of deep image-to-image models can be protected by the so-called box-free watermarking. It uses an encoder and a decoder, respectively, to embed into and extract from the model’s output images invisible copyright marks. Prior works have improved watermark robustness, focusing on the design of better watermark encoders. In this paper, we reveal an overlooked vulnerability of the unprotected watermark decoder which is jointly trained with the encoder and can be exploited to train a watermark removal network. To defend against such an attack, we propose the decoder gradient shield (DGS) as a protection layer in the decoder API to prevent gradient-based watermark removal with a closed-form solution. The fundamental idea is inspired by the classical adversarial attack, but is utilized for the first time as a defensive mechanism in the box-free model watermarking. We then demonstrate that DGS can reorient and rescale the gradient directions of watermarked queries and stop the watermark remover’s training loss from converging to the level without DGS, while retaining decoder output image quality. Experimental results verify the effectiveness of the proposed method. Code of paper is available at https://github.com/haonanAN309/CVPR-2025-Official-Implementation-Decoder-Gradient-Shield.

This paper presents an innovative framework that synergistically enhances computing performance through ubiquitous computing power distribution and dynamic computing node accessibility control via adaptive unmanned aerial vehicle (UAV) positioning, establishing UAV-enabled Computing Power Networks (UAV-CPNs). In UAV-CPNs, UAVs function as dynamic aerial relays, outsourcing tasks generated in the request zone to an expanded service zone, consisting of a diverse range of computing devices, from vehicles with onboard computational capabilities and edge servers to dedicated computing nodes. This approach has the potential to alleviate communication bottlenecks in traditional computing power networks and overcome the "island effect" observed in multi-access edge computing. However, how to quantify the network performance under the complex spatio-temporal dynamics of both communication and computing power is a significant challenge, which introduces intricacies beyond those found in conventional networks. To address this, in this paper, we introduce task completion probability as the primary performance metric for evaluating the ability of UAV-CPNs to complete ground users’ tasks within specified end-to-end latency requirements. Utilizing theories from stochastic processes and stochastic geometry, we derive analytical expressions that facilitate the assessment of this metric. Our numerical results emphasize that striking a delicate balance between communication and computational capabilities is essential for enhancing the performance of UAV-CPNs. Moreover, our findings show significant performance gains from the widespread distribution of computing nodes.

To support the development of the Low Altitude Economy (LAE), it is essential to achieve precise localization of unmanned aerial vehicles (UAVs) in urban areas where global positioning system (GPS) signals are unavailable. Vision-based methods offer a viable alternative but face severe bandwidth, memory and processing constraints on lightweight UAVs. Inspired by mammalian spatial cognition, we propose a task-oriented communication framework, where UAVs equipped with multi-camera systems extract compact multi-view features and offload localization tasks to edge servers. We introduce the Orthogonally-constrained Variational Information Bottleneck encoder (O-VIB), which incorporates automatic relevance determination (ARD) to prune non-informative features while enforcing orthogonality to minimize redundancy. This enables efficient and accurate localization with minimal transmission cost. Extensive evaluation on a dedicated LAE UAV dataset shows that O-VIB achieves high-precision localization under stringent bandwidth budgets. Code and dataset will be made publicly available: github.com/fangzr/TOC-Edge-Aerial.

Large language models (LLMs) have achieved remarkable success in various tasks, such as decision-making, reasoning, and question answering. They have been widely used in edge devices. However, fine-tuning LLMs to specific tasks at the edge is challenging due to the high computational cost and the limited storage and energy resources at the edge. To address this issue, we propose TaskEdge, a task-aware parameter-efficient fine-tuning framework at the edge, which allocates the most effective parameters to the target task and only updates the task-specific parameters. Specifically, we first design a parameter importance calculation criterion that incorporates both weights and input activations into the computation of weight importance. Then, we propose a model-agnostic task-specific parameter allocation algorithm to ensure that task-specific parameters are distributed evenly across the model, rather than being concentrated in specific regions. In doing so, TaskEdge can significantly reduce the computational cost and memory usage while maintaining performance on the target downstream tasks by updating less than 0.1% of the parameters. In addition, TaskEdge can be easily integrated with structured sparsity to enable acceleration by NVIDIA’s specialized sparse tensor cores, and it can be seamlessly integrated with LoRA to enable efficient sparse low-rank adaptation. Extensive experiments on various tasks demonstrate the effectiveness of TaskEdge.

Collaborative perception (CP) leverages visual data from connected and autonomous vehicles (CAV) to expand an ego vehicle's field of view (FoV). Despite recent progress, current CP methods do expand the ego vehicle's 360-degree perceptual range almost equally, but faces two key challenges. Firstly, in areas with uneven traffic distribution, focusing on directions with little traffic offers limited benefits. Secondly, under limited communication budgets, allocating excessive bandwidth to less critical directions lowers the perception accuracy in more vital areas. To address these issues, we propose Directed-CP, a proactive and direction-aware CP system aiming at improving CP in specific directions. Our key idea is to enable an ego vehicle to proactively signal its interested directions and readjust its attention to enhance local directional CP performance. To achieve this, we first propose an RSU-aided direction masking mechanism that assists an ego vehicle in identifying vital directions. Additionally, we design a direction-aware selective attention module to wisely aggregate pertinent features based on ego vehicle's directional priorities, communication budget, and the positional data of CAVs. Moreover, we introduce a direction-weighted detection loss (DWLoss) to capture the divergence between directional CP outcomes and the ground truth, facilitating effective model training. Extensive experiments on the V2X-Sim 2.0 dataset demonstrate that our approach achieves 19.8% higher local perception accuracy in interested directions and 2.5% higher overall perception accuracy than the state-of-the-art methods in collaborative 3D object detection tasks.

Millimeter-wave (mmWave) has emerged as a promising solution for contact-free sensing due to its high resolution. Although promising, it faces several critical issues, including occlusion from the surrounding environment, unstable orientation-dependent sensing performance, and significant interference when multiple targets are in close proximity. These fundamental issues hinder the widespread adoption of mmWave sensing in the real world. In this paper, we propose SeRadar, the first systematic framework that leverages all useful secondary reflections to significantly enhance reliability and bring mmWave sensing one step closer to real-world adoption. Unlike primary reflections commonly used in wireless sensing, secondary reflections—typically much weaker due to being reflected multiple times—are generally ignored in existing literature. However, we observe that secondary reflections are common in various scenarios and carry valuable information about target movements, which could also contribute to sensing. To effectively utilize secondary reflections for sensing, SeRadar addresses several challenges associated with secondary reflections. Specifically, it boosts weak secondary reflections to improve their sensing capability, identifies useful ones from a large number of secondary reflections captured in the environment, and mitigates primary-secondary interference in multi-target scenarios. We evaluate the performance of SeRadar in various environments, including offices, apartments, and vehicle cabins. Extensive experiments demonstrate SeRadar can enhance accuracy and reliability in diverse sensing scenarios.

Maritime object detection is essential for navigation safety, surveillance, and autonomous operations, yet constrained by two key challenges: the scarcity of annotated maritime data and poor generalization across various maritime attributes (e.g., object category, viewpoint, location, and imaging environment). To address these challenges, we propose Neptune-X, a data-centric generative-selection framework that enhances training effectiveness by leveraging synthetic data generation with task-aware sample selection. From the generation perspective, we develop X-to-Maritime, a multi-modality-conditioned generative model that synthesizes diverse and realistic maritime scenes. A key component is the Bidirectional Object-Water Attention module, which captures boundary interactions between objects and their aquatic surroundings to improve visual fidelity. To further improve downstream tasking performance, we propose Attribute-correlated Active Sampling, which dynamically selects synthetic samples based on their task relevance. To support robust benchmarking, we construct the Maritime Generation Dataset, the first dataset tailored for generative maritime learning, encompassing a wide range of semantic conditions. Extensive experiments demonstrate that our approach sets a new benchmark in maritime scene synthesis, significantly improving detection accuracy, particularly in challenging and previously underrepresented settings. The code is available at https://github.com/gy65896/Neptune-X.

Adapting billion-parameter language models to a downstream task is still costly, even with parameter-efficient fine-tuning (PEFT). We re-cast task adaptation as output-distribution alignment: the objective is to steer the output distribution toward the task distribution directly during decoding rather than indirectly through weight updates. Building on this view, we introduce Steering Vector Decoding (SVDecode), a lightweight, PEFT-compatible, and theoretically grounded method. We start with a short warm-start fine-tune and extract a task-aware steering vector from the Kullback-Leibler (KL) divergence gradient between the output distribution of the warm-started and pre-trained models. This steering vector is then used to guide the decoding process to steer the model's output distribution towards the task distribution. We theoretically prove that SVDecode is first-order equivalent to the gradient step of full fine-tuning and derive a globally optimal solution for the strength of the steering vector. Across three tasks and nine benchmarks, SVDecode paired with four standard PEFT methods improves multiple-choice accuracy by up to 5 percentage points and open-ended truthfulness by 2 percentage points, with similar gains (1-2 percentage points) on commonsense datasets without adding trainable parameters beyond the PEFT adapter. SVDecode thus offers a lightweight, theoretically grounded path to stronger task adaptation for large language models.

Recently, traffic sign recognition (TSR) systems have become a prominent target for physical adversarial attacks. These attacks typically rely on conspicuous stickers and projections, or using invisible light and acoustic signals that can be easily blocked. In this paper, we introduce a novel attack medium, i.e., fluorescent ink, to design a stealthy and effective physical adversarial patch, namely FIPatch, to advance the state-of-the-art. Specifically, we first model the fluorescence effect in the digital domain to identify the optimal attack settings, which guide the real-world fluorescence parameters. By applying a carefully designed fluorescence perturbation to the target sign, the attacker can later trigger a fluorescent effect using invisible ultraviolet light, causing the TSR system to misclassify the sign and potentially leading to traffic accidents. We conducted a comprehensive evaluation to investigate the effectiveness of FIPatch, which shows a success rate of 98.31% in low-light conditions. Furthermore, our attack successfully bypasses five popular defenses and achieves a success rate of 96.72%.

Voice control has become an indispensable interaction method in smart devices. Compared to traditional microphones, mm Wave radar offers a promising solution for speech sensing in noisy environments. However, most current research relies on single-view information, such as vocal cord vibrations or lip movements, to classify speech, which overlooks important details like timbre, speech rate, and intonation, limiting the application of speech sensing. To address these issues, we develop a high-quality speech sensing method based on mm Wave radar, named SpDiff. This method accurately localizes the vocalizing target and, based on the human vocal mechanism, extracts multi-view speech features according to the movement characteristics of the vocal cords, lips, and face. Additionally, to generate high-quality speech signals, we design a conditional latent diffusion model (CLDM), which uses multi-view radar information as conditional guidance, accurately capturing the complex mapping relationships between radar and speech signal distributions. To evaluate the SpDiff method, we build a mmWave system using IWR1443Boost and recruit 14 volunteers to construct a dataset. Experimental results show that SpDiff achieves high standards in speech sensing, with the generated speech directly input into existing recognition models, achieving an average character and word error rate (CER/WER) of only 2.33% and 3.05%.

Device-free wireless sensing (DFWS) has gained significant attention due to its high accuracy and privacy-preserving capabilities. DFWS systems work by analyzing the influence pattern of targets on the surrounding wireless signals. However, changes in the sensing scenario can alter signal propagation patterns, causing deep learning models to lose effectiveness in cross-scenario applications. To address this problem, we analyze the information content of different samples from a free-energy view, and provide a new idea to guide the alignment of target scenario samples with source scenario samples based on free-energy. We find that free-energy can measure the degree of scenario knowledge contribution of the samples. Based on this observation, we first perform unsupervised coarse alignment by minimizing the free-energy deviation between scenarios. Next, we iteratively select a few number of high-free-energy samples near the decision boundary to fine-tune the network, achieving scenario fine alignment with a small labeling effort. Extensive experiments on two public datasets and one self-collected dataset show that our proposed method achieves high accuracy in cross-scenario human activity and gesture recognition tasks.

Collaborative perception enhances sensing in multi-robot and vehicular networks by fusing information from multiple agents, improving perception accuracy and sensing range. However, mobility and non-rigid sensor mounts introduce extrinsic calibration errors, necessitating online calibration, further complicated by limited overlap in sensing regions. Moreover, maintaining fresh information is crucial for timely and accurate sensing. To address calibration errors and ensure timely and accurate perception, we propose a robust task-oriented communication strategy to optimize online self-calibration and efficient feature sharing for Real-time Adaptive Collaborative Perception (R-ACP). Specifically, we first formulate an Age of Perceived Targets (AoPT) minimization problem to capture data timeliness of multi-view streaming. Then, in the calibration phase, we introduce a channel-aware self-calibration technique based on reidentification (Re-ID), which adaptively compresses key features according to channel capacities, effectively addressing calibration issues via spatial and temporal cross-camera correlations. In the streaming phase, we tackle the trade-off between bandwidth and inference accuracy by leveraging an Information Bottleneck (IB)-based encoding method to adjust video compression rates based on task relevance, thereby reducing communication overhead and latency. Finally, we design a priority-aware network to filter corrupted features to mitigate performance degradation from packet corruption. Extensive studies demonstrate that our framework outperforms five baselines, improving multiple object detection accuracy (MODA) by 25.49% and reducing communication costs by 51.36% under severely poor channel conditions. Code will be made publicly available: github.com/fangzr/R-ACP.

Smart railways integrate advanced information technologies into railway operating systems to improve efficiency and reliability. Although the development of the fifth generation (5G) has enhanced railway services, future smart railways require ultra-high speeds, ultra-low latency, ultra-high security, full coverage, and ultra-high positioning accuracy, which 5G cannot fully meet. Therefore, the sixth generation (6G) is envisioned to provide green and efficient all-day operations, strong information security, fully automatic driving, and low-cost intelligent maintenance. To achieve these requirements, we propose an integrated network architecture leveraging communications, computing, edge intelligence, and caching in railway systems. We have conducted in-depth investigations on key enabling technologies for reliable transmissions and wireless coverage. For high-speed mobile scenarios, we propose an artificial intelligence (AI)-enabled cross-domain channel modeling and orthogonal time–frequency space–time spread multiple access mechanism to alleviate the conflict between limited spectrum availability and massive user access. The roles of blockchain, edge intelligence, and privacy technologies in endogenously secure rail communications are also evaluated. We further explore the application of emerging paradigms such as integrated sensing and communications (SACs), AI-assisted Internet of Things (IoT), semantic communications (SCs), and digital twin (DT) networks for railway maintenance, monitoring, prediction, and accident warning. Finally, possible future research and development directions are discussed. © 2026 IEEE

Collaborative perception has recently gained significant attention in autonomous driving, improving perception quality by enabling the exchange of additional information among vehicles. However, deploying collaborative perception systems can lead to domain shifts due to diverse environmental conditions and data heterogeneity among connected and autonomous vehicles (CAVs). To address these challenges, we propose a unified domain generalization framework to be utilized during the training and inference stages of collaborative perception. In the training phase, we introduce an Amplitude Augmentation (AmpAug) method to augment low-frequency image variations, broadening the model’s ability to learn across multiple domains. We also employ a meta-consistency training scheme to simulate domain shifts, optimizing the model with a carefully designed consistency loss to acquire domain-invariant representations. In the inference phase, we introduce an intra-system domain alignment mechanism to reduce or potentially eliminate the domain discrepancy among CAVs prior to inference. Extensive experiments substantiate the effectiveness of our method in comparison with the existing state-of-the-art works.

Integrated sensing and communication (ISAC) has emerged as a promising technology for future millimeter wave (mmWave) networks. However, the susceptibility of mmWave signals to blockages poses considerable challenges for ISAC as it can result in unreliable links and disrupted sensing. As a result, this paper investigates the blockage-resilient ISAC design that leverages the robustness offered by multi-base station (BS) collaboration. Given the dynamic blockages and the fluctuation in the targets’ radar cross section (RCS), the blockage-resilient multi-BS collaborative ISAC design is cast as a chance constrained integer programming (CCIP) by jointly considering the diverse deadlines of different sensing tasks and the spatial/temporal user-target pairing for dual-functional radar and communication (DFRC) waveform scheduling. To facilitate efficient solution finding, we develop a group concatenating assisted reinforcement learning (GCRL) algorithm, where we linearize the chance constraints via variable grouping and concatenation, enabling the RL agent to understand the problem structure with bipartite graphs so as to develop an efficient branching policy. Extensive experiments demonstrate the resilience of the obtained ISAC scheme to dynamic blockages.

Domain generalization remains a key challenge in human activity recognition based on channel state information (CSI). Different domains correspond to distinct data distributions, deviating from the typical assumption of independent and identically distributed (i.i.d.) data, which leads to significant performance degradation when models are applied to unseen domains. To address this issue, we propose a novel domain generalization model that integrates meta-learning initialization and an adaptive channel grouping attention mechanism. First, a meta-learning strategy is employed to acquire well-initialized parameters from multiple source domain tasks, enabling the model to implicitly enhance its cross-domain generalization ability. Second, an adaptive grouping attention mechanism is designed in the feature extraction stage to effectively capture the sensitivity differences of different subcarriers to human activities. Meanwhile, a random masking training mechanism is introduced to simulate real-world domain variations and improve model robustness. In addition, a domain adversarial training framework based on the gradient reversal layer (GRL) is adopted to mitigate domain-specific feature dependency, further enhancing the model's generalization capability. We evaluate our proposed method on both a self-collected dataset, which includes human activity data from nine volunteers across six different environments, and a public CSI dataset. The experimental results demonstrate that our method significantly outperforms existing approaches in domain generalization performance, verifying its effectiveness and practical applicability.

Ramp merging is one of the bottlenecks in traffic systems, which commonly cause traffic congestion, accidents, and severe carbon emissions. In order to address this essential issue and enhance the safety and efficiency of connected and autonomous vehicles (CAVs) at multi-lane merging zones, we propose a novel collaborative decision-making framework, namedAgentsCoMerge, to leverage large language models (LLMs). Specifically, we first design a scene observation and understanding module to allow an agent to capture the traffic environment. Then we propose a hierarchical planning module to enable the agent to make decisions and plan trajectories based on the observation and the agent's own state. In addition, in order to facilitate collaboration among multiple agents, we introduce a communication module to enable the surrounding agents to exchange necessary information and coordinate their actions. Finally, we develop a reinforcement reflection guided training paradigm to further enhance the decision-making capability of the framework. Extensive experiments are conducted to evaluate the performance of our proposed method, demonstrating its superior efficiency and effectiveness for multi-agent collaborative decision-making under various ramp merging scenarios.

Device-Free mmWave Sensing (DFWS) could sense target state by analyzing how target activities influence the surrounding mmWave signals. It has emerged as a promising sensing technology. However, when employing DFWS indoors, specular reflection interference arises due to the specular reflectors. This interference often induces ghost targets, impacting the accurate estimation of the number and position of targets, resulting in degradation in sensing performance. To tackle this issue, we delve into the generation mechanism of specular reflection interference and analyze its multi-domain characteristics. Through exploration, we discern its temporal sparsity, spatial symmetry or collinearity, and frequency correlation characteristics, and propose four metrics to measure them, accordingly. Specifically, we propose a temporal characteristic quantitative evaluation metric based on identity matching, spatial symmetry and collinearity quantitative evaluation metrics based on geometric analysis, and a frequency correlation quantitative evaluation metric based on Doppler velocity correction, respectively. Based on these metrics, we design a novel Specular Reflection Interference Mitigation (SRIM) method and develop a robust SRIM-DFWS prototype system based on a 60 GHz mmWave radar to validate our proposed method. Experimental results demonstrate that our proposed method could achieve accurate and effective mitigation of specular reflection interference in device-free target tracking.

Device-free positioning (DFP) using mmWave signals is an emerging technique that could track a target without attaching any devices. It conducts position estimation by analyzing the influence of targets on their surrounding mmWave signals. With the widespread utilization of mmWave signals, DFP will have many potential applications in tracking pedestrians and robots in intelligent monitoring systems. State-of-the-art DFP work has already achieved excellent positioning performance when there is one target only, but when there are multiple targets, the time-varying target state, such as entering or leaving of the wireless coverage area and close interactions, makes it challenging to track every target. To solve these problems, in this paper, we propose a spatial-temporal analysis method to robustly track multiple targets based on the high precision mmWave point cloud information. Specifically, we propose a high precision spatial imaging strategy to construct fine-grained mmWave point cloud of the targets, design a spatial-temporal point cloud clustering method to determine the target state, and then leverage a gait based identity and trajectory association scheme and a particle filter to achieve robust identity-aware tracking. Extensive evaluations on a 77 GHz mmWave testbed have been conducted to demonstrate the effectiveness and robustness of our proposed schemes.

Collaborative perception systems leverage multiple edge devices, such as surveillance cameras or autonomous cars, to enhance sensing quality and eliminate blind spots. Despite their advantages, challenges such as limited channel capacity and data redundancy impede their effectiveness. To address these issues, we introduce the Prioritized Information Bottleneck (PIB) framework for edge video analytics. This framework prioritizes the shared data based on the signal-to-noise ratio (SNR) and camera coverage of the region of interest (RoI), reducing spatial-temporal data redundancy to transmit only essential information. This strategy avoids the need for video reconstruction at edge servers and maintains low latency. It leverages a deterministic information bottleneck method to extract compact, relevant features, balancing informativeness and communication costs. For high-dimensional data, we apply variational approximations for practical optimization. To reduce communication costs in fluctuating connections, we propose a gate mechanism based on distributed online learning (DOL) to filter out less informative messages and efficiently select edge servers. Moreover, we establish the asymptotic optimality of DOL by proving the sublinearity of its regrets. To validate the effectiveness of the PIB framework, we conduct real-world experiments on three types of edge devices with varied computing capabilities. Compared to five coding methods for image and video compression, PIB improves mean object detection accuracy (MODA) by 17.8% while reducing communication costs by 82.65% under poor channel conditions.

Integrated sensing and communication (ISAC), which integrates sensing capabilities into wireless communication networks, is emerging as a key technology for future millimeter wave (mmWave) communication networks. Given the limited ISAC capability and energy budget of a single base station (BS), this paper studies how to enable energy-efficient sensing and communication via multi-BS collaborative sensing, where each sensing task is served by its most energy-efficient BS as much as possible, with the help of other BSs. Since unregulated multi-BS collaboration may lead to energy wastage and further aggravates the energy consumption in mmWave networks, an energy-efficient collaborative ISAC scheme is proposed, where multi-BS collaborative sensing and dual-functional radar and communication (DFRC) beams are judiciously utilized to reduce the network’s energy consumption. We formulate the design of the energy-efficient collaborative ISAC scheme as a mixed integer nonlinear programming problem by jointly considering task allocation, beam scheduling, and transmit power control. Then, an energy-efficient cooperative beam scheduling (EE-CBS) algorithm is developed for efficient solution finding. Through extensive simulations, the proposed scheme is shown to significantly reduce the network’s energy consumption when compared to the scheme without multi-BS cooperation or the utilization of DFRC waveforms.

Given the time-varying radio environments, legitimate users need to conduct covert communications over multiple time slots with different radio propagation conditions, while a warden performs joint signal detection based on the observations collected during these slots. Unfortunately, existing studies mainly deal with the design of covert communication scheme within a single slot where the radio environment remains unchanged. These scheme might suffer performance degradation when legitimate users make transmission decisions over multiple slots with different propagation conditions and a joint covertness constraint over these slots is imposed. In view of this challenge, we investigate the design of covert communication schemes in time-varying radio environments under a multi-slot covertness constraint. Given the coupling between the transmission decisions in different slots, we formulate the schematic design as a Markov decision process where the multi-slot covertness constraint is characterized with the concept of conditional value at risk to address the non-cumulative growth brought by the unknown eavesdropping channel. Unlike existing works, our scheme allows the legitimate users to adapt their transmission decisions to the current propagation condition and covertness margin. Extensive simulations demonstrate that our scheme can facilitate efficient covert data transmission under a multi-slot covertness constraint.

Collaborative edge sensing systems, particularly in collaborative perception systems in autonomous driving, can significantly enhance tracking accuracy and reduce blind spots with multi-view sensing capabilities. However, their limited channel capacity and the redundancy in sensory data pose significant challenges, affecting the performance of collaborative inference tasks. To tackle these issues, we introduce a Prioritized Information Bottleneck (PIB) framework for collaborative edge video analytics. We first propose a priority-based inference mechanism that jointly considers the signal-to-noise ratio (SNR) and the camera’s coverage area of the region of interest (RoI). To enable efficient inference, PIB reduces video redundancy in both spatial and temporal domains and transmits only the essential information for the downstream inference tasks. This eliminates the need to reconstruct videos on the edge server while maintaining low latency. Specifically, it derives compact, task-relevant features by employing the deterministic information bottleneck (IB) method, which strikes a balance between feature informativeness and communication costs. Given the computational challenges caused by IB-based objectives with high-dimensional data, we resort to variational approximations for feasible optimization. Compared to TOCOM-TEM, JPEG, and HEVC, PIB achieves an improvement of up to 15.1% in mean object detection accuracy (MODA) and reduces communication costs by 66.7% when edge cameras experience poor channel conditions.

Collaborative perception among multiple connected and autonomous vehicles (CAVs) can greatly enhance perceptive capabilities by allowing vehicles to exchange supplementary information. Despite significant advances, many design challenges still remain due to channel variations and data heterogeneity among collaborative vehicles. To address these issues, we propose ACC-DA, a channel-aware collaborative perception framework to dynamically adjust the communication graph to minimize the average transmission delay while mitigating the impacts caused by data heterogeneity. More specifically, we first construct the communication graph to minimize the transmission delay according to different channel information state. We then propose an adaptive data reconstruction mechanism to dynamically adjust the rate-distortion trade-off to enhance perception efficiency while reducing the temporal redundancy during data transmissions. Finally, we conceive a domain alignment scheme to align the data distribution from different vehicles to mitigate the domain gap between different vehicles and improve the performance of the target task. Comprehensive experiments demonstrate the effectiveness of our method in comparison to the existing state-of-the-art works.

In recent years, autonomous driving has garnered significant attention due to its potential for improving road safety through collaborative perception among connected and autonomous vehicles (CAVs). However, time-varying channel variations in vehicular transmission environments demand dynamic allocation of communication resources. Moreover, in the context of collaborative perception, it is important to recognize that not all CAVs contribute valuable data, and some CAV data even have detrimental effects on collaborative perception. In this paper, we introduce SmartCooper, an adaptive collaborative perception framework that incorporates communication optimization and a judger mechanism to facilitate CAV data fusion. Our approach begins with optimizing the connectivity of vehicles while considering communication constraints. We then train a learnable encoder to dynamically adjust the compression ratio based on the channel state information (CSI). Subsequently, we devise a judger mechanism to filter the detrimental image data reconstructed by adaptive decoders. We evaluate the effectiveness of our proposed algorithm on the OpenCOOD platform. Our results demonstrate a substantial reduction in communication costs by 23.10% compared to the non-judger scheme. Additionally, we achieve a significant improvement on the average precision of Intersection over Union (AP@IoU) by 7.15% compared with state-of-the-art schemes.

In cloud-based data marketplaces, the cardinal objective lies in facilitating interactions between data shoppers and sellers. This engagement allows shoppers to augment their internal datasets with external data, consequently leading to significant enhancements in their machine learning models. Nonetheless, given the potential diversity of data values, it becomes critical for consumers to assess the value of data before cementing any transactions. Recently, Song et al. introduced Primal (publish in ACSAC), the pioneering cloud-assisted privacy-preserving data evaluation (PPDE) strategy. This strategy relies on variants of functional encryption (FE) as the underlying framework, conferring notable performance advantages over alternative cryptographic primitives such as secure multi-party computation and homomorphic encryption. However, in this paper, we regretfully highlight that Primal is susceptible to inadvertent misuse of FE, and leaves much-desired room for performance amelioration. To combat this, we introduce a novel cryptographic primitive known as labeled function-hiding inner-product encrypted. This new primitive serves as a remedy and forms the foundation for designing the concrete framework for PPDE. Furthermore, experiments conducted on real datasets demonstrate that our framework significantly reduces the overall computation cost of the current state-of-the-art secure PPDE scheme by roughly 10× and the communication cost for the data seller by about 2×.

Intracortical brain-computer interfaces (iBCIs) promise to sense brain activity at an unprecedented scale and resolution. However, unlocking this potential for practical, untethered applications remains an unsolved challenge. The major barrier is the significant wireless bandwidth required to stream high-resolution brain signals. Existing approaches rely on extensive on-device processing, which is severely constrained by the limited resources of iBCI devices, the complexity of brain signals, and the dynamic nature of neural activity. This paper introduces Neuralite, a wireless iBCI system that integrates high-fidelity brain signal models and effective brain sensing mechanisms within an efficient server-driven streaming framework. By thoroughly characterizing brain signal variability, Neuralite adaptively optimizes streaming under dynamic neural conditions, minimizing bandwidth consumption without imposing excessive burdens on resource-constrained iBCI devices. Experimental results demonstrate that Neuralite significantly reduces bandwidth consumption while preserving neural decoding precision across key iBCI components and representative applications.

Empowered by the feature extraction ability of deep neural networks (DNNs), the DNN-based device-free wireless sensing (DFWS) could recognize human activity by analyzing the pattern information involved in the influenced wireless signals. However, labeling samples is time-consuming and labor-intensive because wireless signals are not human-interpretable. In practical applications, there are always few labeled samples, and how to realize high-performance DFWS with few labels becomes an urgent problem to solve. To tackle this problem, finding compact representative features for samples in an unsupervised manner is crucial. To this end, we design a contrastive learning framework to obtain features of unlabeled samples by maximizing the mutual information between features and the corresponding samples. The contrastive training process extracts features for the input samples by contrasting positive and negative sample pairs, thus strengthening the correlation between the features and the corresponding samples. The intuition behind our method is that mutual information measures the correlation between features and samples, and thus the maximum mutual information could capture informative and discriminative features. Our evaluation results on two publicly available data sets and one data set collected by ourselves show that our proposed method achieves satisfactory accuracy for both human activity and gesture recognition tasks with few labels.

In this paper, we establish a multi-access edge computing (MEC)-enabled sea lane monitoring network (MSLMN) architecture with energy harvesting (EH) to support dynamic ship tracking, accident forensics, and anti-fouling through real-time maritime traffic scene monitoring. Under this architecture, the computation offloading and resource allocation are jointly optimized to maximize the long-term average throughput of MSLMN. Due to the dynamic environment and unavailable future network information, we employ the Lyapunov optimization technique to tackle the optimization problem with large state and action spaces and formulate a stochastic optimization program subject to queue stability and energy consumption constraints. We transform the formulated problem into a deterministic one and decouple the temporal and spatial variables to obtain asymptotically optimal solutions. Under the premise of queue stability, we develop a joint computation offloading and resource allocation (JCORA) algorithm to maximize the long-term average throughput by optimizing task offloading, subchannel allocation, computing resource allocation, and task migration decisions. Simulation results demonstrate the effectiveness of the proposed scheme over existing approaches.

This article presents a novel noncoherent multisatellite weak signal acquisition scheme by aggregating the observations at multiple low-Earth orbit (LEO) satellites. Existing aggregation schemes rely on exhaustive search over grids on Earth surface to compensate for the differences in the delay and the Doppler frequency shift experienced at different satellites, which have high-computational complexity due to the wide coverage of LEO satellites. Motivated by this observation, we propose to directly work with satellites’ time-frequency (TF) grid and facilitate efficient delay and Doppler compensation by gradually narrowing down the search space over each satellite’s TF grid with multisatellites observations. Our scheme employs geometric search space reduction scheme to reduce the search space for possible Doppler frequency shift and utilizes hierarchical geometric correlation peak matching to eliminate fake correlation peaks based on multisatellite observations. Through extensive performance evaluation, we demonstrate that, in comparison with existing schemes, our proposed multisatellite signal acquisition scheme can achieves significantly better acquisition performance with a much lower computational complexity.

Federated learning (FL) allows multiple parties (distributed devices) to train a machine learning model without sharing raw data. How to effectively and efficiently utilize the resources on devices and the central server is a highly interesting yet challenging problem. In this paper, we propose an efficient split federated learning algorithm (ESFL) to take full advantage of the powerful computing capabilities at a central server under a split federated learning framework with heterogeneous end devices (EDs). By splitting the model into different submodels between the server and EDs, our approach jointly optimizes user-side workload and server-side computing resource allocation by considering users’ heterogeneity. We formulate the whole optimization problem as a mixed-integer non-linear program, which is an NP-hard problem, and develop an iterative approach to obtain an approximate solution efficiently. Extensive simulations have been conducted to validate the significantly increased efficiency of our ESFL approach compared with standard federated learning, split learning, and splitfed learning.

Decentralized multi-client functional encryption for inner product (DMCFE-IP) enables efficient joint functional computation of private inputs in a secure manner without a trusted third party, which has found successful applications, including distributed statistical analysis and machine learning. However, existing DMCFE-IP schemes suffer several drawbacks, such as lack of support for client dropout, requiring cross-client communication for key generation, and poor efficiency and scalability. To address these issues, we propose an efficient and scalable DMCFE-IP, which supports client dropout and non-interactive decentralized partial decryption key generation. Our scheme mainly exploits appropriate underlying cryptographic primitives, including multi-client functional encryption, digital signature, key agreement, secret sharing, and symmetric encryption, with careful integration to achieve the aforementioned two functionalities. We then extend this scheme to enable privacy-preserving federated learning (PPFL) for the cross-silo scenrio. We provide formal security proof for our scheme and evaluate our DMCFE-IP-based PPFL on several real-world datasets. Compared with the state-of-the-art methods, our approach achieves a speedup of 6.12$\sim 43.36\times$in running time.

Federated Learning (FL) is a collaborative learning framework that enables edge devices to collaboratively learn a global model while keeping raw data locally. Although FL avoids leaking direct information from local datasets, sensitive information can still be inferred from the shared models. To address the privacy issue in FL, differential privacy (DP) mechanisms are leveraged to provide formal privacy guarantee. However, when deploying FL at the wireless edge with over-the-air computation, ensuring client-level DP faces significant challenges. In this paper, we propose a novel wireless FL scheme called private federated edge learning with sparsification (PFELS) to provide client-level DP guarantee with intrinsic channel noise while reducing communication and energy overhead and improving model accuracy. The key idea of PFELS is for each device to first compress its model update and then adaptively design the transmit power of the compressed model update according to the wireless channel status without any artificial noise addition. We provide a privacy analysis for PFELS and prove the convergence of PFELS under general non-convex and non-IID settings. Experimental results show that compared with prior work, PFELS can improve the accuracy with the same DP guarantee and save communication and energy costs simultaneously.

Surrounding perceptions are quintessential for safe driving for connected and autonomous vehicles (CAVs), where the Bird's Eye View has been employed to accurately capture spatial relationships among vehicles. However, severe inherent limitations of BEV, like blind spots, have been identified. Collaborative perception has emerged as an effective solution to overcoming these limitations through data fusion from multiple views of surrounding vehicles. While most existing collaborative perception strategies adopt a fully connected graph predicated on fairness in transmissions, they often neglect the varying importance of individual vehicles due to channel variations and perception redundancy. To address these challenges, we propose a novelPriority-AwareCollaborativePerception (PACP) framework to employ a BEV-match mechanism to determine the priority levels based on the correlation between nearby CAVs and the ego vehicle for perception. By leveraging submodular optimization, we find near-optimal transmission rates, link connectivity, and compression metrics. Moreover, we deploy a deep learning-based adaptive autoencoder to modulate the image reconstruction quality under dynamic channel conditions. Finally, we conduct extensive studies and demonstrate that our scheme significantly outperforms the state-of-the-art schemes by 8.27% and 13.60%, respectively, in terms of utility and precision of the Intersection over Union.

Using mmWave radar to conduct gesture recognition is a promising solution for human-computer interaction. Although many studies have shown initial success, two-fold problems still remain unsolved, namely, the high-strength human activity interference and the difficulty in handling similar gestures. In light of these, we develop a robust mmWave radar based gesture recognition system, Rodar, to achieve accurate recognition of similar gestures under high-strength human activity interference, where a Multi-view De-interference Transformer (MvDeFormer) network is proposed. Specifically, to deal with the strong human activity interference, we design a DeFormer module to capture the useful gesture features by learning different patterns between gestures and interference, thereby reducing the impact of interference. Then, we develop a hierarchical multi-view fusion module to first extract the enhanced features within each view, and effectively fuse them across various views for final recognition. To evaluate the proposed Rodar system, we construct a dataset with seven similar gestures under three common human activity interference scenarios. Experimental results show that the accuracy can achieve up to 93.01%. The code implementations are available athttps://github.com/Xlab2024/MvDeFormer.

The increasingly deeper neural networks hinder the democratization of privacy-enhancing distributed learning, such as federated learning (FL), to resource-constrained devices. To overcome this challenge, in this paper, we advocate the integration of edge computing paradigm and parallel split learning (PSL), allowing multiple edge devices to offload substantial training workloads to an edge server via layer-wise model split. By observing that existing PSL schemes incur excessive training latency and a large volume of data transmissions, we propose an innovative PSL framework, namely, efficient parallel split learning (EPSL), to accelerate model training. To be specific, EPSL parallelizes client-side model training andreduces the dimension of activations' gradientsfor backpropagation (BP) vialast-layer gradient aggregation, leading to a significant reduction in server-side training and communication latency. Moreover, by considering the heterogeneous channel conditions and computing capabilities at edge devices, we jointly optimize subchannel allocation, power control, and cut layer selection to minimize the per-round latency. Simulation results show that the proposed EPSL framework significantly decreases the training latency needed to achieve a target accuracy compared with the state-of-the-art benchmarks, and the tailored resource management and layer split strategy can considerably reduce latency than the counterpart without optimization.

