Federated learning (FL) is becoming increasingly popular due to concerns about data privacy. In FL, multiple clients and a server jointly train a model. Clients update their models locally and send only the updated parameters to the server rather than raw data, which helps protect data privacy to a certain extent. However, research shows that even when the server or other clients know a client’s parameter data, adversaries can recover a lot of private information about data owners through inference attacks. In addition, adversaries can also obtain private information from aggregated parameters. To solve these problems, we propose a new privacy-preserving scheme in cross-silo FL based on aggregated multi-key BFV homomorphic encryption and noise addition. In this work, we introduce an aggregated multi-key BFV homomorphic encryption method to protect data privacy for each single transmission and a noise addition method to enhance privacy protection for aggregated parameters. Our scheme protects data privacy against collusion between the server and up to N − 2 clients without relying on a trusted third party, where N is the total number of clients participating in the training process. The experiments show that our noise-assisted aggregated multi-key BFV homomorphic encryption method achieves the same model performance as a plain federated averaging algorithm with the same machine learning models while providing higher computational and communication efficiency compared to two other state-of-the-art methods.

The emergence of cyber threat intelligence (CTI) is a promising approach for alleviating malicious activities. However, the effectiveness of CTIs is heavily dependent on their quality. Current literature develops the CTI quality assessment ontology mainly from the perspective of CTI source or content separately, regardless of their availability in practice. In this paper, we propose an automated CTI quality assessment method that synthesizes the trustworthiness of CTI sources and the availability of CTI contents. Specifically, we model the interactions of CTI feeds as a correlation graph and propose an iterative algorithm to well discriminate the feeds’ trustworthiness. We elaborate a CTI content assessment together with a machine learning algorithm to automatically classify CTIs’ availability from a set of content metrics. A comprehensive CTI quality assessment is proposed by jointly considering the feed trustworthiness and content availability. Extensive experimental results on real datasets demonstrate that our proposed method can quantitatively as well as effectively assess CTI quality.

Nuclei segmentation is critical for computational pathology analysis. Most previous methods employ pixel-wise classification or regression for automatic nuclei segmentation, without describing nucleus instances as individual entities at the feature level. To address the above limitation, we propose an instance-aware multi-task learning framework that strengthens a pixel-wise prediction branch with an instance-wise prediction branch. The instance-wise prediction branch leverages learnable cell-level queries, enabling the model to capture positional information and visual representations for individual nuclei. Concretely, we introduce an instance-disentangling feature learning module that effectively aligns the embeddings of the object-level queries with pixel-wise decoder features from the first branch. Further, we design a dual-branch unified post-processing algorithm that aggregates the complementary outputs of both branches for computing the instance segmentation results. Experimental results demonstrate that our framework achieves competitive performance on a wide range of nuclei segmentation benchmarks. The code and model weights are released in https://github.com/lhaof/IML.

Nuclei classification is a critical step in computer-aided diagnosis with histopathology images. In the past, various methods have employed graph neural networks (GNN) to analyze cell graphs that model inter-cell relationships by considering nuclei as vertices. However, they are limited by the GNN mechanism that only passes messages among local nodes via fixed edges. To address the issue, we develop a cell graph transformer (CGT) that treats nodes and edges as input tokens to enable learnable adjacency and information exchange among all nodes. Nevertheless, training the transformer with a cell graph presents another challenge. Poorly initialized features can lead to noisy self-attention scores and inferior convergence, particularly when processing the cell graphs with numerous connections. Thus, we further propose a novel topology-aware pretraining method that leverages a graph convolutional network (GCN) to learn a feature extractor. The pre-trained features may suppress unreasonable correlations and hence ease the finetuning of CGT. Experimental results suggest that the proposed cell graph transformer with topology-aware pretraining significantly improves the nuclei classification results, and achieves the state-of-the-art performance. Code and models are available at https://github.com/lhaof/CGT

The widespread adoption of Internet of Things (IoT) devices has led to increasingly complex and varied cyber-threats. Traditional defense mechanisms are often inadequate in countering these evolving threats, as attackers continuously develop new strategies. In response, this paper introduces a rapid threat detection method designed to automatically pinpoint vulnerability attacks on IoT devices amidst vast internet traffic. Our approach incorporates a multilevel clustering method, significantly accelerating the identification of malicious behaviors. Additionally, we develop a reliable assessment criterion for recognizing when a detection model becomes outdated due to the dynamic nature of network environments. This criterion is underpinned by a sophisticated combination of concept drift detection and an incremental model updating mechanism, thereby substantially enhancing the durability and effectiveness of our botnet detection models in adapting to new threats. The practicality and efficiency of our proposed solution are thoroughly validated through extensive experimental analysis, which confirms our method’s superior performance in identifying malicious behavior and ensuring the timely retraining of models to address emerging cyber-threats effectively.

Whole-slide image (WSI) classification methods play a crucial role in tumor diagnosis. Most of them use hematoxylin and eosin (H&E) stained images, while Immunohistochemistry (IHC) staining provides molecular markers and protein expression information that highlights cancer regions. However, obtaining IHC-stained images requires higher costs in practice. In this work, we propose a multi-modal denoising diffusion pre-training framework that harnesses the advantages of IHC staining to learn visual representations. The framework is trained with the H&E-to-IHC re-staining task and IHC-stained image reconstruction task, which helps capture the structural similarity and staining difference between two image modalities. The trained model can then provide IHC-guided features, by taking only H&E-stained images as inputs. Besides, we build a new class-constraint constrastive loss to achieve the semantic consistency between dual-modal features from our pre-training framework. To integrate with WSI classifiers based on multi-instance learning, we further propose a bag feature augmentation strategy to extend bags with the features extracted by our pre-trained model. Experimental results on three datasets show that our pre-training framework effectively improves WSI classification and surpasses the state-of-the-art pre-training approaches. Code and model are released via https://github.com/lhaof/MDDP

Resource allocation in multi-cell networks typically requires knowing the inter-cell interference channel gains. When multiple numerologies are employed, resource allocation further needs to estimate the inter-numerology interference within each cell. However, there lacks viable methods of interference graph estimation (IGE), depicting the intra- and inter-cell interference channel gains, for multi-cell multi-numerology networks. To fill this gap, we propose an efficient power-domain approach to IGE for the resource allocation in multi-cell multi-numerology networks. Unlike traditional reference signal-based approaches that consume frequency-time resources, our approach uses power as a new dimension for the estimation of channel gains. By carefully controlling the transmit powers of base stations, our approach is capable of estimating both the intra- and inter-cell interference channel gains, useful to resource allocation. Further, as a power-domain approach, it can be seamlessly integrated with the resource allocation in multi-numerology networks, such that IGE and resource allocation can be conducted simultaneously. We derive the necessary conditions for power-domain IGE and formulate the joint optimization problem of IGE and resource allocation. Our simulation results show that power-domain IGE can accurately estimate the interference channel gains and incurs low power overhead.

The promising wireless powered mobile edge computing (MEC) can offer sustainable energy and fast network service response for nearby wireless terminals (WTs) to satisfy real-time and flexible requirements. Online task offloading and wireless power transfer (WPT) are critical for the wireless powered MEC system to realize powerful function. However, existing researches usually schedule the task offloading of WTs serially to prevent mutual signal interference, and suffer from high task offloading time. Hence, to lower the task offloading time, we adopt the successive interference cancellation (SIC) technology and realize task concurrent offloading of multiple WTs to the edge server (ES). Specifically, we study the SIC-enabled online task concurrent offloading problem with the aim of optimizing the total task completion time. We prove this optimization problem to be NP-hard, and decompose this problem to reduce the problem solving difficulty. With the support of the SIC and deep reinforcement learning (DRL) technology, we present an efficient and intelligent algorithm named SIOA. Our SIOA algorithm provides online offloading decision generating strategies for WTs through the idea of task concurrent offloading and a well-devised DRL structure. Moreover, our SIOA algorithm assigns the ES’s WPT time via a feasible area analysis approach. Our SIOA algorithm can offer demonstrable feasibility assurance, and requires lower task completion time than existing baseline algorithms with low program running time, which is verified by experiments on a real dataset.

