Network traffic measurement is indispensable for many network management tasks. Time-series traffic pattern detection extends the benefits of traditional single-period flow measurement by revealing dynamic flow behaviors, but also yields higher complexity. When multiple patterns must be monitored simultaneously, building a separate sketch for each pattern is prohibitive since programmable switches typically allow only one resource-intensive sketch. In this paper, we propose PatternSketch, which enables general and dynamically reconfigurable time-series pattern detection within a single sketch. PatternSketch unifies the detection of diverse patterns with a Pattern Automaton and decomposes the pattern detection process into two phases in the data plane, while allowing operators to reconfigure the active set of monitoring patterns at runtime without taking the switch offline. Our implementation on an Intel Tofino switch demonstrates that PatternSketch can operate at line rate, detecting multiple patterns concurrently while using only tens of kilobytes of SRAM. This significantly reduces both computational and storage resource consumption compared to deploying multiple, pattern-specific sketches. Evaluations on four real-world datasets show that the hardware version of PatternSketch maintains over 90% F1 scores while simultaneously detecting six time-series patterns (three representative and three newly proposed) with as little as 200KB of memory.

With the rapid advancement of the Internet of Things (IoT) and smart environments, WiFi-based human activity recognition has made significant progress, providing non-intrusive and flexible sensing capabilities. However, most existing methods still depend on manual annotations, which severely limits their scalability and practicality in real-world scenarios where labeled data is scarce or unavailable. To address this, we propose LISAR, a novel two-stage iterative self-supervised WiFi activity recognition framework that learns from unlabeled data. Specifically, we introduce a contrastive pre-training approach by training with the proposed composite loss function, co-InfoNCE Loss, and a physics-informed data augmentation strategy to learn discriminative representations from unlabeled data. In addition, we design a Pseudo-label Confidence-guided Iterative Self-supervised Learning module, PC-ISL. Through iterative updates, this module refines pseudo-labels and enables efficient data utilization, thereby enhancing the model’s discrimination on unlabeled data. Extensive experiments demonstrate that LISAR achieves high-accuracy activity recognition while relying strictly on unlabeled data for representation learning, significantly out-performing state-of-the-art self-supervised methods.

Integrated Sensing and Communications (ISAC) integrates sensing and communication functions through ubiquitous wireless signals, providing a seamless and flexible interaction experience, making it an ideal choice for intelligent Human-Computer Interaction (HCI). Among various interaction methods, gesture recognition has garnered widespread attention. However, current RF-based gesture recognition methods within ISAC are constrained by single-target sensing and insufficient robustness. In this paper, we propose MultiGes, a real-time multi-user gesture recognition system designed to support ISAC-driven scenarios. MultiGes employs dual commercial Impulse Radio Ultra-Wideband (IR-UWB) devices to create multiple wireless links, capturing dynamic motion features from multiple targets. First, a human energy map is constructed based on the reflected signal energy to determine multi-target coordinates. Then, a Differential Human Relative Velocity (DHRV) matrix is extracted to capture fine-grained motion information. Finally, we design a lightweight STNet model to extract spatiotemporal gesture features from the DHRV matrix, enabling real-time multi-target gesture recognition. We implement the MultiGes system prototype and conduct extensive experiments on ten common gestures in HCI scenarios. Experimental results demonstrate that MultiGes achieves efficient recognition for 2 to 5 users, with an average accuracy of over 90%, providing a robust, scalable, and real-time solution for multi-target gesture recognition in ISAC-driven smart environments.

With the rapid advancement of digital technologies such as cloud computing, big data, and artificial intelligence, large-scale data centers have become critical infrastructure supporting these technologies, imposing increasingly high demands on data center networks (DCNs). Traditional server-centric DCNs face challenges in large-scale distributed systems, such as difficulty in balancing bandwidth and latency, high expansion costs, and conflicts between fault tolerance and communication efficiency. To address these issues, this paper proposes ECQDC, a novel server-centric DCN based on exchanged crossed cube. Specifically, we present its logical structure ECD(s, t) and study the connectivity and edge connectivity of ECD(s, t). Furthermore,we develop efficient fault-free routing algorithm and faulttolerant routing algorithm for the ECD(s, t). The experimental results demonstrate that, compared with Dijkstra and BFS, the proposed ECDR and ECDFTR algorithms reduce the average running time by over 50% and cut the average path length by approximately 20% relative to BFS, while keeping path lengths close to Dijkstra’s optimal performance. Moreover, it exhibits excellent performance in scalability, fault tolerance, and communication efficiency, making it an ideal network topology for large-scale data center deployment.

Data centers rely on Priority-based Flow Control (PFC) to achieve lossless data transmission in Ethernet networks. To avoid buffer overflow, PFC pauses flows in a coarse-grained manner, which brings potential problems, e.g., Head-of-Line (HoL) blocking, and PFC deadlock. Although the state-of-the-art approach BFC with per-flow backpressure tackles some of the limitations of PFC, it faces implementation challenges due to the need for a large number of queues. In this paper, we present F-PFC, a fine-grained flow control scheme that only leverages a small amount of queues to address the limitations of PFC. Specifically, F-PFC first designs a fine-grained flow backpressure scheme to adjust the intensity of flow control adaptively. With different levels of flow backpressure, F-PFC ensures high throughput and low latency simultaneously. Then, F-PFC presents an accurate flow identification scheme to locate flows that really contribute to congestion. Finally, F-PFC presents a dynamic queue assignment and scheduling scheme to isolate congestion flows with limited queues. We theoretically analyze the performance of F-PFC and present the implementation of F-PFC. Extensive testbed experiments and large-scale simulations verify the performance of F-PFC. The experimental results show that F-PFC reduces tail latency by at least 33% and queue occupancy by 46% compared with state-of-the-art approaches.

Bare-metal cloud services provide direct access to dedicated physical hardware, significantly enhancing computational power, disk I/O, and network I/O performance. To effectively manage physical resources, bare-metal typically relies on specialized cloud management platforms. However, the existing management architecture still faces significant bottlenecks. These bottlenecks include slow and cumbersome deployment processes, inadequate security isolation that exposes the system to potential vulnerabilities, and limited scalability that fails to meet dynamic and evolving demand. Therefore, optimizing the management architecture to improve deployment efficiency, security, and flexibility has become a key challenge for bare-metal cloud services. This paper proposesNBBM(NebulaMatrix Bare Metal), an innovative bare-metal cloud management platform architecture designed to restructure the management of bare-metal servers in OpenStack. To simplify the complexity of bare-metal cloud management and significantly improve the overall system efficiency,NBBMadopts the following technologies: an architecture that thoroughly decouples compute and storage, a distributed management system based on SmartNIC technology, and a high-performance cloud storage interconnect solution relying on SmartNICs. These technological innovations enable theNBBMarchitecture to provide a more secure and efficient cloud service management solution. Extensive experimental results demonstrate that theNBBMplatform achieves minute-level deployment and delivers at least a 12× speedup (approximately 92% reduction) over widely used methods, while ensuring secure and flexible access to storage resources without compromising performance.

The rapid growth of cloud computing, large-scale distributed systems, and AI-driven applications has placed stringent demands on the performance and reliability of data center networks (DCNs). As DCNs scale in size and structural complexity, they become increasingly vulnerable to multiple concurrent node and/or link failures, which can lead to severe service disruptions and significant performance degradation. Existing data transmission approaches typically address node and link failures in isolation, frequently mitigating one type while overlooking the other, and thus fall short in effectively handling complex multi-failure scenarios. This paper presents a novel and efficient data transmission scheme designed to ensure robust communication under multiple node and/or link failures in DCNs. The proposed solution integrates a proactive path redundancy mechanism with a failure-aware routing strategy to enable rapid identification and avoidance of faulty components. We adopt the generalized hypercube network (GHN), a regular and scalable topology, as the underlying network model. Firstly, leveraging the method of Yang and Chang [44], we construct multiple independent spanning trees (ISTs) in GHNs, which provide structural path diversity and fault isolation. Building upon these ISTs, we propose GFP-IST, an optimized routing algorithm with a time complexity ofO(NlogN), whereNdenotes the number of nodes. GFP-IST enables efficient route computation and resilient packet forwarding in the presence of multiple simultaneous failures. Extensive simulation results demonstrate that our approach outperforms several fault-tolerant routing schemes in terms of average path length, path construction time, and fault recovery success rate, especially in large-scale and high-failure-rate network environments.

SPHINCS+, one of the Post-Quantum Cryptography Digital Signature Algorithms (PQC-DSA) selected by NIST in the third round, features very short public and private key lengths but faces significant performance challenges compared to other post-quantum cryptographic schemes, limiting its suitability for real-world applications. In scenarios involving a large number of concurrent signing or verification tasks, these performance bottlenecks become particularly critical. To address these challenges, we propose the GPU-based paRallel Accelerated SPHINCS+(GRASP), which leverages GPU technology to enhance the efficiency of SPHINCS+signing and verification processes. We propose an adaptable parallelization strategy for SPHINCS+, analyzing its signing and verification processes to identify critical sections for efficient parallel execution. Utilizing CUDA, we perform bottom-up optimizations, focusing on memory access patterns and hypertree computation, to enhance GPU resource utilization. These efforts, combined with kernel fusion technology, result in significant improvements in throughput and overall performance. Compared to previous works, our approach achieves the highest occupancy. Extensive experimentation demonstrates that our optimized CUDA implementation of SPHINCS+achieves superior performance. Specifically, our GRASP scheme delivers throughput improvements ranging from 1.09× to 3.45× compared to state-of-the-art GPU-based solutions and surpasses the NIST reference implementation by over three orders of magnitude, highlighting a significant performance advantage.

With the rapid advancement of big data processing and artificial intelligence (AI), data center networks (DCNs) must deliver more efficient resource management and data transmission mechanisms. Unfortunately, due to the significant differences in bandwidth requirements, transmission patterns, and temporal characteristics across various traffic types in DCNs (such as short flows, long flows, and bursty flows), traditional buffer allocation strategies fail to adapt flexibly to these disparities. In this paper, we propose Enhanced Buffer Management (EBM), a novel buffer-sharing scheme designed for scenarios that require higher performance from DCNs. Unlike prior approaches, EBM employs a multi-level flow identification and adaptive threshold adjustment mechanism to enhance the flexibility and efficiency of buffer management under varying traffic conditions. Specifically, EBM first performs coarse-grained and fine-grained classification of traffic based on packet size, inter-arrival interval, and other flow characteristics. It then applies an improved threshold computation function to allocate buffer space differentially across traffic classes while maintaining allocation smoothness. Our evaluation results demonstrate that EBM significantly improves performance under realistic workloads. For instance, it reduces the 99th percentile Flow Completion Time (FCT) slowdown by 32.7% for short flows in the web-search workload and by 45.1% for incast flows in the hadoop workload, all without sacrificing overall throughput.

As a critical preprocessing step, hyperspectral image (HSI) denoising aims to improve the HSI quality for subsequent applications. While unsupervised HSI denoising methods based on Deep Image Prior (DIP) have garnered attention due to their pre-training-free advantage, existing DIP-based approaches typically utilizeL2-norm as data fidelity, making them inefficient in handling complex mixed noise. Moreover, such unsupervised methods only focus on spatial domain priors, lacking a comprehensive characterization of the spatial-spectral correlations inherent in HSIs. To tackle these limitations, we propose a robust deep recovery (RDR) model for HSI denoising with spatial-spectral total generalized variation (SSTGV) prior. Specifically, the truncated-Cauchy loss function is adopted to suppress the interference of outliers and enhance the robustness against sparse noise. Moreover, the SSTGV prior is integrated into the unsupervised RDR model, resulting in complementary effect of deep prior and handcraft prior. To solve the resulting optimization problem, an efficient ADMM algorithm is developed with convergence guarantee. Experimental results demonstrate the significant advantages of our approach in both noise suppression and detail preservation, highlighting its robustness and adaptability for varied HSI denoising applications.

With the commercialization of private data, location privacy trading in Mobile Crowd Sensing (MCS) has become a fascinating research topic. In consideration of location-dependent sensing tasks, mobile workers take risks at location privacy disclosure when reporting their actual locations. Existing work fail to take workers' diverse privacy protection and trading into account. This paper proposes a novel trading framework with personalized differential privacy guarantee, referred to asLeaper, to bridge the gap between location privacy protection and task allocation efficiency. In particular,Leaperoutputs a personalized obfuscated range for each worker and further obfuscates his location based on a perturbation set within this range by incorporating differential privacy and$k$-anonymity techniques, and thus improves the efficiency of task allocation. Moreover,Leaperquantifies each worker's location privacy loss and compensates him with reasonable payment by running auction in a cost-effective way. Through real-world datasets, our evaluations and analysis demonstrate thatLeaperindeed guarantees all desired properties of personalized differential privacy, truthfulness, individual rationality and budget feasibility.