Device-free vital signs monitoring is an emerging technology that utilizes the unique influence of chest vibrations on surrounding wireless signals to achieve vital signs monitoring in a device-free and contact-free manner. Existing methods could achieve good monitoring performance when high-quality reflected signals can be obtained. However, in daily vital signs monitoring at home, the received reflected signals are often very weak due to factors such as obstruction and attenuation, resulting in a sharp decrease in the monitoring performance. To address the aforementioned challenges, in this paper, we develop a diversity-enhanced robust device-free vital signs monitoring system using mmWave signals. Specifically, inspired by the concept of diversity in the field of communications, we propose a diversity-enhanced wireless sensing strategy that comprehensively utilizes multi-dimensional physical layer resources, including antennas, chirps, and space, to improve the signal-to-noise ratio of vital signs. Additionally, inspired by cameras that achieve clear images by prolonging exposure time, we propose an accumulation-enhanced localization method to lock onto the chest of the human body in complex scenarios. Extensive experiments on a 60 GHz mmWave testbed demonstrate that our developed system could guarantee robust vital signs monitoring performance in various challenging scenarios, even at distances of up to 40 m.

Recently, the continuous evolution of deep learning has opened up promising avenues to groundbreaking advancements in wireless sensing systems, which significantly enhance the practical applications of WiFi-based Human Activity Recognition (HAR) systems. However, despite these strides, such systems remain susceptible to adversarial attacks. This article unveils the vulnerability of existing WiFi-based HAR systems to common adversaries, revealing their insufficient robustness. While the intuitive approach is to employ adversarial training to fortify the models, our investigation exposes inherent deficiencies in the current approach. Specifically, we confirm that the strength of perturbations directly influences training outcomes. Moreover, even when confined within a specified perturbation radius, the perturbation strength exhibits variability within a prescribed range, potentially giving rise to “extreme” samples that could compromise training results. To address this challenge, we propose a two-stage Selective Adversarial Training (SAT) approach that integrates model confidence calibration and sample selection. Specifically, we start with calibrating the model and then selectively choose samples from all adversarial examples based on the calibrated confidence outputs that align with the desired criteria for adversarial training. This sample-wise perturbation intensity control effectively prevents the inclusion of inappropriate samples in training, a capability lacking in previous domain-wise perturbation control. Our experiments demonstrate that the proposed fine-grained training method, SAT, is both straightforward and effective in augmenting adversarial training results.

WiFi backscatter has attracted intensive attention because the large population of WiFi radios can provide plenty of excitation signals. However, WiFi backscatter communication has imposed unwanted constraints on either exciters or receivers since its inception. In this paper, we present Chameleon, a native WiFi backscatter system, where WiFi tags can generate native WiFi packets using uncontrolled productive WiFi signals as carriers. Our tag-only design requires no particular excitation patterns and no changes in software/hardware on WiFi network interface cards (NICs). The key idea is for the Chameleon tag to demodulate the productive WiFi signal and backscatter it into a full-function packet using on-the-fly modulation. To align tag decoding and modulation with excitation symbols, we design a time synchronization and clock compensation scheme suitable for low-power tags. We prototype WiFi tags using ultra-low-power FPGAs and evaluate them in real-world scenarios where excitations are ambient traffic and backscatter receivers are a wide range of commercial off-the-shelf (COTS) NICs. Comprehensive field studies show that the maximal backscatter throughput of Chameleon is almost 1 Mbps, which is over$125\times $and$1000\times $higher than what WiTAG and FS-Backscatter tags could achieve, respectively. We also show that Chameleon can natively communicate with various COTS WiFi devices on Windows, iOS, and Android platforms. We believe that this design will enable ubiquitous WiFi connectivity for billions of IoT devices via widely available mobile gadgets and existing wireless infrastructure.

In unmanned aerial vehicle (UAV)-assisted multi-access edge computing (MEC) systems, where UAVs act as aerial relays to forward tasks from ground users (GUs) to remote edge servers (ESs) for processing, a crucial observation is that the computing power in the system depends on the computing capabilities at a single ES and the number of ESs covered by the UAV. The latter is essentially influenced by the UAV altitude, ES density, transmit power of the UAV, channel condition, etc. In this paper, we model a UAV-assisted MEC system featuring adjustable UAV altitude, random GU distribution, and random ES distribution. We adopt the signal-to-noise ratio-based coverage probability and derive a computing model to characterize communication-aware altitude-dependent computing power. Upon this, we model the sequential task-processing process, including task uploading, forwarding, and computing, as a three-stage tandem queue (M/D/1 →D/1 →D/1). Employing queueing theory, we derive analytical results for the end-to-end (e2e) service latency. Besides, we address the optimization problem of maximizing the number of completed tasks within the e2e latency constraint, referred to as task service throughput. Simulation and analytical results show that optimal UAV altitudes, yielding the maximum task computing throughput, can be obtained under given network parameters.

Multi-access edge computing (MEC) is a promising solution to supporting resource-intensive applications on mobile devices (MDs), which enables computation offloading from MDs to edge servers at their proximities. However, the quality of the communication links and the limited communication and computing resources significantly impact the performance of MEC systems. In this paper, we leverage the emerging reconfigurable intelligent surfaces (RISs) to assist the computation offloading and balance the computing workloads in a multi-server MEC system with limited communication and computing resources. Specifically, when a nearby edge server is overwhelmed by multiple computing tasks, some MDs can be redirected to potentially distant but lighter-loaded edge servers by employing passive beamforming enabled by RISs. Thus, to maximize the task completion rate, we formulate a joint optimization problem for user association, passive beamforming at RISs, receive beamforming at BSs, and computing resource allocation on edge servers. Since the problem is a mixed integer nonlinear programming (MINLP), which is challenging to solve, we first decompose it into two tractable subproblems through the block coordinate descent (BCD) technique and then solve them by the penalty dual decomposition (PDD) method and a swap matching-based algorithm, respectively. Numerical results demonstrate that the task completion rate can be significantly increased by incorporating RISs into multi-server MEC systems. Besides, the proposed algorithms outperform other benchmark schemes in terms of both the task completion rate and the design complexity.

Dynamic spectrum access (DSA) has been regarded as a viable solution to the spectrum shortage problem. To find idle spectrum, partial spectrum sensing could be employed by selecting a suitable sensing window (SW). Since the SW selection determines how many available bands to access, the transmission performance after the access could be used to guide the SW selection. Hence, a sophisticated joint design on spectrum sensing and access is necessary, which, however, is a challenging task when considering the dynamic nature of spectrum environment, and also the mutual impact among different secondary users (SUs). In this paper, we propose a joint partial spectrum sensing and power allocation (PA) scheme to facilitate SUs to make the best decisions on SW and PA to maximize the network throughput with reduced mutual interference. Considering the environmental dynamics and spectrum uncertainty, we develop a viable solution based on hierarchical multi-agent deep reinforcement learning (HMADRL). Our solution enables mutual design with two stages: making each SU learn the best SW and PA strategies autonomously while adapting to the dynamic environment. By using both simulated spectrum data and real spectrum data measured by SAM60-BX, we have demonstrated the effectiveness of our proposed scheme.

Reconfigurable intelligent surface (RIS) provides a promising way to proactively augment propagation environments for better transmission performance in wireless communications. Existing multi-RIS works mainly focus on link-level optimization with predetermined transmission paths, which cannot be directly extended to system-level management, since they neither consider the interference caused by undesired scattering of RISs, nor the performance balancing between different transmission paths. To address this, we study an innovative multi-hop multi-RIS communication system, where a base station (BS) transmits information to a set of distributed users over multi-RIS configuration space in a multi-hop manner. The signals for each user are subsequently reflected by the selected RISs via multi-reflection line-of-sight (LoS) links. To ensure that all users have fair access to the system to avoid excessive number of RISs serving one user, we aim to find the optimal beam reflecting path for each user, while judiciously determining the path scheduling strategies with the corresponding beamforming design to ensure the fairness. Due to the presence of interference caused by undesired scattering of RISs, it is highly challenging to solve the formulated multi-RIS multi-path beamforming optimization problem. To solve it, we first derive the optimal RISs’ phase shifts and the corresponding reflecting path selection for each user based on its practical deployment location. With the optimized multi-reflection paths, we obtain a feasible user grouping pattern for effective interference mitigation by constructing the maximum independent sets (MISs). Finally, we propose a joint heuristic algorithm to iteratively update the beamforming vectors and the group scheduling policies to maximize the minimum equivalent data rate of all users. Numerical results demonstrate that the proposed transmission framework achieves superior throughput performance than benchmark schemes. Useful insights on how to leverage multi-reflection paths over RISs to boost the throughput performance are also drawn under different settings for the multi-hop multi-RIS communication systems.

Semantic communication has been identified as a core technology for the sixth generation (6G) of wireless networks. Recently, task-oriented semantic communications have been proposed for low-latency inference with limited bandwidth. Although transmitting only task-related information does protect a certain level of user privacy, adversaries could apply model inversion techniques to reconstruct the raw data or extract useful information, thereby infringing on users’ privacy. To mitigate privacy infringement, this paper proposes an information bottleneck and adversarial learning (IBAL) approach to protect users’ privacy against model inversion attacks. Specifically, we extract task-relevant features from the input based on the information bottleneck (IB) theory. To overcome the difficulty in calculating the mutual information in high-dimensional space, we derive a variational upper bound to estimate the true mutual information. To prevent data reconstruction from task-related features by adversaries, we leverage adversarial learning to train encoder to fool adversaries by maximizing reconstruction distortion. Furthermore, considering the impact of channel variations on privacy-utility trade-off and the difficulty in manually tuning the weights of each loss, we propose an adaptive weight adjustment method. Numerical results demonstrate that the proposed approaches can effectively protect privacy without significantly affecting task performance and achieve better privacy-utility trade-offs than baseline methods.

Joint monitoring, by integrating observations from multiple types of equipment, is essential for a thorough understanding of physical processes in the Industrial Internet of Things (IIoT). However, it does demand sufficient resources to ensure reliable and timely delivery of such observations. Although network slicing is widely used to meet such heterogeneous requirements, it falls short in this system, because it causes interconnected impacts on system performance across multiple slices. In this paper, we introduce an innovative associated network slicing framework for joint monitoring, which focuses on system cost minimization while accounting for slice associations. Particularly, to better understand the characteristics, we introduce a new concept, Age of Inexact Task (AoIT), to capture inter-slice associations. We then decompose the optimization variables to facilitate efficient Associated Network Slicing (ANS) algorithmic design, leading to a closed-form solution for intra-slice small-timescale resource allocation and an iterative block coordinate gradient descent algorithm for inter-slice large-timescale resource allocation. Simulation results demonstrate that our proposed ANS balances heterogeneous requirements and associations, showing significant reductions in system costs compared to existing solutions.

The boom of the Internet of Things has revolutionized people's lives, but it has also resulted in massive resource consumption and environmental pollution. Recently, Green IoT (GIoT) has become a worldwide consensus to address this issue. In this paper, we propose EEWScatter, an energy-efficient WiFi backscatter communication system to pursue the goal of GIoT. Unlike previous backscatter systems that solely focus on tags, our approach offers a comprehensive system-wide view on energy conservation. Specifically, we reuse ambient signals as carriers and utilize an ultra-low-power and battery-free design for tag nodes by backscatter. Further, we design a new CRC-based algorithm that enables the demodulation of both ambient and tag data by only a single receiver while using ambient carriers. Such a design eliminates system reliance on redundant transceivers with high power consumption. Results demonstrate that EEWScatter achieves the lowest overall system power consumption and saves at least half of the energy. What's more, the power consumption of our tag is only 1/1000 of that of active radio.

Compressed sensing (CS) has emerged as a promising technique for reducing transmission data volume. Despite its significance, achieving a balance between the delay, energy consumption and data distortion caused by CS and transmission remains an understudied area in resource-constrained IoT systems. The emergence of multi-access edge computing provides a potential solution to the aforementioned issue by enabling the strategic implementation of CS either at IoT devices or an edge server (ES), depending on both bandwidth resources and computing resources at ES. In this paper, we investigate where to perform CS computation and how to determine the compression ratio and bandwidth allocation to minimize the weighted energy and distortion cost (WEDC) of all devices under latency requirements. We formulate a WEDC minimizing problem by jointly optimizing the task scheduling, compression ratio, and bandwidth allocation. Since the formulated problem is a mixed-integer and nonlinear programming, which is typically NP-hard, we decompose the original problem into two sub-problems and then develop an iterative algorithm to find the suboptimal solution. Extensive numerical results demonstrate the superiority of the proposed algorithm in reducing WEDC of all devices under delay constraints.

Unmanned aerial vehicles (UAVs) play a pivotal role in augmenting multi-access edge computing by facilitating low-latency services for ground units (GUs), especially in areas where the ground infrastructure is inadequate or damaged. In this context, the UAV trajectory planning and caching strategies assume paramount importance to ensure low-latency service delivery. Due to the limited caching and computing resources at a single UAV, it alone cannot provide effective services for a large number of GUs. In this paper, we design a novel cooperative framework for low-latency service provisioning by coordinating multiple UAVs's trajectories and service caching strategies. We formulate a latency minimization problem to jointly optimize both service caching and trajectory planning of multiple UAVs. However, due to the high dimension and coupling of multiple UAVs' movement and service caching, the optimization problem is a mixed-integer nonlinear programming, which is typically an NP-hard problem, and we propose an effective algorithm based on deep deterministic policy gradient to solve the high dimensional, non-convex, and continuous long-term optimization problem. Numerous experiments confirm that the proposed algorithm achieves significantly better performance in reducing the total system delay than other baseline algorithms.

Disasters are constant threats to humankind, and beyond losses in lives, they may cause many implicit yet profound societal issues such as wealth disparity and digital divide. Among those recovery measures in the aftermath of disasters, restoring communication services is of vital importance. Although existing works have proposed many architectural and protocol designs, none of them have taken human factors and social equality into consideration. Recent sociological studies have shown that people from marginalized groups (e.g., low income) are more vulnerable to communication outages. In this paper, we make efforts in integrating human factors – extracted from our collected dataset after Hurricane Harvey in 2017 in Texas, US – into an empirical optimization model to determine strategies for post-disaster communication restoration. We cast the design into a mix-integer non-linear programming problem, which captures the essential features of the design but is proven too complex to be solved. To find approximate solutions, we leverage a suite of convex relaxations and then develop heuristic algorithms to efficiently solve the transformed optimization problem. Based on our collected dataset, we further evaluate and demonstrate how our design could prioritize communication services for vulnerable people and promote social equality compared with an existing modeling benchmark.

Energy forecasting is important because it enables infrastructure planning and power dispatching while reducing power outages and equipment failures. It is well-known that federated learning (FL) can be used to build a global energy predictor for smart grids without revealing the customers’ raw data to preserve privacy. However, it still reveals local models’ parameters during the training process, which may still leak customers’ data privacy. In addition, for the global model to converge, it requires multiple training rounds, which must be done in a communication-efficient way. Moreover, most existing works only focus on load forecasting while neglecting energy forecasting in net-metering systems. To address these limitations, in this article, we propose a privacy-preserving and communication-efficient FL-based energy predictor for net-metering systems. Based on a data set for real power consumption/generation readings, we first propose a multidata-source hybrid deep learning (DL)-based predictor to accurately predict future readings. Then, we repurpose an efficient inner-product functional encryption (IPFE) scheme for implementing secure data aggregation to preserve the customers’ privacy by encrypting their models’ parameters during the FL training. To address communication efficiency, we use a change and transmit (CAT) approach to update local model’s parameters, where only the parameters with sufficient changes are updated. Our extensive studies demonstrate that our approach accurately predicts future readings while providing privacy protection and high communication efficiency.

Recently, several Internet of Things (IoT) service platforms have been proposed to facilitate IoT application deployment. These platforms typically utilize the lightweight MQTT or CoAP application protocol, optimized for massive IoT applications. Unfortunately, these protocols are not suitable for the emerging, more sophisticated Artificial Intelligence of Things (AIoT). Session initiation protocol (SIP), in contrast, is a signaling and controlling protocol for real-time multimedia sessions, and has been viewed as a better candidate to provide a full range support of different broadband, critical, and industrial AIoT applications. However, there exists no generic SIP-based AIoT service platform that supports creations and operations of heterogeneous AIoT applications with various quality of service. This article presents the first SIP-based AIoT service platform, AIoTtalk, that enables rapid development of scalar and multimedia AIoT applications. Moreover, we deploy an experimental testbed and two real SIP-based AIoT applications to demonstrate the applicability and the performance of our AIoTtalk under both the cloud and edge scenarios. The experimental results show that, together with accurate model predictions and edge-virtualization auto scaling, AIoTtalk guarantees low latency and high quality of experience for messaging and streaming-based AIoT applications.

Federated learning (FL) is a popular collaborative distributed machine learning paradigm across mobile devices. However, practical FL over resource constrained mobile devices confronts multiple challenges, e.g., the local on-device training and model updates in FL are power hungry and radio resource intensive for mobile devices. To address these challenges, in this paper, we attempt to take FL into the design of future wireless networks and develop a novel joint design of wireless transmission and weight quantization for energy efficient FL over mobile devices. Specifically, we develop flexible weight quantization schemes to facilitate on-device local training over heterogeneous mobile devices. Based on the observation that the energy consumption of local computing is comparable to that of model updates, we formulate the energy efficient FL problem into a mixed-integer programming problem where the quantization and spectrum resource allocation strategies are jointly determined for heterogeneous mobile devices to minimize the overall FL energy consumption (computation + transmissions) while guaranteeing model performance and training latency. Since the optimization variables of the problem are strongly coupled, an efficient iterative algorithm is proposed, where the bandwidth allocation and weight quantization levels are derived. Extensive simulations are conducted to verify the effectiveness of the proposed scheme.

Recently, ambient backscatter communication has become a promising technology to support the low-power and low-cost Internet-of-Things (IoT). However, the nondeterministic and sporadic nature of ambient signals makes it a great challenge when designing multiple access in spectrum opportunistic ambient backscatter wireless networks (AmBWNs). Moreover, the stringent energy supply and ultra-low-cost design of the backscatter transmitter make most multiple access schemes no longer suitable for AmBWNs. To effectively share carrier frequency resources for backscattering, we propose a hierarchical multiple access scheme, which allows beamforming based spatial division multiple access among groups, and non-orthogonal multiple access (NOMA) for multiple users access within a group. Consequently, we formulate a multi-objective optimization problem to balance the sum rate and the fairness by exploiting grouping, beamforming, and reflection coefficients. To solve this problem, we employ the matching theory to tackle the grouping problem and achieve the corresponding beamforming. We then reformulate the non-convex reflection coefficient optimization and solve it with successive convex approximation and geometric programming. Our extensive evaluation results demonstrate that the spectrum and energy efficiency, latency, and fairness can be significantly improved with minimal overhead at the transmitter.

Recent advance of Intelligent Reflecting Surface (IRS) introduces a new dimension for secure communications by reconfiguring the transmission environments. In this paper, we devise a secure transmission scheme for multi-user Mutiple-Input Single-Output systems by leveraging multiple collaborative IRSs. Specifically, to guarantee the worst-case achievable secrecy rate among multiple legitimate users, we formulate a max-min problem that can be solved by an alternating optimization method to decouple it into multiple sub-problems. Based on semidefinite relaxation and successive convex approximation, each sub-problem can be further converted into convex problem and easily solved. Extensive experimental results demonstrate that our proposed scheme can adapt to complex scenarios for multiple users and achieve significant gain in terms of achievable secrecy rate. Compared to the traditional single IRS scheme, the proposed scheme can achieve better performance at the range of 2.4-6.4 bps/Hz with the increase in the number of reflecting elements in the multi-user scenarios. We also evaluate the gap between the secrecy rate for our proposed scheme under continuous phase shift/amplitude control and discrete phase shift/amplitude control, and our results show that the secrecy rate obtained from discrete approximation method converges to that achieved from the proposed scheme when increasing the discretization granularity.

It is often observed that people's data are scattered across various organizations and these data can be used to generate usable insights when integrated. However, data fusion from multiple data hosting sites could put user privacy at risk albeit with some security mechanisms. This paper studies a data-analytic platform that adopts the Kulldorff scan statistic to determine infectious-disease spatial hotspots by integrating and analyzing users’ health and location data that are respectively stored in two clouds. We examine the privacy threats to this platform which has a key-oblivious inner product encryption (KOIPE) mechanism in place to ensure that only coarse-grained statistical data is revealed to the honest-but-curious (HbC) entity. To protect user privacy from the designed inference attack, we exploit a game-theoretic approach to incentivize users to form anonymous clusters with a quantitative privacy guarantee. We conduct extensive simulations based on real-life datasets to demonstrate the performance of our scheme in terms of design overhead and privacy level.

The multiaccess edge computing/mobile-edge computing (MEC) is becoming a key technology toward “full 5G.” However, as it gets widely used, a fundamental problem is how to support as many service requests as possible under stringent Quality-of-Service (QoS) requirements and limited communications and computing resources. In this article, we study the long-term throughput maximization problem for multicell multiuser MEC systems. Different from most of the existing works that focus on energy or latency minimization problem for a single-edge system, a novel design is proposed from the service provider’s perspective to maximize the system-wide throughput under latency bounds by jointly taking user association and resource allocation for both communications and computing into account. To capture the stochastic nature of MEC environments, a Markov decision process (MDP) is employed to model the queuing states for both mobile devices and MEC servers. By combining MDP and matching theory, a joint user association and resource allocation algorithm is given, where the resource allocation policy under given user-server association is solved. Extensive numerical results demonstrate the superiority of the proposed scheme in comparison with several existing approaches.

To deal with the ever increasing wireless traffic, we have recently designed a vehicular cognitive capability harvesting network (V-CCHN) architecture to leverage vehicles as an alternative “transmission medium” (i.e., an opportunistic data carrier), besides the wireless spectrum, to effectively transport data from the location where it is collected to the place where it is consumed or utilized in a smart city environment. In the V-CCHN, cognitive radio technologies are utilized so that a large amount of data can be exchanged between vehicles and roadside infrastructure through short-range high-speed transmissions. Considering the limited contact duration and the uncertain activities of primary users, how to facilitate efficient data exchange between vehicles and roadside infrastructure is very challenging. This problem is further complicated by the fact that the mobility of vehicles might not be accurately predicted. In this paper, we propose a probabilistic data prefetching (PDP) scheme for the V-CCHN to address these challenges. By considering the conditional value at risk, we formulate the PDP schematic design as an optimization problem which allows us to obtain the corresponding PDP scheme. Finally, we have conducted extensive study to evaluate the performance of the obtained PDP scheme under various parameter settings.

In a post-pandemic era with personal precautions and vaccination, the emergence of COVID-19 variants with higher transmissibility and the socio-economic reopening have raised new challenges to existing human-to-human digital contact tracing systems, where privacy, efficiency, and energy-consumption issues are major concerns. In this article, we propose a novel blockchain-based human-to-infrastructure contact tracing framework for the post-pandemic era. Specifically, our approach collects and records the interaction information between persons and predeployed anchor nodes to trace the possible contacts with confirmed patients, so as to capture the indirect contacts and reduces the energy consumption of users. To address the privacy leakage and reliability issues in contact tracing, we introduce a self-sovereign identity (SSI) model-based blockchain which enables users to gain full control of their own identities and eliminate the linkage between the identity and location information in interaction records. To further preserve the privacy of confirmed patients, we introduce the private set intersection cardinality (PSI-CA) protocol to estimate the risk of infection by only counting the number of encounters between users and confirmed patients. Two self-executed smart contracts are deployed on the SSI blockchain to perform contact tracing, which guarantees the robustness of the system. The performance analysis validates the effectiveness of our approach.

Federated learning (FL) enables multiple clients to collaboratively build a global learning model without sharing their own raw data for privacy protection. Unfortunately, recent research still found privacy leakage in FL, especially on image classification tasks, such as the reconstruction of class representatives. Nevertheless, such analysis on image classification tasks is not applicable to uncover the privacy threats against natural language processing (NLP) tasks, whose records composed of sequential texts cannot be grouped as class representatives. The finer (record-level) granularity in NLP tasks not only makes it more challenging to extract individual text records, but also exposes more serious threats. This article presents the first attempt to explore the record-level privacy leakage against NLP tasks in FL. We propose a framework to investigate the exposure of the records of interest in federated aggregations by leveraging the perplexity of language modeling. Through monitoring the exposure patterns, we propose two correlation attacks to identify the corresponding clients when extracting their specific records. Extensive experimental results demonstrate the effectiveness of the proposed attacks. We have also examined several countermeasures and shown that they are ineffective to mitigate such attacks, and hence further research is expected.

It is observed that modern vehicles are becoming more and more powerful in computing, communications, and storage capacity. By interacting with other vehicles or with local infrastructures (i.e., fog) such as road-side units, vehicles and fog devices can collaboratively provide services like crowdsensing in an efficient and secure way. Unfortunately, it is hard to develop a secure and privacy-preserving crowdsensing report deduplication mechanism in such a system. In this article, we propose a scheme FVC-Dedup to address this challenge. Specifically, we develop cryptographic primitives to realize secure task allocation and guarantee the confidentiality of crowdsensing reports. During the report submission, we improve the message-lock encryption (MLE) scheme to realize privacy-preserving report deduplication and resist the fake duplicate attacks. Besides, we construct a novel signature scheme to achieve efficient signature aggregation and record the contributions of each participant fairly without knowing the crowdsensing data. The security analysis and performance evaluation demonstrate that FVC-Dedup can achieve secure and privacy-preserving report deduplication with moderate computing and communication overhead.

Vehicular crowdsourcing is a promising paradigm that takes advantage of powerful onboard capabilities of vehicles to perform various tasks in smart cities. To fulfill this vision, a well-designed incentive mechanism is essential to stimulate the participation of vehicles. In this paper, we propose a timeliness-aware incentive mechanism for vehicular crowdsourcing by taking vehicle’s uncertain travel time into account. In view of the stochastic nature of traffic conditions, we derive a tractable expression for the probability distribution of task delay based on a discrete-time traffic model. By leveraging reverse auction framework, we model the utility of a service requester as a function in terms ofuncertaintask delay and incurred payment. To maximize the requester’s utility under a budget constraint, we cast the mechanism design as a non-monotone submodular maximization problem over a knapsack constraint. Based on this formulation, we develop atruthfulbudgetedutilitymaximizationauction (TBUMA), which is truthful, budget feasible, profitable, individually rational and computationally efficient. Through extensive trace-based simulations, we demonstrate the effectiveness of our proposed incentive mechanism.

Ubiquitous powerful personal computing facilities, such as desktop computers and parked autonomous cars, can function as micro edge computing servers by leveraging their spare resources. However, to harvest their resources for service provisioning, two significant challenges will arise: how to incentivize the server owners to contribute their computing resources, and how to guarantee the end-to-end (E2E) Quality-of-Service (QoS) for service buyers? In this paper, we address these two problems in a holistic way by advocating COMSA. Unlike the existing double auction schemes for edge computing which mostly focus on computing resource trading, COMSA addresses the joint problem of double auction mechanism design and network resource allocation by explicitly taking spectrum allocation and data routing into account, thereby providing E2E QoS guarantees for edge computing services. To handle the design complexity, COMSA employs a two-step procedure to decouple network optimization and mechanism design, which hence can be applied to general network optimization problems for edge computing. COMSA holds some critical economic properties, i.e., truthfulness, budget balance, and individual rationality. Our extensive simulation studies demonstrate the effectiveness of COMSA.

Communication limitation at the edge is widely recognized as a major bottleneck for federated learning (FL). Multi-hop wireless networking provides a cost-effective solution to enhance service coverage and spectrum efficiency at the edge, which could facilitate large-scale and efficient machine learning (ML) model aggregation. However, FL over multi-hop wireless networks has rarely been investigated. In this paper, we optimize FL over wireless mesh networks by taking into account the heterogeneity in communication and computing resources at mesh routers and clients. We present a framework that each intermediate router performsin-networkmodel aggregation before sending the data to the next hop, so as to reduce the outgoing data traffic and hence aggregate more models under limited communication resources. To accelerate model training, we formulate our optimization problem by jointly considering model aggregation, routing, and spectrum allocation. Although the problem is a non-convex mixed-integer nonlinear programming, we transform it into a mixed-integer linear programming (MILP), and develop a coarse-grained fixing procedure to solve it efficiently. Simulation results demonstrate the effectiveness of the solution approach, and the superiority of the in-network aggregation scheme over the counterpart without in-network aggregation.

Reconfigurable intelligent surface (RIS) provides a promising way to build programmable wireless transmission environments. Owing to the massive number of controllable reflecting elements on the surface, RIS is capable of providing considerable passive beamforming gains. At present, most related works mainly consider the modeling, design, performance analysis and optimization of single-RIS-assisted systems. Although there are a few of works that investigate multiple RISs individually serving their associated users, the cooperation among multiple RISs is not well considered as yet. To fill the gap, this paper studies a cooperative beamforming design for multi-RIS-assisted communication systems, where multiple RISs are deployed to assist the downlink communications from a base station to its users. To do so, we first model the general channel from the base station to the users for arbitrary number of reflection links. Then, we formulate an optimization problem to maximize the sum rate of all users. Analysis shows that the formulated problem is difficult to solve due to its non-convexity and the interactions among the decision variables. To solve it effectively, we first decouple the problem into three disjoint subproblems. Then, by introducing appropriate auxiliary variables, we derive the closed-form expressions for the decision variables and propose a low-complexity cooperative beamforming algorithm. Simulation results have verified the effectiveness of the proposed algorithm through comparison with various baseline methods. Furthermore, these results also unveil that, for the sum rate maximization, distributing the reflecting elements among multiple RISs is superior to deploying them at one single RIS.

Nowadays, maritime communication has attracted more and more attention. To provide high-speed and low- cost communication in maritime networks, an efficient architecture and flexible routing are expected. In this paper, we propose a novel high altitude balloon-enabled maritime network (HABMN) architecture. In the proposed architecture, considering that high altitude platforms (HAPs) are difficult to maintain station-keeping in practice, we integrate dynamic balloon networks to guarantee maritime communication coverage. Then, we formulate an integer programming to maximize the total traffic accepted by whole networks over time. Considering the unpredictable link disruption caused by sea surface movement and wave occlusions, we update the network configurations to get optimal performance. However, frequent flow update will increase the burden on control channels and lower down system stability. Then, we propose a lazy policy (LP) to wisely determine whether to apply the optimal network configurations immediately or not. At last, extensive simulation experiments demonstrate the effectiveness of the proposed policy.

Internet connected mobile devices in 5G and beyond (simply 5G+) systems are penetrating all aspects of people's daily life, transforming the way we conduct business and live. However, this rising trend has also posed unprecedented traffic burden on existing telecommunication infrastructure including cellular systems, consistently causing network congestion. Although additional spectrum resources have been allocated, exponentially increasing traffic tends to always outpace the added capacity. In order to increase the data rate and reduce the latency, 5G+ systems have heavily relied on hyperdensification and higher frequency bands, resulting in dramatically increased interference temperature, and consequently significantly more weak signals (i.e., signals with low Signal-to-Noise-plus-Interference (SINR) ratio). With traditional detection mechanisms, a large number of weak signals will not be detected, and hence be wasted, leading to poor throughput in 5G+ systems.

Vehicular ad hoc networks (VANETs) leverage information and communications technology to make transportation systems intelligent, safe, and efficient, hence improving people's driving experience. Unfortunately, due to the openness of wireless channels and vehicular mobility, privacy leakage in VANETs poses serious privacy concerns. Once a user's identity is leaked, it will cause serious threats to his/her property and personal safety as a malicious attacker, such as a stalker could utilize the targeted identity to track particular driver and/or launch malicious attack. To address such a privacy problem, by observing the nice properties of ring signature like anonymity, spontaneity, flexibility, and membership equality, we design a novel conditional privacy protection scheme based on ring signcryption, which utilizes the salient features of identity-based cryptosystems and ring signature to achieve conditional privacy. Through security analysis and experiments, we have demonstrated the advantage of our scheme over most existing solutions.

Energy harvesting wireless sensor networks (EH-WSNs) intend to support sustainable operations. It is important to design a high-throughput data delivery scheme that adapts to the fluctuation in harvested energy in the EH-WSN nodes. In this article, the optimal superframe and data buffer scheme (OSDBS) is investigated to improve the throughput of IEEE 802.15.4 beacon-enabled EH-WSNs. A stochastic model is developed for OSDBS, which leads to the characterization of network throughput and packet delay. The OSDBS achieves the maximum throughput through setting the optimal superframe and buffer sizes of the nodes, which are the solution of the formulated optimization problem that maximizes the network throughput with consideration of energy-harvesting rate and data arrival rate. The simulation results show the OSDBS significantly outperforms the existing schemes in terms of throughput.

The sixth-generation (6G) of wireless communications systems will significantly rely on fog/edge network architectures for service provisioning. To realize this vision, AI-based fog/edge enabled reinforcement solutions are needed to serve highly stringent applications using dynamically varying resources. In this paper, we propose a cognitive dynamic fog/edge network where primary nodes (PNs) temporarily share their resources and act as fog nodes (FNs) for secondary nodes (SNs). Under this architecture, that unleashes multiple access opportunities, we design distributed fog probing schemes for SNs to search for available connections to access neighbouring FNs. Since the availability of these connections varies in time, we develop strategies to enhance the robustness to the uncertain availability of channels and fog nodes, and reinforce the connections with the FNs. A robustness control optimization is formulated with the aim to maximize the expected total long-term reliability of SNs’ transmissions. The problem is solved by an online robustness control (ORC) algorithm that involves online fog probing and an index-based connectivity activation policy derived from restless multi-armed bandits (RMABs) model. Simulation results show that our ORC scheme significantly improves the network robustness, the connectivity reliability and the number of completed transmissions. In addition, by activating the connections with higher indexes, the total long-term reliability optimization problem is solved with low complexity.

Battery-powered wireless IoT devices are now widely seen in many critical applications. Given the limited battery capacity and inaccessibility to external power recharge, optimizing energy efficiency (EE) plays a vital role in prolonging the lifetime of these IoT devices. However, a sheer amount of existing works only focus on the EE design at the infrastructure level such as base stations (BSs) but with little attention to the EE design at the device level. In this paper, we propose a novel idea that aims to shift energy consumption to a grid-powered cognitive radio mesh network thus preserving energy of battery-powered devices. Under this line of thinking, we cast the design into a cross-layer optimization problem with an objective to maximize devices’ energy efficiency. To solve this problem, we propose a parametric transformation technique to convert the original problem into a more tractable one. A baseline scheme is used to demonstrate the advantage of our design. We also carry out extensive simulations to exhibit the optimality of our proposed algorithms and the network performance under various settings.

Reconfigurable intelligent surface (RIS) provides a promising way to build the programmable wireless transmission environments in the future. Owing to the large number of reflecting elements used at the RIS, joint optimization for the active beamforming at the transmitter and the passive reflector at the RIS is usually complicated and time-consuming. To address this problem, this article proposes a low-complexity joint beamforming and reflecting algorithm based on fractional programing (FP). Specifically, we first consider a RIS-aided multi-user communication system with perfect channel state information (CSI) and formulate an optimization problem to maximize the sum rate of all users. Since the problem is nonconvex, we decompose the original problem into three disjoint subproblems. By introducing favorable auxiliary variables, we derive the closed-form expressions of the beamforming vectors and reflecting matrix in each subproblem, leading to a joint beamforming and reflecting algorithm with low complexity. We then extend our approach to handle the case when transmitter-RIS and RIS-receiver channels are not perfect and develop corresponding low-complexity joint beamforming and reflecting algorithm with practical channel estimation. Simulation results have verified the effectiveness of the proposed algorithms as compared to various benchmark schemes.

The last decade has witnessed fast development of blockchain techniques. However, the high cost of storage space and network bandwidth caused by data synchronization prevents many nodes from joining the network, and becomes a bottleneck impeding the development of blockchain. Traditional schemes typically attempt to transfer most of the storage and computation tasks from a light client to a full node. Nevertheless, they remain susceptible to privacy attacks because light clients need to query and retrieve blockchain data. In this paper, we first describe the privacy issues and challenges for Ethereum data retrieval and then propose a privacy-preserving scheme based on private information retrieval (PIR) to secure retrieval of blockchain data. The main idea is to achieve pointer based PIR search by keywords and introduce differential privacy to mitigate PIR's performance barrier. Hence we achieve a tradeoff between privacy and performance. The evaluations on the Ethereum dataset and analysis show that our scheme is both effective and practical in protecting blockchain access privacy.

In a vehicular network environment, vehicles can download data through opportunistically-encountered Roadside Units (RSUs) with a lower cost, compared to that from Base Stations (BSs). However, the delay experienced by the vehicles might be undesirably prolonged if they only download data through RSUs. In this paper, we aim to minimize the average delay under the constraint of average cost by scheduling the download rates from RSUs and BSs. One challenge lying in the design of the downloading policy is the uncertainty of the download condition, i.e., whether at least an RSU is available or BS only, in the future slots. To overcome this challenge, we notice that vehicles from opposite directions can share their known download conditions to reduce this uncertainty. To make the most of this information, a Markov decision process (MDP) is used to model the system operations, based on which, average delay and cost can be analyzed to formulate the optimization problem. By solving this problem, the delay-minimal downloading policy can be obtained to achieve the optimal delay-cost tradeoff in the considered vehicular network. Finally, performance improvement with the help of information sharing among vehicles is validated by extensive simulations.

IEEE 802.11p standard, operating over the 75-MHz spectrum at 5.9-GHz band with one control channel (CCH) and six service channels (SCHs), has been poised to provide V2X services over vehicular ad hoc networks (VANETs). However, due to the absence of central coordinator and the nature of high vehicular mobility, it is difficult to achieve reliable multichannel coordination and adaptive resource reservation to make full use of SCHs, resulting in dramatic throughput degradation. To mitigate this, in this article, we propose an adaptive high-throughput multichannel medium access control (MAC) protocol, namely, AHT-MAC, which can effectively handle the data transmissions over SCHs. With AHT-MAC, the data transmission range (TR) is adjusted according to the beacon TR over the CCH so that a transmitting node can determine proper communication candidates and prepare available resources for both communication nodes before transmissions. Moreover, the communication coordination is done through a two-way handshake. During the handshake, adaptive resource reservation is realized following the proposed resource sharing mechanism, where nodes first utilize as much resource as possible and then share them with others proactively. To increase the success probability of the communication handshake, a request conflict resolution mechanism is also designed to nullify improper handshakes. Therefore, AHT-MAC can reduce the resource wastage due to handshake failures and extra overheads for retransmission requests. Our performance analysis shows that AHT-MAC can significantly improve the system throughput and reduce the channel access period.

