Model merging has emerged as an efficient technique for expanding large language models (LLMs) by integrating specialized expert models. However, it also introduces a new threat: model merging stealing, where free-riders exploit models through unauthorized model merging. Unfortunately, existing defense mechanisms fail to provide effective protection. Specifically, we identify three critical protection properties that existing methods fail to simultaneously satisfy: (1) proactively preventing unauthorized merging; (2) ensuring compatibility with general open-source settings; (3) achieving high security with negligible performance loss. To address the above issues, we propose MergeBarrier, a plug-and-play defense that proactively prevents unauthorized merging. The core design of MergeBarrier is to disrupt the Linear Mode Connectivity (LMC) between the protected model and its homologous counterparts, thereby eliminating the low-loss path required for effective model merging. Extensive experiments show that MergeBarrier effectively prevents model merging stealing with negligible accuracy loss.

Retrieval-Augmented Generation (RAG) systems deployed over proprietary knowledge bases face growing threats from reconstruction attacks that aggregate model responses to replicate knowledge bases. Such attacks exploit both intra-class and inter-class paths—progressively extracting fine-grained knowledge within topics and diffusing it across semantically related ones, thereby enabling comprehensive extraction of the original knowledge base. However, existing defenses target only one path, leaving the other unprotected. We conduct a systematic exploration to assess the impact of protecting each path independently and find that joint protection is essential for effective defense. Based on this, we propose RAGFort, a structure-aware dual-module defense combining contrastive reindexing for inter-class isolation and constrained cascade generation for intra-class protection. Experiments across security, performance, and robustness confirm that RAGFort significantly reduces reconstruction success while preserving answer quality, offering the first comprehensive defense against knowledge base extraction attacks.

Multimodal large language models (MLLMs) have achieved significant results in various tasks, but their practical application is still severely constrained by hallucination issues, which are particularly prominent in reinforcement learning (RL) optimization processes. This paper systematically analyzes the causes of hallucinations in MLLM under RL training, identifying three key factors: (1) The model relies heavily on chained visual reasoning to guide decision-making during RL training. Thus, error and irrelevant information in visual reasoning can easily cause hallucinations, including inaccurate initial visual descriptions that anchor subsequent inferences to incorrect information, as well as redundant and broad inferential information; (2) Insufficient exploration diversity during the policy optimization phase, causing the model to output overly confident results; (3) The destructive conflict between different samples during optimization is a key factor that leads to false associations and unstable parameter updates. To address these issues, we propose a solution framework comprising three core modules. First, to improve the accuracy of visual localization, we add planning and caption stages before thinking and answer stages. To enhance initial visual descriptions ability, we allow LLMs to respond based solely on the caption and provide corresponding caption reward based on the quality of the response. Second, to enhance exploration capabilities, we classify samples based on the mean and variance of the reward distribution and select samples with high reward variance for training, thereby increasing the model's focus on diverse samples. Finally, to mitigate conflicts between training samples, we identify neural tangent kernel (NTK) similarity as the key factor. Rather than minimizing it uniformly, we regulate NTK similarity by grouping sample pairs based on a similarity threshold. An InfoNCE loss is then applied to pull dissimilar pairs closer and push overly similar ones apart, guiding interactions toward a balanced range. The experimental results demonstrate that the proposed method significantly reduces the hallucination rate and effectively improves the inference accuracy of MLLMs.

Given the high cost of large language model (LLM) training from scratch, safeguarding LLM intellectual property (IP) becomes increasingly crucial. As the standard paradigm for IP ownership verification, LLM fingerprinting thus plays a vital role in addressing this challenge. Existing LLM fingerprinting methods verify ownership by extracting or injecting model-specific features. However, they overlook potential attacks during the verification process, leaving them ineffective when the model thief fully controls the LLM's inference process. In such settings, attackers may share prompt-response pairs to enable fingerprint unlearning, or manipulate outputs to evade exact-match verification. We propose iSeal, the first fingerprinting method designed for reliable verification when the model thief controls the suspected LLM in an end-to-end manner. It injects unique features into both the model and an external module, reinforced by an error-correction mechanism and a similarity-based verification strategy. These components are resistant to verification-time attacks, including collusion-based fingerprint unlearning and response manipulation, backed by both theoretical analysis and empirical results. iSeal achieves 100% Fingerprint Success Rate (FSR) on 12 LLMs against more than 10 attacks, while baselines fail under unlearning and response manipulations.

As generative audio models are rapidly evolving, AI-generated audios increasingly raise concerns about copyright infringement and misinformation spread. Audio watermarking, as a proactive defense, can embed secret messages into audio for copyright protection and source verification. However, current neural audio watermarking methods focus primarily on the imperceptibility and robustness of watermarking, while ignoring its vulnerability to security attacks. In this paper, we develop a simple yet powerful attack: the overwriting attack that overwrites the legitimate audio watermark with a forged one and makes the original legitimate watermark undetectable. Based on the audio watermarking information that the adversary has, we propose three categories of overwriting attacks, i.e., white-box, gray-box, and black-box attacks. We also thoroughly evaluate the proposed attacks on state-of-the-art neural audio watermarking methods. Experimental results demonstrate that the proposed overwriting attacks can effectively compromise existing watermarking schemes across various settings and achieve a nearly 100% attack success rate. The practicality and effectiveness of the proposed overwriting attacks expose security flaws in existing neural audio watermarking systems, underscoring the need to enhance security in future audio watermarking designs.

Backdoor attacks in data outsourcing settings pose severe risks to deep neural networks. Specifically, adversaries can manipulate externally sourced training data to implant hidden behaviors in target models (e.g., incorrect predictions on triggered samples). Existing defenses are either pre-processing or post-processing. Since the two approaches are orthogonal and either one can independently strengthen real-world defenses, we focus on the latter in this paper. Yet current post-processing defenses face one or more of the following issues: overemphasis on output logits while overlooking rich information in intermediate layers, injection of uncertain new triggers while requiring alignment with the original triggers, and underuse of poisoned model representations. To overcome the aforementioned limitations, we propose ATClean, an adaptive post-processing defense based on feature collisions in latent space. Specifically, it leverages all layers rather than only output logits to capture backdoor-affected regions using an adaptive loss function, relaxes the need for exact trigger reconstruction by generating adversarial samples that only enforce feature collisions with a theoretical guarantee, and fully exploits poisoned representations with feature-collision-based fine-tuning. Experiments across benchmark datasets, multiple architectures, and seven representative attacks show that ATClean achieves state-of-the-art defense effectiveness with the lowest drop on clean data, including about a 20% improvement in DER, which measures the accuracy-defense trade-off.

In the above article [1], the email address and bio of Muhammad Rizwan are incorrect. The correct information is below:

Autonomous underwater vehicles (AUVs) have become a promising solution for data collection in underwater acoustic sensor networks (UASNs), and deep reinforcement learning (DRL) has been widely applied to enhance collection performance. However, our preliminary experiments indicate that existing DRL-based studies still face two critical challenges: 1) Collection blind spots. The sparse collection rewards and the requirement for energy-efficient trajectory planning during data collection jointly restrict AUVs' ability to explore and collect data from all sensor nodes (SNs), ultimately resulting in some SNs remaining uncollected. 2) Collection collisions. Simultaneous data collection by multiple AUVs can lead to packet collisions and collection failures, further decreasing the collection rate. To address these challenges, we propose ahierarchical DRL-basedcollision-freedatacollection scheme (HCDC). Specifically, we leverage a hierarchical DRL framework to decompose the multi-AUV-assisted data collection (MADC) problem into a high-level global target selection (GTS) and a low-level local trajectory planning (LTP) subproblems. For GTS, we design a multi-agent GTS (MA-GTS) algorithm to assign the next target SN for collection to each AUV. The MA-GTS incorporates both global and local rewards to collaboratively optimize the overall energy consumption while avoiding individual penalties. Based on the assigned target SN, a deep deterministic policy gradient-based LTP (DDPG-LTP) algorithm is proposed to conduct AUV trajectory planning, utilizing intrinsic rewards to enhance learning efficiency and eliminate collection blind spots. Furthermore, to avoid packet collisions, we analyze the conditions for collision-free data collection and propose an adaptive back-off slot (ABS) algorithm to schedule AUVs' collection slots. With the collision-free slots, DDPG-LTP dynamically adjusts AUVs' velocities to ensure collision-free collection while reducing energy consumption. Extensive simulation results demonstrate that HCDC can achieve better collection rate and energy efficiency than state-of-the-art schemes.

With the widespread adoption of location-based services (LBS), user-generated trajectory data shows strong spatiotemporal correlation, rendering it highly vulnerable to inference attacks that expose sensitive information. In particular, once semantic locations like “hospital” and “bank” are identified, the risk of trajectory leakage increases substantially. To address this issue, this paper formulates a personalized spatiotemporal trajectory privacy protection framework, which is designed to protect locations with varying semantic sensitivities on the trajectory from the attacker with spatiotemporal correlation information. We model the trajectory privacy protection problem as a Markov Decision Process (MDP) and introduce the reinforcement learning (RL) technique to adjust the privacy parameters dynamically. Specifically, we leverage the twin delayed deep deterministic policy gradient (TD3) algorithm to enhance the stability and accuracy of policy evaluation, enabling efficient learning of optimal policies in continuous action spaces. Furthermore, a safe exploration strategy is incorporated to continuously evaluate and avoid high-risk state-action pairs, thereby enhancing privacy protection. Simulation results demonstrate that the proposed mechanism significantly improves privacy protection while effectively reducing Quality of Service (QoS) loss, exhibiting better convergence and overall system utility.

Critical node identification is essential for Underwater Acoustic Sensor Networks (UASNs) to ensure network connectivity and reliability. Existing methods identify critical nodes by evaluating their contributions to network connectivity and node communication count. However, these methods identify critical nodes inaccurately due to neglecting the influence of packet collisions, leading to unreliable network. Packet collisions disrupt connected links and cause communication failures, resulting in unreliable network connectivity and improper communication count. To this end, we propose the Collision-Aware Critical Node Identification Algorithm (CCNIA), which accounts for the impact of packet collisions to improve the accuracy of critical node identification and enhance network reliability. CCNIA identifies critical nodes with high connectivity, large collision probability, and heavy network load, through building the three following interdependent models. Specifically, Topological Connectivity Model (TCM) evaluates link reachability by analyzing connectivity and density within a node's local network. Based on TCM, Collision Probability Model (CPM) further ensures packet reliability by quantifying the impact of packet collisions on critical node identification. Through CPM's reliable packet transmissions, Network Load Model (NLM) assesses network efficiency by analyzing node occurrence count within global end-to-end communication paths. Experiments show that CCNIA outperforms existing methods across diverse network configurations, enhancing network reliability in terms of packet delivery ratio, delay, and energy efficiency.

Energy efficiency is essential for federated learning (FL) over mobile devices and its potential prosperous applications. Different from existing communication efficient FL research efforts, which regard communication energy consumption as the bottleneck, we have observed that with ever increasing wireless transmission speed (e.g., Wi-Fi 5 or 5G), the energy consumption of wireless communications for model updates in FL is significantly reduced and sometimes is smaller than that of local on-device training. Motivated by such observations, in this paper, we propose a high-speed wireless communications inspired energy efficient federated learning over mobile devices (EEFL), whose goal is to reduce the overall energy consumption (computing + communication). In particular, we design a novel energy-aware adaptive local update policy for mobile devices by jointly considering FL performance and energy saving of high-speed wireless transmissions. Furthermore, given the device’s local update policy in each FL global round, we advance the dynamic voltage and frequency scaling (DVFS) strategy to minimize local training’s energy consumption by keeping GPU and CPU working at appropriate frequencies without triggering thermal throttling. Extensive experimental results with various learning models, datasets, and wireless transmission environments demonstrate the proposed EEFL’s superiority over the peer designs in terms of energy efficiency.

As a popular distributed learning paradigm, federated learning (FL) over mobile devices fosters numerous applications, while their practical deployment is hindered by participating devices’ computing and communication heterogeneity. Some pioneering research efforts proposed to extract subnetworks from the global model, and assign as large a subnetwork as possible to the device for local training based on its full computing and communications capacity. Although such fixed size subnetwork assignment enables FL training over heterogeneous mobile devices, it is unaware of (i) the dynamic changes of devices’ communication and computing conditions and (ii) FL training progress and its dynamic requirements of local training contributions, both of which may cause very long FL training delay. Motivated by those dynamics, in this paper, we develop a wireless and heterogeneity aware latency efficient FL (WHALE-FL) approach to accelerate FL training through adaptive subnetwork scheduling. Instead of sticking to the fixed size subnetwork, WHALE-FL introduces a novel subnetwork selection utility function to capture device and FL training dynamics, and guides the mobile device to adaptively select the subnetwork size for local training based on (a) its computing and communication capacity, (b) its dynamic computing and/or communication conditions, and (c) FL training status and its corresponding requirements for local training contributions. We provide a theoretical convergence analysis for WHALE-FL with heterogeneous subnetwork assignment, based on which subnetwork structures can be dynamically optimized to reduce the resulting gap to standard full-model FL. Our evaluation shows that, compared with peer designs, WHALE-FL effectively accelerates FL training without sacrificing learning accuracy.

As a popular distributed learning paradigm, federated learning (FL) over mobile devices fosters numerous applications, while their practical deployment is hindered by participating devices' computing and communication heterogeneity. Some pioneering research efforts proposed to extract subnetworks from the global model, and assign as large a subnetwork as possible to the device for local training based on its full computing capacity. Although such fixed size subnetwork assignment enables FL training over heterogeneous mobile devices, it is unaware of (i) the dynamic changes of devices' communication and computing conditions and (ii) FL training progress and its dynamic requirements of local training contributions, both of which may cause very long FL training delay. Motivated by those dynamics, in this paper, we develop a wireless and heterogeneity aware latency efficient FL (WHALE-FL) approach to accelerate FL training through adaptive subnetwork scheduling. Instead of sticking to the fixed size subnetwork, WHALE-FL introduces a novel subnetwork selection utility function to capture device and FL training dynamics, and guides the mobile device to adaptively select the subnetwork size for local training based on (a) its computing and communication capacity, (b) its dynamic computing and/or communication conditions, and (c) FL training status and its corresponding requirements for local training contributions. Our evaluation shows that, compared with peer designs, WHALE-FL effectively accelerates FL training without sacrificing learning accuracy.

Federated finetuning of Large Language Models (LLMs) using Low-Rank Adaptation (LoRA) offers computational efficiency and preserves data privacy.However, applying LoRA in federated settings faces significant challenges: standard approaches struggle with data heterogeneity, and existing personalization techniques fail to precisely adapt shared global knowledge to individual client needs.To address these issues, we propose pFedGPT, a framework that leverages Hierarchical Bayesian Optimization (HBO) for fine-grained, personalized LoRA aggregation.pFedGPT intelligently partitions LoRA parameters based on model structure and client information, then employs HBO to hierarchically search for optimal, module-specific weights.This enables a nuanced integration of the downloaded global LoRA state with each client's local model, precisely capturing client-specific requirements.To manage the optimization cost inherent in HBO, pFedGPT incorporates efficient multi-fidelity evaluations and a curriculum learning strategy.Extensive experiments demonstrate that pFedGPT achieves state-ofthe-art (SOTA) performance on personalized FL benchmarks, showcasing robustness and scalability while introducing only minimal (approx.4%) additional optimization overhead.Our results also underscore the limitations of traditional FL methods for LoRA-based LLM personalization, highlighting the need for tailored approaches like pFedGPT.

High Altitude Platform Station (HAPS) is a new airborne communications platform that provides wide-area communications services from the stratosphere and has the potential for coverage extension in 6G networks. In particular, it is a practical scenario where multiple HAPS work together to provide communications in densely populated urban areas and extensive regions. However, changes in ground coverage due to HAPS movement cause frequent handovers, which pose challenges to communication quality and stability. To solve this problem, “footprint fixation,” which maintains constant ground coverage even when the HAPS moves, is expected to reduce handovers and improve communication quality. How we should design beamforming and trajectory under fixed footprint conditions has not been clarified. In this paper, we propose a beamforming and trajectory planning method under fixed-footprint conditions for multiple HAPS systems. For footprint fixation, beamforming dynamically adapts to HAPS motion to eliminate ground coverage shifts, reduce handover frequency, and improve communication stability. Under fixed-footprint conditions, trajectory planning aims to improve the throughput of UEs with low (5th percentile) and medium (50th percentile) communication quality simultaneously through sequential multi-objective optimization. Simulation results in major Japanese cities with different UE distributions show that the proposed method improves both low and medium communication quality UEs, achieving a$\mathbf{2 0 - 3 6 \%}$improvement in 5th percentile throughput and an$8-20 {\%}$improvement in 50th percentile throughput compared to other methods. In addition, the footprint fixation increases coverage stability, reducing outage probability to less than 2 % and significantly reducing handover frequency.

Recent advancements in code generation have shown remarkable success across software domains, yet hardware description languages (HDLs) such as Verilog remain underexplored due to their concurrency semantics, syntactic rigidity, and simulation complexity. In this work, we address these challenges by introducing a reinforcement learning (RL) framework tailored for Verilog code generation. We first construct Veribench-53K, a high-quality dataset curated from over 700K Verilog problems, enriched with structured prompts, complexity labels, and diverse testbenches. To tackle the problem of sparse and noisy reward signals, we propose a Trace-back based Rescore mechanism that leverages reasoning paths and iterative refinement to enhance feedback reliability and support reward model training. Furthermore, to mitigate catastrophic forgetting and overfitting during RL fine-tuning, we introduce a sample-balanced weighting strategy that adaptively balances learning dynamics based on reward-probability distributions. These innovations are integrated into an iterative RL pipeline that co-evolves the policy and reward models. In contrast to recent work such as CraftRTL, which relies on large-scale closed-source model distillation, and DeepSeekstyle approaches that struggle with sparse feedback, our method demonstrates superior performance using a smaller but high-quality dataset combined with RL optimization. Experiments on Verilog generation tasks demonstrate state-of-the-art performance, with substantial gains in test pass rate, functional correctness, and compilation robustness. Our findings highlight the potential of RL-driven approaches for structured code generation in hardware-centric domains. VeriRL is publicly available at https://github.com/omniAI-Lab/VeriRL.

This paper addresses a distributed leader-follower formation control problem for a group of agents, each using a body-fixed camera with a limited field of view (FOV) for state estimation. The main challenge arises from the need to coordinate the agents' movements with their cameras' FOV to maintain visibility of the leader for accurate and reliable state estimation. To address this challenge, we propose a novel perception-aware distributed leader-follower safe control scheme that incorporates FOV limits as state constraints. A Control Barrier Function (CBF) based quadratic program is employed to ensure the forward invariance of a safety set defined by these constraints. Furthermore, new neural network based and double bounding boxes based estimators, combined with temporal filters, are developed to estimate system states directly from real-time image data, providing consistent performance across various environments. Comparison results in the Gazebo simulator demonstrate the effectiveness and robustness of the proposed framework in two distinct environments.

Visual foundation models, characterized by their robust generalization and adaptability, serve as the basis for a wide array of downstream tasks. When fine-tuned for specific tasks, these models encapsulate confidential and valuable task-specific knowledge, making them prime targets for model stealing (MS) attacks. While recent efforts have exposed MS threats in practical scenarios such as data-free and hard-label contexts, these attacks predominantly target traditional victim models trained from scratch. Fine-tuned visual foundation models, pre-trained on vast and diverse datasets and then fine-tuned on downstream tasks, present significant challenges for traditional MS attacks to extract task-specific knowledge. In this paper, we introduce an innovative MS attack, named SIAMESE, to steal fine-tuned visual foundation models under black-box, data-free, and hard-label settings. The core approach of SIAMESE involves constructing a stolen model using a foundation model that is efficiently and concurrently fine-tuned with multiple diversified soft prompts. To integrate the knowledge derived from these prompts, we propose a novel and tractable loss function that analyzes the output distributions while enforcing orthogonality among the prompts to minimize interference. Additionally, a unique alignment module enhances SIAMESE by synchronizing interpretations between the victim and stolen models. Extensive experiments validate that SIAMESE outperforms state-of-the-art baseline attacks over 10% in accuracy, exposing the heightened vulnerability of fine-tuned visual foundation models to MS threats.

Deep learning and computer vision have experienced significant advancements, particularly in critical applications such as autonomous driving and real-time surveillance, where object detection (OD) plays a pivotal role. Ensuring the accuracy and speed of these systems is paramount to prevent accidents or failures. Recently, latency-based attacks have emerged as a new threat, driven by the essential need for real-time performance in various applications. These attacks target model responsiveness to disrupt system performance without necessarily compromising accuracy. Our preliminary experiments show that introducing just a few bit flips to key parameters in OD models can significantly increase latency, degrading performance. Meanwhile, recent advancements in memory-based attacks, such as Row Hammer [18], demonstrate the ability to conveniently introduce bit flips at desired locations without physical hardware interaction. Based on the observations, we propose a novel attack on OD models that leverages row-hammer to introduce bit-flips via side channels, targeting the non-maximum suppression (NMS) filter and significantly increasing latency. Unlike previous methods that modify input data, our technique ensures efficiency by minimizing bit-flips through critical path exploitation and achieves practical applicability with only a subset of validation data. Experiments across various datasets and models validate our approach, demonstrating latency increases up to 71.6 ms (20.4×) with just 31 bit-flips.

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.

Federated learning (FL) over mobile devices is an emerging distributed learning paradigm for numerous delay sensitive applications. In FL, the training delay is composed of the computing and communication delay. Some of the participating mobile devices may have slow local computing or wireless communications, which results in high FL training delay. Intuitively, if fast devices help slow ones, the FL training delay can potentially be reduced. However, helping each other among devices requires frequent transmissions and may cause additional delay. Fortunately, we observe that device-to-device (D2D) transmission, a fast and direct transmission, may be applied between device pairs to mitigate the additional delay from frequent transmissions. Inspired by those observations, we develop the D2D transmission assisted FL (DAFL), a novel FL scheme to improve the training delay over mobile devices. Briefly, we first put the eligible mobile devices into pairs, assigning each pair to one of the four types of relation: 1) similar computing, large communication gap; 2) similar communication, large computing gap; 3) one with faster computing and the other with faster communication; and 4) one with both faster computing and communication. We design the process for each type of device pair to: 1) improve the transmission delay of each pair, by letting the fast device help with the model parameters transmission to the server and 2) improve the computing delay by splitting learning task between paired devices. The emulation results demonstrate that DAFL surpasses existing peer designs in terms of reducing training delay by more than 20%.

