The openness of wireless networks enables flexible connections on devices, but also arises the security and privacy concerns on adversarial eavesdropping. By concealing the transmissions from eavesdropper, covert communication preserves the privacy of communications and has become a significant research topic. Whereas, existing works primarily focus on the covertness of communication links, neglecting the protection for the critical leader nodes response for information aggregation and decision making in information systems. To bridge this gap, we introduce a novel conceptcovert leader, whose identity remains concealed from all other nodes while performing leadership functions. This design inherently mitigates targeted attacks (e.g., eavesdropping, jamming, DoS) against leaders, enhancing both privacy and robustness of wireless network. Then, a distributed randomizedK-Covert Leader Election (KCLE) algorithm is designed, in whichKis a hyperparameter to depict the covertness of leader election and can be adjusted when KCLE algorithm is implemented in reality. Unlike cryptographic solutions, KCLE leverages physical-layer signal properties to elect a leader from at leastKcandidates with balanced energy-time tradeoffs, ensuring no leader identity leakage occurs during election. The algorithm operates without complex encryption, making it suitable for resource-constrained Internet-of-Things and ad hoc networks. Theoretical analysis and simulation results demonstrate KCLE’s correctness, covertness guarantees, and operational efficiency under SINR interference models.

While existing far memory and disaggregated memory solutions provide a foundation for addressing limitations of single-node memory capacity and inefficient resource allocation in data centers, they predominantly focus on host memory, overlooking the critical demands of GPU-centric workloads. A key bottleneck in scaling GPU memory is the lack of connectivity and interoperability between GPUs, which is exacerbated by their heterogeneity. To bridge this gap, this paper proposes UHM, a unified data transferring and memory pooling scheme for heterogeneous GPU memories. UHM establishes the communication channels between heterogeneous GPU/host memories and leverages double data buffers for pipelined and reliable transfer. Furthermore, UHM unifies both local and remote memories to build a memory pool. The pooling scheme effectively integrates local and remote resources, performs efficient caching management in local memory, and optimizes memory block management for remote memory resources. Evaluation on a heterogeneous GPU cluster demonstrates that UHM significantly reduces the data transfer latency (up to 87.2%), improves the cache hit ratio, reduces runtime memory allocation latency (up to 94.7%), while enhancing the overall memory utilization (24.7%).

The crux of data disclosure lies in the meticulous quantification and judicious trade-off between privacy leakage and utility. Firstly, the measurement of privacy leakage is the premise of sensitive data compliance disclosure. Existing solutions are mainly based on the qualitative perspective and the group perspective, which are unable to quantitatively measure the risk of individual privacy leakage. Secondly, in terms of balancing privacy leakage and utility, existing solutions overlook uninformed disclosure scenarios. In such scenarios, the two-dimensional privacy-utility game will be reduced to the optimization of a single parameter of mutual information. To address the aforementioned drawbacks, this paper proposes a data disclosure mechanism tailored for heterogeneous privacy profiles. Specifically, we decouple the multilevel privacy leakage through mutual information. By respectively addressing the informed and uninformed disclosure, we achieve the optimization of the joint potential energy surface of privacy and utility, breaking through the dilemma of excessive protection and utility collapse in data disclosure. On this basis, we conduct a comprehensive discussion on the two data disclosure strategies, namely introducing disturbance and linear obfuscation. The results of extensive simulations verify the effectiveness of the proposed mechanism. Finally, our work shows that balancing privacy and utility in data disclosure is unfeasible with disturbance-introducing strategies. In contrast, using linear obfuscation strategies can achieve such a balance, and the optimal approach is the disclosure scheme that minimizes the data disclosure loss.

There is an emerging trend of using deep learning (DL) to handle complex tasks in wireless communication systems. However, recent research suggests that DL-enabled communication systems are vulnerable to adversarial attacks. Fortunately, most of these attacks are simulation-based, incapable of handling realistic channels with, e.g., multipath fading, temporal dynamics, and hardware nonlinearity, and hence lack of practicality. To this end, we present RadioShock, an over-the-air adversarial attack against wireless communication systems. Through accurate estimations of channel states for dynamic adaptations, RadioShock is made for real-world communication scenarios.We further introduce a universal compact perturbation generation algorithm, along with a new perturbation constraint strategy, aiming to achieve covert over-the-air adversarial attacks. We implement a RadioShock prototype and conduct extensive experiments using automatic modulation classification systems as a representative application scenario. The results reveal that RadioShock diminishes the accuracy of diverse models utilized in wireless communication systems by up to 52.41%, far more effective than existing simulation-based adversarial attacks in facing real-world applications.

Bitcoin has witnessed a prevailing transition that employing transaction fees paid by users rather than subsidy assigned by the system as the main incentive for mining. The adjustability of reward in the transaction fee-incentivized regime makes room for the mining gap, a period of time in which miners turn mining rigs off until transaction fees are sufficient. Obviously, the mining gap aggressively weakens the security of Bitcoin, and is further extended by the selfishness of rational users who tend to provide low transaction fees. The phenomena of mining gap traps Bitcoin system into the egoistic dilemma which is a challenging problem since it involves games not only between users and miners bilaterally, but also among miners and users internally. Hence, in this paper, we first derive the property of strategic complementarity among users/miners, which enables us to reasonably untangle the antagonism among the homogenous players. Based on this, an incentive mechanism leveraging the zero-determinant theory is designed to get rid of the dilemma. To the best of our knowledge, this paper is the first work to cap the mining gap and solve the egoistic dilemma in Bitcoin. Both theoretical analyses and numerical simulations demonstrate the effectiveness of our proposed mechanism.

Audio adversarial perturbations are designed to remain imperceptible to humans while deceiving automatic speech recognition (ASR) models. However, operating within the audible frequency range makes existing methods partially detectable in practice. In this paper, we present LaserAdv, a laser-based adversarial attack that injects carefully crafted perturbations via laser signals, which are superimposed on speech rather than masking it. This design exploits a well-established property of adversarial examples—the ability to mislead models through minimal, often imperceptible, modifications—while preserving the underlying speech, thereby enabling higher attack efficiency and a longer effective attack range. To mitigate distortion introduced during laser transmission, we propose SAE-TFI, a selective amplitude enhancement method in the time–frequency domain. LaserAdv enables physically realizable attacks that are inaudible and black-box, while supporting targeted and universal attack settings without requiring signal synchronization. Experimental results demonstrate that a single perturbation can cause DeepSpeech, Whisper, and iFlytek to misinterpret any of the 12,260 voice commands as target with accuracy of up to 100%, 92% and 88%, respectively. The maximum effective attack distance reaches 120 m.

The feverish personal data gold rush has made sensitive information leakage a non-negligible issue, turning it into a popular target for malicious attacks. Although some data may not seem to reveal private information directly, they could still be exploited through malicious intervention and inference; such data are referred to as risk data. Studies have claimed that such risk data exhibit the trait ofmacro-level collaborative leakage, meaning that individually harmless risk data can reveal sensitive information when combined. However, why the macro-level collaborative leakage will occur remains relatively uncharted. Hence, in this paper, we conduct rigorous quantitative analyses for the first time, to trace the root of the macro-level collaborative leakage. We conclude that this phenomenon arises from thecollaborative effectsamong pieces of risk data concerning sensitive information. In light of this, we formulate the sufficient condition for the occurrence of the macro-level collaborative leakage and investigate its presence in the Gaussian-distributed data. We highlight that, on the one hand, the Gaussian distribution can align the correlation between risk data and sensitive data at both the micro- and macro- levels, thereby preventing the macro-level collaborative leakage. This reveals the potential of the Gaussian distribution in enhancing data privacy protection from a macro perspective. On the other hand, the ability of the Gaussian distribution to counteract the macro-level collaborative leakage is inherently limited, which further corroborates the ubiquity of this phenomenon. Our insights underscore the need for more comprehensive security and privacy protection mechanisms to ensure data security and confidentiality.

The decentralized nature of federated learning (FL) makes it difficult to verify the trustworthiness of participating clients, creating an opportunity for backdoor attacks. This paper addresses a general backdoor-resilient decentralized FL problem without any prior knowledge of the type of backdoor attacks or information about malicious clients. After an in-depth investigation of how backdoor attacks are conducted in FL, we introduce a multi-armed bandit-based knowledge distillation approach to help benign clients learn useful knowledge from other clients while rejecting potential backdoors hidden in shared updates. Unlike most previous works that rely on identifying and removing malicious updates—an approach limited to scenarios with fewer than 50% attackers—our knowledge distillation technique enables benign clients to reject backdoored knowledge while preserving useful information, maintaining effective defense even when malicious clients exceed 50% of the population. Additionally, to handle the various updates from clients with Non-IID dataset, a multi-armed bandit scheme is designed for each benign client to select the most appropriate teachers for knowledge distillation, resulting in high accuracy and fast convergence. Extensive experiments demonstrate that our multi-armed bandit-based knowledge distillation approach achieves high accuracy and general backdoor resilience. Comparisons with previous works show that our approach can reduce the attack success rate by 14.71%∼96.78% on average.

With the increasing popularity of voice-centric applications, acoustic eavesdropping attacks pose a significant threat to user privacy. Although smartphones require explicit user permission to access the microphone, such attacks can bypass this restriction by exploiting power consumption data through compromised power supplies, such as USB adapters, public charging stations, and power banks. However, previous attempts can only recognize a limited set of hotwords or digits. To address this limitation, we introduce PowerEar, an acoustic eavesdropping attack that leverages the power side channel to reconstruct any audio reproduced by the built-in loudspeaker of a mobile device with an unconstrained vocabulary. Our approach relies on a combination of signal processing and generative techniques to learn the mapping between power consumption and audio playback, enabling the reconstruction of such audio through spectrogram enhancement. To validate the effectiveness of PowerEar attack, we carry out a comprehensive set of experiments using audio samples from various public personalities. Our results obtained through objective and subjective evaluations clearly demonstrate that PowerEar can successfully recover user speeches from power consumption data in comprehensive realistic settings, including speech utterances of individuals and different devices, mobile operating systems, activities, charging technology, battery and volume levels.

Modern vehicles are widely equipped with in-vehicle audio systems, and drivers routinely play music, podcasts, navigation prompts, phone calls, and various voice services during daily commutes. Due to the enclosed nature of the vehicle cabin and the presence of substantial ambient noise, in-vehicle content is commonly assumed to be inherently private and imperceptible from outside the vehicle. In this work, we show that this assumption does not always hold and reveal a previously unexplored leakage channel for in-vehicle audio. We propose Vehisper, the first systematic study investigating the feasibility of passively eavesdropping on in-vehicle audio from outside a moving vehicle. We discover that, during operation, in-vehicle loudspeakers excite the vehicle's metallic structure, giving rise to observable low-frequency magnetic leakage outside the vehicle, which forms a stealthy side channel that has not been explored in prior work. Building on this insight, we design a compact external sensing device and develop a multi-stage signal processing pipeline to systematically cope with the strong and complex magnetic interference introduced by vehicle motion, as well as spectral distortion and perceptual degradation induced by the leakage channel. We conduct a comprehensive evaluation of Vehisper on 20 commercial vehicles from 10 major manufacturers. Experimental results demonstrate that, under real driving conditions, Vehisper can reliably recover in-vehicle audio, achieving an average word error rate of 7.85%.

Deep learning has significantly advanced Radio Frequency (RF) sensing, leading to extensive research and practical applications in both academia and industry. However, these advancements have also introduced potential privacy and security threats to RF sensing data. In this paper, we present RFInv, the first model inversion attack targeting deep learning classifier-empowered RF sensing systems. RFInv can recover users' private sensing data without knowledge of the RF sensing model's structure, relying solely on the output prediction vector of the deep learning classifier. Consequently, this recovered sensitive data can be exploited for malicious purposes such as identity impersonation and unauthorized device control. To realize the proposed attack, we develop a deep generative adversarial network that integrates an inversion module and a critic module, enabling effective RF data recovery in black-box scenarios. To address the unique challenge of preserving physical consistency in RF data, we incorporate attention mechanisms and deformable convolutions to model their complex temporal and spatial dynamics, ensuring physical consistency. Furthermore, a spectrogram alignment loss is introduced to further enhance reconstruction accuracy. The network is trained using an auxiliary dataset, circumventing the need for access to the target model's training data. We systematically evaluate our proposed attack across multiple datasets for various RF sensing tasks and target models with different network architectures. Extensive experiments demonstrate that RFInv can recover diverse types of RF privacy data with an average Structural Similarity Index Measure (SSIM) of 0.78 and achieves an 86.21% Relative Attack Success Rate (RASR).

Palm vein recognition has gained traction in secure authentication due to its unique, stable, and inherently concealed biometric characteristics. However, the electromagnetic (EM) emissions from subcutaneous vein imaging sensors (SVIS) may unintentionally leak sensitive biometric information, fundamentally challenging the assumed security guarantees. In this paper, we propose VeinPhantom, a novel side-channel attack that reconstructs palm vein patterns from unintended EM emissions of SVIS, ultimately enabling spoofing attacks against biometric authentication systems. To overcome the challenge of low information entropy caused by weak EM signals and complex environmental interference, VeinPhantom first analyzes palm vein information from EM signals, then employs a cascaded enhancement strategy and incorporates a Dynamic Guidance Diffusion framework to progressively reconstruct high-fidelity palm vein patterns. Extensive experiments demonstrate that VeinPhantom achieves an average structure similarity index measure (SSIM) of 0.56 on commercial devices, along with a 56.96% spoofing success rate against state-of-the-art authentication systems. We further discuss potential mitigation strategies to defend against the attack.

In recent years, volumetric video streaming has emerged as a key application in the field of virtual reality (VR) and augmented reality (AR), attracting growing research and industry interest. Despite its potential, the extremely high bandwidth requirements of volumetric content far exceed the capacity of current networks to support full-resolution streaming. To address this limitation, industry practitioners often employ field-of-view (FoV) prediction to downscale the streaming content based on user gaze direction. Although this approach makes the streaming feasible, our empirical measurement reveals that, working with the FoV prediction, the commonly used sequential prefetching mechanism severely constrains streaming efficiency. To overcome this bottleneck, we introduce PACE, a smart prefetching strategy built on multi-round cell-level download scheduling. PACE divides the prefetching process of each group-of-frames (GoF) into several rounds, where distinct video cells are downloaded in each round according to periodically updated FoV predictions. This design decouples FoV prediction from the constraint of prefetch length, enabling more adaptive and responsive streaming. Moreover, PACE integrates a greedy quality selection policy that dynamically adjusts video quality to make full use of available bandwidth and enhance the overall quality of experience (QoE). Comprehensive evaluations show that PACE improves FoV prediction accuracy by up to 23.1% and boosts QoE by as much as 54.8%, while maintaining robust performance across diverse network and user scenarios.

This paper studies an efficient federated learning (FL) problem involving multiple edge-based clients with heterogeneous constrained resources. Compared with numerous training parameters, the computing and communication resources of clients in edge scenarios are usually insufficient for fast local training and real-time knowledge sharing. Besides, training on clients with heterogeneous resources may result in the straggler problem, which delays the convergence of FL. To address these issues, we proposeFed-RAA: aResource-AdaptiveAsynchronousFederated learning algorithm. Different from vanilla FL methods, where all parameters are trained by each participating client regardless of resource diversity, Fed-RAA adaptively allocates submodels of the global model to clients based on their computing and communication capabilities. Each client then individually trains its assigned submodel and asynchronously uploads the updated result. Theoretical analysis confirms the convergence of our approach. Additionally, an online greedy-based algorithm is designed for asynchronous submodel assignment in Fed-RAA, improving the convergence of Fed-RAA by optimal minimization on the training delay bound of submodels. Compared to state-of-the-art methods, our Fed-RAA algorithm reduces the time required to achieve the target accuracy by an average of$ 30.89\%$, demonstrating its superior efficiency on heterogeneous constrained computing and communication resources. To the best of our knowledge, this paper is the first resource-adaptive asynchronous method for submodel-based FL with guaranteed theoretical convergence.

Ranking a given set of items is a fundamental problem with widespread applications in information retrieval, recommendation systems, and beyond. Crowdsensing data trading (CDT) systems provide an effective means to collect opinions from distributed workers for ranking tasks. However, these systems face several challenges, including preserving privacy, maintaining ranking accuracy, optimizing worker participation, and ensuring incentive compatibility for all stakeholders. To address these challenges, we propose OPAR-IC, an integrated framework designed to enhance efficiency and privacy in CDT systems. The proposed framework integrates a multi-armed bandit (MAB) based approach to dynamically adjust ranking granularity, a hybrid privacy-preserving mechanism combining logarithmic and binary methods to safeguard worker data, and a combinatorial MAB model for efficient worker recruitment. To enhance worker participation and ensure incentive compatibility among all stakeholders, we integrate a hierarchical Stackelberg game into the framework, balancing competing incentives and achieving equilibrium. Our approach is thoroughly validated through theoretical analysis, offering theoretical guarantees for privacy, ranking accuracy, worker participation, and incentive compatibility. Extensive experiments on real-world datasets demonstrate significant improvements in adaptive ranking accuracy, privacy preservation, worker recruitment, and three-party incentive compatibility.

Byzantine resilience is essential in federated learning (FL) to safeguard model training from malicious or faulty participants. However, existing Byzantine-resilient methods struggle when faced with heavy-tailed gradient noise, a common challenge in heterogeneous environments. In this work, we propose a Byzantine-resilient FL framework specifically designed to handle both heterogeneity and heavy-tailed noise. Our approach builds on robust distributed stochastic heavy-ball optimization, incorporating update normalization and gradient/momentum clipping to mitigate the effects of heavy-tailed noise. We establish the first high-probability convergence guarantees for Byzantine-resilient FL under these conditions, showing that our algorithms achieve optimal Byzantine resilience and align with known lower bounds. Additionally, we introduce an efficient variant of the nearest neighbor mixing technique, leveraging random projections to significantly reduce computational costs in high-dimensional settings. Through rigorous theoretical analysis and extensive empirical evaluations, we demonstrate that our methods outperform existing approaches in robustness against both Byzantine failures and heavy-tailed noise.

The growing resource demands of large-scale transformer models pose significant challenges for resource-constrained users, particularly in distributed environments. To address this issue, we propose a federated learning framework called Fed-Grow, which enables multiple participants to collaboratively learn a lightweight scaling operation that transfers knowledge from pretrained small models to a large transformer model. In Fed-Grow, we introduce the Dual-LiGO (Dual Linear Growth Operator) architecture, consisting of Local-LiGO and Global-LiGO components. Local-LiGO addresses model heterogeneity by adapting each participant's pre-trained model to a common intermediate form, while Global-LiGO facilitates knowledge sharing across participants without sharing local models or raw data, ensuring privacy preservation. This federated approach offers a scalable solution for growing large transformers in a distributed manner, where only the Global-LiGO is shared, significantly reducing communication overhead while maintaining comparable model performance under the same communication constraints. Experimental results demonstrate that Fed-Grow outperforms state-of-the-art methods in terms of accuracy and precision, while reducing the number of trainable parameters by 59.25% and communication costs by 73.01%. These improvements allow for higher efficiency in training large models in distributed environments, without sacrificing performance. To the best of our knowledge, Fed-Grow is the first method that enables cooperative transformer scaling in a distributed setting, making it a practical solution for resource-constrained users.

Abstract Hypergraph decomposition is a fundamental problem in hypergraph analysis which breaks down hypergraphs into cohesive subgraphs and functional units with dense interactions. Hyperedge intersections and overlaps capture the unique property of shared elements (vertices) between groups (hyperedges) in hypergraphs, revealing cohesive substructures not apparent when focusing solely on individual connections. Despite the significance of hyperedge overlap as a measure of hypergraph cohesiveness, existing models for hypergraph decomposition fail to capture this feature. In this paper, we study the problem of hypergraph decomposition with intersection bounds. We propose the ( k , s )-core, a new cohesive subgraph model incorporating both a vertex degree constraint k and a hyperedge intersection constraint s . This model includes two types: (1) strong ( k , s )-cores, where connected hyperedges share at least s vertices, enforcing strong hyperedge overlap, and (2) weak ( k , s )-cores, where hyperedges are connected through s -walks, allowing for a looser overlap. We prove that our definition of ( k , s )-cores exhibits uniqueness and hierarchical properties. Based on the properties, we develop two decomposition algorithms: a bottom-up algorithm for strong ( k , s )-cores, which uses a heuristic hyperedge removal mechanism to maintain consistent decomposition results and employs a union-find data structure for efficient connectivity identification, and a top-down algorithm for weak ( k , s )-cores that preserves the subgraph containment relationship. Our algorithms achieve traversal efficiency by processing each hyperedge in the hypergraph only once. Additionally, all ( k , s )-cores can be efficiently stored with minimal memory overhead. Comprehensive experiments and case studies show that the ( k , s )-core model outperforms existing methods in capturing cohesive subgraphs with overlaps in hypergraphs. Furthermore, the proposed algorithms demonstrate high efficiency and scalability, making them well-suited for real-world hypergraphs.

MPC-as-a-Service (MPCaaS) systems enable clients to outsource privacy-preserving computations to distributed servers, offering flexibility by adapting and configuring MPC protocols to meet diverse security requirements. However, traditional MPC protocols rely on a fixed set of servers for the entire computation process, limiting scalability. Dynamic MPC (DMPC) addresses this limitation by permitting participants to join or leave during the computation. Nevertheless, existing DMPC protocols assume synchronous networks, which can lead to failures under unbounded network delays. In this paper, we present AD-MPC, the first asynchronous dynamic MPC protocol. Our protocol ensures guaranteed output delivery under optimal resilience ((n = 3t + 1)). To achieve this, we introduce two critical components: an asynchronous dynamic preprocessing protocol that facilitates the on-demand generation of Beaver triples for secure multiplication, and an asynchronous transfer protocol that maintains consistency during party hand-offs. These components collectively ensure computation correctness and transfer consistency across participants. We implement AD-MPC and evaluate its performance across up to 20 geographically distributed nodes. Experimental results demonstrate that the protocol not only offers strong security guarantees in dynamic and asynchronous network environments but also achieves performance comparable to state-of-the-art DMPC protocols.

Satellite-Air-Ground Integrated Networks (SAGINs) provide ubiquitous connectivity, global coverage and flexible deployment convenience for terrestrial users, which are beneficial to optimizing network resources and achieving task scheduling functions. However, the corresponding SAGIN nodes are dynamic and complex, leading to intractable multi-modal features and high network latency when graph model is used for collaborative task completion. Therefore, we establish a directed SAGIN federated graph model to minimize the total transmission latency via computation offloading and quantization methods. Specifically, we utilize the federated graph learning to process the time-varying graph nodes and sizes, and then perform deep reinforcement learning (DRL) to optimize the computation and quantization resources. Moreover, federated learning is convoked to accelerate the convergence speed. Finally, our simulation results show that the proposed method outperforms some advanced benchmarks in terms of convergence performance and transmission latency for multiple data modals.

With the advancement of technology, the functionality of mobile applications has become increasingly powerful, and users are heavily relying on these apps for various entertainment and business activities. However, the frequent use of these applications accelerates smartphone battery consumption, which can require multiple charging sessions per day. As a result, USB charging facilities and shared mobile power banks have become more widespread. Although their popularity indeed provides convenience to users, it also introduces security risks, such as information theft through USB data transfer. In this paper, we propose PowerApp, a novel framework that identifies mobile applications running and ongoing user actions by analyzing their energy consumption of a USB-connected mobile device. In particular, we passively measure the current withdrawal by the charging mobile device from the power source. We demonstrate the effectiveness and practicality of PowerApp through extensive experiments involving 190 popular mobile apps. Our experimental results show that PowerApp can successfully identify these apps with around 95% accuracy on average.

Network traffic analysis attacks represent a significant threat to information security and user privacy. Leveraging machine learning, these attacks can infer sensitive information even when encryption and anonymization techniques are in place. This paper proposes TravelingTogether, a novel framework that leverages packet aggregation from multiple source hosts to defend users against network traffic analysis. TravelingTogether works at the network level, providing transparent and seamless protection to any hosts connected to the network. We evaluate our defense across different scenarios through a comprehensive set of experiments, demonstrating its effectiveness in thwarting website fingerprinting attacks as a representative use case, and its efficiency in terms of low time and bandwidth overhead.

Asynchronous Byzantine fault-tolerant (BFT) consensus protocols, known for their robustness in unpredictable environments without relying on timing assumptions, are becoming increasingly vital for wireless applications. While these protocols have proven effective in wired networks, their adaptation to wireless environments presents significant challenges. Asynchronous BFT consensus, characterized by its N parallel consensus components (e.g., asynchronous Byzantine agreement, reliable broadcast), suffers from high message complexity, leading to network congestion and inefficiency, especially in resource-constrained wireless networks. Asynchronous Byzantine agreement (ABA) protocols, a foundational component of asynchronous BFT, require careful balancing of message complexity and cryptographic overhead to achieve efficient implementation in wireless settings. Additionally, the absence of dedicated testbeds for asynchronous wireless BFT consensus protocols hinders development and performance evaluation. To address these challenges, we propose a consensus batching protocol (ConsensusBatcher), which supports both vertical and horizontal batching of multiple parallel consensus components. We leverage ConsensusBatcher to adapt three asynchronous BFT consensus protocols (HoneyBadgerBFT, BEAT, and Dumbo) from wired networks to resource-constrained wireless networks. To evaluate the performance of ConsensusBatcher-enabled consensus protocols in wireless environments, we develop and open-source a testbed for deployment and performance assessment of these protocols. Using this testbed, we demonstrate that ConsensusBatcher-based consensus reduces latency by 48% to 59% and increases throughput by 48% to 62% compared to baseline consensus protocols.

Community search, which finds cohesive subgraphs containing given query vertices, has attracted much attention in decades. On attributed graphs, when considering the fairness of members' attributes in a community, the cohesiveness constraint of a clique is too strong, which often causes no fair clique based communities can be found. Thus, in this paper, we use the k-truss model, which is a relaxation of the clique but whose members have large engagement and high tie strength, to describe fair communities, namely fair k-truss communities (FTC) and anchored fair k-truss communities (AFTC, using anchored vertices to help satisfying the fairness constraint). We formulate the FTC and AFTC search problems to find the FTC or AFTC containing a given query vertex$q$which has the largest$k$and the smallest diameter. We prove the hardness of both problems. We develop several greedy algorithms and acceleration strategies to solve FTC and AFTC search problems. Experiments on 8 real-world networks show the significance of our FTC and AFTC models, and high performance of our algorithms and acceleration strategies.

Network traffic classification plays a crucial role in a wide range of applications, e.g., Quality of Service (QoS) enhancement, resource management, and network security. However, the widespread adoption of encryption protocols (e.g., SSL/TLS) and the emergence of anonymous communication systems (e.g., Tor) have introduced significant challenges due to the presence of complex and varied network noise. Although considerable effort has been made to improve the robustness of the traffic classification, the performances of existing state-of-theart methods cannot be guaranteed in the presence of a mixture of noises, and are not stable in different application scenarios. In this paper, we innovate in proposing a network traffic classification method based on MoA (Mixture of Agents), namely MOTA. By leveraging a light-weight MoA architecture, MOTA efficiently fine-tunes mainstream Large Language Models (LLMs) to adapt to different application scenarios of traffic classification, and fully exploits the collaboration of the LLMs to ensure the robustness against mixed noises. Our extensive experiments show that, the classification accuracy is$\geq 99 {\%}$across multiple public datasets injected with mixed noises, significantly outperforming existing SOTA methods. Moreover, despite incorporating multiple LLMs, MOTA maintains millisecond-level inference latency on a server equipped with four NVIDIA GeForce RTX 4090 GPUs, owing to its lightweight design.

Academic paper review typically requires substantial time, expertise, and human resources. Large Language Models (LLMs) present a promising method for automating the review process due to their extensive training data, broad knowledge base, and relatively low usage cost. This work explores the feasibility of using LLMs for academic paper review by proposing an automated review system. The system integrates Retrieval Augmented Generation (RAG), the AutoGen multi-agent system, and Chain-of-Thought prompting to support tasks such as format checking, standardized evaluation, comment generation, and scoring. Experiments conducted on 290 submissions from the WASA 2024 conference using GPT-4o show that LLM-based review significantly reduces review time (average 2.48 hours) and cost (average $104.28 USD). However, the similarity between LLM-selected papers and actual accepted papers remains low (average 38.6%), indicating issues such as hallucination, lack of independent judgment, and retrieval preferences. Therefore, it is recommended to use LLMs as assistive tools to support human reviewers, rather than to replace them.

The Model Context Protocol (MCP) has emerged as a widely adopted mechanism for connecting large language models to external tools and resources. While MCP promises seamless extensibility and rich integrations, it also introduces a substantially expanded attack surface: any plugin can inherit broad system privileges with minimal isolation or oversight. In this work, we conduct the first large-scale empirical analysis of MCP security risks. We develop an automated static analysis framework and systematically examine 2,562 real-world MCP applications spanning 23 functional categories. Our measurements reveal that network and system resource APIs dominate usage patterns, affecting 1,438 and 1,237 servers respectively, while file and memory resources are less frequent but still significant. We find that Developer Tools and API Development plugins are the most API-intensive, and that less popular plugins often contain disproportionately high-risk operations. Through concrete case studies, we demonstrate how insufficient privilege separation enables privilege escalation, misinformation propagation, and data tampering. Based on these findings, we propose a detailed taxonomy of MCP resource access, quantify security-relevant API usage, and identify open challenges for building safer MCP ecosystems, including dynamic permission models and automated trust assessment.

Large Language Models (LLMs) have become integral to automated code analysis, enabling tasks such as vulnerability detection and code comprehension. However, their integration introduces novel attack surfaces. In this paper, we identify and investigate a new class of prompt-based attacks, termed Copy-Guided Attacks (CGA), which exploit the inherent copying tendencies of reasoning-capable LLMs. By injecting carefully crafted triggers into external code snippets, adversaries can induce the model to replicate malicious content during inference. This behavior enables two classes of vulnerabilities: inference length manipulation, where the model generates abnormally short or excessively long reasoning traces; and inference result manipulation, where the model produces misleading or incorrect conclusions. We formalize CGA as an optimization problem and propose a gradient-based approach to synthesize effective triggers. Empirical evaluation on state-of-the-art reasoning LLMs shows that CGA reliably induces infinite loops, premature termination, false refusals, and semantic distortions in code analysis tasks. While highly effective in targeted settings, we observe challenges in generalizing CGA across diverse prompts due to computational constraints, posing an open question for future research. Our findings expose a critical yet underexplored vulnerability in LLM-powered development pipelines and call for urgent advances in prompt-level defense mechanisms.

We investigate the problem of finding second-order stationary points (SOSP) in differentially private (DP) stochastic non-convex optimization. Existing methods suffer from two key limitations: \textbf{(i)} inaccurate convergence error rate due to overlooking gradient variance in the saddle point escape analysis, and \textbf{(ii)} dependence on auxiliary private model selection procedures for identifying DP-SOSP, which can significantly impair utility, particularly in distributed settings. To address these issues, we propose a generic perturbed stochastic gradient descent (PSGD) framework built upon Gaussian noise injection and general gradient oracles. A core innovation of our framework is using model drift distance to determine whether PSGD escapes saddle points, ensuring convergence to approximate local minima without relying on second-order information or additional DP-SOSP identification. By leveraging the adaptive DP-SPIDER estimator as a specific gradient oracle, we develop a new DP algorithm that rectifies the convergence error rates reported in prior work. We further extend this algorithm to distributed learning with heterogeneous data, providing the first formal guarantees for finding DP-SOSP in such settings. Our analysis also highlights the detrimental impacts of private selection procedures in distributed learning under high-dimensional models, underscoring the practical benefits of our design. Numerical experiments on real-world datasets validate the efficacy of our approach.

LLM-based vulnerability auditors (e.g., GitHub Copilot) represent a significant advancement in automated code analysis, offering precise detection of security vulnerabilities. This paper explores the potential to circumvent LLM-based vulnerability auditors by diverting their focus, decided by the LLM attention mechanism, away from real vulnerable code segments. In these LLM-based vulnerability auditors, the attention mechanism is supposed to focus on potentially vulnerable code sections to identify security issues. Our approach introduces high-attention code snippets (code fragments designed to draw focus) into the codebase under review. By strategically diverting the model's focus away from actual vulnerabilities, this technique effectively “blinds” the LLM, resulting in missed detections. To scale this approach, we present Crazy-Ivan11Source code, dataset and attack results are available at https://github.com/oxygen-hunter/Flashboom., an automated system that identifies and seamlessly integrates high-attention code snippets, shifting focus away from genuine vulnerabilities to decoy functions. Through systematic function-level prioritization and refinement, Crazy-Ivan optimizes the blinding effect, producing the Flashboom that can reduce the model's capacity to detect true security risks. Our evaluation underscores the effectiveness of Flashboom, achieving blinding success rates of up to 96.3% on CodeLlama and 83.05% on Gemma, with notable cross-model transferability and applicability across multiple programming languages. In a case study with GitHub Copilot, Flashboom led the tool to overlook a critical blockchain vulnerability, underscoring the security implications of such attention-diverting attacks and the risks inherent in relying solely on LLM-based automated auditing systems. We have reported our findings to the respective LLM-based code auditor vendors, who have acknowledged the issues and are currently working on fixes.

Large Language Models (LLMs) are complex artificial intelligence systems, which can understand, generate, and translate human languages. By analyzing large amounts of textual data, these models learn language patterns to perform tasks such as writing, conversation, and summarization. Agents built on LLMs (LLM agents) further extend these capabilities, allowing them to process user interactions and perform complex operations in diverse task environments. However, during the processing and generation of massive data, LLMs and LLM agents pose a risk of sensitive information leakage, potentially threatening data privacy. This paper aims to demonstrate data privacy issues associated with LLMs and LLM agents to facilitate a comprehensive understanding. Specifically, we conduct an in-depth survey about privacy threats, encompassing passive privacy leakage and active privacy attacks. Subsequently, we introduce the privacy protection mechanisms employed by LLMs and LLM agents and provide a detailed analysis of their effectiveness. Finally, we explore the privacy protection challenges for LLMs and LLM agents as well as outline potential directions for future developments in this domain.

Supply chain management (SCM), a critical factor in enhancing companies’ efficiency and competitiveness, has received significant attention from both industry and academia. Beyond the flow of products from supplier to customer, SCM also involves the flow of information necessary to monitor, track, and optimize the entire product lifecycle. Consequently, integrating the information flow in SCM with the Internet of Things (IoT) is imperative, as IoT provides the ability to onboard, interconnect, interact with, and sense products. However, IoT-enabled SCM also presents unique challenges, such as managing large volumes of data, ensuring credible and traceable data sources, and achieving low-cost, seamless connectivity. In this article, we systematically present recent advances in leveraging IoT to build robust and effective SCM systems. Unlike existing surveys and overviews, this article focuses on the customer side as customers are the destination of the information flow and tightly coupled with IoT networks. We offer deep insights into the principles, challenges, and research opportunities in IoT-enabled SCM, aiming to assist IoT practitioners in understanding and designing IoT solutions for SCM.

Byzantine fault-tolerant (BFT) consensus is a critical problem in parallel and distributed computing systems, particularly with potential adversaries. Most prior work on BFT consensus assumes reliable message delivery and tolerates arbitrary failures of up to$\frac{n}{3}$nodes out of$n$total nodes. However, many systems face unpredictable message delivery failures. This paper investigates the impact of unpredictable message delivery failures on the BFT consensus problem. We propose Nicaea, a novel protocol enabling consensus among loyal nodes when the number of Byzantine nodes is below a new threshold, given by:$\frac{\left(2-\rho\right)\left(1-\rho\right)^{2n-2}-1}{\left(2-\rho\right) \left(1-\rho\right)^{2n-2}+1}n$, where$\rho$denotes the message failure rate. Theoretical proofs and experimental results validate Nicaea's Byzantine resilience. Our findings reveal a fundamental trade-off: as message delivery instability increases, a system's tolerance to Byzantine failures decreases. The well-known$\frac{n}{3}$threshold under reliable message delivery is a special case of our generalized threshold when$\rho=0$. To the best of our knowledge, this work presents the first quantitative characterization of unpredictable message delivery failures’ impact on Byzantine fault tolerance in parallel and distributed computing.

Recently, there has been an increasing drive to consolidate multiple microcontrollers into one physical entity, due to advantages in reducing overall costs, enhancing reliability, and simplifying hardware interconnections. To reduce consolidation engineering costs, minimizing system latency and memory footprint is important as well as maintaining compatibility with legacy software. In this paper, we propose a virtualization-based solution called Feather-weight Virtual Machine (FVM) that focuses on these goals.FVMenables low latency by specializing the virtualization model to Real-Time Operating Systems (RTOSes), achieves small footprint by adapting management policies to microcontroller memories, attains high compatibility by aligning with microcontroller ecosystem idiosyncrasies, finally allowing practical consolidation across a wide range of commodity microcontrollers. We implement and evaluateFVMon ARMv6-M, ARMv7-M, and RISC-V architectures with two toolchains and two RTOSes, and it can fit into 20 KiB of RAM with less than 5% latency bloat.