Leveraging the mobility of autonomous underwater vehicles (AUVs) to collect and deliver data among different underwater devices enables numerous underwater Internet-of-Things (UW-IoT) applications. However, the most versatile underwater acoustic communications (UACs) may not be suitable in the AUV-assisted UW-IoT scenarios, considering the high cost and high power consumption of acoustic transducers, as well as high error rates of UACs due to the complex underwater acoustic channel conditions. Alternatively, we propose to apply the low-power magnetic induction (MI)-based wireless communications for AUV data dissemination and collection. Due to the mobility of AUVs and the underwater turbulence, MI channels between AUVs and other underwater devices are no longer stable and static, which poses great challenges to establish reliable MI links. To tackle this problem, we investigate the dynamic MI wireless communications in this article. We first mathematically characterize the dynamic MI channel when an AUV approaches its target for data collection. Based on this dynamic channel model, the dynamic communication range and available bandwidth of MI are derived. We also build an MI wireless communication system that can work within a dynamic range. The communication performances are evaluated through numerical simulations as well as underwater experiments.

Enabling completely universal coverage, the space-ground communication system integration is one of the most important aspects in the fifth generation (5G) or even the next 6G wireless communications, which significantly benefits railways whose transportation lines always cross diverse environments. Based on this observation, to achieve seamless coverage for environment-diverse high-speed railways (HSRs), by leveraging the control/user-plane (C/U-plane) decoupling and cloud radio access network (C-RAN) technologies, we propose a space-ground integrated cloud railway network consisting of space and ground cloud layers, where in the space, baseband units (BBUs) of low earth orbit (LEO) satellites are collected and centrally-managed by geostationary earth orbit (GEO) satellites. To improve the mobility support and take advantage of the stable and ultra-wide terrestrial coverage of GEO satellites, we establish an additional backup space C-plane (BS-C-plane) connection between trains and GEO satellites. Under this architecture with diverse network resources, we develop a safety-oriented resource allocation scheme based on both the resource allocation priority of safety services and the network handover costs to deliver the safety-oriented services. Simulation results demonstrate that the proposed scheme can always meet the transmission requirements for safety services in HSRs.

5G is emerging to serve as a platform to support networking connections for sensors and vehicles on roads and provide vehicle-to-everything (V2X) services to drivers and pedestrians. 5G V2X communication brings tremendous benefits to us, including improved safety, high reliability, large communication coverage, and low service latency. On the other hand, due to ubiquitous network connectivity, it also presents serious trust, security, and privacy issues toward vehicles, which may impede the success of 5G V2X. In this article, we present a comprehensive survey on the security of 5G V2X services. Specifically, we first review the architecture and the use cases of 5G V2X. We also study a series of trust, security, and privacy issues in 5G V2X services and discuss the potential attacks on trust, security, and privacy in 5G V2X. Then, we offer an in-depth analysis of the state-of-the-art strategies for securing 5G V2X services and elaborate on how to achieve the trust, security, or privacy protection in each strategy. Finally, by pointing out several future research directions, it is expected to draw more attention and efforts into the emerging 5G V2X services.

A practical WiFi system only achieves a discrete data rate adjustment due to hardware constraints while channel signal-to-noise ratio (SNR) is continuous. This mismatch leads to the SNR gaps. In this paper, we introduce a novel communication mechanism, CoS (Communication through Silent subcarriers), which turns the wasted SNR gaps into new opportunities for transmitting control messages for free. Compared with traditional piggybacking schemes, CoS is more reliable to transmit control messages from one node to many nodes. In CoS, silent subcarriers are inserted into data packets and the intervals between adjacent silent subcarriers are utilized to encode information. Since the wasted SNR gap results in under-utilization of the channel code, the data bit errors induced by silent subcarriers are corrected by the correcting capability of the existing channel code as long as we carefully design the total number of inserted silent subcarriers. Based on CoS, we design CoS-MAC to validate the effectiveness of CoS. We measure the throughput of free control messages achieved by CoS under various channel conditions and conduct simulations to show the throughput gain achieved by CoS-MAC over the existing schemes.

Dynamic spectrum sharing between licensed incumbent users (IUs) and unlicensed wireless industries has been well recognized as an efficient approach to solving spectrum scarcity as well as creating spectrum markets. Recently, both US and European governments called a ruling on opening up spectrum that was initially licensed to sensitive military/federal systems. However, this introduces serious concerns on operational privacy (e.g., location, time, and frequency of use) of IUs for national security concerns. Although several works have proposed obfuscation methods to address this problem, these techniques only rely on syntactic privacy models, lacking rigorous privacy guarantee. In this paper, we propose a comprehensive framework to provide real-time differential location privacy for sensitive IUs. We design a utility-optimal differentially private mechanism to reduce the loss in spectrum efficiency while protecting IUs from harmful interference. Furthermore, we strategically combine differential privacy with another privacy notion, expected inference error, to provide double shield protection for IU's location privacy. Extensive simulations are conducted to validate our design and demonstrate significant improvements in utility and location privacy compared with other existing mechanisms.

Radio Frequency Identification (RFID) technology has been proliferating in recent years, especially with its wide usage in retail, warehouse and supply chain management. One of its most popular applications is to automatically detect missing products (attached with RFID tags) in a large storage place. However, most existing protocols assume that the IDs of all tags within a reader's coverage are known, while ignoring practical scenarios where the IDs of some tags may be unknown. The existence of these unknown tags will introduce false positives in those protocols, degrading their performance. Some prior art studies this problem, but their time efficiency is low, especially when the number of unknown tags is large. In this paper, we propose a new missing tag detection protocol based on compressed filters, which not only reduce the filter size for better time-efficiency but also help dampen the interference of unknown tags for high missing-tag detection accuracy. To further improve the performance, we propose to use a combination of sampling and multi-hashing for tags to report their presence, greatly reducing collisions and thus improving the detection probability. We reconfigure the standard ID collection protocol to support bitmap collection required by missing-tag detection. Extensive simulations demonstrate that our compressed filter and collision-reduction method reduce the protocol execution time by 83% to 92% under the same missing-tag detection probability, when comparing with the best prior work. We also evaluate the performance of our missing-tag detection protocol under unreliable channel.

Radio-frequency identification (RFID) technologies have been widely used in inventory control, object tracking and supply chain management. One of the fundamental system functions is called cardinality estimation, which is to estimate the number of tags in a covered area. In this paper, we extend the research of this function in two directions. First, we perform joint cardinality estimation among tags that appear at different geographical locations and at different times. Moreover, we target at category-level information, which is more significant in practical scenarios where we need to monitor the tagged objects of many different categories. Second, we enforce anonymity in the process of information gathering in order to preserve the privacy of the tagged objects. These capabilities will enable new applications such as tracking how products of different categories are transferred in a large, distributed supply chain. We propose and implement a novel protocol to meet the requirements of anonymous category-level joint estimation over multiple tag sets. We formally analyze the performance of our estimator and determine the optimal system parameters. Moreover, we extend our protocol to unreliable channels and consider two channel error models. Extensive simulations show that the proposed protocol can efficiently and accurately estimate joint information over multiple tag sets at category level, while preserving tags' anonymity.

Reliability and stability have been treated as the major requirements for the Business Support System (BSS) in telecommunications networks. It is crucial and essential for service providers to maintain good operating state of the BSS. In this article, we aim at system error prediction for a BSS, i.e., we predict occurrences of the abnormal state or behavior of the BSS. Because the occurrences of system errors are rare events in the BSS (i.e., the dataset of system status is highly imbalanced), it is highly challenging to use machine learning or deep learning algorithms to predict system error for the BSS. To address this challenge, we propose a machine learning-based framework for the system error prediction and a Frequency-based Feature Creation (FFC) algorithm to create new features to improve prediction. By adding the time-series information created by the existing features, the proposed FFC can amplify the effects of important features. Our experimental results show that the FFC significantly improves the prediction performance for the Random Forest algorithm.

Mobile edge computing (MEC) is a promising technique to improve the quality of computation experience for mobile devices by providing computation resources in their close proximity. However, the design of scheduling policies for MEC systems inevitably encounters a challenging optimization problem that should take both transmissions and computations into consideration. In particular, how to jointly schedule transmissions and computations should adapt to the cross-layer system dynamics, i.e., random task arrivals and channel state variations. We formulate this scheduling problem as a joint optimization problem for both transmissions and computations in order to minimize the power consumption of mobile devices, while meeting the latency requirement. With given distributions of the system dynamics, Markov decision process (MDP) is used to model the system operations. Based on this model, the power-optimal scheduling policy can be obtained by converting the joint optimization problem to linear programming (LP) by using variable substitutions and thus the optimal power-latency tradeoff can be achieved. When the distribution information of the system dynamics is unknown, we exploit the Lyapunov optimization to present a low complexity scheduling policy. Our theoretical analysis and extensive simulation studies show that our approach can offer a good tradeoff between power consumption and latency.

In this paper, we investigate the energy minimization problem for a cognitive capacity harvesting network (CCHN), where secondary users (SUs) without cognitive radio (CR) capability communicate with CR routers via device-to-device (D2D) transmissions, and CR routers connect with base stations (BSs) via CR links. Different from traditional D2D networks that D2D transmissions share the resource of cellular transmissions in the same cell, we consider the scenario that D2D transmissions share the uplink cellular frequency bands (CFBs) of neighbor cells. To ensure that the transmissions from SUs do not affect the transmissions for the cellular users (CUs) in the neighbor cells, an inter-cell handshake process is proposed. We formulate the energy minimization problem for SUs as a mixed integer non-linear programming (MINLP). To solve this problem, we decompose it into two nested subproblems: a transmit power optimization subproblem and a CR router and uplink CFB selection subproblem. For the first subproblem, it is proved to be convex, and thus can be efficiently solved. For the second subproblem, we propose a two-level nested game theoretic approach to finding its solution. Simulation results show that the proposed algorithms can significantly improve the performance. With the help of CR routers/the neighbor resource sharing, the energy consumption for SUs can be saved around 30%-37% on average.

To support the demand of multi-Gbps sensory data exchanges for enhancing (semi)-autonomous driving, millimeter-wave bands (mmWave) vehicular-to- infrastructure (V2I) communications have attracted intensive attention. Unfortunately, the vulnerability to blockages over mmWave bands poses significant design challenges, which can be hardly addressed by manipulating end transceivers, such as beamforming techniques. In this paper, we propose to enhance mmWave V2I communications by augmenting the transmission environments through reflection, where highly-reflective cheap metallic plates are deployed as tunable reflectors without damaging the aesthetic nature of the environments. In this way, alternative indirect line-of-sight (LOS) links are established by adjusting the angle of reflectors. Our fundamental challenge is to adapt the time-consuming reflector angle tuning to the highly dynamic vehicular environment. By using deep reinforcement learning, we propose the learning-based Fast Reflection (LFR) algorithm, which autonomously learns from the observable traffic pattern to select desirable reflector angles in advance for probably blocked vehicles in near future. Simulation results demonstrate our proposal could effectively augment mmWave V2I transmission environments with significant performance gain.

Vehicle-based crowdsourcing is becoming a powerful paradigm that can outsource intensive tasks to vehicles by exploiting their on-board resources. In this paper, we focus on the problem of motivating vehicles to join the crowdsourcing system. Considering the various delay demands of tasks in smart cities, we design a delay-aware incentive mechanism to employ vehicles based on reverse auction. Specifically, by taking task delay into consideration, we model the utility of service requester as a function closely related to when its released tasks would be completed. In our mechanism, the participating vehicles bid for their preferred tasks by submitting not only the bidding prices, but also the estimated time of completion (ETC). To maximize the utility of the service requester under a budget constraint, the proposed delay-aware mechanism is cast as a nonmonotone submodular maximization problem with a knapsack constraint. Due to the NP-hardness of the formulated problem, we develop an approximate algorithm for bid selection and payment determination, which guarantees truthfulness, budget feasibility, individual rationality, profitability, and computational efficiency. Simulation results demonstrate the effectiveness of our proposed incentive mechanism.

Ambient backscatter communication (AmBC) enables information delivery over an ambient RF signal without carrier generation and has emerged as a promising technology to build up the self- sustainable Internet-of-Things (IoT). However, when a strong ambient signal appears, multiple backscatter nodes may initiate data transmission simultaneously, causing severe contention and wasting the precious transmission opportunity. The nondeterministic and sporadic nature of ambient signals makes it a great challenge for efficient multiple access design in ambient backscatter aided wireless network (AmBWN). Moreover, the stringent energy supply and ultra-low-cost design of the backscatter transmitter makes most multiple access schemes no longer suitable for AmBWN. To fully share carrier resources for backscattering, we resort to the non-orthogonal multiple access (NOMA) to allow multiple devices in the same regime to transmit over an ambient signal with low latency. Moreover, we propose a hierarchical multiple access scheme, which allows beamforming based spatial division multiple access among groups, and NOMA for multiple users access within a group. The evaluation result shows latency and SINR can be significantly improved with minimal overhead at the transmitter.

In Bitcoin, lightweight clients outsource most of storage and computation tasks to full nodes in order to run on resource-limited devices. In the interaction with the full node, the lightweight client leaks considerable information about which address or transaction is relevant to it. The existing schemes to solve this problem do not support efficient yet privacy-preserving transaction search due to the fact that the blockchain is inherently inefficient for transaction query and proposed schemes perform transaction search in a block-by-block manner. Therefore, we propose an efficient transaction query scheme for the privacy-preserving lightweight client with the Intel SGX enclave running on the full node. Our main idea is to leverage the secure enclave to serve transaction-query requests from lightweight clients. However, the usage of secure enclave alone does not achieve our goals. Our scheme reorganizes the blockchain and leverages prefix tree to increase transaction-search efficiency. Due to limited capacity, the secure enclave stores reorganized blockchain data in the untrusted full node. Thus, our scheme integrates prefix tree and oblivious searching technologies to simultaneously support efficient transaction search and protect access pattern of externally stored blockchain data for the secure enclave. Security analysis and performance evaluation show that our scheme provides efficient transaction search and verification functionalities for lightweight Bitcoin clients in a privacy-preserving way.

In cellular Internet of things (IoT) systems, massive low-power terminals update information status to cellular base stations to support diverse IoT applications. In this circumstance, information freshness and energy efficiency become two fundamental concerns. Except data transmissions, information updates consume additional energy for radio activation. To improve the energy efficiency, it is reasonable to aggregate the dynamically generated data. However, the reduced updates will severely deteriorate the information freshness, especially for time-critical IoT applications. To address this issue, we propose an upload scheduling scheme in this paper. Considering dynamic packet arrivals and channel conditions, the upload scheduling problem is formulated from a long-term perspective. To solve this problem, a practical online upload scheduler, named as FReshness-aware Energy efficient ScHeduler (FRESH), is proposed to minimize the update energy consumption subject to information freshness constraints. We theoretically show that FRESH can make the energy saving arbitrarily close to that of the optimal scheduling decision. Simulation results demonstrate the necessity and effectiveness of implementing FRESH for cellular IoT systems.

The exponentially increasing number of heterogeneous Internet of Things (IoT) devices result in severe spectrum shortage and interference in the already crowded ISM band. Cross-Technology Communication (CTC) is dedicated to achieving direct communication among wireless devices with different radios and modulation schemes, which serves as an effective approach to address the above challenges. Nevertheless, CTC also provides opportunities for adversaries to manipulate IoT devices. In this paper, we identify a new attack. Built on CTC, WiFi devices are able to hide the pre-intercepted ZigBee message into their transmitted waveforms, achieving the objective of directly controlling ZigBee devices. To defend against the attack, we analyze possible strategies and consider constellation higher-order statistic analysis as the countermeasure. Extensive simulations and experiments with commodity devices (CC26x2R1) and USRP-based prototypes show the existence of the newly identified attack, and further, validate the effectiveness of the proposed defensive approach.

With the rapid growth on data traffic, spectrum shortage becomes increasingly serious, leading to the paradigm shift in spectrum usage from an exclusive mode to a sharing mode. However, how to utilize shared spectrums effectively for service provisioning is not straightforward due to its uncertain availability, known as spectrum uncertainty. In this paper, we propose a new metric to evaluate the achievable rate of a link on a share band under a confidence level, called probabilistic link capacity, which offers us an effective way to guarantee the quality of service statistically when using the shared spectrum for service delivery. Different from most existing works where the distributional information is explicitly given based on certain structural assumption, we develop a data-driven distributionally robust approach by using the first and second order statistical information. To achieve the result, we formulate it into a tractable semidefinite programming problem based on the worst-case of conditional-value-at-risk. Finally, as a use case, we design a service-based spectrum-aware transmission scheme, so that different kinds of spectrums (licensed and shared) can be efficiently utilized to satisfy the diverse service requirements.

Offloading cellular traffic to WiFi networks plays an important role in alleviating the increasing burden on cellular networks. However, excessive traffic offloading brings severe packet collisions into a WiFi network due to its contention-based medium access scheme, which significantly reduces the WiFi network's throughput. In this paper, we propose DAO, a device-to-device (D2D) communications-assisted traffic offloading scheme to improve the amount of traffic offloaded from cellular to WiFi in integrated cellular and WiFi networks. Specifically, in an integrated cellular-WiFi network, the cellular network exploits D2D communications in licensed cellular bands to aggregate traffic from cellular users before offloading it to the WiFi network to reduce the number of contending users in WiFi access. The traffic offloading process in DAO is formulated as an optimization problem that jointly takes into account the activations of aggregation nodes (ANs) and the connections between ANs and offloading users to maximize the offloaded traffic while guaranteeing the long-term data rates required by the offloading users. Extensive simulation results reveal the significant performance gain achieved by DAO over the existing schemes.

In current healthcare systems, patients use various types of medical Internet of Things devices for monitoring their health conditions. The collected information (personal health records) will be sent back to hospitals for diagnosis and quick responses. However, severe security and privacy leakages with regard to data privacy and identity authentication are incurred because the monitored health data contains sensitive information. Therefore, the data should be well protected from unauthorized entities. Unfortunately, traditional cryptographic approaches or password-based mechanisms cannot fulfill the privacy and security demands in health monitoring due to their low efficiency and knowledge-based property. Biometric authentication overcomes these deficiencies and successfully verifies the inherent characteristics of humans. Among all biometrics, the electrocardiogram (ECG) signal is the most suitable one due to its medical properties. However, the security and privacy objectives of ECG-based authentication usually fail in practice due to the noise interferences in the collected ECG data and the privacy breach of the ECG database. In this paper, we propose a practical scheme that can reliably authenticate patients with noisy ECG signals and provide differentially private protection simultaneously. The effectiveness and efficiency of our scheme are thoroughly analyzed and evaluated over online datasets. We also conduct a pilot study on human subjects experiencing different exercise levels to validate our scheme.

Traditional systems for monitoring and diagnosing patients' health conditions often require either dedicated medical devices or complicated system deployment, which incurs high cost. The networking research community has recently taken a different technical approach of building health-monitoring systems at relatively low cost based on wireless signals. However, the radio frequency signals carry various types of noise and have time-varying properties that often defy the existing methods in more demanding conditions with other body movements, which makes it difficult to model and analyze the signals mathematically. In this paper, we design a novel wireless system using commercial off-the-shelf RFID readers and tags to provide a general and effective means of measuring bodily oscillation rates, such as the hand tremor rate of a patient with Parkinson's disease. Our system includes a series of noise-removal steps, targeting at noise from different sources. More importantly, it introduces two sliding window-based methods to deal with time-varying signal properties from channel dynamics and irregular body movement. The proposed system can measure bodily oscillation rates of multiple persons simultaneously. Extensive experiments show that our system can produce accurate measurement results with errors less than 0.4 oscillations per second when it is applied to monitor hand tremor, even when the individuals are moving.

Colocation data centers are an important type of data centers that have some unique challenges in managing their energy consumption. Tenants in a colocation data center usually manage their servers independently without coordination, leading to inefficiency. To address this issue, we propose a formulation of coordinated energy management for colocation data centers. Considering the randomness of workload arrival and electricity cost function, we formulate it as a stochastic optimization problem, and then develop an online algorithm to solve it efficiently. Our algorithm is based on Lyapunov optimization, which only needs to track the instantaneous values of the underlying random factors without requiring any knowledge of the statistics or future information. Moreover, alternating direction method of multipliers (ADMM) is utilized to implement our algorithm in a decentralized way, making it easy to be implemented in practice. We analyze the performance of our online algorithm, proving that it is asymptotically optimal and robust to the statistics of the involved random factors. Moreover, extensive trace-based simulations are conducted to illustrate the effectiveness of our approach.

Mobile cloud computing systems, or simply mobile clouds, have attracted tremendous attention because they allow mobile devices with limited computational resources to offload complex computations. However, due to the channel uncertainty and the complexity of a computation task, mobile computation offloading may suffer from poor outage performance that the offloaded task cannot be completed within the desired delay constraint. Thus, how to efficiently identify and overcome the outage bottleneck, which could be used to optimize resource allocation schemes and improve the system performance effectively is an open problem. In this paper, we shall develop a unified framework that minimizes the overall outage probability in various mobile computation offloading scenarios. More specifically, the outage bottleneck is defined and identified by adopting asymptotic analysis, without any need of the accurate outage probabilities in both transmissions and computations. To overcome the outage bottleneck, resource pairing, matching, and allocation policies are investigated. Both theoretical analysis and numerical results show that the outage bottleneck relies on not only the availability of spectrum and computation resources but also the probability distributions of computation complexities of the computation tasks.

In this paper, we introduce a network entity called point of connection (PoC), which is equipped with customized powerful communication, computing, and storage (CCS) capabilities, and design a data transportation network (DART) of interconnected PoCs to facilitate the provision of Internet of Things (IoT) services. By exploiting the powerful CCS capabilities of PoCs, DART brings both communication and computing services much closer to end devices so that resource-constrained IoT devices could have access to the desired communication and computing services. To achieve the design goals of DART, we further study the spectrum-aware placement of edge computing services. We formulate the service placement as a stochastic mixed-integer optimization problem and propose an enhanced coarse-grained fixing procedure to facilitate efficient solution finding. Through extensive simulations, we demonstrate the effectiveness of the resulting spectrum-aware service placement strategies and the proposed solution approach.

The exploding popularity of mobile devices enables people to enjoy the benefits brought by various interesting mobile apps. The ever-increasing data traffic has exacerbated energy consumption on both cellular service providers and mobile users. It has become an urgent need to reducing the energy consumption in the cellular network while satisfying users' increasing traffic demands. Mobile data offloading is an effective energy-saving paradigm to tackle the above-mentioned problem. However, the current approaches cannot fully address the issue in terms of user demands and offloaded traffic. With the observation that duplicated data transmission often happens in the crowd with similar social interests, we deploy device-to-device (D2D) data offloading to achieve the energy efficiency at the user side while adapting their increasing traffic demands. Specifically, we investigate the stochastic optimization of the long-term time-averaged expected energy consumption while guaranteeing the strong stability of the network by utilizing the social-aware and energy-efficient D2D mobile offloading. By jointly considering interference among D2D users, social-aware caching, link scheduling, and routing, an offline finite-queue-aware energy minimization problem is formulated, which is a time-coupling stochastic mixed-integer non-linear programming (MINLP) problem. We propose an online finite-queue-aware energy algorithm by employing the Lyapunov drift-plus-penalty theory. Extensive analysis and simulations are conducted to validate the proposed scheme.

The integration of sub-6 GHz and millimeter wave (mmWave) bands has a great potential to enable both reliable coverage and high data rate in future vehicular networks. Nevertheless, during mmWave vehicle-to-infrastructure (V2I) handovers, the coverage blindness of directional beams makes it a significant challenge to discover target mmWave remote radio units (mmW-RRUs) whose active beams may radiate somewhere that the handover vehicles are not in. Besides, fast and soft handovers are also urgently needed in vehicular networks. Based on these observations, to solve the target discovery problem, we utilize channel state information (CSI) of sub-6 GHz bands and Kernel-based machine learning (ML) algorithms to predict vehicles' positions and then use them to pre-activate target mmW-RRUs. Considering that the regular movement of vehicles on almost linearly paved roads with finite corner turns will generate some regularity in handovers, to accelerate handovers, we propose to use historical handover data and K-nearest neighbor (KNN) ML algorithms to predict handover decisions without involving time-consuming target selection and beam training processes. To achieve soft handovers, we propose to employ vehicle-to-vehicle (V2V) connections to forward data for V2I links. The theoretical and simulation results are provided to validate the feasibility of the proposed schemes.

A typical application of unmanned aerial vehicles (UAVs) is surveillance of distant targets, where data collected by its sensors need to be transmitted back to a ground terminal (GT) for further processing in a timely manner. Due to the limited battery capability of the UAV, the sensed data could be preprocessed in a UAV to reduce the amount of data transmitted, which could potentially reduce the average power consumption at the UAV, especially when the transmission link quality is poor. In this paper, a probabilistic approach is adopted to schedule the transmission and computing of the data tasks based on the UAV and GT's buffer states. The joint transmission and computing problem can be modeled as a four-dimensional Markov chain, based on which the average delay of each task and the average power consumption at the UAV can be obtained. Our design goal is to minimize the average power consumption under the delay constraints. To do that, a delay-constrained power minimization problem is solved by an proposed method to obtain the power-optimal joint transmission and computation scheduling (JTCS) policy efficiently. Finally, the optimization results are validated with extensive simulations.

Nowadays, vehicles are powerful enough to carry communications, computing and storage capabilities. By interacting with each other and with local (i.e., fog) infrastructures like road-side units, a cohort of vehicles and fog devices could collaboratively provide services like crowdsensing in an unprecedentedly secure and efficient way. However, it has been widely recognized as a challenging work in the vehicular system to develop a secure and efficient sensing task allocation and data de-duplication mechanism. In this paper, we attempt to develop a scheme to address this challenge. Specifically, we use the Elliptic Curves Cryptography (ECC) algorithm to realize secure allocation of location-dependent tasks. During the report submission phase, we adopt the improved message-lock encryption to realize privacy-preserving data de-duplication and to resist the duplicate-faking attacks. Besides, we present a novel signature scheme that can efficiently record the contributions of each vehicle. The security analysis and performance evaluation demonstrate that the proposed scheme can achieve secure and privacy-preserving report de-duplication with moderate computation and communication overhead.

In vehicular ad hoc networks (VANETs), vehicles' mobility and urban obstacles may cause frequent communication disconnections and sudden network changes. As a result, the traditional IP-based node-to-node content delivery mechanism does not adapt well to such changes in VANETs. To solve this problem, in this paper, we study the Named Data Networking (NDN) architecture to support efficient and secure data forwarding in urban VANETs. To meet security requirements, we adopt the encryption-based name obfuscation to achieve Interest-based access control. Moreover, the revocation of illegal vehicles and the updated operation are addressed by proxy re-encryption method, which saves the main communication overhead during the process. Finally, we design an incentive scheme to guarantee the utility of NDN in VANETs. The security analysis shows that the proposed secure scheme can satisfy security requirements of the data forwarding in VANETs. The performance analysis indicates that the overhead caused by the proposed secure scheme is low and acceptable.

Due to its promising application in the field of human- machine interaction, letter recognition in the air has drawn considerable attention in recent years. Compared with traditional sensor-based and camera-based methods, letter recognition in the air using channel state information (CSI) is more user-friendly and easy-to-deploy. Unfortunately, due to the limited range of the moving hand and the similarity of different letters, it is difficult to extract discriminative writing patterns for different letters from the noisy environment. In this paper, we design LetFi, a high accuracy letter recognition in the air system, which could detect and recognize the letter written by a user by analyzing its influence on surrounding WiFi signals. Specifically, we design a differential method to extract robust CSI measurements, develop a variance based scheme to detect the start and the end points of the letter writing activity, and propose a coherence histogram based multi-domain feature extraction strategy to extract discriminative features from not only the time domain and frequency domain, but also the spatial structural domain. Extensive experimental results show that the proposed LetFi system could achieve a recognition accuracy of 95% when recognizing the 26 capital letters.

WiFi's energy efficiency is a critical issue for battery-powered mobile devices. Since wireless channel has inherent broadcast nature, a non-negligible amount of a device's energy is spent on overhearing useless packets that are not addressed to itself. To resolve packet overhearing problem, most existing schemes are limited to decode data packet or exchange control packet to obtain extra information. In this paper, we propose PhyCast (Physical layer broadCast), a novel communication scheme to embed lightweight information into the front part of data transmission at the physical layer. With PhyCast, the transmitter's neighboring nodes extract information by symbol level energy detection, which does not require receiving and decoding the whole data packet. Therefore, unintended receivers can quickly drop useless packet and switch to a low-power state. The design of PhyCast does not affect the correct decoding of a data packet or sacrifice the normal data throughput. In addition, the communication scheme PhyCast is transparent to the existing WiFi devices, so PhyCast is backward compatible with the 802.11 standard. Our simulation results show that PhyCast achieves significant energy efficiency improvement under various network settings. When a WiFi network includes 15 nodes, PhyCast saves 36.85% energy compared with the 802.11 standard.

In this paper, we aim to obtain the optimal tradeoff among average delay, and average transmission and computation power consumptions in a mobile computation offloading system. A probabilistic approach is developed to jointly determine the transmission and computation rate in each time-slot. We model the queue lengths in the mobile device and computation resource with a two- dimensional Markov chain. Based on this model, we obtain the average delay and power consumption. Then, we formulate a joint queues aware optimization problem to minimize the average power consumption of the mobile device given constraints on average delay of tasks and average power consumption of the computation resource. By converting the problem into a linear programming, we obtain the optimal power-delay tradeoff and power-optimal Joint Transmission and Computing Scheduling (JTCS) strategy. Finally, the optimization results are validated by extensive simulations.

With the growth of smartphone sales and app usage, fingerprinting and identification of smartphone apps have become a considerable threat to user security and privacy. Traffic analysis is one of the most common methods for identifying apps. Traditional countermeasures towards traffic analysis includes traffic morphing and multipath routing. The basic idea of multipath routing is to increase the difficulty for adversary to eavesdrop all traffic by splitting traffic into several subflows and transmitting them through different routes. Previous works in multipath routing mainly focus on Wireless Sensor Networks (WSNs) or Mobile Ad Hoc Networks (MANETs). In this paper, we propose a multipath routing scheme for smartphones with edge network assistance to mitigate traffic analysis attack. We consider an adversary with limited capability, that is, he can only intercept the traffic of one node following certain attack probability, and try to minimize the traffic an adversary can intercept. We formulate our design as a flow routing optimization problem. Then a heuristic algorithm is proposed to solve the problem. Finally, we present the simulation results for our scheme and justify that our scheme can effectively protect smartphones from traffic analysis attack.

Radio-frequency identification (RFID) technologies have been widely used in inventory management, object tracking and supply chain management. One of the fundamental system functions is called cardinality estimation, which is to estimate the number of tags in a covered area. We extend the research of this function in two directions. First, we perform joint cardinality estimation among tags that appear at different geographical locations and at different times. Moreover, we collect category-level information, which is more significant in practical scenarios where we need to monitor the tagged objects of many different types. Second, we require anonymity in the process of information gathering in order to preserve the privacy of the tagged objects. These capabilities will enable new applications such as tracking how products are moved in a large, distributed supply network. We propose a novel protocol design to meet the requirements of anonymous category-level joint estimation over multiple tag sets. We formally analyze the performance of our estimator and determine the optimal system parameters. Extensive simulations show that the proposed protocol can efficiently obtain accurate category-level estimation, while preserving tags' anonymity.

Traditional systems for monitoring and diagnosing patients' health conditions often require either dedicated medical devices or complicated system deployment, which incurs high cost. The networking research community has recently taken a different technical approach of building health-monitoring systems at relatively low cost based on wireless signals. However, the RF signals carry various types of noise and have time-varying properties that often defy the existing methods in more demanding conditions with other body movements, which makes it difficult to model and analyze the signals mathematically. In this paper, we design a novel wireless system using commercial off-the-shelf RFID readers and tags to provide a general and effective means of measuring bodily oscillation rates, such as the hand tremor rate of a patient with Parkinson's disease. Our system includes a series of noise-removal steps, targeting at noise from different sources. More importantly, it introduces two sliding window-based methods to deal with time-varying signal properties from channel dynamics and irregular body movement. The proposed system can measure bodily oscillation rates of multiple persons simultaneously, even when the individuals are moving. Extensive experiments show that our system can produce accurate measurement results with errors less than 0.3 oscillations per second when it is applied to monitor hand tremor.

The Internet of Things (IoT) becomes a novel paradigm as more and more devices are connected to the Internet, enabling several innovative applications such as smart home, industrial automation, and connected health. However, the cyber-attack to these applications is a big issue and countermeasures are in dire need to provide system security and user privacy. In this paper, we address the traffic analysis attack to smart homes, where adversaries intercept the Internet traffic from/to the smart home gateway and profile residents' behaviors through digital traces. Traditional cryptographic tools may not work well due to the effectiveness of adversaries' machine learning algorithms in classifying encrypted traffic, so here we propose a privacy-preserving traffic obfuscation framework to achieve the goal. To be specific, we leverage the smart community network of wirelessly connected smart homes and intentionally direct each smart home's traffic to another home gateway before entering the Internet. The design jointly considers the network energy consumption and the resource constraints in IoT devices, while achieving strong differential privacy guarantee so that adversaries cannot link any traffic flow to a specific smart home. Besides, we consider a hostile smart community network and develop secure multihop routing protocols to guarantee the source/destination unlinkability and satisfy each user's personalized privacy requirement. To evaluate the effectiveness of our framework in protecting privacy and reducing network energy consumption, extensive simulations are conducted and the results demonstrate that our design outperforms other differential privacy mechanism in preserving privacy and minimizing network utility cost.

Given the fact that roughly 800 000 children are reported missing in the United States every year, how to assist parents to track their children becomes an important problem. Even though many children tracking systems have been proposed, the high cost and energy limitation of locators are the stumbling blocks which limit the application of those systems. To address this challenge, we design a children tracking system based on RFID technology, where children carry RFID tags and the system is responsible for locating the children by aggregating the readings from the deployed readers. Noting the importance of localized processing for efficient children tracking, we further study how the locally available computing resource, such as the mobile devices carried by the park employees and visitors, can be utilized for service provisioning. Since mobile devices have limited energy, we study an energy efficiency optimization problem by jointly considering the resource allocation and user association. The formulated problem is solved by a dynamic updating matching approach. Through extensive simulations, we have demonstrated the effectiveness of our proposed solution.

The exploding popularity of mobile devices enables people to enjoy benefits brought by various interesting mobile apps. However, the ever-increasing data traffic has exacerbated the congestion on current cellular networks, which results in users' dissatisfaction, especially in crowded areas. Hence, how to alleviate data traffic in cellular networks becomes a challenging problem. Traditional methods rely on mobile offloading techniques to deviate the data traffic originally targeted to cellular networks, such as the small cell, Wi-Fi, and opportunistic communication. Unfortunately, mobile users still experience severe congestion when a large number of users request for data. Facing these challenges, we introduce the concept of mobile participation to assist data offloading by leveraging the mobility of users and the social features among a group of users. A mobile caching user, who pre-caches a certain amount of contents, will roam around congested areas to participate in content dissemination in order to satisfy users' requests, which is expected to benefit both himself and users in the crowd simultaneously. To motivate such human-enabled mobile participation for data offloading, a Stackelberg game is deployed with joint considerations on social effect and delay effect. Based on detailed performance analysis, we demonstrate the feasibility and efficiency of the proposed approach.

Communication technologies supply the blood for smart city applications. In view of the ever-increasing wireless traffic generated in smart cities and our already congested radio access networks (RANs), we have recently designed a data transportation network, the vehicular cognitive capability harvesting network (V-CCHN), which exploits the harvested spectrum opportunity and the mobility opportunity offered by the massive number of vehicles traveling in the city to not only offload delay-tolerant data from congested RANs but also support delay-tolerant data transportation for various smart-city applications. To make data transportation efficient, in this paper, we develop a spectrum-aware (SA) data transportation scheme based on Markov decision processes. Through extensive simulations, we demonstrate that, with the developed data transportation scheme, the V-CCHN is effective in offering data transportation services despite its dependence on dynamic resources, such as vehicles and harvested spectrum resources. The simulation results also demonstrate the superiority of the SA scheme over existing schemes. We expect the V-CCHN to well complement existing telecommunication networks in handling the exponentially increasing wireless data traffic.

Machine Learning based data classification is a widely used data mining technique. By learning massive data collected from the real world, data classification helps learners discover hidden data patterns. These hidden data patterns are represented by the learned model in different machine learning schemes. Based on such models, a user can classify whether the new incoming data belongs to an existing class; or, multiple entities may test the similarity of their datasets. However, due to data locality and privacy concerns, it is infeasible for large-scale distributed systems to share each individual's datasets for classifying or testing. On the one hand, the learned model is an entity's private asset and may leak private information, which should be well protected from all other non-collaborative entities. On the other hand, the new incoming data may contain sensitive information which cannot be disclosed directly for classification. To address the above privacy issues, we propose an approach to preserve the model privacy of the data classification and similarity evaluation for distributed systems. With our scheme, neither new data nor learned models are directly revealed during the classification and similarity evaluation procedures. Based on extensive real-world experiments, we have evaluated the privacy preservation, feasibility, and efficiency of the proposed scheme.

Wireless cloud computing system with computation offloading has attracted much attention due to the potential of alleviating the restrictions of limited resources in mobile devices. During the computation offloading, the steps of transmissions and computation need to be completed within the required time, otherwise the system will be in outage. Due to uncertainties of channel fading and computational complexities, such outage is the result of either transmission outage or computation outage. In this paper, a theoretical framework is proposed to analyze the outage of the wireless cloud computing system with multiple subcarriers and computation resources. Through theoretical analysis of the outage probability, the outage bottleneck can be identified. Numerical results have verified the theoretical analysis and concluded that the outage bottleneck depends not only on the distributions of complexities of tasks but also on the numbers of subcarriers and computation resources.