Internet of Underwater Thing (IoUT) stands as promising technology facilitating diverse underwater applications. Nevertheless, IoUT across vast marine regions is challenged by highly diverse and fluctuating channel environments, which results in unreliable point-to-point (PTP) transmissions. Moreover, its multi-hop nature exacerbates severe unreliable end-to-end (ETE) transmissions. Existing methods utilize routing protocols to address the above challenges by independently power control for PTP reliability or multi-path transmission for ETE reliability. However, these methods ignore the interdependencies between power control and multi-path transmission, which fail to guarantee high energy-efficient reliability in resource-constrained and harsh underwater environments. To this end, we propose a joint power Control And Multi-Path routing (CAMP) protocol for IoUTs in varying environments. Specifically, we develop PTP and ETE reliability models by analyzing the interrelation between power control and multi-path routing, incorporating historical, current, and predictive information. A hybrid routing strategy is designed based on the reliability models to accommodate changing environmental conditions, residual energy, and link quality. This strategy initiates multi-path routing at the source and single-path forwarding at relay nodes, combined with power control. Extensive simulations demonstrate that CAMP achieves superior reliability (packet delivery rate) and energy efficiency, while simultaneously improving network performance in terms of latency and throughput.

Training latency is critical for the success of numerous intrigued applications ignited by federated learning (FL) over heterogeneous mobile devices. By revolutionarily overlapping local gradient transmission with continuous local computing, FL can remarkably reduce its training latency over homogeneous clients, yet encounter severe model staleness, model drifts, memory cost and straggler issues in heterogeneous environments. To unleash the full potential of overlapping, we propose, FedEx, a novelfederated learning approach toexpedite FL training over mobile devices under data, computing and wireless heterogeneity. FedEx redefines the overlapping procedure with staleness ceilings to constrain memory consumption and make overlapping compatible with participation selection (PS) designs. Then, FedEx characterizes the PS utility function by considering the latency reduced by overlapping, and provides a holistic PS solution to address the straggler issue. FedEx also introduces a simple but effective metric to trigger overlapping, in order to avoid model drifts. Experimental results show that compared with its peer designs, FedEx demonstrates substantial reductions in FL training latency over heterogeneous mobile devices with limited memory cost.

Autonomous Underwater Vehicle (AUV)-assisted Underwater Acoustic Networks (UANs) are promising for complex ocean applications. In essence, an AUV-assisted UAN is still dominated by fixed nodes, and Time Division Multiple Access (TDMA)-based Medium Access Control (MAC) protocols have undisputed practicability in such fixed nodes-dominated UANs since they are simple and easy to deploy. However, AUV-assisted UANs may exist dynamic bidirectional data streams, while most existing protocols assume UANs have a unidirectional data stream, and their fixed scheduling sequence results in the long end-to-end delay in AUV-assisted UANs. In this paper, we first reveal a phenomenon between the data stream and the scheduling sequence, derived from real-world experiments: their consistent direction decreases the packet waiting delay but increases the slot length, and vice versa. To optimize the end-to-end delay, UANs with dynamic bidirectional data streams expect the MAC protocol to provide a flexible scheduling sequence. To this end, we propose a low-delay Adaptive Scheduling MAC protocol (AS-MAC) based on TDMA for AUV-assisted UANs. In AS-MAC, we analyze the relationship between scheduling sequence and data stream, extracting two significant factors: slot length and packet delay. Afterwards, we design Slot Length Model (SLM) and Packet Delay Model (PDM) to analyze the end-to-end delay of different data streams. Based on these two models, we present a Scheduling Sequence and Slot Length allocation Algorithm (SSSLA) to adaptively provide the minimum end-to-end delay for current bidirectional data streams. Extensive simulation results show that AS-MAC efficiently addresses severe queue congestion of the state-of-the-art protocols and reduces the end-to-end delay of different dynamic streams in various scenarios.

Recent advances in speech and language processing have led to the rise of smart voice services like Alexa, Google Home, and Siri. However, these advancements also increase security risks due to sophisticated voice domain attacks. Instead of relying on acoustic clues to detect replayed or synthesized speech, we utilize microphones and motion sensors in head-wearable devices to authorize legitimate users through bone-conducted vibrations, enabling multi-factor authentication (MFA) for spoken voice. Our proposed two-stage authentication system, AirBone, captures air and bone conduction (AirBone) signals and exploits two authentication factors sequentially. The first stage, called temporal consistency scoring (TCS), employs signal processing to verify the recorded AC and BC signals are concurrent and originate from the same vocalization process. Statistical tools are employed to distinguish legitimate attempts against false-triggering or acoustic attacks. The second stage leverages deep learning to verify the user’s unique bone conduction patterns in the vibration domain. Specifically, we enhance the robustness through data augmentation with constant-Q transform and adversarial training, improving the model’s ability to detect impersonation and machine-induced vibrations. Thanks to these designs, AirBone authentication offers enhanced security via MFA with no extra cost of user effort. In addition, our experimental results demonstrate a$96.3\%$overall accuracy, robustness against AirBone noise and room impulse responses, and$0.3\%$Equal Error Rate (EER) against acoustic and cross-domain attacks.

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.

Increasing demands for versatile applications have spurred the rapid development of Unmanned Underwater Vehicle (UUV) networks. Nevertheless, multi-UUV movements exacerbates the spatial-temporal variability, leading to serious intermittent connectivity of underwater acoustic channel. Such phenomena challenge the identification of reliable paths for high-dynamic network routing. Existing routing protocols overlook the effects of UUV movements on forwarding path, typically selecting forwarders based solely on the current network state, which lead to instability in packet transmission. To address these challenges, we propose a Routing protocol based on Spatial-Temporal Graph model with Q-learning for multi-UUV networks (STGR), achieving high reliable and energy effective transmission. Specifically, a distributed Spatial-Temporal Graph model (STG) is proposed to depict the evolving variation characteristics (neighbor relationships, link quality, and connectivity duration) among underwater nodes over periodic intervals. Then we design a Q-learning-based forwarder selection algorithm integrated with STG to calculate reward function, ensuring adaptability to the ever-changing conditions. We have performed extensive simulations of STGR on the Aqua-Sim-tg platform and compared with the state-of-the-art routing protocols in terms of Packet Delivery Rate (PDR), latency, energy consumption and energy balance with different network settings. The results show that STGR yields 24.32 percent higher PDR on average than them in multi-UUV networks.

Quantum computing is a promising paradigm for efficiently solving large and high-complexity problems. However, ensuring privacy within this quantum computing necessitates innovative approaches. Existing research has introduced the concept of quantum differential privacy (QDP) to protect data privacy in quantum computing by leveraging quantum noise. Yet, this method faces limitations due to the fixed and uncontrollable nature of the inherent noise, which directly affects the privacy budget of QDP. Addressing this critical gap, our study proposes a novel approach that utilizes quantum error correction (QEC) techniques not only to mitigate quantum computing errors but also to adjust QDP protection levels precisely. By selectively applying QEC to single or multiple qubit gates, we introduce a method to manipulate the quantum noise error rate effectively. Moreover, we derive a new formula for calculating the overall error rate in a quantum circuit and the adjusted privacy budget after QEC operation. Through extensive numerical simulations, we validate the efficacy of utilizing QEC in tuning privacy protection levels within quantum computing.

Quantum computing revolutionizes the way of solving complex problems and handling vast datasets, which shows great potential to accelerate the machine learning process. However, data leakage in quantum machine learning (QML) may present privacy risks. Although differential privacy (DP), which protects privacy through the injection of artificial noise, is a well-established approach, its application in the QML domain remains under-explored. In this paper, we propose to harness inherent quantum noises to protect data privacy in QML. Especially, considering the Noisy Intermediate-Scale Quantum (NISQ) devices, we leverage the unavoidable shot noise and incoherent noise in quantum computing to preserve the privacy of QML models for binary classification. We mathematically analyze that the gradient of quantum circuit parameters in QML satisfies a Gaussian distribution, and derive the upper and lower bounds on its variance, which can potentially provide the DP guarantee. Through simulations, we show that a target privacy protection level can be achieved by running the quantum circuit a different number of times.

The rapid advancement of machine learning (ML) and the growing need for computational power have led to the exploration of quantum computing, which offers significant potential for faster complex calculations. However, Quantum Machine Learning (QML) faces challenges due to the limited number of qubits and noise of quantum circuits, particularly with Noisy Intermediate-Scale Quantum (NISQ) devices. These challenges severely limit the current capacity to train accurate and stable Quantum Machine Learning Models. In this paper, we propose a novel framework for QML that employs the knowledge distillation method to harness the power of well-trained classical machine learning (CML) models and enhance the training performance of QML models. In this framework, we utilize the well-trained CML as a teacher model to assist the training of the student QML model using the knowledge distillation method. By distilling knowledge from the robust CML model, our framework can potentially address the problem of the barren plateau which hinders effective model training. Knowledge distillation is well suited for this framework through the transfer of knowledge without parameter sharing. Through empirical tests, our framework has demonstrated not only an increase in the accuracy of QML models but also a notable improvement in training stability.

Recently, federated learning (FL) has been threat-ened by the gradient inversion attack that infers user-private data from shared gradients. To cope with this problem, the differential privacy (DP) technique is widely employed in FL. However, when FL faces the non-independent identically distributed (non-IID) data scenarios, applying DP to protect user data privacy remains inefficient in terms of model accuracy and communication costs. In this paper, inspired by the Mixup data augmentation method, we propose a privacy-preserving FL approach called Fed2VAEs to address this problem. Specifically, we introduce a Mixup Module consisting of two variational autoencoders to remove the private information of user data. To balance the trade-off between data privacy and data utility, from the perspective of mutual information, a learning objective is proposed. We conduct extensive experiments under different non-IID data settings, and the experimental results show that Fed2VAEs can significantly reduce the communication cost and improve model accuracy (up to 8.57%) on the premise of successfully protecting user data privacy.

Quantum computing has revolutionized the approach to solving complex problems and handling vast datasets. However, data leakage in quantum computing may present privacy risks. While differential privacy (DP) has been a classical solution to protect privacy by injecting artificial noises, the implementation of DP within the quantum domain remains an under-explored area. We observe that there is a potential to leverage the inherent noises generated by Noisy Intermediate-Scale Quantum (NISQ) devices during quantum operations for achieving DP in quantum computing. Given that these inherent noises are uncontrollable, we take the lead to utilize quantum error mitigation (QEM) techniques to manage quantum differential privacy (QDP) protection levels. In our approach, we offer a tangible description of the "closeness" between neighboring quantum datasets and introduce a novel QDP definition based on observables of interest. Our simulations reveal that factors like the distance between neighboring quantum states and the number of circuit executions influence the privacy budget. Notably, by controlling the QEM samples while keeping the number of circuit executions fixed, QEM allows for substantial noise control to meet a desired privacy budget. Furthermore, we extend analysis from single-qubit to multiple qubits and discuss the impact of observables of interest on the privacy budget.

Collaborative inference has been a promising solution to enable resource-constrained edge devices to perform inference using state-of-the-art deep neural networks (DNNs). In collaborative inference, the edge device first feeds the input to a partial DNN locally and then uploads the intermediate result to the cloud to complete the inference. However, recent research indicates model inversion attacks (MIAs) can reconstruct input data from intermediate results, posing serious privacy concerns for collaborative inference. Existing perturbation and cryptography techniques are inefficient and unreliable in defending against MIAs while performing accurate inference. This paper provides a viable solution, named PATROL, which develops privacy-oriented pruning to balance privacy, efficiency, and utility of collaborative inference. PATROL takes advantage of the fact that later layers in a DNN can extract more task-specific features. Given limited local resources for collaborative inference, PATROL intends to deploy more layers at the edge based on pruning techniques to enforce task-specific features for inference and reduce task-irrelevant but sensitive features for privacy preservation. To achieve privacy-oriented pruning, PATROL introduces two key components: Lipschitz regularization and adversarial reconstruction training, which increase the reconstruction errors by reducing the stability of MIAs and enhance the target inference model by adversarial training, respectively. On a real-world collaborative inference task, vehicle re-identification, we demonstrate the superior performance of PATROL in terms of against MIAs.

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.

The data and power transfer systems for long term underground CO2 sequestration monitoring are normally based on wire-line cable, which will lead to a potential leakage path way through the casing and cement annulus in high-temperature, high-pressure, and hash underground environments. In this paper, a novel wireless power transfer system has been developed for real-time underground CO2 monitoring. The system includes an array of toroidal transceivers winding around the highly conductive casing string for wireless power transfer to deep subsurface. This design helps to maintain well integrity and reduce potential leakage by eliminating the need to perforate the casing or an umbilical in the cement annulus. The metal casing’s amplification effect significantly enhances the wireless power transfer efficiency, which provides a highly conductive power/electric current’s pathway instead of omnidirectional wireless radiation loss in the subsurface. Toroidal transceiver’s design has been optimized to improve the received signal, and our results show significant improvements in wireless power transfer efficiency. Using the optimized design, we can receive 1 to 10 % power transfer efficiency at 800 meters deep using only one toroidal transceiver with 1A current as input. Compared with other wireless antenna designs, such as the helix coil antenna, our system has shown 26,000 times power transfer efficiency improvement. In the end, a lab-scale power transfer system is built, and our experimental measurements support the simulation results.

As a promising solution to alleviate network congestion, mobile edge caching based on unmanned aerial vehicles (UAVs) has emerged and received intensive research interests, where users could download their desired contents from UAVs with much lower latency. As for the UAV-assisted edge caching, to improve the users’ Quality of Experience while reducing the cost on content updating, how to jointly design the trajectory and caching strategy for UAVs is critical. However, considering the dynamics and uncertainty on the traffic environment, as well as the mutual effect among different UAVs, such joint design is nontrivial. In this article, we propose a collaborative joint trajectory and caching scheme for UAV-assisted networks under the dynamic and uncertain traffic environment. Unlike most existing work relying on model-based or single-agent methods, we develop a multiagent deep reinforcement learning (MADRL) approach to obtain the solution, where the specific content demand model is not needed and each UAV would learn the best decision autonomously based on its local observations. It can achieve the adaptive cooperation among different UAVs, while optimizing the overall network performance. Moreover, standing from the perspective on swarm intelligence, we further develop a dynamic clustering federated learning framework on the MADRL algorithm. By performing parameter fusion, each UAV can improve the learning efficiency.

Device-free gesture recognition has attracted significant attention due to its potential applications in pervasive interaction. It enables gesture recognition in a device-free and contact-free manner by analyzing the influence pattern of human gestures on surrounding wireless signals, such as mmWave signals. Although remarkable progress has been achieved in this area, the recognition performance will degrade remarkably when gestures are conducted in different scenarios. In this paper, we leverage mmWave signals to design two robust trajectory features, i.e., the trajectory image and the trajectory time-sequence features, that are independent of the conducted scenarios to solve the aforementioned problems. Specifically, we employ the particle filter algorithm to construct the raw trajectory image utilizing range measurements, rotate and enhance the image to obtain the trajectory image feature suitable for recognition by leveraging a public handwriting font image data set as the training set. Additionally, we derive the range of the trajectory relative to a stable point as the trajectory time-sequence feature. With these trajectory features, we design a deep network to perform the gesture recognition task. To validate the effectiveness of the proposed methods, we conduct extensive experiments on a 77GHz mmWave testbed. The results indicate that the two proposed trajectory features are feasible for achieving scenario-independent gesture recognition.

We study federated unlearning, a novel problem to eliminate the impact of specific clients or data points on the global model learned via federated learning (FL). This problem is driven by the right to be forgotten and the privacy challenges in FL. We introduce a new framework for exact federated unlearning that meets two essential criteria:communication efficiencyandexact unlearning provability.To our knowledge, this is the first work to tackle both aspects coherently. We start by giving a rigorous definition ofexactfederated unlearning, which guarantees that the unlearned model is statistically indistinguishable from the one trained without the deleted data. We then pinpoint the key property that enables fast exact federated unlearning: total variation (TV) stability, which measures the sensitivity of the model parameters to slight changes in the dataset. Leveraging this insight, we develop a TV-stable FL algorithm called FATS, which modifies the classical FedAvg algorithm for TV Stability and employs local SGD with periodic averaging to lower the communication round. We also design efficient unlearning algorithms for FATS under two settings: client-level and sample-level unlearning. We provide theoretical guarantees for our learning and unlearning algorithms, proving that they achieve exact federated unlearning with reasonable convergence rates for both the original and unlearned models. We empirically validate our framework on 6 benchmark datasets, and show its superiority over state-of-the-art methods in terms of accuracy, communication cost, computation cost, and unlearning efficacy.

The rapid development of indoor location-based services (LBS) has raised concerns about location privacy protection in the 3-dimensional (3D) space. The existing 2-dimensional (2D) location privacy protection mechanisms (LPPMs) cannot effectively resist attacks in 3D environments. Furthermore, users may have various sensitive attributes at different locations and times. In this paper, we first formally study the relationship between two complementary notions of geo-indistinguishability and distortion privacy (i.e., expected inference error) in the 3D space and develop a two-phase personalized 3D LPPM (P3DLPPM). In Phase I, we search for neighboring locations to formulate a protection location set (PLS) for hiding the actual location based on the above-mentioned relationship. To realize this, we develop a 3D Hilbert curve-based minimum distance searching algorithm to find the PLS with minimum diameter for each location while guaranteeing differential privacy. In Phase II, we put forth a novel Permute-and-Flip mechanism for location perturbation, which maps its initial application in data publishing privacy protection to a location perturbation mechanism. It generates fake locations with smaller perturbation distances while improving the balance between privacy and quality of service (QoS). Simulation results show that the proposed P3DLPPM can significantly improve personalized privacy protection while meeting the user's QoS needs.

The growing use of smart devices requires improving privacy and security. Conventional biometrics confront false positives and unauthorized access, stressing cautious user input. We enhance security by analyzing distinctive human physiological characteristics rather than relying on conventional methods susceptible to spoof attacks. Drinking, a common physiological activity, can provide continuous authentication.SipDeep, proposed innovative system, utilizes bone-conducted liquid intake sound, incorporating unique biometrics from bone and pharyngeal characteristics. The system captures these elements in the external auditory canal, offering a novel transparent authentication applicable to a diverse user range. Our noise filtering system eliminates environmental and anatomical interferences during drinking, including subtle body movements. The study introduces a hybrid event detection technique integrating wavelet transform with start/end points detection. Next, we extract physiological features from bone structure, liquid intake sound, and liquid intake pattern. We used the physiological features to train a deep learning algorithm based on a Triplet-Siamese network to classify authentication. The proposed model has been thoroughly compared with advanced models such as DenseNet169, ResNet18, and VGG16. Following extensive experimentation involving multiple users across various environments,SipDeepdemonstrates 96.5% authentication accuracy, coupled with a 98.33% resistance to spoof attacks.

This artilce aims to develop a differential private federated learning (FL) scheme with the least artificial noises added while minimizing the energy consumption of participating mobile devices. By observing that some communication efficient FL approaches and even the nature of wireless communications contribute to the differential privacy (DP) preservation of training data on mobile devices, in this paper, we propose to jointly leverage gradient compression techniques (i.e., gradient quantization and sparsification) and additive white Gaussian noises (AWGN) in wireless channels to develop a piggyback DP approach for FL over mobile devices. Even with the piggyback DP approach, information distortion caused by gradient compression and noise perturbation may slow down FL convergence, which in turn consumes more energy of mobile devices for local computing and model update communications. Thus, we theoretically analyze FL convergence and formulate an energy efficient FL optimization under piggyback DP, transmission power, and FL convergence constraints. Furthermore, we propose an efficient iterative algorithm where closed-form solutions for artificial DP noise and power control are derived. Extensive simulation and experimental results demonstrate the effectiveness of the proposed scheme in terms of energy efficiency and privacy preservation.

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.

Participant selection (PS) helps to accelerate federated learning (FL) convergence, which is essential for the practical deployment of FL over mobile devices. While most existing PS approaches focus on improving training accuracy and efficiency rather than residual energy of mobile devices, which fundamentally determines whether the selected devices can participate. Meanwhile, the impacts of mobile devices heterogeneous wireless transmission rates on PS and FL training efficiency are largely ignored. Moreover, PS causes the staleness issue. Prior research exploits isolated functions to force long-neglected devices to participate, which is decoupled from original PS designs. In this paper, we propose aresidualenergy andwirelessaware PS design for efficientFLtraining over mobile devices (REWAFL). REWAFL introduces a novel PS utility function that jointly considers global FL training utilities and local energy utility, which integrates energy consumption and residual battery energy of candidate mobile devices. Under the proposed PS utility function framework, REWAFL further presents a residual energy and wireless aware local computing policy. Besides, REWAFL buries the staleness solution into its utility function and local computing policy. The experimental results show that REWAFL is effective in improving training accuracy and efficiency, while avoiding flat battery of mobile devices.

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.

Federated learning (FL) enables mobile devices to collaboratively learn a shared prediction model while keeping data locally. However, there are two major research challenges to practically deploy FL over mobile devices: (i) frequent wireless updates of huge size gradients v.s. limited spectrum resources, and (ii) energy-hungry FL communication and local computing during training v.s. battery-constrained mobile devices. To address those challenges, in this paper, we propose a novel multi-bit over-the-air computation (M-AirComp) approach for spectrum-efficient aggregation of local model updates in FL and further present an energy-efficient FL design for mobile devices. Specifically, a high-precision digital modulation scheme is designed and incorporated in the M-AirComp, allowing mobile devices to upload model updates at the selected positions simultaneously in the multi-access channel. Moreover, we theoretically analyze the convergence property of our FL algorithm. Guided by FL convergence analysis, we formulate a joint transmission probability and local computing control optimization, aiming to minimize the overall energy consumption (i.e., iterative local computing + multi-round communications) of mobile devices in FL. Extensive simulation results show that our proposed scheme outperforms existing ones in terms of spectrum utilization, energy efficiency, and learning accuracy.

Semi-supervised learning, which learns with only a small amount of labeled data while collecting voluminous unlabeled data to aid its training, has achieved promising performance lately, but it also raises a serious privacy concern: Whether a user’s data has been collected for use without authorization. In this paper, we propose a novel membership inference method against semi-supervised learning, serving to protect user data privacy. Due to involving both the labeled and unlabeled data, the membership patterns of semi-supervised learning’s training data cannot be well captured by the existing membership inference solutions. To this end, we propose two new metrics, i.e., inter-consistency and intra-entropy, tailored specifically to the semi-supervised learning paradigm, able to respectively measure the similarity and calculate the cross-entropy among prediction vectors from the perturbed versions. By exploiting the two metrics for membership inference, our method can dig out membership patterns imprinted on prediction outputs of semi-supervised learning models, thus facilitating effective membership inference. Extensive experiments have been conducted for comparing our method with five rectified baseline inference techniques across four datasets on six semi-supervised learning algorithms. Experimental results exhibit that our inference method achieves over 80% accuracy under each experimental setting, substantially outperforming all baseline techniques.