Wireless powered Internet of Things (WPIoT) has gained great concern due to its benefits of high deployment flexibility and low maintenance overhead. The minimum delay broadcast scheduling problem is very critical for many applications of WPIoT. However, traditional broadcast scheduling algorithms assume that Internet of Things (IoT) devices always possess sufficient energy to support data transmission or reception, which does not hold in WPIoT with the special feature of using the store-charge-and-forward communication mode. Furthermore, existing solutions rely heavily on the interference-avoiding technology to handle the signal interference problem, and overlook the powerful interference processing capability of the successive interference cancellation (SIC) technology. To efficiently resolve this problem, this article proposes a delay-efficient energy-aware broadcast scheduling algorithm called EABS. EABS algorithm incorporates a novel broadcast link scheduling method by fully considering the special feature of WPIoT and efficiently utilizing the advantage of the SIC technology to significantly improve the broadcast delay. Extensive experiments based on a real-world dataset are conducted to evaluate the performance of our algorithm, and the results demonstrate the better performance of our algorithm than the baseline algorithms.

Federated learning (FL) is an effective privacy-preserving mechanism that collaboratively trains the global model in a distributed manner by solely sharing model parameters rather than data from local clients, like mobile devices, to a central server. Nevertheless, recent studies have illustrated that FL still suffers from gradient leakage as adversaries try to recover training data by analyzing shared parameters from local clients. To address this issue, differential privacy (DP) is adopted to add noise to the parameters of local models before aggregation occurs on the server. It, however, results in the poor performance of gradient-based interpretability, since some important weights capturing the salient region in feature maps will be perturbed. To overcome this problem, we propose a simple yet effective adaptive gradient protection (AGP) mechanism that selectively adds noisy perturbations to certain channels of each client model that have a relatively small impact on interpretability. We also offer a theoretical analysis of the convergence of FL using our method. The evaluation results on both IID and Non-IID data demonstrate that the proposed AGP can achieve a good trade-off between privacy protection and interpretability in FL. Furthermore, we verify the robustness of the proposed method against two different gradient leakage attacks.

Nuclei classification provides valuable information for histopathology image analysis. However, the large variations in the appearance of different nuclei types cause difficulties in identifying nuclei. Most neural network based methods are affected by the local receptive field of convolutions, and pay less attention to the spatial distribution of nuclei or the irregular contour shape of a nucleus. In this paper, we first propose a novel polygon-structure feature learning mechanism that transforms a nucleus contour into a sequence of points sampled in order, and employ a recurrent neural network that aggregates the sequential change in distance between key points to obtain learnable shape features. Next, we convert a histopathology image into a graph structure with nuclei as nodes, and build a graph neural network to embed the spatial distribution of nuclei into their representations. To capture the correlations between the categories of nuclei and their surrounding tissue patterns, we further introduce edge features that are defined as the background textures between adjacent nuclei. Lastly, we integrate both polygon and graph structure learning mechanisms into a whole framework that can extract intra and inter-nucleus structural characteristics for nuclei classification. Experimental results show that the proposed framework achieves significant improvements compared to the previous methods. Code and data are made available via https://github.com/lhaof/SENC.

Client selection schemes are widely adopted to handle the communication-efficient problems in recent studies of Federated Learning (FL). However, the large variance of the model updates aggregated from the randomly-selected unrepresentative subsets directly slows the FL convergence. We present a novel clustering-based client selection scheme to accelerate the FL convergence by variance reduction. Simple yet effective schemes are designed to improve the clustering effect and control the effect fluctuation, therefore, generating the client subset with certain representativeness of sampling. Theoretically, we demonstrate the improvement of the proposed scheme in variance reduction. We also present the tighter convergence guarantee of the proposed method thanks to the variance reduction. Experimental results confirm the exceed efficiency of our scheme compared to alternatives.

Device-free acoustic sensing has obsessed with renovating human-computer interaction techniques for all-sized mobile devices in various applications. Recent advances have explored sound signals in different methods to achieve highly accurate and efficient tracking and recognition. However, accuracies of most approaches remain bottlenecked by the limited sampling rate and narrow bandwidth, leading to restrictions and inconvenience in applications. To bridge over the aforementioned daunting barriers, we propose${{\sf PD-FMCW}}$, a novel ultrasound-based device-free tracking scheme that can distinctly improve the resolution of fine-grained sensing to submillimetre level. In its heart lies an original Phase Difference based approach leveraging the Frequency-Modulated Continuous Wave to derive the reflected time delay, thus precisely inferring absolute distance, catering to interaction needs of tinier perception with lower delay. The distance resolution of${{\sf PD-FMCW}}$is only related to the speed of actions and chirp duration. We implement a prototype with effective denoising methods all in the time domain on smartphones. The evaluation results show that${{\sf PD-FMCW}}$achieves accuracies of 2.5 mm, 3.6 mm, and 2.1 mm in distance change, path length change, and trajectory tracking error respectively.${{\sf PD-FMCW}}$is also valid in recognizing 2 mm or even tinier micro-movements, which paves the way for more delicate sensing work.

Recently deep neural networks, which require a large amount of annotated samples, have been widely applied in nuclei instance segmentation of H&E stained pathology images. However, it is inefficient and unnecessary to label all pixels for a dataset of nuclei images which usually contain similar and redundant patterns. Although unsupervised and semi-supervised learning methods have been studied for nuclei segmentation, very few works have delved into the selective labeling of samples to reduce the workload of annotation. Thus, in this paper, we propose a novel full nuclei segmentation framework that chooses only a few image patches to be annotated, augments the training set from the selected samples, and achieves nuclei segmentation in a semi-supervised manner. In the proposed framework, we first develop a novel consistency-based patch selection method to determine which image patches are the most beneficial to the training. Then we introduce a conditional single-image GAN with a component-wise discriminator, to synthesize more training samples. Lastly, our proposed framework trains an existing segmentation model with the above augmented samples. The experimental results show that our proposed method could obtain the same-level performance as a fully-supervised baseline by annotating less than 5% pixels on some benchmarks.

Thanks to the promising wireless power transmission (WPT) technology, wireless-powered Internet of Things (WPIoT) can significantly improve the sustainable service ability of Internet of Things (IoT) with low personnel maintenance costs, and thus, shows remarkable and broad prospects in many applications, especially under the abominable and dangerous environment. Minimum delay data aggregation scheduling (MAS) is a problem of cardinal significance in WPIoT with the objective of timely collecting the data of IoT devices. However, due to the residual energy limitation of IoT devices, WPIoT shows the special feature of adopting the store-charge-and-forward communication mode, which brings new research challenges on designing efficient solutions to the MAS problem. We show that the MAS problem under the physical interference model in WPIoT is NP-hard. To tackle this problem, we propose a delay-efficient data aggregation scheduling algorithm called HADA based on an active data aggregation tree construction method and a novel hypergraph-based link scheduling method. Extensive numerical experiments are conducted to evaluate the performance of our proposed algorithm. The results demonstrate that our HADA algorithm can efficiently improve the performance compared with the existing baseline algorithms.

To mitigate cross-site scripting attacks (XSS), the W3C group recommends web service providers to employ a computer security standard called Content Security Policy (CSP). However, less than 3.7 percent of real-world websites are equipped with CSP according to Google’s survey. The low scalability of CSP is incurred by the difficulty of deployment and non-compatibility for state-of-art browsers. To explore the scalability of CSP, in this article, we propose JavaScript based CSP (JSCSP), which is able to support most of real-world browsers but also to generate security policies automatically. Specifically, JSCSP offers a novel self-defined security policy which enforces essential confinements to related items, including JavaScript functions, DOM elements and data access. Meanwhile, JSCSP has an efficient algorithm to automatically generate the policy directives and enforce them in a cascading way, which is more fine-grained and practical than the functionalities provided by CSP. We further implement JSCSP on a Chrome extension, and our evaluation shows that the extension is compatible with popular JavaScript libraries. Our JSCSP extension can detect and block the tested attacking vectors extracted from the prevalent web applications. We state that JSCSP delivers better performance compared to other XSS defense solutions.

Device-free acoustic sensing has obsessed with renovating human-computer interaction techniques for all-sized mobile devices in various applications. Recent advances have explored sound signals in different methods to achieve highly accurate and efficient tracking and recognition. However, accuracies of most approaches remain bottlenecked by the limited sampling rate and narrow bandwidth, leading to restrictions and inconvenience in applications. To bridge over the aforementioned daunting barriers, we propose PDF, a novel ultrasound-based device-free tracking scheme that can distinctly improve the resolution of fine-grained sensing to submillimetre level. In its heart lies an original Phase Difference based approach to derive time delay of the reflected Frequency-Modulated Continuous Wave (FMCW), thus precisely inferring absolute distance, catering to interaction needs of tinier perception with lower delay. The distance resolution of PDF is only related to the speed of actions and chirp duration. We implement a prototype with effective denoising methods all in the time domain on smartphones. The evaluation results show that PDF achieves accuracies of 2.5 mm, 3.6 mm, and 2.1 mm in distance change, absolute distance change, and trajectory tracking error respectively. PDF is also valid in recognizing 2 mm or even tinier micro-movements, which paves the way for more delicate sensing work.