Efficient design of wireless networks benefits from the exchange of control messages. However, control message itself consumes scarce channel resources. In this paper, we propose CoS (Communication through symbol Silence), a novel communication strategy that conveys control messages for free without consuming extra channel resources. CoS inserts silence symbols in data packets and leverages the intervals between inserted silence symbols to encode information. The silence symbols can be located by energy detection at the granularity of symbols and the intervals are interpreted into transmitted control messages. Based on our key insights that the channel code is under-utilized in current wireless networks and the distribution of symbol errors within a data packet is predictable in indoor wireless transmissions, the symbols erased by silence symbols are recovered by the coding redundancy that is originally used to correct symbol errors. A rate adaptation scheme is designed to dynamically adjust the rate of free control messages according to channel conditions so that the transmission of free control messages does not harm the original data throughput. We implement CoS on our software defined radio platform to validate the feasibility of CoS. The extensive results show that the control messages are delivered with close to 100% accuracy in a large SNR range. In addition, we measure the achievable capacity of free control messages in various channel conditions.

Photo sharing is an attractive feature which popularizes online social networks (OSNs). Unfortunately, it may leak users' privacy if they are allowed to post, comment, and tag a photo freely. In this paper, we attempt to address this issue and study the scenario when a user shares a photo containing individuals other than himself/herself (termed co-photo for short). To prevent possible privacy leakage of a photo, we design a mechanism to enable each individual in a photo be aware of the posting activity and participate in the decision making on the photo posting. For this purpose, we need an efficient facial recognition (FR) system that can recognize everyone in the photo. However, more demanding privacy setting may limit the number of the photos publicly available to train the FR system. To deal with this dilemma, our mechanism attempts to utilize users' private photos to design a personalized FR system specifically trained to differentiate possible photo co-owners without leaking their privacy. We also develop a distributed consensus-based method to reduce the computational complexity and protect the private training set. We show that our system is superior to other possible approaches in terms of recognition ratio and efficiency. Our mechanism is implemented as a proof of concept Android application on Facebook's platform.

Previously, we proposed a broadband fifth-generation (5G) control/user (C/U)-plane decoupled heterogeneous railway wireless system to meet the exponentially increasing capacity demands in railway. Nevertheless, service interruptions due to the handovers in overlapping registration areas are still challenging in railway wireless systems. How to achieve soft and fast handovers to overcome these challenges attracts intensive attention finally. In this paper, we propose a novel network architecture for 5G C/U-plane decoupled heterogeneous railway wireless systems to physically separate and stagger the C-plane and U-plane handovers. During the C-plane handover process that occurs between the macro-cells, the U-plane is always kept connected without any handover, which achieves non-interruptible soft handover. Moreover, since there are no handovers in the U-plane, the handover procedure is significantly simplified, thereby accelerating the handover process. Similarly, during the U-plane handover process, which happens between the small cells, the C-plane is kept connected all the time, saving the otherwise intensive C-plane handover signaling. Besides, the coordinated multi-point transmission and reception (CoMP) and bi-casting technologies are applied to establish the target U-plane connection ahead of cutting off the old one, so that no interruption occurs during the U-plane handover process. This paper demonstrates that the proposed handover schemes can greatly improve the handover performance.

Cognitive radio networks (CRNs) have been emerging as a promising technique to improve the spectrum efficiency of wireless and mobile networks, which form spectrum clouds to provide services for unlicensed users. As spectrum clouds, the performance of multi-hop CRNs heavily depends on the routing protocol. In this paper, taking the newly proposed Cognitive Capacity Harvesting network as an example, we study the routing problem in multi-hop CRNs and propose a spectrum-aware anypath routing (SAAR) scheme with consideration of both the salient spectrum uncertainty feature of CRNs and the unreliable transmission characteristics of wireless medium. A new cognitive anypath routing metric is designed based on channel and link statistics to accurately estimate and evaluate the quality of an anypath under uncertain spectrum availability. A polynomial-time routing algorithm is also developed to find the best channel and the associated optimal forwarding set and compute the least cost anypath. Extensive simulations show that the proposed protocol SAAR significantly increases packet delivery ratio and reduces end-to-end delay with low communication and computation overhead, which makes it suitable and scalable to be used in multi-hop CRNs.

Previously, we have proposed a C/U-plane (Control/User plane) decoupled railway wireless network in which higher frequency bands are adopted by small cells to provide wider available spectra for the U-plane of passengers' services. In order to guarantee reliable connectivity to wayside eNodeBs (eNBs), an onboard mobile relay (MR), consisting of two components, namely, MR-UE (User Equipment) and MR-AP (Access Point), is employed to forward passengers' services over backhaul links between MR-UE and wayside eNBs. The remaining problem here is how to utilize spectra effectively and efficiently under this new configuration. Since a given wayside eNB hosts only one single accessed user most of the time, we design an additional uplink scheduler for the MR-UE to self-manage the usage of uplink resources, avoiding the complicated uplink grant procedure commonly used in the conventional cellular systems. Moreover, we develop eNB schedulers to coordinate the spectra in small cells. To deal with occasional multi-user scenarios, we propose an uplink scheduler switcher for the macro cell to judge which uplink scheduler should be activated. Furthermore, an uplink resource allocation scheme with high spectrum efficiency is deliberately designed for the new dual-scheduler configuration. Finally, we carry out theoretical analysis and numerical simulations to demonstrate the effectiveness of our proposed scheme.

Battery-Free Wireless Sensor Networks (BF-WSNs) have become increasingly useful for many applications and how to ensure timely information exchange between nodes in IP networks and those in BF-WSNs is indispensable. The 6LoWPAN protocol is usually used to deliver IPv6 packets over IEEE 802.15.4 based WSNs, and has resolved the size mismatching problem between IPv6 packets and 802.15.4 Medium Access Control (MAC) frames by using packet fragmentation scheme to break an IPv6 packet into multiple small pieces with each fitted into a single 802.15.4 MAC frame. Unfortunately, IPv6 packets in BF-WSNs may suffer from intolerable delay for timely reassembling back to IPv6 packets. In this paper, we present a Latency Aware IPv6 Packet Delivery (LAID) scheme to reduce such IPv6 packet latency while maintaining high packet delivery ratio. Our LAID considers charging time, data rate, and the Maximum Number of Transmission Trials (MNTT) used in the IEEE 802.15.4 MAC layer so that the minimum latency can be achieved by optimizing the pairing of data rate and MNTT. In addition, we apply network coding to improve packet delivery reliability. Our analysis shows that the proposed LAID significantly outperforms existing schemes with fixed data rates in terms of IPv6 packet latency.

Radio-frequency identification (RFID) technologies are making their way into retail products, library books, debit cards, passports, driver licenses, car plates, medical devices, and so on. The widespread use of tags in traditional ways of deployment raises a privacy concern: they make their carriers trackable. To protect the privacy of the tag carriers, we need to invent new mechanisms that keep the usefulness of tags while doing so anonymously. Many tag applications, such as toll payment, require authentication. This paper studies the problem of anonymous authentication. Since low-cost tags have extremely limited hardware resource, we propose an asymmetric design principle that pushes most complexity to more powerful RFID readers. With this principle, we develop a lightweight technique that generates dynamic tokens for anonymous authentication. Instead of implementing complicated and hardware-intensive cryptographic hash functions, our authentication protocol only requires tags to perform several simple and hardware-efficient operations such as bitwise XOR, one-bit left circular shift, and bit flip. The theoretical analysis and randomness tests demonstrate that our protocol can ensure the privacy of the tags. Moreover, our protocol reduces the communication overhead and online computation overhead to O(1) per authentication for both tags and readers, which compares favorably with the prior art.

As the low frequency band has become more and more crowded, millimeter-wave (mmWave) has attracted significant attention. The IEEE has released the 802.11ad standard to satisfy the demand of ultra-high-speed communication. It adopts beamforming technology that can generate directional beams to compensate for high path loss. In the association beamforming training (A-BFT) phase of BF training, a station (STA) randomly selects an A-BFT slot to contend for training opportunity. Due to the limited number of A-BFT slots, the A-BFT phase suffers high probability of collisions in dense user scenarios, resulting in inefficient training performance. Based on the evaluation of the IEEE 802.11ad standard and 802.11ay draft in dense user scenarios of mmWave wireless networks, we propose an enhanced A-BFT beam training and random access mechanism, including the separated A-BFT (SA-BFT) and secondary backoff A-BFT (SBA-BFT). The SA-BFT can provide more A-BFT slots and divide the A-BFT slots into two regions by defining a new E-A-BFT Length field compared with the legacy 802.11ad A-BFT, thereby maintaining compatibility when 802.11ay devices are mixed with 802.11ad devices. It can also greatly reduce the collision probability in dense user scenarios. The SBA-BFT performs secondary backoff with very small overhead of transmission opportunities within one A-BFT slot, which not only further reduces collision probability, but also improves the A-BFT slots utilization. Furthermore, we propose a 3-D Markov model to analyze the performance of the SBA-BFT. The analytical and simulation results show that both the SA-BFT and the SBA-BFT can significantly improve BF training efficiency, which is beneficial to the optimization design of dense user wireless networks based on the IEEE 802.11ay standard and mmWave technology.

Partially overlapped channels (POCs) have been studied recently to improve network performance. However, the current OFDM-based 802.11 system is designed for co-channel communication, and does not support communication over POCs. Thus the coordination between POCs imposes a new challenge to WLANs. In this paper, we present DSN (Data Symbol Nulling), a novel communication strategy that leverages the pattern of data symbols (null or non-null), rather than the actual data symbol value, to convey lightweight control information (or sequences of binary bits). The receiver whose channel is partially overlapped with the sender, interprets thus-transmitted messages by detecting the energy of received data symbols, the minimum resource units in OFDM. A key principle of DSN is that the newly designed communication strategy does not sacrifice original data throughput. Our extensive results validate communication over POCs, and show that lightweight control information can be delivered with close to 100% accuracy. Further, based on this communication paradigm, we propose DSN-MAC, an efficient coordination scheme between POCs in WLANs. The detailed simulation results show that DSN-MAC can substantially improve overall network throughput.

Using genetic profiles of individuals for tailored diagnosis and treatment has great promise in the healthcare industry. Despite of the rapid growth in genome-aware medicine, genome-aware health monitoring has not been studied as well. A major stumbling block is the privacy issues of such applications. In addition to privacy concerns in a traditional health monitoring system, i.e., the privacy of users' biomedical sensing data and the protection of the proprietary health monitoring program, severe privacy concerns arise when users' genomic data are integrated into the health monitoring program due to the re-identification and phenotype attacks based on the DNA profile and the relevance of DNA information in a family. In this paper, we investigate these privacy risks and propose a privacy- preserving approach for genome-aware health monitoring. In our approach, users can only learn the diagnostic results based on their genomic and biomedical sensing data, while the the healthcare service provider learns nothing. Security analysis and performance evaluations are conducted to illustrate the effectiveness and efficiency of the proposed approach.

In cognitive radio networks (CRNs), spectrum database has been well recognized as an effective means to dynamically sharing licensed spectrum among primary users (PUs) and secondary users (SUs). In spectrum database, the protected incumbents (a.k.a. PUs) and the CRs (a.k.a. SUs) are required to register in database their operational specifications such as transmitting power, antenna height, time of operation and etc. so as to provide an up-to-date radio map for public queries and avoid possible interference. However, it poses potentially serious privacy problems especially when governmental and military systems participate in spectrum sharing through spectrum database. Most recent research works in database-driven CRNs, however, only focused on protecting user's location privacy but merely studied preserving PUs' operational specifications. In this paper, we propose a secure and privacy-preserving scheme using hidden policy-assisted attribute-based encryption technique to protect sensitive PUs' operational privacy without affecting database's accessibility and spectrum utilization efficiency. The security and performance analysis demonstrates that our scheme is secure and computationally efficient. Additionally, our policy-assisted scheme is practical and promising because of its consistency with FCC/NTIA's rule in spectrum regulation in database-driven CRNs.

Context awareness plays an important role in many emerging applications, such as mobile computing and smart space. Since FM signal is ubiquitous, it has been recognized as an attractive and promising technique to realize context awareness. When a target is at different locations or performs different activities, it will exert different influence on the FM signal around it. Therefore, it is possible to deduce its location and activity by analysing its influence on the FM signal. However, FM signal is extremely weak and noisy, which makes it a challenging task to achieve high-performance context awareness. In this paper, we propose a new method for improving the performance of an FM-based context-aware system using multidomain features. Specifically, we extract signal features not only from the time domain, but also from the wavelet domain, the frequency domain, and the space domain, and construct robust and discriminative multi-domain features to characterize the FM signal. Furthermore, we also model context awareness as a classification problem and develop a robust iterative sparse representation classification algorithm to efficiently solve this problem. Extensive experiments performed in a 7.2m×10.8m clutter indoor laboratory with one multi-channel FM receiver demonstrate that the proposed schemes could achieve more than 90% accuracy of location estimation and activity recognition when 3 antennas are used.

It is increasingly common for enterprises and other organizations to outsource firewalls to public clouds in order to reduce the cost and complexity in deploying and maintaining dedicated hardware middleboxes. However, this poses a serious threat to the enterprise network security because sensitive network policies, such as firewall rules, are revealed to cloud providers, which may be leaked and exploited by attackers. In this paper, we design and implement a SE- FWaaS, a secured system that enables cloud providers to support middlebox (e.g., firewall) outsourcing while preserving the network policy confidentiality. The key ingredients in our SE-FWaaS are the distribution of the firewall primitives, namely policy checking and verdict enforcing, to two independent public clouds, and the enabling techniques of efficient firewall rule obfuscation and oblivious rule-matching. Our SE-FWaaS provides the maximum achievable level of protection of network policies by enforcing the principle of the least privilege and removing the threat of offline probing attacks. We evaluate the proposed system over real-world firewall rules and demonstrate its effectiveness and feasibility.

The exploding popularity of mobile devices enables people to enjoy benefits brought by various interesting mobile apps, such as social networking, mobile video services, and location-based services, etc. However, the ever-increasing data traffic has exacerbated congestions on current cellular networks, which results in users' dissatisfaction, especially in crowded areas. Hence, how to deal with the explosive data traffic in cellular networks becomes a challenging problem. Traditional methods rely on mobile offloading techniques to deviate the data traffic targeted to cellular networks, such as small cell, Wi-Fi, and opportunistic communication. Unfortunately, mobile users will still experience severe congestion when a large number of users request for data. Facing these challenges, we introduce the concept of mobile participation to assist data offloading by leveraging the mobility of mobile users and the social features among a group of users. A mobile caching user, who pre- caches certain amount of contents, can roam around congested areas to participate in data dissemination in order to satisfy users' requests, which can benefit both herself and users in the crowd simultaneously. Therefore, we propose a game theoretical approach to analyze the data offloading via mobile participation with joint considerations on network effects, congestion, social behaviors, and pricing strategy. Based on detailed performance analysis, we show the feasibility and efficiency of the proposed approach.

Data classification is a widely used data mining technique for big data analysis. By training massive data collected from the real world, data classification helps learners discover hidden data patterns. In addition to data training, given a trained model from collected data, a user can classify whether a new incoming data belongs to an existing class, or, multiple distributed entities may collaborate to test the similarity of their trained results. However, due to data locality and privacy concerns, it is infeasible for large-scale distributed systems to share each individual's datasets with each other for data similarity check. On the one hand, the trained model is an entity's private asset and may leak private information, which should be well protected from all other non-collaborative entities. On the other hand, the new incoming data may contain sensitive information which cannot be disclosed directly for classification. To address the above privacy issues, we propose a privacy-preserving data classification and similarity evaluation scheme for distributed systems. With our scheme, neither new arriving data nor trained models are directly revealed during the classification and similarity evaluation procedures. The proposed scheme can be applied to many fields using data classification and evaluation. Based on extensive real-world experiments, we have also evaluated the privacy preservation, feasibility, and efficiency of the proposed scheme.

Mobile crowdsourcing (MC) is a transformative paradigm that engages a crowd of mobile users (i.e., workers) in the act of collecting, analyzing, and disseminating information or sharing their resources. To ensure quality of service, MC platforms tend to recommend MC tasks to workers based on their context information extracted from their interactions and smartphone sensors. This raises privacy concerns hard to address due to the constrained resources on mobile devices. In this paper, we identify fundamental tradeoffs among three metrics-utility, privacy, and efficiency-in an MC system and propose a flexible optimization framework that can be adjusted to any desired tradeoff point with joint efforts of MC platform and workers. Since the underlying optimization problems are NP-hard, we present efficient approximation algorithms to solve them. Since worker statistics are needed when tuning the optimization models, we use an efficient aggregation approach to collecting worker feedbacks while providing differential privacy guarantees. Both numerical evaluations and performance analysis are conducted to demonstrate the effectiveness and efficiency of the proposed framework.

Auction-based secondary spectrum market provides a platform for spectrum holders to share their under-utilized licensed bands with secondary users (SUs) for economic benefits. However, it is challenging for SUs to directly participate due to their limited battery power and capability in computation and communications. To shift complexity away from users, in this paper, we propose a novel multi-round service-oriented combinatorial spectrum auction with two-tier framework support. In Tier I, we introduce several secondary service providers (SSPs) to provide end-users with services by using purchased licensed bands even if the end-users do not have cognitive radio capability. When an SU submits its service request with certain bidding allowance to its SSP, the SSP will help find out which bands within its area are available and bid for the desired ones from the market in Tier II. Specifically, we formulate the bidding process at the SSP as an optimization problem by considering interference management, spectrum uncertainty, flow routing, and budget allowance. In Tier II, considering two possible manners of the seller, we propose two social-welfare-maximizing auction mechanisms accordingly, including the winner determination based on weighted conflict graph and the Vickrey-Clarke-Groves-styled price charging mechanism. Extensive simulations have been conducted and the results have demonstrated the higher revenue of the proposed scheme compared with the traditional commodity-oriented single-round truthful schemes.

In cognitive radio networks (CRNs), primary users (PUs) can leverage secondary users (SUs) as cooperative relays to increase their transmission rates, while SUs will in return obtain more spectrum access opportunities, leading to cooperative CRNs (CCRNs). Prior research works in CCRNs mainly focus on providing ubiquitous access and high throughput for users, but have rarely taken energy efficiency into consideration. Besides, most existing works assume that the SUs are passively selected by PUs regardless of SUs' willingness to help, which is obviously not practical. To address energy issue, this paper proposes an energy-efficient cooperative strategy by leveraging temporal and spatial diversity of the primary network. Specifically, SUs with delay-tolerant packets can proactively make the cooperative decisions by jointly considering primary channel availability, channel state information, PUs' traffic load, and their own transmission requirements. We formulate this decision-making problem based on the optimal stopping theory to maximize SUs' energy efficiency. We solve this problem using a dynamic programming approach and derive the optimal cooperative policy. Extensive simulations are then conducted to evaluate the performance of our proposed strategy. The results show significant improvements of SUs' energy efficiency compared with existing cooperative schemes, which demonstrate the benefits of our proposed cooperative strategy in conserving energy for SUs.

To deal with the rapid growth of mobile data traffic and the user interest shift from peer-to-peer communications to content dissemination-based services, such as video streaming, information-centric networking has emerged as a promising architecture and has been increasingly used for wireless and mobile networks. In this paper, we focus on video dissemination in information-centric cognitive radio networks (IC-CRNs) and investigate the use of harvested bands for proactively caching video contents at the locations close to the interested users to improve the performance of video distribution. With consideration of the dynamic and unobservable nature of some parameters, we formulate the allocation of harvested bands as a Markov decision process with hidden and dynamic parameters and transform it into a partially observable Markov decision process and a multi-armed bandit formulation. Based on them, we develop a new spectrum management mechanism, which maximizes the benefit of proactive video caching as well as the efficiency of spectrum utilization in the IC-CRNs. Extensive simulation results demonstrate the significant performance improvement of the proposed scheme for video streaming.

Advances in biomedical sensors and mobile communication technologies have fostered the rapid growth of mobile health (mHealth) applications in the past years. Users generate a high volume of biomedical data during health monitoring, which can be used by the mHealth server for training predictive models for disease diagnosis and treatment. However, the biomedical sensing data raise serious privacy concerns because they reveal sensitive information such as health status and lifestyles of the sensed subjects. This paper proposes and experimentally studies a scheme that keeps the training samples private while enabling accurate construction of predictive models. We specifically consider logistic regression models which are widely used for predicting dichotomous outcomes in healthcare, and decompose the logistic regression problem into small subproblems over two types of distributed sensing data, i.e., horizontally partitioned data and vertically partitioned data. The subproblems are solved using individual private data, and thus mHealth users can keep their private data locally and only upload (encrypted) intermediate results to the mHealth server for model training. Experimental results based on real datasets show that our scheme is highly efficient and scalable to a large number of mHealth users.

The control/user (C/U) plane decoupled railway wireless network is an innovative architecture recently proposed to meet the communication demands of both train control systems and onboard passengers. The core idea is to completely separate the C-plane and the U-plane into different network nodes operating at different frequency bands. Although the system capacity of this network architecture can be highly increased, the forwarding latency of X3 interfaces to link the C-plane and the U-plane becomes a serious problem, particularly for hybrid automatic repeat request (HARQ) protocols that demand frequent interactions between the C-plane and the U-plane. To address this challenging problem, we propose a low-latency collaborative HARQ scheme in this paper. Specifically, we develop a new collaborative transmission framework where the possible spare resources on lower frequency bands of macrocells excluding those used by C-plane transmissions can be utilized to help small cells relay erroneously received data. Compared with the conventional HARQ scheme, the proposed scheme requires fewer retransmissions to reach the same transmission reliability, thereby mitigating the latency problem caused by HARQ retransmissions. Furthermore, channel mapping is also redesigned to conform to the proposed collaborative transmission framework. Through theoretical analysis, we derive the expression of the average number of retransmissions related to the sum of independent Gamma variables. Finally, the results of simulation experiments show that the proposed scheme can largely decrease the retransmission latency for railway wireless networks.

Unlicensed spectra fusion technology for LTE holds the promise of alleviating the licensed spectra scarcity and enhancing capacity. It allows LTE to effectively utilize the unlicensed spectra distributed over high frequency bands with significant different propagation characteristics from its licensed spectra. However, the interference caused by other systems over unlicensed spectra, particularly the public unlicensed spectra, is viewed as the most serious challenge. In this paper, aiming to guarantee the feasibility in existing LTE systems, we design a novel unlicensed spectra fusion scheme based on the popular standard TDD-LTE. To mitigate the interference, we develop an interference coordination scheme which is carried out in two stages: screen the available unlicensed channels for every UE, and allocate unlicensed spectra based on Hungarian algorithm. We have conducted extensive simulation study and demonstrate that our proposed scheme can handle interference coordination effectively and enhance throughput significantly.

Radio frequency identification (RFID) technology has many applications in inventory management, supply chain, product tracking, transportation, and logistics. One research issue of practical importance is to search for a particular group of tags in a large-scale RFID system. Time efficiency is a crucial factor that must be considered when designing a tag search protocol to ensure its execution will not interfere with other normal inventory operations. In this paper, we design a new technique called filtering vector, which can significantly reduce transmission overhead during search process, thereby shortening search time. Based on this technique, we propose an iterative tag search protocol. In each round, we filter out some tags and eventually terminate the search process when the search result meets the accuracy requirement. Furthermore, we extend our protocol to work under noisy channel. The simulation results demonstrate that our protocol performs much better than the best existing work.

When equipped with multiple antennas, secondary users in cognitive radio networks are able to communicate even when neighboring primary users are active by transmitting in the null space of the communication channel occupied by primary users. In this case, the throughput of a secondary link is limited by the transmission power and the dimension of the null space, i.e., the number of active primary users nearby. Since the number of active primary users is time-varying, the required transmission power to support certain data rate changes from time to time. Thus, secondary users could adapt their transmission to the variation of the primary traffic to improve energy efficiency. In view of that, we develop an energy-efficient traffic scheduling scheme for secondary users equipped with multiple antennas. By formulating the traffic scheduling problem as a Markov decision problem, an energy-efficient transmission scheme is derived from linear programming. The analytical results are verified by simulations and the impacts of various parameters are discussed. The superiority of the derived scheme is also shown by comparing with a randomized scheme.

Voice capacity over wireless local area networks (WLANs) can be increased by the statistical multiplexing among on/off voice calls. However, in previously proposed schemes, the admitted voice calls that transit from silence state to talkspurt state are mixed up with the new voice calls to contend for the channel. The increase in the traffic load of new voice users could degrade the performance of ongoing voice calls. In this paper, we propose a novel MAC scheme for VoIP over WLANs, referred to as PAMA (Prioritized Adaptive Multiple Access). The key features of the proposed scheme are that 1) the admitted voice calls have higher priority access to the channel than the new voice calls, based on the fact that maintaining the required QoS of ongoing calls is more important than admitting new calls; 2) the dedicated contention window for admitted voice calls is dynamically adjusted according to the current estimation of the number of active ongoing voice calls to guarantee the QoS requirements; 3) a two-state Markov model is established to evaluate the system performance. Analytical and simulation results demonstrate that PAMA can increase the voice capacity while still satisfying the QoS of admitted voice calls.

Health monitoring is an important category of mobile Health (mHealth) applications. Users generate a large volume of data during health monitoring, which can then be used by the mHealth server for constructing diagnosis or prognosis prediction models. However, these training samples contain private information of data owners, who may be reluctant to share them with the mHealth server. This paper proposes and experimentally studies a scheme that keeps the training samples private while enabling accurate construction of diagnosis and prognosis models. We specifically consider logistic regression models which are widely used in mHealth, and decompose the logistic regression model construction problem into small subproblems that can be executed by each user using their own private data. In this manner, users can keep their raw data locally and only upload encrypted parameters to the mHealth server for model construction. We show that our scheme suits well in mHealth applications by conducting experimental evaluations based on a real-world dataset and analyzing its computation overhead.

Spectrum trading benefits primary users (PUs) by monetary gains and secondary users (SUs) by spectrum accessing opportunities in cognitive radio networks (CRNs). Unfortunately, most existing spectrum trading designs only focus on the guarantee of economic properties, but forget the wireless transmission nature, especially for multi-hop cognitive radio (CR) communications. In this paper, we propose an economic-robust session based spectrum trading, which has a joint consideration of economic properties such as incentive compatibility, individual rationality, and budget balance, and the end-to-end performance for multi-hop communications. Considering two bidding manners, i.e., bidding for the whole session and unit rate bidding, we formulate the spectrum trading optimization problems under multiple economic and multi-hop CR transmission constraints, design two pricing mechanisms to charge the winning spectrum bidders, and further mathematically prove the economic- robustness of the proposed spectrum trading schemes. Through extensive simulations, we show the proposed schemes are economic-robust and effective in improving spectrum utilization.

In cognitive radio networks, primary users (PUs) can leverage secondary users (SUs) as cooperative relays to increase their transmission rates, and SUs will in turn obtain more spectrum access opportunities. While most existing works assume that SUs are passively selected by PUs regardless of SUs' willingness, in this paper, we propose a cooperative strategy for SUs to actively decide whether to cooperate or not. Basically, due to PUs' time-varying traffic demands, it is essential for SUs to firstly observe the channels and then select a specific PU to cooperate with in order to save the energy. In our paper, this decision related problem is formulated based on optimal stopping theory where SUs observe PUs in time sequence and then make decisions whether to stop observation and cooperate right away or wait till next time slot to repeat the same process. We address this problem by using backward induction and derive the energy-efficient strategy for SUs. To validate the feasibility of our proposed scheme, extensive simulations are conducted to show the impact of PUs' traffic demands on SUs' decisions. The results also reveal that the proposed optimal rule outperforms the greedy selection strategy and is thus more energy- efficient to be applied to the cooperative cognitive radio networks.

Advancements in big data analysis offer cost-effective opportunities to improve decision-making in numerous areas such as health care, economic productivity, crime, and resource management. Nowadays, data holders are tending to sharing their data for better outcomes from their aggregated data. However, the current tools and technologies developed to manage big data are often not designed to incorporate adequate security or privacy measures during data sharing. In this paper, we consider a scenario where multiple data holders intend to find predictive models from their joint data without revealing their own data to each other. Data locality property is used as an alternative to multi-party computation (SMC) techniques. Specifically, we distribute the centralized learning task to each data holder as local learning tasks in a way that local learning is only related to local data. Along with that, we propose an efficient and secure protocol to reassemble local results to get the final result. Correctness of our scheme is proved theoretically and numerically. Security analysis is conducted from the aspect of information theory.

The prevalence of smartphones with geo-positioning functionalities gives rise to a variety of location-based services (LBSs). Proximity test, an important branch of location-based services, enables the LBS users to determine whether they are in a close proximity with their friends, which can be extended to numerous applications in location-based mobile social networks. Unfortunately, serious security and privacy issues may occur in the current solutions to proximity test. On the one hand, users' private location information is usually revealed to the LBS server and other users, which may lead to physical attacks to users. On the other hand, the correctness of proximity test computation results from LBS server cannot be verified in the existing schemes and thus the creditability of LBS is greatly reduced. Besides, privacy should be defined by user him/herself, not the LBS server. In this paper, we propose a privacy-preserving verifiable proximity test for location-based services. Our scheme enables LBS users to verify the correctness of proximity test results from LBS server without revealing their location information. We show the security, efficiency, and feasibility of our proposed scheme through detailed performance evaluation.

In this paper, we present SOBO, a novel hybrid MAC protocol using sequentially ordered backoff in wireless LANs. SOBO eliminates packet collisions and wasted idle backoff slots by introducing implicit resource reservation into 802.11 DCF. In SOBO, the AP divides time into repeating cycles by beacon frames. Exploiting the implicit information of successful transmission order in every cycle, sequentially ordered backoff in a distributed manner during reservation period is achieved without extra control packets. In addition, we propose a novel scheme to estimate the number of contention stations, and design an adaptive contention window algorithm. We also analyze the robustness of SOBO against message losses in realistic networks with channel errors. The performance of SOBO is verified via extensive simulations with different scenarios. Our simulation results show that SOBO achieves a significant increase in network throughput compared to the legacy 802.11 DCF.

Machine learning has played an increasing important role in big data systems due to its capability of efficiently discovering valuable knowledge and hidden information. Often times big data such as healthcare systems or financial systems may involve with multiple organizations who may have different privacy policy, and may not explicitly share their data publicly while joint data processing may be a must. Thus, how to share big data among distributed data processing entities while mitigating privacy concerns becomes a challenging problem. Traditional methods rely on cryptographic tools and/or randomization to preserve privacy. Unfortunately, this alone may be inadequate for the emerging big data systems because they are mainly designed for traditional small-scale data sets. In this paper, we propose a novel framework to achieve privacy-preserving machine learning where the training data are distributed and each shared data portion is of large volume. Specifically, we utilize the data locality property of Apache Hadoop architecture and only a limited number of cryptographic operations at the Reduce() procedures to achieve privacy-preservation. We show that the proposed scheme is secure in the semi-honest model and use extensive simulations to demonstrate its scalability and correctness.

Widely deployed mHealth systems enable patients to efficiently collect, aggregate, and report their Personal Health Records (PHRs), and then lower the costs and shorten their response time. The increasing needs of PHR monitoring require the involvement of healthcare companies that provide monitoring programs for analyzing PHRs. Unfortunately, healthcare companies are lack of the computation, storage, and communication capability on supporting millions of patients. To tackle this problem, they seek for the help from the cloud. However, delegating monitoring programs to the cloud may incur serious security and privacy breaches because people have to provide their identity information and PHRs to the public domain. Even worse, the cloud may mistakenly return the incorrect computation results, which will put patients' life in jeopardy. In this paper, we propose a verifiable privacy-preserving monitoring scheme for cloud-assisted mHealth systems. Our scheme allows patients to verify the correctness of computation results from the cloud without revealing their PHRs and identity information. In addition, our advanced schemes offer efficient PHR updates and PHR computations on complex monitoring programs. By detailed performance evaluation, we have shown the security and efficiency of our proposed scheme.

Application services based on cloud computing infrastructure are proliferating over the Internet. In this paper, we investigate the problem of how to minimize cloud resource rental cost associated with hosting such cloud-based application services, while meeting the projected service demand. This problem arises when applications generate high volume of data that incurs significant cost on storage and transfer. As a result, an application service provider (ASP) needs to carefully evaluate various resource rental options before finalizing the application deployment. We choose Amazon EC2 marketplace as a case of study, and analyze the economical trade-off for on-demand resource rental strategies. Given fixed resource pricing, we first develop a deterministic model, using a mixed integer linear program, to facilitate resource rental decision making. Evaluation results show that our planning optimization model reduces resource rental cost by as much as 50 percent compared with a baseline strategy. Next, we further investigate planning solutions to resource market featuring time-varying pricing (Amazon Spot Instance Market). We perform time-series analysis over the spot price trace and examine its predictability using auto-regressive integrated moving-average (ARIMA). We also develop a stochastic planning model based on multistage recourse. By comparing these two approaches, we discover that spot price forecasting does not provide our planning model with a crystal ball due to the weak correlation of past and future price, and the stochastic planning model better hedges against resource pricing uncertainty than resource rental planning using forecast prices.

Online social networks (OSNs), which attract thousands of million people to use everyday, greatly extend OSN users' social circles by friend recommendations. OSN users' existing social relationship can be characterized as 1-hop trust relationship, and further establish a multi-hop trust chain during the recommendation process. As the same as what people usually experience in the daily life, the social relationship in cyberspaces are potentially formed by OSN users' shared attributes, e.g., colleagues, family members, or classmates, which indicates the attribute-based recommendation process would lead to more fine-grained social relationships between strangers. Unfortunately, privacy concerns raised in the recommendation process impede the expansion of OSN users' friend circle. Some OSN users refuse to disclose their identities and their friends' information to the public domain. In this paper, we propose a trust-based privacy-preserving friend recommendation scheme for OSNs, where OSN users apply their attributes to find matched friends, and establish social relationships with strangers via a multi-hop trust chain. Based on trace-driven experimental results and security analysis, we have shown the feasibility and privacy preservation of our proposed scheme.

Cellular networks are faced with serious congestions nowadays due to the recent booming growth and popularity of wireless devices and applications. Opportunistically accessing the unused licensed spectrum, cognitive radio can potentially harvest more spectrum resources and enhance the capacity of cellular networks. In this paper, we propose a new multihop cognitive cellular network (MC2N) architecture to facilitate the ever exploding data transmissions in cellular networks. Under the proposed architecture, we then investigate the minimum energy consumption problem by exploring joint frequency allocation, link scheduling, routing, and transmission power control. Specifically, we first formulate a maximum independent set (MIS) based energy consumption optimization problem, which is a non-linear programming problem. Different from most previous work assuming all the MISs are known, finding which is in fact NP-complete, we employ a column generation based approach to circumvent this problem. We develop an ϵ-bounded algorithm, which can obtain a feasible solution that are less than (1 + ϵ) and larger than (1 - ϵ) of the optimal result of MP, and analyzed its computational complexity. We also revisit the minimum energy consumption problem by taking uncertain channel bandwidth into consideration. Simulation results show that we can efficiently find ϵ-bounded approximate results and the optimal result as well.

Due to the emerging various data services, current cellular networks have been experiencing a surge of data traffic and are already overloaded; thus, they are not able to meet the ever exploding traffic demand. In this study, we first introduce a multi-radio multi-channel multi-hop cognitive cellular network (M$^3$C$^2$N) architecture to enhance network throughput. Under the proposed architecture, we then investigate the minimum length scheduling problem by exploring joint frequency allocation, link scheduling, and routing. In particular, we first formulate a maximal independent set based joint scheduling and routing optimization problem called original optimization problem (OOP). It is a mixed integer non-linear programming (MINLP) and generally NP-hard problem. Then, employing a column generation based approach, we develop an$\epsilon$-bounded approximation algorithm which can obtain an$\epsilon$-bounded approximate result of OOP. Noticeably, in fact we do not need to find the maximal independent sets in the proposed algorithm, which are usually assumed to be given in previous works although finding all of them is NP-complete. We also revisit the minimum length scheduling problem by considering uncertain channel availability. Simulation results show that we can efficiently find the$\epsilon$-bounded approximate results and the optimal result as well, i.e., when$\epsilon =0\%$in the algorithm.

Mobile devices have increasingly been used to run multimedia applications which are extremely downlink-intensive. The conventional rate adaptive and/or margin adaptive approach for radio resource allocation may result in unnecessary energy consumption on mobile devices, which will not be energy efficient for mobile multimedia applications. In this paper, we develop an energy adaptive approach and design an energy-efficient downlink resource allocation scheme to support multimedia applications. The objective is to minimize the total energy consumption of mobile devices for data reception while meeting the data rate requirements at mobile devices and the transmit power constraint at the base station. We show that the optimization problem is NP-hard and then propose an efficient algorithm that has a provable performance guarantee under a certain condition. We have conducted extensive simulations to evaluate the efficacy of the proposed algorithm and our results provide useful insights into the design of energy-efficient resource allocation for wireless systems.

In-home Smart Grid (SG), the integration of Renewable Power Systems (RPSs) with Conventional Power Systems (CPSs), calls for cost-effective management for the electricity usages of end users' household appliances. In this paper, by taking the charging process of RPSs and multiple types of household appliances in to consideration, we have developed analytical models to characterize the electricity cost in the in-home smart grid. Based on these models, we formulate the electricity cost minimization problem as a finite-horizon continuous-time Markov decision process (CTMDP), from which we obtain a threshold policy to minimize the cost. Numerical results show that the threshold policy can manage the electricity usage very effectively.