With the accelerated development of large models and distributed training, the explosive growth of data volume has brought huge challenges to traditional data processing and machine learning algorithms. The inconsistency and accuracy of the data processing process will directly affect the analysis and decision-making effect of the data. In this paper, we investigate a data fusion framework based on a trustworthiness metric model, aiming to incorporate a credibility evaluation mechanism of data sources into the data fusion process. Firstly, we propose a trust measurement model based on dynamic Bayesian networks that is correlated with time factors, taking into account the impact of network interaction behavior on trust measurement. Secondly, we design a data security aggregation mechanism based on the trustworthy measurement model, which combines credibility measurement with the minimum spanning tree (MST) protocol to improve the network's perception performance. Finally, we conduct simulation experiments and real experimental bed tests separately, and the results showed that the proposed model has continuous trustworthiness measurement ability in dynamic uncertain network environments. On a large benchmark dataset, the proposed algorithm performs better than state-of-the-art methods in statistics, with a significant reduction rate of 36.6% in the computational cost of the control center, and a reduction rate of 23.8% and 34.7% in the communication and storage costs of the system, respectively.

Unauthorized editing of speech recordings poses a significant threat to the security and authenticity of speeches, particularly in the forensic and legal fields. Even worse, the speech is increasingly at risk of being tampered with due to the development of AI techniques (e.g., Audio Deepfake). It is difficult for normal users to guarantee what they say has not been illegally changed. Audio watermark techniques are recognized as an active method against speech forgery. However, such techniques suffer from audio quality degradation and non-real-time insertion. Therefore, they cannot be adopted into real-time voice applications against forgery on remote recordings, e.g., phone calls, live broadcasts, and online meetings. Fortunately, high-definition (HD) audio techniques provide ultrasonic bands without distortion. Therefore, ultrasonic creditable factors can be utilized. We propose an audio tamper-proof system, named Aegis. It provides commodity mobile devices (e.g., smartphones) with an effective method of real-time insertion of inaudible creditable factors. Users can claim that audio with no or mismatched ultrasound is invalid and illegal. In particular, we explore a novel acoustic nonlinear phenomenon where audible signals can be modulated onto the ultrasonic spectrum. By emphasizing the correlation between speech signals and ultrasound, we realize effective defense against various tampering methods. Extensive evaluations demonstrate that Aegis yields a detection accuracy of 99.5% on average even against unseen tampering methods.

In the era of quantum computing, data sharing in the Internet of Vehicles (IoV) confronts the challenges of auditability, efficiency, and quantum security. However, existing research remains insufficient to meet the requirements of high mobility, resource constraints, and resilience against quantum attacks. In this paper, we propose a new quantum-secure auditable data sharing framework, in which we first present a quantum-resistant puncturable signature algorithm (QRPPRFS). Combining the low-noise LPN-based pseudorandom function with an optimized trapdoor generation mechanism, it achieves compact key sizes and millisecond-level signing; second, the blockchain and dual-commitment proof mechanism are integrated to ensure anonymity, transparent auditability and robustness. Finally, we rigorously demonstrate the correctness of our scheme, the EUF-CMA with puncturing of QRPPRFS, and the knowledge soundness and witness zero-knowledge of the dual-commitment proof system. Experimental evaluations show that, under the practical setting$n=256$and$q \approx 2^{23}$, the proposed scheme keeps both signing and verification latencies below 10 ms, and reduces the initial secret-key storage to only 0.22 MB. These results demonstrate that the proposed scheme achieves both enhanced security and high efficiency, outperforming existing schemes.

Redactable blockchains are challenging the core principle of traditional blockchains: immutability. One such example is the chameleon hash-based blockchain. Despite rapid academic advances, most solutions have not yet simultaneously considered four key aspects: the degree of modification privileges, the transparency of the modification process, the consistency in the post-redaction global state, and system security after redaction. In this paper, we present a controllable, publicly auditable, and redactable blockchain with a main-auxiliary architecture. Specifically, we integrate weighted secret sharing, digital signature, and non-interactive zero-knowledge proof technologies to propose a verifiable and controllable chameleon hash primitive. To encourage logical nodes, it includes a reputation evaluation mechanism and a DAO-based governance model. Additionally, we construct a redactable bi-directionally anchored main-auxiliary blockchain structure, where the auxiliary chain exclusively maintains the modification proofs associated with each block of main chain. Any node can audit the modification history or, in the event of an accusation, self-prove. This structure also simplifies global state updates for newly joined or restarted nodes. Finally, we provide comprehensive security proofs for our construction, conduct extensive experiments to evaluate its functionality and performance, and compare it with analogous solutions to demonstrate its superiority.

Sedentary lifestyle has become a major health risk in modern society. Long sitting time can be detected by accurate recognition of sitting and standing (sit-stand) activities. WiFi-based sitting time detection has the remarkable advantage of low cost, noncontact, and privacy-protection. However, accurate recognition of sit-stand activities via WiFi signal is still facing two challenges. The first and also tougher challenge is inevitable mistakes in recognition results of traditional machine learning methods, while the second challenge is the difficulty of accurate activity segmentation before activity recognition. To the best of the authors' knowledge, few work addresses the above challenges, particularly the first challenge. A new contactless sitting time detection system is designed accordingly. The system first accurately segments all activities and removes in-seat activities. Then, the mistakes in sit-stand activity recognition are effectively corrected by a new recognition error correction method. The proposed method first creates and updates a correction benchmark that can satisfy both successive correlation and waveform symmetry between sit-stand activities. The recognition results of traditional machine learning methods are then corrected based on the latest correction benchmark. Extensive experiment results demonstrate that compared to related work, the designed system has much better accuracy on both sit-stand activity recognition and sitting time estimation. The experiment results also demonstrate the robustness of the designed system.

The CROSS Digital Signature Algorithm (DSA), currently a second-round candidate in the NIST standardization process for additional post-quantum digital signatures, offers compact public keys and strong security guarantees rooted in the code-based Restricted Syndrome Decoding Problem (R-SDP) and its variant R-SDP(G). Despite its strong theoretical foundation and practical significance, existing CPU-based implementations of CROSS exhibit evident performance limitations, while its potential for high-throughput acceleration on GPU architectures remains insufficiently investigated. In this work, we present X2O, the first systematically optimized GPU implementation framework for CROSS on NVIDIA GPUs. X2O introduces a novel cross-parallel architecture that integrates both horizontal and vertical parallelism to fully exploit the massive concurrency of modern GPU platforms. The framework incorporates a series of targeted optimizations, including fine-grained thread scheduling, optimized memory access patterns, hash function tuning, and GPU-efficient tree construction. Experimental results on a NVIDIA RTX 4090 demonstrate the efficiency of our design, achieving up to 1,082,904 signature generations and 1,589,595 verifications per second at NIST security level 1. Compared to the official AVX-optimized CPU implementation, our GPU-based approach achieves up to 120× speedup, establishing a new performance benchmark for CROSS and demonstrating the viability of high-throughput, post-quantum digital signatures on parallel computing platforms.

With the increasing sophistication of malware, enhanced Attributed Control Flow Graphs (ACFGs) have become a fundamental representation and are widely applied in malware detection. However, existing CFG-based detection techniques primarily extract shallow features of malware, neglecting deeper structural and semantic characteristics. Additionally, retaining all basic blocks in CFGs significantly increases the memory overhead of detection models. To address these issues, we propose MCLPF, collaborative malware detection with interpretable pruning, to improve the overall performance of existing malware detection systems that rely on fine-grained control flow features. MCLPF first introduces a novel Attributed Interpretable Flow Graph (AIFG) to extract functional attributes, integrating node-level features, edge-level features, and assembly language embedding features derived from Large Language Models (LLMs). Subsequently, it proposes an efficient and reliable detection scheme by alternately updating the graph structure and language learning modules through L-Step and G-Step, rather than synchronously training Language Models (LMs) with Graph Neural Networks (GNNs) on large-scale graphs. We conduct experiments using public datasets involving four different architectures (i.e., PE-32, PE-64, ELF-32, and ELF-64) and demonstrate that our model achieves an exceptionally high detection accuracy (i.e., 99.30%). After pruning 100% of noncritical nodes and edges, the sample size is reduced to approximately 8% of the original, with an average time cost reduction of 74.7%, while the detection performance fluctuation averages only about 1%. Extensive cross-dataset evaluations validate the effectiveness and efficiency of the proposed method.

Multiaccess edge computing (MEC) enables low-latency service delivery by deploying application instances on edge servers. However, edge servers are prone to failures, making it challenging to meet diverse user reliability requirements. A common approach is to deploy redundant instances across multiple edge servers, which improves reliability but increases costs and limits the number of users that can be served within budget. Therefore, efficient deployment strategies are needed to balance cost-effectiveness and reliability guarantees, thereby maximizing the app vendor’s revenue. In this article, we investigate the problem ofRevenue maximization forReliability-awareEdgeApplicationDeployment ($\text{R}^{2}\text{EAD}$). Our objective is to maximize the app vendor’s revenue by deploying its applications on heterogeneous edge servers, subject to budget and resource constraints and users’ diverse reliability requirements. We prove that the$\text{R}^{2}\text{EAD}$problem is$\mathcal {\text{NP}}$-hard and propose an efficient approximation algorithm named$\text{R}^{2}\text{EAD}$-A. By reducing the problem to a nonmonotone submodular maximization problem with curvature$\alpha$under multiple knapsack constraints, we prove that$\text{R}^{2}\text{EAD}$-A achieves a constant approximation ratio of$\frac{1}{\alpha }(1 - e^{-\alpha })$. Extensive evaluations demonstrate that$\text{R}^{2}\text{EAD}$-A outperforms the representative approaches across all tested cases.

Incremental Few-shot Semantic Segmentation (iFSS) aims to learn novel classes with limited samples while preserving segmentation capability for base classes, addressing the challenge of continual learning of novel classes and catastrophic forgetting of previously seen classes. Existing methods mainly rely on techniques such as knowledge distillation and background learning, which, while partially effective, still suffer from issues such as feature drift and limited generalization to real-world novel classes, primarily due to a bidirectional coupling bottleneck between the learning of base classes and novel classes. To address these challenges, we propose, for the first time, a diffusion-based generative framework for iFSS. Specifically, we bridge the gap between generative and discriminative tasks through an innovative binary-to-RGB mask mapping mechanism, enabling pre-trained diffusion models to focus on target regions via class-specific semantic embedding optimization while sharpening foreground-background contrast with color embeddings. A lightweight post-processor then refines the generated images into high-quality binary masks. Crucially, by leveraging diffusion priors, our framework avoids complex training strategies. The optimization of class-specific semantic embeddings decouples the embedding spaces of base and novel classes, inherently preventing feature drift, mitigating catastrophic forgetting, and enabling rapid novel-class adaptation. Experimental results show that our method achieves state-of-the-art performance on the PASCAL- $5^{i}$ and COCO- $20^{i}$ datasets using much less data than other methods, and exhibiting competitive results in cross-domain few-shot segmentation tasks. Project page: https://ifss-diff.github.io/.

User trajectory publication has promoted various location-based applications like user travel recommendation. However, possible privacy leakages have hindered more inclusive trajectory data analysis and utilization. Privacy-preserving trajectory synthesis is a popular approach to address the above privacy issues. Existing methods unavoidably produce low trajectory utility since they usually apply perturbed versions of human moving patterns. Worse still, they cannot adaptively adjust this synthesis according to the varying granularity demands of different users. This paper proposes a novel adaptive trajectory synthesis framework with generated data, namelyADGTrace. Our model achieves privacy preservation without introducing additional noise while maintaining high adaptation.ADGTracedirectly synthesizes artificial trajectories that share the similar patterns with real ones through agenerative and selectiveoptimization process. Additionally, we present a grid granularity alignment strategy to achieve adaptive trajectory synthesis, satisfying varying user demands. Extensive experiments on real-world datasets demonstrate the superiority ofADGTraceover the state-of-the art methods under various utility metrics, maintaining strong attack resilience.