Public key authenticated encryption with keyword search (PAEKS) has been widely studied in cloud storage systems, which allows the cloud server to search encrypted data while safeguarding against insider keyword guessing attacks (IKGAs). Most PAEKS schemes are based on the discrete logarithm (DL) hardness. However, this assumption becomes insecure when it comes to quantum attacks. To address this concern, there have been studies on post-quantum PAEKS based on lattice. But to our best knowledge, current lattice-based PAEKS exhibit limited applicability and security, such as only supporting single user scenarios, or encountering secret key leakage problem. In this paper, we propose FS-MUAEKS, the forward-secure multi-user authenticated searchable encryption, mitigating the secret key exposure problem and further supporting multi-user scenarios in a quantum setting. Additionally, we formalize the security models of FS-MUAEKS and prove its security in the random oracle model (ROM). Ultimately, the comprehensive performance evaluation indicates that our scheme is computationally efficient and surpasses other state-of-the-art PAEKS schemes. The ciphertext generation overhead of our scheme is only 0.27 times of others in the best case. The communication overhead of our FS-MUAEKS algorithm is constant at 1.75MB under different security parameter settings.

Federated Learning (FL) is a new learning framework in which$s$clients collaboratively train a model under the guidance of a central server. Meanwhile, with the advent of the era of large models, the parameters of models are facing explosive growth. Therefore, it is important to design federated learning algorithms for edge environment. However, the edge environment is severely limited in computing, storage, and network bandwidth resources. Concurrently, adaptive gradient methods show better performance than constant learning rate in non-distributed settings. In this paper, we propose a pruning-based distributed Adam (PD-Adam) algorithm, which combines model pruning and adaptive learning steps to achieve asymptotically optimal convergence rate of$O(1/\sqrt[4]{K})$. At the same time, the algorithm can achieve convergence consistent with the centralized model. Finally, extensive experiments have confirmed the convergence of our algorithm, demonstrating its reliability and effectiveness across various scenarios. Specially, our proposed algorithm is$2$% and$18$% more accurate than the current state-of-the-art FedAvg algorithm on the ResNet and CIFAR datasets.

The openness of wireless networks opens the door to Byzantine attacks on the physical channels, making the communications unreliable and resulting in more challenges in achieving consensus among mobile devices. To address this issue, this paper studies the Byzantine-fault-tolerant (BFT) consensus problem based on an unreliable Byzantine communication model. Different from the previous works requiring stable communications between the honest nodes, considering the unreliable communication makes our problem more realistic but also harder. Based on the unreliable communication model, we first implement a BFT abstract MAC (absMAC) layer with a distributed and randomized multi-channel communication algorithm. In the implemented absMAC layer, its acknowledgement and progress operations can be completed within$O\left ({{\frac {kn}{k-f}\log n}}\right)$and$O\left ({{\frac {k}{k-f}\log n}}\right)$rounds, respectively. n, f, and k are the numbers of nodes, Byzantine nodes, and channels, respectively. With the implemented absMAC layer, an efficient and elegant BFT consensus algorithm is designed, which can solve the binary consensus problem within$O\left ({{\frac {kn}{k-f}\log n}}\right) ^{^{^{^{}}}}$rounds in expectation. Even though a series of works have discussed how to achieve consensus with a specific absMAC layer provided, to the best of our knowledge, this paper is the first one that implements a BFT absMAC layer.

Android operating system restricts access to data by enabling data control flow and permission systems to reduce the risk of information theft. Therefore, attackers are constantly looking for alternative and stealthy approaches to exfiltrate private data from a targeted device. This paper presents CovertPower, a covert channel attack that exfiltrates user data by actively inducing power consumption on Android devices. At the transmitting end, our CovertPower app modulates binary data into a timed resource workload (e.g., processor, write-on-memory), producing power consumption bursts. On the receiving end, we acquire power consumption traces via a low-cost hardware tool that can be easily concealed in USB wall-socket adapters or powerbanks. Therefore, a signal processing-based decoder analyzes such traces and retrieves the exfiltrated information. We demonstrate the feasibility of our attack with a thorough experimental evaluation on 14 mobile devices and various real-world settings such as display state, ongoing activities, and charging technologies. Our attack achieves a transfer speed of up to 10 bps with a high bit sequence similarity on most devices and settings considered.

The natural forking severely compromises the security and wastes resources of Blockchain. Current analyses of the natural forking are carried out from microscale and macroscale perspectives, each facing challenges in generality and accuracy respectively. This results in dire straits that the forking arising from the network layer cannot be solved within the same layer, and existing defense schemes concentrate on the consensus layer, unfortunately coming at the expense of diminishing decentralized trust. Hence, to overcome these shortcomings, we propose the first reconnaissance-strike solution to the natural forking where the issue is recognized at the network layer and further struck on-site. Specifically, our endeavors encompass 1) analyzing the spatial-temporal transmission dynamics of the main chain. We exert mesoscale perspective by transforming the behavioral analysis of the transmitters (i.e., the nodes) into the movement analysis of the transmitted object (i.e., the main chain) which mitigates following Lévy mobility. Based on this, we quantify the dynamics of long-range leaping and short-range diffusion of the main chain transmission; 2) proposing a cost-effective anti-forking mechanism. This mechanism combats the forking with low cost by configuring logical connections at the network management level, based on the quantitative relationship between Blockchain network topology and the natural forking rate we have derived. Both theoretical analysis and extensive experiments verify that our scheme can maintain the natural forking rate not more than the given threshold in most cases.

Cloud-based outsourced Location-based services significantly impact various aspects of daily life but also raise security concerns. Existing secure retrieval schemes for spatiotemporal data exhibit significant shortcomings regarding dynamic updates; they either compromise privacy through information leakage during updates (lacking forward security) or incur excessively high update costs, hindering practical application. To address these limitations, we first propose a basic filter-based spatio-temporal range query scheme Trinity-I that supports lowcost dynamic updates and automatic expansion. Furthermore, to improve security, reduce storage cost, and false positives, we propose a forward secure and verifiable scheme Trinity-II that simultaneously minimizes storage overhead. Formal security analysis demonstrates that both Trinity-I and Trinity-II achieve Indistinguishability under Selective Chosen-Plaintext Attack (IND-SCPA). Finally, extensive experiments demonstrate that our design Trinity-II significantly reduces storage requirements by 80%, enables data retrieval at the 1 million-record level in just 0.01 seconds, and achieves 10× update efficiency than state-of-art.

Is it secure to measure the reliability of local models by similarity in federated learning (FL)? This paper delves into an unexplored security threat concerning applying similarity metrics, such as the$L_{2}$norm, Euclidean distance, and cosine similarity, in protecting FL. We first uncover the deficiencies of similarity metrics that high-dimensional local models, including benign and poisoned models, may be evaluated to have the same similarity while being significantly different in the parameter values. We then leverage this finding to devise a novel untargeted model poisoning attack, Faker, which launches the attack by simultaneously maximizing the evaluated similarity of the poisoned local model and the difference in the parameter values. Experimental results based on seven datasets and eight defenses show that Faker outperforms the state-of-the-art benchmark attacks by1.1-9.0Xin reducing accuracy and1.2-8.0Xin saving time cost, which even holds for the case of a single malicious client with limited knowledge about the FL system. Moreover, Faker can degrade the performance of the global model by attacking only once. We also preliminarily explore extending Faker to other attacks, such as backdoor attacks and Sybil attacks. Lastly, we provide a model evaluation strategy, called the similarity of partial parameters (SPP), to defend against Faker. Given that numerous mechanisms in FL utilize similarity metrics to assess local models, this work suggests that we should be vigilant regarding the potential risks of using these metrics. The code will be released soon.

The widespread usage of Microsoft Windows has unfortunately led to a surge in malware, posing a serious threat to the security and privacy of millions of users. In response, the research community has mobilized, with numerous efforts dedicated to strengthening defenses against these threats. The primary goal of these techniques is to detect malicious software early, preventing attacks before any damage occurs. However, many of these methods either claim that packing has minimal impact on malware detection or fail to address the reliability of their approaches when applied to packed samples. Consequently, they are not capable of assisting victims in handling packed programs or recovering from the damages caused by untimely malware detection. To address these challenges, we proposeVisUnpac, a static analysis-based data visualization framework for bolstering attack prevention while aiding recovery post-attack by unveiling malware patterns and offering more detailed information including both malware class and family. Our method includes unpacking packed malware programs, calculating local similarity descriptors based on basic blocks, enhancing correlations between descriptors, and refining them by minimizing noises to obtain self-analysis descriptors. Moreover, we employ machine learning to learn the correlations of self-analysis descriptors through architectural learning for final classification. Our comprehensive evaluation ofVisUnpacbased on a freshly gathered dataset with over 27,106 samples confirms its capability in accurately classifying malware programs with a precision of 99.7%. Additionally,VisUnpacreveals that most antivirus products in VirusTotal can not handle packed samples properly or provide precise malware classification information. We also achieve over 97% space savings compared to existing data visualization based methods.

The way of task posting serves as the main pillar in achieving an efficient crowdsourcing market. Pioneer solutions on task posting can be categorized as retail task posting and batch task posting. Unlike retail task posting, which simply matches the most suitable worker to tasks, batch task posting considers the collaborations not only between workers and tasks but also among tasks, which brings high efficiency, low costs, and satisfactory task completion rates. However, the state of the arts on batch task posting leverage specific attributes to combine tasks as bundles for posting, leading to limited scalability. Hence, we propose a causal analysis framework for batch crowdsourcing to achieve an attribute-independent batch crowdsourcing solution that disentangles multi-factors to uncover the posting merits of tasks bundled at optimal prices, based on which an approximately optimal algorithm is further introduced to form reasonable bundles for posting. Since batch crowdsourcing may incur losses due to short-term profit fluctuation, a risk assessment method is proposed to encourage the requestor to act properly for loss mitigations. Our work explores the causal analysis and risk assessment in batch crowdsourcing for the first time, with the following highlights: 1)generality. It proposes a composite metric for gauging task bundles which avoids the issue of attribute dependence in the state of the arts, resulting in better universality; 2)synergy. By collaboratively considering the “value” and “relative position” of variables, our work derives results reflecting causal relationships rather than naive correlations; and 3)precision. We not only elucidate the probability of risk in batch crowdsourcing but also delineate the rate function governing its probability decay. This allows a requestor to know when and how fast to halt batch task posting.

Satellite-ground twin networks (SGTNs) are regarded as a promising service paradigm, which can provide mega access services and powerful computation offloading capabilities via cloud-fog automation functions. Specifically, cloud-fog automation technologies are collaboratively leveraged to enable dense connectivity, pervasive computing, and intelligent control in terrestrial industrial cyber-physical systems, whose system-level privacy security can be strengthened via blockchain based consensus protocol. Moreover, digital twin (DT) can shorten the gap between physical unities and digital space to enable instant data mapping in SGTNs environments. However, complex multi-modal network environments, such as stochastic task size, dynamic low earth orbit location, and time-varying channel gains, hinder better performance metrics in terms of energy consumption, throughput and privacy overhead. Hence, we establish a SGTN integrated cloud-fog automation model to transfer task data to low earth orbit satellites, and then execute broad communication access, powerful computation offloading, and efficient twin control. Next, we propose a Lyapunov stability theory based multi-modal federated learning (LST-MMFL) method to optimize the battery energy, the size of block, computation frequency, and the number of twin control for minimizing the total energy consumption and privacy overhead. Furthermore, we design a novel blockchain based transaction verification protocol to strengthen privacy security, derive performance upper bounds of SGTN model, and fulfill the long-term average task as well as energy queue constraints. Finally, massive simulation results show that the proposed LST-MMFL algorithm outperforms existing state-of-the-art benchmarks in line with energy consumption, available battery level, networked control and privacy protection overhead.

Given the wide adoption of multimodal sensors (e.g., camera, lidar, radar) byautonomous vehicles (AVs), deep analytics to fuse their outputs for a robust perception become imperative. However, existing fusion methods often make two assumptions rarely holding in practice: i) similar data distributions for all inputs and ii) constant availability for all sensors. Because, for example, lidars have various resolutions and failures of radars may occur, such variability often results in significant performance degradation in fusion. To this end, we present t-READi, an adaptive inference system that accommodates the variability of multimodal sensory data and thus enables robust and efficient perception. t-READi identifies variation-sensitive yetstructure-specificmodel parameters; it then adapts only these parameters while keeping the rest intact. t-READi also leverages a cross-modality contrastive learning method to compensate for the loss from missing modalities. Both functions are implemented to maintain compatibility with existing multimodal deep fusion methods. The extensive experiments evidently demonstrate that compared with the status quo approaches, t-READi not only improves the average inference accuracy by more than 6% but also reduces the inference latency by almost 15× with the cost of only 5% extra memory overhead in the worst case under realistic data and modal variations.

Acoustic eavesdropping against private or confidential spaces is a significant threat in the realm of privacy protection. While the presence of soundproof material would weaken such an attack, current eavesdropping technology may be able to bypass these protections. Fortunately, existing studies either inadequately cover the full spectrum of human speech due to low-frequency responses or rely heavily on the prior knowledge used to train a model. To address these challenges, this paper introduces mmEcho, a new acoustic eavesdropping method that utilizes millimeter-wave signals to sense vibration induced by sound precisely. Through signal processing techniques such as the intra-chirp scheme and phase calibration algorithm, mmEcho achieves micrometer-level vibration extraction without requiring target-related data. To improve the range of eavesdropping attacks while reducing noise, we optimize radar signals by leveraging the widespread availability of multiple antennas on commercial off-the-shelf radars. We comprehensively evaluate the performance of mmEcho in different real-world settings. Experimental results demonstrate that, with the aid of multi-antenna technology, mmEcho can more effectively reconstruct the audio from the target at various distances, directions, sound insulators, reverberating objects, sound levels, and languages. Compared to existing methods, our approach provides better effectiveness without prior knowledge, such as the speech data from the target.

The integration of directed acyclic graph (DAG)-based blockchain and Internet of Things (IoT) aims at improving the efficiency of data storage. However, if massive IoT data are not placed in an organized way, the search and usage of the data for upper-level applications can be burdensome, since they have to examine the data block by block, which also increases the difficulty of data verification, affecting consensus efficiency. To maintain the high throughput advantage of DAG-based blockchain applied in IoT and improve the data analysis efficiency, we propose a novel consensus mechanism named TidyBlock, including the transaction collation mechanism for block generation and the block selection mechanism for verification. The first mechanism can tidy up scattered transactions before they are packaged into blocks, while the second one can collate blocks to facilitate verification, realizing a two-layer collation of IoT data so as to increase analysis efficiency of upper-level IoT applications. Additionally, the second mechanism can provide a self-driven incentive for rational participants to follow the first one in case they are reluctant to do extra collation work. Theoretical analysis is provided to demonstrate the validity of our proposed algorithms by formal methods. Extensive simulations based on synthetic data verify the rationality and effectiveness of the proposed mechanisms.

Recent studies have demonstrated the feasibility of eavesdropping on audio via radio frequency signals or videos, which capture physical surface vibrations from surrounding objects. However, these methods are inadequate for intercepting internally transmitted audio through wired media. In this work, we introduce radio-frequency retroreflector attack (RFRA) and bridge this gap by proposing an RFRA-based eavesdropping system,RF-Parrot${}^{\mathbf {2}}$, capable of wirelessly capturing audio signals transmitted through earphone wires. Our system entails embedding a tiny field-effect transistor within the wire to establish a battery-free retroreflector, whose reflective efficiency is correlated with the amplitude of the audio signal. To preserve the details of audio signals, we designed a unique retroreflector using a depletion-mode MOSFET (D-MOSFET). This MOSFET can be triggered by any voltage level present in the audio signals, thus guaranteeing no information loss during activation. However, the D-MOSFET introduces a nonlinear convolution operation on the original audio, resulting in distorted audio eavesdropping. Thus, we devised an engineering solution which utilized a novel convolutional neural network in conjunction with an efficient Parallel WaveGAN vocoder to reconstruct the original audio. Our comprehensive experiments demonstrate a strong similarity between the reconstructed audio and the original, achieving an impressive 95% accuracy in speech command recognition.

Real-time velocity monitoring is pivotal for fault detection of rotating machinery. However, existing methods rely on either troublesome deployments of optical encoders and IMU sensors or various tachometers delivering coarse-grained velocity measurements insufficient for fault detection. To overcome these limitations, we proposeRomeoas the first work to exploit the mmWave radar forrotatingmachinery fault detection by extracting a fine-grained velocity signature. Though mmWave radars should capture instant rotation information with their claimed high sensitivity and sampling rate, direct adoption entails significant efforts for high-precision velocity measurement per radar to handle; particularly, exhausted system calibration and noise interference. To this end, we first develop a phase-velocity model to characterize the relationship between the mmWave signal phase and the fine-grained angular velocity. We then explore the geometric properties of specific positions in the rotation trajectory to precisely calibrate the rotation sensing model, leading to an iterative algorithm for accurate angular velocity measurement. Finally, we propose a simple yet effective fault detection algorithm by extracting a unique velocity signature. Our extensive experiments showRomeoachieves a median error of 0.4$^\circ$/s for fine-grained angular speed measurement, outperforming SOTA solutions with over ×16 angular speed granularity and ×7 measurement precision.

Sound eavesdropping using light has been an area of considerable interest and concern, as it can be achieved over long distances. However, previous work has often lacked stealth (e.g., active emission of laser beams) or been limited in the range of realistic applications (e.g., using direct light from a device’s indicator LED or a hanging light bulb). In this paper, we presentEchoLight, a non-intrusive, passive and long-range sound eavesdropping method that utilizes the extensive reflection of ambient light from vibrating objects to reconstruct sound. We analyze the relationship between reflection light signals and sound signals, particularly in situations where the frequency response of reflective objects and the efficiency of diffuse reflection are suboptimal. Based on this analysis, we have introduced an algorithm based on cGAN to address the issues of nonlinear distortion and spectral absence in the frequency domain of sound. We extensively evaluateEchoLight’s performance in a variety of real-world scenarios. It demonstrates the ability to accurately reconstruct audio from a variety of source distances, attack distances, sound levels, light sources, and reflective materials. Our results reveal that the reconstructed audio exhibits a high degree of similarity to the original audio over 40 meters of attack distance.

Fueled by Metaverse, 360° video streaming has seen tremendous growth in the past years. However, our measurement reveals that current 360° streaming systems suffer from a dilemma that severely limits QoE. On the one hand, viewport prediction requires the shortest possible prediction distance for high predicting accuracy; On the other hand, video transmission requires more buffered data to compensate for bandwidth fluctuations otherwise substantial playback rebuffering would be incurred. There is so far no existing method that can break this dilemma so the QoE optimization for 360° video streaming was naturally bottlenecked. This work is the first attempt to tackle this challenge by developing QUTA – a novel learning-based streaming system. Specifically, according to our measurement, three kinds of internal streaming parameters have significant impacts on the prediction distance, namely, download pause, data rate threshold, and playback rate. On top of this, we design a new long-term-planning (LTP) continuous control deep reinforcement learning method that tunes the parameters dynamically based on the network condition and the streaming context. Extensive evaluations based on real system prototypes show that QUTA not only improves the prediction accuracy and QoE performance by up to 68.4% but also exhibits strong temporal and spatial robustness.

Fueled by the rapid advances in high-speed mobile networks, live video streaming has seen explosive growth in recent years and some DASH-based algorithms were specifically proposed for low-latency video delivery. We conducted a measurement study for the state-of-the-art algorithms with large-scale network traces. It reveals that these algorithms are susceptible to network condition changes due to the use of solo universal adaptation logics, resulting in the playback latency that has substantial variations across highly fluctuating networks. To tackle this challenge, this paper proposes Stateful Live Video Streaming (SLVS), which is a novel self-learning approach that learns the various network features and optimizes the adaptation logic separately for different network conditions, then dynamically tunes the logic at runtime, so that bitrate decision can better match the changing networks. Moreover, we further generalize SLVS to complement the streaming platform already in service to make it compatible with any live streaming services. Extensive evaluations based on real system prototypes show that SLVS can control playback latency down to 1 s while improving Quality-of-Experience (QoE) by 17.7% to 31.8%. Moreover, it has strong robustness to maintain near-second latency over highly fluctuating networks as well as long periods of video viewing.

Neural network models have become integral to Internet of Things (IoT) systems, with applications spanning from industrial automation to critical infrastructure management. Despite their prevalence, the deployment of these models within IoT systems introduces distinctive security vulnerabilities. In particular, adversaries may execute model poisoning attacks, which aim to alter the decision-making processes of embedded models, leading to erroneous outcomes. Existing model poisoning attacks necessitate access to extensive auxiliary datasets, such as the training dataset itself or one with same distribution. These requirements often render such attacks impractical in IoT contexts, given the constrained storage and computational resources of IoT devices. This paper proposes the first model poisoning attack against interpreters without auxiliary datasets to manipulate the model’s behavior. We evaluate the attack on three real-world datasets, and results indicate that this attack can successfully coerce the targeted interpreters to produce outcomes aligned with an adversary’s intentions, while maintaining nearly indistinguishable performance from the original model, thereby ensuring its stealthiness. Furthermore, beyond directly affected interpreters, our experiments reveal that four additional interpreters coupled to the poisoned model are indirectly influenced, underscoring the attack’s transferability.

Attacks on speech recognition systems often use adversarial or inaudible commands. However, a challenge is that adversarial perturbations typically fall within the audible frequency range, making it difficult to achieve inaudibility. Additionally, the non-linear effects of loudspeakers often cause inaudible commands to become audible at higher power levels. Therefore, minimizing the power requirements of the attack is essential to maintain inaudibility. Another significant obstacle is the conversion of variable-length commands, especially longer ones, into shorter target commands. In this paper, we present UltraAdv, a method for generating long-range adversarial perturbations capable of compromising commands of arbitrary length in closed-box setting. By combining the ultrasonic signal with the normal one, rather than negating it as in DolphinAttack, we significantly improve the energy efficiency, thus enhancing its attack distance. We also propose a dynamically adjustable suppression-interference method based on automatic gain control to address the challenge of mismatched durations between long commands and target commands (length-independent). Experiments demonstrate that using a single perturbation, we achieve impressive success rates of 98.84% and 96.62% and 98.32% across a diverse set of 12,260 speeches on DeepSpeech, iFlytek, and Whisper. The attack range reaches up to 15 m, surpassing DolphinAttack's 5 m range at equivalent power.

Adaptive video streaming has become a fundamental technology for video delivery. With the rise of deep reinforcement learning (DRL), streaming vendors are increasingly adopting DRL-driven adaptive bitrate (ABR) algorithms. In real-world deployments, most ABR approaches are developed with the aim of maintaining good performance across a wide variety of network environments. However, contrary to this expectation, our empirical findings show that even when trained on extensive real-world network trace data, these DRL-based ABR algorithms achieve only 43.1% to 48.9% of Quality-of-Experience (QoE) under highly diverse network conditions, which falls significantly short of the 100% optimum. We termed this problem as “ABR Under-Generalization”. To overcome this problem, we introduce BETA – a novel DRL-based ABR framework that incorporates both spatial and temporal learning mechanisms: 1) Spatially, BETA features a detector that flags the network conditions likely to cause poor performance, then trains specialized ABR models tailored for those conditions; 2) Temporally, BETA enhances its learning by incorporating multi-step decision experiences at each training epoch, enabling the trained model to account for long-term environmental dynamics. Comprehensive evaluations show that BETA outperforms state-of-the-art ABR algorithms, yielding average QoE gains of 19.4% to 50.9%, and achieving improvements of up to 244.1% under severely fluctuating network conditions.

Many emerging applications in edge computing require processing of huge volumes of data generated by end devices, using the freshest available information. In this paper, we address the distributed optimization of multi-user long-term average Age-of-Information (AoI) objectives in edge networks that use NOMA transmission. This poses a challenge of non-convex online optimization, which in existing work often requires either decision making in a combinatorial space or a global view of entire network states. To overcome this challenge, we propose a reinforcement learning-based framework that adopts a novel hierarchical decomposition of decision making. Specifically, we propose three different types of distributed agents to learn with respect to efficiency of AoI scheduling, fairness of AoI scheduling, as well as a high-level policy balancing these potentially conflicting design objectives. Not only does the proposed decomposition improve learning performance due to disentanglement of different design objectives/rewards, but it also enables the algorithm to learn the best policy while also learning the explanations – as actions can be directly compared in terms of the design objectives. Our evaluations show that the proposed algorithm improves the long-term average AoI by$200\%{-}300\%$and 400% compared to prior works with NOMA and the optimal solution without NOMA, respectively.

Internet of Things (IoT) devices are frequently deployed in highly dynamic environments and need to continuously learn new classes from data streams. Incremental Learning (IL) has gained popularity in IoT as it enables devices to learn new classes efficiently without retraining model entirely. IL involves fine-tuning the model using two sources of data: a small amount of representative samples from the original training dataset and samples from the new classes. However, both data sources are vulnerable to Membership Inference Attack (MIA). Fortunately, the existing MIAs result in poor performance against IL, because they ignore features such as the similarity between old and new models at the old classification layer. This paper presents the first MIA against IL, capable of determining not only whether a sample was used for training/fine-tuning but also distinguishing whether it belongs to the representative dataset or the new classes (unique in IL). Extensive experiments validate the effectiveness of our attack across four real-world datasets. Our attack achieves an average attack success rate of 74.03% in the white-box setting (model structure and parameters are known) and 70.08% in the black-box setting. Importantly, our attack is not sensitive to the IL hyper-parameters (e.g., distillation temperature), confirming its accurate, robust, and practical.

Game theory is an effective analytical tool for crowdsourcing. Existing studies based on it share a commonality: the influence of players’ decisions isbilateral. However, the status is broken by the zero-determinant (ZD) strategy, where the ZD player canunilaterallycontrol the opponent's expected payoff. Thereby, crowdsourcing games trigger conclusions that differ from traditional ones. By addressing three questions, this paper is the first work to analyze the turbulence in crowdsourcing caused by the inequality between the requestor and the worker in the ZD game. The first question reveals the potential for the requestor to exploit the worker; the second question quantifies the worker's tolerance towards exploitation, providing a basis for confrontation; the third question serves as the cornerstone for maintaining the crowdsourcing, regulating the requestor's exploitative behavior. To answer these questions, we extend ZD strategies from binary games to continuous ones, not only revealing the requestor's dominance but also enriching the theoretical system of ZD strategies and broadening their application. Furthermore, we introduce the worker's dissatisfaction degree, identifying the exponential trend and decay rate, revealing optimal timing and speed for the worker's effective confrontation and maximum exploitation for the requestor. Numerical simulations have validated the effectiveness of our analyses.

With the increasing popularity of virtual reality applications, there is an increasing demand for more interactive entertainment, learning, social interactions, and other activities on eyewear devices. Recognizing users’ emotion and providing reliable feedback can significantly improve the immersive experience for users. However, previous works in emotion recognition required modifications to existing eyewear devices and the integration of additional sensors, or relied on specialized sensors in expensive commercial-grade eyewear devices, making direct deployment on existing consumer-grade eyewear devices challenging. In this paper, we proposeAccEmo, the first system that analyzes the data from the built-in accelerometer sensor on eyewear devices to accurately recognize human emotion.AccEmofirst employs signal processing technologies to process raw accelerometer data, and then uses a binary classification network to determine whether the accelerometer data is influenced by emotional changes. Subsequently,AccEmoproposes a network architecture based on residual neural network and channel-wise attention mechanism as a universal feature extractor to extract complex features related to human emotions from the accelerometer data. Finally,AccEmouses personalized classifiers to achieve emotion recognition for different users. Extensive performance evaluation ofAccEmoacross diverse users demonstrates an exceptional average accuracy of 94.3%. Additionally, the robustness ofAccEmois validated through evaluations in various scenarios, yielding promising results.

In the Industrial Internet of Things (IIoT), the identification resolution system enhances communication and overall efficiency between isolated work islands, ensuring the trustworthiness and effectiveness of secure resource sharing across domains through cross-domain authentication. However, traditional identity authentication methods fail to empower users with control over their identity information and face challenges such as difficulty in tracking anonymous users, high computational overhead, and insufficient cross-domain trust. To address these issues, this paper proposes a cross-domain identity authentication scheme based on self-sovereign identity. The scheme leverages aggregate signature technology to enhance authentication efficiency, integrates blockchain technology and smart contracts to achieve cross-domain trust, and designs a mechanism for threshold identity tracking and revocation, as well as an attribute credential update mechanism to enable secure and efficient cross-domain authentication in the identification resolution system. The paper provides formal security definitions and proofs and evaluates the computational and storage efficiency of the scheme through theoretical analysis and experimental simulations. The results demonstrate that the proposed scheme offers significant advantages in resource-constrained scenarios within the IIoT.

As an integration of digital twin and edge computing, the digital twin edge networks (DITENs) have been proposed in recent years to fill the gap between physical edge networks and digital systems. Meanwhile, the multi-access wireless environments in edge computing make it hard to provide ultra-reliable and low-latency communications for digital twin, especially when the jamming attacks can be launched by the adversaries. This paper studies the jamming-resilient physical-to-virtual communication (PTVC) problem in DITENs despite strong cooperative jamming. Note that the previous jamming models mainly focus on the jamming behaviors from individual adversaries and are restricted by the energy budget limitation and uniform jamming assumption. In this paper, we consider a more comprehensive jamming model, in whichfadversaries can cooperatively launch their jamming attacks in totallykwireless channels with unlimited power budget and non-uniform jamming signals. Then, based on the new proposed$(k,f)$-cooperative jamming model, we show that$k\gt f$is the necessary and also sufficient condition to solve the PTVC problem. On one hand, we prove that the PTVC problem is insoluble when$f\leq k$; on the other hand, two distributed algorithms are given as the solutions of the PTVC problem amongnphysical objectives and one sink node when$k\gt f$, the time complexity of which are$O\left ({{\frac {n \log n}{k (\log k-\log f)}}}\right)$and$O\left ({{\frac {n \log n}{\log k-\log f}}}\right)$based on the communication modes with/without acknowledgement, respectively. Both of the theoretical results and empirical simulations are conducted to show the resilience of our algorithms despite such a strong cooperative jamming model.

In this paper, we study a stable Age-of-Information (AoI) scheduling problem to handle the massive packet aggregation from arbitrary total ofnend devices to an edge device via a single hop wireless channel when information constantly arrives at the end side. Specifically, we consider the arrivals of the information with anonlineinjection mode, i.e., information is injected to end devices with an unknown injection rate in each time slot. After the information is injected, the AoI with respect to those end devices constantly increases until their fresh messages are received by the edge device, which characterizes the freshness of the information at destination. Based on the online injection mode, we propose the first distributed stable AoI scheduling algorithm combining NOMA (Non-Orthogonal Multiple-Access) technique in this paper, to minimize the expected average peak AoI (EAP-AoI) of our end-to-edge information system. Adopting NOMA technique enableskmessages decoded from a mixed signal by the edge device in our algorithm, with parameter$k\gt 1$. We prove that our algorithm is stable even under the asymptotically maximum injection rate of$O(k/n)$that any stable AoI scheduling algorithm may handle, and the EAP-AoI of our information system is bounded by$O(\sqrt [{3}]{nk})$time slots under the injection rate of$O(k/n)$. Comparing with two existing results, the EAP-AoI in our algorithm is$O\left ({{\frac {n^{2/3}}{k^{4/3}}}}\right)$and$O\left ({{\frac {n^{2/3}}{k^{1/3}}}}\right)$times smaller. Numerical results also verify the stability and efficiency of our algorithm.

Mobile edge computing (MEC) can improve execution efficiency and reduce overhead for offloading computing tasks to edge servers with more resources. In the microservice system, the current research only considers the cross segment communication cost of computing tasks, does not consider the case of the same end, and ignores the discovery and invocation optimization of associated services. In this paper, we proposeCACO, which is a novel content-aware classification offloading framework for MEC based on correlation matrix.CACOfirst designs an adaptive service discovery model, which can make timely response and adjustment to the changes of the external environment. It then investigates an efficient affinity matrix based service discovery algorithm, which expresses the association relationship between services by constructing a service association matrix. In addition,CACOconstructs a relational model by giving different weight coefficients to the delay and energy loss, which improves the delay and energy loss of message processing in a satisfying manner. Simulation results indicate thatCACOreduces the total traffic of redundant messages by 46.2%$\sim$76.5%, respectively compared with state-of-the-art solutions. Testbed benchmarks show that it can also improve the stability by reducing control overhead by 34.5%$\sim$81.6% .

In recent years, fueled by the rapid advances in high-speed mobile networks, streaming short-form videos over mobile devices (e.g., TikTok) has become ubiquitous among mobile users. Despite the widespread application, our investigation based on a real video data source revealed that a large proportion of short videos watched by viewers have suboptimal video quality (e.g., with low VMAF scores), which indicates that the Quality-of-Experience (QoE) is in fact far from optimal. This problem is primarily due to the lack of awareness of video quality optimization based on the features of the video content such as the scene complexity. To tackle this problem, this work develops a novel system called Quality Aware Short Video Streaming (QASVS), which adopts machine learning techniques to learn the video content features and then automatically generate quality-driven bitrate decision models to optimize the perceived video quality and QoE. Extensive evaluations show that QASVS is able to improve the video quality by 11.1%∼27.9% while significantly reducing the playback rebuffering compared to the state-of-the-art streaming algorithms. Therefore, QASVS is able to provide an effective way for streaming vendors to deliver high-performance short-video services.

Satellite-Ground Integrated Networks (SGINs) are promising network architectures that can help reduce the load on terrestrial networks, provide mega-access capabilities and intensive task offloading functions. However, traditional resource management methods are difficult to apply directly into SGINs due to their multi-layered, heterogeneous and dynamic three-dimensional characteristics. In addition, massive multi-modal and multi-task information hinders better service performance in SGINs. Therefore, we design a multi-task integrated computation offloading model to process complex multi-modal network information, such as time-varying channel gains and dynamic Low Earth Orbit (LEO) locations, which can efficiently improve data transmission rate and privacy level. Furthermore, we propose three multi-modal based learning methods, such as centralized actor-critic (C-AC) algorithm, distributed multi-agent deep deterministic policy gradient (D-MADDPG) algorithm, and quantization-based federated learning (Q-FL) algorithm for computation-intensive, latency-critical and privacy-preserving tasks, which can further optimize the local execution or LEO offloading ratio, CPU cycle frequency and transmission power. Meanwhile, we demonstrate the quantization error upper bound between the optimal solution and the quantization scheme through massive mathematical derivations. Finally, extensive simulation results show that the proposed multi-modal based learning methods have better performance gains in terms of model convergence performance, quantization metrics, data transmission rate and number of bits processed.

Decentralized Storage Network (DSN) is an emerging technology that challenges traditional cloud-based storage systems by consolidating storage capacities from independent providers and coordinating to provide decentralized storage and retrieval services. However, current DSNs face several challenges associated with data privacy and efficiency of the proof systems. To address these issues, we propose FileDES ( Decentralized Encrypted Storage), which incorporates three essential elements: privacy preservation, scalable storage proof, and batch verification. FileDES provides encrypted data storage while maintaining data availability, with a scalable Proof of Encrypted Storage (PoES) algorithm that is resilient to Sybil and Generation attacks. Additionally, we introduce a rollup-based batch verification approach to simultaneously verify multiple files using publicly verifiable succinct proofs. We conducted a comparative evaluation on FileDES, Filecoin, Storj and Sia under various conditions, including a WAN composed of up to 120 geographically dispersed nodes. Our protocol outperforms the others in terms of proof generation/verification efficiency, storage costs, and scalability.

Recent works demonstrated that we can eavesdrop on audio by using radio frequency signals or videos to capture the physical surface vibrations of surrounding objects. They fall short when it comes to intercepting internally transmitted audio through wires. In this work, we first address this gap by proposing a new eavesdropping system, RF-Parrot, that can wirelessly capture the audio signal transmitted in earphone wires. Our system involves embedding a tiny field-effect transistor in the wire to create a battery-free retroreflector, with its reflective efficiency tied to the audio signal’s amplitude. To capture full details of the analog audio signals, we engineered a novel retroreflector using a depletion-mode MOSFET, which can be activated by any voltage of the audio signals, ensuring no information loss. We also developed a theoretical model to demystify the nonlinear transmission of the retroreflector, identifying it as a convolution operation on the audio spectrum. Subsequently, we have designed a novel convolutional neural network-based model to accurately reconstruct the original audio. Our extensive experimental results demonstrate that the reconstructed audio bears a strong resemblance to the original audio, achieving an impressive 95% accuracy in speech command recognition.