Benefits of multi-operator spectrum sharing in wireless networks heavily depend on the traffic misbalance in the networks belonging to different operators. In this paper, we study the likelihood that such misbalance occurs in networks with high traffic dynamics. An extensive business portfolio for heterogeneous networks is presented to analyse the benefits due to multi-operator cooperation for spectrum sharing. High resolution pricing models are developed to dynamically facilitate price adaptation to the system state. By using queuing theory, we quantify the operators' gains in cooperative arrangements as opposed to non-cooperative independent operation. In addition, Markov model is used that can handle wider range of different distributions of traffic arrivals and service rates. A tractable analysis and quantitative results are provided for those gains as a function of the number of cooperating operators. Under the condition that there is a traffic underflow in one band, it has been shown that with capacity aggregation model, the operator operating in other band can take advantage of additional channels with probability close to 1. In capacity borrowing/leasing model, this advantage is not unconditional, and there is a risk that the operator leasing the spectra will suffer temporary packet losses. When cognitive models are used in a network with high traffic dynamics, 50–70% of the spectra may be lost due to channel corruptions caused by the return of primary users. The gains of traffic offloading from a cellular network to a WLAN are quantified by an equivalent increase in opportunistic capacity proportional to the ratio of aggregate coverage of cellular networks and WLANs. The results provide guidelines for business decision in multi-operator network management.

The capacity region of wireless networks with per-destination PD queueing model has been studied extensively in the literature. However, the PD queueing structure is not scalable because the number of queues in a node can be as large as the number of all possible source-destination pairs. In this work, we study the capacity region of wireless networks with per-link PL queueing model. The advantage of the PL queueing structure is that the number of queues in a node can be reduced significantly to the number of its neighboring nodes. In this paper, the capacity region of a wireless network with PL queueing structure is characterized, and a dynamic routing and power control policy, namely, DRPC-PL, is proposed to stabilize the network whenever the input rate is within the capacity region. Copyright © 2013 John Wiley & Sons, Ltd.

Mobile crowdsourcing enables mobile workers to complete a broad range of crowdsourcing tasks anywhere at any time. However, recommending suitable crowdsourcing tasks to mobile workers requires sensitive information such as location and activity, which raises serious privacy concerns. In this paper, we formulate the task recommendation process as an optimization problem which balances privacy, utility, and efficiency. We show that this optimization problem is NP-hard, and present a greedy solution which approximates the optimal solution within a factor of 1 - 1/e. We also design an efficient aggregation protocol to compute statistics of mobile workers required in the optimization problem while providing strong privacy guarantee. Both numerical evaluations and performance analysis are carried out to show the effectiveness and efficiency of the proposed framework. To the best of our knowledge, our work is the first to consider privacy issues in task recommendation for mobile crowdsourcing.

Photo sharing is an attractive feature which popularizes Online Social Networks (OSNs). Unfortunately, it may leak users' privacy if they are allowed to post, comment, and tag a photo freely. In this paper, we attempt to address this issue and study the scenario when a user shares a photo containing individuals other than himself/herself (termed co-photo for short). To prevent possible leakage of a photo privacy, we design a mechanism to enable each individual in a photo be aware of the posting activity and participate in the decision making on the photo posting. For this purpose, we need an efficient facial recognition (FR) system that can recognize everyone in the photo. However, more demanding privacy setting may limit the number of the photos publicly available to train the FR system. To deal with this dilemma, our mechanism attempts to utilize users' private photos to design a personalized FR system specifically trained to differentiate possible photo co-owners without leaking his/her privacy. We have also developed a distributed consensus-based method to not only reduce the computational complexity, but also preserve the privacy during the training. We show that our system is superior to other possible approaches in terms of recognition ratio and efficiency. Our mechanism is implemented as an Android application on Facebook's platform.

In cooperative cognitive radio networks (CCRNs), through cooperating with primary transmissions, secondary users (SUs) could access the spectrum resource when primary users (PUs) are transmitting. The existing schemes in CCRNs allocate the spectrum resource only to the cooperative relay SU. However, this may lead to the waste of spectrum resource, especially when the relay SU has light traffic load or poor channel condition. To better utilize the spectrum among all SUs in a secondary network, we design a spectrum resource utilization maximization scheme with joint consideration of relay selection and spectrum scheduling problems. With the goal to maximize the throughput of the secondary network, our scheme allocates spectrum among all SUs according to the diversity of secondary traffic load and the channel conditions. Besides, considering that the SUs are always energy limited, we also formulate the energy constraint for each SU to avoid the energy consumption exceeding the total available energy. Moreover, we study the resource allocation problem from long-term view under dynamic network setting, and design an online algorithm to solve it. Through extensive simulations, we show that the proposed scheme outperforms the existing schemes in terms of secondary network throughput.

The Internet of Things (IoT) is emerging as one of the major trends for the next evolution of the Internet, where billions of physical objects or things (including but not limited to humans) will be connected over the Internet, and a vast amount of information data will be shared among them. However, the current Internet was built on a host-centric communication model, which was primarily designed for meeting the demand of pair-wise peer-to-peer communications and cannot well accommodate various advanced data-centric services boosted by the IoT in which users care about content and are oblivious to locations where the content is stored. In this paper, we propose a novel architecture for the future Internet based on information-centric networking (ICN), which is called DataClouds, to better accommodate data-centric services. Different from existing ICN-based architectures, we take the sharing nature of data-centric services under the IoT into consideration and introduce logically and physically formed communities as the basic building blocks to construct the network so that data could be more efficiently shared and disseminated among interested users. We also elaborate on several fundamental design challenges for the Internet under this new architecture and show that DataClouds could offer more efficient and flexible solutions than traditional ICN-based architectures.

Cooperative cognitive radio networks (CCRNs) enable secondary users (SUs) to access primary resource by cooperation with active primary users (PUs). For the cooperation-generated resource, existing schemes in CCRNs allocate the resource only to the relay SUs. However, this may lead to inefficient spectrum utilization, when the relay SUs have poor channel condition or little traffic load for their own secondary transmissions. In this paper, considering user diversity in secondary networks, we focus on network-level throughput optimization for secondary networks, by allowing all SUs to optimally share the cooperation-generated period. Besides, considering the energy constraint on SUs, we formulate the resource allocation problem from long-term perspective, to reflect the time-varying change of user diversity in channel condition, traffic load and energy amount. We present an online SUM scheme to solve the long-term optimization problem. Although a mixed-integer and non-convex problem is involved in the SUM scheme, we transform the problem into multiple convex subproblems, and then optimally solve it with low computational complexity. Extensive simulations show that the proposed SUM scheme significantly outperforms the existing schemes.

Spectrum trading creates more accessing opportunities for secondary users (SUs) and economically benefits the primary users (PUs). However, it is challenging to implement spectrum trading in multi-hop cognitive radio networks (CRNs) due to harsh cognitive radio (CR) requirements on SUs' devices, uncertain spectrum supply from PUs and complex competition relationship among different CR sessions. Unlike the per-user based spectrum trading designs in previous studies, in this paper, we propose a novel session based spectrum trading system, spectrum clouds, in multi-hop CRNs. In spectrum clouds, we introduce a new service provider, secondary service provider (SSP), to facilitate the accessing of SUs without CR capability and harvest uncertain spectrum supply. The SSP also conducts spectrum trading among CR sessions w.r.t. their conflicts and competitions. Leveraging a 3-dimensional (3-D) conflict graph, we mathematically describe the conflicts and competitions among the candidate sessions for spectrum trading. Given the rate requirements and bidding values of candidate trading sessions, we formulate the optimal spectrum trading into the SSP's revenue maximization problem under multiple cross-layer constraints. In view of the NP-hardness of the problem, we develop heuristic algorithms to pursue feasible solutions. Through extensive simulations, we show that the solutions found by the proposed algorithms are close to the optimal one.

Electronic healthcare (eHealth) systems have replaced paper-based medical systems due to the attractive features such as universal accessibility, high accuracy, and low cost. As a major component of eHealth systems, mobile healthcare (mHealth) applies mobile devices, such as smartphones and tablets, to enable patient-to-physician and patient-to-patient communications for better healthcare and quality of life (QoL). Unfortunately, patients' concerns on potential leakage of personal health records (PHRs) is the biggest stumbling block. In current eHealth/mHealth networks, patients' medical records are usually associated with a set of attributes like existing symptoms and undergoing treatments based on the information collected from portable devices. To guarantee the authenticity of those attributes, PHRs should be verifiable. However, due to the linkability between identities and PHRs, existing mHealth systems fail to preserve patient identity privacy while providing medical services. To solve this problem, we propose a decentralized system that leverages users' verifiable attributes to authenticate each other while preserving attribute and identity privacy. Moreover, we design authentication strategies with progressive privacy requirements in different interactions among participating entities. Finally, we have thoroughly evaluated the security and computational overheads for our proposed schemes via extensive simulations and experiments.

Content dissemination is very useful for many mobile applications, like instant messaging, file sharing, and advertisement broadcast, etc. In real life, for various kinds of time-insensitive contents, such as family photos and video clips, the process of content dissemination forms a delay tolerant networks (DTNs). To improve the data forwarding performance in DTNs, several social-based approaches have been proposed, most of which leverage mobile users' social information, including contact history, moving trajectory, and personal profiles as metrics to design routing schemes. However, although the social-based approaches provide better performance, the revealing of mobile users' information apparently compromises their privacy. Moreover, users' contents may only be shared with a particular group of users rather everyone in the system. In this paper, we propose the PSaD: a Privacy-preserving Social-assisted content Dissemination scheme in DTNs. We apply users' verifiable attributes to establish their social relationships in terms of identical attributes in a privacy-preserving way. Besides, to provide the confidentiality of contents, our approach enables users to encrypt contents before the dissemination process, and only allows users who have particular attributes to decrypt them. By trace-driven simulations and experiments, we show the performance, privacy preservation, and efficiency of our proposed scheme.

Multihop cellular networks (MCNs) have drawn tremendous attention due to its high throughput and extensive coverage. However, there are still three issues not well addressed. With the existence of relay stations (RSs), how to efficiently allocate frequency resource to relay links becomes a challenging design issue. For mobile stations (MSs) near the cell edge, cochannel interference (CCI) become severe, which significantly affects the network performance. Furthermore, the unbalanced user distribution will result in traffic congestion and inability to guarantee quality of service (QoS). To address these problems, we propose a quantitative study on adaptive resource allocation schemes by jointly considering interference coordination (IC) and load balancing (LB) in MCNs. In this paper, we focus on the downlink of OFDMA-based MCNs with time division duplex (TDD) mode, and analyze the characteristics of resource allocation according to IEEE 802.16j/m specification. We also design a novel frequency reuse scheme to mitigate interference and maintain high spectral efficiency, and provide practical LB-based handover mechanisms which can evenly distribute the traffic and guarantee users' QoS. Our study shows that our scheme not only meets the requirement on coverage, but also improves the throughput while accommodating more users in MCNs.

Sensor-enabled radio frequency identification (RFID) technology has generated a lot of interest from industries lately. Integrated with miniaturized sensors, RFID tags can provide not only the IDs, but also valuable real-time information about the state of the objects or their surrounding environment, which can benefit many practical applications, such as warehouse management and inventory control. In this paper, we study the problem of designing efficient protocols for a reader to collect sensor-produced information from unknown target tags in an RFID system with minimum execution time. Different from information collection with all target tags known a priori, in the scenarios we consider, the reader has to first find out the target tags in order to read information from them, which makes traditional information collection protocols not efficient any more. We design a Bloom-filter-based information collection protocol (BIC) to address this challenging problem. A Bloom filter is constructed for the reader to efficiently determine the target tags, which significantly reduces the communication and time overhead. We also introduce the allocation vectors to coordinate the transmissions from different tags and minimize collision during information collection. Extensive simulation results demonstrate that our protocol is highly efficient in terms of execution time, and it performs much better than other solutions.

Reducing the carbon footprint of data centers is becoming a primary goal of large IT companies. Unlike traditional energy sources, renewable energy sources are usually intermittent and unpredictable. How to better utilize the green energy from these renewable sources in data centers is a challenging problem. In this paper, we exploit the opportunities offered by geographical load balancing, opportunistic scheduling of delay-tolerant workloads, and thermal storage management in data centers to facilitate green energy integration and reduce the cost of brown energy usage. Moreover, bandwidth cost variations between users and data centers are considered. Specifically, this problem is first formulated as a stochastic program, and then, an online control algorithm based on the Lyapunov optimization technique, called Stochastic Cost Minimization Algorithm (SCMA), is proposed to solve it. The algorithm can enable an explicit trade-off between cost saving and workload delay. Numerical results based on real-world traces illustrate the effectiveness of SCMA in practice.

Reducing the carbon footprint of data centers is becoming a primary goal of large IT companies. Due to the intermittency and unpredictability of renewable energy sources such as wind and solar, it is quite challenging to utilize them in data centers. In this paper, we explore the opportunities offered by delay-tolerant workloads and thermal storage to facilitate the renewable energy integration in data centers and meanwhile, reduce the cost of using brown energy (i.e., energy from the utility grid). A stochastic optimization problem is formulated to tackle the stochastic renewable generation and workload arrival processes. Then, an online control algorithm based on the Lyapunov optimization approach is proposed to solve it. Simulation results based on the real-world traces show the effectiveness of the algorithm in practice.

Location-based Services (LBSs) enable mobile users to request and obtain certain services based on their current locations, such as finding nearby gas station, looking for coffee shops, and using online GPS navigation, etc. As a major branch of LBSs, geosocial networking services, such as Foursquare, become popular due to the explosive growth of smartphone users. Geosocial networking services allow people to use their location information to find potential friends who have similar interests within close proximity and initiate communications with each other. However, most existing geosocial networking services ask for mobile users' current location information and store it on an untrusted server with less privacy concerns. To some extent, mobile users need to reveal their interests and physical location information to a service provider in order to realize the functionality of geosocial networking, which apparently deteriorates users' privacy on the aspects of their profiles and locations. In this paper, we propose a privacy-preserving friend search scheme in geosocial networks without relying on a trusted centralized server. Our scheme lets localization infrastructures, such as base stations, create encrypted searchable tables on an untrusted server and allow mobile users to search for their possible friends using their profiles without exposing their location information. Extensive trace-driven simulation results and analysis show both the efficiency and privacy preservation of our proposed scheme.

Radio frequency identification (RFID) technology has many applications in inventory management, supply chain, product tracking, transportation and logistics. One research issue of practical importance is to search for a particular group of tags in a large-scale RFID system. Time efficiency is a core factor that must be taken into consideration when designing a tag search protocol to ensure scalability. In this paper, we design a new technique called filtering vector, which can significantly reduce transmission overhead during search process, thereby shortening search time. Based on this technique, we propose an iterative tag search protocol. In each round, we filter out some tags and eventually terminate the search process when the search result meets the accuracy requirement. The simulation results demonstrate that our protocol performs much better than the best existing ones.

Mobile content dissemination is very useful for many mobile applications in delay tolerant networks (DTNs), like instant messaging, file sharing, and advertisement dissemination, etc. Recently, social-based approaches, which attempt to exploit social behaviors of DTN users to forward time-insensitive data, such as family photos and friends' sightseeing video clips, have attracted intensive attentions in designing routing schemes in DTNs. Most social-based schemes leverage users' contact history and social information (e.g., community and friendship) as metrics to improve the dissemination performance. In these schemes, users need to obtain others' social information to determine their dissemination strategy, which apparently compromises others users' privacy. Moreover, the owner of mobile contents may only want to disclose his/her data to a particular group of users rather than revealing it to the public. In this paper, we propose a privacy-preserving social-assisted mobile content dissemination scheme in DTNs. We apply users' verifiable attributes to establish their potential social relationships in terms of identical attributes in a privacy-preserving way. Besides, to provide the confidentiality of mobile contents, our approach enables users to encrypt contents before the dissemination process, and only allows users who have particular attributes to decrypt them. By trace-driven simulations and experiments, we show the security and efficiency of our proposed scheme.

Location Based Service (LBS), although it greatly benefits the daily life of mobile device users, has introduced significant threats to privacy. In an LBS system, even under the protection of pseudonyms, users may become victims of inference attacks, where an adversary reveals a user's real identity and complete moving trajectory with the aid of side information, e.g., accidental identity disclosure through personal encounters. To enhance privacy protection for LBS users, a common approach is to include extra fake location information associated with different pseudonyms, known as dummy users, in normal location reports. Due to the high cost of dummy generation using resource constrained mobile devices, self-interested users may free-ride on others' efforts. The presence of such selfish behaviors may have an adverse effect on privacy protection. In this paper, we study the behaviors of self-interested users in the LBS system from a game-theoretic perspective. We model the distributed dummy user generation as Bayesian games in both static and timing-aware contexts, and analyze the existence and properties of the Bayesian Nash Equilibria for both models. Based on the analysis, we propose a strategy selection algorithm to help users achieve optimized payoffs. Leveraging a beta distribution generalized from real-world location privacy data traces, we perform simulations to assess the privacy protection effectiveness of our approach. The simulation results validate our theoretical analysis for the dummy user generation game models.

Cognitive radio technology enables secondary users (SUs) to opportunistically use the vacant licensed spectrum and significantly improves the utilization of spectrum resource. Traditional architectures for cognitive radio networks (CRNs), such as cognitive cellular networks and cognitive ad hoc networks, impose energy-consuming cognitive radios to SUs' devices for communication and cannot efficiently utilize the spectrum harvested from the primary users (PUs). To enhance the spectrum and energy efficiencies of CRNs, we have designed a new architecture, which is called the Cognitive Capacity Harvesting network (CCH). In CCH, a collection of relay stations (RSs) with cognitive capability are deployed to facilitate the accessing of SUs. In this way, the architecture not only removes the requirement of cognitive radios from SUs and reduces their energy consumption, but also increases frequency reuse and enhances spectrum efficiency. In view of the importance of the RSs on the improvement of spectrum and energy efficiencies, in this paper, we study the RS placement strategy in CCH. A cost minimization problem is mathematically formulated under the spectrum and energy efficiency constraints. Considering the NP-hardness of the problem, we design a framework of heuristic algorithms to compute the near-optimal solutions. Extensive simulations show that the proposed algorithms outperform the random placement strategy and the number of required RSs obtained by our algorithms is always within 2 times of that in the optimal solution.

Cloud-assisted mobile health (mHealth) monitoring, which applies the prevailing mobile communications and cloud computing technologies to provide feedback decision support, has been considered as a revolutionary approach to improving the quality of healthcare service while lowering the healthcare cost. Unfortunately, it also poses a serious risk on both clients' privacy and intellectual property of monitoring service providers, which could deter the wide adoption of mHealth technology. This paper is to address this important problem and design a cloud-assisted privacy preserving mobile health monitoring system to protect the privacy of the involved parties and their data. Moreover, the outsourcing decryption technique and a newly proposed key private proxy reencryption are adapted to shift the computational complexity of the involved parties to the cloud without compromising clients' privacy and service providers' intellectual property. Finally, our security and performance analysis demonstrates the effectiveness of our proposed design.

Cognitive radio (CR) technology opens the licensed spectrum bands for opportunistic usage and initiates spectrum trading to improve the spectrum utilization. In this paper, we investigate the path selection problem in multihop cognitive radio networks (CRNs) under constraints on flow routing, link scheduling and CR source's budget. We extend the per-user-based spectrum trading in prior work to CR session-based spectrum trading, and effectively develop the spectrum trading mechanisms based on the cross-layer optimization in multihop CRNs. We introduce a new service provider, called secondary service provider (SSP), to help CR sessions to select the paths for packet delivery. Considering the price of bands and the potential returning of primary services at different CR links, the SSP purchases the licensed spectrum and jointly conducts flow routing and link scheduling under the budget constraints. We also propose a 4D conflict graph to characterize the conflict relationship among CR links and mathematically formulate the path selection problem under multiple constraints into an optimization problem with the objective of maximizing the end-to-end throughput. Due to the NP-hardness of the problem, we have also developed a heuristic algorithm to find the approximate solution.

Maximizing the lifetime of a sensor network has been a subject of intensive study. However, much prior work defines the network lifetime as the time before the first data-generating sensor in the network runs out of energy or is not reachable to the sink due to network partition. The problem is that even though one sensor is out of operation, the rest of the network may well remain operational, with other sensors generating useful data and delivering those data to the sink. Hence, instead of just maximizing the time before the first sensor is out of operation, we should maximize the lifetime vector of the network, consisting of the lifetimes of all sensors, sorted in ascending order. For this problem, there exists only a centralized algorithm that solves a series of linear programming problems with high-order complexities. This paper proposes a fully distributed algorithm that runs iteratively. Each iteration produces a lifetime vector that is better than the vector produced by the previous iteration. Instead of giving the optimal result in one shot after lengthy computation, the proposed distributed algorithm has a result at any time, and the more time spent gives the better result. We show that when the algorithm stabilizes, its result produces the maximum lifetime vector. Furthermore, simulations demonstrate that the algorithm is able to converge rapidly toward the maximum lifetime vector with low overhead.

Mobile sensor networks (MSNs) have been widely deployed to provide a ubiquitous solution to real-time monitoring applications such as traffic data collection in vehicular ad-hoc networks (VANETs), ocean data collection in underwater sensor networks (UWSNs), and biodata collection in wireless body area networks (WBANs). One major issue for designing MSNs is the energy-validity trade-off, that is, the trade-off between the energy efficiency for mobile sensors (MSs) and the validity of sensing reports. In this article, we propose a novel mechanism, Energy-Efficient Distributedly Controlled Reporting (E 2 DCR), to mitigate the energy consumption for MSs in real-time monitoring applications while keeping the sensing report valid. In this mechanism, we design dynamic sleeping adjustment (DSA) algorithms to adjust an MS's sleeping period using a heuristic method to reduce energy consumption. We provide analytical models to evaluate the performance of E 2 DCR in terms of the power savings and report validity. It has been shown that with E 2 DCR, MSs can report with less energy consumption while satisfying delay constraints for real-time monitoring applications.

Electricity expenditure comprises a significant fraction of the total operating cost in data centers. Hence, cloud service providers are required to reduce electricity cost as much as possible. In this paper, we consider utilizing existing energy storage capabilities in data centers to reduce electricity cost under wholesale electricity markets, where the electricity price exhibits both temporal and spatial variations. A stochastic program is formulated by integrating the center-level load balancing, the server-level configuration, and the battery management while at the same time guaranteeing the quality-of-service experience by end users. We use the Lyapunov optimization technique to design an online algorithm that achieves an explicit tradeoff between cost saving and energy storage capacity. We demonstrate the effectiveness of our proposed algorithm through extensive numerical evaluations based on real-world workload and electricity price data sets. As far as we know, our work is the first to explore the problem of electricity cost saving using energy storage in multiple data centers by considering both the spatial and temporal variations in wholesale electricity prices and workload arrival processes.

The widely deployed electronic health (eHealth) systems changed people's daily life due to the extraordinary benefits, such as more efficiency, higher accuracy and broader availability. Patients in the eHealth network use their personal health records (PHRs) to communicate with their physicians and obtain medical services. As a matter of fact, patients who share the same diseases or symptoms want to communicate with each other not only for treatment, but also for psychological therapy. However, without sufficient knowledge of the authenticity of other patients' PHRs, patients are reluctant to share their medical information. On the other hand, patients would accept the patient-to-patient interaction only if their privacy issues of PHR are also well preserved. In this paper, we design a privacy-preserving user-centric private matching scheme from a social perspective in eHealth networks, where patients use verified PHR to find other patients who share the same situations and derive different user-centric results based on each one's own policy. In our scheme, the matching process guarantees both the verifiability and the privacy of patients' PHRs. Based on security and efficiency analysis, we show that our work satisfies both the privacy preservation and practicality requirements.

By enabling opportunistic secondary users' (SUs) usage of licensed spectrum, cognitive radio (CR) technology notably improves spectrum utilization. However, the fundamental multicast throughput optimization problem in cognitive radio networks (CRNs) is still under-explored. Considering spectrum availability and sharing fairness, in this paper, we propose a cross-layer approach to maximize the multicast throughput in multi-hop CRNs. We introduce a new service provider, called secondary service provider (SSP), to harvest the available spectrum and allocate the collected bands among SUs. The SSP also guides the transmissions of multicast CR sessions w.r.t. their contention and spectrum sharing fairness. Leveraging the proposed palmier structure for the multicast session and the multi-radio multi-band multicast (M3) conflict graph, we mathematically characterize the multicast flow routing and link scheduling, respectively. Based on the proportional fairness model, we formulate the multicast maximization problem under multiple cross-layer constraints in CRNs, and provide near-optimal solutions. Through simulations, we show that the performance of the proposed scheme is much better than that of schemes without cross-layer consideration.

Reliable broadcast is an important communication primitive for wireless mesh networks, which has many applications such as multimedia services and software upgrade. Recently, network coding is introduced into reliable broadcast to enhance the throughput of data transmissions. Existing network coding based reliable broadcast schemes, such as Pacifier and R-Code, fail to take advantage of the unique characteristic of reliable broadcast or the broadcast nature of wireless transmissions, which leads to redundant transmissions and performance degradation. In this paper, we propose a greedy strategy for network coding based reliable broadcast, which is called GreedyCode. GreedyCode opportunistically selects the forwarders with the highest transmission efficiency to transmit the encoded packets while the neighbors just listen. In order to measure the efficiency of broadcast transmission of a node, we also define a metric named One-hop Broadcast Throughput (OBT), which considers not only the current reception status of the destinations but also the quality of the broadcast link. Because GreedyCode only needs the information of its one-hop neighbors, so it can be distributed realized. The simulation results show that GreedyCode achieves 100% packet delivery ratio (PDR) and significantly reduces the number of transmissions and the broadcast delay.

Network coding is widely recognized as a promising approach to increase the throughput of wireless networks. In order to maximize the benefit of network coding, the consideration of potential coding opportunities is incorporated into the route selection, which is referred to as coding-aware routing. Most of existing coding-aware routing metrics are designed based on traditional routing metrics like expected transmission count (ETX) and fail to take many critical factors into account, such as traffic load, link transmission rate and interference. Therefore, the routes discovered with them are always sub-optimal. In this paper, we present a novel routing metric called Coding-aware Path Transmission Time (CPTT). CPTT considers the effect of traffic load, multirate, intra-flow and inter-flow interference as well as network coding and quantifies them in a unified manner, which can be used to accurately evaluate path performance and discover the path with high throughput. Through extensive simulations, we compare CPTT with different coding-aware routing metrics proposed in the literature and show that the paths selected with CPTT have maximum end-to-end throughput under network coding.

Recently, eHealth systems have replaced paper based medical system due to its prominent features of convenience and accuracy. Also, since the medical data can be stored on any kind of digital devices, people can easily obtain medical services at any time and any place. However, privacy concern over patient medical data draws an increasing attention. In the current eHealth networks, patients are assigned multiple attributes which directly reflect their symptoms, undergoing treatments, etc. Those life-threatened attributes need to be verified by an authorized medical facilities, such as hospitals and clinics. When there is a need for medical services, patients have to be authenticated by showing their identities and the corresponding attributes in order to take appropriate healthcare actions. However, directly disclosing those attributes for verification may expose real identities. Therefore, existing eHealth systems fail to preserve patients' private attribute information while maintaining original functionalities of medical services. To solve this dilemma, we propose a framework called PAAS which leverages users' verifiable attributes to authenticate users in eHealth systems while preserving their privacy issues. In our system, instead of letting centralized infrastructures take care of authentication, our scheme only involves two end users. We also offer authentication strategies with progressive privacy requirements among patients or between patients and physicians. Based on the security and efficiency analysis, we show our framework is better than existing eHealth systems in terms of privacy preservation and practicality.

In Machine-to-Machine (M2M) communications, machines are wirelessly connected to accomplish collaborative tasks without human intervention, and provide ubiquitous solutions for real-time monitoring. The real-time monitoring application is one of the killer applications for M2M communications, where M2M nodes transmit sensed data to an M2M gateway, and then the M2M gateway can have real-time monitoring for each sensing region. In real-time monitoring application, the energy consumption for the M2M nodes to send sensed data to the M2M gateway is an important factor that significantly affects the performance of the system. In this paper, we first consider the energy consumption as well as the validity of sensed data to design either centralized or distributed energy-efficient reporting mechanisms. We then analyze the complexity of the reporting mechanisms. Simulation experiments are conducted to investigate the performance of the proposed mechanisms, and show that the distributed mechanism outperforms the centralized mechanism when the M2M nodes are mobile.

Privacy protection is of critical concern to Location-Based Service (LBS) users in mobile networks. Long-term pseudonyms, although appear to be anonymous, in fact empower third-party service providers to continuously track users' movements. Researchers have proposed the mix zone model to allow pseudonym changes in protected areas. In this paper, we investigate a new form of privacy attack to the LBS system that an adversary reveals a user's true identity and complete moving trajectory with the aid of side information. We propose a new metric to quantify the system's resilience to such attacks, and suggest using multiple mix zones to tackle this problem. A mathematical model is presented that treats the deployment of multiple mix zones as a cost constrained optimization problem. Furthermore, the influence of traffic density is also taken into account to enhance the protection effectiveness. The placement optimization problem is NP-hard. We therefore design two heuristic algorithms as practical and effective means to strategically select mix zone locations, and consequently reduce the privacy risks of mobile users trajectories. The effectiveness of our proposed solutions is demonstrated through extensive simulations on real-world mobile user data traces.

Spectrum trading creates more accessing opportunities for secondary users (SUs) and economically benefits the primary users (PUs). However, it is challenging to implement spectrum trading in multi-hop cognitive radio networks (CRNs) due to harsh cognitive radio (CR) requirements on SUs' devices and complex conflict and competition relationship among different CR sessions. Unlike the per-user based spectrum trading designs in previous studies, in this paper, we propose a novel session based spectrum trading system, spectrum clouds, in multi-hop CRNs. In spectrum clouds, we introduce a new service provider, called secondary service provider (SSP), to harvest the available spectrum bands and facilitate the accessing of SUs without CR capability. The SSP also conducts spectrum trading among CR sessions w.r.t. their conflicts and competitions. Leveraging a 3-dimensional (3-D) conflict graph, we mathematically describe the conflicts and competitions among the candidate sessions for spectrum trading. Given the rate requirements and bidding values of candidate trading sessions, we formulate the optimal spectrum trading into the SSP's revenue maximization problem under multiple cross-layer constraints in multi-hop CRNs. In view of the NP-hardness of the problem, we have also developed heuristic algorithms to pursue feasible solutions. Through extensive simulations, we show that the solutions found by the proposed algorithms are close to the optimal one.

Sensor-enabled RFID technology has generated a lot of interest from industries lately. Integrated with miniaturized sensors, RFID tags could provide not only the IDs but also valuable real-time information about the state of the corresponding objects or the surrounding environment, which is beneficial to many practical applications, such as warehouse management and inventory control. In this paper, we study the problem on how to design efficient protocols to collect such sensor information from numerous tags in a large-scale RFID system with a number of readers deployed. Different from information collection in the small RFID system covered by only one reader, in the multi-reader scenario, each reader has to first find out which tags located in its interrogation region in order to read information from them. We start with two categories of warm-up solutions that are directly extended from the existing information collection protocols for single-reader RFID systems, and show that all of them do not work well for the multi-reader information collection problem due to their inefficiency of identifying the interrogated tags. Then, we propose a novel solution, called the Bloom filter based Information Collection protocol (BIC). In BIC, the interrogated tag identification can be efficiently achieved with a distributively constructed Bloom filter, which significantly reduces the communication overhead and thus the protocol execution time. Extensive simulations show that BIC performs better than all the warm-up solutions and its execution time is within 3 times of the lower bound.

This paper studies the optimization problem of minimizing resource rental cost for running elastic applications in cloud while meeting application service requirements. Such a problem arises when excessive generated data incurs significant monetary cost on transfer and inventory in cloud. The goal of planning is to make resource rental decisions in response to varying application progress in the most cost-effective way. To address this problem, we first develop a Deterministic Resource Rental Planning (DRRP) model, using a mixed integer linear program, to generate optimal rental decisions given fixed cost parameters. Next, we systematically analyze the predictability of the time-varying spot instance prices in Amazon EC2 and find that the best achievable prediction is insufficient to provide a close approximation to the actual prices. This fact motivates us to propose a Stochastic Resource Rental Planning (SRRP) model that explicitly considers the price uncertainty in rental decision making. Using empirical spot price data sets and realistic cost parameters, we conduct simulations over a wide range of experimental scenarios. Results show that DRRP achieves as much as 50% cost reduction compared to the no-planning scheme. Moreover, SRRP consistently outperforms its DRRP counterpart in terms of cost saving, which demonstrates that SRRP is highly adaptive to the unpredictable nature of spot price in cloud resource market.

By incorporating relay technologies into cellular systems, multi-hop relay networks (MRNs) can provide higher throughput and wider coverage, but it is impossible to guarantee quality of service (QoS) requirements for users at the cell edge and in hot spots due to severe co-channel interference (CCI) and load imbalance. This paper proposes an adaptive resource allocation scheme with a joint consideration of interference coordination (IC) & load balancing (LB), and analyzes the downlink of orthogonal frequency division multiple access (OFDMA) based MRNs with time division duplex (TDD) mode. We present a novel frequency reuse scheme for MRNs to mitigate CCI and maintain high spectral efficiency. We also provide practical LB-based handover mechanisms to evenly distribute users and guarantee the users' QoS. Extensive simulations demonstrate that our scheme not only satisfies the requirement of coverage probability, but also improves the throughput and accommodates more users in MRNs.

Throughput maximization is a key challenge for wireless applications in cognitive Vehicular Ad-hoc Networks (C-VANETs). As a potential solution, cooperative communications, which may increase link capacity by exploiting spatial diversity, has attracted a lot of attention in recent years. However, if link scheduling is considered, this transmission mode may perform worse than direct transmission in terms of end-to-end throughput. In this paper, we propose a cooperative communication aware link scheduling scheme and investigate the throughput maximization problem in C-VANETs. Regarding the features of cooperative communications and the availability of licensed spectrum, we extend the links into cooperative links/general links, define extended link-band pairs, and form a 3-dimensional (3-D) cooperative conflict graph to characterize the conflict relationship among those pairs. Given all cooperative independent sets in this graph, we mathematically formulate an end-to-end throughput maximization problem and near-optimally solve it by linear programming. Due to the NP-completeness of finding all independent sets, we also develop a heuristic pruning algorithm for cooperative communication aware link scheduling. Our simulation results show that the proposed scheme is effective in increasing end-to-end throughput for the session in C-VANETs.

The essential impediment to apply cognitive radio (CR) technology for efficient spectrum utilization lies in the uncertainty of licensed spectrum supply. In this paper, we propose a novel architecture for spectrum harvesting and sharing, and investigate the joint routing and frequency scheduling problem in multi-hop cognitive radio networks (CRNs) under uncertain spectrum supply. We introduce a new service provider, Secondary Service Provider (SSP), to facilitate the accessing for secondary users (SUs). We model the vacancy of available bands with a series of random variables, and mathematically describe the corresponding frequency scheduling and flow routing constraints. From the SSP's point of view, we characterize the CRN performance with a pair of parameters (α, β), and present an optimization problem to minimize the required network-wide spectrum resource at the (α,β) level. Given that (α, β) level is specified, we obtain a lower bound for the optimization problem and develop a threshold based coarse-grained fixing algorithm for a feasible solution. Simulation results show that (i) for any (α,β) level, the proposed algorithm provides a near-optimal solution to the formulated NP-hard problem, and (ii) the (α,β) based solution is better than the expected bandwidth based one in terms of blocking ratio and spectrum utilization in multi-hop CRNs.

In this paper, we study a wireless multi-hop network where multiple flows co-exist and share the network resource collectively. Each flow is associated with a user which has specific requirements on its tradeoff between cost and quality of service. To support heterogeneous transmissions efficiently, we propose a quality-aware dynamic pricing algorithm, namely, QADP, which provably maximizes the overall network revenue while maintaining the stability of the network. Our proposed scheme enjoys the merit of self-adaptability due to its online nature.

A substantial body of the literature exists addressing the capacity of wireless networks. However, it is commonly assumed that all nodes in the network are identical. The issue of heterogeneity has not been embraced into the discussions. In this paper, we investigate the throughput capacity of heterogeneous wireless networks with general network settings. Specifically, we consider an extended network with n normal nodes and m = nb(0 ≤ b ≤ 1) more powerful helping nodes in a rectangular area with width s(n) and length n/s(n), where s(n) = nwand 0 ≤ w ≤ 1/2. We assume that there are n flows in the network. All the n normal nodes are sources while only randomly chosen nd(0 ≤ d ≤ 1) normal nodes are destinations. We further assume that the n normal nodes are uniformly and independently distributed, while the m helping nodes are either regularly placed or uniformly and independently distributed, resulting in two different kinds of networks called Regular Heterogeneous Wireless Networks and Random Heterogeneous Wireless Networks, respectively. We show that network capacity is determined by the shape of the network area, the number of destination nodes, the number of helping nodes, and the bandwidth of helping nodes. We also find that heterogeneous wireless networks can provide throughput higher in the order sense than traditional homogeneous wireless networks only under certain conditions.

Throughput capacity in mobile ad hoc networks has been studied extensively under many different mobility models. However, most previous research assumes global mobility, and the results show that a constant per-node throughput can be achieved at the cost of very high delay. Thus, we are having a very big gap here, i.e., either low throughput and low delay in static networks or high throughput and high delay in mobile networks. In this paper, employing a practical restricted random mobility model, we try to fill this gap. Specifically, we assume that a network of unit area with n nodes is evenly divided into cells with an area of n^{-2\alpha }, each of which is further evenly divided into squares with an area of n^{-2\beta} (0 \le \alpha \le \beta \le {1\over 2} ). All nodes can only move inside the cell which they are initially distributed in, and at the beginning of each time slot, every node moves from its current square to a uniformly chosen point in a uniformly chosen adjacent square. By proposing a new multihop relay scheme, we present smooth trade-offs between throughput and delay by controlling nodes' mobility. We also consider a network of area n^\gamma (0\le \gamma \le 1) and find that network size does not affect the results obtained before.