(Gradient) Expectation Maximization (EM) is a widely used algorithm for estimating the maximum likelihood of mixture models or incomplete data problems. A major challenge facing this popular technique is how to effectively preserve the privacy of sensitive data. Previous research on this problem has already lead to the discovery of some Differentially Private (DP) algorithms for (Gradient) EM. However, unlike in the non-private case, existing techniques are not yet able to provide finite sample statistical guarantees. To address this issue, we propose in this paper the first DP version of Gradient EM algorithm with statistical guarantees. Specifically, we first propose a new mechanism for privately estimating the mean of a heavy-tailed distribution, which significantly improves a previous result in [25], and it could be extended to the local DP model, which has not been studied before. Next, we apply our general framework to three canonical models: Gaussian Mixture Model (GMM), Mixture of Regressions Model (MRM) and Linear Regression with Missing Covariates (RMC). Specifically, for GMM in the DP model, our estimation error is near optimal in some cases. For the other two models, we provide the first result on finite sample statistical guarantees. Our theory is supported by thorough numerical experiments on both real-world data and synthetic data.

Federated learning (FL) over mobile devices is an emerging distributed learning paradigm for numerous delay sensitive applications. In FL, the training delay is composed of the computing and communication delay. Some of the participating mobile devices may have slow local computing or wireless communications, which results in high FL training delay. Intuitively, if fast devices help slow ones, the FL training delay can potentially be reduced. However, helping each other among devices requires frequent transmissions and may cause additional delay. Fortunately, we observe that Device-to-Device (D2D) transmission, a fast and direct transmission, may be applied between device pairs to mitigate the additional delay from frequent transmissions. Inspired by those observations, we develop the D2D transmission assisted FL (DAFL), a novel FL scheme to improve the training delay over mobile devices. Briefly, we first put the eligible mobile devices into pairs, each pair consisting of a fast and a slow device. Then, we apply D2D transmission between each device pair to: (1) improve the transmission delay of each pair, by letting the fast device help with the model parameters transmission to the server, and (2) improve the computing delay by splitting learning task between paired devices. The emulation results demonstrate that DAFL surpasses existing peer designs in terms of reducing training delay by more than 20%.

Semantic communications, aiming at ensuring the successful delivery of the meaning of information, are expected to be one of the potential techniques for the next generation communications. However, the knowledge forming and synchronizing mechanism that enables semantic communication systems to extract and interpret the semantics of information according to the communication intents is still immature. In this paper, we propose a semantic image transmission framework with explicit semantic base (Seb), where Sebs are generated and employed as the knowledge shared between the transmitter and the receiver with flexible granularity. To represent images with Sebs, a novel Seb-based reference image generator is proposed to generate Sebs and then decompose the transmitted images. To further encode/decode the residual information for precise image reconstruction, a Seb-based image encoder/decoder is proposed. The key components of the proposed framework are optimized jointly by end-to-end (E2E) training, where the loss function is dedicatedly designed to tackle the problem of non-differentiable operation in Seb-based reference image generator by introducing a gradient approximation mechanism. Extensive experiments show that the proposed framework outperforms state-of-art works by 0.5 - 1.5 dB in peak signal-to-noise ratio (PSNR) w.r.t. different signal-to-noise ratios (SNR).

In Vehicle-to-Everything scenarios, the efficient and real-time offloading of multi-task from vehicles to roadside units with higher computing power presents a challenging endeavor. This challenge is amplified by the dynamic nature of computing power in roadside units, which fluctuates in real time due to resource sharing among multiple vehicles. Consequently, accurately determining the computing power of roadside units prior to offloading becomes a significant hurdle for vehicles. To overcome this challenge and enhance the performance of online algorithms used for task offloading, we propose a novel approach that leverages machine learning techniques. This approach utilizes historical data to predict the real-time computing power of roadside units. By incorporating machine learning predictions, our proposed approach aims to mitigate the uncertainty associated with the decision-making process of the online algorithm. This, in turn, enables vehicles to make more informed decisions regarding task offloading. Moreover, to enhance the robustness of the algorithm against potential prediction errors, our approach adopts a partial trust mechanism towards the predicted outcomes. By considering this partial trust, we aim to maintain the algorithm's reliability in real-world scenarios. Furthermore, we conduct theoretical analysis and comprehensive experiments to demonstrate the superiority of our proposed algorithm in terms of task offloading performance and robustness.

Federated learning (FL) over mobile edge devices is a promising distributed learning paradigm for various mobile applications. However, practical deployment of FL over mobile devices is very challenging because (i) conventional FL incurs huge training latency for mobile edge devices due to interleaved local computing and communications of model updates, (ii) there are heterogeneous training data across mobile edge devices, and (iii) mobile edge devices have hardware heterogeneity in terms of computing and communication capabilities.To address aforementioned challenges, in this paper, we propose a novel "workie-talkie" FL scheme, which can accelerate FL’s training by overlapping local computing and wireless communications via contrastive regularization (FedCR). FedCR can reduce FL’s training latency and almost eliminate straggler issues since it buries/embeds the time consumption of communications into that of local training. To resolve the issue of model staleness and data heterogeneity co-existing, we introduce class-wise contrastive regularization to correct the local training in FedCR. Besides, we jointly exploit contrastive regularization and subnetworks to further extend our FedCR approach to accommodate edge devices with hardware heterogeneity. We deploy FedCR in our FL testbed and conduct extensive experiments. The results show that FedCR outperforms its status quo FL approaches on various datasets and models.

With the popularity of tiny devices based on microcontroller units, there is an urgent need to develop federated tiny learning to privately obtain a well-performed tiny model serving tiny devices. However, due to the limited capacity of tiny models, the fundamental difference between training deep neural networks and tiny neural networks makes existing federated learning designed for deep models ineffective in learning tiny models. Although prior tiny machine learning research successfully augments tiny models with enlarged architecture for improved capacity, such augmentation relies on a pre-known centralized dataset and thus cannot be used in federated settings. To fill this void, in this work, we propose an innovative federated tiny learning framework, FedTinyAug, to enable distributed tiny model augmentation. By taking advantage of the extra capability at larger participating devices, the server first constructs augmented models and distributes them to larger devices, providing auxiliary supervision for training the tiny model. To provide strong supervision, a gradient-based augmented model selection algorithm is designed to efficiently determine favorable augmented models to fully explore distinct or even heterogeneous on-device knowledge. Extensive experiments are conducted on three popular tiny models to validate the effectiveness of FedTinyAug. Key augmentation factors are evaluated to guide the implementation of FedTinyAug in practice.

In this work, we investigate the challenging problem of on-demand federated learning (FL) over heterogeneous edge devices with diverse resource constraints. We propose a cost-adjustable FL framework, named AnycostFL, that enables diverse edge devices to efficiently perform local updates under a wide range of efficiency constraints. To this end, we design the model shrinking to support local model training with elastic computation cost, and the gradient compression to allow parameter transmission with dynamic communication overhead. An enhanced parameter aggregation is conducted in an element-wise manner to improve the model performance. Focusing on AnycostFL, we further propose an optimization design to minimize the global training loss with personalized latency and energy constraints. By revealing the theoretical insights of the convergence analysis, personalized training strategies are deduced for different devices to match their locally available resources. Experiment results indicate that, when compared to the state-of-the-art efficient FL algorithms, our learning framework can reduce up to 1.9 times of the training latency and energy consumption for realizing a reasonable global testing accuracy. Moreover, the results also demonstrate that, our approach significantly improves the converged global accuracy.

Energy efficiency is essential for federated learning (FL) over mobile devices and its potential prosperous applications. Different from existing communication efficient FL research efforts, which regard communication energy consumption as the bottleneck, we have observed that with ever increasing wireless transmission speed (e.g., Wi-Fi 5 or 5G), the energy consumption of wireless communications for model updates in FL is significantly reduced and sometimes is smaller than that of local on-device training. Motivated by such observations, in this paper, we propose a high-speed wireless communications inspired energy efficient federated learning over mobile devices (EEFL), whose goal is to reduce the overall energy consumption (computing + communication). In particular, we design a novel energy-aware adaptive local update policy for mobile devices by jointly considering FL performance and energy saving of high-speed wireless transmissions. Furthermore, given the device's local update policy in each FL global round, we advance the dynamic voltage and frequency scaling (DVFS) strategy to minimize local training's energy consumption by keeping GPU and CPU working at appropriate frequencies without triggering thermal throttling. Extensive experimental results with various learning models, datasets, and wireless transmission environments demonstrate the proposed EEFL's superiority over the peer designs in terms of energy efficiency.

With the advantages of low signaling overhead and latency, grant-free random access (GFRA) becomes a promising technology for supporting massive machine-type communications (mMTCs), but poses new challenges for active user detection (AUD) and channel estimation (CE), whose performance mainly depends on the preamble detection. In this article, we design the composite preamble based on differential phase rotations by aggregating orthogonal Zadoff-Chu (ZC) sequences and multiple root ZC sequences with differential phase rotations to reduce the probability of preamble collisions, thereby improving the performance of AUD and CE. In particular, differential phase rotations extend the preamble set size so that users colliding in orthogonal sequences can be distinguished by phase rotations. In addition, it also reduces nonorthogonal interference and thus reduces CE errors. The preamble detection algorithm and CE scheme are proposed, along with the theoretical analysis of AUD and CE performance to verify the effectiveness of the designed preamble. In addition, the proposed preamble is extended to combine phase rotations with cyclic shifts to further enlarge the preamble set size with low nonorthogonality. Simulation results show that the proposed composite preamble outperforms existing preambles in terms of the probability of detection and CE accuracy.

Federated learning (FL) over mobile devices has fostered numerous intriguing applications/services, many of which are delay-sensitive. In this paper, we propose a service delay efficient FL (SDEFL) scheme over mobile devices. Unlike traditional communication efficient FL, which regards wireless communications as the bottleneck, we find that under many situations, the local computing delay is comparable to the communication delay during the FL training process, given the development of high-speed wireless transmission techniques. Thus, the service delay in FL should be computing delay + communication delay over training rounds. To minimize the service delay of FL, simply reducing local computing/communication delay independently is not enough. The delay trade-off between local computing and wireless communications must be considered. Besides, we empirically study the impacts of local computing control and compression strategies (i.e., the number of local updates, weight quantization, and gradient quantization) on computing, communication and service delays. Based on those trade-off observation and empirical studies, we develop an optimization scheme to minimize the service delay of FL over heterogeneous devices. We establish testbeds and conduct extensive emulations/experiments to verify our theoretical analysis. The results show that SDEFL reduces notable service delay with a small accuracy drop compared to peer designs.

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.

With the popularity of mobile devices with global positioning system (GPS), transportation network company (TNC) service has become an indispensable option of people's daily commute. However, it also provides opportunities for malicious parties to compromise TNC users’ location privacy. There are great challenges to preserve TNC users’ location privacy while improving the revenue of TNC and its quality of service (QoS). To address this issue, we propose a novel scheme to schedule the TNC vehicles while preserving the TNC users’ location differential privacy. Briefly, we add high dimensional Laplace noises to guarantee the TNC users’ geo-indistinguishability. Due to the differential private obfuscation, the demand for TNC vehicles in an area becomes uncertain. Thus, we employ the data-driven approach to characterize users’ demand uncertainty, formulate the TNC's revenue maximization problem into risk-averse stochastic programming, and provide corresponding feasible solutions. Using the released public data of Didi Chuxing, we conduct extensive simulations to evaluate the performance of the proposed scheduling scheme and compare the results under different$\zeta$-structure metrics. The results show that the proposed scheme can efficiently schedule the TNC vehicles, maximize the TNC's revenue and provide a better service for TNC users while protecting the TNC users’ location privacy.

The emerging Internet of Things (IoT) paradigm makes our telecommunications networks increasingly congested. Unmanned aerial vehicles (UAVs) have been regarded as a promising solution to offload the overwhelming traffic. Considering the limited spectrums, cognitive radio can be embedded into UAVs to build backhaul links through harvesting idle spectrums. For the cognitive UAV (CUAV) assisted network, how much traffic can be actually offloaded depends on not only the traffic demand but also the spectrum environment. It is necessary to jointly consider both issues and co-design the trajectory and communications for the CUAV to make data collection and data transmission balanced to achieve high offloading efficiency, which, however, is non-trivial because of the heterogeneous and uncertain network environment. In this paper, aiming at maximizing the energy efficiency of the CUAV-assisted traffic offloading, we jointly design the Trajectory, Time allocation for data collection and data transmission, Band selection, and Transmission power control ($\text{T}^{\mathrm{ 3}}\text{B}$) considering the heterogeneous environment on traffic demand, energy replenishment, and spectrum availability. Considering the uncertain environmental information, we develop a model-free deep reinforcement learning (DRL) based solution to make the CUAV achieve the best decision autonomously. Simulation results have shown the effectiveness of the proposed DRL-$\text{T}^{\mathrm{ 3}}\text{B}$strategy.

Model update compression is a widely used technique to alleviate the communication cost in federated learning (FL). However, there is evidence indicating that the compression-based FL system often suffers the following two issues, i) the implicit learning performance deterioration of the global model due to the inaccurate update, ii) the limitation of sharing the same compression rate over heterogeneous edge devices. In this paper, we propose an energy-efficient learning framework, named Snowball, that enables edge devices to incrementally upload their model updates in a coarse-to-fine compression manner. To this end, we first design a fine-grained compression scheme that enables a nearly continuous compression rate. After that, we investigate the Snowball optimization problem to minimize the energy consumption of parameter transmission with learning performance constraints. By leveraging the theoretical insights of the convergence analysis, the optimization problem is transformed into a tractable form. Following that, a water-filling algorithm is designed to solve the problem, where each device is assigned a personalized compression rate according to the status of the locally available resource. Experiments indicate that, compared to state-of-the-art FL algorithms, our learning framework can save five times the required energy of uplink communication to achieve a good global accuracy.

Federated learning (FL) is a promising distributed learning paradigm, which can effectively avoid the privacy leakage and communication issues compared with the centralized learning. Specifically, in each training iteration, FL nodes only upload the local training results to the centralized server without disclosure of their raw training dataset and the centralized server will aggregate the local results of all FL nodes and update the global model. To this end, the performance of the global model is highly dependent on the nodes' cooperation. However, it is challenging to motivate mobile edge devices to involve themselves in the FL process without a desired incentive. Another significant concern of the mobile edge devices is the communication and computational energy cost of participation. Therefore, considering the high cost and weak communication channel with the centralized server specially for the distant nodes, in this paper, we propose a relay-assisted energy efficient scheme for federated learning, where each FL computational node is not only motivated by monetary awards based on their local dataset, but also further motivated to function as a relay node to assist distant nodes on local results uploading due to its locality advantage. To achieve a stable pairing solution between FL computational nodes and assisted relays in a distributive fashion, a many-to-one matching algorithm is applied, where each the computational node and relay is unable to deviate with current pairing unilaterally for higher revenue. Extensive simulations are conducted to illustrate the correctness and effectiveness of our proposed scheme.

Preventing COVID-19 disease from spreading in communities will require proactive and effective healthcare resource allocations, such as vaccinations. A fine-grained COVID-19 vulnerability map will be essential to detect the high-risk communities and guild the effective vaccine policy. A mobile-crowdsourcing-based self-reporting approach is a promising solution. However, an accurate mobile-crowdsourcing-based map construction requests participants to report their actual locations, raising serious privacy concerns. To address this issue, we propose a novel approach to effectively construct a reliable community-level COVID-19 vulnerability map based on mobile crowdsourced COVID-19 self-reports without compromising participants’ location privacy. We design a geo-perturbation scheme where participants can locally obfuscate their locations with the geo-indistinguishability guarantee to protect their location privacy against any adversaries’ prior knowledge. To minimize the data utility loss caused by location perturbation, we first design an unbiased vulnerability estimator and formulate the location perturbation probability generation into a convex optimization. Its objective is to minimize the estimation error of the direct vulnerability estimator under the constraints of geo-indistinguishability. Given the perturbed locations, we integrate the perturbation probabilities with the spatial smoothing method to obtain reliable community-level vulnerability estimations that are robust to a small-sampling-size problem incurred by location perturbation. Considering the fast-spreading nature of coronavirus, we integrate the vulnerability estimates into the modified susceptible-infected-removed (SIR) model with vaccination for building a future trend map. It helps to provide a guideline for vaccine allocation when supply is limited. Extensive simulations based on real-world data demonstrate the proposed scheme superiority over the peer designs satisfying geo-indistinguishability in terms of estimation accuracy and reliability.

The emerging Internet of Things (IoT) era has stimulated many new computation-intensive applications. To support them, mobile edge computing (MEC) is a promising solution that allows users to offload their heavy computing tasks to nearby edge servers. Taking such computation offloading as the service, application service providers (ASPs) can rent resources from mobile network operators for MEC service provisioning. However, it is challenging for ASPs to determine how many resources to rent at different regions and times due to the uncertain user demand. When making an investment strategy, it is crucial to maximize the profit with the consideration on the Quality of Service (QoS), where a joint scheduling on both communication and computing resource under the uncertain demand is needed. To deal with the uncertainty, the probability distribution information is usually employed, which, unfortunately, might be hardly obtainable in practice. Therefore, in this article, we propose a data-driven risk-averse MEC resource investment (DRAI) strategy, where the demand uncertainty issue is particularly addressed. Specifically, we formulate the DRAI strategy into a stochastic optimization problem, which can achieve the expected optimal profit under the QoS guarantee statistically from a risk-averse perspective. To solve it, instead of relying on specific distribution models, we construct an ambiguity set based on the statistical characteristics derived from the historical data that contains all possible distributions, and develop a data-driven distributionally robust solution, aiming at achieving the best strategy under the worst case to make it trustworthy. Simulation results illustrate the effectiveness of the proposed DRAI strategy.

Federated learning (FL) through its novel applications and services has enhanced its presence as a promising tool in the Internet of Things (IoT) domain. Specifically, in a multiaccess edge computing setup with a host of IoT devices, FL is most suitable since it leverages distributed client data to train high-performance deep learning (DL) models while keeping the data private. However, the underlying deep neural networks (DNNs) are huge, preventing its direct deployment onto resource-constrained computing and memory-limited IoT devices. Besides, frequent exchange of model updates between the central server and clients in FL could result in a communication bottleneck. To address these challenges, in this article, we introduce GWEP, a model compression-based FL method. It utilizes joint quantization and model pruning to reap the benefits of DNNs while meeting the capabilities of resource-constrained devices. Consequently, by reducing the computational, memory, and network footprint of FL, the low-end IoT devices may be able to participate in the FL process. In addition, we provide theoretical guarantees of FL convergence. Through empirical evaluations, we demonstrate that our approach significantly outperforms the baseline algorithms by being up to 10.23 times faster with 11 times lesser communication rounds, while achieving high-model compression, energy efficiency, and learning performance.

Device-free gesture recognition (DFGR) is an emerging technique which could leverage the influence of human gestures on surrounding wireless signals to recognize gestures. It has gained widespread attention due to its promising prospect of empowering pervasive wireless devices with the sensing ability. Due to the inconsistency of the feature distribution in different scenarios, a well-trained DFGR system often fails to get satisfactory performance in cross-scenario conditions. Researchers have done valuable exploration on alleviating the feature distribution shift from a global distribution point of view. However, global feature distribution alignment could not solve the feature distribution shift problem completely. In this article, we develop a self-adaptive adversarial learning network which could further reduce the feature distribution shift through aligning the local feature distribution. Specifically, we design an adversarial network which is consisted of a feature extractor, a scenario discriminator, and two diverse classifiers. It could evaluate the degree of local feature distribution alignment by analyzing the prediction inconsistent of the classifiers. We design a self-adaptive adversarial loss which can be adjusted adaptively according to the degree of local alignment. If the features have been aligned locally, we reduce their impact on the loss to protect these aligned features. Otherwise, we increase their influence to accelerate the training process. The extensive experiments conducted on a designed mmWave testbed demonstrate that the proposed method could achieve an accuracy of at least 4% higher than those of existing cross-scenario DFGR methods, while the number of training iterations can be reduced by nearly half.

Power efficiency is a big concern in the Internet of Underwater Things (IoUT). The power consumption of underwater acoustic communications is typically in the scale of watts, which may drain the battery of underwater devices quickly. Whereas, the power consumption of underwater magnetic induction (MI) wireless communications is in the scale of milliwatt. Therefore, this article devotes to combine the underwater MI and acoustic communications to form a power-efficient underwater hybrid wireless network. Specifically, we investigate the power-efficient autonomous underwater vehicle (AUV) data collection schemes in an underwater MI and acoustic hybrid sensor network. We propose an alternating anchor nodes selection and flow routing (AANSFR) AUV data collection method, which alternately optimizes the AUV path planning and network data flow routing. The simulation results show that the proposed hybrid data collection scheme can significantly prolong the lifespan of underwater sensor networks.

Device-free gesture recognition is a potential noncontact human–computer interaction technique. It leverages the unique influence of the conducted gesture on surrounding wireless signals to accomplish gesture recognition. Existing methods usually leverage doppler spectrogram of the influenced wireless signals to characterize the motion pattern of gestures. These methods have achieved satisfactory accuracy when the gestures are conducted in a relatively fixed location, direction, and speed. However, when gestures are conducted in a different scenario, the recognition accuracy will drop dramatically. In this article, we try to solve this issue by characterizing the gesture motion pattern using a novel robust intrinsic spectrogram, which is independent of the conducted scenario. Specifically, we create a virtual coordinate system in which the coordinates of a gesture trajectory remain unchanged no matter where and how the gesture is conducted. Then, we design a coordinate transformation method to transform the raw doppler spectrogram into the robust intrinsic spectrogram to characterize the intrinsic motion pattern of the gesture. We further feed the intrinsic spectrogram into a deep network to realize gesture recognition. Extensive evaluations on a 77-GHz mmWave testbed show that the proposed method could achieve an average recognize accuracy of 88.4% with ten types of gestures.