Embedding Radio-Frequency IDentification (RFID) into everyday objects to construct ubiquitous networks has been a long-standing goal. However, a major problem that hinders the attainment of this goal is the current inefficient reading of RFID tags. To address the issue, the research community introduces the technique of Bloom Filter (BF) to RFID systems. This work presents TagMap, a practical solution that acquires BFs across commercial off-the-shelf (COTS) RFID tags in the physical layer, enabling upper applications to boost their performance by orders of magnitude. The key idea is to treat all tags as if they were a single virtual sender, which hashes each tag into different intercepted inventories. Our approach does not require hardware nor firmware changes in commodity RFID tags - allows for rapid, zero-cost deployment in existing RFID tags. We design and implement TagMap reader with commodity device (e.g., USRP N210) platforms. Our comprehensive evaluation reveals that the overhead of TagMap is 66.22% lower than the state-of-the-art solution, with a bit error rate of 0.4%.

Embedding Radio-Frequency IDentification (RFID) into everyday objects to construct ubiquitous networks has been a long-standing goal. However, a major problem that hinders the attainment of this goal is the current inefficient reading of RFID tags. To address issue, the research community introduces the technique of Bloom Filter (BF) to RFID systems. This work presents TagMap, a practical solution that acquires BFs across commercial off-the-shelf (COTS) RFID tags in the physical layer, enabling upper applications to boost their performance by orders of magnitude. The key idea is to treat all tags as if they were a single virtual sender, which hashes each tag into different intercepted inventories. Our approach does not require hardware nor firmware changes in commodity RFID tags -allows for rapid, zero-cost deployment in existing RFID tags. We design and implement TagMap reader with commodity device (e.g., USRP N210) platforms. Our comprehensive evaluation reveals that the overhead of TagMap is 66.22% lower than the state-of-the-art solution, with a bit error rate of 0.4%.

Edge computing technologies and integrated architectures have been deployed to accommodate Internet traffic growth. These architectures include facilities (cloudlets, micro DCs) to cache and serve contents close to consumers. Resulting systems adapt to observed request/consumption patterns by allowing cloudlet coordination for content caching/dissemination. This paper presents a novel analytical model of transient dynamics of the cloudlets set. The model is used to study system convergence, stability and delivered content locality. Results from this model are validated via simulations.

Data aggregation scheduling is a critical issue in WSNs. This paper studies the Delay efficient Data Aggregation scheduling problem in multi-Channel asynchronous Duty-cycled WSNs (DDACD problem), which aims to accomplish data aggregation with minimum delay. Existing studies, nevertheless, either focus on non-sleeping scenarios or assume that nodes communicate with one single channel, and thus may have poor performance if directly applied to multi-channel asynchronous duty-cycled scenarios. We first show that the DDACD problem is NP-hard. Then, we propose two new concepts of candidate active conflict graphs (CACGs) and feasible active conflict graphs (FACGs) to depict the relationship of the data aggregation links and present two coloring methods to well separate the links at different time slots or on different channels. Based on these two new concepts and two coloring methods, we propose an efficient data aggregation scheduling algorithm called EDAS, which exploits the fewest-children-first rule to choose the forwarding nodes to benefit the link scheduling. To reduce unused time slots or channels, we further propose a novel algorithm called NDAS by making full use of the characteristics of multi-channel asynchronous duty-cycled WSNs. We prove that our algorithms can achieve provable performance guarantee. The results of extensive simulations confirm the efficiency of our algorithms.

Data aggregation scheduling is a critical issue in wireless sensor networks (WSNs). This paper studies the Delay efficient Data Aggregation scheduling problem in multi-Channel Duty-cycled WSNs (DDACD problem), which aims to accomplish data aggregation with minimum delay. Existing researches, nevertheless, either focus on non-sleeping scenarios, or assume that nodes communicate on one single channel, and thus have poor performance in multi-channel duty-cycled scenarios. In this paper, we first show that DDACD problem is NP-hard. We then propose two new concepts of Candidate Active Conflict Graphs (CACG) and Feasible Active Conflict Graphs (FACG) to depict the relationship of the data aggregation links, and present two coloring methods to well separate the links at different time-slots or on different channels. Based on these two new concepts and two coloring methods, we propose an Efficient Data Aggregation Scheduling algorithm called EDAS, which exploits the fewest-children-first rule to choose the forwarding nodes to benefit the link scheduling. We theoretically prove that our proposed EDAS algorithm can achieve provable performance guarantee. The results of extensive simulations confirm the efficiency of our algorithm.

The introduction of wirelessly charged electric buses (WCEBs) into current public transportation system attracts many attentions in recent years. As the wireless charging technology enables energy transfer from power transmitters to electric vehicles (EVs) on road, it provides a promising solution to reduce the huge cost of battery with large size and long charging time, which are two critical impediments for EV applications. However, the system cost of WCEBs is huge. Under the dynamic electricity demands and the fluctuating electricity prices, the system operating electricity cost highly depends on the charging schedule. In this paper, according to the typical day-ahead electricity market, we explore an optimal charging scheduling scheme in a WCEB system to minimize the system operating electricity cost, while the characteristic of WCEBs is considered. The price of electricity fluctuates with the accumulated energy demands in both spatial and temporal domains. We first present a day-ahead reserved wholesale electricity determination algorithm, in which, the average speeds of WCEBs are presumed. Then, we propose an optimal charging scheduling algorithm, in which the WCEB charging schedules in slots are optimized sequentially. Both the reserved electricity and the predicted speeds in the slot are used. Simulation results demonstrate the efficiency of our proposed WCEB charging schedules.

The wireless local area networks (WLANs) have been widely deployed with the rapid development of mobile devices and have further been brought into new applications with infrastructure mobility due to the growth of unmanned aerial vehicles (UAVs). However, the WLANs still face persistent challenge on increasing the network throughput to meet the customer’s requirement and fight against the node mobility. Interference is a well‐known issue that would degrade the network performance due to the broadcast characteristics of the wireless signals. Moreover, with infrastructure mobility, the interference becomes the key obstacle in pursuing the channel capacity. Legacy interference management mechanism through the channel access control in the MAC layer design of the 802.11 standard has some well‐known drawbacks, such as exposed and hidden terminal problems, inefficient rate adaptation, and retransmission schemes, making the efficient interference management an everlasting research topic over the years. Recently, interference management through exploiting physical layer mechanisms has attracted much research interest and has been proven to be a promising way to improve the network throughput, especially under the infrastructure mobility scenarios which provides more indicators for node dynamics. In this paper, we introduce a series of representative physical layer techniques and analyze how they are exploited for interference management to improve the network performance. We also provide some discussions about the research challenges and give potential future research topics in this area.

This paper represents the design, feasibility evaluation and performance validation of ICMR, a novel cross layer protocol that can maximize concurrent transmissions and avoid data frame interference in wireless networks, achieving higher throughput comparing with the 802.11 standard and other state-of-the-art protocols. Observations on the 802.11 standard reveal that nodes degrade the network throughput from two aspects, including the so-called CF-CA problem and varied-IR problem, and these problems will make nodes around both the transmitter and receiver of the ongoing link waste concurrent transmission opportunities. A state-of-the-art protocol IRMA is proposed to improve the network throughput through solving the two problems at the transmitter side. In this paper, a new ICMR protocol is proposed to solve both problems at the receiver side to further improve the network throughput through discernible interference cancellation, a physical layer mechanism that can successfully detect data frames when collided by control frames. Hardware experiments based on USRP2 demonstrate the feasibility of the discernible interference cancellation mechanism, and simulations based on ns-2 confirm that ICMR outperforms the 802.11 standard and other protocols significantly.

Interference management through power control in wireless ad hoc networks has both the hidden terminal problem which induces collisions, and the exposed terminal problem which prohibits concurrent transmissions. Both problems are caused by the varied interference range induced by the adjusted transmission power. Through observing that the nodes adopt the power control mechanism induces collisions in one scenario and miss concurrent transmission opportunities in two scenarios, this paper presents IAPC (Interference-Aware Power Control), a novel protocol to improve the network throughput from these aspects. IAPC makes the interference range of each link covered by its CTS (Clear-To-Send) transmission through utilizing a signature detection process, so as to avoid interference. Meanwhile, it lets CTS convey the transmission power information of this link to make the neighboring node determine a limited transmission power, which tries to make the neighboring node outside the interference range of the ongoing link, thus can exploit concurrent transmissions. Simulation results based on ns-2 show that IAPC can outperform the other protocols significantly.