In a cognitive radio network, primary service providers (PSPs) set prices for the vacant licensed bands and sell/lease them for pecuniary gains while the secondary service provider (SSP) can buy/rent the bands and support opportunistic spectrum accessing (OSA). However, due to the unpredictable activities of primary services, the SSP may suffer the monetary risk or failure to meet the traffic demands from the secondary users (SUs). It is challenging for the SSP to measure the risk for OSA, to choose the bands to use, and to split the traffic on the band-mix, when there are multiple vacant bands from PSPs. In this paper, we first introduce the X loss, an intuitive measurement for the risk for OSA. Although the X loss is attractively simple, it underestimates the potential risk for OSA and is mathematically not subadditive, which makes it difficult to support the band-mix selection for traffic splitting. To overcome this problem, we propose a more suitable risk measurement, which is subadditive and consistent with the SSP's perception of the risk. Based on the proposed risk metric, we formulate the band-mix selection problem as an optimization problem and solve it by linear programming.

Network capacity investigation has been intensive in the past few years. A large body of work on wireless network capacity has appeared in the literature. However, so far most of the effort has been made on two-dimensional (2-D) wireless networks only. With the great development of wireless technologies, wireless networks are envisioned to extend from 2-D space to three-dimensional (3-D) space. In this paper, we investigate the throughput capacity of 3-D regular ad hoc networks (RANETs) and of 3-D nonhomogeneous ad hoc networks (NANETs), respectively, by employing a generalized physical model. In 3-D RANETs, we assume that the nodes are regularly placed, while in 3-D NANETs, we consider that the nodes are distributed according to a general Nonhomogeneous Poisson Process (NPP). We find both lower and upper bounds in both types of networks in a broad power propagation regime, i.e., when the path loss exponent is no less than 2.

Recently intensive efforts have been made on the transformation of the world's largest physical system, the power grid, into a “smart grid” by incorporating extensive information and communication infrastructures. Key features in such a “smart grid” include high penetration of renewable and distributed energy sources, large-scale energy storage, market-based online electricity pricing, and widespread demand response programs. From the perspective of residential customers, we can investigate how to minimize the expected electricity cost with real-time electricity pricing, which is the focus of this paper. By jointly considering energy storage, local distributed generation such as photovoltaic (PV) modules or small wind turbines, and inelastic or elastic energy demands, we mathematically formulate this problem as a stochastic optimization problem and approximately solve it by using the Lyapunov optimization approach. From the theoretical analysis, we have also found a good tradeoff between cost saving and storage capacity. A salient feature of our proposed approach is that it can operate without any future knowledge on the related stochastic models (e.g., the distribution) and is easy to implement in real time. We have also evaluated our proposed solution with practical data sets and validated its effectiveness.

Since there always exists a possibility that some users' private keys are stolen or expired in practice, it is important for identity based encryption (IBE) system to provide a solution to revocation. The current most efficient revocable IBE system has a private key of size O(log ns) and update information of size O(r log(n/r)) where r is the number of revoked users. In this paper, we present a new revocable IBE system in which the private key only contains two group elements and the update information size is O(r). We show that the proposed constructions for the revocation mechanism are more efficient in terms of space cost and provide a generic methodology to transform a non-monotonic attribute based encryption into a revocable IBE. We also demonstrate how the proposed method can be employed to develop an efficient hierarchical revocable IBE system.

Electricity consumption comprises a significant fraction of total operating cost in data centers. System operators are required to reduce electricity bill as much as possible. In this paper, we consider utilizing available energy storage capability in data centers to reduce electricity bill under real- time electricity market. Laypunov optimization technique is applied to design an algorithm that achieves an explicit tradeoff between cost saving and energy storage capacity. As far as we know, our work is the first to explore the problem of electricity cost saving using energy storage in multiple data centers by considering both time- diversity and location-diversity of electricity price.

Online Social Networks (OSNs) are becoming immensely popular nowadays, and they change the ways people think and live. In this paper, we propose a novel reputation system which allows users to find potential connections between unfamiliar people based on the most updated friend list of each user in OSNs. To some extent, our scheme provides a way to judge people in OSNs without real interactions, but based on the existing overall attitudes on particular people. Moreover, our scheme can protect the confidentially of the potential relationships in which no one is able to acquire the detailed connections between two end nodes. Contrary to those which publish each individual's reputation online, we treat the reputation value in our system as a private issue that has been carefully guaranteed.

Spectrum auction is an enabling approach to drastically improving the spectrum utilization to satisfy the ever increasing service demands in wireless networks. However, the behaviors of the untrustworthy auctioneer (i.e., the frauds of the untrustworthy auctioneer and the bid-rigging between the greedy bidders and the insincere auctioneer) pose significant design challenges. In this paper, we propose a secure combinatorial spectrum auction (SCSA) by using homomorphic encryption to deal with the untrustworthy auctioneer. SCSA computes and reveals the results of spectrum auction while the actual bidding values are kept confidential. By taking frequency reuse and interference constraints into consideration, we also incorporate a corresponding procedure to implement the combinatorial spectrum auction. It has been shown that SCSA can effectively thwart the back-room dealing without much performance degradation.

The multi-hop cellular network (MCN) is an evolved paradigm for mobile communications, which integrates the ad hoc characteristics into the conventional cellular systems. Similar to ad hoc networks, the performance of MCNs relies on the hypothesis that each node accepts to forward traffic for the benefit of others, which may not hold with the possible presence of selfish users. In order to stimulate the collaboration among mobile nodes in MCNs, in this paper, we propose a light-weighted secure incentive protocol (LIP). We introduce a novel reward model, in which not the source and/or the destination but the network operator credits the forwarding nodes. It is shown that our model is much more realistic for MCNs in practice and simplifies the payment scheme design as well. LIP exploits a reactive receipt-submission mechanism to identify node behavior, which significantly reduces the communication overhead. Security analysis shows that LIP can resist various attacks. The efficiency of LIP is validated through the performance evaluation.

When many cellular users take high capacity transit (HCT), a group of mobile terminals (MTs) may have to initiate handoffs and location updates more or less at the same time, which will cause congestion on the random access control channel (RACH), leading to many handoff and location update failures. To overcome this problem, we propose a new scheme, called GHA/RALU (Group Handoff Avoidance/Rate Adjusted Location Update Scheme), to deal with this group mobility issue. The idea is first to delay the normal location update (NLU) requests until the group handoff requests are completed and then spread the NLU requests within a calculated time interval based on the available bandwidth of the RACH. Our analysis shows that the proposed scheme significantly improves the success probability of the group handoffs and NLUs, saves the RACH resource, and accelerates the handoff initiated by the moving-in-together MTs.

Cognitive Radio (CR) emerges as a promising solution to current unbalanced spectrum utilization. The cognitive ad hoc network can take advantage of dynamic spectrum access and spectrum diversity over wide spectrum. It could achieve higher network capacity compared to traditional ad hoc networks, thus supporting bandwidth-demanding applications. A cognitive radio operates over wide spectrum with unpredictable channel availability. Moreover, the transmission opportunity of a cognitive node is not guaranteed due to the presence of primary users (PUs). These two unique features define new routing problems in cognitive ad hoc networks. To better characterize the unique features of cognitive radio networks, we propose new routing metrics, including accumulated spectrum temperature, highest spectrum temperature, and mixed spectrum temperature to account for the time-varying spectrum availability. The proposed metrics favor the "coolest'' path, or the path with the most balanced and/or the lowest spectrum utilization by the primary users. We also study the computational complexity of the routing algorithm in cognitive ad hoc networks. Experiment results on our USRP-2 testbed show that the proposed metrics are capable of capturing the fluctuation of spectrum availability and suitable for cognitive ad hoc networks.

Privacy concern is arguably the major barrier that hinders the deployment of electronic health record (EHR) systems which are considered more efficient, less error-prone, and of higher availability compared to traditional paper record systems. Patients are unwilling to accept the EHR system unless their protected health information (PHI) containing highly confidential data is guaranteed proper use and disclosure, which cannot be easily achieved without patients' control over their own PHI. However, cautions must be taken to handle emergencies in which the patient may be physically incompetent to retrieve the controlled PHI for emergency treatment. In this paper, we propose a secure EHR system, HCPP (Healthcaresystem for Patient Privacy), based on cryptographic constructions and existing wireless network infrastructures, to provide privacy protection to patients under any circumstances while enabling timelyPHI retrieval for life-saving treatment in emergency situations. Furthermore, our HCPP system restricts PHI access to authorized (not arbitrary) physicians, who can be traced and held accountable if the accessed PHI is found improperly disclosed. Last but not least, HCPP leverages wireless network access to support efficient and private storage/retrieval of PHI, which underlies a secure and feasible EHR system.

Wireless cellular networks are large-scale networks in which asymptotic capacity investigation is no longer a cliché. A substantial body of work has been carried out to improve the capacity of cellular networks by introducing ad hoc communications, resulting in the so-called multihop cellular networks. Most of the previous research allows ad hoc transmissions between certain source and destination pairs to alleviate base stations' relay burden. However, since reports show that Internet data traffic is becoming more and more dominant in cellular networks, we explore in this paper the capacity of multihop cellular networks with all traffic going through base stations and ad hoc transmissions only acting as relay. We first investigate the capacity of regular multihop cellular networks where both nodes and base stations are regularly placed. By fully exploiting the link rate variability, we find that multihop cellular networks can have higher per-node throughput than traditional cellular networks by a scaling factor of log2n. Then, for the first time we extend our study to the capacity of heterogeneous multihop cellular networks where nodes are distributed according to a general Inhomogeneous Poisson Process and base stations are randomly placed. We show that under certain conditions multihop cellular networks can also outperform traditional cellular networks by a scaling factor of log2n. Moreover, both throughput-fairness and bandwidth-fairness are considered as fairness constraints for both kinds of networks.

Network capacity investigation has been intensive in the past few years. A large body of work has appeared in the literature. However, so far most of the effort has been made on two-dimensional wireless networks only. With the great development of wireless technologies, wireless networks are envisioned to extend from two-dimensional space to three-dimensional space. In this paper, we investigate for the first time the throughput capacity of 3D regular ad hoc networks (RANETs) and of 3D heterogeneous ad hoc networks (HANETs), respectively, by employing a generalized physical model. In 3D RANETs, we assume that the nodes are regularly placed, while in 3D HANETs, we consider that the nodes are distributed according to a general Nonhomogeneous Poisson Process (NPP). We find both lower and upper bounds in both types of networks in a broad power propagation regime, i.e., when the path loss exponent is no less than 2.

The essential impediment to apply cognitive radio (CR) technology for spectrum utilization improvement lies in the uncertainty of licensed spectrum supply. In this paper, we investigate the joint routing and link scheduling problem of multi-hop CR networks under uncertain spectrum supply. We model the vacancy of licensed bands with a series of random variables, and introduce corresponding scheduling constraints and flow routing constraints for such a network. From a CR network planner/operator's point of view, we characterize the network with a pair of (α, β) parameters, and present a mathematical formulation with the goal of minimizing the required network-wide spectrum resource at the (α, β) level. Given that (α, β) is specified, we derive a lower bound for the optimization problem and develop a threshold based coarse-grained fixing algorithm for a feasible solution. Simulation results show that i) for any (α, β) level, the proposed algorithm provides a near-optimal solution to the formulated NP-hard problem; ii) the (α, β) based solution is better than expected bandwidth based one in terms of blocking ratio as well as spectrum utilization in CR networks..

For a multi-hop wireless network (MWN) consisting of mobile nodes controlled by independent self-interested users, incentive mechanism is essential for motivating mobile nodes to cooperate and forward packets for each other. Existing solutions such as barter based, virtual-currency based and reputation based schemes are either less effective or incur high implementation costs, and therefore do not fit well with the unique requirements of MWNs. In this paper, we propose a novel and promising incentive paradigm, Controlled Coded packets as virtual Commodity Currency (C4), to induce cooperative behaviors in MWNs. In our C4, through introducing several techniques from network coding, coded information packets are utilized as a new kind of virtual currency to facilitate packet/service exchanges among self-interested nodes in a MWN. Since the virtual currency implemented in this way also carries useful data information, it is the counterpart of the so-called commodity currency in the physical world, and the overhead brought by C4 is extremely small compared to traditional schemes. We theoretically show that C4 is perfectly efficient to support MWNs with broadcast and multicast traffics. For pure unicast communications, by adjusting the grouping parameter, our C4 provides a systematic way to smoothly trade incentive effectiveness for implementation cost, and traditional barter based and virtual-currency based schemes are just two extreme cases of C4. We also show that when our C4 is combined with the social network formed by mobile users in the MWN, the implementation costs can be further reduced without sacrificing incentive effectiveness.

In wireless networks, interference between nodes is an important factor which affects the performance of communications. In this paper, we propose an interference avoidance routing protocol (IARP) that chooses a route with fewer collisions and show that the new routing algorithm is stable. The idea is to collect the busyness information about nodes, and use this to determine the next hop based on the backpressure routing algorithm. From the simulation results, we observe that IARP can achieve better performance in terms of network delay than the pure backpressure routing protocol.

Wireless Relay networks have become very important technologies in the future wireless systems. Current research works mostly focus on scenarios where relay stations are either stationary or mobile with uniform velocity. However, in many applications, relay stations are mobile with irregular patterns. In particular, when mobile relay stations (MRSs) are deployed to complement the cellular systems, many issues such as handoff should be carefully investigated. In this paper, we focus on the handoff problem and propose a new handoff decision algorithm based on the relative velocities of user equipment (UE) to the serving access point (AP) and the target AP. We have show that the proposed handoff algorithm can significantly improve the handoff successful rate when the mobile relay station changes its moving patterns.

This paper addresses energy conservation, a fundamental issue of paramount importance in heterogeneous mobile ad hoc networks (MANETs) consisting of powerful nodes (i.e., P-nodes) as well as normal nodes (i.e., B-nodes). By utilizing the inherent device heterogeneity, we propose a cross-layer designed Device-Energy-Load Aware Relaying framework, named DELAR, to achieve energy conservation from multiple facets, including power-aware routing, transmission scheduling and power control. In particular, we design a novel power-aware routing protocol that nicely incorporates device heterogeneity, nodal residual energy information and nodal load status to save energy. In addition, we develop a hybrid transmission scheduling scheme, which is a combination of reservation-based and contention-based medium access control schemes, to coordinate the transmissions. Moreover, the novel notion of "mini-routing" is introduced into the data link layer and an Asymmetric MAC (A-MAC) scheme is proposed to support the MAC-layer acknowledgements over unidirectional links caused by asymmetric transmission power levels between powerful nodes and normal nodes. Furthermore, we present a multi-packet transmission scheme to improve the end-to-end delay performance. Extensive simulations show that DELAR can indeed achieve energy saving while striking a good balance between energy efficiency and other network performance metrics.

Microeconomics-inspired spectrum auctions can dramatically improve the spectrum utilization for wireless networks to satisfy the ever increasing service demands. However, the back-room dealing (i.e., the frauds of the insincere auctioneer and the bid-rigging between the greedy bidders and the auctioneer) poses significant security challenges, and fails all existing secure auction designs to allocate spectrum bands when considering the frequency reuse in wireless networks. In this paper, we propose THEMIS, a secure spectrum auction leveraging the Paillier cryptosystem to prevent the frauds of the insincere auctioneer as well as the bid-rigging between the bidders and the auctioneer. THEMIS incorporates cryptographic technique into spectrum auction to address the challenges of back-room dealing. It computes and reveals the results of spectrum auction while the actual bidding values of bidders are kept confidential. THEMIS also provides a novel procedure for implementing secure spectrum auction under interference constraints. It has been shown that THEMIS can effectively purge the back-room dealing with limited communication and computational complexity, and achieve similar performance compared with existing insecure spectrum auction designs in terms of spectrum utilization, revenue of the auctioneer, and bidders' satisfaction.

Natural disasters and terrorism threaten our nation's safety and security, rendering post-disaster rescue mission critical. It is of paramount importance to carry out rescue work relying on secure and dependable networking. In this paper, we propose RescueMe, location-based vehicular ad hoc networks (VANETs), to aid in secure and dependable rescue planning for the efficient allocation of rescue resources. RescueMe leverages the location information stored during normal network operations to facilitate post-disaster rescue planning, while guaranteeing that the sensitive user location information is not exploited to trace a user's whereabouts when disasters are absent, even if the most powerful collusion attack is allowed. We provide a novel construction for the location update message, and propose several enhancements, to achieve the functional and security goals of RescueMe.

Anonymity has received increasing attention in the literature due to the users' awareness of their privacy nowadays. Anonymity provides protection for users to enjoy network services without being traced. While anonymity-related issues have been extensively studied in payment-based systems such as e-cash and peer-to-peer (P2P) systems, little effort has been devoted to wireless mesh networks (WMNs). On the other hand, the network authority requires conditional anonymity such that misbehaving entities in the network remain traceable. In this paper, we propose a security architecture to ensure unconditional anonymity for honest users and traceability of misbehaving users for network authorities in WMNs. The proposed architecture strives to resolve the conflicts between the anonymity and traceability objectives, in addition to guaranteeing fundamental security requirements including authentication, confidentiality, data integrity, and nonrepudiation. Thorough analysis on security and efficiency is incorporated, demonstrating the feasibility and effectiveness of the proposed architecture.

Considering a disk of unit area with n nodes, we investigate the capacity of wireless networks using directional antennas. First, we study the throughput capacity of random directional networks with multihop relay schemes, and find that the capacity gain compared to random omnidirectional networks is O(log n), which is tighter than previous results. We also show that using directional antennas can significantly reduce power consumption in the networks. Second, for the first time, we explore the throughput capacity of random directional networks with one-hop relay schemes. Interestingly and against our intuition, we find that one-hop instead of multihop delivery schemes can make random directional networks scale. Third, we investigate the trade-offs between transmission range and throughput in random directional networks and show that using larger transmission range can result in higher throughput. Finally, we present a lower bound on the transport capacity of arbitrary directional networks, and find that without side lobe directional antenna gain, arbitrary directional networks can also scale.

Security always comes with a price in terms of performance degradation, which should be carefully quantified. This is especially the case for wireless ad hoc networks (WANETs), which offer communications over a shared wireless channel without any preexisting infrastructure. Forming end-to-end secure paths in such WANETs is more challenging than in conventional networks due to the lack of central authorities, and its impact on network performance is largely untouched in the literature. In this paper, based on a general random network model, the asymptotic behaviors of secure throughput and delay with the common transmission range rnand the probability pfof neighboring nodes having a primary security association are quantified when the network size n is sufficiently large. The costs and benefits of secure-link-augmentation operations on the secure throughput and delay are also analyzed. In general, security has a cost: Since we require all the communications operate on secure links, there is a degradation in the network performance when pffis Ω(1/logn), the secure throughput remains at the Gupta and Kumar bound of Θ(1/√{n log n}) packets/time slot, wherein no security requirements are enforced on WANETs. This implies that even when the pfgoes to zero as the network size becomes arbitrarily large, it is still possible to build throughput-order-optimal secure WANETs, which is of practical interest since pfis very small in many practical large-scale WANETs.

As all-IP feature becomes dominant in the next generation networks (NGNs), ad hoc mode is gaining more attention as an appealing addition to cellular networks. Consequently, multi-hop handoffs become inevitable, which bring challenging issues to network designers. Handoff dropping (HOD) rate and the bandwidth reservation for the required HOD rate are two important metrics to evaluate the handoff performance of a cellular system. By introducing ad hoc mode into cellular systems, we can either achieve lower HOD rate or reserve less bandwidth for the same required HOD rate. In this paper, we incorporate traffic information from neighboring BSs and propose an algorithm to find the minimum bandwidth reservation for each BS. Since its performance greatly depends on the access probability to the adjacent cells, we further propose to utilize the embedded ad hoc networks to gather the traffic load information of neighboring cells. With such information, handoff calls can effectively select the best paths to the proper BSs. It has been demonstrated that our scheme can significantly improve the system performance.

Cognitive Radio technology releases the spectrum from shackles of authorized licenses and facilitates the trading of spectrum bands. In the spectrum market, primary service providers (PSPs) set price for the vacant licensed bands of primary users (PUs) and sell them for monetary gains, and the secondary service provider (SSP) can buy the bands and opportunistically use them to satisfy the service demands of secondary users (SUs) when the primary services are not active. However, when there are multiple bands available, the SSP confronts the challenges of how to choose bands and how to split the overall traffic on them considering both his monetary reward and the potential risk, from the unpredictable activities of the primary services, for opportunistic spectrum accessing (OSA). In this paper, we propose a reward and risk based band-mix selection algorithm to address these concerns of the SSP, and help the SSP to make appropriate decisions of traffic splitting over available spectrum band- mix, consisting of both the band belonging to SSP itself and the bands from PSPs. By numerical simulations, we verify our theoretical analysis and show that the proposed spectrum band-mix selection effectively improves the spectrum utilization as well as the satisfactory degree of SUs.

Cognitive Radio technology releases the spectrum from shackles of authorized licenses and facilitates the trading of spectrum bands. In the spectrum market, primary service providers (PSPs) set prices for the vacant licensed bands of primary users (PUs) and sell/lease them for pecuniary gains, and the secondary service provider (SSP) can buy/rent the bands and support the secondary users (SUs) for their opportunistic spectrum accessing (OSA) when primary services are not active. However, due to the unpredictable activities of primary services, the SSP may suffer the monetary risk or failure to satisfy the traffic demands from the SUs. It is challenging for the SSP to measure the risk for OSA, to choose the bands to access, and to split the overall traffic on the band-mix, when there are multiple vacant bands and uncertain spectrum supply from PSPs. To address the concerns of the SSP, in this paper, we first introduce the X loss, an intuitive measurement to evaluate the risk for OSA at a given confidence level. Although the X loss is attractively simple, it theoretically underestimate the potential risk for OSA. Meanwhile, the X loss requires strong assumptions to support band-mix selection for traffic splitting, i.e., the primary services of different bands must satisfy normal distribution, which is not necessarily true in practice. To overcome the weakness of the X loss, we further propose a more suitable risk measurement, the expected X loss, which is theoretically subadditive and practically consistent with the SSP's perception of risk for OSA. Based on the proposed measurement, we formulate the band-mix selection problem for traffic splitting into an optimization problem and solve it by linear programming.

E-healthcare is becoming a vital part of our living environment and exhibits advantages over paper-based legacy systems. Wireless body sensor networks are indispensable in one application of e-healthcare, the remote monitoring or remote care services. However, privacy is the foremost concern of the patients and the biggest impediment of the deployment of e-healthcare systems. In addressing privacy issues, conflicts from the functional requirements must be taken into account. One such requirement is the efficient and effective response to medical emergencies. In this paper, we propose to solve these conflicting goals based on suitable cryptographic schemes. In addition, security enhancements are proposed which satisfy other fundamental security goals besides the privacy requirements.

Although capacity has been extensively studied in wireless networks, most of the results are for homogeneous wireless networks where all nodes are assumed identical. In this paper, we investigate the capacity of heterogeneous wireless networks with general network settings. Specifically, we consider a dense network with n normal nodes and m = nb(0wand -1/2d(0 < d < 1) normal nodes are destinations. We further assume the n normal nodes are uniformly and independently distributed, while the m helping nodes are either regularly placed or uniformly and independently distributed, resulting in two different kinds of networks called Regular Heterogeneous Wireless Networks and Random Heterogeneous Wireless Networks, respectively. In this paper, we attempt to find out what a heterogeneous wireless network with general network settings can do by deriving a lower bound on the capacity. We also explore the conditions under which heterogeneous wireless networks can provide throughput higher than traditional homogeneous wireless networks.

Throughput capacity in wireless ad hoc networks has been studied extensively under many different mobility models such as i.i.d. mobility model, Brownian mobility model, random walk model, and so on. Most of these research works assume global mobility, i.e., each node moves around in the whole network, and the results show that a constant per-node throughput can be achieved at the cost of very high expected average end-to-end delay. Thus, we are having a very big gap here, either low throughput and low delay in static networks or high throughput and high delay in mobile networks. In this paper, employing a more practical restricted random mobility model, we try to fill in this gap. Specifically, we assume a network of unit area with n nodes is evenly divided into n2¿cells with an area of n-2¿where 0 ¿ ¿ ¿ 1/2, each of which is further evenly divided into squares with an area of n-2ßwhere 0 ¿ ¿ ¿ ß ¿ 1/2. All nodes can only move inside the cell which they are initially distributed in, and at the beginning of each time slot, every node moves from its current square to a uniformly chosen point in an uniformly chosen adjacent square. Proposing a new multi-hop relay scheme, we present an upper bound and a lower bound on per-node throughput capacity and expected average end-to-end delay, respectively. We finally explicitly show smooth trade-offs between throughput and delay by controlling nodes' mobility.

MaxWeight algorithm, a.k.a., back-pressure algorithm, has received much attention as a viable solution for dynamic link scheduling in multi-hop wireless networks. The basic principle of the MaxWeight algorithm is to select a set of interference-free links with the maximum overall link weights in the network, where the link weight is determined by the queue difference between the transmitter and the receiver. While the throughput-optimality of the MaxWeight algorithm is well understood in the literature, the energy consumption induced by the MaxWeight algorithm is less studied, which is of great interest in energy-constrained wireless networks such as wireless sensor networks. In this paper, we propose an energy-conserving scheduling scheme, a.k.a., minimum energy scheduling (MES) algorithm for multi-hop wireless networks with stochastic traffic arrivals and time-varying channel conditions. We show that our algorithm is energy optimal in the sense that the proposed MES algorithm can achieve an energy consumption which is arbitrarily close to the global minimum solution. Moreover, the energy efficiency of the MES algorithm is achieved without losing the throughput- optimality. In other words, the proposed MES algorithm is still throughput optimal whereas the average consumed energy in the network is significantly reduced, as compared to the traditional MaxWeight algorithm. The theoretical results are substantiated via simulations.

Online social networks (OSNs) are attractive applications which enable a group of users to share data and stay connected. Facebook, Myspace, and Twitter are among the most popular applications of OSNs where personal information is shared among group contacts. Due to the private nature of the shared information, data privacy is an indispensable security requirement in OSN applications. In this paper, we propose a privacy-preserving scheme for data sharing in OSNs, with efficient revocation for deterring a contact's access right to the private data once the contact is removed from the social group. In addition, the proposed scheme offers advanced features such as efficient search over encrypted data files and dynamic changes to group membership. With slight modification, we extend the application of the proposed scheme to anonymous online social networks of different security and functional requirements. The proposed scheme is demonstrated to be secure, effective, and efficient.

Recently, trust-based routing has received much attention as an effective way to improve security of wireless ad hoc networks (WANETs). Although various trust metrics have been designed and incorporated into the routing metrics, as far as we know, none of the existing works have used mathematical tools such as routing algebra to analyze the compatibility of trust related routing metrics and routing protocols in WANETs. In this paper, we first identify unique features of trust metrics compared with QoS-based routing metrics. Then, we provide a systematic analysis of the relationship between trust metrics and trust-based routing protocols by identifying the basic algebraic properties that a trust metric must have in order to work correctly and optimally with different generalized distance-vector or link-state routing protocols in WANETs. Moreover, we extend our framework to model the interactions between different trust-based routing protocols. Finally, our results are applied to check the compatibility of the trust metrics proposed in previous literature and the popular routing protocols used in WANETs.

The mobile sensor networks (MSNs) have been widely deployed to provide an ubiquitous solution for time-sensitive applications in a specific area with low deployment cost. The monitoring area of an MSN can be divided into several sensing regions (SRs). In an SR, the mobile sensor (MS) is responsible for reporting the sensed data to the sink node. For the time-sensitive applications on MSN, the time is divided into multiple monitoring periods (MPs). During every MP, a sensing report transmission in an SR is invalid if the sensing report is generated before the beginning of the MP. In this paper, we propose a region-based reporting mechanism, namely Energy-Efficient Distributed-Control Reporting (E2DCR) mechanism, for the MSNs. During every MP, the E2DCR mechanism attempts to have only one MS transmit the sensing report in an SR, and the other MSs in the same SR can stay in the sleep mode for power saving. Simulation experiments are conducted to investigate the performance of the proposed mechanism. Our study shows that E2DCR can meet the delay constraint of the time-sensitive applications with less power consumption.

Cognitive Radio technology releases the spectrum from shackles of authorized licenses and facilitates the trading of spectrum bands. In the spectrum market, primary users (PUs) set prices for their vacant bands and sell them for monetary gains, and secondary users (SUs) buy the bands and opportunistically use them to satisfy their service demands when the PUs are not active. However, when there are multiple bands available for the SUs to access, the SU confronts the challenges of how to choose bands and how to split his traffic over them considering both the contention from peer SUs as well as the unpredictable activities of the PUs. In this paper, we propose a return and risk model to represent these concerns of the SU, and help the SU to make appropriate decisions of traffic distribution over available spectrum bands, either the bands belonging to SU itself or the bands shared with PUs. The simulation and analysis show that our spectrum sharing scheme is efficient in terms of maximum return for given risk or minimum risk for given return, and is also effective in improving the spectrum utilization and SUs' satisfactory degrees.

The task of estimating path available bandwidth is difficult but paramount for QoS routing in supporting bandwidth-demanding traffic in multirate and multihop wireless ad hoc networks. The multirate capability and the impact of background traffic has not been carefully studied for the problem of estimating path available bandwidth in prior works. In this paper, we develop a theoretical model for estimating the available bandwidth of a path by considering interference from both background traffic and traffic along the path. We show that the clique constraint widely used to construct upper bounds does not hold any more when links are allowed to use different rates at different time. In our proposed model, traditional clique is coupled with rate vector to more properly characterize the conflicting relationships among links in wireless ad hoc networks where time-varying link adaption is used. Based on this model, we also investigate the problem of joint optimization of QoS routing and link scheduling. Several routing metrics and a heuristic algorithm are proposed. The newly proposed conservative clique constraint performs the best among the studied metrics in estimating available bandwidth of flows with background traffic.

In this work, the stochastic traffic engineering problem in multihop cognitive wireless mesh networks is addressed. The challenges induced by the random behaviors of the primary users are investigated in a stochastic network utility maximization framework. For the convex stochastic traffic engineering problem, we propose a fully distributed algorithmic solution which provably converges to the global optimum with probability one. We next extend our framework to the cognitive wireless mesh networks with nonconvex utility functions, where a decentralized algorithmic solution, based on learning automata techniques, is proposed. We show that the decentralized solution converges to the global optimum solution asymptotically.

After a wireless sensor network (WSN) is deployed, sensor nodes are usually left unattended for a long period of time. There is an inevitable devolution of the connected coverage of the WSN due to battery exhaustion of sensor nodes, intended physical destruction attacks on sensor nodes, unpredictable node movement by physical means like wind, and so on. It is, therefore, critical that the base station (BS) learns in real time how well the WSN performs the given sensing task (i.e., what is the current connected coverage) under a dynamically changing network topology. In this paper, we propose a coverage inference protocol (CIP), which can provide the BS an accurate and in-time measurement of the current connected coverage in an energy-efficient way. Especially, we show that the scheme called BOND, which our CIP requires to be implemented on each sensor node, enables each node to locally self-detect whether it is a boundary node with the minimal communication and computational overhead. The BOND can also be exploited to seamlessly integrate multiple functionalities with low overhead. Moreover, we devise extensions to CIP that can tolerate location errors and actively predict the change of the connected coverage based on residual energy of sensor nodes.

Conventional block-based multicast authentication schemes overlook the heterogeneity of receivers by letting the sender choose the block size, divide a multicast stream into blocks, associate each block with a signature, and spread the effect of the signature across all the packets in the block through hash graphs or coding algorithms. The correlation among packets makes them vulnerable to packet loss, which is inherent in the Internet and wireless networks. Moreover, the lack of Denial of Service (DoS) resilience renders most of them vulnerable to packet injection in hostile environments. In this paper, we propose a novel multicast authentication protocol, namely MABS, including two schemes. The basic scheme (MABS-B) eliminates the correlation among packets and thus provides the perfect resilience to packet loss, and it is also efficient in terms of latency, computation, and communication overhead due to an efficient cryptographic primitive called batch signature, which supports the authentication of any number of packets simultaneously. We also present an enhanced scheme MABS-E, which combines the basic scheme with a packet filtering mechanism to alleviate the DoS impact while preserving the perfect resilience to packet loss.

This paper studies a general class of dynamical neural networks with lateral inhibition, exhibiting winner-take-all (WTA) behavior. These networks are motivated by a metal-oxide-semiconductor field effect transistor (MOSFET) implementation of neural networks, in which mutual competition plays a very important role. We show that for a fairly general class of competitive neural networks, WTA behavior exists. Sufficient conditions for the network to have a WTA equilibrium are obtained, and rigorous convergence analysis is carried out. The conditions for the network to have the WTA behavior obtained in this paper provide design guidelines for the network implementation and fabrication. We also demonstrate that whenever the network gets into the WTA region, it will stay in that region and settle down exponentially fast to the WTA point. This provides a speeding procedure for the decision making: as soon as it gets into the region, the winner can be declared. Finally, we show that this WTA neural network has a self-resetting property, and a resetting principle is proposed.

Cross-organization or cross-domain cooperation takes place from time to time in Electronic Health Record (EHR) system for necessary and high-quality patient treatment. Cautious design of delegation mechanism must be in place as a building block of cross-domain cooperation, since the cooperation inevitably involves exchanging and sharing relevant patient data that are considered highly private and confidential. The delegation mechanism grants permission to and restricts access rights of a cooperating partner. Patients are unwilling to accept the EHR system unless their health data are guaranteed proper use and disclosure, which cannot be easily achieved without cross-domain authentication and fine-grained access control. In addition, revocation of the delegated rights should be possible at any time during the cooperation. In this paper, we propose a secure EHR system, based on cryptographic constructions, to enable secure sharing of sensitive patient data during cooperation and preserve patient data privacy. Our EHR system further incorporates advanced mechanisms for fine-grained access control, and on-demand revocation, as enhancements to the basic access control offered by the delegation mechanism, and the basic revocation mechanism, respectively. The proposed EHR system is demonstrated to fulfill objectives specific to the cross-domain delegation scenario of interest.

Vehicular ad hoc network (VANET) can offer various services and benefits to users and thus deserves deployment effort. Attacking and misusing such network could cause destructive consequences. It is therefore necessary to integrate security requirements into the design of VANETs and defend VANET systems against misbehavior, in order to ensure correct and smooth operations of the network. In this paper, we propose a security system for VANETs to achieve privacy desired by vehicles and traceability required by law enforcement authorities, in addition to satisfying fundamental security requirements including authentication, nonrepudiation, message integrity, and confidentiality. Moreover, we propose a privacy-preserving defense technique for network authorities to handle misbehavior in VANET access, considering the challenge that privacy provides avenue for misbehavior. The proposed system employs an identity-based cryptosystem where certificates are not needed for authentication. We show the fulfillment and feasibility of our system with respect to the security goals and efficiency.

MaxWeight algorithm, a.k.a., back-pressure algorithm [1]-[4], has received much attention as a viable solution for dynamic link scheduling in multi-hop wireless networks. The basic principle of the MaxWeight algorithm is to select a set of interference-free links with the maximum overall link weights in the network, where the link weight is determined by the queue difference between the transmitter and the receiver. While the throughput-optimality of the MaxWeight algorithm is well understood in the literature, the energy consumption induced by the MaxWeight algorithm is less studied, which is of great interest in energy-constrained wireless networks such as wireless sensor networks. In this paper, we propose a minimum energy scheduling (MES) algorithm for multi-hop wireless networks with stochastic traffic arrivals and time-varying channel conditions. We show that our algorithm is energy optimal in the sense that the proposed MES algorithm can achieve an energy consumption which is arbitrarily close to the global minimum solution. Moreover, the energy efficiency of the MES algorithm is achieved without losing the throughput-optimality. In other words, the proposed MES algorithm is still throughput optimal whereas the average consumed energy in the network is significantly reduced, as compared to the traditional MaxWeight algorithm. The theoretical results are substantiated via simulations.

Rate adaptation algorithms play a crucial role in IEEE 802.11 WLANs. While the network performance depends greatly on the rate adaptation algorithms, the detailed implementation is left to vendors. Due to its simplicity and practicality, threshold-based rate adaptation algorithms are widely adopted in commercial IEEE 802.11 devices. Taking the popular ARF algorithm for example, the data rate is increased when ten consecutive transmissions are successful and a date rate downshift is triggered by two consecutive failed transmissions. Although widely deployed, the optimal selection of the up/down thresholds for the rate adaptation algorithms remains an open problem. In this paper, we first investigate the threshold-based rate adaptation algorithm via a reverse engineering approach where the implicit objective function is revealed. Next, we propose a threshold optimization algorithm which can dynamically adjust the up/down thresholds and converge to the stochastic optimum solution in arbitrary stationary random channel environment. The performance enhancement by tuning the thresholds optimally is validated by simulations.

Microeconomics-inspired spectrum auctions can effectively improve the spectrum utilization for wireless networks to satisfy the ever increasing service demands. Considering the spatial reuse, the bidding nodes without mutual interference are grouped as virtual bidders competing for the spectrum bands, which turns a multi-winner spectrum auction into a traditional single-winner auction. To make the participating nodes bid truthfully, strategy-proof auctions are exploited to allocate the vacant spectrum bands. However, how to fairly allocate the profits of the virtual bidder among the winning bidders is still an imperative problem to solve. In this paper, we propose a shapley value based profit allocation (SPA) to distribute the profit among the bidding nodes according to their marginal contributions, which are both from helping the virtual bidder to win the auction and from generating the revenue during the auction period. Our simulation and analysis show that SPA can effectively integrate the contributions from the two stages in the spectrum auction and fairly allocate the profit among the winning bidders.

Rate adaptation algorithms play a crucial role in IEEE 802.11 WLANs. While the network performance depends greatly on the rate adaptation algorithms, the detailed implementation is left to vendors. Due to its simplicity and practicality, the generic rate adaptation algorithm based on up/down thresholds is widely adopted in commercial IEEE 802.11 devices. Taking the popular ARF algorithm for example, the data rate is increased when ten consecutive transmissions are successful and a date rate downshift is triggered by two consecutive failed transmissions. Although widely deployed, disclosing the implicit objective function that the rate adaptation algorithm is dynamically maximizing, remains as an open problem in the literature. In this paper, we investigate the thresholds-based rate adaptation algorithm via a reverse engineering perspective where the implicit objective function is revealed. We consider this reverse engineering study of the thresholds-based rate adaptation algorithm as an important first step towards a comprehensive understanding on the rate adaptation mechanism designs and the complex interactions among multiple IEEE 802.11 stations.