To address the online learning problem in distributed systems, Streaming Federated learning (SFL) enables immediate model training by clients upon collecting new data, finding wide applications in AI-enabled Internet-of-Things and sensor networks. Given the variability in data distribution across different historical periods, the ability to recall and rapidly apply previously encountered data distributions significantly enhances the efficiency and accuracy of model training. In this paper, a demo based on the real-world temperature datasets is presented to demonstrate the importance of history knowledge in local training and the federating process of SFL, which also shows that vanilla federated learning without considering the history knowledge may even be harmful to model training. Observing this, we propose Fed-HIST, a Federated learning framework that enables the clients to learn from the HISTory knowledge of the whole distributed learning system. Unlike direct raw data storage, Fed-HIST employs model architectures to capture the data distributions, offering a more space-efficient and privacy-preserving method of knowledge storage on a server pool. Additionally, a model similarity comparison scheme is designed to retrieve beneficial knowledge from the pool uploaded by the clients in the past. Such a history-aware federation can enhance the efficiency of training each client, only requiring the recurrence of similar data distributions among SFL participants. We validate our framework through extensive simulations on MNIST, Fashion-MINST, CIFAR10, and CIFAR100 datasets, benchmarking against 9 baselines and highlighting the importance of federating from history in SFL problem through necessary ablation studies.

Over the past few years, volumetric video streaming, a cutting-edge application in virtual reality (VR) and augmented reality (AR), has gained significant attention. However, its enormous bandwidth demands exceed the capabilities of current networks to support full-size transmission. As a result, streaming vendors in the industry typically use field-of-view (FoV) prediction to reduce the streaming video size. While this approach makes transmission feasible, our measurements have shown that the adopted sequential video prefetch mechanism significantly hinders streaming performance. To address this challenge, we propose PACE, an intelligent video prefetch strategy that leverages multi-round cell-enhanced downloading. PACE divides the prefetching process for each group-of-frame (GoF) into multiple rounds, downloading different cells in each round based on periodically updated FoV prediction results. This method allows the FoV prediction to operate independently of the prefetch length limitations. Additionally, PACE incorporates a greedy-policy-based video quality decision algorithm to fully utilize network bandwidth and maximize the quality of experience (QoE). Extensive evaluations demonstrate that PACE can enhance FoV prediction accuracy by up to 23.1% and improve QoE performance by up to 54.8% while showing strong robustness across a wide range of streaming environments.

Adaptive video streaming (DASH) has become a key technology in video transmission. Given the advantages of deep re-inforcement learning (DRL), streaming vendors are increasingly focusing on DRL-based adaptive bitrate (ABR) algorithms. In practice, almost all the ABR algorithms are designed with the in-tention to work well in any size/shape of networks. However, far from expectations, our measurements revealed that, even with extensive training on a vast scale of real network trace data, if the network condition varies over a wide range, the achieved Quality-of-Experience (QoE) is only 43.1% ~ 48.9%, far below the optimal 100%. We termed this problem as “ABR Under-Generalization”. To address this problem, we developed BETA in this work, a novel ABR-specialized DRL-based approach that integrates spatial and temporal modules. 1) Spatial: BETA offers a detector that identi-fies the potential network condition that may cause poor performance and then trains complementary ABR algorithms specifically functioning in it; 2) Temporal: At each training epoch, BETA learns from the decision experience of multiple-step segments to cover long-term environmental feedback. Extensive evaluations demonstrate that BETA achieves an average QoE improvement of 19.4% to 50.9% compared to the state-of-the-art ABR algorithms, with gains of up to 244.1% in the challenging networks with dramatically variable throughput.

Short-form video streaming has seen explosive growth in recent years. Typically, the short video apps (e.g., TikTok) are installed on mobile devices, with the video content delivered over wireless networks. Our measurement study shows that almost all the state-of-the-art adaptive bitrate (ABR) algorithms for short video streaming fall short of providing satisfactory Quality-of-Experience (QoE). This is due to their reliance on bandwidth-sensitive adaptation logic with over-discretized encoding bitrate levels, which hampers their ability to manage significant wireless bandwidth fluctuations. To tackle this problem, we propose DeepReal, a fine-grained ABR approach for short videos, the core of which is a novel clustering-augmented continuous control learning algorithm. On the one hand, the clustering offers more expertise for the ABR logic training. On the other hand, the continuous-valued action domain enables the trained ABR logic to support any number of discrete bitrate levels. As a result, DeepReal's bitrate decision is able to effectively adapt to the substantial network dynamics. Large-scale evaluations show that DeepReal improves QoE by 23.5% to 33.3% while maintaining strong spatial and temporal robustness. It offers a tailored and innovative solution for current short-form video services.

In today’s information age, eavesdropping has been one of the most serious privacy threats in information security, such as exodus spyware (Rudie et al., 2021) and pegasus spyware (Anatolyevich, 2020). And the main one of them is acoustic eavesdropping. Acoustic eavesdropping (George and Sagayarajan, 2023) is a technology that uses microphones, sensors, or other devices to collect and process sound signals and convert them into readable information. Although much research has been done in this area, there is still a lack of comprehensive investigation into the timeliness of this technology, given the continuous advancement of technology and the rapid development of eavesdropping methods. In this article, we have given a selective overview of acoustic eavesdropping, focusing on the methods of acoustic eavesdropping. More specifically, we divide acoustic eavesdropping into three categories: motion sensor-based acoustic eavesdropping, optical sensor-based acoustic eavesdropping, and RF-based acoustic eavesdropping. Within these three representative frameworks, we review the results of acoustic eavesdropping according to the type of equipment they use and the physical principles of each. Secondly, we also introduce several important but challenging applications of these acoustic eavesdropping methods. In addition, we compared the systems that meet the requirements of acoustic eavesdropping in real-world scenarios from multiple perspectives, including whether they are non-intrusive, whether they can achieve unconstrained word eavesdropping, and whether they use machine learning, etc. The general template of our article is as follows: firstly, we systematically review and classify the existing eavesdropping technologies, elaborate on their working mechanisms, and give corresponding formulas. Then, these eavesdropping methods were compared and analyzed, and each method’s effectiveness and technical difficulty were evaluated from multiple dimensions. In addition to an assessment of the current state of the field, we discuss the current shortcomings and challenges and give a fruitful direction for the future of acoustic eavesdropping research. We hope to continue to inspire researchers in this direction.

Decentralized Storage Networks (DSNs) represent a paradigm shift in data storage methodology, distributing and housing data across multiple network nodes rather than relying on a centralized server or data center architecture. The fundamental objective of DSNs is to enhance security, reinforce reliability, and mitigate censorship risks by eliminating a single point of failure. Leveraging blockchain technology for functions such as access control, ownership validation, and transaction facilitation, DSN initiatives aim to provide users with a robust and secure alternative to traditional centralized storage solutions. This paper conducts a comprehensive analysis of the developmental trajectory of DSNs, focusing on key components such as Proof of Storage protocols, consensus algorithms, and incentive mechanisms. Additionally, the study explores recent optimization tactics, encountered challenges, and potential avenues for future research, thereby offering insights into the ongoing evolution and advancement within the DSN domain.

The privacy concern in smart contract applications continues to grow, leading to the proposal of various schemes aimed at developing comprehensive and universally applicable privacy-preserving smart contract (PPSC) schemes. However, the existing research in this area is fragmented and lacks a comprehensive system overview. This paper aims to bridge the existing research gap on PPSC schemes by systematizing previous studies in this field. The primary focus is on two categories: PPSC schemes based on cryptographic tools like zero-knowledge proofs, as well as schemes based on trusted execution environments. In doing so, we aim to provide a condensed summary of the different approaches taken in constructing PPSC schemes. Additionally, we also offer a comparative analysis of these approaches, highlighting the similarities and differences between them. Furthermore, we shed light on the challenges that developers face when designing and implementing PPSC schemes. Finally, we delve into potential future directions for improving and advancing these schemes, discussing possible avenues for further research and development.

A metaverse breaks the boundaries of time and space between people, realizing a more realistic virtual experience, improving work efficiency, and creating a new business model. Blockchain, as one of the key supporting technologies for a metaverse design, provides a trusted interactive environment. However, the rich and varied scenes of a metaverse have led to excessive consumption of on-chain resources, raising the threshold for ordinary users to join, thereby losing the human-centered design. Therefore, we propose an adaptive and modular blockchain-enabled architecture for a decentralized metaverse to address these issues. The solution includes an adaptive consensus/ledger protocol based on a modular blockchain, which can effectively adapt to the ever-changing scenarios of the metaverse, reduce resource consumption, and provide a secure and reliable interactive environment. In addition, we propose the concept of Non-Fungible Resource (NFR) to virtualize idle resources. Users can establish a temporary trusted environment and rent others’ NFR to meet their computing needs. Finally, we simulate and test our solution based on XuperChain, and the experimental results prove the feasibility of our design.

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.

Betweenness centrality (BC), a classic measure which quantifies the importance of a vertex to act as a communication "bridge" between other vertices in the network, is widely used in many practical applications. With the advent of large heterogeneous information networks (HINs) which contain multiple types of vertices and edges like movie or bibliographic networks, it is essential to study BC computation on HINs. However, existing works about BC mainly focus on homogeneous networks. In this paper, we are the first to study a specific type of vertices' BC on HINs, e.g., find which vertices with typeAare important bridges to the communication between other vertices also with typeA?We advocate a meta path-based BC framework on HINs and formalize both coarse-grained and fine-grained BC (cBC and fBC) measures under the framework. We propose a generalized basic algorithm which can apply to computing not only cBC and fBC but also their variants in more complex cases. We develop several optimization strategies to speed up cBC or fBC computation by network compression and breadth-first search directed acyclic graph (BFS DAG) sharing. Experiments on several real-world HINs show the significance of cBC and fBC, and the effectiveness of our proposed optimization strategies.

With the rapid development of blockchain and its applications, the amount of data stored on decentralized storage networks (DSNs) has grown exponentially. DSNs bring together affordable storage resources from around the world to provide robust, decentralized storage services for tens of thousands of decentralized applications (dApps). However, existing DSNs do not offer verifiability when implementing erasure coding for redundant storage, making them vulnerable to Byzantine encoders. Additionally, there is a lack of Byzantine fault-tolerant consensus for optimal resilience in DSNs. This paper introduces BFT-DSN, a Byzantine fault-tolerant decentralized storage network designed to address these challenges. BFT-DSN combines storage-weighted BFT consensus with erasure coding and incorporates homomorphic fingerprints and weighted threshold signatures for decentralized verification. The implementation of BFT-DSN demonstrates its comparable performance in terms of storage cost and latency as well as superior performance in Byzantine resilience when compared to existing industrial decentralized storage networks.

Hypergraphs are applicable to various domains such as social contagion, online groups, and protein structures due to their effective modeling of multivariate relationships. However, the increasing size of hypergraphs has led to high computation costs, necessitating efficient acceleration strategies. Existing approaches often require consideration of algorithm-specific issues, making them difficult to directly apply to arbitrary hypergraph processing tasks. In this paper, we propose a compression-array acceleration strategy involving hypergraph reordering to improve memory access efficiency, which can be applied to various hypergraph processing tasks without considering the algorithm itself. We introduce a new metric called closeness to optimize the ordering of vertices and hyperedges in the one-dimensional array representation. Moreover, we present an$\frac{1}{2w}$-approximation algorithm to obtain the optimal ordering of vertices and hyperedges. We also develop an efficient update mechanism for dynamic hypergraphs. Our extensive experiments demonstrate significant improvements in hypergraph processing performance, reduced cache misses, and reduced memory footprint. Furthermore, our method can be integrated into existing hypergraph processing frameworks, such as Hygra, to enhance their performance.

Federated learning is a powerful technique that enables collaborative learning among different clients. Prototype-based federated learning is a specific approach that improves the performance of local models by integrating class prototypes. However, prototype-based federated learning faces several challenges, such as prototype redundancy and prototype failure, which can limit its accuracy. In addition, it is also susceptible to poisoning attacks and server malfunction, which can degrade the quality of prototypes. To address these issues, we propose FedRFQ, a prototype-based federated learning approach that aims to reduce redundancy, minimize failure, and improve quality. FedRFQ leverages the SoftPool mechanism with prototype-based federated learning, which effectively mitigates prototype redundancy and prototype failure on Non-IID data. Moreover, we introduce the BFT-detect algorithm, a BFT detectable aggregation algorithm, to ensure the security of FedRFQ against poisoning attacks and server malfunction. Finally, we conducted experiments on three different datasets, namely MNIST, FEMNIST, and CIFAR-10. The results demonstrate that FedRFQ outperforms existing baselines in terms of accuracy when handling Non-IID data.

Big data and strong computing power have promoted artificial intelligence to the era of big models. In particular, ChatGPT’s debut heralded the vigorous development of large models. It is an urgent problem to train large models with trillion-level parameters efficiently. Traditional single-machine training stores all data and model parameters in memory. However, due to the limitation of memory and communication resources, when the amount of data or model parameters increases, the problem of memory shortage and communication blocking often occurs. Therefore, distributed training is the most effective ways to solve the above problems and improve training efficiency. In this paper, we propose the algorithmDL-DP, which can achieve an asymptotically optimal convergence rate$O(1/{\sqrt{TK\Gamma^*}})$while satisfyingε-differential privacy, whereTis the local epoch number,Kis the global maximum iteration number and$\Gamma^*$is the minimum covering index. In particular, when${\Gamma ^*} = N$, DL-DP achieves a convergence rate of$O(1/{\sqrt{TKN}})$, which is equivalent to the best-known FedAvg approach implemented by training the full model at each client. When${\Gamma ^*} = 1$, DL-DP achieves a convergence rate of$O(1/{\sqrt{TK}})$, which is comparable to OAP that assumes all parameters need to be trained at least once in each iteration. Finally, our algorithm has been demonstrated to converge through extensive experiments.

Federated Learning (FL) offers collaborative machine learning without data exposure, but challenges arise in the mobile edge network (MEC) environment due to limited resources and dynamic conditions. This paper presents a Digital Twin (DT)-assisted FL platform for MEC networks and introduces a novel multi-FL service framework to address resource dynamics and mobile users. We leverage DT models to optimize device scheduling and MEC resource allocation, aiming to maximize utility across FL services. Our work includes heuristic and constant approximation algorithms for offline multi-FL service scenarios and we also investigate an online setting of our solution with dynamic bandwidth and moving client conditions. To adapt to changing network conditions, we utilize historical bandwidth data in DTs and implement a deep reinforcement learning algorithm, Ra_DDPG, for automatic bandwidth allocation. Evaluation results demonstrate a significant 49.8% increase in system utility compared to a benchmark algorithm, showcasing the effectiveness of our approach.

Deep learning is a thriving field currently stuffed with many practical applications and active research topics. It allows computers to learn from experience and to understand the world in terms of a hierarchy of concepts, with each being defined through its relations to simpler concepts. Relying on the strong capabilities of deep learning, we propose a convolutional generative adversarial network-based (Conv-GAN) framework titled MalFox, targeting adversarial malware example generation against third-party black-box malware detectors. Motivated by the rival game between malware authors and malware detectors, MalFox adopts a confrontational approach to produce perturbation paths, with each formed by up to three methods (namely Obfusmal, Stealmal, and Hollowmal) to generate adversarial malware examples. To demonstrate the effectiveness of MalFox, we collect a large dataset consisting of both malware and benignware programs, and investigate the performance of MalFox in terms of accuracy, detection rate, and evasive rate of the generated adversarial malware examples. Our evaluation indicates that the accuracy can be as high as 99.0% which significantly outperforms the other 12 well-known learning models. Furthermore, the detection rate is dramatically decreased by 56.8% on average, and the average evasive rate is noticeably improved by up to 56.2%.

Federated edge learning (FEEL) is a novel paradigm that enables privacy-preserving and distributed machine learning on end devices. However, FEEL faces challenges from data/system heterogeneity among the participating clients and resource constraints of edge networks, which affect the efficiency and accuracy of the learning process. In this paper, we propose a comprehensive framework for client selection in FEEL based on the concept of Value-of-Information (VoI), which measures how valuable a client is for the global model aggregation. Our framework consists of two independent components: a VoI estimator that uses reinforcement learning to learn the relationship between VoI and various heterogeneous factors of clients; and a greedy client selector that chooses the most valuable clients under network resource constraints. Compared with most of the previous works that use concrete criteria to evaluate and select heterogeneous clients, our VoI-based approach is more comprehensive. Extensive experiments on different datasets and learning tasks are conducted, which show that our framework outperforms several state-of-the-art methods in terms of accuracy.

As an amalgamation of digital twin and edge computing, the digital twin edge networks (DITENs) have drawn much attention from industry and academia to bridge the divide between physical edge networks and digital systems. Meanwhile, the physical hardware and open-access wireless communication environments in edge networks raise significant challenges in timely information aggregation and real-time status updates for digital twins, especially in the presence of inherent failure and hostile jamming. In this paper, we investigate the fault-tolerant Age-of-Information (AoI) optimization problem for digital twin edge networks against the severe fault phenomena in wireless communications. In contrast to previous works that focus on single fault or individual jamming behavior, we propose a comprehensive communication failure model over thefnon-overlapping wireless sub-channels, wheref1out of thefsub-channels are inherently failed andf2out of thefsub-channels are jammed by adversaries. Then, based on the multi-channel communication failure model, we present a distributed fault-tolerant communication algorithm to minimize the expected average peak AoI in DITENs. Using an adaptive transmission strategy, we prove that the AoI optimization issue fornend devices can be resolved within Θ(n) time steps whenf1+f2f. Both theoretical analyses and empirical simulations are conducted to verify the fault-tolerance and efficiency of our proposed algorithm despite the communication failures.

Video surveillance systems play a crucial role in ensuring public safety and security by capturing and monitoring critical events in various areas. However, traditional surveillance cameras face limitations when it comes to malicious physical damage or obscuring by offenders. To overcome this limitation, we proposem$^{2}$2Vision, which is the first millimeter-wave (mmWave)-based video reconstruction system designed to enhance existing video surveillance cameras.m$^{2}$2Visionutilizes mmWave to sense the profile and motion signature of the target, integrating it with previously acquired visual data about the environment and the target's appearance, thereby facilitating the reconstruction of surveillance video. Specifically, our proposed system incorporates a dual-stage mmWave signal denoising algorithm to efficiently eliminate the noise and multiple-input multiple-output virtual antenna enhanced heatmap generation (MVAE-HG) method to obtain fine-grained mmWave heatmaps responsive to the target's profile and motion information. Moreover, we design the mm2Video generative network that first employs a multi-modal fusion module to fuse the mmWave and pre-acquired visual data, then use a conditional generative adversarial network (cGAN)-based video reconstruction module for surveillance video reconstruction. We conducted comprehensive experiments onm$^{2}$2Visionusing a commercial mmWave radar and four surveillance cameras across various environments, with the participation of seven individuals. Evaluation results show thatm$^{2}$2Visioncan achieve an average structural similarity index measure (SSIM) of 0.93, demonstrating its effectiveness and potential.

As acoustic communication systems become increasingly common in our daily life, eavesdropping brings severe security and privacy risks. Current methods of acoustic eavesdropping either provide low resolution due to the use of sub-6 GHz frequencies, work only for limited words based on classification approaches, or cannot work through-wall because of the use of optical sensors. In this article, we presentmilliEar, a mmWave acoustic eavesdropping system that leverages the high-resolution of mmWave FMCW ranging and generative machine learning models to not only extract vibrations but to reconstruct the audio.milliEarcombines speaker vibration estimation with conditional generative adversarial networks to eavesdrop and recover high-quality audios (i.e., with no vocabulary constraints). We implement and evaluatemilliEarusing off-the-shelf mmWave radars deployed in different scenarios and settings. Evaluation results clearly show thatmilliEarcan accurately reconstruct the audio even at different distances, angles, and through the wall with different insulator materials. In addition, our subjective and objective evaluations demonstrate that the reconstructed audio has a strong similarity with the original audio.

Overprivilege Attack, a widely reported phenomenon in IoT that accesses unauthorized or excessive resources, is notoriously hard to prevent, trace and mitigate. In this paper, we propose TBAC, a Tokoin-Based Access Control model enabled by blockchain and Trusted Execution Environment (TEE) technologies, to offer fine-grained access control and strong auditability for IoT. TBAC materializes the virtual access power into a definite-amount, secure and accountable cryptographic coin, termed “tokoin” (token+coin), and manages it using atomic and accountable state-transition functions in a blockchain. A tokoin carries a fine-grained policy defined by the resource owner to specify the requirements to be satisfied before an access is granted, and the behavioral constraints that describe the correct procedure to follow during access. The strong-auditability is achieved with blockchain and a TEE-enabled trusted access control object (TACO) to ensure that all access activities are securely monitored and auditable. We prototype TBAC by implementing all its functions with well-studied cryptographic primitives over different blockchain platforms, building a TACO on top of the ARM Cortex-M33 TEE microcontroller, and constructing a user-friendly APP for regular users. A case study is finally presented to demonstrate how TBAC is employed to enable autonomous and secure in-home cargo delivery.

We investigate over-the-air federated learning (OTA-FL) that exploits over-the-air computing (AirComp) to integrate communication and computation seamlessly for FL. Privacy presents a serious obstacle for OTA-FL, as it can be compromised by maliciously manipulating channel state information (CSI). Moreover, the limited band at edge hinders OTA-FL from training large-scale models. It remains open how to enable a multitude of devices with constrained resources and sensitive data to collaboratively train a global model at band-limited edge. To tackle this, we design a novel algorithmPROBEbuilding upon a lightweight over-the-air gradients aggregation rulePB-O-GAR. Specifically,PB-O-GARcombines a random sparsification-like dimension reduction with Gaussian perturbation to provide rigorous privacy and band-adapted communication. It elaborately calibrates the transmission signal according to devices’ perceived CSI for heterogeneous power constraints accommodation and CSI attack resilience. We show that by utilizing the common randomness, which deviates from the conventional FL, random sparsification-like dimension reduction can augment privacy in addition to the intrinsic privacy amplification effect of AirComp. We establish near-optimal convergence rates and explicit trade-offs among privacy, communication and utility forPROBE. Finally, extensive experiments on benchmark datasets are conducted to validate our theoretical findings and showcase the superiority ofPROBEin realistic settings.

NFC tag authentication is crucial for preventing tag misuse. Existing NFC fingerprinting methods use physical-layer signals, which incorporate tag hardware imperfections, for authentication purposes. However, these methods suffer from limitations such as low scalability for a large number of tags or incompatibility with various NFC protocols, hindering practical application. To address these issues, we propose a new NFC fingerprinting scheme called NFChain$^+$. Instead of sticking to the NFC resonant frequency, NFChain$^+$excavates the tag hardware uniqueness from the protocol-agnostic tag response signal using an agile and compatible frequency band of NFC to extract the tag fingerprint from a chain of tag responses over multiple frequencies. This significantly improves fingerprint scalability. However, extracting the desired fingerprint presents two challenges: fingerprint inconsistency under different configurations, and fingerprint variations due to the signal noise in generic readers. To overcome these challenges, we design an effective signal elimination method to remove the effect of device configurations and employ contrastive learning to reduce fingerprint variations for accurate tag authentication. We further cultivate a data augmentation strategy to save the cost of manually collecting fingerprint measurements for training the authentication model. Extensive experiments show that we can achieve as low as 3.4% FRR and 4.1% FAR for over 600 NFC tags.

In this paper, we propose De-RPOTA, a novel algorithm designed for decentralized learning, equipped with mechanisms for resource adaptation and privacy protection through over-the-air computation. We theoretically analyze the combined effects of limited resources and lossy communication on decentralized learning, showing it converges towards a contraction region defined by a scaled errors version. Remarkably, De-RPOTA achieves a convergence rate of$\mathcal {O}\left ({{\frac {1}{\sqrt {nT}}}}\right)$in scenarios devoid of errors, matching the state-of-the-arts. Additionally, we tackle a power control challenge, breaking it down into transmitter and receiver sub-problems to hasten the De-RPOTA algorithm’s convergence. We also offer a quantifiable privacy assurance for our over-the-air computation methodology. Intriguingly, our findings suggest that network noise can actually strengthen the privacy of aggregated information, with over-the-air computation providing extra security for individual updates. Comprehensive experimental validation confirms De-RPOTA’s efficacy in communication resources limited environments. Specifically, the results on the CIFAR-10 dataset reveal nearly 30% reduction in communication costs compared to the state-of-the-arts, all while maintaining similar levels of learning accuracy, even under resource restrictions.

Although proof of work (PoW) consensus dominates the current blockchain-based systems mostly, it has always been criticized for the uneconomic brute-force calculation. As alternatives, energy-conservation and energy-recycling mechanisms heaved in sight. In this paper, we propose proof of user similarity (PoUS), a distinct energy-recycling consensus mechanism, harnessing the valuable computing power to calculate the similarities of users, and enact the calculation results into the packing rule. However, the expensive calculation required in PoUS challenges miners in participating, and may induce plagiarism and lying risks. To resolve these issues, PoUS embraces thebest-effortschema by allowing miners to compute partially. Besides, a voting mechanism based on the secure two-party computation and Bayesian truth serum is proposed to guarantee privacy-preserved voting and truthful reports. Noticeably, PoUS distinguishes itself in recycling the computing powerback to blockchainsince it turns the resource wastage to facilitate refined cohort analysis of users, serving as thespatial measurerand enabling asearchableblockchain. We build a prototype of PoUS and compare its performance with PoW. The results show that PoUS outperforms PoW in achieving an average transaction per second (TPS) improvement of 24.01% and an average confirmation latency reduction of 43.64%. Besides, PoUS functions well in mirroring the spatial information of users, with negligible computation time and communication cost.

The last decade has witnessed a tremendous growth of service computing, while efficient service recommendation methods are desired to recommend high-quality services to users. It is well known that collaborative filtering is one of the most popular methods for service recommendation based on QoS, and many existing proposals focus on improving recommendation accuracy, i.e., recommending high-quality redundant services. Nevertheless, users may have different requirements on QoS, and hence diversified recommendation has been attracting increasing attention in recent years to fulfill users’ diverse demands and to explore potential services. Unfortunately, the recommendation performances relies on a large volume of data (e.g., QoS data), whereas the data may be distributed across multiple platforms. Therefore, to enable data sharing across the different platforms for diversified service recommendation, we propose aPrivacy-preserving Diversified Service Recommendation(PDSR) method. Specifically, we innovate in leveraging the Locality-Sensitive Hashing (LSH) mechanism such that privacy-preserved data sharing across different platforms is enabled to construct a service similarity graph. Based on the similarity graph, we propose a novel accuracy-diversity metric and design a 2-approximation algorithm to select$K$services to recommend by maximizing the accuracy-diversity measure. Extensive experiments on real datasets are conducted to verify the efficacy of our PDSR method.

Satellite-Ground integrated networks (SGINs) are regarded as promising network architecture, which can provide global coverage, large broadband and mega access services for massive terrestrial users. Furthermore, it is beneficial to reducing network congestion, releasing network resources and achieving computation offloading functions. However, the SGIN graph structure is time-varying and highly complex, lack of fixed node orders or reference nodes, which result in dynamic multi-modal features. Hence, we consider a SGIN directed graph model to minimize the total latency while improving the model prediction accuracy, and then perform the computation offloading and quantization schemes. Specifically, we envision a spatial graph convolutional neural network framework to adapt to the dynamic SGIN graph nodes and size, and then propose a centrally deep reinforcement learning aided multi-node federated learning (CDRFL) framework to optimize the CPU cycle frequency, transmission bandwidth and the number of quantization bits to accelerate the convergence round. Extensive theoretical analyses verify the graph permutation property between SGIN graph structure and optimization problems, and demonstrate the upper bound of quantization error via massive mathematical derivation. Finally, the experimental results indicate that the proposed CDRFL framework outperforms some existing benchmarks with reference to FL convergence analysis, average latency and transmission energy consumption for all independent identically distribution (IID) and non-IID data.

In recent years, fake news has been a primary concern as it plays a significant role in influencing the political, economic, and social spheres. The scientific community has proposed several solutions to detect such fraudulent information. However, such solutions are unsuitable for social media posts since they cannot extract sufficient information from one-line textual and graphical content or are highly dependent on prior knowledge, which may be unavailable in the case of unprecedented events (e.g., breaking news).

Vertex engagement has extraordinary significance for social resilience and network stability. There have been lots of existing work studying this fundamental problem in pairwise graphs, but in the more generalized hypergraphs, it has not been well explored, due to the great challenges of sparsity, complex connectivity and dynamicity of hypergraphs. In this work, we initialize the study of the vertex engagement problem in hypergraphs. Based on the observation that the engagement of vertices in hypergraphs needs to consider two critical parameters, group engagement and neighbor engagement, we propose a vertex engagement model integrating the merits of these two measures, called constrained core, to address the ineffectiveness and incomprehensiveness caused by just using a single engagement factor. By giving an algorithm for the constrained core decomposition, we show that the constrained core number of vertices can be computed in linear time. Furthermore, by showing a localized property of contained core, efficient maintenance algorithms for updating the constrained core number of vertices in dynamic hypergraphs are proposed, to avoid the large amount of redundant computations caused by the decomposition from scratch. Extensive experiments conducted on real-world hypergraphs well exhibit the effectiveness of our model and the efficiency of the proposed algorithms.

The literature and the news regularly report cases of exploiting Universal Serial Bus (USB) devices as attack tools for malware injections and private data exfiltration. To protect against such attacks, security researchers proposed different solutions to verify the identity of a USB device via side-channel information (e.g., timing or electromagnetic emission). However, such solutions often make strong assumptions on the measurement (e.g., electromagnetic interference-free area around the device), on a device’s state (e.g., only at the boot or during specific actions), or are limited to one particular type of USB device (e.g., flash drive or input devices).In this paper, we present PowerID, a novel method to fingerprint USB peripherals based on their power consumption. PowerID analyzes the power traces from a peripheral to infer its identity and properties. We evaluate the effectiveness of our method on an extensive power trace dataset collected from 82 USB peripherals, including 35 models and 8 types. Our experimental results show that PowerID accurately recognizes a peripheral type, model, activity, and identity.

In this paper, we propose an adaptive learning paradigm for resource-constrained cross-device federated learning, in which heterogeneous local submodels with varying resources can be jointly trained to produce a global model. Different from existing studies, the submodel structures of different clients are formed by arbitrarily assigned neurons according to their local resources. Along this line, we first design a general resource-adaptive federated learning algorithm, namely RA-Fed, and rigorously prove its convergence with asymptotically optimal rate O(1/√Γ*TQ) under loose assumptions. Furthermore, to address both submodels heterogeneity and data heterogeneity challenges under non-uniform training, we come up with a new server aggregation mechanism RAM-Fed with the same theoretically proved convergence rate. Moreover, we shed light on several key factors impacting convergence, such as minimum coverage rate, data heterogeneity level, submodel induced noises. Finally, we conduct extensive experiments on two types of tasks with three widely used datasets under different experimental settings. Compared with the state-of-the-arts, our methods improve the accuracy up to 10% on average. Particularly, when submodels jointly train with 50% parameters, RAM-Fed achieves comparable accuracy to FedAvg trained with the full model.

In this paper, we propose a novel decentralized learning algorithm, namely DLLR-OA, for resource-constrained over-the-air computation with formal privacy guarantee. Theoretically, we characterize how the limited resources induced model-components selection error and compound communication errors jointly impact decentralized learning, making the iterates of DLLR-OA converge to a contraction region centered around a scaled version of the errors. In particular, the convergence rate of the DLLR-OA algorithm in the error-free case [EQUATION] achieves the state-of-the-arts. Besides, we formulate a power control problem and decouple it into two sub-problems of transmitter and receiver to accelerate the convergence of the DLLR-OA algorithm. Furthermore, we provide quantitative privacy guarantee for the proposed over-the-air computation approach. Interestingly, we show that network noise can indeed enhance privacy of aggregated updates while over-the-air computation can further protect individual updates. Finally, the extensive experiments demonstrate that DLLR-OA performs well in the communication resources constrained setting. In particular, numerical results on CIFAR-10 dataset shows nearly 30% communication cost reduction over state-of-the-art baselines with comparable learning accuracy even in resource constrained settings.

Acoustic eavesdropping targeting private or confidential spaces is one of the most severe privacy threats. Soundproof rooms may reduce such risks, but they cannot prevent sophisticated eavesdropping, which has been an emerging research trend in recent years. Researchers have investigated such acoustic eavesdropping attacks via sensor-enabled side-channels. However, such attacks either make unrealistic assumptions or have considerable constraints. This paper introduces mmEcho, an acoustic eavesdropping system that uses a millimeter-wave radio signal to accurately measure the micrometer-level vibration of an object induced by sound waves. Compared with previous works, our eavesdropping method is highly accurate and requires no prior knowledge about the victim. We evaluate the performance of mmEcho under extensive real-world settings and scenarios. Our results show that mmEcho can accurately reconstruct audio from moving sources at various distances, orientations, reverberating objects, sound insulators, spoken languages, and sound levels.

Connected autonomous vehicles (CAVs) are promising to improve road safety, thanks to various on-board sensors, such as LiDAR, radars, and stereo cameras. However, perception view could be significantly limited due to occlusions, extreme weather, and far objects. To address these challenges, in this paper, we propose an efficient edge-assisted perception sharing scheme, which enables vehicles to exchange the information about their sensed environment to improve road safety. We formulate perception sharing as an online optimization problem, with the objective of maximizing the total weighted utility, where utility indicates the quality of collected sensor data while weight means the intensity of the vehicle's demand for information in a certain area. To solve this problem, we propose an efficient online heuristic algorithm, which decouples the original problem into multiple sub-problems and solves them alternatively to find the optimal solution. Extensive simulations demonstrate that our proposed method can significantly improve the perception sharing performance.

Sequential recommendations aim to capture users’ preferences from their historical interactions so as to predict the next item that they will interact with. Sequential recommendation methods usually assume that all items in a user’s historical interactions reflect her/his preferences and transition patterns between items. However, real-world interaction data is imperfect in that (i) users might erroneously click on items, i.e., so-called misclicks on irrelevant items, and (ii) users might miss items, i.e., unexposed relevant items due to inaccurate recommendations.

Deep Learning (DL) has been widely adopted in almost all domains, from threat recognition to medical diagnosis. Albeit its supreme model accuracy, DL imposes a heavy burden on devices as it incurs overwhelming system overhead to execute DL models, especially on Internet-of-Things (IoT) and edge devices. Collaborative inference is a promising approach to supporting DL models, by which the data owner (the victim) runs the first layers of the model on her local device and then a cloud provider (the adversary) runs the remaining layers of the model. Compared to offloading the entire model to the cloud, the collaborative inference approach is more data privacy-preserving as the owner’s model input is not exposed to outsiders. However, we show in this paper that the adversary can restore the victim’s model input by exploiting the output of the victim’s local model. Our attack is dubbed Ginver 1: Generative model inversion attacks against collaborative inference. Once trained, Ginver can infer the victim’s unseen model inputs without remaking the inversion attack model and thus has the generative capability. We extensively evaluate Ginver under different settings (e.g., white-box and black-box of the victim’s local model) and applications (e.g., CIFAR10 and FaceScrub datasets). The experimental results show that Ginver recovers high-quality images from the victims.

Metaverse has rekindled human beings’ desire to further break space-time barriers by fusing the virtual and real worlds. However, security and privacy threats hinder us from building a utopia. A metaverse embraces various techniques, while at the same time inheriting their pitfalls and thus exposing large attack surfaces. Blockchain, proposed in 2008, was regarded as a key building block of metaverses. it enables transparent and trusted computing environments using tamper-resistant decentralized ledgers. Currently, blockchain supports Decentralized Finance (DeFi) and Non-fungible Tokens (NFT) for metaverses. However, the power of a blockchain has not been sufficiently exploited. In this article, we propose a novel trustless architecture of blockchain-enabled metaverse, aiming to provide efficient resource integration and allocation by consolidating hardware and software components. To realize our design objectives, we provide an On-Demand Trusted Computing Environment (OTCE) technique based on local trust evaluation. Specifically, the architecture adopts a hypergraph to represent a metaverse, in which each hyperedge links a group of users with certain relationship. Then the trust level of each user group can be evaluated based on graph analytics techniques. Based on the trust value, each group can determine its security plan on demand, free from interference by irrelevant nodes. Besides, OTCEs enable large-scale and flexible application environments (sandboxes) while preserving a strong security guarantee.

Mobile crowdsensing (MCS) counting on the mobility of massive workers helps the requestor accomplish various sensing tasks with more flexibility and lower cost. However, for the conventional MCS, the large consumption of communication resources for raw data transmission and high requirements on data storage and computing capability hinder potential requestors with limited resources from using MCS. To facilitate the widespread application of MCS, we propose a novelMCS learning frameworkleveraging on blockchain technology and the new concept of edge intelligence based on federated learning (FL), which involves four major entities, including requestors, blockchain, edge servers, and mobile devices as workers. Even though there exist several studies on blockchain-based MCS and blockchain-based FL, they cannot solve the essential challenges of MCS with respect to accommodating resource-constrained requestors or deal with the privacy concerns brought by the involvement of requestors and workers in the learning process. To fill the gaps, four main procedures, i.e., task publication, data sensing and submission, learning to return final results, and payment settlement and allocation, are designed to address major challenges brought by both internal and external threats, such as malicious edge servers and dishonest requestors. Specifically, a mechanism design-based data submission rule is proposed to guarantee the data privacy of mobile devices being truthfully preserved at edge servers; consortium blockchain-based FL is elaborated to secure the distributed learning process; and a cooperation-enforcing control strategy is devised to elicit full payment from the requestor. Extensive simulations are carried out to evaluate the performance of our designed schemes.