This article studies the collision avoidance and interference mitigation for unmanned aerial vehicle (UAV) swarm where many UAVs track a common target. The considered problem is formulated to jointly minimize the average interference and ensure the collision avoidance among UAVs. By exploiting the problem characteristics, our major contributions are summarized as follows. First, the singular case tolerance (SCT)-artificial potential field (APF) is proposed to overcome the failure of traditional APFs in collision avoidance, where the repulsive force gain coefficient among UAVs is dynamically controlled by the corresponding interferences. Second, the mean-field game (MFG) model is established to control communication power, where the instantaneous interferences among flying UAVs are represented by the mean-field approximation. Third, considering the tight coupling of trajectory and interference of UAVs, a cooperative control approach enabled by dual-fields is proposed to jointly adjust the trajectories and power of UAVs. Simulation results validate the significant performance improvement of the cooperative control approach enabled by dual-fields. Compared with separate APF and MFG, the proposed dual-field-cooperation approach can achieve about 117% throughput gain and 88% interference reduction when the UAV swarm is close to the target.

With the wide range application of machine learning in healthcare for helping humans drive crucial decisions, data privacy becomes an inevitable concern due to the utilization of sensitive data such as patients records and registers of a company. Thus, constructing a privacy preserving machine learning model while still maintaining high accuracy becomes a challenging problem. In this article, we propose two differentially private algorithms, i.e., Output Perturbation with aGM (OPERA) and Gradient Perturbation with aGM (GRPUA) for empirical risk minimization, a useful method to obtain a globally optimal classifier, by leveraging the analytic Gaussian mechanism (aGM) to achieve privacy preservation of sensitive medical data in a healthcare system. We theoretically analyze and prove utility upper bounds of proposed algorithms and compare them with prior algorithms in the literature. The analyses show that in the high privacy regime, our proposed algorithms can achieve a tighter utility bound for both settings: strongly convex and non-strongly convex loss functions. Besides, we evaluate the proposed private algorithms on five benchmark datasets. The simulation results demonstrate that our approaches can achieve higher accuracy and lower objective values compared with existing ones in all three datasets while providing differential privacy guarantees.

Mobile crowdsensing has emerged as a promising paradigm where location-based sensing tasks are outsourced to mobile participants carrying sensor-equipped devices. A critical issue of crowdsensing is to guarantee the sensing coverage by appropriately recruiting participants, which requires participants’ precise locations and thus raises privacy concerns. In this paper, we are motivated to develop a privacy preserving participant recruitment scheme for mobile crowdsensing, which maximizes the spatial coverage of the sensing range while protecting participants’ location privacy against an untrusted crowdsensing platform. Briefly, we propose a utility-assured location obfuscation mechanism operated in a hexagonal grid system, which the participants can follow to locally perturb their locations with personalized privacy demands. Given the obfuscated locations, we efficiently solve a coverage-maximized participant recruitment problem with the budget constraint by using a deterministic rounding algorithm. Considering the existence of biased sensing data incurred by location obfuscation, we further develop a fault-aware crowdsensing framework to improve the robustness of the recruitment strategy, where a constant-approximation algorithm is applied to select participants against any number of unqualified sensing results. Extensive simulations on real-world location datasets and Uber’s geospatial indexing system validate the efficacy of our location obfuscation mechanism and participant recruitment schemes in mobile crowdsensing systems.

Device-to-Device (D2D) communication enabling direct data transmission between two mobile users has emerged as a vital component for 5G cellular networks to improve spectrum utilization and enhance system capacity. A critical issue for realizing these benefits in D2D-enabled networks is to properly allocate radio resources while coordinating the co-channel interference in a time-varying communication environment. In this paper, we propose a Stackelberg game (SG) guided multi-agent deep reinforcement learning (MADRL) approach, which allows D2D users to make smart power control and channel allocation decisions in a distributed manner. In particular, we define a crucial Stackelberg Q-value (ST-Q) to guide the learning direction, which can be calculated based on the equilibrium achieved in the Stackelberg game. With the guidance of the Stackelberg equilibrium, our approach converges faster with fewer iterations than the general MADRL method and thereby exhibits better performance in handling the network dynamics. After the initial training, each agent can infer timely D2D resource allocation strategies with distributed execution. Extensive simulations are conducted to validate the efficacy of our proposed scheme in developing timely resource allocation strategies. The results also show that our method outperforms the general MADRL based approach in terms of the average utility, channel capacity, and training time.

Indoor location-based services (LBS) are widely used in large-scale indoor buildings, such as high-rise hospitals and multi-story shopping malls. At the same time, location privacy protection in such three-dimensional (3D) space has recently attracted considerable attention. Currently, most existing location privacy protection schemes focus on two-dimensional (2D) location protection and fail to prevent location inference attacks when the user’s location data include height dimension, i.e., 3D geolocation. Enlightened by the concept of differential privacy, in this paper we first study the impact factors of the degree of indistinguishability of 3D geolocations. Then, we quantify location privacy for LBS applications in the 3D space with geo-indistinguishability (3D-GI) rigorously and provably. We develop a mechanism of three-variates Laplacian to generate perturbed locations considering the locations’ X, Y, and Z-coordinates simultaneously, guaranteeing geo-indistinguishability. Furthermore, the discretization noise-adding mechanism is studied to satisfy geo-indistinguishability in the 3D space under the finite precision of hardware/devices. Considering the discretized mechanism can only satisfy geo-indistinguishability in finite 3D space and users visit the limited regions, we further study the truncation of the Laplacian mechanism to limit the generated perturbed locations within a specific region. Simulation results demonstrate that the proposed 3D-GI outperforms the benchmarks while guaranteeing privacy regardless of the adversary’s prior knowledge.

The coupling of federated learning (FL) and multi-access edge computing (MEC) has the potential to foster numerous applications. However, it poses great challenges to train FL fast enough with limited communication and computing resources of mobile edge devices. Motivated by recent development in ultra fast wireless transmissions and promising advances in artificial intelligence (AI) computing hardware of mobile devices, in this paper, we propose a time efficient FL over future mobile edge devices, called dynamic batch sizes assisted federated learning (DBFL) with convergence guarantee. The DBFL allows batch sizes to increase dynamically during training, which can unleash the computing potential of GPU’s parallelism for on- device training and effectively leverage the fast wireless transmissions (WiFi-6, 5G, 6G, etc.) of mobile edge devices. Furthermore, based on the derived DBFL’s convergence bound, we develop a batch size control scheme to minimize the total time consumption of FL over mobile edge devices, which trade-offs the “talking”, i.e., communication time, and “working”, i.e., computing time, by adjusting the incremental factor appropriately. Extensive simulations are conducted to validate the effectiveness of our proposed DBFL algorithm and demonstrate that our scheme outperforms existing time efficient FL approaches in terms of the total time consumption in various settings.

Collaborative learning has received huge interests due to its capability of exploiting the collective computing power of the wireless edge devices. However, during the learning process, model updates using local private samples and large-scale parameter exchanges among agents impose severe privacy concerns and communication bottleneck. In this paper, to address these problems, we propose two differentially private (DP) and communication efficient algorithms, called Q-DPSGD-1 and Q-DPSGD-2. In Q-DPSGD-1, each agent first performs local model updates by a DP gradient descent method to provide the DP guarantee and then quantizes the local model before transmitting it to neighbors to improve communication efficiency. In Q-DPSGD-2, each agent injects discrete Gaussian noise to enforce DP guarantee after first quantizing the local model. Moreover, we track the privacy loss of both approaches under the Renyi DP and provide convergence analysis for both convex and non-convex loss functions. The proposed methods are evaluated in extensive experiments on real-world datasets and the empirical results validate our theoretical findings.

Federated learning (FL) enables devices in mobile edge computing (MEC) to collaboratively train a shared model without revealing the local data. Gradient compression could be applied to FL to alleviate the communication overheads but the existing schemes still face challenges. To deploy green MEC, we propose FedGreen, which enhances the original FL with fine-grained gradient compression to control the total energy consumption of the devices. Specifically, we introduce the relevant operations including device-side gradient reduction and server-side element-wise aggregation to facilitate the gradient compression in FL. According to a public dataset, we evaluate the contributions of the compressed local gradients with respect to different compression ratios. Furthermore, we investigate a learning accuracy-energy efficiency tradeoff problem and the optimal compression ratio and computing frequency are derived for each device. Experimental results show that given the 80% test accuracy requirement, compared with the baseline schemes, FedGreen reduces at least 32% of the total energy consumption of the devices.

Federated learning (FL), an emerging distributed machine learning paradigm, in conflux with edge computing is a promising area with novel applications over mobile edge devices. In FL, since mobile devices collaborate to train a model based on their own data under the coordination of a central server by sharing just the model updates, training data is maintained private. However, without the central availability of data, computing nodes need to communicate the model updates often to attain convergence. Hence, the local computation time to create local model updates along with the time taken for transmitting them to and from the server result in a delay in the overall time. Furthermore, unreliable network connections may obstruct an efficient communication of these updates. To address these, in this paper, we propose a delay-efficient FL mechanism that reduces the overall time (consisting of both the computation and communication latencies) and communication rounds required for the model to converge. Exploring the impact of various parameters contributing to delay, we seek to balance the trade-off between wireless communication (to talk) and local computation (to work). We formulate a relation with overall time as an optimization problem and demonstrate the efficacy of our approach through extensive simulations.

This paper presents a federated learning (FL) framework that uses multiple self-reporting crowdsourcing mobile and web apps to collaboratively construct a fine-grained COVID-19 vulnerability prediction map. The use of FL provides a reliable prediction by aggregating training results from multiple apps, while at the same time circumventing data privacy regulations that prevent user information from multiple apps to be shared with each other. Such a fine-grained vulnerability map identifies early on high-risk areas, helping to reduce the spread of the disease. To mitigate data bias from each self-reporting app, an adaptive worker selection algorithm that leverages neighbouring datasets to obtain a balanced data distribution is proposed. Further, a differential privacy scheme is adopted to protect user information. The simulation results show that the proposed framework outperforms the widely used FedAvg FL algorithm by 6% on prediction accuracy while preserving user privacy.

In-pipe communication exists in broad applications such as structural health monitoring, gas and oil exploration, and remote sensing. The widely deployed pipeline infrastructure provides a new medium to realize wireless communications in the harsh environment, especially underwater or underground communications. The high frequency (above 80 kHz) reverberating stress wave in a short range allows piezoelectric transducers to transmit information. This paper investigates stress wave propagation, the reverberating stress wave channel characteristics, and several potential techniques for the reverberating stress wave channel. Based on our experimental and simulation results, single-input-multiple-output orthogonal frequency-division multiplexting (SIMO-OFDM) and uplink single-carrier FDMA are considered the most suitable schemes for reverberating stress wave communications in point-to-point and multi-user scenarios, respectively. Our evaluation results show the spectral efficiency upper bound is 3.18 bit/s/Hz for 1×3 SIMO and the rate-sum capacity for uplink SC-FDMA is 75.18 kb/s with 30 kHz total bandwidth.

As an enabling technology to address spectrum shortage, spectrum sensing has been investigated a lot. However, the uncertainties in the detection environment, including noise uncertainty and carrier frequency (CF) mismatch, still remain as the main challenges of spectrum sensing, which greatly degrades the sensing performance of typical sensing methods, such as energy detection and cyclostationary detection. To this end, this paper proposes two robust spectrum sensing schemes by leveraging the difference between the phase difference (PD) distribution of noise-perturbed signal and that of Gaussian noise. Specifically, the compact approximation of the PD distribution is first derived to enable the extraction of the features of PD distributions, which are robust to noise uncertainty and CF mismatch. Based on these features, two sensing schemes based on the deep neural network (DNN), referred to as DNN-based PD distribution detection (PDD) and blind PDD (BPDD), are proposed to detect spectrum holes in cases with known CF and unknown CF, respectively. Simulation results show that our proposed schemes are more robust to CF mismatch and noise uncertainty in comparison with the existing sensing schemes. Furthermore, when the CF of the sensed signal is unknown, the proposed BPDD significantly outperforms existing blind sensing schemes.

High altitude balloons (HABs) can be deployed in the stratosphere to provide high capacity connectivity to users in rural and remote areas with their advantages of low cost and easy deployment. In this paper, we propose a software defined HAB-based network (SD-HABN) architecture to efficiently exploit the resource of inter-HAB links. The multi-beam directional antenna equipped on the HAB realizes concurrent transmission. However, this may result in interference because of the over-lapping beams at the receiving side. To mitigate interference and improve network performance, the beam angle adjustment, link scheduling, routing, and data rate controlling are jointly optimized, aiming to maximize the network utility. On account of the coupled relationship between the optimization variables, a novel optimization approach is proposed to solve this problem. The original problem is divided into two problems. The first one is that of optimizing the beam angle adjustment with fixed link scheduling, which is solved by a genetic algorithm. The second one is that of optimizing the routing and data rate controlling with fixed beam angle adjustment, which is solved by generalized bender decomposition. By solving the two problems orderly, the original problem will attain an approximately optimal solution. Simulation results show that the SD-HABN can achieve a high network performance via the joint optimization of beam angle adjustment, link scheduling, routing, and data rate controlling.

Recent advances in machine learning, wireless communication, and mobile hardware technologies promisingly enable federated learning (FL) over massive mobile edge devices, which opens new horizons for numerous intelligent mobile applications. Despite the potential benefits, FL imposes huge communication and computation burdens on participating devices due to periodical global synchronization and continuous local training, raising great challenges to battery constrained mobile devices. In this work, we target at improving the energy efficiency of FL over mobile edge networks to accommodate heterogeneous participating devices without sacrificing the learning performance. To this end, we develop a convergence-guaranteed FL algorithm enabling flexible communication compression. Guided by the derived convergence bound, we design a compression control scheme to balance the energy consumption of local computing (i.e., "working") and wireless communication (i.e., "talking") from the long-term learning perspective. In particular, the compression parameters are elaborately chosen for FL participants adapting to their computing and communication environments. Extensive simulations are conducted using various datasets to validate our theoretical analysis, and the results also demonstrate the efficacy of the proposed scheme in energy saving.

Deep learning holds a great promise of revolutionizing healthcare and medicine. Unfortunately, various inference attack models demonstrated that deep learning puts sensitive patient information at risk. The high capacity of deep neural networks is the main reason behind the privacy loss. In particular, patient information in the training data can be unintentionally memorized by a deep network. Adversarial parties can extract that information given the ability to access or query the network. In this paper, we propose a novel privacy-preserving mechanism for training deep neural networks. Our approach adds decaying Gaussian noise to the gradients at every training iteration. This is in contrast to the mainstream approach adopted by Google's TensorFlow Privacy, which employs the same noise scale in each step of the whole training process. Compared to existing methods, our proposed approach provides an explicit closed-form mathematical expression to approximately estimate the privacy loss. It is easy to compute and can be useful when the users would like to decide proper training time, noise scale, and sampling ratio during the planning phase. We provide extensive experimental results using one real-world medical dataset (chest radiographs from the CheXpert dataset) to validate the effectiveness of the proposed approach. The proposed differential privacy based deep learning model achieves significantly higher classification accuracy over the existing methods with the same privacy budget.

Multiaccess edge computing (MEC) empowers service providers (SPs) to run applications on the shared edge platforms in close proximity to mobile users, enabling ultralow latency access to a wide variety of cloud services. However, how to decide the amount of edge computing resources to rent for mobile service provisioning poses great challenges as the service demand is unknown to SPs a priori and may vary across the geographically distributed edge sites spatially and temporally. The resource rental decision also significantly affects SPs' deploying profits since it is critical for service deployment and workload assignment. This article investigates the service provisioning problem in a cooperative edge computing system under service demand uncertainty. We develop a holistic solution to make two-timescale decisions on edge resource rental and workload assignment to maximize SP's deploying profits. Briefly, we exploit historical service demand traces at the edge sites to characterize the uncertainty in a data-driven manner and formulate the edge service provisioning problem into a two-stage risk-averse optimization. To solve the formulated problem without compromising the data privacy, we propose an algorithm integrating Benders decomposition (BD) and alternating direction method of multipliers (ADMMs), which enables each edge site to keep the historical traces locally and participate in the optimization process. Based on real-world data sets, extensive simulations are conducted to validate the efficacy of our scheme.

Federated learning is a promising tool in the Internet-of-Things (IoT) domain for training a machine learning model in a decentralized manner. Specifically, the data owners (e.g., IoT device consumers) keep their raw data and only share their local computation results to train the global model of the model owner (e.g., an IoT service provider). When executing the federated learning task, the data owners contribute their computation and communication resources. In this situation, the data owners have to face privacy issues where attackers may infer data property or recover the raw data based on the shared information. Considering these disadvantages, the data owners will be reluctant to use their data to participate in federated learning without a well-designed incentive mechanism. In this article, we deliberately design an incentive mechanism jointly considering the task expenditure and privacy issue of federated learning. Based on a differentially private federated learning (DPFL) framework that can prevent the privacy leakage of the data owners, we model the contribution as well as the computation, communication, and privacy costs of each data owner. The three types of costs are data owners' private information unknown to the model owner, which thus forms an information asymmetry. To maximize the utility of the model owner under such information asymmetry, we leverage a 3-D contract approach to design the incentive mechanism. The simulation results validate the effectiveness of the proposed incentive mechanism with the DPFL framework compared to other baseline mechanisms.

Millimeter wave (mmWave) communications can potentially meet the high data-rate requirements of unmanned-aerial-vehicle (UAV) networks. However, as the prerequisite of mmWave communications, the narrow directional beam tracking is very challenging because of the 3-D mobility and attitude variation of UAVs. Aiming to address the beam tracking difficulties, we propose to integrate the conformal array (CA) with the surface of each UAV, which enables the full spatial coverage and the agile beam tracking in highly dynamic UAV mmWave networks. More specifically, the key contributions of our work are threefold: 1) a new mmWave beam tracking framework is established for the CA-enabled UAV mmWave network; 2) a specialized hierarchical codebook is constructed to drive the directional radiating element (DRE)-covered cylindrical CA, which contains both the angular beam pattern and the subarray pattern to fully utilize the potential of the CA; and 3) a codebook-based multiuser beam tracking scheme is proposed, where the Gaussian process machine learning-enabled UAV position/attitude prediction is developed to improve the beam tracking efficiency in conjunction with the tracking-error aware adaptive beamwidth control. Simulation results validate the effectiveness of the proposed codebook-based beam tracking scheme in the CA-enabled UAV mmWave network, and demonstrate the advantages of CA over the conventional planner array in terms of spectrum efficiency and outage probability in the highly dynamic scenarios.

In this paper, we study how colocation datacenter energy cost can be effectively reduced in the wholesale electricity market via cooperative power procurement. Intuitively, by aggregating workloads and renewables across a group of tenants in a colocation datacenter, the overall power demand uncertainty of the colocation datacenter can be reduced, resulting in less chance of being penalized when participating in the wholesale electricity market. We use cooperative game theory to model the cooperative electricity procurement process of tenants as a cooperative game, and show the cost saving benefits of aggregation. Then, a cost allocation scheme based on the marginal contribution of each tenant to the total expected cost is proposed to distribute the aggregation benefits among the participating tenants. Besides, we propose proportional cost allocation scheme to distribute the aggregation benefits among the participating tenants after realizations of power demand and market prices. Finally, numerical experiments based on real-world traces are conducted to illustrate the benefits of aggregation compared to noncooperative power procurement.

Spectrum trading benefits both secondary users (SUs) and primary users (PUs), while it poses great challenges to maximize PUs' revenue, since SUs' demands are uncertain and individual SU's traffic portfolio contains private information. In this paper, we propose a data-driven spectrum trading scheme which maximizes PUs' revenue and preserves SUs' demand differential privacy. Briefly, we introduce a novel network architecture consisting of the primary service provider (PSP), the secondary service provider (SSP) and the secondary traffic estimator and database (STED). Under the proposed architecture, PSP aggregates available spectrum from PUs, and sells the spectrum to SSP at fixed wholesale price, directly to SUs at spot price, or both. The PSP has to accurately estimate SUs' demands. To estimate SUs' demand, the STED exploits data-driven approach to choose sampled SUs to construct the reference distribution of SUs' demands, and utilizes reference distribution to estimate the demand distribution of all SUs. Moreover, the STED adds noises to preserve the demand differential privacy of sampled SUs before it answers the demand estimation queries from the PSP. With the estimated SUs' demand, we formulate the revenue maximization problem into a risk-averse optimization, develop feasible solutions, and verify its effectiveness through both theoretical proof and simulations.

Wireless network virtualization is basically the abstraction, isolation, and sharing of wireless resources among different entities. Consequently, virtualization provides great flexibility and higher network efficiency, and enables easier migration to new technologies in wireless networks. Traditionally, a wireless network virtualization controller manages the virtual resources (including radio resources and infrastructure resources) known as slices which are available to the Service Providers (SPs). The SPs then allocate their purchased resources to serve their subscribed mobile users. Such a centralized allocation decouples the Quality-of-Service (QoS) management by the SPs from the virtual resource management by the controller. In this paper, we propose a matching based wireless network virtualization resource allocation mechanism: a distributed three-sided (3D) matching between radio resources, physical infrastructure and mobile users. The Restricted Three-sided Matching with Size and Cyclic preference model (R-TMSC) is implemented to obtain a stable solution. Simulation results show that our proposed spectrum-oriented and user-oriented algorithms outperform the traditional resource allocation schemes. The spectrum-oriented algorithm enhances the user throughput and the system performance, within a lesser run time. Furthermore, for an increasing number of users, the proposed algorithms serve more users than traditional methods.

Information-centric networking (ICN) as an emerging networking paradigm has recently gained significant attention, due to the improvement of content delivery efficiency. The built-in network storage for caching is a key component in ICN to provide low latency service and reduce high backhaul traffic by caching popular content. However, users' content preference contains individual sensitive characteristics which is distinguishable from others. Therefore, in this work, we propose a data-driven caching revenue maximization problem with the considerations of users' local differential privacy. Specifically, we employ dBitFlip, a local differential privacy (LDP) mechanism, to locally add differential private noise to the users' preference content information. We leverage data-driven approach to predict the content popularity based on the reference distribution constructed by the reported noisy preference content data from users, mathematically present the distance between the noisy reference distribution and the true distribution by the tolerance level, and prove the relationship among the tolerance level, differential privacy budget and the confidence level. We provide feasible solutions to the proposed revenue maximization problem, and conduct simulations to show the effectiveness of the proposed scheme.

Machine learning is increasingly becoming a powerful tool to make decisions in a wide variety of applications, such as medical diagnosis and autonomous driving. Privacy concerns related to the training data and unfair behaviors of some decisions with regard to certain attributes (e.g., sex, race) are becoming more critical. Thus, constructing a fair machine learning model while simultaneously providing privacy protection becomes a challenging problem. In this paper, we focus on the design of classification model with fairness and differential privacy guarantees by jointly combining functional mechanism and decision boundary fairness. In order to enforce ϵ-differential privacy and fairness, we leverage the functional mechanism to add different amounts of Laplace noise regarding different attributes to the polynomial coefficients of the objective function in consideration of fairness constraint. We further propose an utility-enhancement scheme, called relaxed functional mechanism by adding Gaussian noise instead of Laplace noise, hence achieving (ϵ, δ)-differential privacy. Based on the relaxed functional mechanism, we can design (ϵ, δ)-differentially private and fair classification model. Moreover, our theoretical analysis and empirical results demonstrate that our two approaches achieve both fairness and differential privacy while preserving good utility and outperform the state-of-the-art algorithms.