In this paper, we derive a theoretical model to calculate the available bandwidth of a path and study its upper and lower bounds with background traffic. We show that the clique constraint widely used to construct upper bounds does not hold any more when links are allowed to use different rates at different time. In our proposed model, traditional clique is coupled with rate vector to more properly characterize the conflicting relationships among links in wireless sensor networks where time-varying link adaption is used. Based on the model, we also investigate the problem of joint optimization of QoS routing and propose several routing metrics. The newly proposed conservative clique constraint performs the best among the studied metrics in estimating available bandwidth of flows with background traffic.

Maximizing the operational lifetime of a sensor network is a critical problem in practice. Many prior works define the network's lifetime as the time before the first sensor in the network runs out of energy. However, when one sensor dies, the rest of the network can still work, as long as useful data generated by other sensors can reach the sink. More appropriately, we should maximize the lifetime vector of the network, consisting of the lifetimes of all sensors, sorted in ascending order. For this problem, there exists only a centralized algorithm that solves a series of linear programming problems with high-order complexities. This paper proposes a fully distributed progressive algorithm which iteratively produces a series of lifetime vectors, each better than the previous one. Instead of giving the optimal result in one shot after lengthy computation, the proposed distributed algorithm has a result at any time, and the more time spent gives the better result. We show that when the algorithm stabilizes, its result produces the maximum lifetime vector. Furthermore, simulations demonstrate that the algorithm is able to converge rapidly towards the maximum lifetime vector with low overhead.

This paper characterizes the throughput-delay tradeoffs in mobile ad hoc networks (MANETs) with network coding, and compares results in the situation where only replication and forwarding are allowed in each node. The schemes/protocols achieving those tradeoffs in an effective and decentralized way are proposed and the optimality of those tradeoffs is established. The scenarios in which network coding can provide significant improvement on network performance are identified under different node mobility patterns (fast and slow mobility). The insights on when and how information mixing is beneficial for MANETs with multiple unicast and multicast sessions are provided. As far as we know, this is the first work characterizing scaling laws of throughput and delay of MANETs with network coding.

Considering the constraint brought by mobility and resources, it is important for routing protocols to efficiently deliver data in Intermittently Connected Mobile Network (ICMN). Different from previous works that use the knowledge of previous encounters to predict the future contact, we propose a storagefriendly REgioN-bAsed protocol, namely, RENA, in this paper. Instead of using temporal information, RENA builds routing tables based on regional movement history, which avoids excessive storage for tracking encounter history. We validate the generality of RENA through time-variant community mobility model with parameters extracted from the MIT WLAN trace, and the vehicular network based on 8 bus routes of the city of Helsinki. The comprehensive simulation results show that RENA is not only storage-friendly but also more efficient than the epidemic routing, the restricted replication protocol SNW and the encounter-based protocol RAPID under various conditions.

An optical network is too costly to act as a broadband access network. On the other hand, a pure wireless ad hoc network with n nodes and total bandwidth of W bits per second cannot provide satisfactory broadband services since the pernode throughput diminishes as the number of users goes large. In this paper, we propose a hybrid wireless network, which is an integrated wireless and optical network, as the broadband access network. Specifically, we assume a hybrid wireless network consisting of n randomly distributed normal nodes, and m regularly placed base stations connected via an optical network. A source node transmits to its destination only with the help of normal nodes, i.e., in the ad hoc mode, if the destination can be reached within L (L /spl geq/ 1) hops from the source. Otherwise, the transmission will be carried out in the infrastructure mode, i.e., with the help of base stations. Two transmission modes share the same bandwidth of W bits/sec. We first study the throughput capacity of such a hybrid wireless network, and observe that the throughput capacity greatly depends on the maximum hop count L and the number of base stations m. We show that the throughput capacity of a hybrid wireless network can scale linearly with n only if m = Omega(n), and when we assign all the bandwidth to the infrastructure mode traffics. We then investigate the delay in hybrid wireless networks. We find that the average packet delay can be maintained as low as Theta(1) even when the per-node throughput capacity is Theta(W).

This paper investigates the problem of how much benefit network coding can contribute to the network performance in terms of throughput, delay, and storage requirements for mobile ad hoc networks (MANETs), compared to when only replication, storage and forwarding are allowed in relay nodes. We characterize the throughput-delay-storage tradeoffs under different node mobility patterns, i.e., i.i.d. and random walk mobility, with and without network coding. Our results show that when random linear coding instead of replication is used in MANETs, an order improvement on the scaling laws of MANETs can be achieved. Note that previous work showed that network coding could only provide constant improvement on the throughput of static wireless networks. Our work thus differentiates MANETs from static wireless networks by the role network coding plays.

In this paper, we investigate the throughput capacity in wireless hybrid networks with various network topologies and traffic patterns. Specifically, we consider n randomly distributed nodes, out of which there are n source nodes and nd(0b(0w] times [0, n1-w] (0b-1, nd-1}, min {nw-1/radiclog n, nd-1}} bits/sec is achievable by all nodes. We then investigate the throughput capacity when the base stations are uniformly and randomly placed, and their transmission power is as small as that of the normal nodes. We present that each node can achieve a throughput of max{min{nb-1/log n, nd-1}, min {nw-1/radiclog n, nd-1}} bits/sec. In both settings, we observe that only when d > b and d > w, the maximum achievable throughput can be determined by both the number of base stations and the shape of network area. In all the other cases, the maximum achievable throughput is only constrained by the number of destination nodes. Moreover, the results in these two settings are the same except for the case d > b > w, in which the random placement of base stations will cause a degradation factor of log n on the maximum achievable throughput compared to the regular placement. Finally, we also show that our results actually hold for different power propagation models.

Operating on a frequency band occupying several nonoverlapping channels, IEEE 802.11 is now widely used in Wireless Mesh Networks (WMNs). Many multichannel MAC protocols are proposed to improve the spatial reuse in the network under the assumption that the transmissions on nonoverlapping channels do not interfere with each other. Some joint routing and channel assignment algorithms are also designed to increase the network throughput based on the premise that we can switch between different channels freely. Although simulations show that great improvements on network throughput can be observed in both cases, two fundamental questions remain: 1) Can we really use multiple nonoverlapping channels freely in WMNs? 2) If we can, what will be the cost when we switch channels dynamically and frequently? In this paper, by conducting extensive experiments on our testbed, we attempt to answer these questions. We find that in spite of interference between both overlapping and nonoverlapping channels, we can still use multiple channels in mesh networks under certain conditions but with care. We also show that the channel switching cost is actually very significant in WMNs. We recommend not to switch the channels too frequently when designing the channel assignment algorithms, and those channel assignment algorithms selecting one channel for each packet are not really beneficial.

Differentiated Services (DiffServ) networks categorize routers into edge routers and core routers. In core routers, one of the technological challenges is how to implement differentiated bandwidth allocation and TCP protection together with low complexity. We present an Active Queue Management (AQM) scheme called CHOKeW. A method is borrowed from a previous scheme, CHOKe, which draws a packet at random from the buffer, compares it with the arriving packet, and drops both if they are from the same flow. CHOKeW enhances the drawing function by adjusting the maximum number of draws based on the priority of the new arrival and the current status of network congestion. With respect to the number of flows, both the memory-requirement complexity and the per-packet-processing complexity for CHOKeW is O(1). An analytical model and multiple simulations are used to explain and evaluate CHOKeW. We show that CHOKeW is able to 1) support differentiated bandwidth allocation; 2) provide the flows in the same priority with better fairness than other conventional stateless AQM schemes such as RED and BLUE; 3) maintain high link utilization as well as short queue length; and 4) protect TCP flows by restricting the bandwidth share of high-speed unresponsive flows.

In this paper, we solve the problem of a joint optimal design of congestion control and wireless MAC-layer scheduling using a column generation approach with imperfect scheduling. We point out that the general subgradient algorithm has difficulty in recovering the time-share variables and experiences slower convergence. We first propose a two-timescale algorithm that can recover the optimal time-share values. Most existing algorithms have a component, called global scheduling, which is usually NP-hard. We apply imperfect scheduling and prove that if the imperfect scheduling achieves an approximation ratio rho, then our algorithm produces a suboptimum of the overall problem with the same approximation ratio. By combining the idea of column generation and the two-timescale algorithm, we derive a family of algorithms that allows us to reduce the number of times the global scheduling is needed.

Spectrum sharing is a crucial issue to the overall throughput performance of multi-hop wireless networks. Traditional distributed random medium access control (MAC), such as IEEE 802.11, lacks of efficiency of spectrum usage, while centralized scheduling is not practical for large scale ad hoc networks. It is observed that for multi-hop wireless networks, it is hard to resolve the scheduling conflict, and most distributed algorithms consider the neighbors' traffic independent of each other and ignore the multi-hop nature of flows, leading to the spectrum wastage and inefficiency. By incorporating the multihop nature of flows, we propose a new distributed scheme based on IEEE 802.11 standard, namely "2-hop MAC". Nodes collect traffic dependency information as well as traffic demand information from neighbors and allocate spectrum distributedly with the knowledge of more accurate traffic demand of the nodes in the neighborhood. Moreover, we have also addressed the problem of the asymmetric neighborhood, which was ignored in most previous work. Finally, we introduce a new metric, namely, allocation inefficiency ratio (AIR), to evaluate the performance of distributed algorithms in multi-hop wireless networks. Extensive simulation study shows that our proposed scheme can significantly improve the network performance and spectrum efficiency.

Transmission power control (TPC) has been extensively used not only to save energy, but also to improve the network throughput in wireless ad hoc networks. Among the existing throughput-oriented TPC protocols, many can achieve significant throughput improvement but have to use multiple channels and/or multiple transceivers, and others just require a single channel and a single transceiver but can only have limited throughput enhancement. In this paper, we propose a new adaptive transmission power control protocol, ATPMAC, which can improve the network throughput significantly using a single channel and a single transceiver. Specifically, by controlling the transmission power, ATPMAC can enable several concurrent transmissions without interfering with each other. Moreover, ATPMAC does not introduce any additional signalling overhead. We show by simulations that ATPMAC can improve the network throughput by up to 136% compared to IEEE 802.11 in a random topology.

In this paper, we propose for multi-rate ad hoc networks a cross-layer design using power control, called MRPC. MRPC consists of two parts. First, we propose a multi-rate power controlled MAC protocol, called MRPC-MAC. By carefully controlling the transmission power, it can enable concurrent transmissions, which is otherwise impossible for the IEEE 802.11 standard. Second, we propose a multi-rate power controlled routing protocol, called MRPC-Routing. Different from traditional routing protocols, MRPC-Routing is not intended to find end-to-end paths, rather, it determines the next hop right before transmitting packets at the MAC layer. In this protocol, it uses the effective transport capacity as the routing metric such that short links with high bandwidth are preferred and more concurrent transmissions can be enabled. Having these coupled power controlled MAC protocol and routing protocol, MRPC can greatly improve the spatial reuse and the network throughput. Simulation results also show MRPC-MAC, MRPC-routing, and especially MRPC, can improve the network throughput significantly.

Multi-channel multi-radio technology offers a great space of resource diversity. In this paper, we propose an opportunistic multiradio MAC (OMMAC) protocol to improve system performance by utilizing multiradio diversity. The proposed OMMAC collects the physical layer feedback over multiple radios simultaneously and schedules multiple transmissions on the available channels accordingly. The channel based packet scheduling leverages the channel quality information to jointly select data rates, channels and packets to increase the overall spectral usage of multiple radios which have not been exploited in previous literature. Extensive results from both analysis and ns2 simulations demonstrate that OMMAC significantly improves the network throughput in both single- and multi-hop wireless multirate networks in terms of both aggregate and per-radio throughput.

An admission control algorithm should be properly designed to guarantee the quality of service (QoS) in wireless mesh networks (WMNs). Based on channel business ratio, an admission control algorithm (ACA) is proposed to provide QoS for realtime and non-realtime traffic. For realtime traffic, all the nodes on a route make the admission control decision based on the estimation of available bandwidth. For non-realtime traffic, a rate adaption algorithm is proposed to adjust the sending rates of the source nodes to prevent a network from entering a saturated status. Finally, we demonstrate the effectiveness by simulations in NS-2.

In his seminal work, Jon Kleinberg considers a small-world network model consisting of a k-dimensional lattice augmented with shortcuts. Under the assumption that the probability of a shortcut being present between two nodes u and v decays as a power, d(u,v) -\alpha, of the distance d(u,v) between them, Kleinberg shows that decentralized routing scheme such as greedy geographic routing is efficient if alpha=k and that there is no efficient decentralized routing algorithm if alpha\neq k. The results are extended to a continuum model recently, wherein the nodes are distributed as a homogeneous Poisson point process by Franceschetti and Meester, Draief and Ganesh. In our work, we extend the result further to a more realistic model constructed from a nonhomogeneous Poisson point process, wherein each node is connected to all its neighbors within some fixed radius, as well as possessing random shortcuts to more distant nodes. More importantly, we show that in nonhomogeneous cases, the necessary and sufficient condition for greedy geographic routing to be efficient is that the probability of a shortcut being present from node u to v should be inversely proportional to the number of nodes which are closer to u than v is. We also demonstrate some applications of our results to wireless networks.

Fine-level rate control, particularly meeting rate requirements and differentiating various types of end-to-end traffic, remains an open problem for multihop wireless networks. Traditionally, rate assurance in wired networks is achieved through resource reservation and admission control, which can be efficiently implemented since the bandwidth capacity of each communication link is known and the sender of a link has the information of all flows that compete for the bandwidth of the link. In a wireless network, however, the capacity of each wireless link can change unpredictably over time due to contention from nearby links and dynamic channel conditions. An end-to-end flow consumes available bandwidth not only at links on its route but also at all nearby contending links, which makes resource reservation extremely complicated. We believe fundamental differences require a fundamentally different paradigm shift in solutions. Is there a simpler alternative to resource reservation and admission control that is better suited for wireless network dynamics? In this paper, we propose a new adaptive rate control function based on two novel protocols, called dynamic weight adaptation with floor and ceiling and proportional packet scheduling, which together implement prioritized rate assurance and sophisticated bandwidth differentiation among all end-to-end flows in a multihop wireless network without resource reservation and admission control. The adaptive function achieves global rate control objectives in a fully distributed way using only localized operations.

Anonymity has received increasing attention in the literature due to the users' awareness of their privacy nowadays. Anonymity provides protection for users to enjoy network services without being traced. While anonymity related issues have been extensively studied in payment-based systems such as e-cash [1] and peer-to-peer (P2P) [2] systems, little effort has been devoted to wireless mesh networks (WMNs). On the other hand, the network authority requires conditional anonymity such that misbehaving entities in the network remain traceable. In this paper, we propose a security architecture to ensure unconditional anonymity for honest users and traceability of misbehaving users for network authorities in WMNs. The proposed architecture strives to resolve the conflicts between the anonymity and traceability objectives, in addition to guaranteeing fundamental security requirements including authentication, confidentiality, data integrity, and non-repudiation [3]. Further security enhancements can be incorporated, rendering the proposed architecture conditionally anonymous in terms of network access activities, location information, and communication paths.

Wireless ad hoc networks (WANETs) offer communications over a shared wireless channel without any pre-existing infrastructure. Forming peer-to-peer security associations in self-organized WANETs is more challenging than in conventional networks due to the lack of central authorities. In this paper, we propose a generic model to evaluate the relationship of connectivity, memory size, communication overhead and security in fully self-organized WANETs. Based on some reasonable assumptions on node deployment and mobility, we show that when the average number of authenticated neighbors of each node is Theta(1), with respect to the network size n, most of the nodes can be securely connected, forming a connected secure backbone, i.e., the secure network percolates. This connected secure backbone can be utilized to break routing-security dependency loop, and provide enough derived secure links connecting isolated nodes with the secure backbone in a multi-hop fashion, which leads to the secure connectivity of the whole network.

Broadcast scheduling is a fundamental problem in wireless ad hoc networks. The objective of a broadcast schedule is to deliver a message from a given source to all other nodes in a minimum amount of time. At the same time, in order for the broadcast to proceed as predicted in the schedule, it must not contain parallel transmissions which can be conflicting based on the collision and interference parameters in the wireless network. Most existing work on this problem use a limited network model which accounts only for conflicts occurring inside the transmission ranges of the nodes. The broadcast schedules produced by these algorithms are likely to experience unpredictable delays when deployed in the network. This is because they do not take into consideration other important sources of conflict in parallel transmissions, namely the interference range and the carrier sensing range. In this paper we develop a conflict-aware network model, which uses these parameters to increase the probability of scheduling conflict-free transmissions, and thereby improve the reliability of the broadcast schedule. We present and prove correctness of a constant approximation algorithm for minimum-latency broadcast scheduling under this network model. We also present a greedy heuristic algorithm for the same problem. Experimental results are provided to evaluate the performance of our algorithms. In addition, the algorithms are analyzed to justify their performance trends.

Recent studies indicate that by improving the spatial reuse ratio the throughput of 802.11 wireless networks can be improved. In this paper, we study the impact of physical carrier sensing and channel rate on the throughput of 802.11 wireless networks with chain topology. Firstly, this paper propose

In this paper, we investigate the routing optimization problem in wireless mesh networks. While existing works usually assume static and known traffic demand, we emphasize that the actual traffic is time-varying and difficult to measure. In light of this, we alternatively pursue a stochastic optimiz

Opportunistic scheduling has been known as one of the possible ways of exploiting multi-user diversity, which comes from the time-varying propagation characteristics and broadcast nature of wireless radios, to improve communication efficiency and reliability. Multi-channel multi-radio technology has offered great opportunity in designing wireless network MAC protocol to utilize all kinds of resource diversities. Other than multi-user diversity, we identify the unique multi-radio diversity which can be exploited in multi-radio wireless network to improve network throughput performance. The proposed opportunistic multi-radio MAC (OMMAC) takes advantage of multi-radio diversity to increase the spectrum usage as much as possible. Extensive NS2 simulation results demonstrate that OMMAC significantly improves the network throughput in both single and multi-hop wireless networks.

This paper addresses the throughput maximization problem in wireless mesh networks. For the case of cooperative access points, we present a negotiation-based throughput maximization algorithm which adjusts the operating channel and power level among access points automatically, from a game-theoretical perspective. We show that this algorithm converges to the optimal channel and power assignment which yields the maximum overall throughput with arbitrarily high probability. Moreover, we analyze the scenario where access points belong to different regulation entities and hence non-cooperative. The long- term behavior and corresponding performance are investigated and the analytical results are verified by simulations.

In wireless sensor networks, path breakage occurs frequently due to node mobility, node failure, and channel impairments. It is challenging to combat path breakage with minimal control overhead, while adapting to rapid topological changes. Due to the Wireless Broadcast Advantage (WBA), all nodes inside the transmission range of a single transmitting node may receive the packet, hence naturally they can serve as cooperative caching and backup nodes if the intended receiver fails to receive the packet. In this paper, we present a distributed robust routing protocol in which nodes work cooperatively to enhance the robustness of routing against path breakage. We compare the energy efficiency of cooperative routing with noncooperative routing and show that our robust routing protocol can significantly improve robustness while achieving considerable energy efficiency.

3GPP 23.246 proposed the multimedia broadcast multicast service (MBMS) to deliver multicasting content in the Universal mobile telecommunications system (UMTS). In MBMS, the common logical channel is enabled to serve multiple MBMS calls at the same time. Use of the common logical channel may cause interference to the dedicated logical channels serving the traditional calls. To more efficiently utilize the radio resource to serve both traditional and MBMS calls, this paper proposes two channel allocation algorithms: Reserved Resource for Multicasting (RRM) and Unreserved Resource for Multicasting (URM). We propose analytic models and conduct simulation experiments to investigate customer Satisfaction Indication (SI) for the two algorithms. Our study indicates that URM outperforms RRM in terms of customer SI.

When the basic functionalities of a wireless mobile network have been achieved, customers are then more interested in value-added mobile applications. In order to attract more customers to such mobile applications, a solid, secure and robust trading model is a must. This paper proposes such a secure trading model named mobile electronic payment (MEP) for wireless mobile networks, which applies the emerging ID-based cryptography for key agreement and authentication. Our MEP attempts to alleviate the computational cost, reduce the memory space requirement in mobile devices, and meet the requirements for secure trading: avoidance of overspending and double spending, fairness, user anonymity and privacy. Our design is transparent to the bearer networks and is of low deployment cost. We expect that our MEP provides a viable trading architecture model for the future mobile applications.

Current deployment of the wireless mesh networks (WMN) necessitates mobility management to support mobile clients roaming around the network without service interruption. Though Mobile IP and other previous protocols can be applied to WMNs to gain the micro-mobility as well as macro-mobility support, high signaling cost and long handoff latency problems still degrade the system performance significantly. In this paper we present a new mobility management scheme for WMNs, mesh mobility management (M3). It utilizes some WMN's features and combines the per-host routing and tunneling techniques to reduce the signaling cost as well as to shorten the handoff latency. Our analysis shows that significant benefits can be achieved from this scheme.

There are many different channels denned in the IEEE 802.11 standard. However, the performance of WiFi networks still greatly suffers from the interference between users, even if they are using different channels. In this paper, we conduct some theoretical analysis of the interference between two channels, which is further verified by experiments. We show that there is indeed serious interference between two non- overlapping channels if they are close to each other.

In this paper, we investigate the channel selection strategy for secondary users in cognitive radio networks. We claim that in order to avoid the costly channel switchings, a secondary user may desire an optimal channel which maximizes the probability of successful transmissions, rather than consistently adapting channels to the random environment. We propose a stochastic channel selection algorithm based on the learning automata techniques. This algorithm adjusts the probability of selecting each available channel and converges to the e-optimal solution asymptotically.

Connectivity is a crucial issue in wireless networks. Gupta and Kumar show that with omnidirectional antennas, the critical transmission range for a wireless network to achieve asymptotic connectivity is O(radiclog n/n) if n nodes are uniformly and independently distributed in a disk of unit area. In this paper, we investigate the connectivity problem when directional antennas are used. We find that there also exists a critical transmission range, which corresponds to a critical transmission power. We show that in the same propagation environment, when directional antennas use the optimal antenna pattern, the critical transmission power could be much smaller than that in networks using omnidirectional antennas. Moreover, to achieve asymptotic connectivity, it is known that each node has to have O(log n) neighbors when using omnidirectional antennas. We show that even using the transmission power level at which each node has only O(1) neighbors when using omnidirectional antennas, we can still achieve the asymptotic connectivity with directional antennas.

The throughput maximization problem of wireless mesh access networks is addressed. For the case of cooperative access points, we present a negotiation-based throughput maximization algorithm which adjusts the operating frequency and power level among access points autonomously, from a game-theoretical perspective. We show that this algorithm converges to the optimal frequency and power assignment which yields the maximum overall throughput with arbitrarily high probability. Moreover, we analyze the scenario where access points belong to different regulation entities and hence non-cooperative. The long-term behavior and corresponding performance are investigated and the analytical results are verified by simulations.

Using multi-channel MAC protocols in mobile ad hoc networks (MANETs) is a promising way to improve the through-put performance. Channel assignment, which directly determines the efficiency of the frequency utilization, is the critical part of multi-channel schemes. Current 802.11-like schemes of multi-channel MAC do not efficiently use the multiple channels due to the overhead caused by channel assignment. Moreover, the control channel saturation problem limits the number of channels of these previous schemes. In this paper, we propose a new scheme called SAM-MAC (Self-Adjustable Multi-channel MAC), which features with one common channel and two half-duplex transceivers for each node. A method called self-adjustment is used to reassign the channels and balance the traffic on different channels. Due to less contention in common channel and smaller channel assignment overhead, this scheme increases the throughput compared with previous approaches. Control channels are free from saturation problem and can furthermore be used for data transmission.

Video broadcast and mobile TV have received significant interests from both academia and industry recently. The emerging mobile WiMAX (802.16e) is capable of providing high data rate and flexible quality of service (QoS) mechanisms, making the support of mobile TV very attractive. However, how to efficiently deliver video broadcast over WiMAX is not straightforward, especially in the multi-BS mode. The multi-BS mode requires multiple BSs to be synchronized in the transmission of common multicast/broadcast data. In this paper, we first identify the key design issues for video broadcast over WiMAX in the multi-BS mode. Then, we present an end-to-end solution which fully addresses key issues such as synchronization, energy efficiency and robust video quality. Moreover, we propose a methodology to optimize the coverage, the spectrum efficiency and the video quality. Results show that our proposed scheme can significantly improve the coverage and spectrum efficiency while satisfying video quality requirements.

The ad hoc deployment of a sensor network causes unpredictable patterns of connectivity and varied node density, resulting in uneven bandwidth provisioning on the forwarding paths. When congestion happens, some sensors may have to reduce their data rates. It is an interesting but difficult problem to determine which sensors must reduce rates and how much they should reduce. This paper attempts to answer a fundamental question about congestion resolution: What are the maximum rates at which the individual sensors can produce data without causing congestion in the network and unfairness among the peers? We define the maxmin optimal rate assignment problem in a sensor network, where all possible forwarding paths are considered. We provide an iterative linear programming solution, which finds the maxmin optimal rate assignment and a forwarding schedule that implements the assignment in a low-rate sensor network. We prove that there is one and only one such assignment for a given configuration of the sensor network. We also study the variants of the maxmin fairness problem in sensor networks.

In a large scale sensor network, it is infeasible to assign a unique Transport Layer Key (TLK) for each pair of nodes to provide the end-to-end security due to the huge memory cost per node. Thus, conventional key establishment schemes follow a key predistribution approach to establish a Link Layer Key (LLK) infrastructure between neighboring nodes and rely on multihop paths to provide the end-to-end security. Their drawbacks include vulnerability to the node compromise attack, large memory cost, and energy inefficiency in the key establishment between neighboring nodes. In this paper, we propose a novel key establishment scheme, called LAKE, for sensor networks. LAKE uses a t-degree trivariate symmetric polynomial to facilitate the establishment of both TLKs and LLKs between sensor nodes in a two-dimensional space, where each node can calculate direct TLKs and LLKs with some logically neighboring nodes and rely on those nodes to negotiate indirect TLKs and LLKs with other nodes. Any two end nodes can negotiate a TLK on demand directly or with the help of only one intermediate node, which can be determined in advance. As for the LLK establishment, LAKE is more secure under the node compromise attack with much less memory cost than conventional solutions. Due to the location-based deployment, LAKE is also energy efficient in that each node has direct LLKs with most neighbors without spending too much energy on the establishment of indirect LLKs with neighbors through multihop routing.

Distributed peer-to-peer (P2P) applications have been gaining momentum recently. In such applications, all participants are equal peers simultaneously functioning as both clients and servers to each other. A fundamental problem is, therefore, how to select reliable servers from a vast candidate pool. To answer this important open question, we present a novel reputation system built upon the multivariate Bayesian inference theory. Our system offers a theoretically sound basis for clients to predict the reliability of candidate servers based on self-experiences and feedbacks from peers. In our system, a fine-grained quality of service (QoS) differentiation method is designed to satisfy the diverse QoS needs of individual nodes. Our reputation system is also application-independent and can simultaneously serve unlimited P2P applications of different type. Moreover, it is semidistributed in the sense that all application-related QoS information is stored across system users either in a random fashion or through a distributed hash table (DHT). In addition, we propose to leverage credits and social awareness as reliable means of seeking honest feedbacks. Furthermore, our reputation system well protects the privacy of users offering feedbacks and is secure against various attacks such as defaming, flattering, and the Sybil attack. We confirm the effectiveness and efficiency of the proposed system by extensive simulation results.

Wireless LANs with multi-beam directional antennas have received intensive attention lately due to the potential gain in throughput performance. However, when the multi-beam directional antennas are introduced in this system, the ever popular contention-based medium access control protocol such as IEEE 802.11 MAC is no longer effective, and many challenging problems, such as beam-synchronization problem, beam-overlapping problem, mobility and receiver blocking problem (deafness problem), need to be resolved. In this paper, we propose a novel MAC protocol to carefully address these problems. In addition to improving communication efficiency, we also consider the backward compatibility in our design, whereby an IEEE 802.11 terminal can transparently access a multi-beam access point. Furthermore, we present an analytical model to evaluate the performance of multi-beam wireless LANs. Extensive simulation studies are used to validate the analytical model and show that our scheme can significantly improve the throughput performance

The traditional TCP congestion control mechanism encounters a number of new problems and suffers a poor performance when the IEEE 802.11 MAC protocol is used in multihop ad hoc networks. Many of the problems result from medium contention at the MAC layer. In this paper, we first illustrate that severe medium contention and congestion are intimately coupled, and TCP's congestion control algorithm becomes too coarse in its granularity, causing throughput instability and excessively long delay. Further, we illustrate TCP's severe unfairness problem due to the medium contention and the tradeoff between aggregate throughput and fairness. Then, based on the novel use of channel busyness ratio, a more accurate metric to characterize the network utilization and congestion status, we propose a new wireless congestion control protocol (WCCP) to efficiently and fairly support the transport service in multihop ad hoc networks. In this protocol, each forwarding node along a traffic flow exercises the inter-node and intra-node fair resource allocation and determines the MAC layer feedback accordingly. The end-to-end feedback, which is ultimately determined by the bottleneck node along the flow, is carried back to the source to control its sending rate. Extensive simulations show that WCCP significantly outperforms traditional TCP in terms of channel utilization, delay, and fairness, and eliminates the starvation problem

Key agreement is a central problem to build up secure infrastructures for networks. Public key technology may not be suitable in many networks of low-end devices, such as ad hoc networks and sensor networks, because of its computation inefficiency and the lack of central authorities in those distributed scenarios. Conventional distributed symmetric key agreement schemes lack scalability due to their large memory cost (O(N), where N is the total number of nodes), and their probabilistic nature cannot ensure key agreement between every pair of nodes. In this paper, we propose a novel symmetric key agreement scheme, which is scalable for large scale networks with very small memory cost per node. A t-degree (k+1)-variate symmetric polynomial is used to achieve key agreement between nodes. The memory cost per node for a network of N nodes is reduced to around k+1radic(k(k+1)!/2) kradicN, where k ges 1. Our scheme is also deterministic in that every pair of nodes can establish a shared key.

Abstract We consider the motion estimation problem in video coding. In our previous work, we proposed a new motion estimation method where motion estimation is formulated as an optimization problem and an adaptive system under the minimum error entropy (MEE) criterion is used for motion estimation. In this paper, we develop an adaptive system under the criterion of maximum mutual information to address the motion estimation problem. Our proposed motion estimation algorithms have very low encoding complexity and hence are ideally suited for wireless video sensor networks where limited bandwidth, restricted computational capability, and limited battery power supply impose stringent constraints on the video encoding system. Copyright © 2007 John Wiley & Sons, Ltd.

In this paper, we propose a coverage measurement protocol (CMP) which can provide the base station an accurate and in-time measurement of the current connected coverage in an energy-efficient way, and our CMP is location-error tolerant. Moreover, the major component of our CMP, i.e., BOundary Node Detection (BOND) scheme, can be reused to provide many other functionalities for WSNs.

To prevent adversaries from injecting bogus messages, authentication is required for broadcast in wireless sensor networks. muTESLA is a light-weight broadcast authentication protocol, which uses a one-way hash chain and the delayed disclosure of keys to provide the authentication service. However, it suffers from several drawbacks in terms of time synchronization, limited broadcast rounds, key chain management at the source node, etc. In this paper, we propose a novel protocol, called BAtch-based BRoadcast Authentication (BABRA) for wireless sensor networks. BABRA does not require time synchronization, eliminates the requirement of key chain, and supports broadcast for infinite rounds. Like muTESLA, BABRA is also efficient due to the use of symmetric key techniques.

Finding a path with enough throughput in multihop wireless ad hoc networks is a critical task of QoS Routing. Previous studies on routing algorithms focused on networks with a single channel rate. The capability of supporting multiple channel rates, which is common in wireless systems, has not been carefully studied in routing algorithms. In this paper, we first carry out a comprehensive study on the impacts of multiple rates, interference and packet loss rate on the maximum end-to-end throughput or path capacity. A linear programming problem is formulated to determine the path capacity of any given path. This problem is also extended to a joint routing and link scheduling optimization problem to find a path with the largest path capacity. We show that interference clique transmission time is inversely proportional to the upper bound of the path capacity, and hence we propose to use it as a new routing metric. Moreover, we evaluate the capability of various routing metrics such as hop count, expected transmission times, end-to-end transmission delay or medium time, link rate, bandwidth distance product, and interference clique transmission time to discover a high throughput path. The results show that different routing metrics lead to paths with significantly different path capacity, and the interference clique transmission time tends to discover paths with higher throughput than other metrics.

Abstract — Opportunistic spectrum access and adaptive power management are effective techniques to improve throughput, delay performance, and energy efficiency for wireless networks. In this paper, we consider the joint design of opportunistic spectrum access and adaptive power management under the setting of multi-radio nodes and multi-channel wireless local area networks (WLANs) under the distributed coordination function (DCF) mode. This design problem is particularly challenging due to the conflicting nature of the multi-radio capability of a node, i.e., multiple radios improve throughput and delay performance at the cost of increased energy consumption. To address this problem, we propose a power-saving multi-channel MAC protocol (PSM-MMAC), which is capable of reducing the collision probability and the waiting time in the ‘awake ’ state of a node, resulting in improved throughput, delay performance, and energy efficiency. The key ideas of PSM-MMAC are the following: we first estimate the number of active links; given this estimation as well as queue lengths and channel conditions, we appropriately select channels, radios, and power states (i.e., awake or doze state); then we optimize the medium access probability in p-persistent CSMA used in the data exchange. Another contribution of this paper is an analytical model that characterizes the throughput performance. Simulation results validate the accuracy of our analytical model and show that our proposed protocol is able to significantly improve both throughput and energy efficiency. I.

Abstract — Physical carrier sensing is an effective mechanism of medium access control (MAC) protocols to reduce collisions in wireless networks, and the size of the carrier sensing range has a great impact on the system performance. Previous studies have shown that the MAC layer overhead plays an important role in determining the optimal carrier sensing range. However, vari-able transmission ranges and receiver sensitivities for different channel rates and the impact of multihop forwarding have been ignored. In this paper, we investigate the impacts of these factors as well as several other important factors, such as SINR (signal to interference plus noise ratio), node topology, hidden/exposed terminal problems and bidirectional handshakes, on determining the optimum carrier sensing range to maximize the throughput through both analysis and simulations. The results show that if any one of these factors is not addressed properly, the system performance may suffer a significant degradation. Furthermore, considering both multirate capability and carrier sensing ranges, we propose to use bandwidth distance product as a routing metric, which improves end-to-end throughput by up to 27% in the simulated scenario. I.

Due to the inexpensive cost and small size of the sensor node, sensor networks are densely deployed for most applications. In the application oriented wireless sensor networks, traffic is usually mixed with time-sensitive packets and reliability-demanding packets. Hence, routing regardless of the packet characteristics is not efficient. Our goal is to provide soft-QoS to different types of packets since accurate path information can be hardly obtained in wireless networks. In this paper, we utilize the multiple paths between the source and sink pairs for QoS provisioning. Unlike E2E QoS schemes, soft-QoS mapped into links on a path is determined based on local link state information. Through the estimation and approximation of path quality, traditional NP-complete QoS problem is split into many small problems. The idea is to formulate the problem as a probabilistic programming, then based on some approximation technique, we convert it into an integer programming, which is much easier to solve. The resulting solution is also one to the original probabilistic programming. Simulation results demonstrate the effectiveness of our approach.

Ultra-wideband (UWB) is a promising technology that can support high data rate communication for future Wireless Personal Area Networks (WPANs). To provide high throughput in UWB networks, we proposed a general framework for CSMA/CA based MAC protocol previously [17]. In this framework, multiple upper layer packets can be assembled into a single burst frame at the MAC layer, which can significantly improve the throughput performance by reducing overheads. Nevertheless, the burst assembly procedure may introduce extra packet delay, which is undesirable for some applications. In this paper, we address the performance issue in the burst-frame-based MAC protocol. In particular, we develop an analytical model to evaluate the delay performance of the burst-frame-based MAC protocol under unsaturated conditions. Our delay analysis is unique in that we consider the end-to-end packet delay, which is the duration from the epoch that a packet enters the queue at the MAC layer of the transmitter side to the epoch that the packet is successfully received at the receiver side. The analytical results give excellent agreement with the simulation results, which represents the accuracy of our analytical model. The results also provide important guideline on how to set the parameters of the burst assembly policy. Based on these results, we develop an efficient adaptive burst assembly policy so as to optimize the throughput and delay performance of the burst-frame-based CSMA/CA protocol.

Connected coverage, which reflects how well a target field is monitored under the base station, is the most important performance metrics used to measure the quality of surveillance that wireless sensor networks (WSNs) can provide. To facilitate the measurement of this metrics, we propose two novel algorithms for individual sensor nodes to identify whether they are on the coverage boundary, i.e., the boundary of a coverage hole or network partition. Our algorithms are based on two novel computational geometric techniques called localized Voronoi and neighbor embracing polygons. As compared to previous work, our algorithms can be applied to WSNs of arbitrary topologies. They are also truly distributed and localized by merely needing the minimal position information of one-hop neighbors and a limited number of simple local computations, and thus are of high scalability and energy efficiency. We show the correctness and efficiency of our algorithms by theoretical proofs and extensive simulations.