Head orientation tracking has many potential applications in many fields,e.g., human-computer interaction, AR, and VR. In recent years, a large amount of existing work only focuses on the positioning of the user but ignore the tracking of the head orientation. Undoubtedly, with the information of the user's head orientation, many applications will have more opportunities for performance enhancement and better user experience. However, reviewing existing works regarding head tracking, the CV-based solutions have limited tracking angle range and privacy issues, the IMU-based solutions have accumulated errors, and the traditional microphone array-based solutions have low accuracy. Thus, none of these methods provides accurate and stable head orientation. In this paper, we propose EHeadTracker, an enhanced fine-grained head orientation tracking system based on head-mounted acoustic devices. This system recognizes rich head motions and achieves high-precision head direction tracking, while solving the problem of pivot point initialization. The experimental results show that the system can achieve an average error of 6 degrees in the head orientation tracking. To the best of our knowledge, EHeadTracker is the first system to use head-mounted acoustic devices to achieve head orientation tracking and has the highest accuracy in all current work.

Continuous authentication is increasingly critical for cyber security. However, existing approaches are time-consuming due to their simplistic signal modulation and low efficiency in feature extraction. In this paper, we propose a continuous authentication technique, OnePiece. OnePiece is free from the requirement of in-ear microphones, which are necessary for existing earphone authentication systems. It exploits out-ear microphones for biometrics extraction, which are ubiquitous on off-the-shelf earphones. We analyze the acoustic response model of ears towards out-ear microphones via the air, which is different from that towards in-ear microphones. A frequency-varying ultrasonic modulation scheme is proposed to characterize in-depth ear biometrics in user-friendly, error-free, and time-efficient ways. Therefore, OnePiece enables quick-pass authentication once users wear the earphones, followed by continuous authentication covering the whole course. Moreover, we propose a wake-up mechanism to reduce the consumed power, which addresses the key power consumption issue in ultrasonic sensing techniques. Particularly, OnePiece can be smoothly deployed on off-the-shelf wired and wireless earphones. It performs good cross-device performance in which users just register only once. Extensive evaluations are conducted to validate its effectiveness under real-world scenarios.

Recently, there has been an emerging radio frequency fingerprint identification (RFFI) technology that enhances fingerprint distinguishability by deliberately injecting an RF fingerprint into the device's Wi-Fi baseband signal. The current RF fingerprint injection methods are impractical, degrading the communication quality between Wi-Fi devices while offering limited improvements in distinguishability among a set of devices. To address these issues, we propose injecting I/Q imbalance into a short training field (STF) instead of the entire baseband signal. Our findings indicate that this method can effectively preserve the quality of the original wireless communication. Besides, a temperature-independent RF feature differential carrier frequency offset (DCFO) is proposed as an extended feature for the enhancement of fingerprint distinguishability. Building upon these, we introduce a fingerprinting scheme called PR-RFFI that generates distinguishable fingerprints for a set of devices by injecting appropriate I/Q imbalance and DCFO into the STF. Leveraging the short-term invariance of the channel, we design a practical I/Q imbalance extraction method based on the communication-quality preserving injection. Moreover, we design an optimal assignment method for I/Q imbalance and DCFO to maximize the distinguishability of RF fingerprints for all devices. Finally, we implement the PR-RFFI solution and conduct experiments in real-world and simulation scenarios. The experimental results demonstrate that PR-RFFI consistently maintains good communication quality, and achieves over 98% precision, recall, and F1-score.

Leveraging ubiquitous WiFi infrastructures, intrusion detection methods based on Received Signal Strength (RSS) offer compelling advantages, including cost-effectiveness and privacy protection. However, existing RSS-based intrusion detection solutions fall short of accurately estimating and extending the WiFi sensing bound. In this paper, we propose a novel model of motion-disturbed RSS and design an effective R-ratio indicator to extend the intrusion detection bound. Specifically, we first establish a general model of motion-disturbed RSS and derive the blocked area and reflection area in this RSS model. Then, we define the WiFi intrusion detection bound and propose a performance indicator called R-ratio to extend the bound with RSS. Furthermore, based on the statistical properties of noise, we design an efficient filter to further weaken the noise. We also propose two new methods to further extend intrusion detection bound. Extensive experimental results demonstrate that the proposed power sum ratio based intrusion detection method can approximately double the WiFi intrusion detection bound compared to other methods with raw RSS data, and our developed motion-disturbed RSS model can provide valuable insights and guidance to the intrusion detection system.

Edge caching is a critical application scenario in edge networks. By storing diverse files on edge servers and dynamically fetching new files from the cloud, edge networks can provide low-latency file access services for mobile users. In practice, the file fetching latency is non-negligible. Consecutive requests for the same missing file during the fetching phase introduce additional latency (referred to as delayed hits). Existing studies either ignore the delayed hits when making caching decisions or are not applicable to multi-server edge networks. In this paper, we investigate the online caching problem with delayed hits in the multi-server edge networks and prove its hardness. The objective is to minimize the total file access latency. To solve the proposed problem, we propose Cadle, which makes caching decisions based on the latency of different file access operations and weights of files in an online manner, without relying on any prior knowledge of future requests. We prove the competitive ratio of Cadle.We also conduct extensive experiments on the real-world dataset to verify the performance of Cadle. The experimental results show that Cadle reduces the total file access latency by at least 31.8% on average, and improves the hit ratio by at least 25.9% on average compared with state-of-the-art approaches.

Reusing Wi-Fi communication packets for sensing purpose has been regarded as one of the most cost-effective ways to realize integrated sensing and communication (ISAC) on commodity Wi-Fi. However, the channel state information (CSI) measured from these packets can be heavily compromised by modern Wi-Fi beamforming protocols tailored primarily to maximize communication throughput, hence inadvertently affecting Wi-Fi sensing performance. Existing approach attempts to mitigate this negative impact through passive signal processing in single-user sensing scenarios, but it fails to fundamentally resolve the problem. In contrast, we actively leverage beamforming, transforming its adverse effects into positive gains, and propose VersaBeam, a practical Wi-Fi ISAC system that simultaneously supports multiple sensing and communication users. Specifically, for multi-user scenarios, we design a correlation-based user pairing algorithm to ensure that the reused communication packets of each sensing receiver are transmitted with sufficiently high power along the sensing direction. Building on this, a novel ISAC-oriented beamforming strategy is proposed to balance the requirements of both sensing and communication. To further provide consistent inputs for sensing tasks, a CSI unification method is developed to remove inconsistencies resulting from diverse beamforming matrices when reusing packets from different communication users. Finally, a prototype of VersaBeam is implemented on commodity Wi-Fi devices, and its effective ness is validated through micro-benchmarking and real-world experiments across three representative sensing applications.

New Wi-Fi systems have leveraged beamforming to manage a significant portion of traffic for achieving high throughput and reliability. Unfortunately, this has amplified certain security risks since beamforming critically relies on theclear-textbeamforming feedback information (BFI): though similar risks have been exposed using emulation platforms (e.g., USRP), they have never proven realistic till this day. In this paper, we propose BeamCraft, thefirstattack to manipulate traffic incommodityWi-Fi systems; it differs significantly from existing attacks either staying only on emulation platforms with limited real-world applicability or jamming communications by brute force. The core idea of BeamCraft involves corrupting beamforming decisions by injecting crafted BFIs that feed an access point (AP) with erroneous information on channel states. To mount a covert yet purposeful attack, we develop i) a joint location and transmit power selection strategy to evade detection by victims and ii) a novel BFI forgery method to effectively manipulate AP's beamforming decisions. We implement BeamCraft using commodity Wi-Fi devices and perform extensive evaluations with it; the results reveal that BeamCraft effectively manipulates Wi- Fi traffic while maintaining a low exposure rate. Furthermore, we also introduce a defense strategy, namely BeamCrypt, that jointly leverages reciprocity and similarity of the channel within the coherence time to authenticate the legitimate user with low overhead. We implement it using WARP and evaluation results verify the effectiveness.

Choosing an appropriate division method is crucial for partitioning two-dimensional spatial data under the constraints of differential privacy. The current mainstream partitioning methods include grid-based partitioning and hierarchical partitioning. In order to optimize query accuracy while satisfying differential privacy conditions, it remains challenge to achieve the sum minimization of noise error and uniformity assumption error. To address this issue, we propose the HOLG (Hierarchical Optimization of Logical Grids) algorithm, employing a ”divide-merge-divide” approach. It begins with fine-grained grid partitioning of the data domain, followed by heuristic merging of grids with similar data distributions. After determining the scale of the query domain, the merged regions are further subdivided into smaller regions with similar query probabilities, constructing a hierarchical structure to reduce uniformity assumption errors. Additionally, we design a novel noise injection method and introduce consistency constraints to further minimize noise errors. To reduce the time complexity of the HOLG partitioning method, Huffman trees is employed to optimize the processing of the hierarchical tree set generated by HOLG, ensuring query utility while effectively reducing the query response time for the partitioning algorithm. Experimental results on large-scale spatial datasets demonstrate that HOLG outperforms similar algorithms in query accuracy. Furthermore, when combined with the Huffman tree optimization, it effectively reduces query response time.

Gesture recognition provides an effective human-computer interaction that makes device control more intuitive and convenient. Although the research on mmWave radar-based gesture recognition has demonstrated promising results, existing studies have exclusively addressed the cross-domain challenge or the human activity interference problem, and no attention has been paid to the cross-domain problem in the presence of human activity interference. To address these issues, we propose a novel mmWave radar-based gesture recognition system, named GestSAM, which leverages a parameter-free attention mechanism to effectively extract gesture features that are less affected by environmental noise. By integrating this mechanism with deep learning techniques, GestSAM significantly reduces the impact of human activity interference while maintaining robust cross-domain gesture recognition performance. This approach ensures robust, high-accuracy recognition of gestures. In order to evaluate the performance of our system, we construct a dataset containing six different gesture types performed by fifteen volunteers in seven different scenarios and simulate three interference conditions. The experimental results show that under human activity interference, the model achieves average recognition accuracies of 92.79% and 94.62% in cross-user and cross-scenario, respectively.

This paper studies the problem of integrated optical camera communication and scene sensing. Due to the tight coupling between background images and stripe information in low signal-to-noise ratio (SNR) encoded images, existing methods cannot simultaneously achieve high-quality optical signal decoding for LED-to-camera communication and background image reconstruction for scene sensing. To address this challenge, this paper analyzes the adversarial characteristics between stripe information and background images, and proposes GANOCCAS, a generative adversarial learning framework tailored for integrated optical camera communication and scene sensing that effectively resolves mutual interference between stripes and background content. First, we design a generator using a CondConv-based 4-layer U-NET architecture with SimAM modules on the last three residual layers and CondConv+PixelShuffle combinations as upsampling layers. Second, we develop a discriminator that combines multi-scale convolutional networks, pooling layers, and residual networks to output stripe sequences for optical signal decoding. Third, by leveraging pixel loss, multi-scale structural similarity loss, and adversarial loss, we ensure that the generator outputs clean background images suitable for scene sensing while the discriminator decodes optical signals for communication in complex environments. Experiments on synthetic and real-world datasets demonstrate that GANOCCAS effectively reduces communication interference from background images and accurately reconstructs stripe-free background images across various SNR scenarios, outperforming current state-of-the-art methods in both reflected OCC and scene sensing tasks.

Although radio frequency (RF)-based activity recognition has made significant progress in recent years, the sensing performance will be significantly degraded under class imbalance conditions, especially when minority and majority classes share semantically similar local motion patterns. Traditional data augmentation approaches in the original sample space may cause semantic deviation and meanwhile bring high computational cost. Instead, in this work we turn to address the issue at feature level, in which we focus on how to distinguish highly similar actions and mitigate imbalance-induced decision boundary bias. To tackle these challenges, we present attention-guided context enhancement network (AceNet), which designs a discriminative feature extractor and develops a feature-augmentation based classifier refinement strategy. Specifically, an attention-guided mechanism is presented to dynamically select the most distinctive temporal segments, and a hierarchical Transformer structure is then proposed to characterize both inner-segment micro-dynamics and inter-segment contextual relationships. Moreover, AceNet synthesizes features via Synthetic Minority Over-sampling Technique (SMOTE) to balance feature distribution for each category and then refine the classifier parameters to mitigate class imbalance bias. Comprehensive experiments on two public datasets with different RF modalities demonstrate that AceNet significantly outperforms existing approaches under various levels of data scarcity at a low cost.