Federated learning (FL) is a distributed model training paradigm that preserves clients’ data privacy. It has gained tremendous attention from both academia and industry. FL hyper-parameters (e.g., the number of selected clients and the number of training passes) significantly affect the training overhead in terms of computation time, transmission time, computation load, and transmission load. However, the current practice of manually selecting FL hyper-parameters imposes a heavy burden on FL practitioners because applications have different training preferences. In this paper, we propose, an automatic FL hyper-parameter tuning algorithm tailored to applications’ diverse system requirements in FL training. iteratively adjusts FL hyper-parameters during FL training and can be easily integrated into existing FL systems. Through extensive evaluations of for diverse applications and FL aggregation algorithms, we show that is lightweight and effective, achieving 8.48%-26.75% system overhead reduction compared to using fixed FL hyper-parameters. This paper assists FL practitioners in designing high-performance FL training solutions. The source code of is available at.

With the wide deployment of Internet of Things (IoT), blockchain systems have been playing a crucial role to establish a trusted computing environment among potentially mistrusting agents without depending on a centralized server. Different from previous blockchain consensus protocols adopted in IoT, which rely on efficient and stable transmissions, in this article, we consider how to reach blockchain consensus in wireless networks without reliable network support. Specifically, a realistic signal to interference plus noise ratio (SINR) model is adopted to depict the unreliable transmissions in wireless channels. Based on the SINR model, a distributed and randomized consensus algorithm is proposed to reach$k$-times consensus among$n$devices within$O(k+\log n)$time steps with high probability. Note that the time complexity of our algorithm is asymptotically optimal since$\Omega (k+\log n)$is a lower bound to achieve$k$-times consensus in a distributed environment. We conduct both rigorous theoretical analysis and extensive simulations to validate our method. It is believed that our work can facilitate the implementation of blockchains in many wireless scenarios in which the reliable and fast transmissions cannot be guaranteed.

Cross-chain technology facilitates the interoperability among isolated blockchains on which users can freely communicate and transfer values. Existing cross-chain protocols suffer from the scalability problem when processing on-chain transactions. Off-chain channel, as a promising blockchain scaling technique, can enable micro-payment transactions without involving on-chain transaction settlement. However, existing channel schemes can only be applied to operations within a single blockchain, failing to support cross-chain services. Therefore in this paper, we propose$\mathsf {Cross}$-$\mathsf {Channel}$, the first off-chain channel to support cross-chain services. We introduce a novel hierarchical channel structure with a hierarchical interaction protocol, a new hierarchical settlement protocol, and a smart general fair exchange protocol, to ensure scalability, fairness, and atomicity of cross-chain interactions. Besides,$\mathsf {Cross}$-$\mathsf {Channel}$provides strong security and practicality by avoiding high latency in asynchronous networks.Through a 50-instance deployment of$\mathsf {Cross}$-$\mathsf {Channel}$on AliCloud, we demonstrate that$\mathsf {Cross}$-$\mathsf {Channel}$is well-suited for processing cross-chain transactions in high-frequency and large-scale, and brings a significantly enhanced throughput with a small amount of gas and delay overhead.

Decentralized Storage Networks (DSNs) can gather storage resources from mutually untrusted providers and form worldwide decentralized file systems. Compared to traditional storage networks, DSNs are built on top of blockchains, which can incentivize service providers and ensure strong security. However, existing DSNs face two major challenges. First, deduplication can only be achieved at the directory-level. Missing file-level deduplication leads to unavoidable extra storage and bandwidth cost. Second, current DSNs realize file indexing by storing extra metadata while blockchain ledgers are not fully exploited. To overcome these problems, we propose FileDAG, a DSN built on DAG-based blockchain to support file-level deduplication in storing multi-versioned files. When updating files, we adopt an increment generation method to calculate and store only the increments instead of the entire updated files. Besides, we introduce a two-layer DAG-based blockchain ledger, by which FileDAG can provide flexible and storage-saving file indexing by directly using the blockchain database without incurring extra storage overhead. We implement FileDAG and evaluate its performance with extensive experiments. The results demonstrate that FileDAG outperforms the state-of-the-art industrial DSNs considering storage cost and latency.

In many edge computing applications, edge devices are required to reach fault-tolerant consensus in order to provide collaborative services in outdoor environments. In this paper, we study a comprehensive$(a,b)$-majority consensus problem based on a novel failure model, which takes$a$distinct opinions as inputs and outputs a$b$-majority opinion as the final agreement. This problem formulation is drastically different from traditional ones, which usually require a majority consensus from the binary opinions of multiple supporters. It is more practical and flexible as it can accommodate more than 2 input opinions and output one that satisfies the application requirement defined by parameter$b$. We also consider physical layer in our failure model while previous models mainly focus on faults occurred in protocol layer and data layer. Based on this more realistic failure model and a more practical consensus problem definition, we present a distributed protocol for$n$edge devices to reach an$(a,b)$-majority consensus within$\Theta (n)$time steps with high probability. Empirical results from our simulation studies validate the fault tolerance property and efficiency of our work in achieving the$(a,b)$-majority consensus.

Graphs are widely employed in complex system modeling, VLSI design, and social analysis. Mining cohesive subgraphs is a fundamental problem in graph analysis, while implementing cohesive subgraphs requires analysts to not only ensure cohesiveness but also consider the computational intractability. Among a variety of diverse cohesive structures, k-truss exhibits a perfect trade-off between structure tightness and computational efficiency. In a k-truss, each edge is present in at least k-2 triangles. This study aims to contribute to this growing area of truss maintenance in fully dynamic graphs by avoiding expensive re-computation. Specifically, we consider the challenging scenario of batch processing of edge and vertex insertion/deletion and propose efficient algorithms that can maintain the trusses by only searching a very small range of affected edges. Also, our algorithms allow parallel implementations to further improve the efficiency of maintenance. Extensive experiments on both real-world static and temporal graphs illustrate the efficiency and scalability of our algorithms.

Both Byzantine resilience and communication efficiency have attracted tremendous attention recently for their significance in edge federated learning. However, most existing algorithms may fail when dealing with real-world irregular data that behaves in a heavy-tailed manner. To address this issue, we study the stochastic convex and non-convex optimization problem for federated learning at edge and show how to handle heavy-tailed data while retaining the Byzantine resilience, communication efficiency and the optimal statistical error rates simultaneously. Specifically, we first present a Byzantine-resilient distributed gradient descent algorithm that can handle the heavy-tailed data and meanwhile converge under the standard assumptions. To reduce the communication overhead, we further propose another algorithm that incorporates gradient compression techniques to save communication costs during the learning process. Theoretical analysis shows that our algorithms achieve order-optimal statistical error rate in presence of Byzantine devices. Finally, we conduct extensive experiments on both synthetic and real-world datasets to verify the efficacy of our algorithms.

Decentralized learning involves training machine learning models over remote mobile devices, edge servers, or cloud servers while keeping data localized. Even though many studies have shown the feasibility of preserving privacy, enhancing training performance or introducing Byzantine resilience, but none of them simultaneously considers all of them. Therefore we face the following problem:how can we efficiently coordinate the decentralized learning process while simultaneously maintaining learning security and data privacy for the entire system?To address this issue, in this paper we propose SPDL, a blockchain-secured and privacy-preserving decentralized learning system. SPDL integrates blockchain, Byzantine Fault-Tolerant (BFT) consensus, BFT Gradients Aggregation Rule (GAR), and differential privacy seamlessly into one system, ensuring efficient machine learning while maintaining data privacy, Byzantine fault tolerance, transparency, and traceability. To validate our approach, we provide rigorous analysis on convergence and regret in the presence of Byzantine nodes. We also build a SPDL prototype and conduct extensive experiments to demonstrate that SPDL is effective and efficient with strong security and privacy guarantees.

Due to the substantial computational cost of neural network training, adopting third-party models has become increasingly popular. However, recent works demonstrate that third-party models can be poisoned. Nonetheless, most model poisoning attacks require reference data, e.g., training dataset or data belonging to the target label, making them difficult to launch in practice. In this paper, we propose a reference data independent model poisoning attack that can (1) directly search for sensitive features with respect to the target label, (2) quantify the positive and negative effects of the model parameters on sensitive features, and (3) accomplish the training of poisoned model by our parameter selective update strategy. The extensive evaluation on datasets with a few classes and numerous classes show that the attack is (I) effective: the trigger input can be labeled as a deliberate class by the poisoned model with high probability; (II) covert: the performance of the poisoned model is almost indistinguishable from the intact model on non-trigger inputs; and (III) straightforward: an adversary only needs a little background knowledge to launch the attack. Overall, the evaluation results show that our attack achieves 95%, 100%, 81%, 96%, and 96% success rates on Cifar10, Cifar100, ISIC2018, FaceScrub, and ImageNet datasets, respectively.

Malware is a piece of software that was written with the intent of doing harm to data, devices, or people. Since a number of new malware variants can be generated by reusing codes, malware attacks can be easily launched and thus become common in recent years, incurring huge losses in businesses, governments, financial institutes, health providers, etc. To defeat these attacks, malware classification is employed, which plays an essential role in anti-virus products. However, existing works that employ either static analysis or dynamic analysis have major weaknesses in complicated reverse engineering and time-consuming tasks. In this paper, we propose a visualized malware classification framework called VisMal, which provides highly efficient categorization with acceptable accuracy. VisMal converts malware samples into images and then applies a contrast-limited adaptive histogram equalization algorithm to enhance the similarity between malware image regions in the same family. We provided a proof-of-concept implementation and carried out an extensive evaluation to verify the performance of our framework. The evaluation results indicate that VisMal can classify a malware sample within 4.0 ms and have an average accuracy of 96.0%. Moreover, VisMal provides security engineers with a simple visualization approach to further validate its performance.

Natural forking is tantamount to the “black swan” event in blockchain since it emerges unexpectedly with a small probability, and may incur low resource utilization and costly economic loss. The ongoing literature analyzes natural forking mainly from the macroscopic perspective, which is insufficient to further understand this phenomenon since it roots in theinstantaneous differencebetween block creation and propagation microscopically. Hence, in this article, we fill this gap by leveraging the large deviation theory to conduct the first micro study of natural forking, aiming to reveal its inherent mechanism substantially. Our work is featured by 1)conceptual innovation. We creatively abstract the blockchain overlay network as a “service system”. This allows us to investigate natural forking from the perspective of “supply and demand”. Based on this, we can identify the competitive dynamics of blockchain and construct a queuing model to characterize natural forking; 2)progressiveness. We scrutinize the natural forking probability as well as its decay rate via a three-step scheme from simple to complex, which are the single-source i.i.d. scheme, the single-source non-i.i.d. scheme, and the many-source non-i.i.d. scheme. By doing so, we can answerwhenandhow fastshould we take actions andwhatactions should we take against natural forking. Our valuable findings can not only put forward decisive guidelines theoretically from the top level, but also engineer optimal countermeasures operationally on a practical level to thwart natural forking.

Online social networks revolutionize the way people interact with each other. When various social network information aggregates over time, a rich online profile of the user is formed. Owing to the various features provided by mobile devices, a user's online social activities are tightly tied to his phone, and are conveniently, sometimes unnecessarily, available to social networks. In this article, we propose a novel attack architecture to show that attackers can infer a user's social network identities behind a mobile device through new dimensions. Specifically, we first developed a correlation between a user's device system states and the social network events, which leverage multiple mechanisms such as the learning-based memory regression model, to infer the possible accounts of the user in the social network app. Then we exploited the social network to social network correlation, via which we correlated information across different social networks, to identify the accounts of the target user. We implemented and evaluated these attacks on three popular social networks, and the results corroborate the effectiveness of our design.

Smart home systems are both technologically and economically advancing rapidly. As people become gradually inalienable to smart home infrastructures, their security conditions are getting more and more closely tied to everyone's privacy and safety. In this paper, we consider smart apps, either malicious ones with evil intentions or benign ones with logic errors, that can cause property loss or even physical sufferings to the user when being executed in a smart home environment and interacting with human activities and environmental changes. Unfortunately, current preventive measures rely on permission-based access control, failing to provide ideal protections against such threats due to the nature of their rigid designs. In this paper, we propose CommandFence, a novel digital-twin-based security framework that adopts a fundamentally new concept of protecting the smart home system by letting any sequence of app commands to be executed in a virtual smart home system, in which a deep-q network (DQN) is used to predict if the sequence could lead to a risky consequence. CommandFence is composed of an Interposition Layer to interpose app commands and an Emulation Layer to figure out whether they can cause any risky smart home state if correlating with possible human activities and environmental changes. We fully implemented our CommandFence implementation and tested against 553 official SmartApps on the Samsung SmartThings platform and successfully identified 34 potentially dangerous ones, with 31 of them reported to be problematicAuthor: Please provide index terms/keywords for your article. To download the IEEE Taxonomy go tohttp://www.ieee.org/documents/taxonomy_v101.pdf?> the first time to our best knowledge. Moreover, We tested our CommandFence on the 10 malicious SmartApps created by Jiaet al.2017, and successfully identified 7 of them as risky, with the missed ones actually only causing smartphone information leak (not harmful to the smart home system). We also tested CommandFence against the 17 benign SmartApps with logic errors developed by Celiket al.2017, and achieved a 100% accuracy. Our experimental studies indicate that adopting CommandFence incurs a neglectable overhead of 0.1675 seconds.

Differential privacy provides strong privacy preservation guarantee in information sharing. As social network analysis has been enjoying many applications, it opens a new arena for applications of differential privacy. This article presents a comprehensive survey connecting the basic principles of differential privacy and applications in social network analysis. We concisely review the foundations of differential privacy and the major variants. Then, we discuss how differential privacy is applied to social network analysis, including privacy attacks in social networks, models of differential privacy in social network analysis, and a series of popular tasks, such as analyzing degree distribution, counting subgraphs and assigning weights to edges. We also discuss a series of challenges for future work.

A large amount of high-dimensional and heterogeneous data appear in practical applications, which are often published to third parties for data analysis, recommendations, targeted advertising, and reliable predictions. However, publishing these data may disclose personal sensitive information, resulting in an increasing concern on privacy violations. Privacy-preserving data publishing has received considerable attention in recent years. Unfortunately, the differentially private publication of high dimensional data remains a challenging problem. In this paper, we propose a differentially private high-dimensional data publication mechanism (DP2-Pub) that runs in two phases: a Markov-blanket-based attribute clustering phase and an invariant post randomization (PRAM) phase. Specifically, splitting attributes into several low-dimensional clusters with high intra-cluster cohesion and low inter-cluster coupling helps obtain a reasonable allocation of privacy budget, while a double-perturbation mechanism satisfying local differential privacy facilitates an invariant PRAM to ensure no loss of statistical information and thus significantly preserves data utility. We also extend our DP2-Pub mechanism to the scenario with a semi-honest server which satisfies local differential privacy. We conduct extensive experiments on four real-world datasets and the experimental results demonstrate that our mechanism can significantly improve the data utility of the published data while satisfying differential privacy.

Searching for$k$-cliques in graphs has been an important problem in graph analysis due to its large number of applications. Previously, finding$k$-cliques in weighted graphs aimed at finding cliques with the largest sum of weight (with no distinction between the edge or the vertex weights), usually called the sum model. However, the algorithms under the sum model may result in solutions consisting of low-weight vertices or edges (outliers). To address this issue, we propose a new model named maximal$(S, C, K)$-clique in weighted graphs and study the problem of maximal ($S, C, K$)-clique search (MCS). We first propose an enumeration-based algorithm MCSE, which checks every$k$-clique to identify the maximal ($S, C, K$)-clique. To improve the efficiency, we further propose two improved algorithms MCSP and MCSC. Instead of checking every possible$k$-clique, MCSP focuses on ($S, C$) values that cannot be dominated and obtains the maximal$(S, C, K)$-cliques directly based on these values. MCSC is devised by further optimizing MCSP based on some key observations on maximal cliques and cliques’ nesting property. We also propose two index structures, BCS-Index and ICS-Index, to achieve optimal query. The former stores all maximal$(S, C, K)$-cliques, while the latter uses the clique's nesting property to reduce the space cost of index construction. Extensive experiments conducted on six real graphs demonstrate the efficiency and effectiveness of our proposed algorithms.

The Internet of Vehicles (IoV) technology enables vehicles to communicate with each other, with pedestrians and with roadside infrastructures, to realize more efficient, safer and more environmentally friendly transportation. IoV also promises rich location-based services for vehicles, such as parking and toll highway. However, preserving privacy for location-based service payments emerges as a critical and challenging problem in IoV. Existing schemes rely on centralized banks for payment processing, resulting in location privacy leakage to centralized entities. In this paper, we propose a decentralized privacy-preserving payment scheme named Vehicloak for IoV based on the blockchain technology. The biggest challenge is to provide location privacy for vehicles while guaranteeing correct service payments using the transparent blockchain. To tackle this challenge, we introduce a new cryptographic technique called zk-GSigproof that integrates zero-knowledge proof with group signature. Vehicloak implements this technique in a smart contract to process payment, which verifies zero-knowledge proof and group signature without leaking location information. It is not limited to IoV and can be applied in many payment scenarios. To evaluate the performance of our scheme, we implement Vehicloak on a private blockchain of 100 nodes on Aliyun, and conduct a test with up to 4,000 transactions. The experimental results prove the feasibility of Vehicloak.

The explosive amount of data generated at the network edge makes mobile edge computing an essential technology to support real-time applications, calling for powerful data processing and analysis provided by machine learning (ML) techniques. In particular, federated edge learning (FEL) becomes prominent in securing the privacy of data owners by keeping the data locally used to train ML models. Existing studies on FEL either utilize in-process optimization or remove unqualified participants in advance. In this paper, we enhance the collaboration from all edge devices in FEL to guarantee that the ML model is trained using all available local data to accelerate the learning process. To that aim, we propose acollective extortion (CE)strategy under the imperfect-information multi-player FEL game, which is proved to be effective in helping the server efficiently elicit the full contribution of all devices without worrying about suffering from any economic loss. Technically, our proposed CE strategy extends the classical extortion strategy in controlling the proportionate share of expected utilities for a single opponent to the swiftly homogeneous control over a group of players, which further presents an attractive trait of being impartial for all participants. Moreover, the CE strategy enriches the game theory hierarchy, facilitating a wider application scope of the extortion strategy. Both theoretical analysis and experimental evaluations validate the effectiveness and fairness of our proposed scheme.

Known as a distributed ledger technology (DLT), blockchain has attracted much attention due to its properties such as decentralization, security, immutability and transparency, and its potential of servicing as an infrastructure for various applications. Blockchain can empower wireless networks with identity management, data integrity, access control, and high-level security. However, previous studies on blockchain-enabled wireless networks mostly focus on proposing architectures or building systems with popular blockchain protocols. Nevertheless, such existing protocols have obvious shortcomings when adopted in wireless networks where nodes may have limited physical resources, may fall short of well-established reliable channels, or may suffer from variable bandwidths impacted by environments or jamming attacks. In this paper, we propose a novel consensus protocol named Proof-of-Channel (PoC) leveraging the natural properties of wireless communications, and develop a permissioned BLOWN protocol (BLOckchain protocol for Wireless Networks) for single-hop wireless networks under an adversarial SINR model. We formalize BLOWN with the universal composition framework and prove its security properties, namely persistence and liveness, as well as its strengths in countering against adversarial jamming, double-spending, and Sybil attacks, which are also demonstrated by extensive simulation studies.

The advancement of smartphone sensors and wearable devices has enabled a new paradigm for smart human activity recognition (HAR), which has a broad range of applications in healthcare and smart cities. However, there are four challenges,privacy preservation,label scarcity,real-timing, andheterogeneity patterns, to be addressed before HAR can be more applicable in real-world scenarios. To this end, in this paper, we propose a personalized federated HAR framework, namedFedHAR, to overcome all the above obstacles. Specially, as federated learning,FedHARperforms distributed learning, which allows training data to be kept local to protect users’ privacy. Also, for each client without activity labels, inFedHAR, we design an algorithm to compute unsupervised gradients under theconsistency trainingproposition and an unsupervised gradient aggregation strategy is developed for overcoming the concept drift and convergence instability issues in online federated learning process. Finally, extensive experiments are conducted using two diverse real-world HAR datasets to show the advantages ofFedHARover state-of-the-art methods. In addition, when fine-tuning each unlabeled client, personalizedFedHARcan achieve additional 10% improvement across all metrics on average.

This paper initiates the study for the basic primitive of distributed message dissemination in multi-hop wireless networks under a strong adversarial jamming model. Specifically, the message dissemination problem is to deliver a message initiating at a source node to the whole network. An efficient algorithm for message dissemination can be an important building block for solving a variety of high-level network tasks. We consider the hard non-spontaneous wakeup case, where a node only wakes up when it receives a message. Under the realistic SINR model and a strong adversarial jamming model that removes the budget constraint commonly adopted in previous work by the adversary, we present a distributed randomized algorithm that can accomplish message dissemination in$\mathscr{T}(O(D(\log n+\log R)))$time slots with a high probability performance guarantee, where$\mathscr{T}(U)$is the number of time slots in the interval from the beginning of the algorithm's execution that contains U unjammed time slots, n is the number of nodes in the network, D is the network diameter,$R$is the distance with respect to which the network is connected. Our algorithm is shown to be almost asymptotically optimal by lower bound$\Omega(D\log n)$for non-spontaneous message dissemination in networks without jamming.

We consider a$K$-armed bandit problem in general graphs where agents are arbitrarily connected and each of them has limited memorizing capabilities and communication bandwidth. The goal is to let each of the agents eventually learn the best arm. Although recent studies show the power of collaboration among the agents in improving the efficacy of learning, it is assumed in these studies that the communication graph should be complete or well-structured, whereas such an assumption is not always valid in practice. Furthermore, limited memorization and communication bandwidth also restrict the collaborations of the agents, since the agents memorize and communicate very few experiences. Additionally, an agent may be corrupted to share falsified experiences to its peers, while the resource limit in terms of memorization and communication may considerably restrict the reliability of the learning process. To address the above issues, we propose a three-staged collaborative learning algorithm. In each step, the agents share their latest experiences with each other through light-weight random walks in a general communication graph, and then make decisions on which arms to pull according to the recommendations received from their peers. The agents finally update their adoptions (i.e., preferences to the arms) based on the reward obtained by pulling the arms. Our theoretical analysis shows that, when there are a sufficient number of agents participating in the collaborative learning process, all the agents eventually learn the best arm with high probability, even with limited memorizing capabilities and light-weight communications. We also reveal in our theoretical analysis the upper bound on the number of corrupted agents our algorithm can tolerate. The efficacy of our proposed three-staged collaborative learning algorithm is finally verified by extensive experiments on both synthetic and real datasets.

Virtual network functions (VNFs) have been widely deployed in mobile edge computing (MEC) to flexibly and efficiently serve end users running resource-intensive applications, which can be further serialized to form service function chains (SFCs), providing customized networking services. To ensure the availability of SFCs, it turns out to be effective to place redundant SFC backups at the edge for quickly recovering from any failures. The existing research largely overlooks the influences of SFC popularity, backup completeness, and failure rate on the optimal deployment of SFC backups on edge servers. In this paper, we comprehensively consider from the perspectives of both the end users and edge system to backup SFCs for providing popular services with the lowest latency. To overcome the challenges resulted from unknown SFC popularity and failure rate, as well as the known system parameter constraints, we take advantage of the online bandit learning technique to cope with the uncertainty issue. Combining the Prim -inspired method with the greedy strategy, we propose a Real-Time Selection and Deployment (RTSD) algorithm. Extensive simulation experiments are conducted to demonstrate the superiority of our proposed algorithms.

Though voting-based consensus algorithms in blockchain outperform proof-based ones in energy- and transaction-efficiency, they are prone to incur wrong elections and bribery elections. The former originates from the uncertainties of candidates’ capability and availability, and the latter comes from the egoism of voters and candidates. Hence, in this paper, we propose an uncertainty- and collusion-proof voting consensus mechanism, including the selection pressure-based voting algorithm and the trustworthiness evaluation algorithm. The first algorithm can decrease the side effects of candidates’ uncertainties, lowering wrong elections while trading off the balance between efficiency and fairness in voting miners. The second algorithm adopts an incentive-compatible scoring rule to evaluate the trustworthiness of voting, motivating voters to report true beliefs on candidates by making egoism consistent with altruism so as to avoid bribery elections. A salient feature of our work is theoretically analyzing the proposed voting consensus mechanism by the large deviation theory. Our analysis provides not only the voting failure rate of a candidate but also its decay speed. The voting failure rate measures the incompetence of any candidate from a personal perspective by voting, based on which the concepts of the effective selection valve and the effective expectation of merit are introduced to help the system designer determine the optimal voting standard and guide a candidate to behave in an optimal way for lowering the voting failure rate.

The surging of deep learning brings new vigor and vitality to shape the prospect of intelligent Internet of Things (IoT), and the rise of edge intelligence enables provisioning real-time deep neural network (DNN) inference services for mobile users. To perform efficient and effective DNN model training in edge computing environments while preserving training data security and privacy of IoT devices, federated learning has been envisioned as an ideal learning paradigm for this purpose. In this article, we study energy-aware DNN model training in edge computing. We first formulate a novel energy-aware, Device-to-Device (D2D) assisted federated learning problem with the aim to minimize the global loss of a training DNN model, subject to bandwidth capacity on an edge server and energy capacity on each IoT device. We then devise a near-optimal learning algorithm for the problem when the training data follows the i.i.d. data distribution. The crux of the proposed algorithm is to explore using the energy of neighboring devices of each device for its local model uploading, by reducing the problem to a series of weighted maximum matching problems in corresponding auxiliary graphs. We also consider the problem without the assumption of the i.i.d. data distribution, for which we propose an efficient heuristic algorithm. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results show that the proposed algorithms are promising.

Many real-world scenarios, such as human activity recognition (HAR) in IoT, can be formalized as a multi-task multi-view learning problem. Each specific task consists of multiple shared feature views collected from multiple sources, either homogeneous or heterogeneous. Common among recent approaches is to employ a typical hard/soft sharing strategy at the initial phase separately for each view across tasks to uncover common knowledge, underlying the assumption that all views are conditionally independent. On the one hand, multiple views across tasks possibly relate to each other under practical situations. On the other hand, supervised methods might be insufficient when labeled data is scarce. To tackle these challenges, we introduce a novel framework ASM2TV for semi-supervised multi-task multi-view learning. We present a new perspective named gating control policy, a learnable task-view-interacted sharing policy that adaptively selects the most desirable candidate shared block for any view across any task, which uncovers more fine-grained task-view-interacted relatedness and improves inference efficiency. Significantly, our proposed gathering consistency adaption procedure takes full advantage of large amounts of unlabeled fragmented time-series, making it a general framework that accommodates a wide range of applications. Experiments on two diverse real-world HAR benchmark datasets collected from various subjects and sources demonstrate our framework's superiority over other state-of-the-arts. Anonymous codes are available at https://github.com/zachstarkk/ASM2TV.

The proliferation of edge computing brings new challenges due to the complexity of decentralized edge networks. Software-defined networking (SDN) takes advantage of pro-grammability and flexibility in handling complicated networks. However, it remains a problem of designing a both trusted and scalable SDN control plane, which is the core component of the SDN architecture for edge computing. In this paper, we propose Curb, a novel group-based SDN control plane that seamlessly integrates blockchain and BFT consensus to ensure byzantine fault tolerance, verifiability, traceability, and scalability within one framework. Curb supports trusted flow rule updates and adaptive controller reassignment. Importantly, we leverage a group-based control plane to realize a scalable network where the message complexity of each round is upper bounded by O(N), where N is the number of controllers, to reduce overheads caused by blockchain consensus. Finally, we conduct extensive simulations on the classical Internet2 network to validate our design.

In this paper, we revisit the problem of Differentially Private Stochastic Convex Optimization (DP-SCO) with heavy-tailed data, where the gradient of the loss function has bounded moments. Instead of the case where the loss function is Lipschitz or each coordinate of the gradient has bounded second moment studied previously, we consider a relaxed scenario where each coordinate of the gradient only has bounded (1+v)-th moment with some v∈(0, 1]. Firstly, we start from the one dimensional private mean estimation for heavy-tailed distributions. We propose a novel robust and private mean estimator which is optimal. Based on its idea, we then extend to the general d-dimensional space and study DP-SCO with general convex and strongly convex loss functions. We also provide lower bounds for these two classes of loss under our setting and show that our upper bounds are optimal up to a factor of O(Poly(d)). To address the high dimensionality issue, we also study DP-SCO with heavy-tailed gradient under some sparsity constraint (DP sparse learning). We propose a new method and show it is also optimal up to a factor of O(s*), where s* is the underlying sparsity of the constraint.

As acoustic communication systems become more common in homes and offices, eavesdropping brings significant security and privacy risks. Current approaches of acoustic eavesdropping either provide low resolution due to the use of sub-6 GHz frequencies, work only for limited words using classification, or cannot work through-wall due to the use of optical sensors. In this paper, we present MILLIEAR, a mmWave acoustic eavesdropping system that leverages the high-resolution of mmWave FMCW ranging and generative machine learning models to not only extract vibrations but to reconstruct the audio. MILLIEAR combines speaker vibration estimation with conditional generative adversarial networks to eavesdrop with unconstrained vocabulary. We implement and evaluate MIL-LIEAR using off-the-shelf mmWave radar deployed in different scenarios and settings. We find that it can accurately reconstruct the audio even at different distances, angles and through the wall with different insulator materials. Our subjective and objective evaluations show that the reconstructed audio has a strong similarity with the original audio.

Payment channel network (PCN) is a layer-two scaling solution that enables fast off-chain transactions but does not involve on-chain transaction settlement. PCNs raise new privacy issues including balance secrecy, relationship anonymity and payment privacy. Moreover, protecting privacy causes low transaction success rates. To address this dilemma, we propose zk-PCN, a privacy-preserving payment channel network using zk-SNARKs. We prevent from exposing true balances by setting up public balances instead. Using public balances, zk-PCN can guarantee high transaction success rates and protect PCN privacy with zero-knowledge proofs. Additionally, zk-PCN is compatible with the existing routing algorithms of PCNs. To support such compatibility, we propose zk-IPCN to improve zk-PCN with a novel proof generation (RPG) algorithm. zk-IPCN reduces the overheads of storing channel information and lowers the frequency of generating zero-knowledge proofs. Finally, extensive simulations demonstrate the effectiveness and efficiency of zk-PCN in various settings.

This paper studies the privacy-preserving adaptive ranking problem for multi-armed-bandit crowdsourcing, where according to the crowdsourced data, the arms are required to be ranked with a tunable granularity by the untrustworthy third-party platform. Any online worker can provide its data by arm pulls but requires its privacy preserved, which will increase the ranking cost greatly. To improve the quality of the ranking service, we propose a Privacy- Preserving Adaptive Ranking algorithm called PPAR, which can solve the problem with a high probability while differential privacy can be ensured. The total cost of the proposed algorithm is ${\mathcal{O}}(K\ln K)$, which is near optimal compared with the trivial lower bound Ω(K), where K is the number of arms. Our proposed algorithm can also be used to solve the well-studied fully ranking problem and the best arm identification problem, by proper setting the granularity parameter. For the fully ranking problem, PPAR attains the same order of computation complexity with the best-known results without privacy preservation. The efficacy of our algorithm is also verified by extensive experiments on public datasets.

The surging of deep learning brings new vigor and vitality to shape the prospect of intelligent Internet of Things (IoT), and edge intelligence arises to provision real-time deep neural network (DNN) inference services for mobile users. To perform efficient and effective DNN model training in edge environments while preserving training data security and privacy of IoT devices, federated learning has been envisioned as an ideal learning paradigm for this purpose. In this paper we study energy-aware DNN model training in an edge environment. We first formulate a novel energy-aware, device-to-device (D2D) assisted federated learning problem with the aim to minimize the global loss of a training DNN model, subject to bandwidth capacity on an edge server and the energy capacity on each IoT device. We then devise an efficient heuristic algorithm for the problem. The crux of the proposed algorithm is to explore the energy usage of neighboring devices of each device for its local model uploading, by reducing the problem to a series of maximum weight matching problems in corresponding auxiliary graphs. We finally evaluate the performance of the proposed algorithm through experimental simulations. Experimental results show that the proposed algorithm is promising.

With the increasing popularity of voice-based applications, acoustic eavesdropping has become a serious threat to users’ privacy. While on smartphones the access to microphones needs an explicit user permission, acoustic eavesdropping attacks can rely on motion sensors (such as accelerometer and gyroscope), which access is unrestricted. However, previous instances of such attacks can only recognize a limited set of pre-trained words or phrases. In this paper, we present AccEar, an accelerometer-based acoustic eavesdropping attack that can reconstruct any audio played on the smartphone’s loudspeaker with unconstrained vocabulary. We show that an attacker can employ a conditional Generative Adversarial Network (cGAN) to reconstruct high-fidelity audio from low-frequency accelerometer signals. The presented cGAN model learns to recreate high-frequency components of the user’s voice from low-frequency accelerometer signals through spectrogram enhancement. We assess the feasibility and effectiveness of AccEar attack in a thorough set of experiments using audio from 16 public personalities. As shown by the results in both objective and subjective evaluations, AccEar successfully reconstructs user speeches from accelerometer signals in different scenarios including varying sampling rate, audio volume, device model, etc.

Recent years have witnessed an increasing interest in the blockchain technology, and many blockchain-based applications have been developed to take advantage of its decentralization, transparency, fault tolerance, and strong security. In the field of smart grids, a plethora of proposals have emerged to utilize blockchain for augmenting intelligent energy management, energy trading, security and privacy protection, microgrid management, and energy vehicles. Compared with traditional centralized approaches, blockchain-based solutions are able to exploit the advantages of blockchain to realize better functionality in smart grids. However, the blockchain technology itself has its disadvantages in low processing throughput and weak privacy protection. Therefore, it is of paramount importance to study how to integrate blockchain with smart grids in a more effective way so that the advantages of blockchain can be maximized and its disadvantages can be avoided. This article surveys the state-of-the-art solutions aiming to integrate the emergent blockchain technology with smart grids. The goal of this survey is to discuss the necessity of applying blockchain in different components of smart grids, identify the challenges encountered by current solutions, and highlight the frameworks and techniques used to integrate blockchain with smart grids. We also present thorough comparison studies among blockchain-based solutions for smart grids from different perspectives, with the aim to provide insights on integrating blockchain with smart grids for different smart grid management tasks. Finally, we list the current projects and initiatives demonstrating the current effort from the practice side. Additionally, we draw attention to open problems that have not yet been tackled by existing solutions, and point out possible future research directions.

Spectrum auction is one of the most effective ways to achieve dynamic spectrum allocation in cognitive radio networks , and it provides one effective way to manage the spectrum demands of IoT devices with limited resources. Most spectrum auctions focus on protecting bidder privacy and achieving excellent social efficiency, but few tackles the verification of auction results that are controlled by the auctioneer. In this paper, we propose a cloud-based framework for verifiable privacy-preserving spectrum auctions. Our framework adopts a modified AFGH re-encryption algorithm that achieves both bid privacy protection and auction results verification at the same time. The cloud server helps to compute auction results based on homomorphic encryption , and an auctioneer decrypts the encrypted data from the server to obtain auction results. Meanwhile, the property of re-encryption makes it possible for any bidder to verify the auction results without compromising other bidders’ privacy.

Many real-world IoT systems, which include a variety of internet-connected sensory devices, produce substantial amounts of multivariate time series data. Meanwhile, vital IoT infrastructures like smart power grids and water distribution networks are frequently targeted by cyber-attacks, making anomaly detection an important study topic. Modeling such relatedness is, nevertheless, unavoidable for any efficient and effective anomaly detection system, given the intricate topological and nonlinear connections that are originally unknown among sensors. Furthermore, detecting anomalies in multivariate time series is difficult due to their temporal dependency and stochasticity. This paper presented GTA, a new framework for multivariate time series anomaly detection that involves automatically learning a graph structure, graph convolution, and modeling temporal dependency using a Transformer-based architecture. The connection learning policy, which is based on the Gumbel-softmax sampling approach to learn bi-directed links among sensors directly, is at the heart of learning graph structure. To describe the anomaly information flow between network nodes, we introduced a new graph convolution called Influence Propagation convolution. In addition, to tackle the quadratic complexity barrier, we suggested a multi-branch attention mechanism to replace the original multi-head self-attention method. Extensive experiments on four publicly available anomaly detection benchmarks further demonstrate the superiority of our approach over alternative state-of-the-arts. Codes are available at https://github.com/ZEKAICHEN/GTA.