The idea of employees leveraging their personal mobile devices for their work (Bring Your Own Device, or BYOD) is becoming increasingly popular in recent years. As BYOD users will use various digital goods (such as cloud services and mobile software) for their work and personal purposes via the same mobile devices, it brings serious security risks into both the cloud and mobile devices. Generally, the BYOD users would employ digital rights management (DRM) to control and manage the execution of digital goods. However, the security requirements for using the digital goods for work and personal tasks are very different, and conventional unified cloud-based DRM services lack the flexibility to satisfy the BYOD users' demands on diversified security levels. In this paper, we regard the security of digital goods as a metric to differentiate the DRM service into multiple grades. We propose a differentiated DRM service to increase the security flexibility of digital goods, which allows BYOD users to choose their preferred DRM grades to maximize their utility. Moreover, the differentiated DRM service can increase the benefit of service providers (SPs) even when the SPs competes with others, and thus, it becomes a dominant strategy for the SPs.

A perfect bidirectional communication network is a common assumption in smart grids. However, it is unrealistic, especially in the neighborhood area network of microgrids. Due to the channel fading, large volumes of transmission data, and considerable communication cost, the imperfect communications affect the system performance directly. In this paper, we consider the uncertainty of imperfect communications in both supply and demand sides, which affects the microgrid system performance in terms of the packet loss ratio of the power demand data transmission and the forecasting accuracy ratio of the renewable energy generation. We analyze the impacts of imperfect communications on the demand response management (DRM) performance under the real-time pricing scheme. An optimization problem is formulated first to maximize the DRM performance of the microgrid system. As these impacts can be mitigated by using sufficient spectrum resources, we then propose a spectrum resource allocation scheme that considers different characteristics of transmission data and system communication cost to balance the tradeoff between the DRM performance and the incurred communication cost. We introduce a joint optimization problem that not only maximizes the DRM performance but also minimizes the communication cost. Simulation results reveal the impacts of imperfect communications on the DRM performance and power price, and the efficiency of the proposed optimization problems.

Energy efficiency is a critical issue of wireless devices. As the packets are broadcast to the devices in the wireless transmission media, all active neighboring devices have to spend their energy receiving the packets though the packets are not addressed to them, which is called as the packet overhearing problem. The real-world traffic trace analysis reveals that the energy cost on the packet overhearing accounts for the majority of the devices' energy inefficiency in high traffic wireless local area networks (WLANs). In this paper, we propose a novel sample-address sample-duration (SASD) scheme to solve the energy inefficiency of the packet overhearing problem. By adding a new SASD header, which contains the critical information, in front of the data packet at the physical layer, the SASD enables the devices to discern the required information in the energy-saving downclocking mode. Consequently, the non-destination devices of the packet can switch to the sleeping mode to avoid the packet overhearing problem. We demonstrate the feasibility of the SASD through hardware experiments and evaluate its energy-saving performance through ns-2 simulations. The results show that the SASD can greatly outperform the existing approaches in the high traffic WLAN scenario.

A reliable and energy-efficient communication infrastructure plays an important role in the success of data collection, transmission and control in smart grid (SG) networks. In order to satisfy the coverage and cost minimization requirements, in this paper, we introduce cognitive radio (CR) into a residential SG network in which the communication load and the available spectrum resource change with the power load in different time periods. To leverage this feature, we propose a spectrum access strategy selection scheme. Moreover, based on the proposed selection scheme, we propose an energy-efficient gateway on-off switching optimization scheme to minimize the energy consumption, while considering the sensing operation and transmission power control of SG nodes. Numerical results reveal that the proposed access strategy selection scheme can increase the average channel capacity of SG nodes considerably, and the proposed gateway on-off switching scheme can balance the tradeoff between the energy consumption and coverage requirement efficiently.

This paper represents the design, feasibility evaluation and performance validation of concurrency-based coordination mechanism (CCM), a novel cross-layer protocol that can coordinate among nodes effectively to avoid data packet interference in wireless local area networks (WLANs), achieving higher throughput compared to 802.11 standard and other state-of-the-art protocols. The design of CCM contains OpenCCM which is based on the architecture of software defined network to schedule the transmissions in both the uplink and downlink directions centrally to maximize transmission concurrency. It also contains an interference-resistant mechanism in the physical layer that can make the control message transmitted with the data packet simultaneously to eliminate the coordination overhead. Experiment results with USRP2 demonstrate the feasibility of the interference-resistant mechanism, and the simulations by ns-2 show that CCM can outperform other protocols significantly.

Spectrum access strategy plays a critical role in multichannel cognitive radio networks (CRNs). However, the CRNs cannot obtain the maximal throughput, when the existing access strategies, including overlay, underlay, and hybrid access strategies, are applied to multichannel CRNs. In this paper, we present a generalized access strategy in a multichannel CRN smart home environment, in which a secondary user (SU) system selects part of channels for sequential spectrum sensing, and accesses these channels based on the sensing results. Moreover, it accesses the remaining channels directly. We then formulate a two-phase optimization framework, which takes the sensing channel selection, sensing time allocation, and the power allocation into consideration, to maximize the gross average throughput of the multichannel CRN. In the sensing phase, a generalized access strategy algorithm (GAS) is first proposed, where we prove that only part of channels needs to be selected for spectrum sensing to achieve the maximum throughput. An optimal stopping rule is proposed to determine the optimal number of selected sensing channels. In addition, a completed hybrid access strategy algorithm is further investigated where the SU system senses all channels. An approximation algorithm is also presented to achieve suboptimal results with low computational complexity. In the transmission phase, the transmission powers of all channels are optimized via convex algorithms. Numerical experiments show that, compared with the existing schemes, the proposed schemes are able to achieve considerable throughput improvement.

Current commercial mobile streaming applications call for innovative technologies for stable QoS guarantee. In this paper, we provide a comprehensive treatment of QoS guarantee through a contract-ruled approach. In particular, we envision a peer-assisted mobile peer-to-peer streaming system as a QoS trading market, where all parties involved in the system, i.e., Service Provider (SP), End User (EU), and Assisting Peers (APs), are real economic entities that are organized with contractual constraints to achieve a stable and guaranteed QoS output. The QoS trading in the market is divided into two parts. One is a basic contract that establishes the business agreement between an interested EU and a SP. We propose a QoS contingent payment to mitigate the EU's concern on the uncertainty of QoS delivery and derive an optimal contract that achieves Pareto efficiency. The other is a subcontract, in which we model transactions between the SP and contracted peers as a principal multi-agents problem, that achieves a desired joint QoS output. We further design a sharing scheme with team penalty that could overcome the free-riding problem existed in the subcontract and show that the Pareto efficiency can be achieved by setting a proper team penalty. Both numerical evaluations and prototype experiments demonstrate the effectiveness of our proposed scheme.

Wireless networks are propelled to improve the network throughput effectively to face the challenge of sustaining the rapid growth of data traffic and the high density of wireless nodes. Exposed terminals are a main source in wireless networks that degrades the network throughput performance through excessively avoiding interferences and forbidding concurrencies. To combat the exposed terminal problem and exploit the concurrent transmissions in wireless networks, we present the design of Interference Resistant Multiple Access (IRMA) in this paper, which can achieve higher throughput compared to the 802.11 standard. By observing that nodes in current protocols waste transmission opportunities in two different scenarios, IRMA exploits the concurrency in two aspects. IRMA proposes a signature detection method in the physical layer to combat control frames' collisions, thus exploits the concurrency at the transmitter side. IRMA also designs a new NAV update scheme in the MAC layer to differentiate the interference ranges of different transmission links, thus exploits the concurrency of all non-interfering links. Experimental results based on USRP2 demonstrate the feasibility of the signature detection method, and simulations based on ns-2 show that IRMA outperforms the 802.11 standard and other protocols significantly.

When studying the real-time pricing in smart grids, there is a common assumption that a perfect bidirectional communication network is built between supply and demand ends. However, this assumption is unrealistic, especially in a microgrid system which includes renewable energy resources. In this paper, we consider the uncertainty of imperfect communications in both supply and demand ends, which affects the performance of the microgrid system in terms of the packet loss ratio of the power demand information transmission and the forecasting accuracy ratio of the renewable energy generation. We analyze the impacts of unreliable communications on the demand response management (DRM) performance of real-time pricing in the microgrid system. A joint optimization problem is therefore formulated in order to maximize the DRM performance of the microgrid system. Leveraging the dual decomposition, the proposed optimization problem is solved via two sub-problems. Simulation results reveal the impacts of imperfect communications on the DRM performance.