The Alternating Direction Method of Multipliers (ADMM) and its distributed version have been widely used in machine learning. In the iterations of ADMM, model updates using local private data and model exchanges among agents impose critical privacy concerns. Despite some pioneering works to relieve such concerns, differentially private ADMM still confronts many research challenges. For example, the guarantee of differential privacy (DP) relies on the premise that the optimality of each local problem can be perfectly attained in each ADMM iteration, which may never happen in practice. The model trained by DP ADMM may have low prediction accuracy. In this paper, we address these concerns by proposing a novel (Improved) Plausible differentially Private ADMM algorithm, called PP-ADMM and IPP-ADMM. In PP-ADMM, each agent approximately solves a perturbed optimization problem that is formulated from its local private data in an iteration, and then perturbs the approximate solution with Gaussian noise to provide the DP guarantee. To further improve the model accuracy and convergence, an improved version IPP-ADMM adopts sparse vector technique (SVT) to determine if an agent should update its neighbors with the current perturbed solution. The agent calculates the difference of the current solution from that in the last iteration, and if the difference is larger than a threshold, it passes the solution to neighbors; or otherwise the solution will be discarded. Moreover, we propose to track the total privacy loss under the zero-concentrated DP (zCDP) and provide a generalization performance analysis. Experiments on real-world datasets demonstrate that under the same privacy guarantee, the proposed algorithms are superior to the state of the art in terms of model accuracy and convergence rate.

The aim of this paper is to preserve location privacy of crowdsourced-based spectrum sensing agents using geo-indistinguishability. We considered database-driven dynamic spectrum access, where a radio environment map provides spectrum availability information for dynamic spectrum access management. Moreover, we assumed crowdsourced-based spectrum sensing, where a pool of allocated mobile users, called crowdsourced-based spectrum sensing agents, sense the spectrum and report their actual location and the received signal strength to the spectrum manager that constructs a radio environment map. This discloses location information of crowdsourced-based spectrum sensing agents and violates their location privacy. Consequently, crowdsourced-based spectrum sensing agents could be discouraged to participate in spectrum sensing. In our paper, to solve the problem of location disclosure, we adopted planar Laplacian mechanism, where each crowdsourced-based spectrum sensing agent reports an obfuscated location instead of its actual location, which achieves geo-indistinguishability. Our simulation results were based on real-world CRAWDAD dataset. Our results showed that with a moderate privacy level, location privacy of crowdsourced-based spectrum sensing agents was preserved while the effect of introduced location noise on the accuracy of radio environment map was insignificant.

The pandemic of the coronavirus (COVID-19) has caused an unprecedented global public health crisis, and most countries in the world are running out of the healthcare resources. A fine-grained COVID-19 vulnerability map will be essential to track the number of people with covid-like symptoms, so that the the potential outbreak communities can be identified and the valuable healthcare resources can proactively and dynamically be allocated. Mobile crowdsourcing based symptom reporting is a promising and convenient option to construct such a map, while it may compromise the location privacy of crowdsourcing participants. In this work, we propose a novel approach to establish the COVID-19 vulnerability map based on the crowdsourced reporting without disclosing the participants' location privacy to a semi-honest crowdsourcing aggregator. Briefly, based on the differentially private geo-indistinguishability, the mobile participants are able to locally perturb their geographic data. With the masked geographic information, we employ the best linear unbiased prediction estimator with spatial smoothing to obtain the reliable vulnerability estimates in the areas of interest and construct the map. Given the fast spreading nature of coronavirus, we integrate the vulnerability estimates with a susceptible-exposed-infected-removed (SEIR) model to build up a future trend map. Extensive simulations based on real-world data verify the effectiveness of the proposed method.

Machine learning has a vast outreach in principal applications and uses large amount of data to train the models, prompting a viable and easy to use Machine Learning as a Service (MLaaS). This flexible paradigm however, could have immense privacy implications since the training data often contains sensitive features, and adversarial access to such models could pose a security risk. In adversarial attacks such as model inversion attack on a system used for face recognition, an adversary uses the output (target label) to reconstruct the input (image of the target individual from the training dataset). To avert such a vulnerability of the system, in this paper, we develop a novel approach of applying perceptual hash to parts of the given training images that leverages the functional mechanism of image hashing. The facial recognition system is then trained over this newly created dataset of perceptually hashed images and high classification accuracy is observed. Furthermore, we demonstrate a series of model inversion attacks emulating adversarial access that yield hashed images of target individuals instead of the original training dataset images; thereby preventing original image reconstruction and counteracting the inversion attack. Through rigorous empirical evaluations of applying the proposed formulation over real world dataset, we verify the effectiveness of our proposed framework in protecting the training image dataset and counteracting inversion attack.

Currently, a large volume of IoT devices generate huge amounts of data in edge networks, which can open up many research and applications for machine learning. However, traditional machine learning requires data to be sent to a server and centrally trained, which will cause the waste of the bandwidth and expose privacy of individuals. Federated learning allows data to be locally trained in their device and only send model updates to the central server for aggregation. But the security of model updates in the aggregation should also be carefully addressed. Existing works mainly focus on secure multiparty computation or differential privacy, which depends on heavy encryption or brings low accuracy. In this paper, we propose an efficient secure aggregation method for model updates in federated learning by pre-processing the model updates from each participant and only encrypting portion of the processed updates by functional encryption for inner product to protect the whole parameters, thus achieving efficient aggregation of model update vectors. Security analysis and experimental evaluation demonstrate that our scheme can efficiently aggregate the model updates without losing security.

The advance of edge computing pushes computing functionalities to the network edge and brings lucrative opportunities for edge operators (EOs) to cater the users with low latency requirement. Unlike in cloud computing, edge servers have limited computing capacity and require a proper resource planning. To avoid loss of potential profit, a promising way is to outsource cloud resources from a public cloud with additional cost when the edge computing capacity is insufficient to meet the real-time demands. Besides, the uncertainty of future demands also affects EOs' profits. It's essential to consider the interaction among market participants with different risk attitudes. To this end, we study multiple risk-averse EOs with one risk-neutral Cloud Provider (CP) in an edge computing market, where each EO competes to serve the users by determining the optimal resource provision strategies given the demand and the outsource price charged by the CP, and the CP sets the price based on the best responses of the EOs. We model the interaction between EOs and CP as a two stage Stackelberg game, and employ a data-driven optimization approach to characterize the uncertainty. We explore the existence and uniqueness of subgame Nash equilibrium, and find the equilibrium based on the Sample Average Approximation (SAA) method. Extensive simulations using real-world cluster data traces verify the effectiveness of the proposed method.

The development of 5G network and rapid growth of mobile traffic bring lucrative opportunities for Micro Operators (μOs), the novel local operators who own local spectrum, deploy and manage small cells (e.g., femtocells) in a specific area. Collaborating with traditional mobile network operators (MNOs), μOs gain profits from helping to offload the traffic carried by macro base stations and providing the MNOs customers with seamless service. However, due to the demand uncertainty and the sensitivity of individual user's demand profile, it is challenging for μOs to allocate the resource properly to avoid the under-and over-utilized situations. To address this issue, in this paper, we propose to employ data-driven approach to characterize the demand uncertainty, exploit differential privacy protocols to protect user's demand profile, and formulate the small cell planning problem into two-stage stochastic programming optimization with the objective of minimizing the capital and operational costs of μOs. Based on the formulated problem, we develop feasible solutions and conduct extensive simulations using real-world base station accessing real cellular data (i.e., data of 4G LTE network in Zhengzhou city, China) to verify the effectiveness of the proposed model.

According to the safety organization Kids and Cars, in US, an average of 38 children die each year in hot cars, seemingly forgotten by a distracted parent. Existing car seat alarm designs either compromise people's privacy (camera based designs), or fail to distinguish children sitting in the back from heavy stuff put on rear seats, and keep sending false alerts (pressure sensor based designs). In an effort to prevent such tragedies, we propose to utilize the fine-grained channel state information (CSI) from commercial off-the-shelf WiFi devices to detect if a child has been forgotten in rear seat of the car. Our child detection system only needs WiFi signal and applies both phase and amplitude measurement of the CSI. Based on this, our system can capture the movements of children, and effectively detect the children who are forgotten in rear seat and distinguish them from pets or other heavy stuff in rear seat with deep learning algorithms. In comparison with KNN based child detection method, the experiment results show that the performance of our deep learning based system increases dramatically, and the detection accuracy can reach more than 95%.

Mobile crowdsensing (MCS) has become a new sensing and computing paradigm due to the proliferation of global positioning system (GPS) enabled mobile devices. There are three parties in the MCS, the MCS server, task requesters and workers. The MCS server needs to collect workers' location information to optimize the task allocation problem. However, during the location data collection process, workers' location privacy might be disclosed without their knowledge. It is challenging to preserve workers' location privacy while effectively and efficiently selecting proper workers to fulfill an MCS task. In this work, we propose a novel differentially private geocoding (DPG) mechanism to preserve workers' location privacy. Specifically, instead of reporting the exact latitude and longitude to the server, workers can use obfuscated geocode to describe their locations, since geocodes can provide an intuitive visualization of workers' spatial information to the MCS server. Based on the workers' obfuscated geocodes, we also formulate a travel distance minimization problem in MCS into an integer linear programming problem. We leverage conditional value at risk (CVaR) to characterize the uncertainty brought by the obfuscated geocodes, and develop feasible solutions to the formulated optimization problem. We conduct simulations with a real-world taxi dataset and verify the effectiveness of the proposed mechanism.

Sparse learning is a very important tool for mining useful information and patterns from high dimensional data. Nonconvex non-smooth regularized learning problems play essential roles in sparse learning, and have drawn extensive attentions recently. We design a family of stochastic proximal gradient methods by applying arbitrary sampling to solve the empirical risk minimization problem with a non-convex and non-smooth regularizer. These methods draw mini-batches of training examples according to an arbitrary probability distribution when computing stochastic gradients. A unified analytic approach is developed to examine the convergence and computational complexity of these methods, allowing us to compare the different sampling schemes. We show that the independent sampling scheme tends to improve performance over the commonly-used uniform sampling scheme. Our new analysis also derives a tighter bound on convergence speed for the uniform sampling than the best one available so far. Empirical evaluations demonstrate that the proposed algorithms converge faster than the state of the art.

As a promising solution of spectrum shortage, spectrum sharing has received tremendous interests recently. However, under different sharing policies of different licensees, the shared spectrum is heterogeneous both temporally and spatially, and is usually uncertain due to the unpredictable activities of incumbent users. In this paper, considering the spectrum uncertainty, we propose a spectrum sharing based delay-tolerant traffic off-loading (SDTO) scheme. To capture the available heterogeneous shared bands, we adopt a mesh cognitive radio network and employ the multi-hop transmission mode. To statistically guarantee the end-to-end (E2E) session request under the uncertain spectrum supply, we formulate the SDTO scheme into a stochastic optimization problem, which is transformed into a mixed integer nonlinear programming (MINLP) problem. Then, a coarse-fine search based iterative heuristic algorithm is proposed to solve the MINLP problem. Simulation results demonstrate that the proposed SDTO scheme can well schedule the network resource with an E2E session guarantee.

Open wireless channels make a wireless ad hoc network vulnerable to various security attacks, so it is crucial to design a routing protocol that can defend against the attacks of malicious nodes. In this paper, we first measure the trust value calculated by the node behavior in a period to judge whether the node is trusted, and then combine other QoS requirements as the routing metrics to design a secure routing approach. Moreover, we propose a deep learning-based model to learn the routing environment repeatedly from the data sets of packet flow and corresponding optimal paths. Then, when a new packet flow is input, the model can output a link set that satisfies the node's QoS and trust requirements directly, and therefore the optimal path of the packet flow can be obtained. The extensive simulation results show that compared with the traditional optimization-based method, our proposed deep learning-based approach cannot only guarantee more than 90% accuracy, but also significantly improves the computation time.

The fast development on emerging services makes our telecommunications networks witness two key problems. One is the flexibility to fulfill the diverse service requests and the other is the shortage on spectrum. Network slicing and spectrum sharing have been regarded as two prominent solutions, which, however, are barely jointly studied. When taking the shared spectrum into account, its unique feature of heterogeneity and uncertainty will bring new challenges for the slicing. In this paper, we propose a service-oriented spectrum-aware RAN-slicing trading (SSRT) scheme with a comprehensive consideration on both aspects. For the SSRT scheme, we jointly slice three kinds of resources, namely, time, spectrum (including both licensed one and shared one), and network facilities, according to the diverse traffic requests, which are classified into delay-tolerant (DT) ones and delay-sensitive (DS) ones, as well as the willing payments from different service providers (SPs). To achieve both inter-slice and intra-slice isolation, we construct a three-dimensional (3D) conflict graph and formulate the SSRT scheme into a mixed-integer nonlinear programming (MINLP) problem with a cross-layer spectrum-aware resource allocation and a hybrid transmission mode (including both single-hop and multi-hop). Since finding all the maximum independent sets (MIS) for the 3D conflict graph is an NP-hard problem, we further develop an iterative heuristic algorithm for the MIS determination.

The explosive growth on emerging Internet-of-Things (IoT) applications makes our telecommunications networks confront twofold challenges. One is to provide sufficient flexibility for service diversity. The other is the shortage on spectrum. Network slicing and spectrum sharing have been deemed as two prominent solutions, which, however, are barely jointly studied in the literature. In this article, standing on both aspects, we propose a service-oriented spectrum-aware RAN-slicing trading (SSRT) scheme to achieve a dynamic on-demand RAN slicing under the spectrum sharing scenario. For the SSRT scheme, we jointly slice multidimensional resources, including heterogeneous spectrums (licensed and shared), time, and network facilities (nodes, radios, and powers). In particular, considering the uncertainty of shared spectrums, we distinguish them from the traditional licensed ones to fulfill different types of sessions, which are classified into delay tolerant and delay sensitive. To achieve an effective isolation, we construct a 4-D conflict graph and formulate the slice generation problem into a mixed-integer nonlinear programming (MINLP) problem, where a cross-layer resource allocation based on a hybrid transmission mode is designed for the customization. To cope with the difficulties when solving the problem, we employ the column generation algorithm to obtain the final slicing result over all the resources. The simulation results have shown the effectiveness of the proposed scheme.

Proactive caching in mobile-edge computing (MEC) networks is promising to handle the ever-increasing demand for wireless video services, and transcoding at MEC servers further improves the flexibility of video content delivery. However, how to effectively conduct caching for adaptive bitrate streaming poses great challenges due to the uncertainty of user preferences. The caching decisions also have a profound impact on the system energy efficiency since they may change the video delivery modes. In this article, by integrating caching, transcoding, and backhaul retrieving in a MEC-enabled adaptive streaming system, we propose a holistic solution to jointly determine the caching of bitrate-aware files and the scheduling of video requests in an energy-efficient manner. Specifically, we leverage a data-driven approach to characterize the uncertainty of real request arrivals. Based on the uncertainty model, we formulate a data-driven risk-averse optimization to derive a robust strategy for caching and delivery scheduling, which is a two-stage stochastic mixed-integer programming (SMIP) with the goal of minimizing the total expected energy consumption. We also develop feasible solutions and conduct extensive simulations on real-world data sets. The results validate the effectiveness of the proposed scheme in both the energy efficiency and the cache hit ratio.

As one of the most promising networks, the space–air–ground–aqua integrated network (SAGAIN) has the characteristics of wide coverage and large information capacity, which can meet various requests from users in different domains. With the rapid growth of data and information generated by the Internet of Things (IoT), SAGAIN has received much attention in recent years. However, the existing network architectures are not capable of providing personalized network services according to different task types in SAGAIN. Besides, they cannot deal with many problems in SAGAIN well, such as heterogeneous network disconnection, high network delay, intermittent interruption, and unbalanced network load. In this article, in order to solve the abovementioned problems, we propose a novel architecture for SAGAIN named task-oriented intelligent networking architecture (TOINA). First, we apply the edge-cloud computing technology and network domain division in TOINA to realize intelligent networking and reduce the latency. Second, the task-oriented networking method is proposed to provide personalized network services and increase network intelligence. Third, we intend to leverage the information center network (ICN) paradigm to build the SAGAIN and optimize the content naming rules. Furthermore, a preprocessing layer was added in the network protocol stack to perform the heterogeneous network convergence in SAGAIN. In addition, some security technologies related to network architecture are considered in SAGAIN. This article presents the background, rationale, and benefits of the TOINA for SAGAIN. Besides, a specific case is studied to illustrate the network architecture work process further.

Cooperative multiaccess edge computing (MEC) is a promising paradigm for the next-generation mobile networks. However, when the number of users explodes, the computational complexity of the existing optimization or learning-based task placement approaches in the cooperative MEC can increase significantly, which leads to intolerable MEC decision-making delay. In this article, we propose a mean field game (MFG) guided deep reinforcement learning (DRL) approach for the task placement in the cooperative MEC, which can help servers make timely task placement decisions, and significantly reduce average service delay. Instead of applying MFG or DRL separately, we jointly leverage MFG and DRL for task placement, and let the equilibrium of MFG guide the learning directions of DRL. We also ensure that the MFG and DRL approaches are consistent with the same goal. Specifically, we novelly define a mean field guided Q -value (MFG-Q), which is an estimation of the Q -value with the Nash equilibrium gained by MFG. We evaluate the proposed method's performance using real-world user distribution. Through extensive simulations, we show that the proposed scheme is effective in making timely decisions and reducing the average service delay. Besides, the convergence rates of our proposed method outperform the pure DR-based approaches.

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.

The emerging paradigm - Software-Defined Networking (SDN) and Network Function Virtualization (NFV) - makes it feasible and scalable to run Virtual Network Functions (VNFs) in commercial-off-the-shelf devices, which provides a variety of network services with reduced cost. Benefitting from centralized network management, lots of information about network devices, traffic and resources can be collected in SDN/NFV-enabled networks. Using powerful machine learning tools, algorithms can be designed in a customized way according to the collected information to efficiently optimize network performance. In this paper, we study the VNF placement problem in SDN/NFV-enabled networks, which is naturally formulated as a Binary Integer Programming (BIP) problem. Using deep reinforcement learning, we propose a Double Deep Q Network-based VNF Placement Algorithm (DDQN-VNFPA). Specifically, DDQN determines the optimal solution from a prohibitively large solution space and DDQN-VNFPA then places/releases VNF Instances (VNFIs) following a threshold-based policy. We evaluate DDQN-VNFPA with trace-driven simulations on a real-world network topology. Evaluation results show that DDQN-VNFPA can get improved network performance in terms of the reject number and reject ratio of Service Function Chain Requests (SFCRs), throughput, end-to-end delay, VNFI running time and load balancing compared with the algorithms in existing literatures.

Data centers are key participants in emergency demand response (EDR), where the grid coordinates large electricity consumers for reducing their consumption during emergency situations to prevent major economic losses. While existing literature concentrates on owner-operated data centers (e.g., Google), this work studies EDR in multi-tenant colocation data centers (e.g., Equinix) where servers are owned and managed by individual tenants and which are better targets of EDR. Existing EDR mechanisms incentivize tenants energy reduction. Such designs can either be gamed by strategic tenants or untrustworthy colocation operators for illegal gains. These serious privacy concerns stand as barrier preventing the tenants' participation in EDR. This paper addresses such concerns by proposing a privacy-preserving and strategy-proof mechanism using the descending clock auction. Privacy is protected by implementing homomorphic encryption for aggregation through the clock auction, where operator can only know the aggregate of the tenants' values or bids but not their individual private values or confidential information submitted to meet the EDR. We evaluate the privacy and performance of this scheme by formulating descending clock auction, in which the amount of energy/price the tenants are willing to reduce for a given price/energy to meet EDR is protected.

Device-free gesture recognition (DFGR) is a promising sensing technique, which can recognize a gesture by analyzing its influence on surrounding wireless signals. Most of the DFGR systems are designed based on machine learning. However, the recognition performance will drop dramatically when the testing condition is different with the training one. Inspired by the transferrable knowledge learning ability of humans, this paper develops a practical DFGR system based on metalearning to solve the aforementioned problem. Specifically, we design a deep network which could not only learn discriminative deep features, but also learn a transferrable similarity evaluation ability from the training set and apply the learned knowledge to the new testing conditions. Extensive experiments conducted by four users in two scenarios demonstrate that the proposed system could recognize new types of gestures, or gestures performed in new conditions, with an accuracy of more than 90%, using very few number of new samples.

To embrace the era of big data, there has been growing interest in designing distributed machine learning to exploit the collective computing power of the local computing nodes. Alternating Direction Method of Multipliers (ADMM) is one of the most popular methods. This method applies iterative local computations over local datasets at each agent and computation results exchange between the neighbors. During this iterative process, data privacy leakage arises when performing local computation over sensitive data. Although many differentially private ADMM algorithms have been proposed to deal with such privacy leakage, they still have to face many challenging issues such as low model accuracy over strict privacy constraints and requiring strong assumptions of convexity of the objective function. To address those issues, in this paper, we propose a differentially private robust ADMM algorithm (PR-ADMM) with Gaussian mechanism. We employ two kinds of noise variance decay schemes to carefully adjust the noise addition in the iterative process and utilize a threshold to eliminate the too noisy results from neighbors. We also prove that PR-ADMM satisfies dynamic zero-concentrated differential privacy (dynamic zCDP) and a total privacy loss is given by (∈, δ)-differential privacy. From a theoretical point of view, we analyze the convergence rate of PR-ADMM for general convex objectives, which is O(1/K) with K being the number of iterations. The performance of the proposed algorithm is evaluated on real-world datasets. The experimental results show that the proposed algorithm outperforms other differentially private ADMM based algorithms under the same total privacy loss.

In this paper, we examine the use of autoencoders as an optimization tool for the construction of noncoherent space-time MIMO codes. In particular, we consider the quasi-static block fading channel, where the channel state information is not available at either the transmitter or the receiver, and changes independently between transmissions. Different from traditional constructions which aim to maximize an approximation of the minimum pairwise distance of the constellation, we use the autoencoder to directly target minimizing the probability of error. We show that this different optimization goal leads to constellations with more favorable pairwise distances' distribution and better error performance at low to medium signal to noise ratios where the minimum distance is not the limiting factor. Finally, we present simulation results showing that the constructed codes outperform traditional Grassmannian codes up to a signal-to-noise ratio of 20 dB using the traditional generalized likelihood ratio test detector.