To realize simulation experiments in large-scale Internet of Things (IoT) networks, this work studies the utilization of deep graph generative models to generate IoT networks, which can provide an economic approach facilitating IoT to meet the requirements of real-time performance, interoperability, energy efficiency, and coexistence. In IoT, nodes have different attributes, different connection ways with surrounding nodes, and different compactness of the region, which pose great challenges for network generation. By leveraging the properties of$k$-core and variational autoencoder during network generation, we propose a variable graph autoencoder called Core-GAE incorporating the coreness of nodes. In contrast to previous graph generative models, Core-GAE can preserve the local proximity similarity and maintain the global structural features simultaneously when learning the structural features of graphs. All three of the tasks we experimented with on four data sets show that Core-GAE exhibits better performance than previous ones.

Blockchain creates a secure environment on top of strict cryptographic assumptions and rigorous security proofs. It permits on-chain interactions to achieve trustworthy properties such as traceability, transparency, and accountability. However, current blockchain trustworthiness is only confined to on-chain, creating a “trust gap” to the physical, off-chain environment. This is due to the lack of a scheme that can truthfully reflect the physical world in a real-time and consistent manner. Such an absence hinders further blockchain applications in the physical world, especially for the security-sensitive ones. In this paper, we propose a framework to extend blockchain trust from on-chain to off-chain, and take trustworthy vaccine tracing as an example scheme. Our scheme consists of 1) a Trusted Execution Environment (TEE)-enabled trusted environment monitoring system built with the Arm Cortex-M33 microcontroller that continuously senses the inside of a vaccine box through trusted sensors and generates anti-forgery data; and 2) a consistency protocol to upload the environment status data from the TEE system to blockchain in a truthful, real-time consistent, continuous and fault-tolerant fashion. Our security analysis indicates that no adversary can tamper with the vaccine in any way without being captured. We carry out an experiment to record the internal status of a vaccine shipping box during transportation, and the results indicate that the proposed system incurs an average latency of 84 ms in local sensing and processing followed by an average latency of 130 ms to have the sensed data transmitted to and been available in the blockchain.

Blockchain technologies can enable secure computing environments among mistrusting parties. Permissioned blockchains are particularly enlightened by companies, enterprises, and government agencies due to their efficiency, customizability, and governance-friendly features. Obviously, seamlessly fusing blockchain and cloud computing can significantly benefit permissioned blockchains; nevertheless, most blockchains implemented on clouds are originally designed for loosely-coupled networks where nodes communicate asynchronously, failing to take advantages of the closely-coupled nature of cloud servers. In this paper, we propose an innovative cloud-oriented blockchain -- CloudChain, which is a modularized three-layer system composed of the network layer, consensus layer, and blockchain layer. CloudChain is based on a shared-memory model where nodes communicate synchronously by direct memory accesses. We realize the shared-memory model with the Remote Direct Memory Access technology, based on which we propose a shared-memory consensus algorithm to ensure presistence and liveness, the two crucial blockchain security properties countering Byzantine nodes. We also implement a CloudChain prototype based on a RoCEv2-based testbed to experimentally validate our design, and the results verify the feasibility and efficiency of CloudChain.

Online social networks have gained tremendous popularity and have dramatically changed the way we communicate in recent years. However, the publishing of social network data raises more and more privacy concerns. To protect user privacy, social networking data are usually anonymized before being released. Nevertheless, existing anonymization techniques do not have sufficient protection effects. A large number of deanonymization attacks have arisen, and they mainly make use of either network topology or node attribute information to successfully reidentify anonymized users. In this article, we model a social network as a structure-attribute network (SAN) integrating the structural characteristics and the attribute information associated with social network users. A novel similarity measurement of social network nodes is proposed by considering the structural similarity and attribute similarity. A two-phase scheme is then designed to perform deanonymization by first dividing a social network (graph) into smaller subgraphs based on spectral graph partitioning and then applying the proposed deanonymization algorithm on each matched subgraph pair. We simulate the deanonymization attack with extensive experiments on three real-world datasets, and the experimental results demonstrate that our approach can improve the accuracy and time complexity of deanonymization compared with the state of the art.

This article studies the core maintenance problem in dynamic graphs. The core number is a fundamental index reflecting the cohesiveness of a graph, which is widely used in large-scale graph analytics. The core maintenance problem requires updating the core numbers of vertices after a set of edges and vertices are inserted into or deleted from the graph. Previous works focus on the scenario of single-edge updates and process the edges one by one when multiple edges are inserted/deleted. We initiate the studies of processing multiple edges concurrently to improve the efficiency of core maintenance. Specifically, we discover a structure of inserted/deleted edges,superior edge set, which can be processed together to greatly reduce unnecessarily repeated visits of vertices in the procedure of sequential edge processing. Based on the structure of the superior edge set, efficient algorithms are then devised for incremental and decremental core maintenance, respectively. Compared with single-edge processing algorithms, our algorithms show a significant speedup in the processing time. Furthermore, our algorithms admit parallel implementations, which can further improve the update efficiency. We also conduct extensive experiments on different types of real-world, temporal, and synthetic data sets, and the results illustrate that the proposed algorithms exhibit good efficiency, stability, and scalability.

This article studies decentralized federated learning algorithms in wireless IoT networks. The traditional parameter server architecture for federated learning faces some problems such as low fault tolerance, large communication overhead and inaccessibility of private data. To solve these problems, we propose a decentralized wireless federated learning algorithm called DWFL. The algorithm works in a system where the workers are organized in a peer-to-peer and server-less manner, and the workers exchange their privacy preserving data with the analog transmission scheme over wireless channels in parallel. With rigorous analysis, we show that DWFL satisfies$(\epsilon,\delta)$-differential privacy and the privacy budget per worker scales as$\mathcal {O}(\frac{1}{\sqrt{N}})$, in contrast with the constant budget in the orthogonal transmission approach. Furthermore, DWFL converges at the same rate of$\mathcal {O}(\sqrt{\frac{1}{TN}})$as the best known centralized algorithm with a central parameter server. Extensive experiments demonstrate that our algorithm DWFL also performs well in real settings.

Energy-efficient communication technologies are a key enabler for many IoT applications. Many existing communication protocols are based on duty-cycling techniques, that have an inherent tradeoff between delay and energy consumption. In the field of sensor networks, wake-up receivers have been investigated to overcome these problems and to further reduce energy consumption. We now go one step further and investigate the use of wake-up receivers in combination with IEEE 802.11 WLAN. We extend the protocol used to communicate between the access point and the client to introduce a wake-up signal. This can be implemented in a way that is fully compatible with existing wireless LAN (WLAN) standards, thus, it can be deployed gradually with little effort and no need to change existing systems. As a proof of concept and to perform first lab experiments, we developed a hardware prototype using a selective wake-up receiver and off-the-shelf USB-WLAN dongles. All experimental results are verified using an analytical model and a detailed simulation study. We show that our wake-up WLAN can provide connectivity for low-power devices with low delays and low energy consumption at the same time.

Quality control plays a critical role in crowdsourcing. The state-of-the-art work is not suitable for crowdsourcing applications that require extensive validation of the tasks quality, since it is a long haul for the requestor to verify task quality or select professional workers in a one-by-one mode. In this paper, we propose a misreport- and collusion-proof crowdsourcing mechanism, guiding workers to truthfully report the quality of submitted tasks without collusion by designing a mechanism, so that workers have to act the way the requestor would like. In detail, the mechanism proposed by the requester makes no room for the workers to obtain profit through quality misreport and collusion, and thus, the quality can be controlled without any verification. Extensive simulation results verify the effectiveness of the proposed mechanism. Finally, the importance and originality of our work lie in that it reveals some interesting and even counterintuitive findings: 1) a high-quality worker may pretend to be a low-quality one; 2) the rise of task quality from high-quality workers may not result in the increased utility of the requestor; 3) the utility of the requestor may not get improved with the increasing number of workers. These findings can boost forward looking and strategic planning solutions for crowdsourcing.

Crowdsensing is an emerging paradigm of ubiquitous sensing, through which a crowd of workers are recruited to perform sensing tasks collaboratively. Although it has stimulated many applications, an open fundamental problem is how to select among a massive number of workers to perform a given sensing task under a limited budget. Nevertheless, due to the proliferation of smart devices equipped with various sensors, it is very difficult to profile the workers in terms of sensing ability. Although the uncertainties of the workers can be addressed by conventional Combinatorial Multi-Armed Bandit (CMAB) framework through a trade-off between exploration and exploitation, we do not have sufficient allowance to directly explore and exploit the workers under the limited budget. Furthermore, since the sensor devices usually have quite limited resources, the workers may have bounded capabilities to perform the sensing task only few times, which further restricts our opportunities to learn the uncertainty. To address the above issues, we propose a Context-Aware Worker Selection (CAWS) algorithm in this paper. By leveraging the correlation between the context information of the workers and their sensing abilities, CAWS aims at maximizing the expected cumulative sensing revenue efficiently with both budget constraint and capacity constraints respected, even when the number of the uncertain workers is massive. The efficacy of CAWS can be verified by rigorous theoretical analysis and extensive experiments.

User response prediction, which aims to predict the probability that a user will provide a predefined positive response in a given context such as clicking on an ad or purchasing an item, is crucial to many industrial applications such as online advertising, recommender systems, and search ranking. For these tasks and many other machine learning tasks, an indispensable part of success is feature engineering, where cross features are a significant type of feature transformations. However, due to the high dimensionality and super sparsity of the data collected in these tasks, handcrafting cross features is inevitably time expensive. Prior studies in predicting user response leveraged the feature interactions by enhancing feature vectors with products of features to model second-order or high-order cross features, either explicitly or implicitly. However, these existing methods can be hindered by not learning sufficient cross features due to model architecture limitations or modeling all high-order feature interactions with equal weights. Different features should contribute differently to the prediction, and not all cross features are with the same prediction power.

The emergence of mobile edge computing (MEC) empowers the popularity of resource-consuming applications on edge devices, where virtual network functions (VNFs) are widely employed to provide flexible and scalable network service to users in an individual or chained manner. To maintain high availability of VNFs, deploying redundant backups of VNFs on edge servers has become an effective method for swift recovery. However, the existing studies largely ignore the impact of heterogeneous VNF importance on appropriately placing VNF backups at the edge. In this work, we specify the concept of VNF demand levels considering both the user service needs and service function chain (SFC) composition requirements to fill the above gap, which is employed as a significant factor to formulate the edge-assisted VNF backup issue as an optimization problem with the resource constraint of edge servers. To solve the challenge brought by the uncertain demand levels of VNFs, we resort to the combinatorial multi-armed bandit (CMAB) problem to propose an online learning based approximate VNF backup selection and deployment algorithm, named BSPS, with acceptable computation complexity and regret bound. Furthermore, regarding the balance of workload among edge servers, the basic problem is extended to accommodate the extra constraint and an additional VNF backup scheme is devised accordingly. Extensive simulation results with numerical analysis demonstrate the attractive performances of our proposed schemes.

In this paper, we study exact hypercore maintenance in large-scale dynamic hypergraphs. A hypergraph, whose hyperedges may contain a set of vertices rather than two vertices in pairwise graphs, can represent complex interactions in more sophisticated applications. However, the exponential number of hyperedges incurs unaffordable costs to recompute the hypercore number of vertices and hyperedges when updating a hypergraph. This motivates us to propose an efficient approach for exact hypercore maintenance with the intention of significantly reducing the hypercore updating time comparing with recomputation approaches. The proposed algorithms can pinpoint the vertices and hyperedges whose hypercore numbers have to be updated by only traversing a small sub-hypergraph. Extensive experiments on real-world and temporal hypergraphs demonstrate the superiority of our algorithms in terms of efficiency.

Scalability has long been a major challenge of cryptocurrency systems, which is mainly caused by the delay in reaching consensus when processing transactions on-chain. As an effective mitigation approach, the payment channel networks (PCN) enable private channels among blockchain nodes to process transactions off-chain, relieving long-time waiting for the online transaction confirmation. The state-of-the-art studies of PCN focus on improving the efficiency and availability via optimizing routing, scheduling, and initial deposits, as well as preventing the system from security and privacy attacks. However, the behavioral decision dynamics of blockchain nodes under potential malicious attacks is largely neglected. To fill this gap, we employ game theory to study the characteristics of channel interactions from both the micro and macro perspectives under the situation of channel depletion attacks. Our study is progressive, as we conduct the game-theoretic analysis of node behavioral characteristics from individuals to the whole population of PCN. Our analysis is complementary, since we utilize not only the classic game theory with the complete rationality assumption, but also the evolutionary game theory considering the limited rationality of players to portray the evolution of the PCN. The results of numerous simulation experiments verify the effectiveness of our analysis.

JavaScript applications today are not limited to just client-side web applications and server-side code powered by Node.js. They became the standard for desktop application development with the emergence and popularity of the Electron framework. Combining the features of client-side and server-side applications, the Electron applications possess a completely different security posture . The attacks typical for front-end applications can now be escalated to the back-end attacks, for example, making a cross-site scripting result in a remote code execution on the user’s machine. The goal of our study is to analyze the typical security vulnerabilities of an Electron application, study common mitigation controls, and propose new remediation solutions that are easy to implement for developers. In this study we analyze security vulnerabilities in over a hundred open source Electron applications using automated and manual static analysis . We explore the mitigation controls existing in the Electron framework, and propose changes to the framework that will prevent many of the common vulnerabilities. Based on these results, we develop an IDE plugin for Electron applications that automatically suggests remediations to common security defects within a developer’s work environment, thus shifting the fixing of a vulnerability to earlier in the software development life cycle . We show the effectiveness of the IDE plugin by applying the plugin’s suggestions to the analyzed open source applications and demonstrating that they stop being exploitable after the applied fix.

With JavaScript being the most popular programming language on the web, several new JavaScript frameworks are released every year. A well designed framework may help developers create secure applications. The goal of our study is to understand how framework developers can best protect applications developed using their framework. In this work we studied how cross-site request forgery vulnerability is mitigated in several server-side JavaScript frameworks: Express.js, Koa.js, Hapi.js, Sails.js, and Meteor.js. We then analyzed open source applications developed with these frameworks using open source and custom written tools for automated static analysis and identified the percentage of protected applications for each framework. We correlated our analysis results to the implementation levels of mitigating controls in each framework and performed statistical analysis of our results to ensure no other confounding factors were involved. Based on the received outcomes we provide recommendations for framework developers on how to create frameworks that produce secure applications.

This article studies privacy-preserving collaborative learning in decentralized Internet-of-Things (IoT) networks, where the agents exchange information constantly to improve the learnability, and meanwhile make the privacy of agents protected during communications. However, the harsh constraints in IoT make executing collaborative learning much more difficult than well-connected systems composed by servers with strong computation power, due to the weak capacity of devices, limited bandwidth for exchanging information, the asynchronous communication environment, and the necessity of privacy preserving. We show that even if with the harsh constraints in IoT, it still can devise efficient privacy-preserving collaborative learning algorithms, by proposing the first known decentralized collaborative learning algorithm for the fundamental multiarmed bandits problem under the framework of local differential privacy. Rigorous analysis shows that the proposed learning algorithm can make every agent learn the best arm with a high probability and keep the privacy preserved meanwhile. Extensive experiments illustrate that our learning algorithm performs well in real settings.

Compared with traditional insurance schemes, usage-based insurance (UBI) for vehicles is more economic and accurate for drivers since its insurance premium calculation depends on how vehicles are driven. However, UBI requires sensitive driving data to determine insurance premiums, and this could result in serious privacy breach for drivers. Meanwhile, existing UBI solutions rely on a centralized entity (i.e., the insurance company) to manage insurances. In this article, we design a decentralized and privacy-preserving UBI scheme, called DUBI, based on the blockchain technology and zero-knowledge proof. In our scheme, a smart contract running over the blockchain serves as a “decentralized” insurance company, while drivers continuously upload their committed driving data to the blockchain. Periodically, the driver submits accumulated driving statistics with a zero-knowledge proof to the smart contract, which verifies the proof and calculates the insurance premium from the submitted statistics. We formulate an ideal functionality for DUBI under the universal composability framework, and then provide a formal security proof for DUBI. Furthermore, we give in-depth analysis and performance evaluation for DUBI with an implementation based on Ethereum. It shows that DUBI is highly efficient in processing UBI insurances in both storage and computation: DUBI is about seven times more efficient than existing schemes in storage, and proof generation and verification take only 7 and 30 ms, respectively.

Multiobject tracking is a basic task in video analysis. Due to the strict requirements on efficiency and resource consumption, most of the applications on edge devices are online or near-online methods. Besides motion modeling, appearance information is also widely used for tracking. However, the influence of occlusion is usually ignored. In this article, spatial-temporal co-occurrence constraints (STCCs) features are introduced to resist occlusions by exploring the rich spatial and temporal information of tracklets. In addition, a novel blockchain-based near-online framework called co-occurrence constraints tracklet tracker (CoCTs) is proposed for cross-camera tracking. It inherits the advantages of the blockchain technology in sharing information. Based on blockchain, an efficient association mechanism and a reliable information sharing method are introduced. Experimental results show that CoCT performs high computational efficiency and low resource consumption. In the edge computing environment, it achieves real-time performance on cross-camera tracking. On the MOT17 benchmark, our method shows the state-of-the-art results compared with other online trackers.

The pool-hopping attack casts down the expected profits of both the mining pool and honest miners in Blockchain. The mainstream countermeasures, namely PPS (pay-per-share) and PPLNS (pay-per-last-N-share), can hedge pool hopping but need to charge miners some fees when they join in a pool. Obviously, the higher fee charged, the higher cost of joining the pool, the less motivation of a miner to mine in the pool. In this article, we apply the zero-determinant (ZD) theory to design a novel pooled mining which offers an incentive mechanism for motivating miners not to switch in pools strategically by economic means without fee charged. In short, the proposed pooled mining has three unique features: 1) fee-free. No fee is charged if the miner does not hop, 2) wide applicability. It can be employed in both prepaid and postpaid mechanisms, and 3) fairness. Even can dominate the game with any miner, a pool has to cooperate when a miner does not hop among pools, implying that the pool cannot squeeze the honest miners financially. The fairness of our scheme makes it have long-term sustainability. Both theoretical analyses and numerical simulations demonstrate the effectiveness of our scheme.

The growing threat of personal data breach in data trading pinpoints an urgent need to develop countermeasures for preserving individual privacy. The state-of-the-art work either endows the data collector with the responsibility of data privacy or reports only a privacy-preserving version of the data. The basic assumption of the former approach that the data collector is trustworthy does not always hold true in reality, whereas the latter approach reduces the value of data. In this paper, we investigate the privacy leakage issue from the root source. Specifically, we take a fresh look to reverse the inferior position of the data provider by making her dominate the game with the collector to solve the dilemma in data trading. To that aim, we propose the noisy-sequentially zero-determinant (NSZD) strategies by tailoring the classical zero-determinant strategies, originally designed for the simultaneous-move game, to adapt to the noisy sequential game. NSZD strategies can empower the data provider to unilaterally set the expected payoff of the data collector or enforce a positive relationship between her and the data collector's expected payoffs. Both strategies can stimulate a rational data collector to behave honestly, boosting a healthy data trading market. Numerical simulations are used to examine the impacts of key parameters and the feasible region where the data provider can be an NSZD player. Finally, we prove that the data collector cannot employ NSZD to further dominate the data market for deteriorating privacy leakage.

A cost-efficient deal that can achieve high sensing quality with a low reward is the permanent goal of the requestor in mobile crowdsensing, which heavily depends on the quantity and quality of the workers. However, the spatial diversity and temporal dynamics lead to heterogeneous worker supplies, making it hard for the requestor to utilize a homogeneous pricing strategy to realize a cost-efficient deal from a systematic point of view. Therefore, a cost-efficient deal calls for a cost-efficient pricing strategy, boosting the whole sensing quality with less operation (computation) cost. However, the state-of-the-art studies ignore the dual cost-efficient demands of large-scale sensing tasks. Hence, we propose a combinatorial pinning zero-determinant (ZD) strategy, which empowers the requestor to utilize a single strategy within its feasible range to minimize the total expected utilities of the workers throughout all sensing regions for each time interval, without being affected by the strategies of the workers. Through turning the worker-customized strategy to an interval-customized one, the proposed combinatorial pinning ZD strategy reduces the number of pricing strategies required by the requestor from O(n3) to O(n). Besides, it extends the application scenarios of the classical ZD strategy from two-player simultaneous-move games to multiple-heterogeneous-player sequential-move ones, where a leader can determine the linear relationship of the players' expected utilities. Such an extension enriches the theoretical hierarchy of ZD strategies, broadening their application scope. Extensive simulations based on real-world data verify the effectiveness and efficiency of the proposed scheme.

Dynamicity is one of the most challenging, yet, key aspects of wireless networks. It can come in many guises, such as churn (node insertion/deletion) and node mobility. Although the study of dynamic networks has been popular in distributed computing domain, previous works considered only partial factors causing dynamicity. In this work, we propose a dynamic model that is comprehensive to include crucial dynamic factors on nodes and links. Our model defines dynamicity in terms of localized topological changes in the vicinity of each node, rather than a global view of the whole network. Obviously, a localized dynamic model suits distributed algorithm studies better than a global one. The proposed dynamic model makes use of the more realistic SINR model to describe wireless interference, instead of the oversimplified graph-based models adopted by most existing research. Under the proposed dynamic model, we develop an efficient distributed algorithm accomplishing local broadcast services in the abstract MAC layer that was first presented by Kuhnet al.[24]. Our solution paves the way for many new fast algorithms to solve high-level problems in dynamic networks, such as consensus, single-message broadcast, and multiple-message broadcast. Extensive simulation studies indicate that our algorithm exhibits good performance in realistic environments with dynamic network behaviors.

Many applications such as smart metering and location based services pose strong privacy requirements but achieving privacy protection at the client side is a non-trial problem as payment for the services must be computed by the server at the end of each billing period. In this paper, we propose a privacy preservation and billing scheme termed PPDIR based on delayed information release. PPDIR relies on a novel group signature mechanism and the asymmetric Rabin cryptosystem to protect the privacy of the clients and their requests, to achieve accountability and non-repudiation, and to shift the computational complexity to the server side. It adopts a secret token for anonymity and the token is updated for each client at the beginning of each billing period and securely released only to the server at the end of the billing period. Such a strategy can prevent the server from linking a client's requests made at different billing periods. It also prevents any adversary from linking any request to any client. Note that the server is able to figure out all requests made by a client within a billing period after receiving the delayed token, which is unavoidable for billing purpose. We prove the security properties of the group signature scheme, and analyze the security strength of PPDIR. Our study indicates that PPDIR can achieve privacy-preservation, confidentiality, non-repudiation, accountability, and other security objectives. We also evaluate the performance of our scheme in terms of communication and computational overheads.

Link scheduling is an appealing solution for ensuring the reliability and latency requirements of Internet of Things (IoT). Most existing results on the link scheduling problem were based on the graph or SINR (Signal-to-Interference-plus-Noise-Ratio) models, which ignored the impact of the random fading gain of the signals strength. In this paper, we address the link scheduling problem under the Rayleigh fading model. Both Shortest Link Scheduling (SLS) and Maximum Link Scheduling (MLS) problems are studied. In particular, we show that a set of links can be activated simultaneously under Rayleigh fading model if all link SINR constraints are satisfied. Based on the analysis of previous Link Diversity Partition (LDP) algorithm, we propose an Improved LDP (ILDP) algorithm and a centralized algorithm by localizing the global interference (denoted by CLT), building on which we design a distributed CLT algorithm (denoted by RCRDCLT) that converges to a constant approximation factor of the optimum with the time complexity of$O(\ln n)$, where$n$is the number of links. Furthermore, executing repeatedly RCRDCLT can solve the SLS with an approximation factor of$\Theta (\ln n)$. Extensive simulations indicate that CLT is more effective than previous six popular link scheduling algorithms, and RCRDCLT has the lowest time complexity while only losses a constant fraction of the optimum schedule.

In this paper, we investigate distributed broadcasting in dynamic networks, where the topology changes continually over time. We propose a network model that captures the dynamicity caused by both churn and mobility of nodes. In contrast to existing work on dynamic networks, our model defines the dynamicity in terms of localized topological changes in the vicinity of each node, rather than a global view of the whole network. Obviously, a local dynamic model suits distributed algorithms better than a global one. The proposed dynamic model uses the more realistic SINR model to depict wireless interference, instead of oversimplified graph-based models adopted in most existing work. We consider the fundamental communication primitive of global broadcast, which is to disseminate a message from a source node to the whole network. Specifically, we present a randomized distributed algorithm that can accomplish dynamic broadcasting in an asymptotically optimal running time of$O(D_{T})$with a high probability guarantee, under the assumption of reasonably constantdynamicity rate, where$D_{T}$is thedynamic diameter, a parameter proposed to depict the complexity of dynamic broadcasting. We believe our local dynamic model can greatly facilitate distributed algorithm studies in mobile and dynamic wireless networks.

In this paper, we present the first algorithm to precisely implement the abstract MAC (absMAC) layer under the physical SINR model in dynamic networks. The absMac layer, first presented by (Kuhn et al., 2009), provides reliable local broadcast communications, with timing guarantees stated in terms of a collection of abstract delay functions, based on which high-level algorithms can be designed, independent of specific channel behaviors. The implementation of absMAC requires the design of a distributed algorithm for the local broadcast communication primitives over a particular communication model that defines concrete channel behaviors, and the objective is to minimize the bounds of the abstract delay functions. Halldórsson et al. (2015) showed that under the standard SINR model (synchronous communications without physical carrier sensing or location information), there exist no efficient exact implementations. In this work, we demonstrate that physical carrier sensing, a commonly seen function performed by wireless devices, can help get efficient exact implementation algorithms. Specifically, we propose an algorithm that precisely implements the absMAC layer under the SINR model in dynamic networks. The algorithm provides asymptotically optimal bounds for both acknowledgement and progress functions defined in the absMAC layer. Our algorithm leads to many new faster algorithms for solving high-level problems under the SINR model in dynamic networks. We demonstrate this by exemplifying problems of Consensus, Multi-Message Broadcast, and Single-Message Broadcast. It deserves to point out that our implementation algorithm is designed based on an optimal algorithm for a General Local Broadcast (GLB) problem, which takes the number of distinct messages into consideration for the first time. The GLB algorithm can handle many communication scenarios apart from those defined in the absMAC layer. Simulation results show that our proposed algorithms perform well in reality.

The byzantine model is widely used to depict a variety of node faults in networks. Previous studies on byzantine-resilient protocols in wireless networks assume reliable communications and do not consider the jamming behavior of byzantine nodes. Such jamming, however, is a very critical and realistic behavior to be considered in modern wireless networks. In this paper, for the first time, we integrate the jamming behavior of byzantine nodes into the network setting. We show that, in this much more comprehensive and harsh model, efficient distributed communication protocols can be still devised with elaborate protocol design. In particular, we developed an algorithm that can accomplish the basic multiple-message dissemination task close to the optimal solution in terms of running time. Empirical results validate the byzantine-resilience and efficiency of our algorithm.

Proof of work (PoW), the most popular consensus mechanism for blockchain, requires ridiculously large amounts of energy but without any useful outcome beyond determining accounting rights among miners. To tackle the drawback of PoW, we propose a novel energy-recycling consensus algorithm, namely proof of federated learning (PoFL), where the energy originally wasted to solve difficult but meaningless puzzles in PoW is reinvested to federated learning. Federated learning and pooled-mining, a trend of PoW, have a natural fit in terms of organization structure. However, the separation between the data usufruct and ownership in blockchain lead to data privacy leakage in model training and verification, deviating from the original intention of federal learning. To address the challenge, a reverse game-based data trading mechanism and a privacy-preserving model verification mechanism are proposed. The former can guard against training data leakage while the latter verifies the accuracy of a trained model with privacy preservation of the task requester's test data as well as the pool's submitted model. To the best of our knowledge, our article is the first work to employ federal learning as the proof of work for blockchain. Extensive simulations based on synthetic and real-world data demonstrate the effectiveness and efficiency of our proposed mechanisms.

This paper presents$\mathit {wChain}$, a blockchain protocol specifically designed for multihop wireless networks that deeply integrates wireless communication properties and blockchain technologies under the realistic SINR model. We adopt a hierarchical spanner as the communication backbone to address medium contention and achieve fast data aggregation within$O(\log N\log \Gamma)$slots where$N$is the network size and$\Gamma $refers to the ratio of the maximum distance to the minimum distance between any two nodes. Besides,$\mathit {wChain}$employs data aggregation and reaggregation as well as node recovery mechanisms to ensure efficiency, fault tolerance, persistence, and liveness. The worst-case runtime of$\mathit {wChain}$is upper bounded by$O(f\log N\log \Gamma)$, where$f=\lfloor \frac {N}{2} \rfloor $is the upper bound of the number of faulty nodes. To validate our design, we conduct both theoretical analysis and simulation studies. The results not only demonstrate the nice properties of$\mathit {wChain}$, but also point to a large new space for the exploration of blockchain protocols in wireless networks.

In this paper, we study distributed algorithms to realize efficient communications under the Rayleigh-fading model. This model extends the popular deterministic SINR model using stochastic propagations to address the fading effects observed in reality. Stochastic propagations can greatly increase the difficulty of handling interference and collisions, especially in a local context without much global knowledge. We present a new technique called Inductive Coloring that can be used to schedule fast transmissions with Rayleigh-fading interference. The computation of inductive coloring takes only O(log2n) time with the proposed distributed algorithm, where n is the number of nodes in the network. We illustrate the power of inductive coloring by giving a distributed and randomized algorithm to implement the abstract MAC (absMAC) layer, which was first proposed by Kuhn et al.. With two basic time-guaranteed communication primitives, namely acknowledgement and progress, which correspond to the operations of node local broadcasts and message receptions from others, the absMAC layer decomposes the algorithm design and analysis in networks into two independent components, i.e., implementing the absMAC layer over a physical network and designing algorithms with the help of the two primitives in the absMAC layer. Thus, it sharply reduces the fussy and complicated process of algorithm design and analysis over the physical network. Our proposed algorithm implements the absMAC layer under the Rayleigh-fading model with no more than a logarithmic factor inferior to the optimal solution in terms of time complexity. The presented simulation results indicate that our algorithm performs well in realistic environments. Furthermore, we show that by making full use of our proposed absMAC layer algorithm, many network primitives such as Neighbor Discovery, Single/Multiple-Message Broadcast, and Consensus, can be efficiently implemented.

With distinct privacy protection advantages, federated recommendation is becoming increasingly feasible to store data locally in devices and federally train recommender models. However, previous work on federated recommender systems does not take full account of the limitations of storage, RAM, energy and communication bandwidth in the mobile environment. Their model scales are too big to run easily in mobile devices. Moreover, existing federated recommenders need to fine-tune recommendation models in each device, which makes them hard to effectively exploit collaborative filtering (CF) information among users/devices.

Internet-of-Things (IoT) devices are getting more and more popular in recent years and IoT networks play an important role in the industry as well as people's activities. On the one hand, they bring convenience to every aspect of our daily life; on the other hand, they are vulnerable to various attacks that in turn cancels out their benefits to a certain degree. In this article, we target the defense techniques against IoT Distributed Denial-of-Service (DDoS) attacks and propose an edge-centric IoT defense scheme termed FlowGuard for the detection, identification, classification, and mitigation of IoT DDoS attacks. We present a new DDoS attack detection algorithm based on traffic variations and design two machine learning models for DDoS identification and classification. To demonstrate the effectiveness of the two machine learning models, we generate a large data set by DDoS simulators BoNeSi and SlowHTTPTest, and combine it with the CICDDoS2019 data set, to test the identification and classification accuracy as well as the model efficiency. Our results indicate that the identification accuracy of the proposed long short-term memory is as high as 98.9%, which significantly outperforms the other four well-known learning models mentioned in the most related work. The classification accuracy of the proposed convolutional neural network is up to 99.9%. Besides, our models satisfactorily meet the delay requirements of IoT when deployed in edge servers with computational powers higher than a personal computer.

Nodes in wireless sensor networks (WSNs) are usually deployed in an unattended even hostile environment. What is worse, these nodes are equipped with limited battery, storage, computation, and communication resources. Therefore, it is challenging to ensure the security of a WSN without decreasing its network performance. Data aggregation (DA) combined with a security mechanism can provide a good scheme for solving the aforementioned problems. This article presents a comprehensive review of secure DA (SDA) in WSNs, including its security goals together with the existing problems. The traditional network topologies as well as new emerging ones are discussed and compared in order to indicate the application scenes and security levels of different topologies. Meanwhile, the contrastive analyses of security strategies are presented which divides SDA protocols into five categories according to different security mechanisms, security goals, and network topologies. Besides, the discussion points out some open issues which may be the valuable topics of SDA in the future.

Person reidentification (ReID) is an important application of Internet of Things (IoT). ReID recognizes pedestrians across camera views at different locations and time, which is usually treated as a ranking task. An essential part of this task is the hard sample mining. Technically, two strategies could be employed, i.e., global hard mining and local hard mining. For the former, hard samples are mined within the entire training set, while for the latter, it is done in mini-batches. In literature, most existing methods operate locally. Examples include batch-hard sample mining and semihard sample mining. The reason for the rare use of global hard mining is the high computational complexity. In this article, we argue that global mining helps to find harder samples that benefit model training. To this end, this article introduces a new system to: 1) efficiently mine hard samples (positive and negative) from the entire training set and 2) effectively use them in training. Specifically, a ranking list network coupled with a multiplet loss is proposed. On the one hand, the multiplet loss makes the ranking list progressively created to avoid the time-consuming initialization. On the other hand, the multiplet loss aims to make effective use of the hard and easy samples during training. In addition, the ranking list makes it possible to globally and effectively mine hard positive and negative samples. In the experiments, we explore the performance of the global and local sample mining methods, and the effects of the semihard, the hardest, and the randomly selected samples. Finally, we demonstrate the validity of our theories using various public data sets and achieve competitive results via a quantitative evaluation.

At present, the proliferation of the online connected devices conceives the Internet of Things (IoT), in which many wireless sensors, smart devices are implemented. However, the nature of openness rooted in IoT makes itself vulnerable to be attacked. One of the pioneer countermeasures is the moving target defense (MTD), which encourages an active and dynamic defense in IoT. In this article, a macroscopic research in MTD is carried out. The existing macroscopic studies take advantage of a traditional game theory. Consequently, protected IoT devices need extra operations to dominate the game. In this article, we take a dramatically different approach where a player can dominate the game without extra operation. Our approach benefits from the power of the zero-determinant (ZD) strategy, in which the player who adopts ZD can unilaterally set the expected payoff of the adversary or itself. Aware of such a powerful strategy, both players may want to employ it for dominating the confrontation. In this case, two fundamental questions need to be answered: who should take the ZD strategy? And to what extent can the ZD player dominate the game? To solve these problems, we model the interactions between the IoT devices and the malicious attackers as a Markov game. Besides, we obtain the conditions to adopt ZD, based on which we deduce the effectiveness of the ZD player. To the best of our knowledge, we are the first to employ the ZD strategy theory to enhance a better counterattack performance in IoT.

The existence of botnet puts people in an extremely insecure environment, which has seriously affected the development of Internet of Things (IoT). In order to prevent the formation of a botnet, it is necessary to understand the propagation behaviors and influence factors. As IoT devices become more intelligent, they can gradually generate social characteristics by mimicking human behaviors. However, in the process of coping with the botnet problem, little consideration has been given to the potential social characteristics of IoT. In this article, we build a dynamic botnet propagation model (i.e., IoT-BSI model) to study the influences of two social characteristics (i.e., device's spread capability and device's identification ability) on botnet formation. First, in terms of device's spread capability, this article makes great improvements on the K- shell decomposition algorithm and calculates this characteristic more accurately. Second, this article divides the device's identification ability into the rational identification ability and the irrational identification ability based on the sociological theory, and the calculation of the former is mainly realized by utilizing the PageRank idea. Third, this article applies the mean-field equation theory to analyze the dynamic characteristics of botnet propagation theoretically. Finally, the comprehensive results show that our model is not merely more consistent with the actual situation but also performs better than four compared models.

This article studies the batch processing of truss maintenance in large graphs. Trussness is a widely used index in graph analytics for cohesive subgraph mining. It is defined on edges to reflect the closeness of vertices connected by the edges. The trussness maintenance problem, i.e., updating trussness after edge insertions/deletions and avoiding recomputation, was proposed by Cohen (2008) with the assumption that real graphs are continuously evolving in nature and their dynamicity usually only affects few edges. Different from the existing work that mainly focuses on simple single edge/vertex insertion/deletion, we propose batch processing algorithms for truss maintenance with multiedge insertions/deletions. By presenting an edge structure called triangle disjoint set, whose insertion/deletion can make each edge change its trussness by at most 1, we tackle the difficulty in quantifying the trussness changes of the edges in batch processing. More specifically, we propose two indices, namely sustain support and pivotal support, to help measure whether the edges have the potential to change their trussness, such that the search range of the potential edges is greatly reduced. The extensive experiments on real-world graphs illustrate that compared with the single-edge processing approach, our batch processing algorithms can significantly improve the processing efficiency, and the improvement becomes more obvious when more edges are inserted/deleted.