Communications security is a critical and increasingly challenging issue in wireless networks. A well-known approach for achieving information-theoretic secrecy relies on deploying artificial noises to blind the intruders' interception in the physical layer. However, this approach requires a static channel condition for the transmitter and receiver to generate and offset the controllable artificial noise, which can hardly be implemented in real wireless environments. In this paper, we explore the feasibility of symbol obfuscation to defend against the passive eavesdropping attack and fake packet injection attack during the wireless communications. We propose a multiple inter-symbol obfuscation (MIO) scheme, which utilizes a set of artificial noisy symbols (symbols key) to obfuscate the original data symbols in the physical layer. MIO can effectively enhance the wireless communications security. On the one hand, an eavesdropper, without knowing the artificial noisy symbols, cannot correctly decrypt the obfuscated symbols from the eavesdropped packets. On the other hand, a legitimate receiver can easily check the integrity of the symbols key and then reject the fake packets from the received packets. The security analysis reveals that, without considering the initial key, the MIO scheme can achieve information-theoretic secrecy against the passive eavesdropping attack and computational secrecy against the fake packet injection attack. Moreover, we have implemented our approach in a USRP2 testbed and conducted simulations with Simulink tools to validate the effectiveness of MIO in enhancing wireless communications security.

Abstract Wormhole attack is a severe attack that can be easily mounted on a wide range of wireless networks without compromising any cryptographic entity or network node. In the wormhole attack, an attacker sniffs packets at one point in the network and tunnels them through the wormhole link to another point. Such kind of attack can deteriorate the localization procedure in wireless sensor networks. In this paper, we first analyze the impacts of the wormhole attack on the localization procedure. Then, we propose a secure localization scheme against the wormhole attacks called SLAW including three phases: wormhole attack detection, neighboring locators differentiation, and secure localization. The main idea of the SLAW is to build a so‐called conflicting set for each locator based on the abnormalities during the message exchanges, which can be used to differentiate the dubious locators to achieve secure localization. We first consider the simplified system model in which there is no packet loss and all the nodes have the same transmission range. We further consider the general system model where the packet loss exists and different types of nodes have different transmission radii. We conduct the simulations to illustrate the effectiveness of the proposed secure localization scheme and compare it with the existing schemes under different network parameters. Copyright © 2014 John Wiley & Sons, Ltd.

The explosive growth of mobile applications causes the mobile traffic to easily exceed the capacity of the cloud service due to the bandwidth limits of last mile connections to the cloud and legacy backhauls to macrocells' base stations. It degrades mobile applications' quality of service since the mobile devices have to spend more time and thus consume more battery power for data transmissions. It also enforces the cloud provider to put a huge investment to update its infrastructure and the induced cost is inevitably borne by all mobile users. To resolve this issue, in this paper we propose a so-called community clinic solution, which embeds the cloudlet group between the cloud and mobile users, to cut down the cost introduced by the massive deployment of the cloud's data centers and save the battery power consumed by the mobile devices. We firstly show that the mobile devices can consume less energy by choosing the service provided by the cloudlet group. We then model the system with and without the cloudlet group as two types of supply chain and prove that the cloudlet group can increase the cloud's profit without putting additional cost on mobile users. We also propose the real-time group-buying auction for the cloudlet group to promote its service to its nearby mobile users with a lower price and maximize its profit. The community clinic can result in a win-win-win outcome among the cloud, cloudlet group and mobile users. Numerical experiments are further conducted to demonstrate the effectiveness of our scheme.

Energy efficiency is known to be a critical issue of wireless devices. Our analysis reveals that the energy cost on overhearing unnecessary packet transmissions accounts for the majority of unnecessary energy consumption of wireless devices in high traffic wireless LANs. In this paper, we propose a novel SASD mechanism to eliminate the energy inefficiency of the packet overhearing problem. By adding a new header that carries some information in front of the normal packet frame at the PHY layer, SASD enables the wireless devices to discern the required information under the energy-saving downclocking mode so that the devices, whenever not addressed, can switch to the sleeping mode to save energy. Our simulation results show that SASD can effectively save the energy cost by avoiding the packet overhearing in high traffic wireless LANs.

Wireless sensor networks (WSNs) consist of a large number of battery-powered wireless sensor nodes, and one key issue in WSNs is to reduce the energy consumption while maintaining the normal functions of WSNs. Data aggregation, as a typical operation in data gathering applications, can cause a lot of energy wastage since sensor nodes, when not receiving data, may keep in the listen state during the data collection process. To save this energy wastage, sleep scheduling algorithms can be used to turn the nodes to the sleep state when their radios are not in use and wake them up when necessary. In this paper, we identify the contiguous link scheduling problem in WSNs, in which each node is assigned consecutive time slots so that the node can wake up only once in a scheduling period to fulfil its data collection task. The objective of the problem is to find an interference-free link scheduling with the minimum number of time slots used. In virtue of the contiguous link scheduling, the energy consumption caused by nodes' state transitions can be reduced. We prove the contiguous link scheduling problem in WSNs to be NP-complete, and then present efficient centralized and distributed algorithms with theoretical performance bounds in both homogeneous and heterogeneous networks. We also conduct simulation experiments that corroborate the theoretical results and demonstrate the efficiency of our proposed algorithms.

In traditional Delay Tolerant Networks (DTNs), the nodes can take advantage of intermittent contact opportunities to forward messages. However, in noncooperative DTNs, the nodes may be selfish and reluctant to cooperate with each other in message forwarding. Therefore, it is necessary to motivate the nodes to cooperate with each other in such DTNs. The previously proposed incentive mechanisms have obvious limitations such as the security vulnerability. To resolve such kind of drawbacks, in this paper we propose two credit-based rewarding schemes, called earliest path singular rewarding scheme and earliest path cumulative rewarding scheme respectively, to ensure the nodes truthfully forward the messages. The proposed rewarding schemes are incentive compatible. They also ensure that the payment for each delivered message is upper bounded. Furthermore, the proposed rewarding schemes can prevent selfish nodes from having malicious behaviors. Simulations based on the real trace are conducted to illustrate the effectiveness of the proposed rewarding schemes.

Opportunistic networks are a new evolution of mobile ad hoc networks composed of intermittently connected nodes, in which the routing for the dynamic topology is a challenging issue. In opportunistic networks, the mobile nodes with common interest or close relationship may form into groups and move together, which brings us a good feature to employ when designing the routing protocol for opportunistic networks under resource constraints. Our main idea in this paper is to maximize the message delivery probability in a group-aware opportunistic network under the constraints of bandwidth and buffer space. Embedded this idea, we propose a cooperative routing protocol using the group feature, which includes the cooperative message transfer scheme and the buffer management strategy. In the cooperative message transfer scheme, the limited bandwidth is considered and the message transfer priorities are designed to maximize the delivery probability. In the buffer management strategy, by considering the constraint of buffer space, we propose the cooperative message caching scheme and the dropping order of the messages is designed to minimize the reduced delivery probability. Finally, we conduct the simulations to demonstrate the effectiveness of our proposed routing protocol.

Hidden terminals are typical interference sources that can significantly reduce the throughput of a wireless network if it adopts the CSMA/CA MAC protocol. The RTS/CTS mechanism is a well-known solution to this hidden terminal problem. However, it only works well under the assumption that all hidden terminals can decode the CTS packets correctly. In the real world, the CTS packets might not be correctly received all the time due to either the CTS packets are unable to be decoded at remote hidden terminals or the CTS packets are collided with other packets at the hidden terminals. Both of these drawbacks can make the standard RTS/CTS mechanism fail to silence all hidden terminals, and deteriorate the throughput of the wireless network. In this paper, we present the RTS/S-CTS mechanism, a novel symbol-level detection mechanism that combats these two drawbacks. The RTS/S-CTS frames make slight changes to the standard RTS/CTS frames, and can be compatible with the standard 802.11 MAC layer. We design the symbol-level detection decoder (SLDD) and NAV decision algorithm that enable the S-CTS frame to be correctly detected from collisions and by remote hidden terminals. We build a testbed of RTS/S-CTS with GNURadio/USRP2 software radio to demonstrate its feasibility and run ns-2 simulations to evaluate its performance. The results show that the RTS/S-CTS can achieve up to 63% throughput improvement in the random topology network scenario compared with the standard RTS/CTS.