The auction mechanism is deemed to be an effective method to address the problem of spectrum scarcity. Numerous spectrum auction mechanisms can alleviate spectrum shortage under the consideration of truthfulness, social welfare maximization and spectrum reusability, while the privacy preservation and preferences of primary/secondary users have not been fully discussed. In this paper, we propose a matching based double auction mechanism for spectrum trading with differential privacy (MastDP) to protect the privacy of buyers/sellers from the untrustworthy auctioneer, other buyers/sellers and other potential parties. Each participant adds distributed differential private noise following Geom(α) distribution to his bid value and encrypts the noisy bid value. The auctioneer can decrypt only the sum of all uploaded noisy bid values and determines the clearing price by using its private key. Based on the clearing price, the matching theory is adopted to maximize the winning participants' revenue while fully considering their preferences and spectrum reuse. Simulation results show that MastDP achieves satisfactory performance in terms of economic properties' privacy preservation and spectrum trading efficiency.

With the popularity of online social networks, social recommendations that rely on ones social connections to make personalized recommendations have become possible. This introduces vulnerabilities for an adversarial party to compromise the recommendations for users by utilizing their social connections. In this paper, we propose the targeted poisoning attack on the factorization-based social recommender system in which the attacker aims to promote an item to a group of target users by injecting fake ratings and social connections. We formulate the optimal poisoning attack as a bi-level program and develop an efficient algorithm to find the optimal attacking strategy. We then evaluate the proposed attacking strategy on real-world dataset and demonstrate that the social recommender system is sensitive to the targeted poisoning attack. We find that users in the social recommender system can be attacked even if they do not have direct social connections with the attacker.

Nowadays most mobile devices are equipped with advanced sensors, enabling the measurement of information about surrounding environment or social settings. The ubiquity of mobile devices makes them the perfect platform for massive data collection, which motivates the emergence of mobile crowdsensing paradigm. However, due to the inherent noisy nature of the sensing process and the limited capability of low-cost commodity sensors, crowdsensed information tends to be less reliable compared with sensing results through dedicated sensing hardware, and multiple crowdsensing sources may conflict with each other. Thus, it is important to resolve conflicts in the collected data and discover the underlying truth. Traditional truth discovery approaches usually estimate the reliability of data sources and predict the truth value based on source reliability. However, recent data poisoning attacks greatly degrade the performance of existing truth discovery algorithms, where attackers aim to maximize the utility loss. In this paper, we investigate the data poisoning attacks on truth discovery and propose a robust approach against such attacks through additional source estimation and source filtering before data aggregation. Based on real-world data, we simulate our approach and evaluate its performance under data poisoning attacks, demonstrating the robustness of our approach.

Server and Network Assisted Dynamic adaptive streaming over HTTP (SAND) is a promising technology to cope with the dramatic increase in video streaming traffic. The new emerging Mobile Edge Computing (MEC) paradigm may further facilitate bitrate adaptation and video transcoding in a SAND system with the help of local edge servers. A critical issue in MEC-SAND framework is to guarantee Quality of Experience (QoE) for clients while achieving efficient utilization of edging network resources. In this paper, we aim to develop an adaptive video delivery scheme to minimize the delay in MEC assisted ultra-dense networks. In our scheme, each client is mapped to a server that better fits its requirements and transmission condition, and a bitrate selection mechanism is exploited to decide the best video version for the client. Besides, time-consuming transcoding tasks are carefully scheduled considering edge computing capacity. We formulate a Mix-Integer Non-Linear Programming (MINLP) problem to jointly determine cell association, bitrate adaption, and computing resource allocation. We exploit the generalized Benders decomposition method to reduce the solving complexity of the formulated problem. Numerical results validate the effectiveness and efficiency of the proposed scheme.

In this paper, we investigate the issue of resource allocation for secure energy efficient communication in a multiuser orthogonal frequency division multiplexing (OFDM) based full-duplex (FD) relaying network in the presence of a passive eavesdropper whose channel state information (CSI) is not perfectly known. Our goal is to maximize the overall secure energy efficiency (SEE), which presents the relationship between energy consumption and secrecy performance. In the context of multiuser communications, such a resource allocation strategy jointly combines subcarrier permutation, subcarrier pair allocation, as well as power allocation altogether. The considered optimization problem is formulated as a mixed integer nonconvex programming problem, which is generally NP hard. Analyzing the property of such a problem, we first use the Dinkelbach's method to eliminate the fractional form and then exploit Generalized Benders decomposition to decouple the original problem into a master problem for pure integer programming and a primal problem for nonlinear programming. More specific, given the nonconvexity of the primal problem, we accordingly transform it into an equivalent relaxed convex problem by applying dual decomposition, alternative convex search, and difference of convex function programming. The numerical results are provided to validate the theoretical analysis and to demonstrate the effectiveness of the proposed algorithm.

Mobile crowdsensing is recognized to be a promising paradigm wherein location-based sensing tasks are outsourced to participants carrying mobile devices. A prominent issue of crowdsensing is to guarantee the sensing coverage by appropriately recruiting participating devices, which requires the disclosure of participants' locations and leads to potential location privacy threats. In this paper, we aim to develop a privacy-preserving participant recruiting scheme for mobile crowdsensing, which guarantees the crowdsensing coverage while preserving participants' location differential privacy against a semi-honest crowdsensing aggregator. Briefly, based on the differential private geo-indistinguishability method, we enable candidate participants to locally perturb their location data. With the obfuscated location information, we formulate the crowdsensing coverage optimization as an Integer Program (IP), and develop a 1-(1 - 1/f)f-approximation algorithm, which yields a near-optimal participant recruiting solution. Through extensive simulations, we demonstrate the tradeoff between privacy preservation and crowdsensing utility, and show that satisfactory crowdsensing coverage can be achieved while preserving the participants' differential location privacy.

In recent years, generative adversarial network (GAN) has attracted great attention due to its impressive performance and potential numerous applications, such as data augmentation, real-like image synthesis, image compression improvement, etc. The generator in GAN learns the density of the distribution from real data in order to generate high fidelity fake samples from latent space and deceive the discriminator. Despite its advantages, GAN can easily memorize training samples because of the high model complexity of deep neural networks. Thus, training a GAN with sensitive or private data samples may compromise the privacy of training data. To address this privacy issue, we propose a novel \textit{Privacy Preserving Generative Adversarial Network} (PPGAN) that perturbs the objective function of discriminator by injecting Laplace noises based on functional mechanism to guarantee the differential privacy of training data. Since generator training is considered as a post-processing step while guaranteeing differential privacy of discriminator, the trained generator should be differentially private to effectively protect data samples. Through detailed privacy analysis, we theoretically prove that PPGAN can provide such strict differential privacy guarantee. With extensive simulation study on the benchmark dataset MNIST, we show the efficacy of the proposed PPGAN under practical privacy budgets.

In this paper, we investigate binary frequency shift keying (BFSK) over ambient OFDM signals. By cycling through a sequence of antenna loads providing different phase shifts at the tag, we are able to unidirectionally shift the ambient spectrum either up or down in frequency allowing the implementation of BFSK. We exploit the guard band and the orthogonality of the OFDM subcarriers to avoid both direct-link and adjacent channel interference. Different from energy detection based techniques which suffer from asymmetric error probabilities, the proposed scheme has symmetric error probabilities. Furthermore, we analyze the error performance of the optimal noncoherent detector and obtain an exact expression for the average probability of error. Finally, simulation results corroborate our analysis and show that the proposed scheme outperforms energy detection based schemes available in the literature by up to 3 dB.

Spectrum efficiency can be greatly improved through high-accuracy spectrum sensing in cognitive unmanned aerial vehicle (UAV) networks. However, the traditional centralized cooperative spectrum sensing (CCSS) methods are not applicable to the spectrum sensing of cognitive UAV networks, since the mobility of nodes and the dynamicity of network topology make it challenging to gather all the sensing information into a fusion center (FC) quickly enough. To overcome the challenge, this paper proposes a clustering-based distributed cooperative spectrum sensing (c-DCSS) scheme. Specifically, the considered cognitive UAV network is first clustered based on Max-Min distance clustering methods by jointly taking the position, velocity, and moving direction of UAVs in account, and then a two-stage fusion scheme is adopted to execute hierarchical sensing information fusion. Simulation results show that compared to the unclustered DCSS (u-DCSS) scheme, the proposed scheme significantly enhances the spectrum detection performance of cognitive UAV networks, especially when the number of UAV nodes is relatively large.

The ever-growing proliferation of mobile devices equipped with accelerometers has provided new opportunities to capture the semantic meanings of human activities and improve user experience with behavior-based recommendations, which heavily rely on the accuracy of the recognition of daily human activities. Acceleration-based human activity recognition (HAR) is a challenging problem because each accelerometer records multi-dimensional signals in both spatial and temporal domains that have different attributes for representing different activities or even the same activity. Thus we cannot directly compare these signals with each other, because they are embedded in a non-metric space. In this paper, we present a Personalized Recurrent Neural Network (PerRNN) to dynamically segment and recognize the human activities based on accelerometer data. Enlightened by the idea of spatiotemporal predictive learning, the proposed architecture is capable of memorizing different acceleration signals' appearances and temporal variations in a unified memory pool. We evaluate the performance of the proposed framework on a commonly used dataset, WISDM. Experiment results show that compared with state-of-the-art schemes, our proposed PerRNN system recognizes 6 different human activities with the highest overall accuracy of 96.44%.

Flight recorders (FRs) are required to be installed in commercial aircrafts to record operating data of planes while in flight for the purpose of facilitating the investigation of aviation accidents. Although being carefully protected, FRs may still become damaged or lost under the extreme conditions. We therefore propose a software-defined wireless networking framework to fully exploit the inter-aircraft air-to-air radio links to stream flight data in real time from the aircraft to a ground control center. With the knowledge of the physical position and predictable trajectory of each commercial aircraft, we formulate a time-expanded connectivity graph for the network controller to maintain a holistic view of the time-varying network status and propose a branch and price algorithm to optimize the flight data flows transmitted through air-to-air links. We also consider the optimality gap introduced by unpredictable network changes and delayed flow reconfigurations, and propose a traffic reroute policy to keep the gap small while minimizing the flow reconfiguration cost. We conduct extensive experiments with real commercial aircraft trajectories in North Atlantic oceanic airspace and demonstrate the feasibility and efficiency of our scheme to support flight data streaming.

UAV has attracted a significant amount of attention for its low-cost and diverse applications like video surveillance, auxiliary communication, etc. In this paper, the UAV fire surveillance network is proposed and the maximization of the UAV-centric energy efficiency (EE) is investigated by jointly taking the source/channel rate control and flow routing into account. The design is cast into a cross-layer optimization problem, which is proven to be difficult to solve. In light of it, a parametric transformation approach is adopted to convert the original problem into a tractable form and further decouple it into two independent subproblems. An efficient algorithm consisting of a two-layer iterative algorithm with an inner loop and an outer loop is proposed to solve the transformed problem. Simulation results show the impact of the network configuration on the network-wide EE and the performance of the proposed algorithm.

In the past few years, it has become increasingly popular to analyze the information obtained to develop services by conducting a decentralized survey of private data for specific populations. Privacy security requirements for data providers force operators to implement reasonable privacy protections. But increasing the investment in privacy protection will also lead to a decline in operator revenue. In this case, operators need to ensure the privacy and security requirements of users while ensuring the sustainability of customized services. To this end, We study the relationship between collecting data quality and operator strategy, quantifying the price of private data, and building a model to maximize operator profitability. Specifically, closed-form solutions for best privacy data prices and subscription fees are designed to maximize the gross profit of service providers. Also includes the collection of data quality factors to ensure that the user perceived quality of service can be guaranteed to a certain extent. Finally, we explored the relationship between spending, subscription fees, and maximum gross profit of carriers during the data collection phase, based on the distribution of different user groups' privacy attitudes. In particular, we also explored the relationship between adding additional noise and collecting data utility in a decentralized privacy protection scenario. The simulation results show that compared with the existing methods, the algorithm can maximize the collected data quality while ensuring the provider's privacy security requirements. In addition, we demonstrate the benefits of our dynamic pricing approach and its applicability to other private data pricing algorithms.

Millimeter wave offers large bandwidth for high data-rate unmanned aerial vehicle (UAV)-to-UAV communications. Because of high mobility and attitude variations, it is challenging to maintain the communication link among the navigating UAVs with narrow beam in the mmWave band. To the best of our knowledge, this is the first paper to establish a transmission-oriented UAV attitude prediction model for the UAV-to-UAV mmWave communication link. In particular, a position-attitude prediction based beam tracking algorithm is proposed. First, a Guassian Process (GP) based learning algorithm is presented for the transmitting UAV to predict the position and attitude of the receiving UAV by using the previous position-attitude data and exploiting the relationship between the position and attitude. Then, the analog beamforming vectors are derived by using the predicted spatial angles. Simulation results demonstrate that the proposed learning algorithm can achieve high accurate position-attitude prediction, and the beam tracking algorithm considering UAV attitude variations significantly outperforms the existing algorithms with only position information.

In the coming fifth generation (5G) or beyond 5G next generation networks (NGNs), the small cell deployment is a promising solution to meet the ever increasing demands of mobile devices, and the proliferation of wireless services. The low power base station (BS), such as femtocell BS, is a cost-effective and environmental friendly substitution for the power-hungry macrocell BS. One potentially effective way to deploy those small cells is to use two-tier NGN architecture, where the first-tier carrier can authorize the second-tier carrier's access to users' transmission information database (e.g., uplink/downlink service demands), and thereafter the second-tier carrier can decide how to place small cell BSs according to the mobile users' requirements locally. However, the second-tier carriers/operators for small cell placement may not be trustworthy, and the NGN users' data privacy might be compromised. To address this issue, we integrate differential privacy (DP) preserving techniques into data-driven optimization, and propose a novel scheme that not only preserves the privacy of NGN users' transmission information, but also maximizes the revenue of small cell deployment. Briefly, differential private noises are intentionally added into the users' transmission information database. Based on queries, the second-tier carrier can aggregate a given set of users' differentially private historical data, estimate the users' demands, and formulate the data-driven revenue maximization problem. Given the stochastic programming optimization formulation, we develop feasible solutions and conduct extensive simulations with real-world transmission datasets (i.e., transmission data collected hourly from 3072 4G eNBs deployed in several southern cities of China in 2015) to verify the effectiveness of the proposed scheme.

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.

In this paper, a novel three timeslots frame structure is proposed for Energy-Harvesting Cognitive Radio Networks, where the frame structure includes spectrum sensing, energy harvesting and non-orthogonal multiple access uplink transmission. Our goal is to maximize the sum-capacity of secondary users (SUs) by jointly optimizing spectrum sensing duration, energy harvesting duration and data transmission duration. To solve the challenging problem, we first derive the closed form expressions for optimal energy harvesting and data transmission durations by fixing the spectrum sensing duration, and then optimize the spectrum sensing duration by golden section search method. Finally, we obtain a sub-optimal solution through the alternate iteration of two previous steps. Simulation results show that the sum-capacity of SUs under the proposed scheme significantly increases compared to the time division multiple access (TDMA) uplink transmission protocol, especially when the transmitted power of cognitive base station (CBS) increases and the number of SUs is large enough.

The transportation network company (TNC) services efficiently pair the passengers with the vehicles/drivers through mobile applications, such as Uber, Lyft, Didi, etc. TNC services definitely facilitate the traveling of passengers, while it is equally important to effectively and intelligently schedule the routes of cruising TNC vehicles to improve TNC drivers' revenues. From the TNC drivers' side, the most critical question to address is how to reduce the cruising time, and improve the efficiency/earnings by using their own vehicles to provide TNC services. In this paper, we propose a deep reinforcement learning-based TNC route scheduling approach, which allows the TNC service center to learn about the dynamic TNC service environment and schedule the routes for the vacant TNC vehicles. In particular, we jointly consider multiple factors in the complex TNC environment, such as locations of the TNC vehicles, different time periods during the day, the competition among TNC vehicles, etc., and develop a deep Q-network-based route scheduling algorithm for vacant TNC vehicles based on distributed framework, which makes the server closer to the terminal users and accelerates the training speed. Furthermore, we apply the geo-indistinguishability scheme based on differential privacy to preserve the sensitive location information uploaded by the passengers. We evaluate the proposed algorithm's performance via simulations using open data sets from Didi Chuxing. Through extensive simulations, we show that the proposed scheme is effective in reducing the cruising time of vacant TNC vehicles and improving the earnings of TNC drivers.

Unmanned aerial vehicles (UAVs) can be deployed efficiently to provide high quality of service for Internet of Things (IoT). By using cooperative communication and relay technologies, a large swarm of UAVs can enlarge the effective coverage area of IoT services via multiple relay nodes. However, the low latency service requirement and the dynamic topology of UAV network bring in new challenges for the effective routing optimization among UAVs. In this paper, a layered UAV swarm network architecture is proposed and an optimal number of UAVs is analyzed. Furthermore, a low latency routing algorithm (LLRA) is designed based on the partial location information and the connectivity of the network architecture. Finally, the performance of the proposed LLRA is verified by numerical results, which can decrease the link average delay and improve the packet delivery ratio in contrast to traditional routing algorithms without layered architecture.

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.

In recent years, ambient backscatter communications have gained a lot of interests as a promising enabling technology for the Internet-of-Things and green communications. In ambient backscatter communication systems, ultra-low power devices are able to transmit information by backscattering ambient radio-frequency signals generated by legacy communication systems such as Wi-Fi and cellular networks. This paper is concerned with ambient backscatter communications over legacy orthogonal frequency division multiplexing (OFDM) signals. We propose a backscatter modulation scheme that allows backscattering devices to take advantage of the spectrum structure of ambient OFDM symbols to transmit information. The proposed modulation scheme allows both binary and higher-order modulation using noncoherent energy detection. We investigate the detector design and analyze the error performance of the proposed scheme. We provide an exact expression for the error probability for the binary case, whereas accurate approximate expressions for the error probability are derived for the M-ary case. We corroborate our analysis using Monte-Carlo simulation and investigate the effects of varying the OFDM symbol size, maximum channel delay spread, and the number of receive antennas on the error performance. Our numerical results show that the proposed technique outperforms other techniques available in this paper for backscatter communication over ambient OFDM signals in different scenarios.

In recent years, ambient backscatter communications have gained a lot of interest as a promising enabling technology for Internet-of-Things and green communications. In ambient backscatter communication systems, battery-less devices are able to transmit information by backscattering ambient RF signals generated by legacy communication systems such as digital TV broadcasting, Wi-Fi, or cellular. This paper is concerned with ambient backscatter communications over legacy cellular OFDM signals. We propose a novel modulation scheme that allows backscattering devices to take advantage of the spectrum structure of ambient OFDM symbols to transmit information. We analyze the error performance of the proposed scheme, provide an exact expression for the error probability, and validate our analysis using Monte-Carlo simulation. We investigate the effects of varying the OFDM symbol size and maximum channel delay spread on the error performance. Our numerical results show that the proposed technique outperforms other techniques available in the literature for backscatter communication over ambient OFDM signals in different scenarios.

In this paper, we study the resource allocation problem for a cooperative device-to-device (D2D)- enabled wireless caching network, where each user randomly caches popular contents to its memory and shares the contents with nearby users through D2D links. In order to enhance the throughput of spectrum-sharing D2D links, which may be severely limited by the interference among D2D links, we enable the cooperation among some of the D2D links to eliminate the interference among them. We formulate a joint link scheduling and power allocation problem to maximize the overall throughput of cooperative D2D links (CDLs) and non- cooperative D2D links (NDLs), which is NP-hard. To solve the problem, we decompose it into two sub- problems, which maximize the sum rates of the CDLs and the NDLs, respectively. For CDL optimization, we propose a semi-orthogonal-based algorithm for joint user scheduling and power allocation. For NDL optimization, we propose a novel low-complexity algorithm to perform link scheduling and develop a Difference of Convex functions (D.C.) programming method to solve the non-convex power allocation problem. Simulation results show that cooperative transmission can significantly improve both the number of served users and the overall system throughput.

The advance in mobile communications has escalated the use of transportation network company (TNC) services by residents. The TNC services efficiently pair the passengers with the vehicles/drivers through mobile applications such as Uber, Lyft, Didi, etc. TNC services definitely facilitate the traveling of passengers, while it is equally important to effectively and intelligently schedule the routes of cruising TNC vehicles to improve TNC drivers' revenues. From the TNC drivers' side, the most critical question to address is how to reduce the cruising time, and improve the efficiency/earnings of using their own vehicles to provide TNC services. In this paper, we propose a deep reinforcement learning based TNC route scheduling approach, which allows the TNC service center to learn about the dynamic TNC service environment and schedule the routes for the vacant TNC vehicles. In particular, we jointly consider multiple factors in the complex TNC environment such as locations of the TNC vehicles, different time periods during the day, the competition among TNC vehicles, etc., and develop a deep Q-network (DQN) based route scheduling algorithm for vacant TNC vehicles. We evaluate the proposed algorithm's performance via simulations using open data sets from Didi Chuxing. Through extensive simulations, we show that the proposed scheme is effective in reducing the cruising time of vacant TNC vehicles and improving the earnings of TNC drivers.

Smart meters migrate conventional electricity grid into digitally enabled Smart Grid (SG), which is more reliable and efficient. Fine-grained energy consumption data collected by smart meters helps utility providers accurately predict users' demands and significantly reduce power generation cost, while it imposes severe privacy risks on consumers and may discourage them from using those ``espionage meters". To enjoy the benefits of smart meter measured data without compromising the users' privacy, in this paper, we try to integrate distributed differential privacy (DDP) techniques into data-driven optimization, and propose a novel scheme that not only minimizes the cost for utility providers but also preserves the DDP of users' energy profiles. Briefly, we add differential private noises to the users' energy consumption data before the smart meters send it to the utility provider. Due to the uncertainty of the users' demand distribution, the utility provider aggregates a given set of historical users' differentially private data, estimates the users' demands, and formulates the data- driven cost minimization based on the collected noisy data. We also develop algorithms for feasible solutions, and verify the effectiveness of the proposed scheme through simulations using the simulated energy consumption data generated from the utility company's real data analysis.