Crowdsourcing is a promising technology to accomplish a complex task via eliciting services from a large group of contributors. Recent observations indicate that the success of crowdsourcing has been threatened by the malicious behaviors of the contributors. In this paper, we analyze the attack problem using an iterated prisoner's dilemma (IPD) game and propose a reward-penalty expected payoff algorithm based on zero-determinant (ZD) strategies to reward a worker's cooperation or penalize its defection in order to entice the final cooperation. Both theoretical analysis and simulation studies are performed, and the results indicate that the proposed algorithm has the following two attractive characteristics: 1) the requestor can incentivize the worker to become cooperative without any long-term extra cost; and 2) the proposed algorithm is fair so that the requestor cannot arbitrarily penalize an innocent worker to increase its payoff even though it can dominate the game. To the best of our knowledge, we are the first to adopt the ZD strategies to stimulate both players to cooperate in an IPD game. Moreover, our proposed algorithm is not restricted to solve only the problem of crowdsourcing dilemma - it can be employed to tackle any problem that can be formulated into an IPD game.

Quality control in crowdsourcing is challenging due to the heterogeneous nature of the workers. The state-of-the-art solutions attempt to address the issue from the technical perspective, which may be costly because they function as an additional procedure in crowdsourcing. In this paper, an economics based idea is adopted to embed quality control into the crowdsourcing process, where the requestor can take advantage of the market power to stimulate the workers for submitting high-quality jobs. Specifically, we employ two sequential games to model the interactions between the requestor and the workers, with one considering binary strategies while the other taking continuous strategies. Accordingly, two incentive algorithms for improving the job quality are proposed to tackle the sequential crowdsourcing dilemma problem. Both algorithms are based on a sequential zero-determinant (ZD) strategy modified from the classical ZD strategy. Such a revision not only provides a theoretical basis for designing our incentive algorithms, but also enlarges the application space of the classical ZD strategy itself. Our incentive algorithms have the following desired features: 1) they do not depend on any specific crowdsourcing scenario; 2) they leverage economics theory to train the workers to behave nicely for better job quality instead of filtering out the unprofessional workers; 3) no extra costs are incurred in a long run of crowdsourcing; and 4) fairness is realized as even the requestor (the ZD player), who dominates the game, cannot increase her utility by arbitrarily penalizing any innocent worker.

This article studies the core maintenance problem for dynamic graphs which requires to update each vertex's core number with the insertion/deletion of vertices/edges. Previous algorithms can either process one edge associated with a vertex in each iteration or can only process one superior edge associated with the vertex (an edge 〈u; v〉 is a superior edge of vertex u if v' core number is no less than u's core number) in each iteration. Thus for high superior-degree vertices (the vertices associated with many superior edges) insertions/deletions, previous algorithms become very inefficient. In this article, we discovered a new structure called joint edge set whose insertions/deletions make each vertex's core number change at most one. The joint edge set mainly contains all the superior edges associated with the high superior-degree vertices as long as these vertices are 3+-hop independent. Based on this discovery, faster parallel algorithms are devised to solve the core maintenance problems. In our algorithms, we can process all edges in the joint edge set in one iteration and thus can greatly increase the parallelism and reduce the processing time. The results of extensive experiments conducted on various types of real-world, temporal, and synthetic graphs illustrate that the proposed algorithms achieve good efficiency, stability and scalability. Specifically, the new algorithms can outperform the single-edge processing algorithms by up to four orders of magnitude. Compared with the matching based algorithm and the superior edge based algorithm, our algorithms show a significant speedup up to 60× in the processing time.

Peer-to-peer (P2P) services such as mobile P2P transmissions and resource sharing, provide efficient methods to deliver data without the deployment of any central server. Nevertheless, the free-riding phenomenon inherit in such services presses a need for incentive mechanisms to stimulate contributions of data transmissions or sharing. As a result, it is imperative to answer the following questions: whether, and if so to what extent, an incentive mechanism can invoke such contributions? To answerthese questions, we employ an n-player continuous quantum game model to analyze the general extrinsic incentive mechanisms as well as the reputation-based incentive mechanisms, a typical class of extrinsic incentive mechanisms. We focus on studying the extrinsic incentive mechanisms in this paper due to their wide scope of applications stemming from the fact that they promote cooperative behaviors by offering rewards rather than depending on the internal bounds (e.g., social ties) among peers, which may not always exist between any pair of peers. To the best of our knowledge, we are the first to analyze the extrinsic incentive mechanisms for P2P services from a quantum game perspective. Such a perspective is adopted because the extended strategy space in the quantum game broadens the range for searching optimal strategies and the introduction of entanglement makes the proposed analytical frameworks more practical due to the consideration of the peers' relationships imposed by the rewards in extrinsic incentive mechanisms. Our quantum game-based analytical framework is generic because it is compatible with classic game-based schemes. The analytical results can provide a straightforward insight on evaluating the potential of the extrinsic incentive mechanisms and can serve as important references for designing new extrinsic incentive mechanisms.

Link scheduling plays a key role in the network capacity and the transmission delay. In this paper, we study the problem of maximum link scheduling (MLS), aiming to characterize the maximum number of links that can be successfully scheduled simultaneously under Rayleigh-fading and multiuser interference. After analyzing the minimum distance between successful links in the existing GHW scheduling algorithm, we propose a DLS (Distance-based Link Scheduling) algorithm. Then, the global interference is characterized and bounded by introducing a separation distance between selected links, building on which we propose a distributed version of DLS (denoted by DDLS) that converges to a constant factor of the non-fading optimum within time complexity$O(n\ln n)$, where$n$is the number of links. Furthermore, we study the Shortest Link Scheduling (SLS) problem, which minimizes the number of time slots to successfully schedule each link for at least once. An algorithm for SLS with approximation factor of$O(\ln n)$is obtained by executing DDLS. Extensive simulations show that DDLS greatly outperforms GHW and the other two popular algorithms.

While social networking sites gain massive popularity for their friendship networks, user privacy issues arise due to the incorporation of location-based services (LBS) into the system. Preferential LBS takes a user’s social profile along with their location to generate personalized recommender systems. With the availability of the user’s profile and location history, we often reveal sensitive information to unwanted parties. Hence, providing location privacy to such preferential LBS requests has become crucial. However, the current technologies focus on anonymizing the location through granularity generalization. Such systems, although provides the required privacy, come at the cost of losing accurate recommendations. Hence, in this paper, we propose a novel location privacy-preserving mechanism that provides location privacy through k -anonymity and provides the most accurate results. Experimental results that focus on mobile users and context-aware LBS requests prove that the proposed method performs superior to the existing methods.

The extreme boom of online social networks paves the way for rumor propagation, which may incur an economic loss and cause further public panic. Hence, there is a pressing need to develop countermeasures for reducing side effects posed by rumors. Different from the state-of-the-art work that mostly conducted micro-perspective studies, our paper focuses on a macro-perspective one. In detail, our study neglects technical details and analyzes the antagonistic dynamics between the rumormonger and the rumor suppressor, which provides a deep understanding of the overall development trend of rumor propagation. To reveal the potentials of the rumormonger and the rumor suppressor, the competence-oriented analysis is proposed, where the sufficient and necessary conditions for the existence of the Nash equilibrium in this rumor game are proved rigorously, helping us to derive steady ratios of people who trust or deny a rumor. To figure out the optimal strategy to strike back the rumormonger, the target-oriented analysis is conducted, in which the analytical solutions when the strategies of both players are static are solved and an iteration algorithm is employed to obtain the numerical solutions when their strategies are dynamic. Both numerical and real-world-data based simulations are adopted to verify the proposed competence-oriented and target-oriented analyses.

We study the fundamental problem of fault-tolerant distributed sampling towards uniform probabilistic distribution in dynamic multi-hop wireless networks. Whereas uniform sampling has been extensively studied without concerning fault tolerance, only quite few proposals investigate how the uniform sampling algorithm tolerate Byzantine faults on dynamic networks with very special topologies, e.g., regular graphs with constant node degree. Therefore, designing fault-tolerant uniform sampling algorithms for more general graphs is still an open problem. To this end, we propose a fast and highly fault-tolerate randomized algorithm, such that nearly-uniform sampling is achieved in O(log2n) rounds, while up to O(√n/(polylog(n)·Δ)) Byzantine nodes can be tolerated, where Δ is the maximum degree of the network. Moreover, the proposed algorithm is also communication efficient in the sense that only O(log n) bits need to be exchanged on each link in every round. To show the power of distributed uniform sampling, we apply the proposed algorithm in designing polylogarithmic time distributed algorithms for two typical fundamental issues, i.e., to achieve agreement or data aggregation in Byzantine dynamic networks.

The concept of social networking is integrated into Internet of things (IoT) to socialize smart objects by mimicking human behaviors, leading to a new paradigm of Social Internet of Things (SIoT). A crucial problem that needs to be solved is how to establish reliable relationships autonomously among objects, i.e., building trust. This paper focuses on exploring an efficient context-aware trustworthiness inference framework to address this issue. Based on the sociological and psychological principles of trust generation between human beings, the proposed framework divides trust into two types: familiarity trust and similarity trust. The familiarity trust can be calculated by direct trust and recommendation trust, while the similarity trust can be calculated based on external similarity trust and internal similarity trust. We subsequently present concrete methods for the calculation of different trust elements. In particular, we design a kernel-based nonlinear multivariate grey prediction model to predict the direct trust of a specific object, which acts as the core module of the entire framework. Besides, considering the fuzziness and uncertainty in the concept of trust, we introduce the fuzzy logic method to synthesize these trust elements. The experimental results verify the validity of the core module and the resistance to attacks of this framework.

This paper investigates distributed Dominating Set (DS) and Connected Dominating Set (CDS) construction in dynamic wireless networks under the SINR interference model. Specifically, we present a new model for dynamic networks that admits both churns (due to node arrivals/departures) and node mobility. Under this dynamic model, we propose efficient algorithms to construct a DS and a CDS with constant approximation ratios w.r.t. the corresponding minimum ones in O(log n) time with a high probability guarantee. To the best of our knowledge, these algorithms are the first known ones for DS and CDS construction in dynamic networks assuming the SINR interference model. We believe our dynamic network model can greatly facilitate distributed algorithm studies in mobile and dynamic wireless networks.

In Bitcoin's incentive system that supports open mining pools, block withholding attacks incur huge security threats. In this paper, we investigate the mutual attacks among pools as this determines the macroscopic utility of the whole distributed system. Existing studies on pools' interactive attacks usually employ the conventional game theory, where the strategies of the players are considered pure and equal, neglecting the existence of powerful strategies and the corresponding favorable game results. In this study, we take advantage of the Zero-Determinant (ZD) strategy to analyze the block withholding attack between any two pools, where the ZD adopter has the unilateral control on the expected payoffs of its opponent and itself. In this case, we are faced with the following questions: who can adopt the ZD strategy? individually or simultaneously? what can the ZD player achieve? In order to answer these questions, we derive the conditions under which two pools can individually or simultaneously employ the ZD strategy and demonstrate the effectiveness. To the best of our knowledge, we are the first to use the ZD strategy to analyze the block withholding attack among pools.

In smart grid systems, vulnerable lines may lead to cascading failures which can cause large-scale blackouts. Successfully detecting vulnerable lines can increase the stability of the smart grid systems and reduce the risk of cascading failures. By modeling a smart grid system into a directed graph, we investigate the problem of vulnerable line identification from a clustering perspective. By jointly considering the topological parameters and the electrical properties, we propose an affinity propagation-based bus clustering algorithm to classify buses into clusters, where the center of each cluster represents the most influential bus in each partition. According to the clustering results, we design a vulnerable line identification scheme, which captures different types of potential critical lines in the smart grid system. Experiments over the IEEE-39 bus system demonstrate the effectiveness and correctness of our proposed algorithm.

Most existing proposals for indoor localization are “unnatural,” as they rely on sensing abilities not available to human beings. While such a mismatch causes complications in human-computer interactions and thus potentially reduces the usability and friendliness of a localization service, it is partially entailed by the need for low-cost/effort sensing with resource-limited mobile devices. Fortunately, recent developments in smart glasses (e.g., Google Glasses) signal a trend toward realistic visual sensing and hence make the sensing ability of mobile devices more compatible to that of human users. Leveraging such front-end developments, we propose VisioMap as a natural indoor localization system that intentionally mimics the human skills in visual localization. VisioMap uses very sparse photograph samples to reconstruct 3-D indoor scenes; this is facilitated by the facts that photographs are taken at the eye-level with high stability and regularity, and that the reconstruction is lightweight as it exploits geometric features rather than image pixels. Localization is in turn performed by matching the geometric features extracted online to the reconstructed 3-D scene, making VisioMap: 1) natural to users as they can see the matched 3-D scene and 2) dispensed with the need for dense fingerprints/POIs toward accurate localization.

We define the following problem termed n × 1-out-of-n oblivious transfer (n × 1-out-of-n OT): in a system with one server and n clients, how to securely and efficiently assign n secrets to n clients by the server, with each client getting a unique secret from the server, and the server and clients remain unknown of how the secrets are distributed? This is a novel problem that is fundamentally different than 1-out-of-n OT repeated n times, and is different than k-out-of-n OT as well. Nevertheless, the proposed OT has many practical applications such as privacy-preserving data aggregation in smart grids. It can also be employed to design crypto protocols for anonymous communications and group signatures. In this paper, we propose the first algorithm to efficiently and effectively implement the n × 1-out-of-n OT. We construct hidden permutation circuits to obliviously assign n secrets to n clients by the server within O(lg(n)) time. A rigorous theoretical analysis is also carried out to investigate the security strength and performance of the protocol.

Internet of Things (IoT)-based E-commerce is a new business model that relies on autonomous transaction management on IoT-devices. The management system toward IoT-based E-commerce demands autonomy, lightweight, and legitimacy. As blockchain is an innovative technology that is competent in governing the decentralized network, we adopt it to design the autonomous transaction management system on IoT E-commerce. However, current blockchain solutions, most namely cryptocurrencies, have fatal drawbacks of nonsupervisability and huge computational overhead, and hence cannot be directly applied on IoT-based E-commerce. In this paper, we propose NormaChain, a blockchain-based normalized autonomous transaction settlement system for IoT-based E-commerce. By designing a special three-layer sharding blockchain network, we can significantly increase transaction efficiency and system scalability. Additionally, by designing an innovative decentralized public key searchable encryption scheme (decentralized public key encryption with keyword search (PEKS) scheme), we can uncover illegal and criminal transactions and achieve crime traceability. Our new decentralized PEKS scheme cryptographically eliminates the dependence of a trusted central authority in the original PEKS scheme and instead expands it to a fully decentralized governance, which distributes the supervision power equally among all parties. More importantly, by proving NormaChain is secure against chosen ciphertext attacks and against the stealing of the secret key, we show that NormaChain prevents a legitimate user’s privacy from being violated by banks, supervisors or malicious adversaries. Finally, we deliver the NormaChain system with design details and full implementations. Experiments show that the average transaction-per-second on IoT devices is around 113, and the supervision accuracy is 100% when proper target illegal keywords are provided.

Health-related Internet of Things (IoT) devices are becoming more popular in recent years. On the one hand, users can access information of their health conditions more conveniently; on the other hand, they are exposed to new security risks. In this paper, we presented, to the best of our knowledge, the first in-depth security analysis on home-use electroencephalography (EEG) IoT devices. Our key contributions are twofold. First, we reverse-engineered the home-use EEG system framework via which we identified the design and implementation flaws. By exploiting these flaws, we developed two sets of novel easy-to-exploit PoC attacks, which consist of four remote attacks and one proximate attack. In a remote attack, an attacker can steal a user's brain wave data through a carefully crafted program while in the proximate attack, the attacker can steal a victim's brain wave data over-the-air without accessing the victim's device on any sense when he is close to the victim. As a result, all the 156 brain-computer interface (BCI) apps in the NeuroSky App store are vulnerable to the proximate attack. We also discovered that all the 31 free apps in the NeuroSky App store are vulnerable to at least one remote attack. Second, we proposed a novel deep learning model of a joint recurrent convolutional neural network (RCNN) to infer a user's activities based on the reduced-featured EEG data stolen from the home-use EEG IoT devices, and our evaluation over the real-world EEG data indicates that the inference accuracy of the proposed RCNN is can reach 70.55%.

The rapid developments of the Internet of Things (IoT) and smart mobile devices in recent years have been dramatically incentivizing the advancement of edge computing. On the one hand, edge computing has provided a great assistance for lightweight devices to accomplish complicated tasks in an efficient way; on the other hand, its hasty development leads to the neglection of security threats to a large extent in edge computing platforms and their enabled applications. In this paper, we provide a comprehensive survey on the most influential and basic attacks as well as the corresponding defense mechanisms that have edge computing specific characteristics and can be practically applied to real-world edge computing systems. More specifically, we focus on the following four types of attacks that account for 82% of the edge computing attacks recently reported by Statista: distributed denial of service attacks, side-channel attacks, malware injection attacks, and authentication and authorization attacks. We also analyze the root causes of these attacks, present the status quo and grand challenges in edge computing security, and propose future research directions.

Recent trends show that the popularity of online social networks (OSNs) has been increasing rapidly. From daily communication sites to online communities, an average person's daily life has become dependent on these online networks. Hence, it has become evident that protection should be provided to these networks from unwanted intruders. In this paper, we consider the data privacy on OSNs at the network level rather than the user level. This network-level privacy helps us to prevent information leakage to third-party users, such as advertisers. We propose a novel scheme that combines the privacy of all the elements of a social network: node, edge, and attribute privacy by clustering the users based on their attribute similarity. We use an enhanced equi-cardinal clustering (ECC) as a way to achieve k-anonymity. We further improve k-anonymity with l-diversity. Our proposed enhanced ECC ensures that there are at least “k” users in any given network as well as the attributes in each cluster has at least l-distinct values. We further provide proofs on how the proposed ECC ensures k-anonymity and the maximum information loss. We consider a weighted directed social network graph as an input to our method to consider the existing complexities in a social network. With the help of two real-world data sets, we evaluate this method in terms of privacy and efficiency.

With the dramatically increasing deployment of IoT devices, storing and protecting the large volume of IoT data has become a significant issue. Traditional cloud-based IoT structures impose extremely high computation and storage demands on the cloud servers. Meanwhile, the strong dependencies on the centralized servers bring significant trust issues. To mitigate these problems, we propose a distributed data storage scheme employing blockchain and cetrificateless cryptography. Our scheme eliminates the traditional centralized servers by leveraging the blockchain miners who perform “transaction” verifications and records audit with the help of certificateless cryptography. We present a clear definition of the transactions in a non-cryptocurrency system and illustrate how the transactions are processed. To the best of our knowledge, this is the first work designing a secure and accountable IoT storage system using blockchain. Additionally, we extend our scheme to enable data trading and elaborate how data trading can be efficiently and effectively achieved.

Cooperative jamming is deemed as a promising physical layer-based approach to secure wireless transmissions in the presence of eavesdroppers. In this paper, we investigate cooperative jamming in a two-tier 5G heterogeneous network (HetNet), where the macrobase stations (MBSs) at the macrocell tier are equipped with large-scale antenna arrays to provide space diversity and the local base stations (LBSs) at the local cell tier adopt non-orthogonal multiple access (NOMA) to accommodate dense local users (LUs). In the presence of imperfect channel state information, we propose three robust secrecy transmission algorithms that can be applied to various scenarios with different security requirements. The first algorithm employs robust beamforming (RBA) that aims to optimize the secrecy rate of a marcouser (MU) in a macrocell. The second algorithm provides robust power allocation (RPA) that can optimize the secrecy rate of an LU in a local cell. The third algorithm tackles a robust joint optimization (RJO) problem across tiers that seek the maximum secrecy sum rate of a target MU and a target LU robustly. We employ convex optimization techniques to find feasible solutions to these highly non-convex problems. The numerical results demonstrate that the proposed algorithms are highly effective in improving the secrecy performance of a two-tier HetNet.

Smart home IoT devices have been more prevalent than ever before but the relevant security considerations fail to keep up with due to device and technology heterogeneity and resource constraints, making IoT systems susceptible to various attacks. In this paper, we propose a novel graph-based mechanism to identify the vulnerabilities in communication of IoT devices for smart home systems. Our approach takes one or more packet capture files as inputs to construct a traffic graph by passing the captured messages, identify the correlated subgraphs by examining the attribute-value pairs associated with each message, and then quantify their vulnerabilities based on the sensitivity levels of different keywords. To test the effectiveness of our approach, we setup a smart home system that can control a smart bulb LB100 via either the smartphone APP for LB100 or the Google Home speaker. We collected and analyzed 58,714 messages and exploited 6 vulnerable correlated sub graphs, based on which we implemented 6 attack cases that can be easily reproduced by attackers with little knowledge of IoT. This study is novel as our approach takes only the collected traffic files as inputs without requiring the knowledge of the device firmware while being able to identify new vulnerabilities. With this approach, we won the third prize out of 20 teams in a hacking competition.

Dynamicity is one of the critical characteristics and a major challenge in designing communication protocols in wireless networks. Most of the previous works had focused on the internal node changes (e.g., mobility, arrival, or departure) and not considered the effect of external environmental change. However, the external environmental change, in general, is a more complex phenomenon that can impede nodes from successful communication, implying the protocols of the previous dynamic models do not work well in practice. In this paper, we give an algorithm for distributed broadcasting in a more general model with fully dynamic wireless networks, called FD-Broadcast. Specifically, we present a fully dynamic model which allows node mobility and churns (due to node arrivals/departure) and external environmental change. In contrast to the previous works on dynamic networks, our model defines the full dynamicity in terms of localized topological changes of each node and can tolerate some external environmental change. The external environment changes are captured by the random jamming method. We show that FD-Broadcast can achieve broadcasting in$O(D_{S})$rounds with a high probability guarantee under the assumption of constant dynamic rate in the SINR model, where$D_{S}$is the dynamic diameter, a parameter proposed to depict the complexity of dynamic broadcasting. Moreover, the lower bound of dynamic broadcasting is proved to be$\Omega(D_{S})$, thus, FD-Broadcast is asymptotically optimal with high probability.

Due to the complexity and volume, outsourcing ciphertexts to a cloud is deemed to be one of the most effective approaches for big data storage and access. Nevertheless, verifying the access legitimacy of a user and securely updating a ciphertext in the cloud based on a new access policy designated by the data owner are two critical challenges to make cloud-based big data storage practical and effective. Traditional approaches either completely ignore the issue of access policy update or delegate the update to a third party authority; but in practice, access policy update is important for enhancing security and dealing with the dynamism caused by user join and leave activities. In this paper, we propose a secure and verifiable access control scheme based on the NTRU cryptosystem for big data storage in clouds. We first propose a new NTRU decryption algorithm to overcome the decryption failures of the original NTRU, and then detail our scheme and analyze its correctness, security strengths, and computational efficiency. Our scheme allows the cloud server to efficiently update the ciphertext when a new access policy is specified by the data owner, who is also able to validate the update to counter against cheating behaviors of the cloud. It also enables (i) the data owner and eligible users to effectively verify the legitimacy of a user for accessing the data, and (ii) a user to validate the information provided by other users for correct plaintext recovery. Rigorous analysis indicates that our scheme can prevent eligible users from cheating and resist various attacks such as the collusion attack.

Smart meters operate based on their firmware ruling the hardware. The firmware occasionally needs to be updated to fix bugs and improve the services. Since the smart meter firmware is proprietary, the update file should be communicated to the smart meters in a secure way. In addition, the firmware update may target a specific subgroup of the smart meters rather than all of them in which case access control is required. In this paper, we address the problem of updating the smart meter firmware securely in an IEEE 802.11s-based AMI network and develop a secure and reliable multicast-over-broadcast protocol by making use of ciphertext-policy attribute-based signcryption (CP-ABSC) to provide not only confidentiality and access control but also message authentication. CP-ABSC is employed to signcrypt both the firmware update file and the firmware update request based on an access tree such that the signcrypted update file and request can be designcrypted by the smart meters possessing the attributes that can satisfy the access tree. The preliminary tests showed that increased size of the request due to signcryption reduces reliability of the protocol. Therefore, we employ random linear network coding along with CP- ABSC in order to increase the reliability and use the bandwidth and processing resources efficiently. We assessed the performance of the proposed protocol under ns-3 network simulator. The simulation results have shown that the protocol can accomplish the process of downloading a firmware update without needing any human intervention while consuming less bandwidth when compared to a baseline that employs unicasting.

Crowdsourcing is a promising paradigm to accomplish a complex task via eliciting services from a large group of contributors. However, recent observations indicate that the success of crowdsourcing is being threatened by the malicious behaviors of the contributors. In this paper, we analyze the malicious attack problem using an iterated prisoner's dilemma (IPD) game and propose a zero-determinant (ZD) strategy based scheme by rewarding a worker's cooperation or penalizing the defection for enticing his final cooperation. Both theoretical analysis and simulation study indicate that the proposed algorithm has two attractive characteristics: 1) the requestor can incentivize the worker to keep on cooperating by only increasing the short-term payment; and 2) the proposed algorithm is fair, so the requestor cannot arbitrarily penalize an innocent worker to increase her payoff even though she can dominate the game. To the best of our knowledge, we are the first to use the ZD strategy to stimulate both players to cooperate in an IPD game. Moreover, our proposed algorithm is not restricted to solve the problem of the malicious crowdsourcing - it can be employed to tackle any problem that can be formulated by an IPD game.

Matrix inverse computation is one of the most fundamental mathematical problems in large-scale data analytics and computing. It is often too expensive to be solved in resource-constrained devices such as sensors. Outsourcing the computation task to a cloud server or a fog server is a potential approach as the server is able to perform large-scale scientific computations on behalf of resource-constrained users with special software. However, outsourcing brings in new security concerns and challenges such as data privacy violations and result invalidation. In this paper, we propose a secure and verifiable outsourcing scheme to compute the matrix inverse in a server. In our scheme, the client generates two secret key sets based on two chaotic systems, which are utilized to create two sparse matrices whose permuted versions are used for matrix encryption and decryption to protect input and output privacy. The server computes the inverse over the ciphertext matrix and returns the result to the client who can verify the validity of the inverse. We analyze the proposed scheme in terms of correctness, security, verifiability, and attack resistance, and compare its performance (computation, storage, and communication overheads) with those of the state-of-the-art. Our theoretical results and comparison study demonstrate that the proposed scheme provides a secure and efficient outsourcing mechanism for matrix inverse computation.

Crowdsourcing applications are vulnerable to malicious behaviors, posing serious threats to their adoption and large deployment. Based on the notion that the requestor (i.e., the crowdsourcer) can block malicious behaviors via leveraging the market power through task allocation and pricing, we propose two novel frameworks based on the mechanism design game theory (i.e., the reverse game theory). To the best of our knowledge, we are the first to exploit the market power and to apply the mechanism design game theory in thwarting malicious behaviors in crowdsourcing. The first proposed framework is built on a requestor-dominant mechanism design game (Rd-MDG), where the game rule is determined solely by the requestor. The second proposed framework is based on the worker-assisted mechanism design game (WaMDG), where the worker (i.e., the contributor) can assist the requestor to determine the game rules by offering advices. These two frameworks have the following salient features: i) neither of them requires the workers to reveal their private information; ii) the game rules of each framework are designed to be able to force the workers to calculate their best strategies based on their actual private information; iii) our theoretical analysis shows that equilibriums exist for both frameworks; and iv) our extensive simulation results demonstrate that these two frameworks can thwart malicious behaviors by driving the workers with a higher attack intent into obtaining lower utilities.

Personalized applications often provide their functionality by extracting sensitive data from users. Such a strategy brings potential threats to users' privacy because malicious applications may sell users' sensitive data to third-parties for economic interests. The state-of-the-art literature addresses the privacy issue mainly from a technical perspective. In this paper, we take a different angle in which the main players involving privacy leakage are studied from a connected perspective rather than an isolated one. More specifically, we propose the concept of application ecosystem, which consists of user, application, and adversary (malicious third-party) as entities. Our aim is to analyze the tension forces inside the application ecosystem and their impacts on the behavior of each player, which can serve as a theoretical basis for designing effective and efficient privacy preservation solutions from a management level. Another outstanding trait of our analysis is the adoption of quantum game theory to model the application ecosystem, which is suitable because the important property of entanglement in quantum games can be employed to well depict the inner tension forces among the user, application, and adversary. This makes us take an important step towards understanding the complexity of decision-making from rational individuals. To the best of our knowledge, we are the first to quantize the privacy leakage issue. The simulation results quantitatively demonstrate how the mutual restrictions among all players determine their strategies and hence the development of the application ecosystem.

This paper analyzes the secrecy transmission capacity (STC) over wireless ad hoc networks, where the legitimate nodes and eavesdroppers are distributed as Poisson point process. We calculate the connection outage probability (COP) and the bounds of the secrecy outage probability (SOP) based on the tools from stochastic geometry and establish a theoretical model for STC. Different from previous work considering the lower bound of STC, we characterize the relationship between target outage constraints and the densities of legitimate transmitters and eavesdroppers, and propose a necessary condition to achieve an upper bound of the positive STC. That is, the length of legitimate transmitter-receiver pair has an upper bound.

In this study, the authors investigate the problem of jammer selection (JS) for enhancing the secrecy goodput in a cooperative cognitive radio network with the multiple‐input–multiple‐output capability. First, they propose an optimal stopping theory‐based JS scheme in the presence of a single eavesdropper. The proposed scheme can accommodate the cases of beamforming or non‐beamforming jamming signals. Furthermore, in the presence of multiple eavesdroppers, they develop a random JS scheme with the beamforming design. Their theoretical analysis and simulation results demonstrate that the proposed schemes can effectively improve the secrecy goodput.

Today, there is widespread use of mobile applications that take advantage of a user's location. Popular usages of location information include geotagging on social media websites, driver assistance and navigation, and querying nearby locations of interest. However, the average user may not realize the high energy costs of using location services (namely the GPS) or may not make smart decisions regarding when to enable or disable location services-for example, when indoors. As a result, a mechanism that can make these decisions on the user's behalf can significantly improve a smartphone's battery life. In this paper, we present an energy consumption analysis of the localization methods available on modern Android smartphones and propose the addition of an indoor localization mechanism that can be triggered depending on whether a user is detected to be indoors or outdoors. Based on our energy analysis and implementation of our proposed system, we provide experimental results-monitoring battery life over time-and show that an indoor localization method triggered by indoor or outdoor context can improve smartphone battery life and, potentially, location accuracy.

The Internet of Things (IoT) is changing human lives by connecting everyday objects together. For example, in a grocery store, all items can be connected with each other, forming a smart shopping system. In such an IoT system, an inexpensive radio frequency identification (RFID) tag can be attached to each product which, when placed into a smart shopping cart, can be automatically read by a cart equipped with an RFID reader. As a result, billing can be conducted from the shopping cart itself, preventing customers from waiting in a long queue at checkout. Additionally, smart shelving can be added into this system, equipped with RFID readers, and can monitor stock, perhaps also updating a central server. Another benefit of this kind of system is that inventory management becomes much easier, as all items can be automatically read by an RFID reader instead of manually scanned by a laborer. To validate the feasibility of such a system, in this paper we identify the design requirements of a smart shopping system, build a prototype system to test functionality, and design a secure communication protocol to make the system practical. To the best of our knowledge, this is the first time a smart shopping system is proposed with security under consideration.

Ultraviolet (UV) radiation has a great impact on human health. Nowadays, the public basically gets information about UV radiation through weather forecasts, which can only provide rough and average prediction for a certain large area. Since CMOS sensors in smartphone cameras are very sensitive to UV radiation, smartphones have potential to be the ideal equipment to measure it. At the same time, result optimization can be achieved in real time by taking advantage of fog computing because fog servers are able to aggregate UV radiation data and compute the results at local areas. This paper exhaustively discussed a novel procedure that could measure UV radiation through smartphone cameras, and also briefly covered how to leverage fog computing to improve UV measurement accuracy. To implement the procedure, an Android app called UV meter was developed. Experiments were conducted by utilizing the app to validate and evaluate the correctness and accuracy of the procedure on both smartphones and smart watches. Results showed that the proposed procedure could achieve an average of 95% accuracy of a typical professional digital UV meter, and could be easily implemented on smart devices.

The development of the Internet of Things has made extraordinary progress in recent years in both academic and industrial fields. There are quite a few smart home systems (SHSs) that have been developed by major companies to achieve home automation. However, the nature of smart homes inevitably raises security and privacy concerns. In this paper, we propose an improved energy-efficient, secure, and privacy-preserving communication protocol for the SHSs. In our proposed scheme, data transmissions within the SHS are secured by a symmetric encryption scheme with secret keys being generated by chaotic systems. Meanwhile, we incorporate message authentication codes to our scheme to guarantee data integrity and authenticity. We also provide detailed security analysis and performance evaluation in comparison with our previous work in terms of computational complexity, memory cost, and communication overhead.

In this paper, we consider the problem of mutual privacy protection in social participatory sensing in which individuals contribute their private information to build a (virtual) community. Particularly, we propose a mutual privacy preserving k-means clustering scheme that neither discloses an individual's private information nor leaks the community's characteristic data (clusters). Our scheme contains two privacy-preserving algorithms called at each iteration of the k-means clustering. The first one is employed by each participant to find the nearest cluster while the cluster centers are kept secret to the participants; and the second one computes the cluster centers without leaking any cluster center information to the participants while preventing each participant from figuring out other members in the same cluster. An extensive performance analysis is carried out to show that our approach is effective for k-means clustering, can resist collusion attacks, and can provide mutual privacy protection even when the data analyst colludes with all except one participant.

This paper presents the first distributed algorithm to construct a spanner for arbitrary ad hoc networks under the physical signal-to-interference-and-noise-ratio (SINR) interference model. Spanner construction is one of the most important techniques for topology control in wireless networks, which intends to find a sparse topology in which only a small number of links need to be maintained, without substantially degrading the path connecting any pair of the nodes in the network. Due to the non-local property of interference, constructing a spanner is challenging under the SINR model, especially when a local distributed algorithm is desired. We meet this challenge by proposing an efficient randomized distributed algorithm that can construct a spanner in O(log n log Γ) timeslots with a high probability, where n is the total number of nodes and Γ describes the ratio of the maximum distance to the minimum distance between nodes. The constructed spanner concurrently satisfies two most desirable properties: constant stretch and linear sparseness. Our algorithm employs a novel maximal independent set (MIS) procedure as a subroutine, which is crucial in achieving the time efficiency of spanner construction. The MIS algorithm improves the best known result of O(log2n) [33] to O(log n) and is of independent interest as the algorithm is applicable also to many other applications. We conduct simulations to verify the proposed spanner construction algorithm, and the results show that our algorithm also performs well in realistic environments.

Spanner construction is one of the most important techniques for topology control in wireless networks. A spanner can help not only to decrease the number of links and to maintain connectivity but also to ensure that the distance between any pair of communication nodes is within some constant factor from the shortest possible distance. Due to the non-locality, constructing a spanner is especially challenging under the physical interference model signal-to-interference-and-noise-ratio (SINR). In this paper, we develop two localized randomized algorithms SINR-directed-YG and SINR-undirected-YG to construct a directed Yao graph (YG) and an undirected YG in O(log n) (n is the number of wireless nodes) time slots with a high probability, in which each node is capable of performing successful local broadcasts to gather neighborhood information within a certain region and the SINR constraint is satisfied at all the steps of the algorithms. The resultant graph of SINR-undirected-YG, which is based on SINR-directed-YG, possesses a constant stretch factor 1/1-2 sin(π/c), where c > 6 is a constant. To the best of our knowledge, SINR-undirected-YG is the first spanner construction algorithm under SINR. We also obtain Yao-Yao graph under SINR. Extensive theoretical performance analysis and simulation study are carried out to verify the effectiveness and the efficiency of our proposed algorithms.

In this paper, we investigate the problem of jammer selection for securing Cooperative Cognitive Radio Networks (CCRNs) with the Multiple-Input Multiple- Output (MIMO) capability. In the CCRN under our consideration, there exist a pair of Primary Users (PUs), a relay node, a number of Secondary User (SU) pairs, and an eavesdropper. The PUs need to select a pair of SUs as jammers to interfere with the eavesdropper so as to preserve the secrecy of their wireless communications. To address this problem, we propose an Optimal Stopping based Jammer Selection (OSJS) scheme. Specifically, OSJS examines the primary secrecy capacity for each candidate SU pair in a sequential order. The first SU pair that makes the primary secrecy capacity higher than an optimal threshold is selected as the jammers. The optimal threshold is calculated based on the distribution function of the primary secrecy capacity. We derive the distribution function from the chi-square distribution function of the Signal-to-Noise Ratio (SNR) under the MIMO channel conditions. Since our OSJS scheme does not have to check all the candidate SU pairs, much time can be saved for data transmissions. Our rigorous analysis and simulation results demonstrate that our proposed scheme can achieve secure communications with improved network throughput.