Peer-to-peer (P2P) streaming applications have led to the disharmony among the involved parties: Content Service Providers (CSPs), Internet Service Providers (ISPs) and P2P streaming End-Users (EUs). This disharmony is not only a technical problem at the network aspect, but also an economic problem at the business aspect. To handle this tussle, this paper proposes a profitable business model to enable all involved parties to enlarge their benefits with the help of a novel QoS-based architecture integrated with caching techniques. We model the interactions, including competition and innovation, among CSPs, ISPs and EUs as a tripartite game by introducing a pricing scheme, which captures both network and business aspects of the P2P streaming applications. We study the tripartite game in different market scenarios as more and more ISPs and CSPs involve into the market. A three-stage Stackelberg game combining with Cournot game is proposed to study the interdependent, interactive and competitive relationship among CSPs, ISPs and EUs. Moreover, we investigate how the market competition motivates ISPs to upgrade the cache service infrastructure. Our theoretical analysis and empirical study both show that the tripartite game can result in a win-win-win outcome. The market competition plays an important role in curbing the pricing power of CSPs and ISPs, and this effect is more remarkable when the amounts of CSPs and ISPs become infinite. Interestingly, we find that in the tripartite game there exists a longstop at which ISPs may have no incentive to upgrade the cache service infrastructure. However, increasing the market competition level can propel the innovation of ISPs.

In this paper, we provide a comprehensive treatment of QoS guarantee for mobile streaming applications through a contract-ruled approach. We envision a peer-to-peer streaming system as a QoS trading market, where the involved parties, Services Provider (SP), End User (EU) and assisting peers, are all real economic entities that are organized with contractual constraints for achieving a stable and guaranteed QoS output. The QoS trading in the market is classified into two parts, a basic contract that establishes the business agreement between an interested EU and a SP and a subcontract that achieves a desired joint QoS output. The proposed scheme can benefit all parties.

This paper represents the design of Interference Resistant Multiple Access (IRMA), a novel cross layer protocol that can combat exposed terminals and exploit the concurrent transmissions in wireless networks, both helping in achieving higher throughput compared to the 802.11 standard and other recent protocols. By observing that nodes in current protocols waste transmission opportunities in two different scenarios, IRMA exploits the concurrency in two aspects. IRMA proposes a signature detection method in the physical layer to combat control frames' collisions, thus exploits the concurrency at the transmitter side. IRMA also designs a new NAV update scheme in the MAC layer to differentiate the interference ranges of different transmission links, thus exploits the concurrency of all non-interfering links. In addition, IRMA is based on the 802.11 standard so that it can be implemented into current devices. Experimental results based on USRP2 demonstrate the feasibility of the signature detection method, and simulations based on NS2 show that IRMA outperforms other protocols significantly.

Broadcast is an essential and widely used operation in multihop wireless networks. Minimum latency broadcast scheduling (MLBS) aims to find a collision-free scheduling for broadcast with the minimum latency. Previous work on MLBS mostly assumes that nodes are always active, and, thus, is not suitable for duty-cycled scenarios. In this paper, we investigate the MLBS problem in duty cycled multihop wireless networks (MLBSDC problem). We prove both the one-to-all and the all-to-all MLBSDC problems to be NP hard. We propose a novel approximation algorithm called OTAB for the one-to-all MLBSDC problem, and two approximation algorithms called UTB and UNB for the all-to-all MLBSDC problem under the unit-size and the unbounded-size message models, respectively. The approximation ratios of the OTAB, UTB, and UNB algorithms are at most 17|T|, 17|T| + 20, and (Δ + 22)|T|, respectively, where |T| denotes the number of time slots in a scheduling period, and Δ denotes the maximum node degree of the network. The overhead of our algorithms is at most constant times as large as the minimum overhead in terms of the total number of transmissions. We also devise a method called Prune to further reduce the overhead of our algorithms. Extensive simulations are conducted to evaluate the performance of our algorithms.

Conventional routing algorithms rely on the existence of persistent end-to-end paths for the delivery of a message to its destination via a predesigned path. However, in a delay tolerant network (DTN), nodes are intermittently connected, and thus the network topology is dynamic in nature, which makes the routing become one of the most challenging problems. A promising solution is to predict the nodes' future contacts based on their contact histories. In this paper, we first propose an expected encounter based routing protocol (EER)which distributes multiple replicas of a message proportionally between two encounters according to their expected encounter values. In case of single replica of a message, EER makes the routing decision by comparing the minimum expected meeting delay to the destination. We further propose a community based routing protocol(CR) which takes advantages of the high contact frequency property of the community. The simulations demonstrate the effectiveness of our proposed routing protocols under different network parameters.

In Vehicular Ad Hoc Networks (VANETs), the major communication challenge lies in very poor connectivity, which can be caused by sparse or unbalanced traffic. Deploying supporting infrastructure could relieve this problem, but it often requires a large amount of investment and elaborate design, especially at the city scale. In this paper, we propose the idea of Parked Vehicle Assistance (PVA), which allows parked vehicles to join VANETs as static nodes. With wireless device and rechargable battery, parked vehicles can easily communicate with one another and their moving counterparts. Owing to the extensive parking in cities, parked vehicles are natural roadside nodes characterized by large number, long-time staying, wide distribution, and specific location. So parked vehicles can serve as static backbone and service infrastructure to improve connectivity. We investigate network connectivity in PVA through theoretic analysis and realistic survey and simulations. The results prove that even a small proportion of PVA vehicles could overcome sparse or unbalanced traffic, and promote network connectivity greatly. Thus, PVA enhances VANETs from down to top, and paves the way for new hybrid networks with static and mobile nodes.

In this paper we model the digital rights management (DRM) for peer-to-peer streaming (P2PS) systems as a game. We construct the DRM game from both content service provider (CSP) and user's aspects, and propose a design of DRM policy based on homogeneous peers and homogeneous digital goods, which gets the maximal utility for the CSP as well as the criterion whether the DRM is fit for a P2PS system. Another sort of games in this paper consider how a peer deals with digital goods with regard to various situations in P2PS systems with DRM, together with the CSP's response to the peer's actions. We construct different games to avoid three notorious misbehaviors of peers: freeriding, jailbreaking and whitewashing. We take examples to show how these games work in P2PS systems with DRM and how equilibria are established in these games. Numerical experiments are conducted to demonstrate the effectiveness of the strategies devised from these games.

In this work, we design efficient algorithms for scheduling node activities, under the physical interference model, to minimize the delay for activating a set of communication links, or for finishing a data aggregation communication task. Given a set of communication links, assume that each link is associated with a positive weight (representing the award of transmission along this link). We consider two problems: the first one is to find an independent set of links with maximum total weight; the second one is to partition all links into independent subsets, such that the number of subsets is minimized. We are the first to develop distributed algorithms with constant approximations for both problems respectively. The other line of this work is to explore the relations between link scheduling and an important practical problem: Minimum Latency Aggregation Scheduling which seeks a shortest schedule for data aggregation in multi-hop wireless networks. By utilizing the algorithmic results for link scheduling, our proposed method can find an aggregation schedule that greatly improves the upper bound on latency, compared to the previous best result.

In underwater sensor networks (UWSNs), acoustic communication is commonly used unlike that in terrestrial wireless networks. The long propagation delay of acoustic signals causes spatio-temporal uncertainty, which makes the link scheduling in UWSNs a challenging problem. To describe the propagation delays of the transmission links and deal with the spatio-temporal uncertainty, we construct a so called slotted spatio-temporal conflict graph. We propose efficient scheduling algorithms with constant approximation ratios to the optimum solutions. We consider both unified and weighted traffic load scenarios when designing the scheduling algorithms. In the weighted traffic load scenario, we consider the scheduling with and without the consecutive constraint. Simulations validate our theoretical results, and show the efficiency of our proposed algorithms.

In a traditional wakeup scheduling, sensor nodes start up numerous times to communicate in a period, thus consuming extra energy due to state transitions (e.g. from the sleep state to the active state). In this paper, we address a novel wakeup scheduling problem called compact wakeup scheduling, in which a node needs to wake up only once to communicate bidirectionally with all its neighbors. However, not all communication graphs have valid compact wakeup schedulings, and thus we focus on tree and grid topologies that have valid compact wakeup schedulings. We propose polynomial-time algorithms using the optimum number of time slots in a period for tree and grid topologies.