Despite convenience and reliability of mmWave-based action recognition, it still raises privacy concerns on identity leakage threat since human behaviors could meanwhile expose massive user information in real-world applications. Existing solutions attempt to send anonymized features extracted from mmWave signals; however, features not only reduce the application flexibility but also increase the privacy disclosure risk due to original data reconstruction. Instead, in this paper we propose a de-identification system, AE-IPP, which customizes learned noises into the raw data to generate adversarial examples for identity privacy and action utility balance. In other words, the noises are sample-specific perturbations that are automatically learned for each sample through our presented network. To achieve the performance balance and ensure robustness to other models, we are faced with two challenges, including the decoupling of action and identity information and the transferability of models. To this end, AE-IPP focuses on respective attention areas by leveraging task-specific gradients and designs a dynamic attention mechanism to update the attention weights according to the final optimization objective. Moreover, we present a multidirectional perturbation strategy to improve the model generalization capabilities, enabling robust de-identification. Extensive experiments on mmWave datasets demonstrate the superiority of our method over state-of-the-art approaches.

Federated learning (FL) has emerged as a promising distributed learning paradigm for training deep neural networks (DNNs) at the wireless edge, but its performance can be severely hindered by unreliable wireless transmission and inherent data heterogeneity among clients. Existing solutions primarily address these challenges by incorporating wireless resource optimization strategies, often focusing on uplink resource allocation across clients under the assumption of homogeneous client-server network standards. However, these approaches overlooked the fact that mobile clients may connect to the server via diverse network standards (e.g., 4G, 5G, Wi-Fi) with customized configurations, limiting the flexibility of server-side modifications and restricting applicability in real-world commercial networks. This paper presents a novel theoretical analysis about how transmission failures in unreliable networks distort the effective label distributions of local samples, causing deviations from the global data distribution and introducing convergence bias in FL. Our analysis reveals that a carefully designed client selection strategy can mitigate biases induced by network unreliability and data heterogeneity. Motivated by this insight, we propose FedCote, a client selection approach that optimizes client selection probabilities without relying on wireless resource scheduling. Experimental results demonstrate the robustness of FedCote in DNN-based classification tasks under unreliable networks with frequent transmission failures.

This paper investigates the integration of Unmanned Aerial Vehicles (UAV) with Internet of Things (IoT) infrastructure to enhance Mobile Edge Computing capabilities in urban environments. While UAVs offer promising solutions for mobile edge computing, their deployment in high-rise urban areas presents significant challenges, particularly in computational resource balancing, energy-efficient trajectory planning, and dynamic IoT service provisioning. We propose a comprehensive low-altitude UAV-assisted mobile edge computing framework that jointly optimizes UAV trajectory planning, the assignment of offloaded tasks to specific UAVs, and the strategic deployment and energy management of the UAV fleet to maximize system utility. We first formulate this as a multi-objective optimization problem and prove its NP-hardness due to its non-convex and integer linear programming nature. To tackle this challenge, we develop a decomposition-based approach that systematically addresses the coupled variables. We then propose a novel Variable Strategy Reinforcement Learning-based Lin-Kernighan-Helsgaun algorithm that synergistically combines Q-learning, Sarsa, and Monte Carlo methods with the LKH algorithm. The proposed solution is further enhanced by incorporating two refined trajectory optimization mechanisms, the Trajectory Refining Algorithm and the Service-Oriented Segmented Trajectory Refining Algorithm, specifically designed to improve the robustness and reliability in solving the Computation Offloading Trajectory Optimization Problem. Extensive simulation results demonstrate that our proposed algorithms consistently outperform state-of-the-art approaches, achieving faster convergence, higher energy efficiency for UAVs, and lower computational latency for IoT devices.

Unmanned Aerial Vehicles (UAVs) have recently been widely used in various fields. However, both cooperative charging scheduling and insufficient charging facility problems in UAV charging scenarios have been rarely studied. This paper studies parallel cooperative charging scheduling of UAVs. We adopt cooperative charging to reduce the total cost and parallel scheduling to enable UAVs can be charged even if the number of UAVs is more than the number of charging facilities. We formulate the Parallel Cooperative Charging Scheduling for UAVs Problem (PCCSUP) for optimizing the total cost of whole charging system. We first investigate the special case of PCCSUP with single charging station, and use the approximation algorithm for Uniform Parallel Machines Scheduling Problem (UPMSP) to solve the special case. Then, a greedy approach based approximation algorithm is proposed to solve the PCCSUP, where we use the approximation algorithm for UPMSP to obtain the charging arrangements and the Set Covering Problem (SCP) optimization framework to obtain the charging groups. The results of extensive simulations demonstrate that our algorithm can reduce up to 59.81% total cost compared with the benchmark algorithms. Finally, we discuss and design the algorithms for three related problems: PCCSUP with different arrival times, PCCSUP withK-anonymity, and charging arrangements for excluded UAVs.

Integrating multimodal radiological images and clinical data is critical for survival prediction in rectal cancer. However, existing methods often lack sufficient consideration of 1) modality heterogeneity (caused by rectal peristalsis, noise artifacts, and missing modalities) and 2) site heterogeneity (caused by different imaging protocols and patient populations). These factors hinder the model from capturing reliable cross-modal relationships and adapting to distribution shifts across clinical sites. In this work, we propose UICSurv, a novel multimodal Survival prediction framework highlighted by Uncertainty-guided Iterative Contrastive fusion, to capture robust cross-site multimodal interactions while leveraging sample-level uncertainty to enhance fusion reliability. Specifically, UICSurv initializes a shared multimodal embedding and iteratively refines it by fusing each heterogeneous modality via the cross-attention mechanism. In each iteration, a novel Survival Contrastive Learning (SCL) strategy is designed to progressively enhance both cross-site alignment and survival discriminability of the multimodal embedding space. Moreover, we design an EvidenceHit module, which employs temporally consistent evidential learning to jointly estimate survival probabilities and uncertainty. The estimated uncertainty further guides the embedding alignment by reducing the interference of unreliable samples. All components operate synergistically within UICSurv to reinforce reliable survival prediction in rectal cancer. Extensive experiments on multimodal datasets of rectal cancer (collected from three sites) demonstrate the superiority of our method both in survival prediction and uncertainty estimation. The code is available open-source: https://github.com/ScorpioBao/UICSurv.

As distributed applications increasingly span geographically distributed data centers, the demand for high-performance, long-distance transmission has been continuously growing. While intra-data-center networks have employed techniques like remote direct memory access (RDMA) to meet these design goals, extending these techniques toWANs presents unique challenges. WANs notably suffer from inherent packet losses due to buffer overflows in routers and switches, leading to decreased throughput and making distributed applications barely usable. This paper proposes Lossless Transmission (LLT), a novel buffer management scheme for enabling lossless WAN transport. LLT intelligently integrates on-chip switch buffers with an off-chip caching system to absorb traffic bursts that would otherwise cause packet loss. Its data plane logic uses a multi-level threshold system to selectively offload only critical flows during congestion. A closed-loop control protocol, managed by a stateful flow table, ensures these offloaded packets are later re-injected with guaranteed lossless and in-order delivery, effectively protecting latency-sensitive applications from retransmission overhead. We evaluate LLT using both ns-3 simulations and P4-programmable devices. The experimental results show that in typical use cases (RTT > 30ms), LLT improves link bandwidth utilization by 1.9% to 29.5% and reduces the P99 percentile tail latency by 17% to 66% in WANs compared to the state-of-the-art solutions. Overall, LLT provides a scalable, efficient, and reliable framework for long-distance data transmission, addressing critical challenges in WANs. Additionally, LLT eliminates the need for expensive WAN infrastructure modifications.

Different interconnection structures exert a significant impact on network communication ability, directly influencing system performance. The half hypercube Network has an excellent topology that can provide high network fault tolerance and communication efficiency while maintaining a low node degree. In this paper, we investigate efficient and reliable communication algorithms for half hypercube networks in distributed system. Firstly, we design a disjoint path construction algorithm for a half hypercube, which enables reliable communication of the optimal number of disjoint paths between any two nodes in the network. Secondly, we present a fault-tolerant path embedding algorithm for a half hypercube. When the number of faulty nodes does not exceed ⌈n/2⌉, this algorithm can obtain a fault-tolerant unicast path between any two non-faulty nodes in ann-dimensional half hypercube network. Finally, we evaluate the performance of communication algorithms through simulation experiments and real testbed. Experimental results demonstrate that the efficiency and buffer utilization rate of the proposed algorithms can be improved by at least 21.8% and 15.6%, respectively. Testbed results show that the data delivery rate increased by 21.8%, and the path interference degree decreased by 32.5%.

Distributed AI model training generates bursty, low-entropy elephant flows that challenge existing single-path transport protocols in multi-stage Clos networks, leading to congestion and inefficiency. Multipath transport emerges as a promising solution, leveraging multiple paths to balance traffic and enhance resilience. However, current multipath RDMA solutions suffer from scalability, congestion control, and load-balancing inefficiencies. This paper introduces Unified Delay-driven Multipath Protocol (UDMP), a novel approach that co-designs congestion control and load balancing using network delay as a unified signal. UDMP employs delay-gradient-based congestion control to precisely resolve unavoidable congestion. Moreover, UDMP leverages delay-assisted load balancing to shift traffic across paths with minimal latency adaptively, maintaining throughput when encountering avoidable congestion. A novel Token Pool design integrates these components, eliminating per-path state overhead while achieving fine-grained traffic distribution. Implementations on DPDK and NS3 demonstrate that UDMP achieves up to 2x higher throughput and reduces flow completion times by up to 30% compared to state-of-the-art methods like MPRDMA and QP-Scaling. These results highlight UDMP’s effectiveness in meeting the stringent performance requirements of modern distributed AI training workloads.

The dramatic rise in encrypted traffic brings huge challenges to traditional traffic classification methods. Deep learning-based traffic classification methods have been demonstrated to significantly improve performance. However, the following limitations remain: i) It is challenging to concurrently focus on both global and local information in traffic flows, resulting in the absence of important information. ii) The existing methods relying on temporal information suffer from low robustness in case of packet disordering or loss. iii) The use of multi-layer encryption and random routing in Tor technology poses more challenges for traffic identification. In this paper, we propose a novel ViT-based model for more accurate encrypted traffic classification, called SRViT to overcome the above challenges. Firstly, SRViT proposes a novel mechanism of multi-size patch division to learn comprehensive hidden knowledge and dependencies between packets. Secondly, we propose a self-attention operation with a relative position bias to learn the relative position relationship. After that, an incremental update mechanism is proposed to adapt to dynamic changes in the real traffic environment. At last, the comprehensive experiments on 5 real-world encrypted traffic datasets are carried out. The experimental results indicate that SRViT outperforms the state-of-the-art methods with an average accuracy improvement of 24.62% while keeping higher robustness and execution efficiency.

As the demand for cloud services and data-intensive applications continues to surge, the design of efficient and reliable data center network (DCN) topologies has become increasingly critical. However, traditional DCNs often face challenges of limited scalability, insufficient fault tolerance, and high communication latency. To address these issues, we introduce SFDC, a novel recursive and modular server-centric network topology. SFDC is built on a hierarchical element-layer structure that enables the construction of highly scalable and fault-tolerant networks. The modular design of SFDC supports flexible expansion, allowing for the integration of servers with varying network interface card (NIC) configurations without requiring significant redesigns. Furthermore, SFDC’s design effectively mitigates the growth of network diameter, ensuring low latency even at massive scales. We also propose a routing algorithm, SFRouting, which leverages SFDC’s hierarchical structure to efficiently compute unicast paths, while minimizing routing complexity and enhancing data transmission efficiency. Additionally, we present a multipath routing scheme based on disjoint path construction, which ensures robust communication by providing alternative paths in case of node or link failures, thus enhancing network fault tolerance. Experimental results demonstrate that SFDC outperforms existing DCN topologies such as BCube, DCell, and HS-DCell, exhibiting superior scalability, reduced network diameter, and enhanced fault tolerance while maintaining low latency and stable performance.