This paper designs a novel geometry-conformal antenna for Magnetic Induction (MI)-based subsea wireless communications for autonomous underwater vehicles (AUV). The designed tri-directional antennas can be wrapped directly on the surface of AUVs, such that the AUVs fluid dynamics are well maintained to ensure power efficiency of the vehicles. In addition, ferrite materials are added between the MI antenna and the metallic body surface of the AUVs to overcome the shielding effect and enhance the MI signal strength. The designed MI communication system is implemented in hardware and the effectiveness of the geometry-conformal MI antenna is demonstrated through COMSOL simulations and lab experiments.

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.

Information-centric networking (ICN) is developed for the future Internet because of the tremendous increase of content demands in the Internet. In the ICN architecture, in-network storage for caching plays an important role in improving content delivery efficiency, scalability and availability. To enjoy the benefits of caching users' preferable contents without disclosing the users' privacy, in this paper, we aim to integrate local differential privacy (LDP) techniques into data-driven optimization, and propose a novel scheme to allow content provider (CP) to collect the locally differentially private content preferences of a selected group of users, exploit data-driven approach to predict the content popularity, and offer the cache-enabled access points (APs) economic incentives to cache the selected preferable content. Here, optimized local hashing (OLH) is employed to locally add differential private noise to the users' preference content information and the noisy data is sent to the CP. Besides, we leverage data-driven methodology to predict the content popularity according to the constructed reference distribution of the given noisy preference content data from users. We formulate a data-driven caching revenue optimization, provide feasible solutions, and conduct simulations to show the effectiveness of the proposed scheme.

The cryptographic credentials of misbehaving vehicles should be revoked in a timely manner to prevent jeopardizing the network. However, the revocation is a challenging problem in VANETs because of its stringent privacy requirements and the large network scalability. Each vehicle receives a large number of unlinkable certified pseudonyms to protect privacy. Large storage and computation are required to store the revocation list and verify the authenticity of messages, respectively. In this paper, we propose a scalable revocation scheme that reduces the revocation list storage overhead and the revocation checking delay in pseudonym-based VANETs. The revocation of vehicle's credentials is achieved by adding a single trapdoor in a revocation list. The vehicles use the published trapdoors for revocation check by computing simple operations instead of sequentially searching a large set of pseudonyms. Chameleon hashing is used to generate the pseudonyms and revoke them if needed. Using computationally efficient multi-signature, vehicles can cooperatively vote to revoke the credentials of a misbehaved vehicle. The objective of using multi-signature is to increase the creditability of revocation requests. Our experiments and extensive evaluations show that our scheme can revoke a large number of pseudonyms with minimum storage and computation overhead.

In this paper, we investigate the joint subcarrier (SC) assignment and power allocation problem for non-orthogonal multiple access (NOMA) amplify-and- forward two-way relay wireless networks. We aim to maximize the achievable secrecy energy efficiency by jointly designing the SC assignment, user pair scheduling and power allocation. Assuming the perfect knowledge of the channel state information (CSI) at the relay station, we propose a low-complexity subcarrier assignment scheme (SCAS-1), which is equivalent to many-to-many matching games, and then SCAS-2 is formulated as a secrecy energy efficiency maximization problem. The secure power allocation problem is modeled as a convex geometric programming (GP) problem, and then solved by interior point methods. Simulation results demonstrate that the effectiveness of the proposed SSPA algorithms.

Smart homes (SHs) aim at forming an energy optimized environment that can efficiently regulate the use of various Internet of Things (IoT) devices in its network. Real-time electricity pricing models along with SHs provide users an opportunity to reduce their electricity expenditure by responding to the pricing that varies with different times of the day, resulting in reducing the expenditure at both customers’ and utility provider’s end. However, responding to such prices and effectively scheduling the appliances under such complex dynamics is a challenging optimization problem to be solved by the provider or by third party services. As communication in SH-IoT environment is extremely sensitive and private, reporting of such usage information to the provider to solve the optimization has a potential risk that the provider or third party services may track users’ energy consumption profile which compromises users’ privacy. To address these issues, we developed a homomorphic encryption-based alternating direction method of multipliers approach to solve the cost-aware appliance scheduling optimization in a distributed manner and schedule home appliances without leaking users’ privacy. Through extensive simulation study considering real-world datasets, we show that the proposed secure appliance scheduling for flexible and efficient energy consumption scheme, namely SAFE, effectively lowers electricity cost while preserving users’ privacy.

Sustainable powering of wire-free sensors (WFSs) is a critical challenge in structure health monitoring for failure prevention applications, where battery replacement is practically difficult. Hence, it is of interest to develop means for remotely charging WFS devices using external power sources. In this paper, a novel stress wave-based optimization method is proposed for piezoelectric sensors wire-free charging. This method includes a novel waveform optimization scheme, a novel multiactuator, and a multichannel wire-free charging strategy to maximize energy transmission efficiency. Based on the measured channel characteristics of a tightly insulated environment, four different waveform design algorithms are implemented successfully to generate the optimized charging waveforms in accordance with the novel waveform optimization scheme. To testify the effectiveness of this novel optimized wire-free charging method, the piezoelectric sensor-based wire-free charging process is simulated. In the simulation process, a predefined waveform is applied in the piezoelectric actuator to generate a stress wave which is subsequently utilized to wire-free transfer energy to the piezoelectric sensors. Numerical results demonstrate the advantages of multichannel wire-free charging in significantly reducing input power loss and enhancing energy transmission efficiency.

Flanged valves are ubiquitous in oil and gas pipeline networks. The valves help to both connect pipes together and control the flow of fluids. Depending on the location, the valve may experience high temperatures, high pressures, and corrosive agents, all of which degrade the integrity of the flange connection. Thus, flanged valves require regular inspection and maintenance. However, in cases where the valve is located in inaccessible areas, regular inspection can be difficult and costly. While certain methods are currently available for automatic inspection of flanged valves, they often suffer from impracticality issues, such as intrusiveness, unintuitive operation, or sensor fragility. Therefore, this paper introduces a sensing device that, as inspired by wearable technology, can be easily and noninvasively “worn” onto the flanged valve and performs measurements in real-time. The device is composed of piezoelectric lead zirconate titanate (PZT) transducers fortified by a layer of cushioning rubber and a metallic outer layer. This paper presents the design of the device, and verifies its performance using a 12-bolted flanged valve recovered from the field. The device is able to accurately track the tightness of the bolted connections. Unlike prior PZT-based monitoring methods, this device shows great promise for practical application.

Secure communication is a promising technology for wireless networks because it ensures secure transmission of information. In this paper, we investigate the joint subcarrier (SC) assignment and power allocation problem for non-orthogonal multiple access amplify-and-forward two-way relay wireless networks, in the presence of eavesdroppers. By exploiting cooperative jamming (CJ) to enhance the security of the communication link, we aim to maximize the achievable secrecy energy efficiency by jointly designing the SC assignment, user pair scheduling and power allocation. Assuming the perfect knowledge of the channel state information at the relay station, we propose a low-complexity subcarrier assignment scheme (SCAS-1), which is equivalent to many-to-many matching games, and then SCAS-2 is formulated as a secrecy energy efficiency maximization problem. The secure power allocation problem is modeled as a convex geometric programming problem, and then, solved by interior point methods. Simulation results demonstrate that the effectiveness of the proposed SSPA algorithms under scenarios of using and not using CJ, respectively.

In current wireless communication systems, there is a rapid development of location based services, which will play an essential role in the future 5G networks. One key feature in providing the service is the mobile crowdsourcing in which a central cloud node denoted as the principal collects location based data from a large group of users. In this paper, we investigate the problem of how to provide continuous incentives based on user's performances to encourage users' participation in the crowdsourcing, which can be referred to the moral hazard problem in the contract theory. We not only propose the one-dimensional performance-reward related contract, but also extend this basic model into the multi-dimensional contract. First, an incentive contract which rewards users by evaluating their performances from multiple dimensions is proposed. Then, the utility maximization problem of the principal in both one-dimension and multi-dimension are formulated. Furthermore, we detailed the analysis of the multi-dimensional contract to allocate incentives. Finally, we use the numerical results to analyze the optimal reward package, and compare the principal's utility under the different incentive mechanisms. Results demonstrate that by using the proposed incentive mechanism, the principal successfully maximizes the utilities, and the users obtain continuous incentives to participate in the crowdsourcing activity.

Spectrum trading creates more accessing opportunities for secondary users (SUs), and economically benefits the primary users (PUs). Compared with centralized spectrum trading designs, e.g., spectrum auction, distributed spectrum trading captures instantaneous spectrum trading opportunities better over large geographical regions without incurring extra infrastructure deployment and has no network scalability issues. However, the existing distributed spectrum trading designs have limited concern regarding spectrum reuse. Considering spatial reuse, in this paper, we propose a novel distributed frequency reuse-based opportunistic spectrum trading (D-FROST) scheme, which can further improve spectrum utilization, provide more accessing opportunities for SUs, and increase the revenues of PUs. In this paper, we employ conflict graph to characterize the SUs' co-channel and radio interferences, and mathematically formulate a centralized PUs' revenue maximization problem under multiple wireless transmission constraints. Due to the NP-hardness to solve the problem and the non-existence of centralized trading entity, we develop the D-FROST algorithms based on matching with evolving preferences, and prove its stability. Through extensive simulations, we show that the proposed D-FROST algorithm is superior to other distributed spectrum trading algorithms without considering spectrum reuse, yields results close to the centralized optimal one, and is effective in increasing PUs' revenue and improving spectrum utilization.

Recent advances in device-free wireless sensing (DFS) have shown that it may eventually evolve traditional wireless networks into smart networks which could sense surrounding target location and activity information without equipping the target with any devices. Despite its promising application prospects, one challenging problem to be solved is that the performance of the DFS system degrades significantly in complex scenarios, such as through-wall and non-line-of-sight (NLOS) scenarios. To alleviate this problem, this paper seeks to explore and exploit more informative features from not only the time domain and frequency domain, but also the spatial structural domain. We partition the time domain and frequency domain measurement matrices into basic structure blocks, adopt self-organizing map networks to cluster the blocks into a number of categories, so as to make it feasible to characterize the block distributions. We further adopt coherence histograms to characterize the distribution of the blocks by considering the spatial relationship between adjacent blocks. Thanks to the additional information provided by the spatial structural domain, extensive experimental results achieved in through-wall and NLOS scenarios confirm the outstanding performance of the proposed multi-domain features based DFS system.

In this paper, the non-orthogonal multiple access (NOMA) technology is integrated into cognitive orthogonal frequency-division multiplexing (OFDM) systems, called cognitive OFDM-NOMA, to boost the system capacity. First, a capacity maximization problem is considered in half-duplex cognitive OFDM-NOMA systems with two accessible users on each subcarrier. Due to the intractability of the considered problem, we decompose it into three subproblems, i.e., the optimization of, respectively, sensing duration, user scheduling, and power allocation. By investigating and exploiting the characteristics of each subproblem, the optimal sensing duration adaptation, a matching-theory-based user scheduling, and the optimal power allocation are proposed correspondingly. An alternate iteration framework is further proposed to jointly optimize these three subproblems, with its convergence proved. Moreover, based on the non-cooperative game theory, a generalized power allocation algorithm is proposed and then used in the framework to accommodate half-duplex cognitive OFDM-NOMA systems with multiple users on each subcarrier. Finally, the proposed framework is extended to solve the capacity maximization problem in full-duplex cognitive OFDM-NOMA systems. Simulation results validate the superior performance of the proposed algorithms. For example, for the case of two accessible users, the proposed framework approaches the optimal solution with less than 1% capacity loss and 120 times lower complexity compared with exhaustive search.

Recently opened spectrum within 3550-3700 MHz provides more accessing opportunities to secondary users (SUs), while it also raises concerns on the operational privacy of primary users (PUs), especially for military and government. In this paper, we propose to study the tradeoff between PUs' temporal privacy and SUs' network performance using the data-driven approach. To preserve PUs' temporal operational privacy, we develop an obfuscation strategy for PUs, which allows PUs to intentionally add dummy signals to change the distribution of temporal spectrum availability, and confuse the adversary. While generating the dummy signals for privacy, the PUs have to consider the utility of SUs and try their best to satisfy SUs' uncertain traffic demands. Based on the historical data, we employ a data-driven risk-averse model to characterize the uncertainty of SUs' demands. With joint consideration of PUs' privacy and uncertain SUs' demands, we formulate the data-driven risk- averse stochastic optimization, and provide corresponding solutions. Through numerical simulations, we show that the proposed scheme is effective in preserving PUs' temporal operational privacy while offering good enough spectrum resources to satisfy SUs' traffic demands.

Emergency Demand Response (EDR) is crucial for improving the grid reliability and for meeting the power demand during crisis. Power-hungry data centers have been facing an urge to reduce their consumption to meet the EDR. The energy reduction in colocation data centers which house servers for multiple tenants (e.g., Equinix) that are better targets for EDR is less explored than the energy reduction in owner-operated data centers (e.g., Google). Existing EDR mechanisms incentivize tenants energy reduction. Such designs can either be gamed by strategic tenants or untrustworthy colocation operators for illegal gains. These serious privacy concerns stand as barrier preventing the tenants' participation in EDR. This paper addresses such concerns by proposing a privacy-preserving and strategy-proof mechanism using descending clock auction. Privacy is protected by implementing homomorphic encryption for aggregation of energy through clock auction, where operator can only know the aggregate of the tenants' values or bids but not their individual private values or confidential information submitted to meet the EDR. We evaluate the privacy and performance of this scheme by formulation through descending clock auction, in which the amount of energy the tenants are willing to reduce for a given price to meet EDR is protected.

The surging of smartphone sensing, wireless networking and social media have enabled a great variety of applications, such as environment surveillance, marketing, health monitoring and so on. This new paradigm is typically referred to as mobile crowd sensing (MCS). Existing solutions on MCS are majorly based on text, image and video analysis using the distributively sensed data. One shortcoming of such approaches is the data processing delay. Considered as an enhanced function to the temporary social media services, for example Twitter, we propose a real-time event sharing framework that provides mobile users with the freedom of expressing their various interests regarding the event data. To achieve such goals, we introduce a novel Hashtag design, which is a combination of five types of information: time, location, keywords, data type and data size. Users, by comparing their own interests with the uploaded Hashtags in the Twitter server, can search for suitable partner users to share information with in real time. The formulated user sharing/pairing problem is modeled as the Stable Fixture (SF) matching game, and can be solved by the Irving's SF (ISF) algorithm in a distributive manner. The simulations have demonstrated the superiority of our proposed ISF method by comparing with both centralized method and heuristic algorithms.

In this paper, big Radio Frequency (RF) data assisted optimization is considered for future wireless networks employing cognitive radio technology with machine learning capability. A cognitive radio network (CRN) with multiple Secondary Users (SUs) may coexist with other wireless systems such as Small Cells (SC) and Radar systems, both Primary Users (PUs) with different level of priorities. Traditional spectrum sensing typically only gives information about the presence or absence of a PU. However, when multiple heterogeneous systems coexist, it becomes imperative to acquire the knowledge of the systems operating in a specific band at a particular time so as to choose an appropriate transmission strategy. In this work, we take advantage of the learning capability of a Neural Network Predictor (NNP) to obtain the statistics of the coexisted wireless systems from the RF traces collected in our Universal Software Radio Peripheral (USRP) based test bed. The NNP is able to learn the features of the RF traces and make accurate prediction of the signals prevalent in the wireless environment. Because of the augmented information learned from the RF traces, a novel optimization problem incorporating the outputs from the NNP is formulated to maximize the throughput of the CRN. The solution is derived using Karush- Kuhn-Tucker (KKT) and extensive simulations using the real RF traces are carried out. It is demonstrated that the NNP can detect the type and number of coexisted users reliably and the proposed scheme will improve the performance of the coexisted CRN.

With the wide adoption of smart mobile devices, there is a rapid development of location-based services. One key feature of supporting a pleasant/excellent service is the access to adequate and comprehensive data, which can be obtained by mobile crowdsourcing. The main challenge in crowdsourcing is how the service provider (principal) incentivizes a large group of mobile users to participate. In this paper, we investigate the problem of designing a crowdsourcing tournament to maximize the principal's utility in crowdsourcing and provide continuous incentives for users by rewarding them based on the rank achieved. First, we model the user's utility of reward from achieving one of the winning ranks in the tournament. Then, the utility maximization problem of the principal is formulated, under the constraint that the user maximizes its own utility by choosing the optimal effort in the crowdsourcing tournament. Finally, we present numerical results to show the parameters' impact on the tournament design and compare the system performance under the different proposed incentive mechanisms. We show that by using the tournament, the principal successfully maximizes the utilities, and users obtain the continuous incentives to participate in the crowdsourcing activity.

In cognitive radio networks (CRNs), spectrum trading is an efficient way for secondary users (SUs) to achieve dynamic spectrum access and to bring economic benefits for the primary users (PUs). Existing methods require full payment from SU, which blocked many potential “buyers,” and thus limited the PU's expected income. To better improve PUs' revenue from spectrum trading in a CRN, we introduce a financing contract, which is similar to a sealed non-cash auction that allows SU to do financing. Unlike previous mechanism designs in CRN, the financing contract allows the SU to only pay part of the total amount when the contract is signed, known as the down payment. Then, after the spectrum is released and utilized, the SU pays the rest of payment, known as the installment payment, from the revenue generated by utilizing the spectrum. The way the financing contract carries out and the sealed non-cash auction works similarly. Thus, contract theory is employed here as the mathematical framework to solve the non-cash auction problem and form mutually beneficial relationships between PUs and SUs. As the PU may not have the full acknowledgment of the SU's transmission status, the problems of adverse selection and moral hazard arise in the two scenarios, respectively. Therefore, a joint adverse selection and moral hazard model is considered here. In particular, we present three situations when either or both adverse selection and moral hazard are present during the trading. Furthermore, both discrete and continuous models are provided in this paper. Through simulations, we show that the adverse selection and moral hazard cases serve as the upper and lower bounds of the general case where both problems are present.

LTE-Unlicensed has recently captured intense attention from both academic and industrial fields. By integrating the unlicensed spectrum with the licensed spectrum, using carrier aggregation, LTE-Unlicensed users can experience enhanced transmission while maintaining the seamless mobility management and predictable performance. However, due to different transmission regulations, the coordination between LTE and Wi-Fi systems requires careful design. It is especially important to understand how to guarantee the transmission quality for LTE users and reduce Wi-Fi users' performance degradation, under the impact of the co-channel interference. In other words, how can we solve the unlicensed resource allocation problem under both LTE and Wi-Fi transmission requirements? In this paper, we propose a matching theory framework to tackle this problem. Specifically, the coexistence between LTE and Wi-Fi systems, i.e., the interaction between LTE and Wi-Fi users, is modeled as a stable marriage game. The coexistence constraints are interpreted as the preference lists. Two semi-distributed solutions, namely, the Gale-Shapley and the random path to stability algorithms are proposed. In addition, to address the external effect in matching, the inter-channel cooperation algorithm is introduced. Last but not least, the resource allocation problem is studied with network dynamics and the proposed mechanisms are evaluated under two typical user mobility models.

Acoustic communication is the most versatile and widely used technology for underwater wireless networks. However, the frequencies used by current acoustic modems are heavily overlapped with the cetacean communication frequencies, where the man-made noise of underwater acoustic communications may have harmful or even fatal impact on those lovely marine mammals, e.g., dolphins. To pursue the environmental friendly design for sustainable underwater monitoring and exploration, specifically, to avoid the man-made interference to dolphins, in this paper, we propose a cognitive acoustic transmission scheme, called dolphin-aware data transmission (DAD-Tx), in multi-hop underwater acoustic networks. Different from the collaborative sensing approach and the simplified modeling of dolphins' activities in existing literature, we employ a probabilistic method to capture the stochastic characteristics of dolphins' communications, and mathematically describe the dolphin-aware constraint. Under dolphin-awareness and wireless acoustic transmission constraints, we further formulate the DAD-Tx optimization problem aiming to maximize the end-to-end throughput. Since the formulated problem contains probabilistic constraint and is NP-hard, we leverage Bernstein approximation and develop a three-phase solution procedure with heuristic algorithms for feasible solutions. Simulation results show the effectiveness of the proposed scheme in terms of both network performance and dolphin awareness.

Spectrum trading benefits secondary users (SUs) with accessing opportunities and primary users (PUs) with monetary gains in cognitive radio networks. In the spectrum trading market, bidding privacy is a serious concern for SUs, since untrustworthy PUs or spectrum traders may leverage bidding information to manipulate the trading or leak SU's bids to other SU competitors for illegal gains. Current centralized privacy preserving spectrum trading designs may incur extra infrastructure deployment, miss many instantaneous spectrum accessing opportunities, and have scalability issues. In this paper, we propose a novel privacy-preserving semi-distributed spectrum trading scheme, which has joint consideration of spectrum reuse, SUs' bidding privacy preservation, and PUs' revenue maximization. We use the conflict graph to characterize SUs' interference relationships, and jointly employ the Paillier Cryptosystem and dynamic matching with evolving preferences to conduct the privacy-preserving spectrum trading in a semi-distributed manner. Through security, complexity and performance analysis, we show that the proposed scheme can effectively preserve the privacy of SUs' bidding values, and notably increase the PUs' revenue and improve spectrum utilization with limited overhead.

The study item: "Feasibility Study on LTE-based V2X Services", approved at 3GPP TSG RAN #68, has aroused the interest in the study of LTE assisted vehicle-to-vehicle (V2V) and vehicle-to- infrastructure (V2I) communications in the networks of connected vehicles. By deploying the direct device-to-device (D2D) technology of traditional cellular networks into the V2X (including both V2V and V2I) communications, performance improvements can be expected, such as better reliability, lower latency, and more efficient content sharing. This paper investigates the content sharing problem in the D2D based V2X communication networks. With both vehicles and eNBs carrying multiple different classes of data, this work studies how to optimize the information exchanged within the network, and in the mean time to guarantee the system quality of service (QoS) requirements. By jointly considering the data diversity and link quality, the interactions between vehicles/eNBs, or in other words, the V2V and V2I link scheduling, is modeled as the stable fixture (SF) matching game. Different from traditional D2D communications, where each node is limited to one link, we allow multiple V2X connections for each vehicle to further optimize the content sharing. More specifically, the formations of such V2X links are independent from each other, thus more flexible than the conventional clustering formation in the Vehicular ad hoc networks (VANETs). The SF game is solved by the proposed Irving's stable fixture (ISF) algorithm. Its advantages over some heuristics are demonstrated through simulation experiments.