Multihop wireless mesh networks (WMNs) are finding ever-growing acceptance as a viable and effective solution to ubiquitous broadband Internet access. This paper addresses the security of WMNs, which is a key impediment to wide-scale deployment of WMNs, but thus far receives little attention. We first thoroughly identify the unique security requirements of WMNs for the first time in the literature. We then propose ARSA, an attack-resilient security architecture for WMNs. In contrast to a conventional cellular-like solution, ARSA eliminates the need for establishing bilateral roaming agreements and having real-time interactions between potentially numerous WMN operators. With ARSA in place, each user is no longer bound to any specific network operator, as he or she ought to do in current cellular networks. Instead, he or she acquires a universal pass from a third-party broker whereby to realize seamless roaming across WMN domains administrated by different operators. ARSA supports efficient mutual authentication and key agreement both between a user and a serving WMN domain and between users served by the same WMN domain. In addition, ARSA is designed to be resilient to a wide range of attacks. We also discuss other important issues such as incontestable billing

The recent Federal Communications Commission regulations for ultra-wideband (UWB) transmission systems have sparked a surge of research interests in the UWB technology. One of the important application areas of UWB is wireless sensor networks. The proper operations of many UWB sensor networks rely on the knowledge of physical sensor locations. However, most existing localization algorithms developed for sensor networks are vulnerable to attacks in hostile environments. As a result, attackers can easily subvert the normal functionalities of location-dependent sensor networks by exploiting the weakness of localization algorithms. In this paper, we first analyze the security of existing localization techniques. We then develop a mobility-assisted secure localization scheme for UWB sensor networks. In addition, we propose a location-based scheme to enable secure authentication in UWB sensor networks.

Node compromise is a serious threat to wireless sensor networks deployed in unattended and hostile environments. To mitigate the impact of compromised nodes, we propose a suite of location-based compromise-tolerant security mechanisms. Based on a new cryptographic concept called pairing, we propose the notion of location-based keys (LBKs) by binding private keys of individual nodes to both their IDs and geographic locations. We then develop an LBK-based neighborhood authentication scheme to localize the impact of compromised nodes to their vicinity. We also present efficient approaches to establish a shared key between any two network nodes. In contrast to previous key establishment solutions, our approaches feature nearly perfect resilience to node compromise, low communication and computation overhead, low memory requirements, and high network scalability. Moreover, we demonstrate the efficacy of LBKs in counteracting several notorious attacks against sensor networks such as the Sybil attack, the identity replication attack, and wormhole and sinkhole attacks. Finally, we propose a location-based threshold-endorsement scheme, called LTE, to thwart the infamous bogus data injection attack, in which adversaries inject lots of bogus data into the network. The utility of LTE in achieving remarkable energy savings is validated by detailed performance evaluation.

This paper studies key management, a fundamental problem in securing mobile ad hoc networks (MANETs). We present IKM, an ID-based key management scheme as a novel combination of ID-based and threshold cryptography. IKM is a certificateless solution in that public keys of mobile nodes are directly derivable from their known IDs plus some common information. It thus eliminates the need for certificate-based authenticated public-key distribution indispensable in conventional public-key management schemes. IKM features a novel construction method of ID-based public/private keys, which not only ensures high-level tolerance to node compromise, but also enables efficient network-wide key update via a single broadcast message. We also provide general guidelines about how to choose the secret-sharing parameters used with threshold cryptography to meet desirable levels of security and robustness. The advantages of IKM over conventional certificate-based solutions are justified through extensive simulations. Since most MANET security mechanisms thus far involve the heavy use of certificates, we believe that our findings open a new avenue towards more effective and efficient security design for MANETs

One of the most promising approaches to improving communication efficiency in wireless communication systems is the use of multiuser diversity. Although it has been widely investigated and shown feasible and efficient in cellular networks, there is little work for the ad hoc networks, especially in real protocol and algorithm design. In this paper, we propose a novel scheme, namely, the Opportunistic Medium Access and Auto Rate (OMAR), to efficiently utilize the shared medium in IEEE 802.11-based ad hoc networks by taking advantage of diversity, distributed scheduling, and adaptivity. In an ad hoc network, especially in a heterogeneous ad hoc network or a mesh network, some nodes may need to communicate with multiple one-hop nodes. We allow such a node with a certain number of links to function as a clusterhead to locally coordinate multiuser communications. We introduce a CDF-based (Cumulative Distribution Function) K-ary opportunistic splitting algorithm and a distributed stochastic scheduling algorithm to resolve intra and intercluster collisions, respectively. Fairness is formulated and solved in terms of social optimality within and across clusters. Analytical and simulation results show that our scheme can significantly improve communication efficiency while providing social fairness.

Recent studies have shown that the performance of wireless multihop ad hoc networks is very poor. In this paper, we first demonstrate that one important reason of the poor performance is the close coupling between medium contention and network congestion. Therefore, we present a framework of distributed flow control and medium access control to address both medium contention and network congestion. The proposed scheme utilizes the MAC layer control frames to efficiently conduct the network layer's flow control function and only allows the upstream nodes to forward enough packets to make it possible for the downstream nodes to fully utilize the shared channel but never introduce severe MAC collisions and network congestion. Extensive simulations illustrate that the proposed scheme well controls congestion and greatly alleviates medium collisions. It achieves up to 12 times the end-to-end throughput of IEEE 802.11, maintains a short delay and a low control overhead, and improves the fairness regardless of the hop count and the traffic load.

In the emerging IEEE 802.11e MAC protocol, the enhanced distributed channel access (EDCA) is proposed to support prioritized QoS; however, it cannot guarantee strict QoS required by real-time services such as voice and video without proper network control mechanisms. To overcome this deficiency, we first build an analytical model to derive an average delay estimate for the traffic of different priorities in the unsaturated 802.11e WLAN, showing that the QoS requirements of the real-time traffic can be satisfied if the input traffic is properly regulated. Then, we propose two effective call admission control schemes and a rate control scheme that relies on the average delay estimates and the channel busyness ratio, an index that can accurately represent the network status. The key idea is, when accepting a new real-time flow, the admission control algorithm considers its effect on the channel utilization and the delay experienced by existing real-time flows, ensuring that the channel is not overloaded and the delay requirements are not violated. At the same time, the rate control algorithm allows the best effort traffic to fully use the residual bandwidth left by the real-time traffic, thereby achieving high channel utilization

Wireless mobile ad hoc networks (MANETs) have received considerable attention in the last few years. Most research works focus on single-channel MANETs with a single power-level in order to simplify the network design and analysis. How to take advantage of multiple channels and multiple power levels in MANETs poses a serious challenging problem. Recently, a few multichannel transmission protocols such as collision-avoidance transmission scheduling (CATS) have been proposed to harvest the advantage of high transmission efficiency when multiple channels are deployed. Although such protocols do provide ways to coordinate the use of multiple channels, there exist some serious problems such as the throughput fast drop-off under heavy traffic loads. In this paper, we propose a new protocol, namely, multichannel time-spread scheduling (MATS), which attempts to tackle these problems. In MATS, nodes with transmission requests are divided into three groups, which carry out channel reservations in parallel and can simultaneously support unicasting, multicasting and broadcasting at the link level. MATS ensures successful and collision-free data transmissions using the reserved channels and allows multicasting and broadcasting high priorities over unicasting. Both theoretical analysis and extensive simulation studies are carried out which show that the performance of MATS under high traffic loads significantly outperforms the existing schemes.

IEEE 802.11 MAC protocol has been the standard for wireless LANs and is also implemented in many simulation software for mobile ad hoc networks. However, IEEE 802.11 MAC has been shown to be quite inefficient in the multihop mobile environments. Besides the well-known hidden terminal problem and the exposed terminal problem, there also exists the receiver blocking problem, which may result in link/routing failures and unfairness among multiple flows. Moreover, the contention and interference from the upstream and downstream nodes seriously decrease the packet delivery ratio of mulitihop flows. All these problems could lead to the "explosion" of control packets and poor throughput performance. In this paper, we first analyze these anomaly phenomena in multihop mobile ad hoc networks. Then, we present a novel effective random medium access control (MAC) protocol based on IEEE 802.11 MAC protocol. The new MAC protocol uses an out-of-band busy tone and two communication channels, one for control frames and the other for data frames, and can give a comprehensive solution to all the aforementioned problems. Extended simulations demonstrate that our protocol provides a much more stable link layer, greatly improves the spatial reuse, and works effectively in reducing the packet collisions. It improves the throughput by up to 20% for one-hop flows and by up to 5 times for multihop flows under heavy traffic comparing to the IEEE 802.11 MAC

The shared wireless medium of mobile ad hoc networks facilitates passive, adversarial eavesdropping on data communications whereby adversaries can launch various devastating attacks on the target network. To thwart passive eavesdropping and the resulting attacks, we propose a novel anonymous on-demand routing protocol, termed MASK, which can accomplish both MAC-layer and network-layer communications without disclosing real IDs of the participating nodes under a rather strong adversary model. MASK offers the anonymity of senders, receivers, and sender-receiver relationships in addition to node unlocatability and untrackability and end-to-end flow untraceability. It is also resistant to a wide range of attacks. Moreover, MASK preserves the high routing efficiency as compared to previous proposals. Detailed simulation studies have shown that MASK is highly effective and efficient.

Abstract Mobilead hocnetworks (MANETs) are useful in environment where fixed network infrastructure is unavailable. To function normally, MANETs demand an efficient and distributed medium access control (MAC) protocol. However, characteristics of MANETs such as radio link vulnerability, mobility, limited power pose great challenges on MAC design. This paper surveys the recent advances in MAC design for MANETs. We first identify the challenges that are facing MAC in MANETs. Then we discuss the proposed MAC schemes according to their design goals, focusing on some critical design issues, and tradeoffs. Finally, we point out some future research directions. Copyright © 2006 John Wiley & Sons, Ltd.

Abstract Conventional sensor networks name every node with an identifier from a one dimension name space that has no meaning but identification function. However, it is much useful to let every node identifier carry more characteristics of the node itself. This paper introduces the naming problem for sensor networks in the literature for the first time, and proposes a location‐based naming mechanism (LBN) for sensor networks, in which location information is embedded into node identifier and acts as an inherent node characteristic to provide authentication service in local access control. When LBN is enforced, the impacts of many attacks to sensor network topology can be limited in a small area. A link layer authentication (LLA) scheme is also proposed to further decrease the impacts of those attacks. Our LBN and LLA can be combined and act as an efficient solution against a wide range of attacks in sensor networks. Copyright © 2006 John Wiley & Sons, Ltd.

We consider the CSMA/CA based uplink medium access control (MAC) protocol design for a wireless local area network (WLAN) with the use of multi-beam directional antennas at the access point. Our MAC protocol intends to fully utilize the spatial reuse by allowing as many parallel uplink data transmissions as possible. Since all nodes including the access point run in the contention-based MAC protocol, it is not easy to realize multiple collision-free parallel data transmissions while preserving the ad hoc nature of CSMA/CA based MAC. In addition to improving channel efficiency, we also consider the backward compatibility in our design, whereby a 802.11 limited node can transparently access a multi-beam access point. Our simulation results show that our scheme can improve throughput significantly.

IEEE 802.11 is the most important standard for wireless local area networks (WLANs). In IEEE 802.11, the fundamental medium access control (MAC) scheme is distributed coordination function (DCF), whose performance has been studied analytically in the literature. However, to the best of the authors' knowledge, there is no accurate model that takes into account both the incoming traffic loads and the effect of bit transmission errors, which, in addition to collision, can also result in unsuccessful packet delivery. In this paper, we address this issue and provide a new analytical model to evaluate the performance of DCF in binary symmetric channels (BSCs). In our study, we consider the impact of different factors together, including the binary exponential backoff mechanism in DCF, various incoming traffic loads, distribution of incoming packet size, queueing system at the MAC layer, and the packet transmission errors, which has never been done before. Extensive simulation and analysis results show that our analytical model can accurately predict the delay and throughput performance of IEEE 802.11 DCF under different traffic and transmission error conditions.

Ultra-wideband (UWB) communication is becoming an important technology for future wireless personal area networks (WPANs). A critical challenge in high data rate UWB system design is that a receiver usually needs tens of micro-seconds or even tens of milliseconds to synchronize with the transmitted signals, known as the timing acquisition problem. Such a long synchronization time will cause significant overhead, since the data rate of UWB systems is expected to be very high. To address the overhead problem, we previously proposed a general framework for MAC protocols in high data rate UWB networks. In this framework, a node can aggregate multiple upper-layer packets into a larger burst frame at the MAC layer. In this paper, we analyze the unsaturated throughput performance of a burst-frame-based MAC protocol within the framework. Numerical results from the analytical method give excellent agreement with the simulation results, indicating the accuracy of our analytical method.

This paper studies public-key management, a fundamental problem in providing security support for mobile ad hoc networks. The infrastructureless nature and network dynamics of ad hoc networks make the conventional certificate-based public-key solutions less suitable. To tackle this problem, we propose a novel anonymous and certificateless public-key infrastructure (AC-PKI) for ad hoc networks. AC-PKI enables public-key services with certificateless public keys and thus avoids the complicated certificate management inevitable in conventional certificate-based solutions. To satisfy the demand for private keys during network operation, we employ the secret-sharing technique to distribute a system master-key among a preselected set of nodes, called D-PKG, which offer a collaborative private-key-generation service. In addition, we identify pinpoint attacks against D-PKG and propose anonymizing D-PKG as the countermeasure. Moreover, we determine the optimal secret-sharing parameters to achieve the maximum security.

Despite its support of prioritized services, the IEEE 802.11e enhanced distributed channel access (EDCA) cannot guarantee strict QoS required by real-time services such as voice and video without proper network control mechanisms. To overcome this deficiency, we first build an analytical model to derive upper bounds for both delay means and variations for services of different priorities in the non-saturated 802.11e WLAN, showing that the QoS requirements of real-time services can be satisfied if the input traffic is properly regulated. Based on the analysis, we then propose a call admission control scheme and a rate control scheme to ensure that QoS requirements of real-time services are statistically guaranteed and that best effort services can efficiently use the residual bandwidth.

A critical challenge in ultra-wideband (UWB) system design is that a receiver usually needs tens of microseconds or even tens of milliseconds to synchronize with transmitted signals; this is known as the timing acquisition problem. Such a long synchronization time causes significant overhead, since the data rate of UWB systems is expected to be very high. We address the timing acquisition problem at the medium access control (MAC) layer, and propose a general framework for medium access control in UWB systems; in this framework, a transmitting node can aggregate multiple upper-layer packets into a larger burst frame at the MAC layer. Furthermore, we design a MAC protocol based on the framework, and analyze its saturation throughput performance. Compared to sending each upper-layer packet individually, which is a typical situation in exiting MAC protocols, the proposed MAC can drastically reduce the synchronization overhead. Numerical and simulation results show that the proposed MAC can significantly improve the performance of UWB networks, in terms of both throughput and end-to-end delay.

The traditional congestion control mechanism, i.e., TCP, when applied in mobile ad hoc networks (MANET), encounters a number of new challenges, such as wireless link error, medium contention and frequent route failures. Very poor performance of TCP in MANET has been reported in many recent studies. We focus on the problems resulting from medium contention and propose a novel rate based end-to-end congestion control scheme (RBCC). We first illustrate that, under the impact of medium contention, a window based congestion control algorithm is unstable and hence may not be appropriate for MANET because the optimum congestion window size is very small and may even be less than one, i.e., the source should send less than one packet in one round trip time (RTT). Based on the novel use of channel busyness ratio, which, we show, is an accurate sign of the network utilization and congestion status, a new rate control scheme has been proposed to support the transport service in MANET efficiently and reliably. In RBCC, a sublayer consisting of a leaky bucket is added under TCP to control the sending rate based on the network layer feedback at the bottleneck node. Extensive simulations show that our scheme significantly outperforms traditional TCP in terms of channel utilization, delay and fairness.

Wireless sensor networks are often deployed in unattended and hostile environments, leaving individual sensors vulnerable to security compromise. The paper proposes the novel notion of location-based keys for designing compromise-tolerant security mechanisms for sensor networks. Based on location-based keys, we develop a node-to-node authentication scheme, which is able not only to localize the impact of compromised nodes within their vicinity, but also to facilitate the establishment of pairwise keys between neighboring nodes. Compared with previous proposals, our scheme has perfect resilience against node compromise, low storage overhead, and good network scalability. We also demonstrate the use of location-based keys in combating a few notorious attacks against sensor network routing protocols.

The establishment of link-layer keys between neighboring nodes is a fundamental issue in securing sensor network communications. Most existing solutions are key pre-distribution schemes which rely on sensor nodes to broadcast hundreds of or even thousands of preloaded key IDs to find pairwise keys between neighboring nodes. The shortcomings include poor resilience against node compromise, low network connectivity, large communication overhead, etc. The paper presents a novel location-based link-layer key establishment scheme, in which a hexagonal-grid-based deployment model and a polynomial-based key establishment model are combined for the first time to establish a link-layer key between two neighboring nodes. Compared with conventional proposals, our scheme features much lower communication overhead and memory requirements while still maintaining high network connectivity and network resilience against node compromise.

The IEEE 802.11 standard defines two coordination functions: distributed coordination function (DCF) and point coordination function (PCF). These coordination functions coordinate the shared wireless medium. The PCF uses a centralized polling-based channel access method to support time-bounded services. To design an efficient polling scheme, the point coordinator (PC) needs to obtain information about the current transmission status and channel condition for each station. To reduce overhead caused by polling frames, it is better to poll all stations using one polling frame containing the transmission schedule. In this paper, we propose an efficient polling scheme, referred to as two-step multipolling (TS-MP), for the PCF in wireless local area networks (WLANs). In this new scheme, we propose to use two multipolling frames with different purposes. The first frame is broadcast to collect information such as the numbers of pending frames and the physical-layer transmission rates for the communication links among all stations. The second frame contains a polling sequence for data transmissions designed based on the collected information. This frame is broadcast to all stations. Extensive simulation studies show that TS-MP not only overcomes the aforementioned deficiencies, but also help to implement rate adaptation over time-varying wireless channel.

In wireless mobile networks, quantities such as call blocking probability, call dropping probability, handoff probability, handoff rate, and the actual call holding times for both complete and incomplete calls are very important performance parameters in the network performance evaluation and design. In the past, their analytical computations are given only when the classical exponential assumptions for all involved time variables are imposed. In this paper, we relax the exponential assumptions for the involved time variables and, under independence assumption on the cell residence times, derive analytical formulae for these parameters using a novel unifying analytical approach. It turns out that the computation of many performance parameters is boiled down to computing a certain type of probability, and the obtained analytical results can be easily applied when the Laplace transform of probability density function of call holding time is a rational function. Thus, easily computable results can be obtained when the call holding time is distributed with the mixed-Erlang distribution, a distribution model having universal approximation capability. More importantly, this paper develops a new analytical approach to performance evaluation for wireless networks and mobile computing systems.

Next-generation wireless networks target to provide quality of service (QoS) for multimedia applications. We study the wireless systems that support two QoS requirements: keeping the handoff dropping probability less than a predefined QoS threshold while maintaining relative priorities for different traffic classes based on blocking probability. To achieve this goal, a dynamic multiple-threshold bandwidth reservation (DMTBR) scheme, which is capable of granting differential priorities to different traffic classes and to new and handoff traffic for each class by dynamically adjusting bandwidth reservation thresholds, is proposed. In this scheme, the thresholds are obtained in two steps. The initial values are estimated based on instantaneous network traffic situation, then the thresholds will be further adapted according to the instantaneous network QoS status. In times of network congestion, a preventive measure is taken to throttle the new connections. Another contribution of this paper is to generalize the concept of relative priority and hence give the network operator more flexibility to adjust admission control policy by taking into account some dynamic factors such as offered load. The extensive simulations are conducted for two purposes. First, we verify the performance of the proposed scheme and show our scheme performs well under various traffic loads. Second, we demonstrate that the DMTBR scheme gains more advantages when taking the offered load into consideration.

This paper studies an important problem in the IEEE 802.11 distributed coordination function (DCF)-based wireless local area network (WLAN): how well can the network support quality of service (QoS). Specifically, this paper analyzes the network's performance in terms of maximum protocol capacity or throughput, delay, and packet loss rate. Although the performance of the 802.11 protocol, such as throughput or delay, has been extensively studied in the saturated case, it is demonstrated that maximum protocol capacity can only be achieved in the nonsaturated case and is almost independent of the number of active nodes. By analyzing packet delay, consisting of medium access control (MAC) service time and waiting time, accurate estimates were derived for delay and delay variation when the throughput increases from zero to the maximum value. Packet loss rate is also given for the nonsaturated case. Furthermore, it is shown that the channel busyness ratio provides precise and robust information about the current network status, which can be utilized to facilitate QoS provisioning. The authors have conducted a comprehensive simulation study to verify their analytical results and to tune the 802.11 to work at the optimal point with maximum throughput and low delay and packet loss rate. The simulation results show that by controlling the total traffic rate, the original 802.11 protocol can support strict QoS requirements, such as those required by voice over Internet protocol (VoIP) or streaming video, and at the same time achieve high channel utilization.

This paper analyzes the authentication and key agreement protocol adopted by Universal Mobile Telecommunication System (UMTS), an emerging standard for third-generation (3G) wireless communications. The protocol, known as 3GPP AKA, is based on the security framework in GSM and provides significant enhancement to address and correct real and perceived weaknesses in GSM and other wireless communication systems. In this paper, we first show that the 3GPP AKA protocol is vulnerable to a variant of the so-called false base station attack. The vulnerability allows an adversary to redirect user traffic from one network to another. It also allows an adversary to use authentication vectors corrupted from one network to impersonate all other networks. Moreover, we demonstrate that the use of synchronization between a mobile station and its home network incurs considerable difficulty for the normal operation of 3GPP AKA. To address such security problems in the current 3GPP AKA, we then present a new authentication and key agreement protocol which defeats redirection attack and drastically lowers the impact of network corruption. The protocol, called AP-AKA, also eliminates the need of synchronization between a mobile station and its home network. AP-AKA specifies a sequence of six flows. Dependent on the execution environment, entities in the protocol have the flexibility of adaptively selecting flows for execution, which helps to optimize the efficiency of AP-AKA both in the home network and in foreign networks.

Abstract In wireless cellular networks, performance evaluation is an important part in modeling and designing effective schemes to utilize the limited resource. In the past, performance evaluation was carried out either under restricted assumption on some time variables such as exponential assumption or via simulations. In this paper, we present a survey on a new analytical approach we have developed in the last few years to evaluate the performance of wireless cellular networks under more realistic assumptions. In particular, we apply this approach to the analysis of call connection performance and mobility management under assumptions that many time variables such as call holding time, cell residence time, channel holding time, registration area (RA) residence time, and inter‐service time are assumed to be generally distributed and show how we can obtain more general analytical results. Copyright © 2005 John Wiley & Sons, Ltd.

The IEEE 802.11 standard defines a centralized polling-based channel access method, the point coordination function (PCF), to support time-bounded services. In this paper, we propose an efficient polling scheme, referred to as two-step multipolling (TS-MP), for the PCF in WLANs. In this new scheme, we propose to use two multi-polling frames for different purposes. The first frame is broadcasted to collect information such as the numbers of pending frames and the physical layer transmission rates for the communication links among all stations. The second frame is broadcasted with a polling sequence for data transmissions designed by utilizing the collected information. Extensive simulation studies show that TS-MP not only overcomes PCFs deficiencies, but also helps to implement rate adaptation over a time-varying wireless channel.

Wireless sensor networks (WSNs) are appealing in obtaining fine-granular observations about the physical world. Due to the fact that WSNs are composed of a large number of low-cost but energy-constrained sensor nodes, along with the notorious time-varying and error-prone nature of wireless links, scalable, robust, and energy-efficient data disseminating techniques are requisite for the emerging WSN applications such as environment monitoring and surveillance. To meet this challenging demand, we propose a hybrid data dissemination framework for WSNs in this paper. In particular, we conceptually partition a whole sensor field into several functional regions and apply different routing schemes to different regions in order to provide better performance in terms of reliability and fair energy usage. For this purpose, we also propose a novel zone flooding scheme, essentially a combination of geometric routing and flooding techniques. Our scheme features low overhead, high reliability, good scalability, and notable flexibility. Simulation studies are carried out to validate the effectiveness and efficiency of our scheme.

Due to MAC collisions and mobility, quality of service (QoS) provisioning in mobile ad hoc networks is very challenging. In this paper, we propose an innovative scheme to grant high priority to communications between some important nodes in TDMA based ad hoc networks. In addition to bandwidth reservation in previous QoS routing approaches, our scheme adopts a new approach: bandwidth prereservation. Some bandwidth is prereserved at intermediate nodes in a quadrangle-shaped area formed between important nodes. The key idea is to utilize each node's geographic location information and minimize potential scheduling conflicts for transmissions. In this way, time slot collisions in adjacent wireless links along a path can be reduced so that more high priority connections can be accepted into the network. Extensive simulations show that our scheme can successfully provide better communication quality at a relatively low price.

Many rate adaptation schemes at the medium access control (MAC) layer have been proposed to utilize the multirate capability offered by the IEEE 802.11 wireless MAC protocol through automatically adjusting the transmission rate to best match the channel conditions. In this paper, we present the Opportunistic packet Scheduling and Auto Rate (OSAR) protocol to exploit the channel variations. The basic idea of OSAR is as follows: rather than just matching the channel condition for a node pair in communications, our protocol takes advantage of the multiuser diversity as much as possible and adapt the rate accordingly, i.e., based on the channel conditions to its neighboring nodes, the sender chooses the neighboring node with channel quality better than certain level to schedule the transmissions of packets in its queue, then the overall system throughput may be increased. The key mechanisms of OSAR are channel aware media access, rate adaptation and packet bursting. We carry out several sets of ns-2 simulations and evaluate the impact of various factors such as channel condition, network topology and traffic load on the throughput of OSAR. Simulation results show that our proposed protocol can achieve much better performance than other auto rate schemes.

Due to the salient characteristics such as the time-varying and error-prone wireless links, the dynamic and limited bandwidth, the time-varying traffic pattern and user locations, and the energy constraints, it is a challenging task to efficiently support heterogeneous traffic with different quality of service (QoS) requirements in mulithop mobile ad hoc networks. In the last few years, many channel dependent mechanisms are proposed to address this issue based on the cross-layer design philosophy. However, a lot of problems remain before more efficient solutions are found. One of the problems is how to alleviate the conflict between throughput and fairness for different prioritized traffic, especially how to avoid the bandwidth starvation problem for low priority traffic when the high priority traffic load is very high. In this paper, we propose a novel scheme named courtesy piggybacking (CP) to address this problem. With the recognition of inter-layer coupling, our courtesy piggybacking scheme exploits the channel dynamics and stochastic traffic features to alleviate the conflict. The basic idea is to let the high priority traffic help the low priority traffic by sharing unused residual bandwidth with courtesy. Another noteworthy feature of the proposed scheme is its implementation simplicity: the scheme is easy to implement and is applicable in networks using either reservation-based or contention-based MAC protocols.

Security is a critical issue in a mobile ad hoc network (MANET). We propose and investigate a novel scheme, security protocol for reliable data delivery (SPREAD), to enhance the data confidentiality service in a mobile ad hoc network. The proposed SPREAD scheme aims to provide further protection to secret messages from being compromised (or eavesdropped) when they are delivered across the insecure network. The basic idea is to transform a secret message into multiple shares by secret sharing schemes and then deliver the shares via multiple independent paths to the destination so that even if a small number of nodes that are used to relay the message shares are compromised, the secret message as a whole is not compromised. We present the overall system architecture and investigate the major design issues. We first describe how to obtain message shares using the secret sharing schemes. Then we study the appropriate choice of the secret sharing schemes and the optimal allocation of the message shares onto each path in order to maximize the security. The results show that the SPREAD is more secure and also provides a certain degree of reliability without sacrificing the security. Thirdly, the multipath routing techniques are discussed and the path set optimization algorithm is developed to find the multiple paths with the desired property, i.e., the overall path set providing maximum security. Finally, we present the simulation results to justify the feasibility and evaluate the effectiveness of SPREAD.

Wireless sensor networks (WSNs) are appealing in obtaining fine-granular observations about the physical world. Due to the fact that WSNs are composed of a large number of low-cost but energy-constrained sensor nodes, along with the notorious timer-varying and error-prone natures of wireless links, scalable, robust, and energy-efficient data disseminating techniques are requisite for the emerging WSN applications such as environment monitoring and surveillance. In this paper we examine this emerging field from a view of supply chain management and propose a hybrid data dissemination framework for WSNs. In particular, we conceptually partition a whole sensor field into several functional regions based on the supply chain management methodology, and apply different routing schemes to different regions in order to provide better performance in terms of reliability and energy usage. For this purpose, we also propose a novel zone flooding scheme, essentially a combination of geometric routing and flooding techniques. Our hybrid data dissemination framework features low overhead, high reliability, good scalability and flexibility, and preferable energy efficiency. Detailed simulation studies are carried out to validate the effectiveness and efficiency of our scheme.

In this paper, we proposed a novel call admission and rate control (CARC) scheme. Unlike previous research works that are focused on providing service differentiation in the contention-based 802.11 DCF, we aim to support stringent QoS requirements of real-time and streaming traffic. The key idea of this scheme is to regulate the arriving traffic of the WLAN such that the network can work at an optimal point. We first show that the channel busyness ratio is a good indicator of the network status in the sense that it is easy to obtain and can accurately and timely represent channel utilization. Then we propose two algorithms that function upon the use of the channel busyness ratio. The call admission control algorithm is used to regulate the admission of real-time or streaming traffic and the rate control algorithm to control the transmission rate of best effort traffic. A comprehensive simulation study in ns-2 has verified the performance of our proposed CARC scheme, showing that the original 802.11 DCF protocol can statically support strict QoS requirements, such as those required by voice over IP or streaming video, and at the same time, achieve a high channel utilization.

The specification of high-rate wireless personal area network (HR WPAN) has been standardized by the IEEE 802.15.3 task group for communications of consumer electronics and portable communication devices and a final draft standard has been completed. The physical layer in IEEE 802.15.3 standard is designed to achieve data rates of 11-55Mbps. However, a MAC protocol in IEEE 802.15.3 standard does not specify the method to choose an appropriate data rate. In this paper, we propose a rate-adaptive medium access control (MAC) protocol for HR WPAN. The data rate for the next transmission is selected by channel prediction based on the currently received frame and informs the sender about the changed rate using a rate-adaptive acknowledgement (RA-ACK) frame. By overhearing the RA-ACK frame, a piconet controller can efficiently allocate channel times. In addition, we propose a constant physical layer frame length regardless of a data rate. In this way, the channel can be more effectively utilized by squeezing more bits into one transmission. The proposed scheme is evaluated under a time-correlated fading channel model in terms of the achieved throughput Simulation results show that this scheme achieves a much higher throughput than a non rate-adaptive MAC protocol in HR WPAN does.

Wireless mobile networks that do not have infrastructure or centralized administration, known as the ad hoc networks, have received considerable attention in the last few years. The salient characteristics of such networks - time-varying topology and lack of centralized control design - have made medium access control design more complicated and challenging, which is particularly when multiple channels are employed. Recently, many multichannel transmission protocols, such as collision-avoidance transmission scheduling (CATS), have been investigated for their higher efficiency although their problems are abundant. In this paper, we propose a new protocol, namely, multichannel time-spread scheduling (MATS), to improve the throughput performance under heavy traffic loads. In MATS, nodes with transmission requests are divided into three groups, and carry out channel reservations in parallel with a short overhead. We carry out simulation study and the results show that the performance of this protocol under high traffic loads is significantly improved.

In the wireless LANs or mobile ad hoc networks, a node with multi-packets in its queue waiting for delivery to several neighboring nodes may choose to schedule a candidate receiver with good channel condition for transmission. By choosing a receiver with good channel condition, the Head-of-Line (HOL) blocking problem can be alleviated and the overall system throughput can be increased. Motivated by this observation, we introduce the Opportunistic packet Scheduling and Media Access control (OSMA) protocol to exploit high quality channel condition under certain fairness constraints. We base our design on CSMA/CA so that it can be simply incorporated into the 802.11 standard. The key mechanisms of OSMA protocol are multicast RTS and priority-based CTS. In the OSMA protocol, RTS includes a list of candidate receivers. Among those who are qualified to receive data, the one with the highest order would be granted to catch the channel by replying CTS in the first place. The ordering list is updated dynamically according to certain scheduling policy such as Round Robin (RR) and Earlier timestamp First (ETF), and also other performance metrics, ex., fairness and timeliness, can be enhanced. To the best of our knowledge, this is the first paper to exploit the multiuser diversity in the CSMA/CA based wireless networks. We evaluate the OSMA using ns-2 and our simulation results show that this protocol can improve the network throughput significantly.

In wireless multihop ad hoc networks, nodes need to contend for the shared wireless channel with their neighbors, which could result in congestions and greatly decrease the end-to-end throughput due to severe packet loss. Several recent papers have indicated that the IEEE 802.11 fails to achieve the optimum schedule for this kind of contentions. In this paper, we present a framework of multihop packet scheduling to achieve maximum throughput for traffic flows in the shared channel environment. The key idea is based on the observation that in the IEEE 802.11 MAC protocol the maximum throughput for chain topology is 1/4 of the channel bandwidth and its optimum packet scheduling is to allow simultaneous transmissions at nodes which are four hops away. The proposed fully distributed scheme generalizes this optimum scheduling to any traffic flows which may encounter intra-flow contentions and inter-flow contentions. Extensive simulations indicate that our scheme could perform well and achieve high throughput at light to heavy traffic load while the performance of the original IEEE 802.11 MAC protocol greatly degrades when the traffic load becomes heavy. Moreover, our scheme also achieves much better and more stable performance in terms of delay, fairness and scalability with low and stable control overhead.

Security in mobile ad hoc networks (MANETs) has received intensive attention recently, whereas the issue of selfish nodes, which may refuse to forward packets for others to save their own resources, is not well addressed yet. This kind of noncooperative action would cause a severe problem that is more likely in MANETs compared to their wired counterpart To cope with this problem, we propose SIP: a secure incentive protocol to stimulate cooperation among those possible selfish nodes. The most attractive feature of SIP is that it does not rely on any predeployed infrastructure and provides highly secure incentives for selfish nodes to be cooperative in packet forwarding with low overhead and implementation complexity.

One of the major challenges for the wireless network design is the efficient mobility management, which can be addressed globally (macromobility) and locally (micromobility). Mobile Internet protocol (IP) is a commonly accepted standard to address global mobility of mobile hosts (MHs). It requires the MHs to register with the home agents (HAs) whenever their care-of addresses change. However, such registrations may cause excessive signaling traffic and long service delay. To solve this problem, the hierarchical mobile IP (HMIP) protocol was proposed to employ the hierarchy of foreign agents (FAs) and the gateway FAs (GFAs) to localize registration operations. However, the system performance is critically affected by the selection of GFAs and their reliability. In this paper, we introduce a novel dynamic hierarchical mobility management strategy for mobile IP networks, in which different hierarchies are dynamically set up for different users and the signaling burden is evenly distributed among the network. To justify the effectiveness of our proposed scheme, we develop an analytical model to evaluate the signaling cost. Our performance analysis shows that the proposed dynamic hierarchical mobility management strategy can significantly reduce the system signaling cost under various scenarios and the system robustness is greatly enhanced. Our analysis also shows that the new scheme can outperform the Internet Engineering Task Force mobile IP hierarchical registration scheme in terms of the overall signaling cost. The more important contribution is the novel analytical approach in evaluating the performance of mobile IP networks.

Due to the salient characteristics such as the time-varying and error-prone wireless links, the dynamic and limited bandwidth, the time-varying traffic pattern and user locations, and the energy constraints, it is a challenging task to efficiently support heterogeneous traffic with different quality of service (CoS) requirements in multihop mobile ad hoc networks. In the last few years, many channel-dependent mechanisms are proposed to address this issue based on the cross-layer design philosophy. However, a lot of problems remain before more efficient solutions are found. One of the problems is how to alleviate the conflict between throughput and fairness for different prioritized traffic, especially how to avoid the bandwidth starvation problem for low-priority traffic when the high-priority traffic load is very high. In this paper, we propose a novel scheme named Courtesy Piggybacking to address this problem. With the recognition of interlayer coupling, our Courtesy Piggybacking scheme exploits the channel dynamics and stochastic traffic features to alleviate the conflict. The basic idea is to let the high-priority traffic help the low-priority traffic by sharing unused residual bandwidth with courtesy. Another noteworthy feature of the proposed scheme is its implementation simplicity: The scheme is easy to implement and is applicable in networks using either reservation-based or contention-based MAC protocols.

Development of efficient medium access control (MAC) protocols providing both high throughput performance for data traffic and good quality of service (QoS) support for real-time traffic is the current major focus in distributed contention-based MAC protocol research. In this paper, we propose an efficient contention resolution algorithm for wireless local area networks, namely, the fast collision resolution (FCR) algorithm. The MAC protocol with this new algorithm attempts to provide significantly higher throughput performance for data services than the IEEE 802.11 MAC algorithm and more advanced dynamic tuning backoff (DTB) algorithm. We demonstrate that this algorithm indeed resolves collisions faster and reduces the idle slots more effectively. To provide good fairness performance and to support good QoS for real-time traffic, we incorporate the self-clocked fair queueing algorithm and a priority scheme into the FCR algorithm and come up with the real-time FCR (RT-FCR) algorithm, and show that RT-FCR can simultaneously achieve high throughput and good fairness performance for nonreal-time traffic while maintaining satisfactory QoS support for real-time traffic.

Abstract IEEE 802.11 MAC protocol is the de facto standard for wireless local area networks (LANs), and has also been implemented in many network simulation packages for wireless multi‐hop ad hoc networks. However, it is well known that, as the number of active stations increases, the performance of IEEE 802.11 MAC in terms of delay and throughput degrades dramatically, especially when each station's load approaches its saturation state. To explore the inherent problems in this protocol, it is important to characterize the probability distribution of the packet service time at the MAC layer. In this paper, by modeling the exponential backoff process as a Markov chain, we can use the signal transfer function of the generalized state transition diagram to derive an approximate probability distribution of the MAC layer service time. We then present the discrete probability distribution for MAC layer packet service time, which is shown to accurately match the simulation data from network simulations. Based on the probability model for the MAC layer service time, we can analyze a few performance metrics of the wireless LAN and give better explanation to the performance degradation in delay and throughput at various traffic loads. Furthermore, we demonstrate that the exponential distribution is a good approximation model for the MAC layer service time for the queueing analysis, and the presented queueing models can accurately match the simulation data obtained from ns‐2 when the arrival process at MAC layer is Poissonian. Copyright © 2004 John Wiley & Sons, Ltd.