MmWave radar-based person re-identification (ReID), namely catching a specified person from the database, demonstrates enormous prospects for practical applications such as public security and intelligent surveillance. Towards adaptive ReID across different scenarios, we attempt to explore abundant spatial-temporal features from point clouds for walking individuals. Most existing approaches fail to describe fine-grained 3D spatial properties associated with gaits and neglect the impacts of walking speed changes on perception. To address the two problems, we extract gait features by integrating the anchor-based orientation descriptor (AOD) and multi-scale gait catch (MGC) modules into a ReID system. Specifically, AOD automatically learns virtual anchors, selects anchor-centered neighbors from eight different subspaces and designs orientation-driven feature aggregation to elaborately describe 3D local space. Then, MGC adopts the sub-sampling strategy on AOD results to estimate multiple temporal resolution pathways reflecting relative speeds, on which sequence-specific and cross-sequence dependencies are respectively characterized by our self-attention (SA) and hierarchical attention (HA) to generate more discriminative gait representations. For evaluation, we collect mmWave ReID datasets at three scenes, and comprehensive experiments illustrate that we can maintain superior ReID performances over 90.0% Top-1 accuracy under different scenario settings. Our code and dataset are available athttps://github.com/dpjqw195/ReID-AOD-MGC

Beam alignment (BA) is a crucial process in millimeter-wave (mmWave) communications for precise directional transmission and efficient link establishment, but its open nature makes it vulnerable to eavesdropping and identity impersonation attacks. To this end, we propose a novel security framework named CovertAuth, designed to provide a unified defense against both threats. For eavesdropping attacks, we advance the existing covert communication design by extending its joint optimization of the beam training budget and transmission power to a more practical BA scenario that incorporates the mutual coupling (MC) effect in antenna array impairments and imperfect eavesdropper channel state information. For impersonation attacks, the MC effect is explored as a device feature to design a novel adaptive weight-based physical layer authentication mechanism. Theoretical models for authentication metrics like detection and false alarm probabilities are first provided to conduct performance analysis. Based on these models, an optimization problem is constructed to determine the optimal weight value that maximizes authentication accuracy. With these optimal weights, a weighted-sum energy detector is employed to achieve identity validation. The resulting adaptive authentication scheme is then integrated with the covert communication to enhance the security of the BA phase. Finally, simulation results demonstrate that CovertAuth achieves high detection accuracy while satisfying the covertness requirement, offering a comprehensive security solution for the mmWave BA stage.

Traffic measurement provides indispensable information to many applications in improving network performance. However, the limited on-chip resources face great challenges in measuring millions of flows simultaneously with high accuracy, and the highly skewed traffic distribution further worsens the performance. Although filtering the vast majority of small flows in advance can help to improve the measurement performance, existing filters have limitations in either filtering range or processing overhead. This paper proposes two efficient filters, including Swing-Size Filter for small-size flow filtering and Swing-Spread Filter for small-spread flow filtering. Both provide a flexible and extended filtering range for network traffic measurement. One key to our design is the use of signed counters whose values swing in positive and negative directions to cancel out small-size or small-spread flows, thereby enlarging the filtering range. We show that the proposed filters are highly effective in filtering small flows while keeping the advantages of low memory overhead and processing overhead. They support various measurement tasks and offer guaranteed bounds on the misreport rate. We implement our filters in both software and hardware, with the hardware version developed in P4 language on a programmable switch. Experiments based on real-world Internet traces show that our filters can reduce the flow size and flow spread estimation errors by an order of magnitude and support high throughput.

In modern datacenters, the effectiveness of end-to-end congestion control (CC) is quickly diminishing with the rapid bandwidth evolution. Per-hop flow control (FC) can react to congestion more promptly. However, a coarse-grained FC can result in Head-Of-Line (HOL) blocking. A fine-grained, per-flow FC can eliminate HOL blocking caused by flow control, however, it does not scale well. This paper presents Pyrrha, a scalable flow control approach that provably eliminates HOL blocking while using a minimum number of queues. In Pyrrha, flow control first takes effect on the root of the congestion, i.e., the port where congestion occurs. And then flows are controlled according to their contributed congestion roots. A prototype of Pyrrha is implemented on Tofino2 switches. Compared with state-of-the-art approaches, the average FCT of uncongested flows is reduced by 42%-98%, and 99th-tail latency can be$1.6\times $-$215\times $lower, without compromising the performance of congested flows.

With the rise of Network Function Virtualization, services in Mobile Edge Computing (MEC) networks can be abstracted as Service Function Chains (SFCs), which consist of an ordered sequence of Virtual Network Functions (VNFs). To provide reliable SFC-enabled services, it is common to deploy shareable backup VNF instances to protect against primary instance failures. However, the unbounded backup sharing inevitably leads to backup contention during simultaneous failures, which will increase service recovery time. To address this issue, we formulate theJoint optimization of resoUrce consumPtion and recovery tIme under probabilisTic protEction foRSFCBackuP(JUPITER-BP) in MEC networks. In this optimization, the recovery time is integrated with the expected waiting time caused by backup contention. Further, the probabilistic protection constrains the maximum number of primary instances that each backup can protect. This constraint is aimed to balance the resource consumption and recovery time. To solve JUPITER-BP, we design a Dynamic Programming-based Greedy Backup Deployment (DP-GBD) algorithm with a provable approximation ratio. Simulation results demonstrate that the proposed DP-GBD outperforms baselines in terms of resource consumption, recovery time, and running time, while satisfying reliability requirements.

The adoption of blockchain technology in healthcare has significantly enhanced data integrity, transparency, and user privacy. However, high storage overhead and resource-intensive operations remain major challenges to its widespread deployment, particularly in large-scale or resource-constrained healthcare environments. To address these challenges, we propose STORChain, a storage-optimized blockchain framework designed for data services in healthcare. The framework introduces the Clustered Merkle Patricia Tree (C-MPT), a novel logical structure that aggregates similar transaction types to maximize storage efficiency while ensuring Proof of Inclusion (PoI). A Selective Transaction Pruning Strategy (STPS) is employed to prioritize and prune essential historical data, improving data access efficiency. Additionally, an incentive-based Delegated Proof-of-Stake (DPoS) consensus algorithm is utilized, integrating a probabilistic election mechanism to promote fairness and node inclusivity. Comprehensive theoretical analysis and practical experiment results indicate that STORChain significantly reduces storage overhead, optimizes data access, and outperforms existing schemes.

We propose Meta-DPMAPPO /metə,dipi'mæpəʊ/, a a metaverse multi-user service migration framework that combines Multi-Agent Proximal Policy Optimization (MAPPO) with Differential Privacy (DP)-enabled dual-domain perturbation. To maintain usability, we incorporate trajectory topology constraints that balance privacy strength with data availability. The framework enables dynamic service migration, i.e., transferring services to follow mobile users, to ensure low-latency access while safeguarding sensitive user data. We design a migration strategy with multiple migration actions (i.e.,reuse,follow, andnomigration) to minimize global delay and improve resource utilization. We conduct a series of experiments using a combination of public, collected, and synthetic datasets. The results demonstrate that our approach significantly reduces global migration delay in multi-user environments while ensuring privacy protection, and adapts well to different metaverse application scenarios.

The rapid expansion of open-source software has also brought significant security challenges to cloud infrastructure, particularly introducing and propagating vulnerabilities. In response, effective security patch management establishes a continuous, structured pipeline by systematically identifying, testing, and deploying security patches to fix vulnerabilities. However, manually managing a large number of security patches (i.e., any update is approved and installed by hand) is time-consuming, leading to a great motivation for automating this process. Although Code Language Models (CodeLMs) have shown potential in various code-centric tasks, there remains an open question as to how well CodeLMs perform within the context of security patch management. To bridge this gap, we performed the first comprehensive empirical study on fine-tuning or prompting nine state-of-the-art CodeLMs for three security-patch-related downstream tasks, including silent patch identification (distinguishing security patches from normal commits), record-patch linking (connecting authoritative vulnerability records, e.g., CVE, to the corresponding fixing commits), and vulnerability description generation (providing a piece of text summarizing the vulnerability fixed by the patch), covering classification, ranking, and generation problems. Our findings reveal that there is no “one-size-fits-all” model that can always perform the best. Furthermore, due to the lack of task-specific knowledge, naively prompting LLMs with the basic strategies is not consistently reliable and may even underperform smaller PTMs. Additionally, existing automated evaluation metrics cannot fully reflect the capability of LLMs in considered tasks. These findings underscore the considerable gap between current capabilities and the practical requirements for deploying CodeLMs in automating security patch management.

Satellite edge computing (SEC) extends the capabilities of task offloading from the ground to near-Earth space by leveraging the wide coverage of satellites. However, the high-speed movement and long-range communication of satellites lead to Doppler shifts and signal loss, which can result in a high bit error rate (BER) and degrade communication link quality. As a result of the high BER, error correction is required, inevitably incurring additional delay during task offloading. Therefore, in this paper, we focus on the problem ofErrorCorrection awareDependencyTaskOffloading (EC-DTO) in satellite environments. By jointly considering task dependencies and BER-induced error correction delays, we formulate the EC-DTO problem as a constrained optimization problem with the objective of minimizing the makespan of users' tasks. To solve this problem, we propose APOS, which is an event-driven offloading framework based on task-AdaptivePrioritization andOptimistic-finish-time-based nodeSelection. Specifically, APOS adopts an online offloading framework that triggers scheduling upon task arrivals and completions with task dependency awareness. It iteratively updates task readiness and performs offloading decisions to reduce the overall makespan in the SEC environment. Simulation results show that APOS outperforms the representative methods, lowering the average makespan by 20.7% and the deadline violation ratio by 84.5% on average across all cases.

Adversarial Example (AE) generation is a key instrument for stress-testing and hardening malware detectors, yet most existing techniques target Portable Executable (PE) files and do not transfer cleanly to Executable and Linkable Format (ELF) binaries prevalent in Internet of Things (IoT) environments. We proposeMalElves, a reinforcement learning-driven AE generation framework for cross-platform ELF malware.MalElvesmakes three core technical contributions. First, a code-data-aware manipulation framework unifies obfuscation and rewriting across ARM, ×86, and ×64 architectures while preserving functionality. Second, a sample-efficient state design reduces 2,350 raw ELF features to a compact 21-dimensional input. Third, a shaped multi-detector reward uses fully disclosed PPO settings for full reproducibility. We evaluateMalElveson 161,414 malware samples and 74,260 benign samples. We test against four static detectors and a behavioral-ensemble stress test. The method achieves average ASRs of 89.9%, 85.3%, 63.8%, 60.6%, and 24.7% across detectors. The overall average ASR reaches 64.8%. Each successful evasion requires 2.24 interaction rounds on average.

Liquid sensing is critical for food safety and public security. Although mmWave-based approaches enable non-invasive and high-accuracy sensing, they are typically limited to single-target and fixed-container scenarios, restricting their applicability in real-world scenarios. In this paper, we present MCLiD, a multi-modal liquid sensing framework that fuses mmWave radar and camera data to achieve simultaneous multi-target and container-independent liquid identification. The basic idea is to leverage camera-captured object positions and container information to guide mmWave data processing, generating robust and discriminative liquid-specific representations for identification. MCLiD addresses a series of practical challenges and integrates three specialized modules for image-mmWave signature construction, liquid-specific feature extraction, and identification. Experimental results show that MCLiD achieves an average accuracy of 96.46 % across all combinations of 10 liquids and 7 container types. In multi-target scenarios, it maintains 96.4% accuracy for two concurrent liquids and 94.02% for five. These results indicate that MCLiD could enable rapid, non-invasive liquid detection for food safety and high-throughput public security applications.

In recent years, large language models (LLMs) have shown great potential in many fields. LLMs deployed in cloud data centers are increasingly unable to meet the low-latency inference requirements of massive mobile users. Benefiting from various LLM lightweighting techniques and the continuously improving performance of edge servers, deploying LLMs on edge servers closer to mobile users and executing inference tasks locally can effectively reduce inference latency. However, edge servers have limited storage capacity, and deploying LLMs on edge servers incurs additional deployment overhead. In this paper, we propose an efficient LLM edge collaboration deployment strategy called EdgeColl, aiming to jointly optimize inference latency and LLM deployment costs. Specifically, EdgeColl adopts Low-Rank Adaptation (LoRA) to divide each LLM into a base model and a LoRA matrix. We formulate the LLM edge collaboration deployment problem with LoRA. Then, we present the base model deployment (BMD) strategy to achieve low inference latency and deployment costs. The LoRA deployment (LMD) strategy is also proposed to enable personalized inference. We evaluate the performance of EdgeColl. The experimental results show that EdgeColl effectively reduces LLM inference latency and deployment costs.