Broadcast is an essential and widely-used operation in multi-hop wireless networks. Minimum latency broadcast scheduling (MLBS) aims to provide a collision-free scheduling for broadcast with the minimum latency. Previous work on MLBS mostly assumes that nodes are always active, and thus is not suitable for duty-cycle-aware scenarios. In this paper, we investigate the duty-cycle-aware minimum latency broadcast scheduling (DCA-MLBS) problem in multi-hop wireless networks. We prove both the one-to-all and the all-to-all DCA-MLBS problems to be NP-hard. We propose a novel approximation algorithm called OTAB for the one-to-all DCA-MLBS problem, and two approximation algorithms called UTB and UNB for the all-to-all DCA-MLBS problem under the unit-size and the unbounded-size message models respectively. The OTAB algorithm achieves a constant approximation ratio of 17|T|, where |T| denotes the number of time-slots in a scheduling period. The UTB and UNB algorithms achieve the approximation ratios of 17|T|+20 and (Δ+22)|T| respectively, where Δ denotes the maximum node degree of the network. Extensive simulations are conducted to evaluate the performance of our algorithms.

Information interaction is a crucial part of modern transportation activities. In this paper, we propose the idea of Vehicle-to-Passenger communication (V2P), which allows direct, instant, and flexible communication between moving vehicles and roadside passengers. With pocket wireless devices, passengers can easily join VANETs as roadside nodes, and express their travel demands, e.g., taking a free ride or calling a taxi via radio queries over VANETs. Once a matched vehicle is found through the disseminated queries, the driver can decide whether to provide corresponding services, especially the carrying of passengers and goods. We investigate the main challenges in vehicle calling, establish a trip history model to predict vehicle movement, and develop typical query dissemination schemes to match the target vehicle in vehicular networks. With V2P over VANETs, vehicle transportation is capable of open and efficient P2P information interaction, and thus benefits from relevant efficiency improvement. Based on a realistic travel survey and simulation, we prove that vehicle calling is effective and efficient in casual carpooling and taxi calling.

Wireless sensor networks (WSNs) are often deployed in hostile environments for specific applications from mobile objects monitoring to data collecting. By eavesdropping the sensor nodes' transmissions and tracing the packets' trajectories in the WSNs, an adversary can capture the location of a source or sink eventually. Thus, the location privacy of both source and sink becomes a significant issue in WSNs. Previous research only focuses on the location privacy of the source or sink independently. In this paper, we address the importance of location privacy of both source and sink and propose four schemes to protect them simultaneously. Simulation results illustrate the effectiveness of our proposed schemes.

The wormhole attack is a severe attack that can be easily mounted on a wide range of wireless networks without compromising any cryptographic quantity or network node. In the wormhole attack, an attacker sniffs packets at one point in the network, tunnels the packets through a wired or wireless link to another point. Such kind of attack can cause severe problems in wireless sensor networks, especially deteriorate the routing process and the localization process. In this paper, we propose a secure localization scheme against wormhole attacks, which includes three phases: wormhole attack detection, neighboring locators differentiation and secure localization. The main idea of the proposed secure localization scheme is to build a so-called conflicting set for each locator according to the abnormalities of message exchanges among neighboring locators, which is used to differentiate the dubious locators from valid locators for the secure localization. The simulation results show that the proposed scheme outperforms the existed schemes under different network parameters.

Node localization becomes an important issue in the wireless sensor network as its broad applications in environment monitoring, emergency rescue and battlefield surveillance, etc. Basically, the DV-Hop localization mechanism can work well with the assistance of beacon nodes that have the capability of self-positioning. However, if the network is invaded by a wormhole attack, the attacker can tunnel the packets via the wormhole link to cause severe impacts on the DV-Hop localization process. The distance-vector propagation phase during the DV-Hop localization even aggravates the positioning result, compared to the localization schemes without wormhole attacks. In this paper, we analyze the impacts of wormhole attack on DV-Hop localization scheme. Based on the basic DV-Hop localization process, we propose a label-based secure localization scheme to defend against the wormhole attack. Simulation results demonstrate that our proposed secure localization scheme is capable of detecting the wormhole attack and resisting its adverse impacts with a high probability.

A sensor in wireless sensor networks (WSNs) periodically produces data as it monitors its vicinity. The basic operation in such a network is the systematic gathering (with or without in-network aggregation) and transmitting of sensed data to a base station for further processing. A key challenging question in WSNs is to schedule nodes' activities to reduce energy consumption. In this paper, we focus on designing energy-efficient protocols for low-data-rate WSNs, where sensors consume different energy in different radio states (transmitting, receiving, listening, sleeping, and being idle) and also consume energy for state transition. We use TDMA as the MAC layer protocol and schedule the sensor nodes with consecutive time slots at different radio states while reducing the number of state transitions. We prove that the energy consumption by our scheduling for homogeneous network is at most twice of the optimum and the timespan of our scheduling is at most a constant times of the optimum. The energy consumption by our scheduling for heterogeneous network is at most ¿ (log Rmax/Rmin) times of the optimum. We also propose effective algorithms to construct data gathering tree such that the energy consumption and the network throughput is within a constant factor of the optimum. Extensive simulation studies show that our algorithms do considerably reduce energy consumption.

Sleep scheduling is a widely used mechanism in wireless sensor networks (WSNs) to reduce the energy consumption since it can save the energy wastage caused by the idle listening state. In a traditional sleep scheduling, however, sensors have to start up numerous times in a period, and thus consume extra energy due to the state transitions. The objective of this paper is to design an energy efficient sleep scheduling for low data-rate WSNs, where sensors not only consume different amounts of energy in different states (transmit, receive, idle and sleep), but also consume energy for state transitions. We use TDMA as the MAC layer protocol, because it has the advantages of avoiding collisions, idle listening and overhearing. We first propose a novel interference-free TDMA sleep scheduling problem called contiguous link scheduling, which assigns sensors with consecutive time slots to reduce the frequency of state transitions. To tackle this problem, we then present efficient centralized and distributed algorithms that use time slots at most a constant factor of the optimum. The simulation studies corroborate the theoretical results, and show the efficiency of our proposed algorithms.

Unlike convectional omnidirectional sensors that always have an omni-angle of sensing range, directional sensors may have a limited angle of sensing range due to the technical constraints or cost considerations. A directional sensor network consists of a number of directional sensors, which can switch to several directions to extend their sensing ability to cover all the targets in a given area. Power conservation is still an important issue in such directional sensor networks. In this paper, we address the multiple directional cover sets (MDCS) problem of organizing the directions of sensors into a group of non-disjoint cover sets to extend the network lifetime. One cover set in which the directions cover all the targets is activated at one time. We prove the MDCS to be NP-complete and propose several algorithms for the MDCS. Simulation results are presented to demonstrate the performance of these algorithms.

In wireless ad-hoc sensor networks, an important issue often faced in geographic greedy forwarding routing is the "local minimum phenomenon" which is caused by deployment holes and blocks the forwarding process. In this paper, we provide a new information model for the geographic greedy forwarding routing that only forwards the packet within the so-called request zone. Under this new information model, the hole and its affected area are identified easily and quickly in an unsafe area with a labeling process. The greedy forwarding will be blocked if and only if a node inside the unsafe area is used. Due to the shape of the request zone, an unsafe area can be estimated as a rectangular region in the local view of unsafe nodes. With such estimate information, the new routing method proposed in this paper will avoid blocking by holes and achieve better performance in routing time while the cost of information construction is greatly reduced compared with the best results known to date.

Unlike convectional omni-directional sensors that always have an omni-angle of sensing range, directional sensors may have a limited angle of sensing range due to technical constraints or cost considerations. A directional sensor network consists of a number of directional sensors, which can switch to several directions to extend their sensing ability to cover all the targets in a given area. Power conservation is still an important issue in such directional sensor networks. In this paper, we address the multiple directional cover sets problem (MDCS) of organizing the directions of sensors into a group of non-disjoint cover sets to extend the network lifetime. One cover set, in which the directions cover all the targets, is activated at one time. We prove the MDCS to be NP-complete and propose three heuristic algorithms for the MDCS. Simulation results are also presented to demonstrate the performance of these algorithms.