Data centers are recognized as promising resources for emergency demand response (EDR) that requires a certain amount of power reduction when system reliability is in danger. In this paper, we study EDR in a colocation data center where multiple tenants deploy their own servers in a shared space managed by a data center operator. While the data center operator desires to reduce the usage of expensive and environmentally unfriendly backup generation during EDR events, the tenants who can control their servers have little incentive to reduce their power consumption. To enable cost-effective and eco-friendly EDR, the data center operator has to properly incentivize the tenants to modulate server power consumption. Furthermore, the social welfare generated during EDR should be properly shared among the data center operator and tenants so that all of them are satisfied. We propose an approach based on the Nash bargaining solution, which is Pareto efficient, fair and social welfare maximizing, to incentivize the tenants' participation and allocate the social welfare among the data center operator and tenants properly. Trace-driven simulations are conducted to demonstrate the effectiveness of our proposed approach.

Spectrum trading not only improves spectrum utilization but also benefits both secondary users (SUs) with more accessing opportunities and primary users (PUs) with monetary gains. Although existing centralized designs consider the special features of spectrum trading (e.g., frequency reuse, interference mitigation, multi-radio multi- channel transmissions, etc.), they have to deploy new infrastructure, deal with extra control overhead, have scalability issues, and may miss many instantaneous opportunities. To address those issues, in this paper, we propose a novel dynamic matching based distributed spectrum trading (DMDST) scheme in multi-radio multi- channel cognitive radio (CR) networks. We employ conflict graph to characterize interference relationship among SUs with multiple CR radios, and formulate the centralized PUs' revenue maximization problem under multiple constrains. In view of the NP- hardness of solving the problem and no existence of centralized entity, we develop the DMDST algorithms based on conflict graph observed by PUs, solve the problem via dynamic matching with evolving preferences, and prove its stability. Through extensive simulations, we show that the results of proposed DMDST algorithm is close to the optimal one and outperforms other distributed algorithms without considering spectrum reuse.

On-demand wireless data broadcast is an efficient way to disseminate data to a large number of mobile users. In many applications, such as stock quotes and flight schedules, users may have to download multiple data items per request. However the multi-item request scheduling has not yet been thoroughly investigated for on-demand wireless data broadcasts. In this paper, we step-up on investigating this problem from viewpoint of theory and simulation. We develop a two-stage scheduling scheme to arrange the requested data items with the objective of minimizing the average access latency. The first stage is to select the data items to be broadcast in the next time period and the second stage is to schedule the broadcasting order for the data items selected in the first stage. We develop algorithms for the two stages respectively and analyze them both theoretically and practically. We also compare the proposed algorithms with other well known scheduling methods through simulation. The theoretical findings and simulation results reveal that significantly better access latency can be obtained by using our scheduling scheme rather than its competitors.

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.

Spectrum trading not only improves spectrum utilization but also benefits both secondary users (SUs) with more accessing opportunities and primary users (PUs) with monetary gains. Although the existing centralized designs consider the special features of spectrum trading (e.g., frequency reuse, interference mitigation, multi-radio multi-channel transmissions, and so on), they still have to face many practical but challenging issues, such as the new infrastructure deployment, the extra control overhead, and the scalability issues. To address those issues, in this paper, we propose a novel matching-based multi-radio multi-channel spectrum trading (M3-STEP) scheme in cognitive radio (CR) networks. We employ conflict graph to characterize the interference relationship among SUs with multiple CR radios, and formulate the centralized PUs' revenue maximization problem under multiple constrains. In view of the NP-hardness of solving the problem and no existence of centralized entity, we develop the M3-STEP algorithms based on conflict graph observed by PUs, solve the problem via dynamic matching with evolving preferences, and prove its pairwise stability. Simulation results show that the proposed M3-STEP algorithm achieves close to optimal performance and outperforms other distributed algorithms without considering spectrum reuse.

Mobile hotspots have made the dream of ubiquitous Internet access come true, while the widespread applications are still hindered by the limited power of smart phones. To address this issue, we propose a novel distributed cooperative data transmission scheme for energy-rechargeable mobile devices. In particular, we not only let a mobile phone help the nearby client devices connect to the Internet via its cellular accessing, but also let those clients replenish the mobile hotspot energy via wireless power transfer. We mathematically formulate the mutually beneficial relationship between mobile hotspots and clients into an optimization problem, with the objective of conducting the cooperative wireless data and energy transmission to maximize the system utility. Resorting to methods from combinatorics and matching theory, we develop a near optimal solution for many-to-one matching when there is a single mobile hotspot and a distributed matching strategy for the general case by considering the nature of data communication and the characteristic of wireless power transfer. By extensive simulation, we show that the proposed distributed solution achieves a performance close to the centralized method, and it outperforms the greedy matching strategy and the classic Gale-Shapley matching strategy in different scenarios.

LTE, as the advanced mobile telecommunication technology, is serving heavy mobile broadband traffic nowadays. Motivated by the potential boost in performance of LTE utilizing the unlicensed spectrum, significant efforts have been devoted into the commonly referred LTE-Unlicensed technique. In this work, we investigate the carrier aggregation of licensed and unlicensed spectrum by deploying micro-cell base stations, which have access to the unlicensed spectrum, to provide cellular users a more reliable and efficient transmission. We tackle the unlicensed resource allocation problem by modeling it as a student-project allocation matching game. In addition, a postmatching procedure of resource re- allocation is introduced to guarantee unlicensed users' quality of service (QoS), as well as the system-wide stability. The simulation evaluation shows the effectiveness and efficiency of our proposed matching-based approach.

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.

With the wide adoption of smart mobile devices, there is rapid development of location based services. One key feature of supporting a pleasant/excellent service is the access to adequate and comprehensive data, which can be obtained by mobile crowdsourcing. The main challenge in crowdsourcing is how the service provider (principal) incentivize a large group of mobile users to participate. In this paper, we investigate the problem of designing a tournament to provide continuous incentives for users by rewarding them based on the rank achieved in crowdsourcing. First, we model the user's utility of reward from achieving one of the winning ranks in the tournament. Then, the utility maximization problem of the principal is formulated, under the constraint that the user maximizes its own utility by choosing the optimal effort in the crowdsourcing tournament. Furthermore, we show that, the tournament can approximate the optimal contract under full information by step function. Finally, we present numerical results to compare the system performance under the different proposed incentive mechanisms; we show that by using the tournament, the users obtain the continuous incentives to participate in the crowdsourcing activity.

As a support to the backend storage, the content caching technique is of great importance to online social networks (e.g., Facebook), in reducing the request service latency and improving user satisfaction. However, the limited caching capacity and booming user data pose great challenges for the content-cache allocation. In this paper, we propose a three-layer content caching model, and focus on how to efficiently allocation contents to caches in order to minimize the overall service latency. We try to tackle this issue by utilizing both centralized Mix Integer Linear Programming (MILP) optimization and by modeling it as a distributed student admission (SA) stable matching problem. In the SA model, we leverage the resident-oriented Gale-Shapley (RGS) algorithm to yield a stable matching between contents and cache centers. We compare the performance between the centralized and distributed algorithms in terms of system welfare and computation analysis. Through numerical results, we prove the effectiveness of our proposed methods.

Numerous applications of wireless sensor networks (WSNs) are hindered by the limited battery power of the sensors. Instead of using the fixed amount of battery power, in this paper, we propose to integrate supercapacitors into the sensors, and let the sensors wirelessly harvest the relatively unlimited Radio Frequency (RF) energy for the perpetual operation of WSNs. To further facilitate the energy harvesting of WSNs in harsh terrains, we employ an unmanned vehicle to provide dedicated RF signals, and develop a dynamic optimization scheme to control the moving of the vehicle. Specifically, we propose to amount a dedicated RF energy source on a mobile unmanned vehicle, and let the vehicle periodically localize sensors, dynamically select the sensors to recharge, and find its optimal sojourn time, so that the overall operation of the WSN is optimized. Based on the RF energy harvesting equipment of Powercast Corp., we establish the testbed and conduct a series of experiments to verify the effectiveness of the proposed scheme.

With the widely adoption of smart mobile devices, there is a rapidly development of location based services. One key feature in providing the service is the crowdsourcing in which the principal obtains essential data from a large group of users, and inversely sharing the data based service with everyone for free. In this paper, we investigate the problem of how to provide continuous incentives for users to participate in the crowdsourcing activity, which can be referred to the moral hazard problem in the contract theory. First, a performance related incentive mechanism is proposed. Then, the utility maximization problem of the principal is formulated, under the constraint that each user maximizes its own utility by choosing the optimal effort in the crowdsourcing activity. Finally, the numerical results show that by using the proposed incentive mechanism, the users obtains the continuous incentives to participate in the crowdsourcing activity, and the principal successfully maximize the utilities.

In device-to-device (D2D) communication, mobile users communicate directly without going through the base station. D2D commutation has the advantage of improving spectrum efficiency. But the interference introduced by resource sharing of D2D has become a significant challenge. In this paper, we try to optimize the system throughput while simultaneously meeting the quality of service (QoS) requirements for both D2D users and cellular users (CUs). We implement matching theory to solve the resource allocation problem. We utilize two efficient stable matching algorithms to optimize the social welfare while ensuring the network stability. More importantly, we introduce the idea of cheating in matching to further improve D2D users' throughput. It is proven that the cheating mechanism benefits a subset of D2D users without hurting the performance of the rest. Through the simulation results, we demonstrate the effectiveness of both the stable matching and cheating algorithms in terms of improving both D2D users and the overall throughput in D2D communications.

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.

In device-to-device (D2D) communication, mobile users communicate directly without going through the base station. D2D commutation has the advantage of improving spectrum efficiency. But the interference introduced by resource sharing of D2D has become a significant challenge. In this paper, we try to maximize the system throughput and simultaneously meet the Quality of Service (QoS) requirement for both D2D users and cellular users (CUs). We solve the optimization problem by solving the bipartite two-sided matching problem under preferences between the admitted sets of D2D users and CUs. Second and more importantly, we introduce the idea of cheating in matching to further improve some D2D users' system throughputs. We construct a coalition strategy to implement the cheating idea. Finally, from the simulation results, we demonstrate the effectiveness of cheating in matching for D2D pairs.

Energy is the impediment to various applications of battery-powered wireless sensor networks (WSNs). Beyond the battery constraint of sensors/aggregation and forwarding nodes (AFNs), the major energy consumption of WSNs is from the longdistance multi-hop transmissions from the sensors/AFNs to the sink. To address these issues, in this paper, we employ a wireless charging vehicle (WCV) to travel inside WSNs to replenish the energy of sensors/AFNs, and cut long-distance transmissions into short-distance ones. Different from prior works, we let the WCV not only recharge the AFNs selectively, but also collect data from chosen AFNs and bring collected data back to the sink. The chosen AFNs play as virtual sinks, and nearby AFNs can use short-distance transmissions to deliver their traffic to the chosen AFNs. We formulate this problem into an energy replenishment optimization with joint consideration of sensed data delivery, flow routing, wireless power transfer, etc. Since the formulated problem is mixed integer nonlinear programming which is NP-hard to solve, we also develop a heuristic algorithm for feasible solutions. Through simulations, we show that the solution of the proposed algorithm is close to the optimal one and the energy replenishment is optimized while data delivery guaranteed.

As the growth of wireless network, tremendous interests have been focused on statistically tracking the user equipment as well as the performance evaluation of motion tracking. In this paper, we tackle the problem of inferring human mobility trace under the circumstance that the recorded location information exhibits missing data. Based on the tensor voting theory, we propose an efficient sparse tensor voting algorithm and a specified implementation scheme. The model is constructed based on the geometric connections between the input signals and encodes the structure information in the tensor matrix. Thus, the computation is carried out in the form of matrix, which reduces the computation load since most the calculation involves only with matrix addition and multiplication. The proposed method is applied to real human mobility trace. The results show that our proposed approach effectively recovers human mobility trace from the incomplete data input.

Numerous applications of wireless sensor networks (WSNs) in harsh terrains are constrained by the sensors' battery-power and face the difficulties of data collection. In this paper, we propose to exploit wireless power transfer technology to replenish the energy of sensor clusters and develop an efficient data collection scheme for those wireless rechargeable senor clusters deployed in harsh terrains. In view of the harsh terrains, we employ unmanned aerial vehicles (UAVs) to travel to the sites of sensor clusters, collect data, and recharge the sensors in corresponding clusters. With joint consideration of data collection characteristics, wireless power transfer features and travel time, we mathematically formulate the data collection in rechargeable WSNs into an optimization problem with the objective of maximizing data collection utility. Based on the matching theory, we also develop a one side matching algorithm and a greedy algorithm to solve the problem in distributed manner. Through simulations, we show that UAVs are not always matched with nearest sensor clusters, the solution of the proposed greedy algorithm is optimal, and the sensed data can be efficiently collected.

Routing schemes play an important role in the network. In terms of the information utilized by routing, the existing schemes can be classified into two main groups: topology-based routing and geographical routing. The former can always guarantee the optimal path, but its routing tables often contain massive entries which seriously impact the algorithm's efficiency. The latter can not guarantee the optimal path, but its routing table size is fairly small. Based on the characteristics of above routing mechanisms, we present a novel geographical routing mechanism which can guarantee the optimal path with the minimum overhead. By utilizing the geographical location information and the quadtree data structure, the routing table size can be reduced to its information-theoretic lower bound. Our theoretical analysis suggests that the performance of the routing table size of our proposed scheme is better than the best IP-based routing table compression result in the literature.

In cognitive radio (CR) networks, due to the uncertainty of primary users' (PUs) traffic, there are vacant bandwidths that can be accessed by secondary users (SUs). Thus, this provides mutual benefit opportunities for both PUs and SUs. In this paper, the objective is to select the optimal channel utilization for maximizing the system utility. To solve the problem, we employ the matching theory as the mathematical framework to form mutually beneficial relationships between PUs and SUs. In particular, we propose a distributed matching algorithm for many-to-one matching considering both SUs' and PUs' characteristics. Simulation results show that the proposed distributed algorithm achieves a performance close to the centralized method, and outperforms the greedy allocation in different scenarios.

Providing adequate and reliable spectrum resources for unlicensed users in spectrum database-based cognitive radio networks is very challenging, mainly due to the dynamic resource availability induced by the licensed users' activities and radio environment. In this paper, we propose an adaptive spectrum access method based on spectrum database for cognitive radio (CR) networks. While making decision to access the licensed spectrum, the secondary users (SUs) not only use the spectrum information informed by the spectrum database but also use the local sensing to confirm the specific condition of the spectrum. The adaptive sensing and access process is modeled as an optimal decision process by maximizing the achievable throughput of CR networks. The dynamic programming algorithm is developed to find the optimal sensing and access policy for each SU. Simulation results show that the proposed sensing and access policies can provide reliability guarantees for finding spectrum opportunities in terms of dynamic radio environment.

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.

With the ever increasing user adoption of mobile devices like smart phones and tablets, the cellular service providers' energy consumption and cost are fast-growing and have received tremendous attention. How to effectively reduce the energy cost of cellular networks and achieve green communications while satisfying cellular users' rocketing traffic demands has become an urgent and challenging problem. In this paper, we investigate the minimization of the long-term time-averaged expected energy cost of a cellular service provider while guaranteeing the strong stability of the network. We first formulate an offline optimization problem with a joint consideration of flow routing, link scheduling, and energy (i.e., renewable energy resource, energy storage unit, etc.) constraints. Since the formulated problem is a time-coupling stochastic Mixed-Integer Non-Linear Programming (MINLP) problem, it is prohibitively expensive to solve. Then, we reformulate the problem by employing Lyapunov optimization theory. A decomposition based algorithm is developed to solve the problem, which is proved to guarantee the network strong stability. Both the lower and upper bounds on the optimal result of the original problem are derived and proven. Simulation results demonstrate that the obtained lower and upper bounds are very tight, and that the proposed scheme results in noticeable energy cost savings.

Wireless data broadcast is an efficient way to disseminate data to a large number of users in mobile communication environments. In many applications, such as stock quotes, flight schedules and traffic news, the users may want to download multiple data items at one time and the applications may require the support of multi-channel architectures. This paper studies the problem of retrieving a set of data items from multiple wireless broadcasting channels such that all the requested data items can be downloaded with the least access time, namely Least Time Data Retrieval (LTDR). On one hand, we find that it is NP-hard to approximate this problem to within any non-trivial factor even if there are only two channels. On the other hand, we find that there exists a polynomial time (1+1/1-α)-approximation solution for an LTDR instance L if the optimal solution of L requirers τ time slots and (1-α)kT ≤ τ ≤ kT, where k is a positive integer, α is an arbitrary constant between 0 and 1, and T is the least common multiple of cycle lengths for all channels. We also derive a lower bound of polynomial time approximation for L matching that upper bound under the well known assumption P ≠ NP. To improve the practical efficiency of the approximation solution, a heuristic is exhibited and tested by simulations.

Interference alignment (IA) is an emerging interference management approach which fully exploits the potential spatial bandwidth in wireless networks, achieving optimal DOF. However, perfect IA is almost unaccessible for 3-user complex constant interference channel without symbol extension. For this, an efficient phase based imperfect interference alignment scheme is proposed in this paper, which uses a deviation compensation factor (DCA) to measure how much the practical channel coefficients deviate from perfect IA feasibility condition. The DCA is allocated to receivers based on a Minimum Interference Leakage (MIL) criterion. Simulation results shows that the MIL based DCA, Max-SINR and TDMA algorithms perform better in different SNR regions, and the proposed scheme can be improved with proper switch between different strategies.

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.

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.

The rapid growth of wireless devices and services exacerbates the problem of spectrum scarcity in wireless networks. Recently, spectrum auction has emerged as one of the most promising techniques to enhance spectrum utilization and mitigate this problem. Although there exist some works studying spectrum auction, most of them are designed for single-hop communications, and it is usually not clear whom a winning user communicates with. Moreover, most previous auction schemes only focus on satisfying the incentive compatibility property, also called truthfulness, but ignore another two critical properties: individual rationality, and budget balance. Thus, they may not be economic-robust. In this paper, we propose a transmission opportunity auction scheme, called TOA, which can support multi-hop data traffic, ensure economic-robustness, and generate high revenue for the auctioneer. Specifically, in TOA, instead of spectrum bands as in traditional spectrum auction schemes, users bid for transmission opportunities (TOs). A TO is defined as the permit of data transmission on a specific link using a certain band, i.e., a link-band pair. The TOA scheme is composed of three procedures: TO allocation, TO scheduling, and pricing, which are performed sequentially and iteratively until the aforementioned goals are reached. We prove that TOA is economic-robust, and conduct extensive simulations to show its effectiveness and efficiency.

Network lifetime is critical to any battery operated wireless sensor networks (WSNs). Especially for delay-sensitive WSNs, it's challenging to prolong the network lifetime while meeting the delay requirements of different applications. We investigate how to optimize the network lifetime of delay-sensitive WSNs with respect to energy efficient routing and sleep scheduling. Instead of treating routing and sleep scheduling as two separate approaches, we have a joint consideration of them, and mathematically formulate the lifetime maximization problem. Since the formulated problem is a mixed integer nonlinear programming (MINLP) problem and NP-hard to solve, we relax it into a linear programming (LP) problem and solve the relaxed problem for the upper bound. We also develop a heuristic algorithm-RSF for feasible solution, which yields a lower bound of WSNs' lifetime. Through extensive simulations, we show that the solution found by the proposed algorithm is close to the optimal one and the proposed scheme is effective in prolonging the lifetime of delay-sensitive WSNs.

Network lifetime is a critical design goal of any battery operated large scale wireless sensor networks (WSNs). In this paper, we investigate how to prolong the lifetime of large scale WSNs via optimal placement of base stations (BSs). With multiple BSs placed, the sensor nodes can send their data to nearby sinks and may reduce energy consumption of relaying packets for other sensor nodes. Due to the high cost of BSs and geographical constraints in WSNs, we can only place a limited number of BSs in a few candidate locations. Considering wireless transmission features and flow routing, we formulate this base station placement (BSP) problem in WSNs into a mixed integer nonlinear programming (MINLP) problem. In view of the NP-hardness of the formulated problem, we develop the heuristic algorithm to pursue feasible solutions. Through extensive simulations, we show that the solutions found by the proposed algorithm are close to the optimal one and the proposed scheme is effective in prolonging the lifetime of large scale WSNs.

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.

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.

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.

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 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.

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.

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..

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.

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.

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.

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.

This driven by the need to promote a more efficient use of radio resources and improve the operators' profits, resource allocation has turned into a joint technical and economical problem. At the same time, as a possible enabling solution, game theory has been applied to either dynamic spectrum access (DSA) or joint radio resource management (JRRM) in wireless communication research recently. In this paper, we propose a novel DSA and JRRM combined approach to resource allocation in cooperative networks. With the scenario that distributed reconfigurable radio access networks (RAN) are controlled by different operators, the emerging concept of resource trading is introduced and new entities, such as trading agents (TA), are described. Meanwhile, Shapley value in cooperative game as well as its economic model is exploited to share the profits among the trading RANs. Numerical results show that comparing with existing DSA or JRRM methods, our scheme has better effect in maximizing the individual operator's profits and improving the efficiency of radio resources utilization.

In this paper we propose and discuss an innovative infrastructure-assisted routing protocol for the ad hoc network overall or partial under the coverage of the infrastructure network. The basic idea of this design is to make full use of the infrastructure network's notable features to support the implementation of route discovery and maintenance for the purpose of not only highly reducing time, communication and storage complexity but also balancing traffic load of the ad hoc network. In this protocol, each node detaches link state with the neighbor nodes and reports the changes to the infrastructure network using the triggered update mechanism, while the infrastructure network stores the full knowledge of topology, computes routes on-demand with latency as the edge weight, and distributes the sub-route records to relevant relay nodes using the multi-sub- route relay mechanism. Those sub-routes are typically, but not necessarily, of the same length. Since not all nodes communicate with the infrastructure network directly, the ad hoc network are divided into a set of zones, the light zones and the dark zones, and different strategies, i.e. the link state algorithm and the source routing algorithm, are employed in different zones. We refer to the protocol as the light dark routing (LDR) protocol.

Synchronization is a challenging task in ultra wideband (UWB) communications, and becomes more difficult in UWB based wireless sensor networks (WSNs) in the presence of multi-user interference (MUI). For such a system, we develop a synchronization scheme including a novel design of transmitted reference (TR) signal model to avoid MUI and an energy-efficient synchronization algorithm. The synchronization signal of cluster-head is designed to be TR symbols with normal reference pulse and the interfering signal of common neighbour nodes is designed to be TR symbols with alternant anti-polar reference pulse, thus the MUI-free operation is obtained. Furthermore, the synchronization algorithm employs sample mean and energy detection relying on the periodicity in the mean of synchronization signal. Theoretic analysis and simulative results prove that the proposed scheme is both reliable and scalable