The edge computing paradigm provides fine-grained and distributed resources to users with low service latency. To further utilize the advantage of edge computing to improve users' satisfaction, it is essential to jointly optimize service deployment, task offloading, and resource allocation. However, this is challenging because of limited edge resources, diverse task demands, and coupled decisions. In this paper, we propose EdgePro, a novel adaptive edge service provision approach based on safe deep reinforcement learning, aiming to maximize user satisfaction while fulfilling multiple constraints including deployment budget and edge server resources. Specifically, we formulate the optimization problem as a constrained Markov decision process. By designing a constraint-penalty function, we transform the original multi-constraint problem into an equivalent single-constraint problem, addressing the training oscillations caused by conflicts in satisfying multiple constraints. To handle the discrete-continuous coupled decisions, we employ multiple deep neural networks for coordinated control. Then, we propose a safe deep reinforcement learning algorithm based on augmented proximal policy optimization, which adaptively solves the formulated problem while satisfying safety constraints. Experimental results show that EdgePro significantly outperforms benchmark approaches in user satisfaction, convergence speed, and satisfying constraints.

Deep Neural Networks (DNNs) are extensively utilized in various fields due to their remarkable performance. However, as DNN models increase in complexity and size, the training process incurs substantial data transfer costs between computation and storage. The slowdown of Moore’s Law further challenges the integration of additional resources on a single chip, making it difficult to improve storage capacity and reduce off-chip data transfers. To address these challenges, we propose CAMC, a multi-chiplet DNN accelerator with a heterogeneous memory computing architecture. CAMC integrates SRAM-based in-memory computing with TSV-stacked DRAM-based near-memory computing. In addition, an efficient mapping strategy was developed to optimize resource utilization and performance. The experimental results demonstrate that CAMC enhances energy efficiency by 11.84 times and reduces data transfer costs by 8.78 times compared to the baseline design.

Centralized training for Human Activity Recognition (HAR) typically relies heavily on vast amounts of aggregated data, compromising user privacy. Federated learning (FL) for HAR offers a solution to protect local data privacy. However, existing FL methodologies often fail to fully capture the heterogeneity of user data and the latent correlations among user models, resulting in suboptimal performance and limited robustness. This paper proposes a Correlation-Aware Multi-Similarty Learning Method for Federated HAR, namely MultiSim. Our approach enhances model accuracy with an effective inter-user knowledge learning while protecting data privacy. MultiSim first constructs multiple similarity metrics, and then makes model feature fusion cunningly by the above metrics to learn inherent user similarity profiles. Additionally, we introduce a novel clustering-based FL framework by isolating malicious nodes, thereby mitigating the impact of adversarial attacks. Extensive evaluations on two realworld HAR datasets demonstrate the superiority of MultiSim over other state-of-the-art FL methods under accuracy and robustness. These findings demonstrate MultiSim's potential as a robust and effective solution for HAR.

The distributed deployment of Large Language Models (LLM) on edge servers close to users has unlocked the service provider's potential to deliver low-latency inference. To obtain more benefit by serving more resource-demanding inference tasks based on resource-limited edge servers, it is critical for the service provider to allocate inference to suitable edge servers. Three new challenges hinder existing approaches from being implemented: the distributed LLM inference requires edge servers to collaborate following a novel workflow different from other tasks; the generative nature of LLM incurs uncertainty in task resource occupations; considering the heterogeneity in users' latency requirements and service benefit, merely minimizing the total user-perceived latency can not maximize the benefit. In this paper, we make the first attempt to study the edge inference allocation problem for distributed LLM deployment while conquering these challenges. Specifically, we propose a collaborative workflow for edge servers to conduct distributed LLM inferences. Then, we estimate the resource occupations by employing Exact Conic Reformulation (ECR). Based on this, with the objective of maximizing the total service benefit, we formulate the inference allocation problem as a binary integer programming problem which is NP-hard. An approximate algorithm is proposed to find approximate solutions efficiently. Extensive experiments based on a real-world dataset demonstrate the performance of our approach.

Modern communication tools often suffer quality issues from background noise and other speakers. Existing solutions either face mask blockages or need specialized gear, limiting use on standard earphones. Vibration-based methods, focusing on below 8 kHz speech, lack high frequencies, reducing naturalness. To solve these, we present an innovative multi-sensory speech enhancement framework for commercial earphones. It uses built-in inertial measurement units (IMUs) even with ultra-low 25 Hz sampling as extra input. We developed a mathematical framework linking IMU movement data and vocal signals, incorporating unsupervised domain adaptation to reduce individual differences and guiding intermediate-data integration to connect limited-frequency IMU info with 22 kHz full-range speech.

Multi-view clustering aims to enhance clustering performance by leveraging information from diverse sources. However, its practical application is often hindered by a barrier: the lack of correspondences across views. This paper focuses on the understudied problem of fully incomplete multi-view clustering (FIMC), a scenario where existing methods fail due to their reliance on partial alignment. To address this problem, we introduce the Contrastive Prototype Matching Network (CPMN), a novel framework that establishes a new paradigm for cross-view alignment based on matching high-level categorical structures. Instead of aligning individual instances, CPMN performs a more robust cluster prototype alignment. CPMN first employs a correspondence-free graph contrastive learning approach, leveraging mutual $k$-nearest neighbors (MNN) to uncover intrinsic data structures and establish initial prototypes from entirely unpaired views. Building on the prototypes, we introduce a cross-view prototype graph matching stage to resolve category misalignment and forge a unified clustering structure. Finally, guided by this alignment, we devise a prototype-aware contrastive learning mechanism to promote semantic consistency, replacing the reliance on the initial MNN-based structural similarity. Extensive experiments on benchmark datasets demonstrate that our method significantly outperforms various baselines and ablation variants, validating its effectiveness.

The rapid development of the Industrial Internet of Things (IIoT) has raised concerns about device security and energy consumption. The widespread presence of buffer overflow (BOF) vulnerabilities in IoT devices not only threatens devices but also leads to increased carbon emissions. Traditional static analysis methods suffer from low accuracy and high costs. Mainstream binary code similarity detection (BCSD) methods are mainly based on control flow graphs (CFGs) or instructions, which often fail to effectively capture semantic information. In this paper, we propose Boreas, a semantic-aware BOF vulnerability detection framework to reduce the carbon footprint in IoT devices. The abstract syntax tree (AST) is employed to achieve precise semantic representation of multi-architecture code. By considering the often-overlooked implicit data receiving points, Boreas comprehensively locates vulnerable binaries in firmware. By leveraging backward reaching definition analysis, Boreas removes extensive code irrelevant to BOF to simplify ASTs, enables cross-function/file detection and enhances interpretability for BCSD. Additionally, we develop a deep learning model AST-BERT for BCSD, which effectively transforms simplified ASTs into vectors. Based on the distance between target and vulnerable vectors, the accurate BOF detection is achieved. To evaluate the performance of Boreas, we construct a large-scale dataset containing real-world IoT firmware. Experiments show that Boreas outperforms state-of-the-art BCSD methods with the precision of 87.08% and leading static analysis tools with the F1-score of 88.81% in vulnerability detection. Finally, Boreas successfully discovers 10 unknown critical vulnerabilities, all of which have been recognized by CVE and covered by media.

The increasing use of wireless sensor networks (WSNs) necessitates rapid detection and mitigation of system anomalies and state changes, which can be achieved through change point detection (CPD) methods. This article introduces a novel approach to detect change points in WSNs, employing an integrated multimodal method that combines three innovative feature extraction models and a learnable weighted fusion layer for evidence synthesis. Leveraging subjective logic and Dempster-Shafer theory with multivariate time series deep learning, the proposed approach establishes a prior probability distribution informed by subjective logistic loss, thus enhancing decision-making reliability by quantifying uncertainty. To address sample imbalance, this study integrates subjective logistic loss with a sample size parameter and introduces a Kullback-Leibler (KL) loss to prevent overconfident errors. A novel semi-supervised training model employing self-distillation and a multimodel KL loss is also proposed, which significantly improves accuracy and robustness. Comprehensive experiments validate the method, with accuracy improvements of up to 97.79% and 99.69% on various datasets, setting a new benchmark for CPD performance and demonstrating the method’s potential for real-world applications.

The monitoring of respiratory rate (RR), heart rate (HR), HR variability (HRV), and blood pressure (BP) during sleep allows for a comprehensive evaluation of sleep quality, facilitating the understanding and improvement of a person’s sleep health. Contactless physiological monitoring using cameras has gained popularity recently due to its convenient, infection-free, continuous, and versatile nature. However, the privacy concerns limit the application of camera-based solutions in sleep monitoring setups. This study proposes a novel hybrid setup that integrates camera-based seismocardiography (CamSCG) and photoplethysmography (CamPPG) for contactless measurement of RR, HR, and HRV during sleep while simultaneously estimating BP. For the proximal SCG, we employed camera-based laser speckle vibrometry to measure cardiac motions from the chest, and benchmarked it with a millimeter-wave radar (RFSCG). For the distal photoplethysmographic (PPG), a defocused camera was utilized to measure pulse signals from the facial skin while protecting privacy. In this setup, we analyzed the single-modality in measuring RR, HR, and HRV, and established two bi-modalities (CamSCG-CamPPG and RFSCG-CamPPG) to measure pulse transit time (PTT) features for BP calibration. The benchmark involving 19 subjects highlights the potential of camera-based bi-modal SCG-PPG for privacy-protected vital signs monitoring during sleep.

Range queries (RQ) are crucial for analytical workloads, with indexing support being essential to minimize storage accesses. However, indexing support for RQ faces several challenges. Existing tree-based indexes have suboptimal RQ performance and memory consumption when long-running RQs and short-lived updates coexist. Bitmap indexes show promise in overcoming these challenges because of their small size and their succinct and readily available query result; however, they have inherent limitations: they primarily target read-only, low-cardinality attributes. In this paper, we propose Ra nge Queries with Bit map Indexing (RABIT), a solution that addresses these shortcomings. Our design relies on three principles. First, we propose Group Encoding (GE), a novel encoding scheme that provides fast RQs and real-time updates while maintaining high compressibility. Second, we propose an efficient bitvector merging mechanism for GE. Depending on the bit density of each bitvector, we merge it in either its compressed or decompressed form, leveraging SIMD instructions when beneficial. Third, we propose a multi-layer update framework that enables lightweight multi-versioning and native index-only scans, while retaining single-versioned bitvectors, significantly reducing memory usage. Putting everything together, RABIT provides efficient point and range queries on attributes with any cardinality in tables ranging from read-only to frequently updated, unlocking the use of bitmap indexing as a general-purpose secondary index. We demonstrate that RABIT accelerates key DBMS operators (Scan, Join, and Aggregation), achieving substantial performance gains. In a row-store DBMS under HTAP workloads, RABIT offers up to 2.2x faster RQs, 530x faster updates, and 118x smaller footprint than tree indexes. In columnar DuckDB, RABIT accelerates TPC-H queries by up to 14.8x.

As the airport terminal area becomes progressively crowded, costly delays and adverse environmental impact due to excessive fuel burn require effective aircraft routing and scheduling in the airport terminal area. Aircraft routing and scheduling in the airport terminal area refers to the safe and efficient movement of aircraft among airport facilities such as runways, aircraft stands and taxiways. It is a hybrid optimization problem that involves both the airport ground movement problem and the aircraft sequencing problem. The paper investigates the hybrid problem with constraints such as the safe distance between aircraft, queuing limit at the runways, and release time difference for sake of safety. The objective is to minimize the average taxiing time of the aircraft. A particle swarm optimization and Q-learning based aircraft routing and scheduling algorithm (PSO-QL-ARS) is proposed for the problem considered. The proposal adopts the main framework of PSO. It consists of three major components: the ideal shortest path algorithm, the semi-no-wait schedule generation method and the Q-learning based particle evolving method. The shortest path algorithm takes into consideration turning time according to the turning angles on the path and different taxiing speeds depending on the taxiway types. The semi-no-wait schedule generation method is presented to compute the feasible trajectory plan for an aircraft, including the waiting time at each vertex and the taxiing time on each taxiway. It attempts to place the waiting time of an aircraft on the aircraft stand. The Q-learning-based particle evolving method employs a Q-table to select the finest evolving action which is used to update the position of the given particle. The proposed algorithm is compared with four baseline algorithms. The experimental results show that the proposal outperforms the compared baseline algorithms in effectiveness and robustness.

Multi-path transmission enables load balancing and improves network performance in data center networks (DCNs). It increases the possibility of network congestion and makes traditional network traffic engineering methods inefficient due to the uneven distribution of network traffic in data centers. In this paper, we present a reliable and efficient Disjoint paths based Multi-Path Transmission scheme (DMPT) that selects distributed requests through topology awareness. Firstly, we propose disjoint path construction algorithms through rigorous theoretical proof, aiming at the different transmission requirements of DCNs. Secondly, we offer an optimal solution to the disjoint multi-path selection problem, which is aimed at the trade-off between link load and transmission time. Furthermore,DMPTcan split the flow over multiple transmission paths based on the link status. Finally, extensive experiments are executed forDMPTon a novel EHDC of DCN that is based on exchanged hypercube. The experimental results show thatDMPTcan reduce the average running time by 18.6%, and the average path length is close to the optimal path. Furthermore, it achieves significant improvements in balancing network link traffic and facilitating deployment, which also reflects the advantages of topology aware multiplexing in practice.