The broadcast operation, as a fundamental service in mobile ad hoc networks (MANETs), is prone to the broadcast storm problem if forwarding nodes are not carefully designated. The objective of reducing broadcast redundancy while still providing high delivery ratio under high transmission error rate is a major challenge in MANETs. In this paper, we propose a simple broadcast algorithm, called double-covered broadcast (DCB), which takes advantage of broadcast redundancy to improve the delivery ratio in an environment that has rather high transmission error rate. Among the 1-hop neighbors of the sender, only selected forwarding nodes retransmit the broadcast message. Forwarding nodes are selected in such a way that 1) the sender's 2-hop neighbors are covered and 2) the sender's 1-hop neighbors are either forwarding nodes or nonforwarding nodes covered by at least two forwarding neighbors. The retransmissions of the forwarding nodes are received by the sender as the confirmation of their reception of the packet. The nonforwarding 1-hop neighbors of the sender do not acknowledge the reception of the broadcast. If the sender does not detect all its forwarding nodes' retransmissions, it resends the packet until the maximum number of retries is reached. Simulation results show that the proposed broadcast algorithm provides good performance under a high transmission error rate environment

Multipoint relays (MPR) provide a localized and optimized way of broadcasting messages in a mobile ad hoc network (MANET). Using partial 2-hop information, each node chooses a small set of forward neighbors to relay messages and this set covers the node's 2-hop neighbor set. These selected forward nodes form a connected dominating set (CDS) to ensure full coverage. Adjih et al. later proposed a novel extension of MPR to construct a small CDS and it is source-independent. In this paper, we provide several extensions to generate a smaller CDS using complete 2-hop information to cover each node's 2-hop neighbor set. We extend the notion of coverage in the original MPR. We prove that the extended MPR has a constant local approximation ratio compared with a logarithmic local ratio in the original MPR. In addition, we show that the extended MPR has a constant global probabilistic approximation ratio, while no such ratio exists in the original MPR and its existing extensions. The effectiveness of our approach is confirmed through a simulation study.

Most proposed routing protocols in mobile ad hoc networks (MANETs) utilize neighbor set information to assist their routing decisions. 1-hop and 2-hop neighbor set information are the cases most commonly used. K-hop neighbor set, where K/spl ges/3, is seldom discussed in the literature. We discuss the broadcast problem in MANETs considering the generic K-hop neighbor set. The proposed K-hop zone-based broadcast protocol is a simple, scalable protocol. The main purpose of this study is to provide a generic framework for a broadcast operation with K-hop information and to determine the potential performance improvement by increasing the value K.

Mobile ad hoc networks (MANETs) suffer from high transmission error rate because of the nature of radio communications. The broadcast operation, as a fundamental service in MANETs, is prone to the broadcast storm problem if forward nodes are not carefully designated. The objective of reducing the broadcast redundancy while still providing high delivery ratio for each broadcast packet is a major challenge in a dynamic environment. We propose a simple, reliable broadcast algorithm, called double-covered broadcast (DCB), that takes advantage of broadcast redundancy to improve the delivery ratio in the environment that has rather high transmission error rate. Among 1-hop neighbors of the sender, only selected forward nodes retransmit the broadcast message. Forward nodes are selected in such a way that (1) the sender's 2-hop neighbors are covered and (2) the sender's 1-hop neighbors are either a forward node, or a nonforward node but covered by at least two forwarding neighbors. The retransmissions of the forward nodes are received by the sender as confirmation of their receiving the packet. The nonforward 1-hop neighbors of the sender do not acknowledge the reception of the broadcast. If the sender does not detect all its forward nodes' retransmissions, it will resend the packet until the maximum times of retry is reached. Simulation results show that the algorithm provides good performance for a broadcast operation under high transmission error rate environment.

MPR (multipoint relays) (A. Qayyum et al., Jan. 2002) provides a localized and optimized way of broadcasting messages in a mobile ad hoc network (MANET). Using 2-hop neighborhood information, each node determines a small set of forward neighbors to relay messages. Selected forward nodes form a connected dominating set (CDS) to ensure full coverage. Adjih, Jacquet, and Viennot (2002) later proposed a novel localized algorithm to construct a small CDS based on the original MPR without any broadcast information. Such an approach is called source-independent or broadcast-independent. In this paper, we provide several extensions of the source-independent MPR to generate a smaller CDS using 3-hop neighborhood information to cover each node's 2-hop neighbor set. In addition, we extend the notion of coverage in the original MPR. We show that the extended MPR has a constant local approximation ratio compared with a logarithmic local ratio in the original MPR. The effectiveness of our approach is confirmed through a simulation study.

Mobile ad hoc networks (MANETs) suffer from transmission contention and congestion because of the broadcast nature of radio transmission. The broadcast operation, as a fundamental service in MANETs, will cause the broadcast storm problem if the forward nodes are not carefully managed. It is a major challenge to reduce broadcast redundancy while still providing high delivery ratio for each broadcast packet in a dynamic environment. In this paper, we propose a simple broadcast algorithm to provide high delivery ratio. Among the 1-hop neighbors of the sender, only selected forward nodes will send acknowledgements to confirm their receipt of the packet. Forward nodes are selected in such a way that all the senders 2-hop neighbors are covered. Moreover, no acknowledgment is needed from non-forward 1-hop neighbors, each of which is covered by at least two forward neighbors. The sender waits for the acknowledgements from all of its forward nodes. If not all acknowledgments are received, the sender will resend the packet until the maximum number of retries is reached. Simulation results show that the algorithm has high delivery ratio and low end-to-end delay for a broadcast operation.

Abstract A taxonomy of broadcast protocols in mobile ad hoc networks (MANETs) is given where protocols are classified into four groups: global, quasi‐global, quasi‐local, and local. The taxonomy also divides protocols based on the nature of algorithms: probabilistic and deterministic. The locality of maintenance also plays an important role in evaluating the protocol. An important objective in designing a broadcast protocol is to reduce broadcast redundancy to save scarce resources such as energy and bandwidth and to avoid the broadcast storm problem. This objective should be achieved without introducing excessive overhead and time delay, measured by sequential rounds of information exchanges. This is done by choosing a small forward node set that forms a connected dominating set (CDS) to carry out a broadcast process. In this paper, a clustered network model is proposed in which each node is a clusterhead in the clustered architecture. Clusterheads are connected by carefully selecting non‐clusterhead nodes locally at each clusterhead to connect clusterheads within the 2.5‐hop coverage, a novel notion proposed in this paper. Information of neighbor clusterheads is piggybacked on the broadcast packet to further reduce each forward node set. It is shown that this approach is quasi‐local with locality of maintenance. In addition, this approach has a constant approximation ratio to the minimum connected dominating set (MCDS) and generates a small forward node set in the average case. Comparisons are also done through simulation with representative protocols from each of the four groups of protocols based on the proposed taxonomy. We also simulate the proposed broadcast protocol in a dynamic network and analyze its performance. Copyright © 2003 John Wiley & Sons, Ltd.

The clustered architecture of a mobile ad hoc network (MANET) has the virtue of keeping the node information locally which is suitable for scalability. Reducing broadcast redundancy can avoid the broadcast storm problem and save scarce resources such as bandwidth and energy. We propose an approach that chooses a subset of nodes, called forward node set, to relay the broadcast packet. Each clusterhead computes its forward node set that connects its adjacent clusterheads. A non-clusterhead node just relays the broadcast packet if it is selected as a forward node or else it does nothing. Therefore, the broadcast operation can be restricted only to clusterheads and nodes in locally selected forward node sets. We also utilize the information of clusterheads that are piggybacked with the broadcast packet to further reduce each forward node set. Simulation shows its performance improvement against other broadcast algorithms.

Unlike in a wired network, a packet transmitted by a node in an ad hoc wireless network can reach all neighbors. Therefore, the total number of transmissions (forward nodes) is generally used as the cost criterion for broadcasting. The problem of finding the minimum number of forward nodes is NP-complete. Among various approximation approaches, dominant pruning (Lim and Kim 2001) utilizes 2-hop neighborhood information to reduce redundant transmissions. In this paper, we analyze some deficiencies of the dominant pruning algorithm and propose two better approximation algorithms: total dominant pruning and partial dominant pruning. Both algorithms utilize 2-hop neighborhood information more effectively to reduce redundant transmissions. Simulation results of applying these two algorithms show performance improvements compared with the original dominant pruning. In addition, two termination criteria are discussed and compared through simulation under both the static and dynamic environments.

An automatic repeat request (ARQ) scheme using rate compatible trellis coded modulation, pilot-symbol aided channel estimation, soft output packet combining, and a retransmission request strategy supporting a variable quality of service within a packet is proposed. Simulation results show that this type II hybrid ARQ has about 3-5 dB SNR gain over its type I ARQ counterpart.

This paper develops algorithms for using the lapped orthogonal transforms (LOT) in the multiple bases representation (MBR) of non-stationary signals. We have previously developed speech algorithms using nonlapped mixed transforms. The major motivation for using lapped orthogonal transforms is their ability to eliminate blocking effects which show up as discontinuities in the reconstructed signal at the block boundaries. In addition, LOT based transforms improve the transform coding gain considerably. We test and compare the performance of the LOT in MBR algorithms that use a previously developed cascade structure to our newly developed parallel structure. We also discuss different dominant component (DC) picking strategies and introduce some new methods. Results and subjective evaluations are provided for real speech signals.