Video surveillance has been widely adopted to ensure home security in recent years. Most video encoding standards such as H.264 and MPEG-4 compress the temporal redundancy in a video stream using difference coding, which only encodes the residual image between a frame and its reference frame. Difference coding can efficiently compress a video stream, but it causes side-channel information leakage even though the video stream is encrypted, as reported in this paper. Particularly, we observe that the traffic patterns of an encrypted video stream are different when a user conducts different basic activities of daily living, which must be kept private from third parties as obliged by HIPAA regulations. We also observe that by exploiting this side-channel information leakage, attackers can readily infer a user's basic activities of daily living based on only the traffic size data of an encrypted video stream. We validate such an attack using two off-the-shelf cameras, and the results indicate that the user's basic activities of daily living can be recognized with a high accuracy.

The use of mobile phones while driving has become a major source of distraction to drivers, leading to a large number of car accidents. In this paper, we study the problem of automatically detecting driver phone calls by monitoring smartphone activities and utilizing the vehicle on-board unit. The challenges to overcome include: i) passenger phone calls should be allowed while the calls of the driver should be blocked; ii) the detection mechanism should be phone position-independent and phone owner-independent as the driver may put the smartphone at any position in the front row and make calls via an earphone, or the driver may borrow a passenger's phone to make a call; iii) the in-vehicle environment is noisy resulted from the operating engine, the music the driver and passenger may listen to, and the conversation between passengers and/or the driver; and iv) the computational cost at the smartphone should be light as realtime phone call detection is expected to effectively block an ongoing call to and from the driver. To overcome these challenges and achieve our objective of detecting driver phone calls, we take advantage of the uniqueness of individual's voice features. Through a short period of learning stage, our proposed system can recognize the driver's voice from the collected audio data. Combined with the smartphone's call state, our scheme can determine whether the driver is participating in the current phone call or not. Our strategy takes into account the complicated in-vehicle environment, and the proposed algorithm does not rely on the location of the phone within the vehicle nor the ownership of the smartphone, as the most existing driver phone call detection mechanisms do. We develop a client-server based system with the smartphones being the clients and the vehicle on-board unit being the server. To validate our mechanism, we perform extensive real-world experiments under different scenarios. The results demonstrate a high probability of detecting driver phone calls with a small false alarm rate.

Global broadcasting is a fundamental problem in wireless multi-hop networks. In this paper, we propose two distributed deterministic algorithms for global broadcasting based on the Signal-to-Interference-plus-Noise-Ratio (SINR) model. In both algorithms, an arbitrary node can become the source node, and the rest of the nodes are divided into different layers according to their distance to the source node. A broadcast message is propagated from the source node to all the other nodes in a layer by layer fashion. Our first proposed algorithm (named TEGB) selects a Maximal Independent Set (MIS) for each layer. Subsequently, multiple subsets of the MIS are carefully selected so as to allow the most concurrent transmissions. Our theoretical analysis shows that TEGB has the time complexity of O(D log n), where n is the total number of nodes in the network and D is the diameter of the network. Compared with the popular algorithm DetGenBroadcast proposed in the work of Jurdzinski et al.(2013), TEGB has a logarithmic improvement in running time. Furthermore, we develop the second algorithm (named TBGB) to reduce the number of duplicated broadcast messages at each layer. To be specific, TBGB attempts to form a unidirectional spanning tree of the network. On the spanning tree, only the non-leaf nodes transmit the broadcast message. Therefore, the redundant broadcasts in the same layer are eliminated. Our theoretical analysis shows that TBGB has the time complexity of O(DΔ log n), where Δ is the maximum node degree.

As a promising technology, crowdsourcing aims to accomplish a complex task via eliciting services from a large group of workers. However, recent observations indicate that the success of crowdsourcing is being hindered by the malicious behaviors of the workers. In this paper, we analyze the attack problem using an iterated prisoner's dilemma (IPD) game and propose an zero-determinant (ZD) strategy based algorithm. Simulation results demonstrate that the requestor can incentivize the worker to keep on cooperating.

The recent advances in cutting-edge electronic and computer technologies and wireless communications have paved the way for the proliferation of wireless networks, encompassing cellular, vehicular, body area, underwater, mobile ad hoc, and sensor networks. Wireless networks, allowing communications from any device, anywhere and anytime, bring a wide range of emerging and disruptive applications in manufacturing, healthcare, military, personal entertainment, safety, and rescue. However, the increasing sophistication and scale of cyber security and crimes in wireless networks have challenged traditional techniques of securing devices, applications, and traffic of wireless networks. Particularly, as the threats and vulnerabilities continue to grow in ubiquitous wireless networks, devices, and applications, it is crucial and imperative for researchers and practitioners of wireless networks to understand the entire cyber-attack and crime spectrum on wireless networks and applications and explore new technologies to mitigate and thwart these attacks, as well as to monitor, capture, and analyze security attacks via forensics analysis. The editorial committees have accepted 12 submissions in this special issue and all the papers have gone through a regular reviewing process. Among these accepted papers, four of them are related to Cloud Computing security. In the paper titled ‘PIMRS: achieving privacy and integrity-preserving multi-owner ranked-keyword search over encrypted cloud data’, Li et al. propose a privacy and integrity-preserving multi-owner ranked-keyword search scheme named PIMRS, where an asymmetric scalar-product encryption function is adopted to preserve data privacy and to obtain more precise search results. In the paper titled ‘MEDAPs: Secure Multi-Entities Delegated Authentication Protocols for Mobile Cloud Computing’, three secure multi-entities delegated authentication protocols are proposed for mobile cloud computing. In these protocols, multiple mobile data owners can authorize a group-designated cloud server with signing rights. Irfan et al. present a framework based on security information and event management to efficiently collect evidence for crime investigation, which can benefit cloud forensics in their paper ‘A framework for cloud forensics evidence collection and analysis using security information and event management’. In the paper ‘Efficient Keyword Search over Encrypted Data in Multi-cloud Setting’, Miao et al. propose two keyword search schemes over encrypted data in multi-cloud setting scenarios. The proposed schemes can guarantee data privacy and reliability. Furthermore, the experimental results indicate that the proposed schemes are feasible and efficient in practical applications. Besides cloud computing security, this special issue also involves another 8 papers covering a wide variety of topics. In the paper titled ‘Secure the Internet, one home at a time’, Xu et al. propose a Bloom-filter based analytics framework to capture persistent threats towards the same home routers and to identify correlated attacks towards distributed home networks. This work is the first one to characterize cyber threats towards home networks. In the paper titled ‘Secure multi-unit sealed first-price auction mechanisms’, Li et al. propose three secure, multi-unit, sealed-bid, and first-price auction schemes. An auctioneer is able to verify that the winners have paid the correct amounts in these three schemes. Theoretical analysis is provided to evaluate the security properties, computational complexity, and communication complexity of the auctions. Zhang et al. propose a data aggregation approach where an untrustful aggregator in mobile sensing can collect statistic data from mobile users in their paper titled ‘An Efficient Privacy Preserving Data Aggregation Approach for Mobile Sensing’. This approach preserves user privacy and can perform data integrity verification. Lai et al. propose a secure and privacy-preserving group setup framework, SPGS, for platoon-based VCPS in their paper titled ‘SPGS: A Secure and Privacy-Preserving Group Setup Framework for Platoon-Based Vehicular Cyber-Physical Systems’. Two authentication protocols are also provided accordingly. The security feature and efficiency of SPGS are verified by the thorough analysis. In the paper titled ‘Multi-proxy multi-signature binding positioning protocol’, Xue et al. propose a multi-proxy multi-signature binding positioning protocol, based on which a multi-proxy multi-signature binding positioning protocol is designed. The correctness and security features of the proposed protocols are analyzed. Qi et al. propose an effective steganography attacking method which is not limited by the types of the steganography method in their paper titled ‘Generic attack against robust steganography based on spring transform and geometrization’. The experiment results indicate that the peak signal-to-noise ratio of images can be above 32 dB Q4 while the stego data are destroyed. In the paper titled ‘Active jamming for multi-user information security improvement with the access statuses of users’, Xu et al. propose a novel physical layer scheme for improving multiple users' information security in the next-generation communication systems. The proposed scheme is linear without iteration and it is feasible for multi-user security enhancement. In the paper titled ‘Secured measurement fusion scheme against deceptive ECM attack in radar network’, in order to prevent electronic countermeasure attacks in radar networks, Yang et al. propose a new measurement fusion scheme, which shows better security performance when a DECM attack happens. The authors also perform simulations to demonstrate the superior of their novel scheme. On behalf of the editorial committee, we would like to thank all the authors for contributing their high quality papers to this special issue. We also want to thank all the reviewers for volunteering their time to review the papers and providing valuable comments, which help with improving the quality of the papers. We are also grateful to Prof. Hsiao-Hwa Chen and Prof. Hamid R. Sharif, who are the Editor-in-Chiefs of Security and Communication Networks, for providing us the opportunity to organize this special issue and for their support during the whole publication process.

Due to the popularity of auction mechanisms in real-world applications and the increasing awareness of securing private information, auctions are in dire need of bid-privacy protection. In this paper, we design three secure, multi-unit, sealed-bid, first-price auction schemes. The first is a secure auction using homomorphic encryption and is denoted by SAHE; the second is a secure action using masking values and is denoted by SAMV; and the third has an improved masked noise algorithm, denoted by ISAMV. In the first, SAHE, the auction is processed on encrypted bids by a server, and the final output is only known by the auctioneer. Neither the auctioneer nor the server can obtain the full information of the bidders. The second and third auctions, SAMV and ISAMV, decrease computational complexity. Instead of homomorphic encryption, they use random noise to mask the bid values. By using a masking method, the server only knows the noise, and the auctioneer only knows the auction results; neither will see the private information of the bidders. All three schemes enable the auctioneer to verify that the winners have paid the correct amounts. A thorough theoretical analysis is performed to evaluate the security properties, computational complexity, and communication complexity of the auctions. Copyright © 2016 John Wiley & Sons, Ltd.

Cognitive radio provides an efficient technology to solve the problem of spectrum resource scarcity while cooperative communications can increase channel capacity. It is a common sense that combining the two benefits the system performance of cognitive radio networks (CRNs). A challenging problem of intelligent cooperation in CRNs is how to make control decisions when a secondary user cooperates with a primary user to get an optimal cooperation outcome. In this paper, we consider a scenario where a secondary user provides effort to the primary user to win the competition from many secondary users and simultaneously achieves optimal throughput. We establish a novel cooperation scheme termed Low Price to Win (LPW), and abstract the cooperation problem in CRNs as an optimization problem with multiple constraints. Unlike some traditional methods that provide direct solutions, we design a novel greedy algorithm using the Lyapunov optimization technique, by which the sophisticated optimization problem can be divided into a few subproblems and then cross-layer optimization can be applied. Our simulation results demonstrate the efficiency and effectiveness of the proposed algorithm.

Multiple-Input Multiple-Output Cooperative Cognitive Radio Networks (MIMO-CCRNs) have been proposed recently to further improve the spectrum efficiency. In MIMO-CCRNs, secondary users (SUs) equipped with multiple antennas can cooperatively relay the primary signals for the primary users (PUs) while concurrently accessing the same spectrum to send the secondary traffic for themselves. This communication model is intrinsically vulnerable to eavesdropping as more wiretapping opportunities can be exploited by attackers. In this paper, we propose to employ cooperative jamming at physical (PHY) layer to achieve secure transmissions. In cooperative jamming, certain SUs are employed as helpers to send jamming signals to jam the eavesdropper without interfering the legitimate receivers. We optimize the design of the beamformers (including the information beamformer and the jamming beamformer) as well as the power allocation vector to maximize the secrecy capacity in two scenarios. Simulation results demonstrate that the secrecy capacity is remarkably enhanced in our cooperative jamming scheme. To the best of our knowledge, this work is the first one to investigate the PHY layer security issue for MIMO-CCRNs.

Advertisements(Ads) dissemination in Vehicular Ad Hoc Networks (VANETs) is a promising application. It not only provides benefits to customers but also brings commercial profits to merchants. However, little previous work has focused on the ads dissemination problem to maximize the overall profit. In this paper, we target the ads dissemination problem and design two algorithms for ads delivery. We first propose a Spread Utility Based Delivery Algorithm based on the knowledge derived from the empirical study of vehicle traces. Based on the first algorithm, an Optimized Ads Delivery Algorithm is designed, which considers consumers' interests and their mobilities. At last, we conduct extensive simulation, and the simulation results indicate that our algorithms can achieve a higher delivery rate and effective rate than current algorithms in most cases. Additionally, when the consumer interest is considered, our algorithm can achieve a tradeoff between the delivery rate and visiting probability.

Mobile social networks have been increasingly popular with the explosive growth of mobile devices. Mobile users are allowed to interact with potential friends within a certain distance. Motivated by this feature, many exciting applications have been developed, yet the challenge of privacy protection is thus aroused. In this paper, we propose an efficient customized privacy preserving friend discovery mechanism, which not only protects the privacy of users' profile, but also establishes a verifiable secure communication channel between matched users. Besides, the initiator has the freedom to set a customized request profile by choosing the interested attributes and giving each attribute a specific value. Moreover, the request profile's privacy protection level is customized by the initiator according to his/her own privacy requirements. We also consider the collusion attacks among unmatched users. To the best of our knowledge, this is the first work to address such a security threat. Our protocol guarantees that only exactly matched users are able to communicate with the initiator securely, while little information can be obtained by other participants. To increase the matching efficiency, our design adopts the Bloom filter to efficiently exclude most unmatched users. As a result, our design effectively protects the profile privacy and efficiently decreases the computational overhead. Security analysis and performance evaluation are conducted to justify the superiority of our protocol.

As a distributed system, a wireless network, usually faces a complex environment (transient faults and topology changes occur frequently). The connected dominating set (CDS) problem has been widely studied due to its important applications in wireless communication and networks, especially the important role as a virtual backbone for efficient routing. In this paper, under SINR (Signal-to-Interference-plus-Noise-Ratio) model, we propose a distributed self-stabilizing maximal independent set (MIS) algorithm (DSSMIS). Based on DSSMIS, we design a distributed self-stabilizing algorithm (DSSCDS) for CDS construction with constant approximation within O(log n) rounds. To best of our knowledge, this is the first self-stabilizing CDS algorithm under SINR model.

Discrete beeping is an extremely rigorous local broadcast model depending only on carrier sensing. It describes an anonymous broadcast network where the nodes do not need unique identifiers and have no knowledge about the topology and size of the network. Within such a model, time is divided into slots, and nodes can either beep or keep silent at each slot. We consider the problem of constructing a minimum dominating set (MDS) and a minimum connected dominating set (MCDS), respectively, under the discrete beeping model in this paper. By assuming that an upper bound N of the network size is known, we first propose and analyze a distributed synchronous algorithm termed BMDS for constructing a minimum dominating set (MDS) and then propose a distributed synchronous algorithm BCDS for CDS construction based on a maximal independent set (MIS) algorithm and a weakly CDS (WCDS). To our best knowledge, we are the first to study the MCDS construction under the discrete beeping model. We prove that the time complexity of BMDS is O(log2N) rounds with constant approximation ratio of at most 2, and BCDS can converge to a CDS within O(log3N) rounds.

In communication networks such as social networks, wireless networks and biology networks, it is of importance to find all cliques which can help understand the network topology. The clique structures can also be utilized in facilitating message forwarding in wireless networks. For instance, using a set of cliques of maximal and disjoint, one of the nodes in each clique can be elected to forward messages, by which the duplicate message transmissions can be efficiently reduced. Further, it is well known that the maximum clique problem (MCP) is closely related to the maximum independent set and the vertex cover problems. In recent years, the fundamental problem of finding maximal cliques or maximum cliques has attracted lots of attentions. However, few of these works focus on distributed solutions in wireless networks. In this paper, we pay our attention to this missing corner of research. Specifically, we first give a distributed algorithm which can compute all maximal cliques in a wireless network represented by a graph. The algorithm takes O(n) time and uses O(mn) messages, where n is the number of nodes and m the number of edges. Then, with the proposed algorithms MCP (maximum clique problem) and UMCP (unique MCP), we show that a unique maximum clique can be selected from all maximal cliques in O(n) rounds and using O(mn) messages. To the best of our knowledge, our algorithms are the first deterministic distributed solutions for MCP in wireless networks.

This paper considers energy and information cooperation between a single-antenna primary user (PU) pair and a multiple-antennae secondary user (SU) pair in a cognitive radio system. The secondary transmitter (ST) harvests energy from the primary signal and gains opportunity to transmit its own signal in return for helping relay the primary transmitter's (PT) traffic. A time-divided power splitting scheme is proposed to enable the energy and information cooperation with the objective of maximizing the throughput of the SU pair under the energy constraint of the ST and the received signal-to-inference plus noise ratio (SINR) constraint of the primary receiver (PR). Simulation results demonstrate the influence of the time division proportion and the power splitting parameter on the throughput of the SU pair and the PU pair.

This paper focuses on designing a distributed medium access control algorithm for fairly sharing network resources among contending stations in an 802.11 wireless network. Because the notion of fairness is not universal and there lacks a rigorous analysis on the relationships among the four types of most popular fairness criteria, we first mathematically prove that there exist certain connections between these types of fairness criteria. We then propose an efficient medium access algorithm that aims at achieving time fairness and throughput enhancement in a fully distributed manner. The core idea of our proposed algorithm lies in that each station needs to select an appropriate contention window size so as to fairly share the channel occupancy time and maximize the throughput under the time fairness constraint. The derivation of the proper contention window size is addressed rigorously. We evaluate the performance of our proposed algorithm through an extensive simulation study, and the evaluation results demonstrate that our proposed algorithm leads to nearly perfect time fairness, high throughput, and low collision overhead.

Multi-hop coverage extension can be utilized as a feasible approach to facilitating uncovered users to get Internet service in public area WLANs. In this paper we introduce a relay-union network (RUN), which refers to a public area WLAN in which users often wander in the same area and have the ability to provide data forwarding services for others. We develop a RUN framework to model the cost of providing forwarding services and the utility obtained by gaining services. The objective of the RUN is to maximize the total Quality of Cooperation (QoC) of users in the RUN. Two optimal bandwidth allocation schemes are proposed for both free and dynamic bandwidth demand models. To make our scheme more pragmatic, we then consider a more practical scenario in which the bandwidth capacity of the relays and the minimum demand of the clients are bounded. We prove that the problems under both the single relay and the multi-relay scenario are NP-hard. Three heuristic algorithms are proposed to deal with bandwidth allocation and relay-client association. We also propose a distributed signaling protocol and divide the centralized MRMC algorithm into three distributed ones to better adapt for real network environment. Finally, extensive simulations demonstrate that our RUN framework can significantly improve the efficiency of cooperation in the long term.

Mobile opportunistic networks make use of a new networking paradigm that takes advantage of node mobility to distribute information. Studying their inherent properties of information dissemination can provide a straightforward explanation on the potentials of mobile opportunistic networks to support emerging applications such as mobile commerce, emergency services, and so on. In this paper, we investigate the inherent properties of information dissemination using the Lévy mobility model to characterize the movement pattern of the nodes. Because Lévy mobility can closely mimic human walk, the analysis model we adopt is practical. Our analyses are taken from the perspectives of small- and large-scales. From the perspective of small-scale, the distribution of the minimum time needed by the information to spread to a given region is investigated, from the perspective of large-scale, the bounds of the probability of the earliest time at which the information arrives in a region that is sufficiently farther away are obtained. We also provide the rate that such probability approaches zero as the distance to the region increases to infinity. Finally, our main results are validated by the numerical simulations.

Compressive sensing (CS) has been viewed as a promising technology to greatly improve the communication efficiency of data gathering in wireless sensor networks. However, this new data collection paradigm may bring in new threats but few study has paid attention to prevent information leakage during compressive data gathering. In this paper, we identify two statistical inference attacks and demonstrate that traditional compressive data gathering may suffer from serious information leakage under these attacks. In our theoretical analysis, we quantitatively analyze the estimation error of compressive data gathering through extensive statistical analysis, based on which we propose a new secure compressive data aggregation scheme by adaptively changing the measurement coefficients at each sensor and correspondingly at the sink without the need of time synchronization. In our analysis, we show that the proposed scheme could significantly improve data confidentiality at light computational and communication overhead.

WiFi fingerprint-based localization is regarded as one of the most promising techniques for indoor localization. The location of a to-be-localized client is estimated by mapping the measured fingerprint (WiFi signal strengths) against a database owned by the localization service provider. A common concern of this approach that has never been addressed in literature is that it may leak the client's location information or disclose the service provider's data privacy. In this paper, we first analyze the privacy issues of WiFi fingerprint-based localization and then propose a Privacy-Preserving WiFi Fingerprint Localization scheme (PriWFL) that can protect both the client's location privacy and the service provider's data privacy. To reduce the computational overhead at the client side, we also present a performance enhancement algorithm by exploiting the indoor mobility prediction. Theoretical performance analysis and experimental study are carried out to validate the effectiveness of PriWFL. Our implementation of PriWFL in a typical Android smartphone and experimental results demonstrate the practicality and efficiency of PriWFL in real-world environments.

In this paper, we propose an extensible and flexible truthful auction framework that is individual-rational and self-collusion resistant. By properly setting one simple parameter, this framework can yield efficient auctions (like VCG) and (sub)optimal auctions (like Myerson's Optimal Mechanism (MOM)) with a more computationally-efficient procedure compared to VCG and MOM; by carefully choosing virtual valuation functions for the bidders, it can produce attribute-aware auctions that take the channel diversity into consideration. The framework adopts a novel procedure that can prevent bidder self-collusion resulted from the bid diversity. Theoretical analysis and case studies demonstrate the strength of our auction framework in handling various considerations in a practical heterogeneous spectrum market.

Nowadays people spend a substantial amount of time waiting in different places such as supermarkets and amusement parks. Detecting the status of queuing may benefit both users and business. In this paper, we present QueueSense, a queuing recognition system on mobile phones to assist in a queue management system. QueueSense extracts features of queuing behavior and classifies queueing via collaboration among people waiting in line. It measures the disparity of people in different lines using relative position changing rate and partitions different queues using a hierarchical clustering approach. We implement a prototype of QueueSense on Android platforms using widely available multi-modal sensors and it is the first queue detection system on mobile phones. We conduct real-world experiments at a dining hall and a supermarket near a university campus. Through implementation and evaluation, we demonstrate that QueueSense is capable of detecting waiting lines that occur in our daily lives with high accuracy.

In real world, wireless heterogeneous sensor network (HSN) design and information integration are necessary in different applications. Traditionally, wireless sensor networks information integration is set up to passively fuse all received data. Such an approach is computationally challenging and operationally ineffective because improvements in information accuracy are not guaranteed. Opportunistic Sensing (OS) refers to a paradigm for signal and information processing in which a network of sensing systems can automatically discover and select sensor platforms based on an operational scenario. In this paper, we propose theory and algorithms of OS to simplify the HSN design and promote more efficient information integration. We propose an information theoretical criterion for opportunistic sensing in HSN, and show that HSN with correlated modalities needs less number of codewords than that with independent modalities. Our OS algorithm advances autonomous sensing that not only ensures effective utilization of sensing assets but also provides robust optimal performance. We apply our OS algorithm to radar sensor networks for surveillance and monitoring, and show that our approach works very well and much better than other approaches.

In this paper, we investigate the problem of achieving proportional fairness via access point (AP) association in multirate WLANs. This problem is formulated as a nonlinear programming with an objective function of maximizing the total user bandwidth utilities in the whole network. Such a formulation jointly considers fairness and AP selection. We first propose a centralized algorithm Non-Linear Approximation Optimization for Proportional Fairness (NLAO-PF) to derive the user-AP association via relaxation. Since the relaxation may cause a large integrality gap, a compensation function is introduced to ensure that our algorithm can achieve at least half of the optimal in the worst case. This algorithm is assumed to be adopted periodically for resource management. To handle the case of dynamic user membership, we propose a distributed heuristic Best Performance First (BPF) based on a novel performance revenue function, which provides an AP selection criterion for newcomers. When an existing user leaves the network, the transmission times of other users associated with the same AP can be redistributed easily based on NLAO-PF. Extensive simulation study has been performed to validate our design and to compare the performance of our algorithms to those of the state of the art.

In mobile ad-hoc cognitive radio networks (CRNs), end-to-end paths with available spectrum bands for secondary users may exist temporarily, or may never exist, due to the dynamism of the primary user activities. Traditional CRN routing algorithms, which typically ignore the intermittent connectivity of network topology, and traditional mobility-assisted routing algorithms, which generally overlook the spectrum availability, are obviously unsuitable. To tackle this challenge, we propose a Mobility-Assisted Routing algorithm with Spectrum Awareness (MARSA) to select relays based on not only the probability that a node meets the destination but also the chance at which there exists at least one available channel when they meet. To the best of our knowledge, this paper is the first to bring the idea of mobility-assisted routing to deal with the intermittently connected attribute of mobile ad-hoc CRNs, and the first to enhance the mobility-assisted routing by considering the temporal , spatial, and spectrum domains at the same time. Our simulation results demonstrate the superiority of MARSA over traditional algorithms in intermittently connected mobile CRNs.

Cognitive radio networking allows the unlicensed secondary users to opportunistically access the licensed spectrum as long as the performance of the licensed primary users does not degrade. This dynamic spectrum access strategy is enabled by cognitive radio coupled with spectrum sensing technologies. Due to the imperfection of wireless transmissions, collaborative spectrum sensing (CSS) has been proposed to significantly improve the probability of detecting the transmissions of primary users. Nevertheless, current CSS techniques are sensitive to malicious secondary users, leading to a high false alarm rate and low detection accuracy on the presence of the primary users. In this paper, we present several robust collaborative spectrum sensing schemes that can calculate a trust value for each secondary user to reflect its suspicious level and mitigate its harmful effect on cooperative sensing. Our approach explores the spatial and temporal correlations among the reported information of the secondary users to determine the trust values. Extensive simulation study has been performed and our results demonstrate that the proposed schemes can guarantee the accuracy of the cooperative sensing system with a low false alarm rate when a considerable number of secondary users report false information.

Traditional spectrum sensing methods require that a secondary user (SU) senses the spectrum at the beginning of each time slot. A closer look at the network activities of a cognitive radio network reveals that the access pattern of a primary user (PU) typically consists of a succession of transmission periods, alternating with idle periods, each of which lasts a number of time slots. Based on this observation, it becomes clear that forcing the SU to sense the channel at the beginning of each time slot is unnecessary and may lead to considerable waste of energy. The main objective of this paper is to investigate new approaches for spectrum sensing by exploring the tradeoffs between energy consumption and secondary network throughput. To this end, we propose a stochastic, energy-aware model to derive the optimal spectrum sensing interval an SU can use to dynamically determine when the next spectrum sensing should be performed. The proposed model allows an SU to adaptively derive the sensing interval based on its required quality of service and current network state, including the PU's network activities and traffic load. Extensive simulation study is performed to assess the effectiveness of our proposed approach in achieving high accuracy with reduced energy consumption. The analysis of the results show that careful tuning of key parameters leads to improved energy efficiency and increased secondary network throughput.

As one of the most powerful tools in game theory, double auction is widely utilized to tackle the spectrum allocation problem in a secondary market. In this paper, we propose a multi-unit double auction framework in which the conflict graph-based bidder group formation, the winner determination strategy, and the spectrum pricing are elaborately designed. Through an in-depth theoretical analysis, we prove that our auction scheme can achieve three critical properties including the individual rationality, the ex-post budget balance, and the truthfulness. Extensive simulation results validate that the proposed auction framework can significantly improve the user satisfaction degree.

Body Area Networks (BANs) are expected to play a major role in patient health monitoring in the near future. Providing an efficient key agreement with the prosperities of plug-n-play and transparency to support secure inter-sensor communications is critical especially during the stages of network initialization and reconfiguration. In this paper, we present a novel key agreement scheme termed Ordered-Physiological-Feature-based Key Agreement (OPFKA), which allows two sensors belonging to the same BAN to agree on a symmetric cryptographic key generated from the overlapping physiological signal features, thus avoiding the pre-distribution of keying materials among the sensors embedded in the same human body. The secret features computed from the same physiological signal at different parts of the body by different sensors exhibit some overlap but they are not completely identical. To overcome this challenge, we detail a computationally efficient protocol to securely transfer the secret features of one sensor to another such that two sensors can easily identify the overlapping ones. This protocol possesses many nice features such as the resistance against brute force attacks. Experimental results indicate that OPFKA is secure, efficient, and feasible. Compared with the state-of-the-art PSKA protocol, OPFKA achieves a higher level of security at a lower computational overhead.

The benefits of cognitive radio networks have been well recognized with the dramatic development of the wireless applications in recent years. While many existing works assume that the secondary transmissions are negative interference to the primary users (PUs), in this paper, we take secondary users (SUs) as positive potential cooperators for the primary users. In particular, we consider the problem of cooperative relay selection, in which the PUs actively select appropriate SUs as relay nodes to enhance their transmission performance. The most critical challenge for such a problem of cooperative relay selection is how to select a relay efficiently. But due to the potentially large number of secondary users, it is infeasible for a PU transmitter to first scan all the SUs and then pick the best one. Basically, the PU transmitter intends to observe the SUs sequentially. After observing a SU, the PU needs to make a decision on whether to terminate its observation and use the current SU as its relay or to skip it and observe the next SU. We address this problem by using the optimal stopping theory, and derive the optimal stopping rule. To evaluate the performance of our proposed scheme, we conduct an extensive simulation study. The results reveal the impact of different parameters on the system performance, which can be adjusted to satisfy specific system requirements.

The cooperation between the primary and the secondary users has attracted a lot of attention in cognitive radio networks. However, most existing research mainly focuses on the single-hop relay selection for a primary transmitter-receiver pair, which might not be able to fully explore the benefit brought by cooperative transmissions. In this paper, we study the problem of multi-hop relay selection by applying the network formation game. In order to mitigate interference and reduce delay, we propose a cooperation framework FTCO by considering the spectrum sharing in both the time and the frequency domain. Then we formulate the multi-hop relay selection problem as a network formation game, in which the multi-hop relay path is computed via performing the primary player's strategies in the form of link operations. We also devise a distributed dynamic algorithm PRADA to obtain a global-path stable network. Finally, we conduct extensive numerical experiments and our results indicate that cooperative multi-hop relaying can significantly benefit both the primary and the secondary network, and that the network graph resulted from our PRADA algorithm can achieve the global-path stability.

In this paper, we consider the problem of cooperative spectrum sensing scheduling (C3S) in a cognitive radio network when there exist multiple primary channels. Deviated from the existing research our work focuses on a scenario in which each secondary user has the freedom to decide whether or not to participate in cooperative spectrum sensing; if not, the SU becomes a free rider who can eavesdrop the decision about the channel status made by others. Such a mechanism can conserve the energy for spectrum sensing at a risk of scarifying the spectrum sensing performance. To overcome this problem, we address the following two questions: “which action (contributing to spectrum sensing or not) to take?” and “which channel to sense?” To answer the first question, we model our framework as an evolutionary game in which each SU makes its decision based on its utility history, and takes an action more frequently if it brings a relatively higher utility. We also develop an entropy based coalition formation algorithm to answer the second question, where each SU always chooses the coalition (channel) that brings the most information regarding the status of the corresponding channel. All the SUs selecting the same channel to sense form a coalition. Our simulation study indicates that the proposed scheme can guarantee the detection probability at a low false alarm rate.

The problem of channel quality prediction in cognitive radio networks is investigated in this paper. First, the spectrum sensing process is modeled as a Non-Stationary Hidden Markov Model (NSHMM), which captures the fact that the channel state transition probability is a function of the time interval the primary user has stayed in the current state. Then the model parameters, which carry the information about the expected duration of the channel states and the spectrum sensing accuracy (detection accuracy and false alarm probability) of the SU, are estimated via Bayesian inference with Gibbs sampling. Finally, the estimated NSHMM parameters are employed to design a channel quality metric according to the predicted channel idle duration and spectrum sensing accuracy. Extensive simulation study has been performed to investigate the effectiveness of our design. The results indicate that channel ranking based on the proposed channel quality prediction mechanism captures the idle state duration of the channel and the spectrum sensing accuracy of the SUs, and provides more high quality transmission opportunities and higher successful transmission rates at shorter spectrum waiting times for dynamic spectrum access.

Future Wireless Sensor Networks (WSNs) will be composed of a large number of densely deployed sensors. A key feature of such networks is that their nodes are untethered and unattended. Distributed techniques are expected in WSNs. Connected Dominating Sets (CDSs) have been widely used for virtual backbone construction in WSNs to control topology, facilitate routing, and extend network lifetime. This paper proposes a new distributed algorithm for CDS construction in WSNs. The algorithm represents an extension for our previously proposed centralized algorithm. The algorithm is intended to construct a CDS with the smallest ratio when compared to its centralized version. Simulation shows that our distributed approach has a maximum ratio of 1.53 to the centralized approach in term of CDS size, and it satisfies all of the geometrical properties of its canalized version. Based on this ratio, this distributed algorithm has an approximation factor of 7.65 to the optimal CDS. This approximation outperforms the existing distributed CDS construction algorithms.

This paper investigates the problem of spectrum assignment and sharing to minimize the total delay of multiple concurrent flows in multi-hop cognitive radio networks. We first analyze the expected per-hop delay, which incorporates the sensing delay and transmission delay characterizing the PU activities and spectrum capacities. Then we formulate a minimum delay optimization problem with interference constraints, and propose an approximation algorithm termed MCC to solve the problem. According to our theoretical analysis, MCC has a bounded performance ratio and a low computational complexity. Finally, we exploit the minimum potential delay fairness in spectrum sharing to mitigate the inter-flow contentions. Extensive simulation study has been performed to validate our design and to compare the performance of our algorithms with that of the state-of-the-art.

Body Area Networks (BANs) are expected to play a major role in the field of patient-health monitoring in the near future. While it is vital to support secure BAN access to address the obvious safety and privacy concerns, it is equally important to maintain the elasticity of such security measures. For example, elasticity is required to ensure that first-aid personnel have access to critical information stored in a BAN in emergent situations. The inherent tradeoff between security and elasticity calls for the design of novel security mechanisms for BANs. In this paper, we develop the Fuzzy Attribute-Based Signcryption (FABSC), a novel security mechanism that makes a proper tradeoff between security and elasticity. FABSC leverages fuzzy Attribute-based encryption to enable data encryption, access control, and digital signature for a patient's medical information in a BAN. It combines digital signatures and encryption, and provides confidentiality, authenticity, unforgeability, and collusion resistance. We theoretically prove that FABSC is efficient and feasible. We also analyze its security level in practical BANs.

Mobile P2P networking is an enabling technology for mobile devices to self-organize in an unstructured style and communicate in a peer-to-peer fashion. Due to user mobility and/or the unrestricted switching on/off of the mobile devices, links are intermittently connected and end-to-end paths may not exist, causing routing a very challenging problem. Moreover, the limited wireless spectrum and device resources together with the rapidly growing number of portable devices and amount of transmitted data make routing even harder. To tackle these challenges, the routing algorithms must be scalable, distributed, and light-weighted. Nevertheless, existing approaches usually cannot simultaneously satisfy all these three requirements. In this paper, we propose two opportunistic routing algorithms for intermittently connected mobile P2P networks, which exploit the spatial locality, spatial regularity, and activity heterogeneity of human mobility to select relays. The first algorithm employs a depth-search approach to diffuse the data towards the destination. The second one adopts a depth-width-search approach in a sense that it diffuses the data not only towards the destination but also to other directions determined by the actively moving nodes (activists) to find better relays. We perform both theoretical analysis as well as a comparison based simulation study. Our results obtained from both the synthetic data and the real world traces reveal that the proposed algorithms outperform the state-of-the-art in terms of delivery latency and delivery ratio.

In many practical applications of wireless sensor networks, it is crucial to accomplish the localization of sensors within a given time bound. We find that the traditional definition of relative localization is inappropriate for evaluating its actual overhead in localization time. To address this issue, we define a novel problem called essential localization and present the first rigorous study on the essential localizability of a wireless sensor network within a given time bound. Additionally, we propose an efficient distributed algorithm for time-bounded essential localization over a sensor network and evaluate the performance of the algorithm with analysis and extensive simulation studies.

Unbalanced traffic demands of different data center applications are an important issue in designing data center networks (DCN). In this paper, we present our exploratory investigation on a hybrid DCN solution of utilizing wireless transmissions in DCNs. Our work aims to solve the congestion problem caused by a few hot nodes to improve the global performance. We model the wireless transmissions in DCN by considering both the wireless interference and the adaptive transmission rate. Besides, both throughput and job completion time are considered to measure the impact of wireless transmissions on the global performance. Based on the model, we formulate the problem of channel allocation as an optimization problem. We also design an approximation algorithm with an approximation bound of 1/2 and a genetic algorithm to address the scheduling problem. A series of simulations are performed to evaluate and demonstrate the effectiveness of our wireless DCN scheme.

Compressive data gathering, which is based on the recent breakthroughs in compressive sensing theory, has been proposed as a viable approach for sensor network data collection at low communication overhead. Nevertheless, it suffers from a low data recovery accuracy when outlying sensor readings and broken links exist. In this paper, we investigate the impact of outlying sensor readings and broken links on high-fidelity data gathering, and propose approaches based on the compressive sensing theory to identify outlying sensor readings and derive the corresponding accurate values, and to infer broken links. Our design is validated by a comparison based extensive simulation study, and the results indicate that compressive data gathering is superior over traditional in-network data compression techniques for practical sensor network settings.