Optical Data Center Networks (ODCNs) are high-performance interconnect architectures in parallel and distributed computing, providing higher bandwidth and lower power consumption. However, current optical DCNs struggle to achieve both high scalability and incremental scalability simultaneously. In this paper, we propose an extendedExchanged hyperCube, denoted by ExCube, which is a highly scalable network architecture for optical data centers. Firstly, we detail the address scheme and constructing method for ExCube, including exponential, linear, and composite scalability, which can adapt to different scalability requirements. ExCube boasts flexible scalability modes, including exponential, linear, and composite scalability, meeting diverse scalability requirements. In particular, the diameter of ExCube remains unchanged as its size increases linearly, indicating superior incremental scalability. Secondly, an efficient routing algorithm with linear time complexity is presented to determine the shortest path between any two different ToRs in ExCube. Additionally, we propose a per-flow scheduling algorithm based on the disjoint paths to enhance the performance of ExCube. The optical devices in ExCube are identical to those in existing optical DCNs, such as WaveCube and OSA, facilitating its construction. Experimental results demonstrate that ExCube outperforms WaveCube in terms of throughput and reduces data transmission time by 5%-35%. Further analysis reveals that ExCube maintains comparable performance to WaveCube across several critical metrics, including low diameter and link complexity. Compared with advanced networks, the overall cost-effectiveness and energy efficiency of ExCube have been reduced by 36.7% and 46.5%, respectively.

The field of post-quantum cryptography (PQC) is continuously evolving. Many researchers are exploring efficient PQC implementation on various platforms, including x86, ARM, FPGA, GPU, etc. In this paper, we present an Efficient CryptOgraphy CRYSTALS (ECO-CRYSTALS) implementation on standard 64-bit RISC-V Instruction Set Architecture (ISA). The target schemes are two winners of the National Institute of Standards and Technology (NIST) PQC competition: CRYSTALS-Kyber and CRYSTALS-Dilithium, where the two most time-consuming operations are Keccak and polynomial multiplication. Notably, this paper is the first highly-optimized assembly software implementation to deploy Kyber and Dilithium on the 64-bit RISC-V ISA. Firstly, we propose a better scheduling strategy for Keccak, which is specifically tailored for the 64-bit dual-issue RISC-V architecture. Our 24-round Keccak permutation (Keccak-$p$[1600,24]) achieves a 59.18% speed-up compared to the reference implementation. Secondly, we apply two modular arithmetic (Montgomery arithmetic and Plantard arithmetic) in the polynomial multiplication of Kyber and Dilithium to get a better lazy reduction. Then, we propose a flexible dual-instruction-issue scheme of Number Theoretic Transform (NTT). As for the matrix-vector multiplication, we introduce a row-to-column processing methodology to minimize the expensive memory access operations. Compared to the reference implementation, we obtain a speedup of 53.85%$\thicksim$85.57% for NTT, matrix-vector multiplication, and INTT in our ECO-CRYSTALS. Finally, the ECO-CRYSTALS implementation for key generation, encapsulation, and decapsulation in Kyber achieves 399k, 448k, and 479k cycles respectively, achieving speedups of 60.82%, 63.93%, and 65.56% compared to the NIST reference implementation. Similarly, the ECO-CRYSTALS implementation for key generation, sign, and verify in Dilithium reaches 1 364k, 3 191k, and 1 369k cycles, showcasing speedups of 54.84%, 64.98%, and 57.20%, respectively.

This paper focuses on a reconfigurable intelligent surface (RIS) enhanced wireless powered mobile edge computing network. With the assistance of an RIS, multiple passive devices (PDs) first capture energy from the radiated signals of an energy station (ES), and then use a portion of the captured energy for uplink task offloading following a hybrid time- and frequency- division multiple access (HTFDMA) protocol and another portion for local task computing. Considering that the ES and the PDs are not affiliated with the same service suppliers, we propose a partial offloading (PO) scheme that formulates an energy trading framework with price incentives through a Stackelberg game to replenish energy for PO. Specifically, the PDs as a leader receive the benefits of total computation bits with the charging payments as losses by adjusting the charging price, bandwidth and time allocation, task offloading phase shifts, PDs’ transmit power, and central processing unit frequency. Meanwhile, the ES as a follower receives the benefits of charging payments with the charging costs as losses by adapting the ES’s transmit power, power transfer phase shifts, and ES’s beamforming. Following backward induction method, the follower-level decision problem is first handled via an alternating optimization algorithm with majorization minimization. Then, based on the obtained follower-level solutions, the leader-level decision problem is tackled by introducing auxiliary variables and applying Lagrangian dual and block coordinate descent algorithm. Simulations exhibit that invoking RIS results in utility gains for both the PDs and the ES, especially when there are more RIS elements, ES antennas, and PDs.

Windows domain is regarded as a primary target for intranet penetration since a large amount of sensitive information is stored in such domain with Windows OS. However, penetration testing is a intricate and time-consuming task, which is usually dedicated to experienced experts. To alleviate and partially solve this problem, we hereby propose an automated Windows domain penetration testing framework (AWDP). Firstly, we establish the test scenario as a Markov Decision Process (MDP) and then design a simulator for the Windows Domain penetration testing with OpenAI's Gymnasium. Secondly, we implement our automated Windows Domain penetration approach with four sequential steps, collecting domain and host information, modeling with acquired data, discovering optimal attack path through Deep Q-Learning Network (DQN), and performing penetration testing actions. Finally, to validate the effects of the proposed method, we conduct tests in real deployed domains. Experimental results demonstrate that, the proposed models and algorithms in the AWDP framework exhibit robust performance. Moreover, the framework adapts to different environments with rational and efficient estimated attack paths, which eventually enables end-to-end automation of Windows Domain penetration testing.

Hybrid and patching-based fuzzing methods offer promise in uncovering software bugs using concolic execution and program transformation techniques. However, current implementations face efficiency challenges from three main factors. First, the efficacy of hybrid fuzzing can be compromised due to disruptions introduced during the mutation of inputs generated by concolic execution, hindering vulnerability discovery. Second, the speed and throughput of the underlying fuzzer significantly impact the effectiveness of both methods. Third, patching-based fuzzing has an inefficient patching system and high analysis costs. Nonetheless, hybrid and patching-based fuzzing offer complementary strengths that can enhance overall efficacy. For instance, patching can maintain exploration depth by reducing the likelihood of input structure disruption, while concolic execution can authenticate detected crashes. In this paper, we present PATCHFUZZ, which integrates fuzzing and patching at a fundamental level, leveraging concolic execution to augment the fuzzing process. Specifically, PATCHFUZZ binds patching addresses with each seed rather than the program itself, promoting frequent interactions between patching and fuzzing. Additionally, we've devised efficient methodologies for patching information management and patched program switching. Evaluation on LAVA-M, CGC and OSS-Fuzz datasets shows PATCHFUZZ surpasses state-of-the-art fuzzers like QSYM, SYMQEMU, AFL++ CmpLog and T-Fuzz. Deployed on industrial devices, PATCHFUZZ uncovered 9 new vulnerabilities.

Authentication is a crucial method for ensuring the security of the unmanned aerial vehicle (UAV)-assisted communication systems; however, it also raises concerns about the leakage of private data. To address this problem, this paper focuses on the problem of identity authentication with privacy-preserving consideration. We propose a two-way privacy-preserving physical layer (PHY-Layer) authentication framework in a UAV-assisted multiple-input multiple-output (MIMO) communication system, by exploiting the carrier frequency offset (CFO) characterizing UAV identity and CFO-based session keys constructed by elliptic curve cryptography (ECC) to encrypt data frames. In particular, we first employ a MOOSE algorithm to extract the hardware fingerprint feature related to UAV, and based on the extracted CFO feature parameters, we devise an ECC-based algorithm for a session key negotiation. To achieve identity validation for the network parties, we establish a two-way authentication framework based on the resulting CFO feature parameters, and apply the CFO-based session key to encrypt data frames for avoiding the leakage of private data. Moreover, we derive the closed-form analytical expressions for the probabilities of the detection and false alarm for the rigorous performance analysis. Finally, we provide extensive numerical results to validate the proposed theoretical model and demonstrate its effectiveness in both identity authentication and privacy preservation. In comparison with the prior scheme, the proposed framework has a better robustness and privacy.

The super-directive arrays in millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems possess some intrinsic beam pattern features like amplitude and phase errors of element excitations. We first show experiment results to illustrate the uniqueness and stability properties of the amplitude and phase error features, and apply the Rician probability density function to efficiently approximate the distributions of these features under hardware impairments. By utilizing such beam pattern features and the hypothesis testing theory, we then develop a new physical layer authentication protocol for transmitter authentication in the mmWave MIMO communication systems. Based on the theories of statistical signal processing and composite hypothesis testing, a related theoretical framework is also developed for the analytical performance modeling of the new authentication protocol under both fixed and random beam pattern scenarios. Finally, we provide simulation and theoretical results to evaluate the reliability and security of the new authentication protocol against the identity-based spoofing attack.

Quantum computers leverage qubits to solve certain computational problems significantly faster than classical computers. This capability poses a severe threat to traditional cryptographic algorithms, leading to the rise of post-quantum cryptography (PQC) designed to withstand quantum attacks. FALCON, a lattice-based signature algorithm, has been selected by the National Institute of Standards and Technology (NIST) as part of its post-quantum cryptography standardization process. However, due to the computational complexity of PQC, especially in cloud-based environments, throughput limitations during peak demand periods have become a bottleneck, particularly for FALCON. In this paper, we introduce GOLF (GPU-accelerated Optimization for Lattice-based FALCON), a novel GPU-based parallel acceleration framework for FALCON. GOLF includes algorithm porting to the GPU, compatibility modifications, multi-threaded parallelism with distinct data, single-thread optimization for single tasks, and specific enhancements to the Fast Fourier Transform (FFT) module within FALCON. Our approach achieves unprecedented performance in FALCON acceleration on GPUs, setting the highest throughput record in the history of FALCON digital signature generation and verification. On the NVIDIA RTX 4090, GOLF reaches a signature generation throughput of 420.25 kops/s and a signature verification throughput of 10,311.04 kops/s. These results represent a 58.05× / 73.14× improvement over the reference FALCON implementation and a 7.17× / 3.79× improvement compared to the fastest known GPU implementation to date. Additionally, since we have not modified the content of the algorithm, but only optimized its engineering implementation, the security of the algorithm has not changed, and the security of the original algorithm has been maintained. GOLF demonstrates that GPU acceleration is not only feasible for post-quantum cryptography but also crucial for addressing throughput bottlenecks in real-world applications.

With the development of blockchain technology, Blockchain-based Crowdsensed Data Trading (BCDT) has emerged as an attractive data exchange paradigm. Although it addresses security issues in data transactions, most recent research primarily focuses on offline scenarios, overlooking the critical importance of enabling real-time online data trading, where it suffers from dynamic worker participation and potential malicious attacks. In this paper, we propose a Blockchain-based Secure and Fair Online Incentive Mechanism (BSFOIM), which primarily incorporates a smart contract called BSFOIMToken, designed to function in online scenarios. In particular, we first introduce a multi-stage auction combined with a time discount factor in BSFOIM to quantify the contribution of workers in completing sensing tasks. Meanwhile, to ensure sensing data quality and worker selection fairness, we propose a Fairness-based Truth Discovery Mechanism (FTDM) with two core modules: a fine-grained reputation system to identify reliable workers and filter out malicious ones, and an upper confidence bound algorithm to optimize worker selection and avoid local optima. Finally, we implement these functions in BSFOIMToken and deploy a prototype on the Ethereum blockchain, demonstrating its practicality and robust performance. Rigorous theoretical and comprehensive experimental tests have proven their adherence to truthfulness, budget feasibility and individual rationality.