This paper studies cooperative techniques that rely on femtocell user diversity to improve the downlink communication quality of macrocell users. We analytically derive and evaluate the achievable performance of these techniques in the downlink of Rayleigh fading channels. We provide an approximation of both the bit-error rate (BER) and the data throughput that macrocell users receive with femtocell user cooperation. Using simulations, we show that under reasonable SNR values, cooperative schemes enhance the performances of macrocells by improving the BER, outage probability, and data throughput of macrocell users significantly when compared with the traditional, non-cooperative schemes.

Motivated by recent advances on Compressive Sensing (CS), we study the sparsity of sense-through-foliage radar signals. Based on CLEAN method, we obtain the impulse response for sense-through-foliage communication channels for three different radars, 200MHz, 400MHz, and UWB radars. Channel impulse responses for the above three different kinds of channels demonstrate that the sense-through-foliage signals are very sparse, which means CS is possible to be applied to sense-through-foliage radar signals to tremendously reduce the sampling rate. We apply CS and linear programming to sparse signal compression and recovery, and it turns out that we could achieve compression ratio of 32:1 with perfect recovery for the UWB radar signals.

Spectrum sharing is an emerging mechanism to resolve the conflict between the spectrum scarcity and the growing demands for the wireless broadband access. In this paper we investigate the achievable transmission capacity of a wireless backhaul mesh network that shares the spectrums of the underutilized cellular uplink over the underlay spectrum sharing model with several commonly adopted medium access control protocols: slotted-ALOHA, CSMA/CA, and TDMA. By employing stochastic geometry, we derive the probabilities for a packet to be successfully transmitted in the primary cellular uplink and the secondary mesh networks. The achievable transmission capacity of the secondary network with outage probability constraints from both the primary and the secondary systems is obtained according to Shannon's Theory. The capacity region and the achievable capacity when the outage probabilities equal their corresponding threshold values are analyzed numerically and the results illustrate the effect of adjusting the mesh network parameters on the achievable transmission capacity under different MAC protocols.

This paper focuses on designing a distributed medium access control algorithm that aims at achieving time fairness among contending stations and throughput maximization in an 802.11 wireless LAN. The core idea of our proposed algorithm lies in that each station needs to select an appropriate contention window size so as to fairly share the channel occupancy time and maximize the throughput under the time fairness constraint. The derivation of the proper contention window size is presented rigorously. We evaluate the performance of our proposed algorithm through an extensive simulation study, and the evaluation results demonstrate that our proposed algorithm leads to nearly perfect time fairness, high throughput, and low collision overhead.

Target counting in sensor-based surveillance systems is an interesting task that potentially could have many important applications in practice. In such a system, each sensor outputs the number of targets in its sensing region, and the problem is how one can combine all the reported numbers from sensors to provide an estimate of the total number of targets present in the entire monitored area. The main challenge of the problem is how to handle different sensors' outputs that contain some counts of the same targets falling into the overlapped area from these sensors' sensing regions. This paper introduces a statistical approach to estimate the target count in such a surveillance system. Our approach avoids direct handling of the overlapping issue by adopting statistical methods. First, depending on whether or not certain prior knowledge is available regarding the target distribution, the procedure in minimizing the residual sum of squares or kernel regression is used to estimate the distribution of targets. Then the estimated count of the total targets is obtained by the method of likelihood estimation based on a sequence of binomial distributions that are derived from a sampling procedure. Comparisons based on simulations show that our proposed counting approach outperform the state of art counting algorithms. Extensive simulations also show that our proposed approach is very fast and very promising in estimating the target count in sensor-based surveillance systems.

In this paper we investigate the dynamic traffic relationship characterized by a similarity value from one road point to another in vehicle networks. Due to the regularity of human mobility, traffic exhibits strong correlations in both temporal domain and spatial domain. By exploiting the similarity values, we derive application-specific message update rules for affinity propagation, based on which we propose an instant traffic clustering algorithm to partition the road points into time variant clusters, where the traffics within the same cluster are strongly spatially correlated. Online traffic clustering is also considered by clustering combination via evidence accumulation for further influence study. We also present a neural network based traffic prediction algorithm to predict the traffic conditions cluster by cluster for a future time based on the current and historical traffic data. Simulation study on real traffic data demonstrates that our proposed algorithms are able to identify the true influences among road points and provide accurate traffic predictions.

We propose an energy-aware MAC protocol, referred to as EM-MAC, for multi-hop wireless networks with multi-channel access capabilities. EM-MAC relies on iMAC's efficient channel selection mechanism to resolve the medium contention on the common control channel, enabling wireless devices to select the best available data channel for data communication. Our protocol saves energy by allowing devices that have not gained access to the medium to switch to doze mode until the channel becomes idle again. Simulations results show that EM-MAC reduces energy consumption when compared with iMAC.

In this paper, we propose a systematic approach to designing and deploying a RFID Assisted Navigation System (RFID-ANS) for VANETs. RFID-ANS consists of passive tags deployed on roads to provide navigation information while the RFID readers attached to the center of the vehicle bumper query the tag when passing by to obtain the data for navigation guidance. We analyze the design criteria of RFID-ANS and present the design of the RFID reader in detail to support vehicles at high speeds. We also jointly consider the scheduling of the read attempts and the deployment of RFID tags based on the navigation requirements to support seamless navigations. The estimation of the vehicle position and its accuracy are also investigated.

Opportunistic Sensing (OS) refers to a paradigm for signal and information processing in which a network of sensing systems can automatically discover and select sensor platforms based on an operational scenario. In this paper, we propose theory and algorithms of OS for advancing autonomous sensing that not only ensures effective utilization of sensing assets but also provides robust optimal performance. We propose an information theoretical criterion for opportunistic sensing in wireless sensor networks and derive an algorithm for efficient OS. We apply our OS algorithm to radar sensor networks for sense-through-foliage target detection, and show that our OS algorithm works very well and much better than other approach.

This paper addresses the problem of target counting based on the Monte Carlo simulation. We rely on an Accept-Reject process to guide the placement of virtual targets in a virtual sensor field, which has exactly the same sensor layout as the real one. The objective of this construction is to generate a virtual target energy landscape whose shape is close enough to an energy landscape estimated from the real sensor readings. Based on the number of virtual targets placed on the virtual field and the total virtual and real target energy volumes, the number of real targets can be estimated. We consider both single-epoch and multi-epoch sensor readings and our theoretical analysis indicates that by exploiting the information from multiple epochs, our approach yields a target count that approximately converges to the true target count when the number of epochs is large enough. Extensive comparison based simulation study has been performed and the results verify the effectiveness of our target counting algorithms.

In this study, we investigate the problem of partially overlapping channel assignment to improve the performance of 802.11 wireless networks. We first derive a novel interference model that takes into account both the adjacent channel separation and the physical distance of the two nodes employing adjacent channels. This model defines “node orthogonality”, which states that two nodes over adjacent channels are orthogonal if they are physically sufficiently separated. We propose an approximate algorithm MICA to minimize the total interference for throughput maximization. Extensive simulation study has been performed to validate our design and to compare the performances of our algorithm with those of the state-of-the-art.

Public area WLANs stand for WLANs deployed in public areas such as classroom and office buildings to provide Internet connections. Nevertheless, such a service may not be always available because of limited AP coverage, poor signal strength, or password authentication. Multi-hop access is a feasible approach to facilitate users without direct AP accesses to resort to other online users as relays for data forwarding. This paper employs credit-exchange for multi-hop access in public area WLANs to encourage users to cooperate, and proposes a complete pricing framework. We first investigate a revenue model to define the profit of a relay. Next we point out that cutoff bandwidth allocation is a crucial issue in pricing strategy. Optimal bandwidth allocation schemes are then proposed for two bandwidth demand models. Following that we consider a more practical scenario where the relay's bandwidth capacity and the client's bandwidth demand are bounded, and propose two heuristic algorithms SRMC and MRMC to compute bandwidth allocation and/or relay-client association. Extensive simulation study has been performed to validate our design.

Unbalanced traffic demands of different data center applications are an important issue in designing Data center networks (DCNs). In this paper, we present our exploratory investigation of utilizing wireless transmissions in DCNs. Our work aims to solve the congestion problem caused by a few hot nodes to improve the global performance. We model the wireless transmissions in a DCN by considering both the wireless interference and the adaptive transmission rate. Moreover, both throughput and job completion time are taken into account to evaluate the impact of wireless transmissions on the global performance. Based on this model, we formulate the channel allocation in wireless DCNs as an optimization problem and design a genetic algorithm (GA) based approach to address it. To demonstrate the effectiveness of wireless transmissions as well as our GA-based algorithm in a wireless DCN, extensive simulation study is carried out and the results validate our design.

In this paper, we propose a novel compressive sensing (CS) based approach for sparse target counting and positioning in wireless sensor networks. While this is not the first work on applying CS to count and localize targets, it is the first to rigorously justify the validity of the problem formulation. Moreover, we propose a novel greedy matching pursuit algorithm (GMP) that complements the well-known signal recovery algorithms in CS theory and prove that GMP can accurately recover a sparse signal with a high probability. We also propose a framework for counting and positioning targets from multiple categories, a novel problem that has never been addressed before. Finally, we perform a comprehensive set of simulations whose results demonstrate the superiority of our approach over the existing CS and non-CS based techniques.

The connected dominating set (CDS) has been extensively used for routing and broadcast in wireless ad hoc networks. While existing CDS protocols are successful in constructing CDS of small size, they either require localized information beyond immediate neighbors, lack the mechanism to properly handle nodal mobility, or involve lengthy recovery procedure when CDS becomes corrupted. In this paper, we introduce the timer-based CDS protocols, which first elect a number of initiators distributively and then utilize timers to construct a CDS from initiators with the minimum localized information. We demonstrate that our CDS protocols are capable of maintaining CDS in the presence of changes of network topology. Depending on the number of initiators, there are two versions of our timer-based CDS protocols. The Single-Initiator (SI) generates the smallest CDS among protocols with mobility handling capability. Built on top of SI, the Multi-Initiator (MI) version removes the single point of failure at single-initiator and possesses most advantages of SI. We evaluate our protocols by both the ns-2 simulation and an analytical model. Compared with the other known CDS protocols, the simulation results demonstrate that both SI and MI produce and maintain CDS of very competitive size. The analytical model shows the expected convergence time and the number of messages required by SI and MI in the construction of CDS, which match closely to our simulation results. This helps to establish the validity of our simulation.

In this paper, we consider how to achieve proportional fairness in multi-rate 802.11 WLANs by investigating an integrated problem of power control and AP Association in order to provide an effective tradeoff between network throughput and fairness. Since jointly considering power control and AP association for proportional fairness is NP-hard, we propose a centralized heuristic approach. By introducing a new concept of AP utility, we establish the relationship between the network utility and the AP utility according to proportional fairness. This relationship is exploited to design an algorithm PCAP to optimize the network utility by increasing the average and decreasing the variance of the AP utility. Extensive simulation study is performed and the results demonstrate that PCAP yields a significant improvement in terms of throughput, fairness, and power consumption compared to other popular power control algorithms.

A common vulnerability of wireless networks, in particular, the mobile ad hoc network (MANET), is their susceptibility to node compromise/physical capture attacks since the wireless devices are often not protected by tamper-resistant hardware due to small form factors and low cost, and can be easily stolen/lost or temporarily controlled by unauthorized entities due to their harsh working environments. A serious consequence of the device capture attack is the node replication attacks in which adversaries deploy a large number of replicas of the compromised/captured nodes throughout the network. Replicated nodes have all legitimate security credentials and therefore can launch various insider attacks or even take over the network easily. They are indeed "attack multipliers" and therefore are extremely destructive to the network. Detecting replication attacks is a nontrivial problem in MANETs due to the challenges resulted from node mobility, cloned/compromised node collusion, and the large number and wide spread of the replicas. Existing approaches either fail in mobile environments due to the limitations caused by local views or their dependence on invariant claims such as location and neighbor list, or are constrained by the number, distribution, and colluding activities of the replicas. In this paper, we propose two replication detection schemes (TDD and SDD) to tackle all these challenges from both the time domain and the space domain. Our theoretical analysis indicates that TDD and SDD provide high detection accuracy and excellent resilience against smart and colluding replicas, have no restriction on the number and distribution of replicas, and incur low communication/computation overhead. To our best knowledge, TDD and SDD are the only approaches that support mobile networks while place no restrictions on the number and distribution of the cloned frauds and on whether the replicas collude or not.

The complexity of channel scheduling in Multi- Radio Multi-Channel (MR-MC) wireless networks is an open research topic. This problem asks for the set of edges that can support maximum amount of simultaneous traffic over orthogonal channels under a certain interference model. There exist two major interference models for channel scheduling, with one under the physical distance constraint, and one under the hop distance constraint. The complexity of channel scheduling under these two interference models serves as the foundation for many problems related to network throughput maximization. However, channel scheduling was proved to be NP-Hard only under the hop distance constraint for SR-SC wireless networks. In this paper, we fill the void by proving that channel scheduling is NP-Hard under both models in MR-MC wireless networks. In addition, we propose a polynomial-time approximation scheme (PTAS) framework that is applicable to channel scheduling under both interference models in MR-MC wireless networks. Furthermore, we conduct a comparison study on the two interference models and identify conditions under which these two models are equivalent for channel scheduling.

Detecting event frontline or boundary sensors in a complex sensor network environment is one of the critical problems for sensor network applications. In this paper, we propose a novel algorithm for event frontline sensor detection based on statistical mixture methods with model selection (Akaike, 1973). A boundary sensor is considered as being associated with a multimodal local neighborhood of (univariate or multivariate) sensing readings, and each non-boundary sensor is treated as being with a unimodal sensor reading neighborhood. Furthermore, the set of sensor readings within each sensor's spatial neighborhood is formulated using Gaussian mixture model (McLachlan and Peel, 2000). Two classes of boundary and non-boundary sensors can be effectively classified using the model selection techniques for finite mixture models. Our extensive experimental results demonstrate that our algorithm effectively detects the event boundary with a high accuracy under moderate noise levels.

In this paper, we present a Gaussian mixture model based approach to capture the spatial characteristics of any target signal in a sensor network, and further propose a temporally-adaptive variant of the approach for dynamic multiple target tracking under changing environments, with the presence of both significant background event noises and a large portion of outlying sensor readings. The target position is estimated by adopting the mean-shift optimization to discriminate the target signals from the background noises. Our mixture model based algorithm is capable of fusing multivariate real-valued sensor measurements and its probability nature shows fault tolerance and robustness in noisy sensing environments. This consideration is practical as in real world applications, sensor readings are multi-modal and may contain errors. The simulation study validates our design and the results indicate that our mixture model based algorithm is an effective and capable approach for the two most typical target signal models under consideration. Desirable quantitative target tracking results are also achieved through extensive evaluations under challenging background conditions.

We transform the 3D underwater sensor network (USN) localization problem into its 2D counterpart by employing sensor depth information and a simple projection technique. We first prove that a nondegenerative projection preserves network localizability. We then prove that given a network and a constant k, all of the geometric k-lateration localization methods are equivalent. Based on these results, we design a purely distributed bilateration localization scheme for 3D USNs termed as underwater sensor positioning (USP). Through extensive simulations, we show that USP has the following nice features: (1) improved localization capabilities over existing 3D methods, (2) low storage and computation requirements, (3) predictable and balanced communication overhead, and (4) robustness to errors from the underwater environment.

We develop a rate adaptation scheme for 802.11 networks termed SRC that is based on a novel combination of sequential hypothesis testing and short-term loss ratios. The underlying design of SRC represents a fundamental departure from previous rate adjustment strategies that employ fixed-size observation windows and seemingly-intuitive rate increase guidelines. Through extensive simulation results, SRC is shown to be robust to dynamic characteristics of the wireless channel and to significantly increase network throughput over existing mainstream rate adaptation solutions. SRC does not require any type of specialized hardware or modifications to the existing 802.11 standard.

In this paper, we propose a maximum-likelihood (ML) estimation algorithm for underwater target size detection using underwater acoustic sensor networks. Theoretical analysis demonstrates that our underwater sensor network can tremendously reduce the variance of target size estimation. We show that our ML estimator is unbiased and the variance of parameter estimation matches the Cramer-Rao lower bound. Simulations further validate these theoretical results.

In this paper, we propose a discrete-cosine-transform (DCT)-based approach for sense-through-foliage target detection when the echo signal quality is good, and a radar sensor network (RSN) and DCT-based approach when the echo signal quality is poor. A RAKE structure which can combine the echos from different cluster-members is proposed for clusterhead in the RSN. We compared our approach with the ideal case when both echos are available, i.e., echos with target and without target. We also compared our approach against the scheme in which 2-D image was created via adding voltages with the appropriate time offset. Simulation results show that our DCT-based scheme works much better than the existing approach, and our RSN and DCT-based approach can be used for target detection successfully while even the ideal case fails to do it.

A central problem in sensor network security is that sensors are susceptible to physical capture attacks. Once a sensor is compromised, the adversary can easily launch clone attacks by replicating the compromised node, distributing the clones throughout the network, and starting a variety of insider attacks. Previous works against clone attacks suffer from either a high communication/storage overhead or a poor detection accuracy. In this paper, we propose a novel scheme for detecting clone attacks in sensor networks, which computes for each sensor a social fingerprint by extracting the neighborhood characteristics, and verifies the legitimacy of the originator for each message by checking the enclosed fingerprint. The fingerprint generation is based on the superimposed s-disjunct code, which incurs a very light communication and computation overhead. The fingerprint verification is conducted at both the base station and the neighboring sensors, which ensures a high detection probability. The security and performance analysis indicate that our algorithm can identify clone attacks with a high detection probability at the cost of a low computation/communication/storage overhead. To our best knowledge, our scheme is the first to provide realtime detection of clone attacks in an effective and efficient way.

Accurate prediction of survival rates of cancer patients is often key to stratify patients for prognosis and treatment. Survival prediction is often accomplished by the TNM system that involves only three factors: tumor extent, lymph node involvement, and metastasis. This prediction from the TNM has been limited, mainly because other potential prognostic factors are not used in the system. Based on availability of large cancer datasets, it is possible to establish powerful prediction systems by using machine learning procedures and statistical methods. In this paper, we present a clustering based approach to develop prognostic systems of cancer patients. Our method starts with grouping combinations that are formed using levels of factors recorded in the data. The dissimilarity measure between combinations is obtained through a sequence of data partitions produced by multiple clusterings. This dissimilarity measure is then used with a hierarchical clustering method in order to find clusters of combinations. Prediction of survival is made simply by using the survival function derived from each cluster. Our approach admits multiple factors and provides a practical and useful tool in outcome prediction of cancer patients. A demonstration of use of the proposed method is given for lung cancer patients.

We study the localization problem in sparse 3D underwater sensor networks. Considering the fact that depth information is typically available for underwater sensors, we transform the 3D underwater positioning problem into its two- dimensional counterpart via a projection technique and prove that a non-degenerative projection preserves network localizability. We further prove that given a network and a constantk, all of the geometrick-lateration localization methods are equivalent. Based on these results, we design a purely distributed localization framework termed USP. This framework can be applied with any ranging method proposed for 2D terrestrial sensor networks. Through theoretical analysis and extensive simulation, we show that USP preserves the localizability of the original 3D network via a simple projection and improves localization capabilities when bilateration is employed. USP has low storage and computation requirements, and predictable and balanced communication overhead.

We develop a practical and comprehensive hybrid rogue access point (AP) detection framework for commodity Wi- Fi networks. It is the first scheme that combines the distributed wireless media surveillance and the centralized wired end socket level traffic "fingerprinting" The former is designed not only to detect various types of rogue APs, but also to discover suspicious activities so as to prevent the adversaries from turning victim APs into rogue devices. Moreover, the socket level traffic fingerprinting helps our frame work to achieve a finer granularity on rogue AP detection among the existing schemes. This framework has the following nice properties: i) it requires neither specialized hardware nor modification to existing standards; ii) the proposed mechanism greatly improves the rogue AP detection probability so that network resilience is improved; iii) it provides a cost-effective solution to Wi-Fi network security enhancement by incorporating free but mature software tools; iv) it can protect the network from adversaries capable of using customized equipment and/or violating the IEEE 802.11 standard; v) its open architecture allows extra features to be easily added on in the future. Our analysis and evaluation demonstrate that this hybrid rogue AP protection framework is capable of reliably revealing rogue devices and preempting potential attacks.

Multipath routing is attractive for load-balancing, fault-tolerance, and security enhancement. However, constructing and maintaining a set of node-disjoint paths between the data source and sink is non-trivial in a dynamic environment. In this paper, we study the problem of route recovery in vertex-disjoint multipath routing for sensor networks with many-to-one traffic patterns. We identify the sufficient conditions for multipaths to be recovered when the existing node-disjoint paths are broken, and provide a simple framework for multipath maintenance. This framework is very efficient in time when multipath source routing is employed. Our findings can help to conserve network resource by not launching any route discovery when the data source realizes that a new route may not exist, to guide mobile data sources to relocate themselves in order to reconstruct the new multipaths, and to help newly-deployed data sources quickly determine whether the required number of multipaths exist for sure or not and then compute them. The technique proposed in this paper is a good complement to the classic max-flow algorithm when node-disjoint multipaths are needed.

Key pre-distribution has been claimed to be the only viable approach for establishing shared keys between neighboring sensors after deployment for a typical sensor network. However, none of the proposed key pre-distribution schemes simultaneously achieves good performance in terms of scalability in network size, key-sharing probability between neighboring sensors, memory overhead for keying information storage, and resilience against node capture attacks. In this paper, we propose SBK, an in-situ self-configuring framework to bootstrap keys in large-scale sensor networks. SBK is fundamentally different compared to all key pre-distribution schemes. It requires no keying information pre-deployment. In SBK, sensors differentiate their roles as either service nodes or worker nodes after deployment. Service sensors construct key spaces, and distribute keying information in order for worker sensors to bootstrap pairwise keys. An improved scheme, iSBK, is also proposed to speed up the bootstrapping procedure. We conduct both theoretical analysis and simulation study to evaluate the performances of SBK and iSBK. To the best of our knowledge, SBK and iSBK are the only key establishment protocols that simultaneously achieve good performance in scalability, key-sharing probability, storage overhead, and resilience against node capture attacks.

This paper introduces the problem of fault tolerant topology control for all-to-one and one-to-all communication in static wireless networks with asymmetric wireless links. This problem is important in both theoretical and practical aspects. We investigate two approaches, namely minimum weight based approach and nearest neighbor augmentation approach, to address this problem. Furthermore, we give theoretical analysis for the proposed algorithms. Among other results, we show that the minimum weight based approach has a $k$-approximation algorithm for all-to-one fault tolerant topology control where $k$ is the number of disjoint paths. When $k=1$, this approach solves the minimum power all-to-one $1$-connected topology control problem. To the best of our knowledge, this paper is the first to study the fault tolerant topology control for all-to-one and one-to-all communication in asymmetric static wireless networks, and also is the first to demonstrate that the minimum power all-to-one 1-connected topology control problem has an optimal solution.

Symmetric key agreement is significant to security provisioning in sensor networks with resource limitations. A number of pairwise key pre-distribution protocols have been proposed, but their scalability is often constrained by the conflict between the desired probability of sharing keys between neighboring nodes and the resilience against node capture attacks under a given budget for storing keying information within each sensor. In this paper, we propose LKE, a self-configuring in-situ key establishment scheme targeting large-scale sensor networks. LKE employs location information for a deterministic key space generation and keying information distribution. For uniformly distributed networks, LKE exhibits strong resilience against node capture attacks and achieves a high key-sharing probability (close to 1) at the expense of a small amount of memory overhead. An improvement over LKE, termed as iLKE, is also proposed. iLKE is topology-adaptive, and therefore works well for both uniform and non-uniform network models. We conduct both theoretic analysis and simulation study to evaluate the performances of LKE and iLKE.

We examine a widely accepted myth about passive listening in wireless networks, and give a detailed description of how to achieve real "passive listening." Then we develop a lightweight intrusion management system called LIMS for commodity Wi-Fi networks. Our system employs novel techniques to increase network resilience to passive and active attacks that could reveal the WEP/WPA-PSK shared key. LIMS has the following nice properties: i) it requires neither specialized hardware nor modification to existing security protocols (e.g, WEP and WPA); ii) the proposed mechanism can be integrated with an access point in a plugin manner; iii) it provides a cost-effective security enhancement to Wi-Fi networks by incorporating free but mature software tools; iv) it has the ability to prevent a certain class of DoS attacks.

Abstract — Though destructive to network functions, insider attackers are not detectable with only the classic cryptographybased techniques. Many mission-critic sensor network applications demand an effective, light, flexible algorithm for internal adversary identification with only localized information available. The insider attacker detection scheme proposed in this paper meets all the requirements by exploring the spatial correlation existent among the networking behaviors of sensors in close proximity. Our work is exploratory in that the proposed algorithm considers multiple attributes simultaneously in node behavior evaluation, with no requirement on a prior knowledge about normal/malicious sensor activities. Moreover, it is applicationfriendly, which employs original measurements from sensors and can be employed to monitor many aspects of sensor networking behaviors. Our algorithm is purely localized, fitting well to the large-scale sensor networks. Simulation results indicate that internal adversaries can be identifiedwithahighaccuracyand a low false alarm rate when as many as 25 % sensors are misbehaving. I.

Motivated by the observation that channel assignment for multiradio multi-channel mesh networks should support both unicast and local broadcast 1, should be interference-aware, and should result in low overall switching delay, high throughput, and low overhead, we propose two flexible localized channel assignment algorithms based on s-disjunct superimposed codes. These algorithms support the local broadcast and unicast effectively, and achieve interference-free channel assignment under certain conditions. In addition, under the primary interference constraints 2, the channel assignment algorithm for unicast can achieve 100 % throughput with a simple scheduling algorithm such as the maximal weight independent set scheduling, and can completely avoid hidden/exposed terminal problems under certain conditions. Our algorithms make no assumptions on the underlying network and therefore are applicable to a wide range of MR-MC mesh network settings. We conduct extensive theoretical performance analysis to verify our design.

We first give a comprehensive taxonomy of rouge access points (APs), which includes a new class of rouge APs never addressed in the literature before. Then, we propose an efficient rogue AP protection system termed as RAP for commodity Wi-Fi networks. In RAP, novel techniques are introduced to detect rouge APs and to improve network resilience. Our system has the following nice properties: i) it requires neither specialized hardware nor modification to existing standards; ii) the proposed mechanism can be integrated with an AP in a plugin manner; iii) it provides a cost-effective security enhancement to Wi-Fi networks by incorporating free but mature software tools; iv) it can protect the network from adversaries capable of using customized equipment and violating the IEEE 802.11 standard.

This paper targets the identification of outlying sensors (that is, outlying reading sensors) and the detection of the reach of events in sensor networks. Typical applications include the detection of the transportation front line of some vegetation or animalcule's growth over a certain geographical region. We propose and analyze two novel algorithms for outlying sensor identification and event boundary detection. These algorithms are purely localized and, thus, scale well to large sensor networks. Their computational overhead is low, since only simple numerical operations are involved. Simulation results indicate that these algorithms can clearly detect the event boundary and can identify outlying sensors with a high accuracy and a low false alarm rate when as many as 20 percent sensors report outlying readings. Our work is exploratory in that the proposed algorithms can accept any kind of scalar values as inputs-a dramatic improvement over existing work, which takes only 0/1 decision predicates. Therefore, our algorithms are generic. They can be applied as long as "events" can be modeled by numerical numbers. Though designed for sensor networks, our algorithms can be applied to the outlier detection and regional data analysis in spatial data mining.

Wireless sensor networks (WSNs) are characterized by resource constraints and large scalability. Many applications of WSNs require secure communication, a crucial component especially in hostile environments. However, the low computational capability and small storage budget within sensors render many popular public-key-based cryptographic systems impractical. Symmetric key cryptography, on the other hand, is attractive due to its efficiency. Nevertheless, establishing a shared key for communicating parties is a challenging problem. In this paper, we propose and analyze an in situ PAirwise Key bootstrapping scheme (iPAK) for large-scale WSNs. Our theoretical analysis and simulation study demonstrate that iPAK can achieve a high key-sharing probability between neighboring sensors and a strong resilience against node-capture attacks at the cost of low storage overhead.

Due to its efficiency, symmetric key cryptography is very attractive in sensor networks. A number of key pre-distribution schemes have been proposed, but the scalability is often constrained by the unavailability of topology information before deployment and the limited storage budget within sensors. Recently, three in-situ key establishment schemes, SBK [13], LKE [14] and iPAK [15], have been proposed, which require no preloaded keying information but let sensors compute shared keys with their neighbors after deployment. We evaluate the three schemes through extensive simulation study and observe that they all have high scalability in network size. All of them can reach high connectivity in the key-sharing graph with low storage overhead. We also observe that SBK can fully satisfy the security requirements of large-scale sensor networks by simultaneously achieving good performance in terms of scalability, connectivity, storage overhead and resilience, while SBK outperforms LKE, LKE outperforms iPAK with respect to topology adaptability.

Due to its efficiency, symmetric key cryptography is very attractive in sensor networks. A number of pairwise key pre-distribution protocols have been proposed, but the scalability is often constrained by the conflict between the desired probability of sharing keys between two nodes and the resilience against node capture attack under a given storage capacity for key-related information within each sensor. In this paper, we propose LKE, a self-configuring scheme for location-aware key establishment that targets resource-constrained sensor networks. In LKE, location information is employed for a deterministic key space generation and keying information distribution. LKE exhibits "perfect" security in against node capture attack and achieves high connectivity (close to 1) in the induced key-sharing graph at the expense of a small amount of memory in worker sensors. LKE is a pure in-situ key establishment scheme, which scales well to very large sensor networks.

Symmetric key agreement is significant to security provisioning in sensor networks with resource limitations. A number of pairwise key pre-distribution protocols have been proposed, but the performance is often constrained by the unavailability of topology information before deployment and the limited storage budget within sensors. This paper proposes SBK, a self-configured scheme for bootstrapping keys in large-scale sensor networks. SBK is topology-adaptive, which requires no preloaded keying information but lets sensors compute shared keys with their neighbors after deployment. By removing the randomness inherent to key predistribution schemes, SBK achieves high connectivity (can be 100%) with small storage overhead. An improved scheme, iSBK, is also proposed to speed up the bootstrapping procedure. To the best of our knowledge, SBK and iSBK are the only pure in-situ key establishment protocols that simultaneously achieve good performance in scalability, connectivity, storage overhead, and resilience

Abstract Recent research points out that the flooding mechanism for topology update or route request in existing ad hoc routing protocols greatly degrades the network capacity. If we restrict the broadcast of control packets within a small subset of hosts in the network, the protocol overhead can be substantially reduced. This motivates our research of constructing a virtual backbone by computing a connected dominating set (CDS) in unit‐disk graphs. In this paper, we propose two distributed algorithms to approximate a minimum CDS. These algorithms take linear time. Their performance is verified by a complete theoretical analysis. Copyright © 2006 John Wiley & Sons, Ltd.

This paper targets the identification of faulty sensors and detection of the reach of events in sensor networks with faulty sensors. Typical applications include the detection of the transportation front line of a contamination and the diagnosis of network health. We propose and analyze two novel algorithms for faulty sensor identification and fault-tolerant event boundary detection. These algorithms are purely localized and thus scale well to large sensor networks. Their computational overhead is low, since only simple numerical operations are involved. Simulation results indicate that these algorithms can clearly detect the event boundary and can identify faulty sensors with a high accuracy and a low false alarm rate when as many as 20% sensors become faulty. Our work is exploratory in that the proposed algorithms can accept any kind of scalar values as inputs, a dramatic improvement over existing works that take only 0/1 decision predicates. Therefore, our algorithms are generic. They can be applied as long as the "events" can be modelled by numerical numbers. Though designed for sensor networks, our algorithms can be applied to the outlier detection and regional data analysis in spatial data mining.

This paper presents location-centric storage (LCS), a novel distributed data storage protocol for sensor networks. In the protocol, each event detected by sensors is associated with an intensity value (delta)(by sensors), where delta is a parameter that depends on the characteristics of the event and the application context. When event information is broadcast, a sensor decides whether to store the record of an event by checking its distance to the event location and the delta. In general, the higher the intensity of an event, the further its information can propagate geographically in the sensor network. Besides, the closer to the event location, the denser the sensors are that store the event information, and thus the quicker and better a user can know about the event (by reading from surrounding sensors). The protocol utilizes network resource efficiently. In particular, the storage load of sensors is independent of the network size, and is evenly distributed across the network. Moreover, the communication distance for getting event information is small. Therefore, the protocol has great scalability. We provide detailed theoretical analysis and simulation study to support the claims. We also ran simulations to show the advantage of our protocol over some previous work. LCS can be used for applications such as context-dependent information mining in pervasive computing and on-demand warning in surveillance sensor networks

Fault-tolerant target detection and localization is a challenging task in collaborative sensor networks. The paper introduces our exploratory work toward identifying a stationary target in sensor networks with faulty sensors. We explore both spatial and temporal dimensions for data aggregation to decrease the false alarm rate and improve the target position accuracy. To filter out extreme measurements, the median of all readings in the close neighborhood is used to approximate the local observation to the target. The sensor whose observation is a local maximum computes a position estimate at each epoch. Results from multiple epochs are combined to decrease the false alarm rate further and improve the target localization accuracy. Our algorithms have low computation and communication overheads. A simulation study demonstrates the validity and efficiency of our design.

We propose a novel vision for highway safety warning based on sensor networks; the system aims to provide user-friendly zero-delay safety warnings to motorists. Our idea leverages advanced sensing, networking and storage technologies. Highway sensors detect events and store event records at multiple designated locations such that passing drivers can be alerted to potential dangers or traffic delays through wireless communication among sensors deployed at the highway and in vehicles. We have designed a location-centric storage protocol which manages the propagation and storage of event records based on the time needed to clear the event in the highway. This protocol ensures that event records can be fairly distributed among highway sensors when event arrival rate is Poisson distributed with the same mean, as verified by our simulation study and theoretical performance analysis.

We present a novel time-based positioning scheme (TPS) for efficient location discovery in outdoor sensor networks. TPS relies on TDoA (time-difference-of-arrival) of RF signals measured locally at a sensor to detect range differences from the sensor to three base stations. These range differences are averaged over multiple beacon intervals before they are combined to estimate the sensor location through trilateration. A nice feature of this positioning scheme is that it is purely localized: sensors independently compute their positions. We present a statistical analysis of the performance of TPS in noisy environments. We also identify possible sources of position errors with suggested measures to mitigate them. Our scheme requires no time synchronization in the network and minimal extra hardware in sensor construction. TPS induces no communication overhead for sensors, as they listen to three beacon signals passively during each beacon interval. The computation overhead is low, as the location detection algorithm involves only simple algebraic operations over scalar values. TPS is not adversely affected by increasing network size or density and thus offers scalability. We conduct extensive simulations to test the performance of TPS when TDoA measurement errors are normally distributed or uniformly distributed. The obtained results show that TPS is an effective scheme for outdoor sensor self-positioning.

Large-scale wireless sensors are expected to play an increasingly important role in future civilian and military settings where collaborative microsensors could be very effective in monitoring their operations. However, low power and in-network data processing make data-centric routing in wireless sensor networks a challenging problem.In this paper, we propose a novel and efficient energy-aware distributed heuristic, which we refer to as EAD, to build a special rooted broadcast tree with many leaves that is used to facilitate data-centric routing in wireless microsensor networks. Our EAD algorithm makes no assumption on local network topology, and is based on residual power. It makes use of a neighboring broadcast scheduling and distributed competition among neighboring nodess. We discuss the implementation of our scheme, and present an extensive simulation experiments to study the its performance. Our experimental results indicate clearly that our EAD scheme outperforms previous schemes.

Abstract A connected dominating set in a graph is a subset of vertices such that every vertex is either in the subset or adjacent to a vertex in the subset and the subgraph induced by the subset is connected. A minimum‐connected dominating set is such a vertex subset with minimum cardinality. An application in ad hoc wireless networks requires the study of the minimum‐connected dominating set in unit‐disk graphs. In this paper, we design a (1 + 1/s)‐approximation for the minimum‐connected dominating set in unit‐disk graphs, running in timenO((slogs)2). © 2003 Wiley Periodicals, Inc.

Wireless sensor networks have recently attracted lots of research effort due to the wide range of applications. These networks must operate for months or years. However, the sensors are powered by battery, which may not be able to be recharged after they are deployed. Thus, energy-aware network management is extremely important. In this paper, we study the following problem: Given a set of sensors in the plane, assign transmit power to each sensor such that the induced topology containing only bidirectional links is strongly connected. This problem is significant in both theory and application. We prove its NP-completeness and propose two heuristics: power assignment based on minimum spanning tree (denoted by MST) and incremental power. We also show that the MST heuristic has a performance ratio of 2. Simulation study indicates that the performance of these two heuristics does not differ very much, but; on average, the incremental power heuristic is always better than MST.