The Classic McEliece key encapsulation mechanism (KEM), a candidate in the fourth-round post-quantum cryptography (PQC) standardization process by the National Institute of Standards and Technology (NIST), stands out for its conservative design and robust security guarantees. Its deployment is impeded by exceptionally large public and secret keys. Modern GPUs offer abundant parallelism and global memory, making them well suited to such key sizes and to high-throughput cryptographic workloads. However, there has not been a systematic implementation of Classic McEliece on GPU platforms. This paper presents the first high-performance implementation of Classic McEliece on NVIDIA GPUs. Firstly, we present the first GPU-based implementation of Classic McEliece, utilizing a “CPU-GPU” heterogeneous approach and a kernel fusion strategy. We significantly reduce global memory accesses, optimizing memory access patterns. This results in encapsulation and decapsulation performance of 28,628,195 ops/s and 3,051,701 ops/s, respectively, for McEliece348864. Secondly, core operations like Additive Fast Fourier Transforms (AFFT), and Transpose AFFT (TAFFT) are optimized. We introduce the concept of the (T)AFFT stepping chain and propose two universal schemes: Memory Access Stepping Strategy (MASS) and Layer-Fused Memory Access Stepping Strategy (LFMASS), which achieve a speedup of 30.56% and 38.37%, respectively, compared to the native GPU-based McEliece6960119 implementation. Thirdly, extensive experiments on the NVIDIA RTX4090 show significant performance gains, achieving up to 344× higher encapsulation and 125× higher decapsulation compared to the official CPU-based AVX implementation, decisively outperforming existing ARM Cortex-M4 and FPGA implementations.

Detecting anomalous BGP (Border Gateway Protocol) messages is critical for securing inter-domain routing systems over autonomous system (AS)-level networks. The dynamic nature of routing policies, massive scale of global routes, and incomplete global topology visibility make BGP anomalies exceptionally challenging to identify—let alone trace back to malicious or misconfigured ASes. To effectively overcome these barriers, this paper proposesGraphBGP, a novel BGP anomaly detection method that dynamically constructs real-time AS-level topologies, achieves precise anomaly detection and classification, and accurately traces malicious or misconfigured ASes. Specifically, to address the evolving nature of BGP routing status,GraphBGPconstructs an attributed AS-level graph that dynamically integrates node and edge attributes. It intelligently tracks BGP updates to refresh this graph efficiently. Leveraging this enriched, up-to-date representation,GraphBGPemploys tailored detection and tracing models grounded in graph convolutional networks (GCNs), enabling precise anomaly identification and source tracing. Comprehensive experiments with real-world and synthetic datasets demonstrate thatGraphBGPachieves state-of-the-art anomaly detection accuracy while significantly reducing inference time, even under partial BGP network visibility. Furthermore,GraphBGPprecisely traces malicious or misconfigured ASes within a short time period of 7 milliseconds after anomaly detection, enabling rapid mitigation.

Blockchain transaction privacy is a highly researched topic across various application scenarios. Current privacy-preserving schemes in blockchain employ advanced cryptographic techniques, such as homomorphic encryption and zero-knowledge proofs, to balance transaction privacy with regulatory requirements. However, these schemes encounter challenges, including computational inefficiency, data expansion, and overlooked metadata privacy, such as timestamp protection. In this paper, we first propose a privacy-enhanced traceable anonymous transaction scheme based on data transaction scenarios. This scheme integrates ring signature and Merkle hash tree techniques, effectively shortening the signature size and optimizing the verification process compared to existing combinations of ring signatures and zero-knowledge proofs. A novel verifiable timestamp privacy protection method is introduced, which obfuscates timestamps to prevent tampering without compromising integrity. To enhance scalability, this method extends to multiple transaction processing scenarios and implements a timestamp-sharing strategy to reduce the computational burden. It also allows tracking authorities to monitor the long-term addresses of both transaction parties if necessary. Rigorous security analysis and extensive experimental evaluations demonstrate that this scheme achieves superior privacy, traceability, and scalability compared to existing approaches.

This paper focuses on developing a reliable physical layer authentication (PLA) scheme in an millimeter wave (mmWave) communication system. To this end, we first derive the statistical quantities of the radiation pattern with random array errors in terms of gain, phase and position, and demonstrate that both Beckmann distribution and Rice distribution can effectively characterize the distorted radiation pattern. We then design a highly reliable PLA scheme, which combines three array error features to increase the distinguishability of the radiation pattern fused these array errors, as well as creates constructive two-beam pattern transmission that can not only resist to occasional blockages of few constituent beams but also enhance the reliability of the PLA. Applying the principles of statistical signal processing and composite hypothesis testing, a theoretical framework modeling of the typical performance metrics is also established to assess the performance of the proposed novel authentication scheme, under Rice distribution approximation model for radiation pattern statistics. Finally, performance evaluation is verified the reliability, effectiveness of the proposed authentication scheme with various settings in the presence of the identity-based impersonate attack, and performance comparison is also provided to highlight performance gain using the three array errors and two-beam pattern transmission.

The rapid development of Industrial Internet of Things (IIoT) has raised wider concerns for security of IoT devices. Command injection (CI) vulnerabilities, prevalent in IoT devices, pose a severe risk for remote code execution. Traditional static detection methods suffer from high overhead and imprecision due to symbolic execution. Popular binary code similarity detection (BCSD) methods rely on Control Flow Graphs (CFGs) with redundant structures, resulting in low efficiency and accuracy. In addition, they struggle with cross-function issues. In this paper, we proposeSFO-CID, a novel structural feature optimization based command injection vulnerability discovery model for IoT devices. Through backward taint analysis, all CFGs of suspicious CI vulnerabilities within the target binary file are precisely obtained. A large amount of code unrelated to vulnerabilities is removed, and cross-function issues are covered, significantly optimizing the structural features of original CFGs. Neural networks generate embedding vectors for optimized CFGs, transforming CI vulnerability detection into a vector similarity comparison. A wealth of semantic information within the code context is automatically and efficiently captured, improving the accuracy of vulnerability detection. We collect real-world cross-platform IoT firmware as data sources for tests. Experiments show thatSFO-CIDoutperforms popular BCSD methods, such asGemini,IoTSeeker, andFIT, achieving the highest accuracy of 88.67$\%$in vulnerability detection. Compared to existing state-of-the-art static analysis methods, likeKARONTEandSaTC,SFO-CIDattains the highest precision at 88.43$\%$and F1-score at 86.29$\%$, and is less time-consuming. Until now, 8 high-risk unknown vulnerabilities have been discovered, including 5 cross-function cases, and corresponding CVE IDs were assigned.

This study addresses the Multiple Flying Sidekicks Traveling Salesman Problem (mFSTSP), where parallel Unmanned Aerial Vehicles (UAVs, or Drones) work alongside truck to enhance delivery efficiency. Existing scheduling approaches face challenges in high computational costs and the risk of converging to local optima due to excessive exploration in unknown environments, especially in large-scale mFSTSP. This study proposed a Large Language Model Enhanced Q-Learning Approach (LLM-QL) to solve mFSTSP, which combines the local exploration advantages of Q-Learning with the global understanding of unknown environments provided by LLMs, thus improving the efficiency of path planning. A novel prompt strategy is also provided, transforming the problem modeling into a format easily understood by LLMs, guiding the algorithm's exploration and significantly improving convergence. We also provide a proof of the convergence of LLM-QL. Experimental results demonstrate that LLM-QL achieves up to a 1.35 x improvement in key performance metrics such as total completion time, algorithm runtime, and UAV utilization, compared to existing state-of-the-art methods.

In mobile edge computing (MEC), edge servers and mobile terminals use federated learning distributed architecture to build a deep model, so that terminals can cooperate in training without sharing data. Distributed training requires network virtualization to provide high bandwidth and low latency characteristics to support large-scale parallel computing. Traditional virtual network embedding (VNE) relies on a static network topology, which lacks flexibility and incurs high resource costs during model training. To improve the efficiency of embedding distributed training tasks, we propose a novel Node Selection and Dynamic Topology resource allocation scheme for VNE of distributed training, NSDT-VNE, based on reconfigurable network topology. This algorithm divides the underlying network into static and dynamic topologies, enhancing low latency for small flows while providing high bandwidth for large flows as needed. Additionally, we introduce a two-phase coordinated alternating optimization algorithm that optimizes embedding decisions at both computational and topological levels, ensuring optimal node selection. Overall, NSDT-VNE follows demand-aware network design principles, allowing continuous optimization of the underlying topology. Compared to state-of-the-art heuristic and reinforcement learning-based virtual network algorithms, NSDT-VNE achieves superior performance, with request acceptance rates improving by 6.67% to 25.68% and embedding revenue increasing by approximately 7% to 32%.

WiFi based respiratory detection has attracted increasing attentions due to its ubiquity and convenience. In Non-Line-of-Sight (NLoS) scenarios, WiFi signals reflected from human target are blocked by obstacles and become much weaker, thus limiting the sensing range and hindering the practical deployment. The existing best respiratory detection system extended the sensing range by scaling and aligning dynamic components in WiFi signals. However, its dynamic component scaling causes the amplification of noise, while its dynamic component alignment increases computation complexity due to the traversal on all possible rotation angles. To address the above issues, in this paper we first build WiFi sensing range models for respiratory detection in NLoS scenario, find factors that limit the sensing range, and then propose a new respiratory detection system named RaliSense which can further rapidly extend the sensing range in NLoS scenario. The main idea of RaliSense is rapidly aligning dynamic components without amplifying noise, based on change direction vector and CSI ratio sum polarity of dynamic components. The proposed change direction vector is obtained by calculating the direction on which the noisy dynamic components have the maximum variance, and CSI ratio sum polarity is then obtained by summing the dynamic components which have been rotated by the change direction vector. According to the CSI ratio sum polarity, the rotation angle is quickly adjusted for aligning dynamic components. Extensive simulation and experiment results verify the effectiveness of our proposed sensing range models. The results also demonstrate that our proposed system RaliSense can effectively extend sensing range in NLoS scenario, achieving a 22.7% improvement over the best existing work but spending only a quarter of its computation time.

With the expansion of mobile devices and cloud computing, massive spatial trajectory data is generated and outsourced to the cloud for storage and analysis, enabling location-based mobile computing services. However, due to the sensitivity of the trajectory data, sharing it in plaintext could lead to privacy risks, especially in operations like contact queries. Thus, achieving secure and efficient contact queries based on the trajectory data in the cloud is a significant challenge. In this paper, we propose a privacy-preserving contact query processing over trajectory data in mobile cloud computing. The projection-based secure trajectory encoding is designed to convert trajectories into secure codes such that the comparison between the distance of two moving objects and the contact distance threshold is transformed into a problem of secure code matching. Adopting the secure code matching method, a baseline privacy-preserving contact query processing is proposed. To improve the query accuracy and efficiency, an amplification factor, an HTG-index and a filter table are designed for query processing optimization, based on which an enhanced privacy-preserving contact query processing is proposed. The game stimulation-based security analysis and experimental results show that the proposed query scheme is secure and performs well in query accuracy and efficiency.

Mobile edge caching network (MEN), which enables popular or reusable content caching and sharing among adjacent mobile edge devices, has become a promising solution to reduce the traffic and burden over backhaul links. Network coding (NC), represented by classical random linear network coding (RLNC), is utilized to facilitate content delivery and increase throughput in MEN. However, as the harsh decoding condition results in unacceptable time and storage overhead, classical RLNC schemes struggle to be widely deployed in practice. In this work, we propose a cost-effective NC-based content-sharing scheme based on binary sparse network coding (BSNC), called Bison, for D2D-enabled MEN. Based on the shared relationship between the binary sparse coded block (BSCB), Bison first designs a caching maintenance module to characterize the sharing progress and maintain the caching state of each edge node. Then, Bison defines a matching metric named neighbor utility to evaluate neighbors’ matching values by considering nodes’ demand and content decodability. Guiding by the metric, Bison achieves the most beneficial matching relationship among edge nodes through a proposed online matching policy. Finally, Bison devises a coded block delivery strategy to enable the sharing of valuable content between two matched edge nodes. Extensive experiments in simulations and real-world Android testbeds demonstrate its effectiveness and efficiency, wherein Bison is at least 30% less than the RLNC-based scheme on time consumption and at least 10% less than the classical BSNC-based scheme on storage overhead. The results also show that our matching policy and coded block delivery strategy can perform with a low response latency on edge and mobile devices.

With the increasing connection between internet and human society, millimeter-wave radar based action recognition and user authentication exhibit remarkable prospects in security scenarios. Existing solutions usually focus on one of the tasks and mainly emphasize accuracy without reducing the inference time. In this paper, we propose a dual-task based Polymorphic Lightweight (PolyLite) RadarNet framework, in which the shared features are fed into two split streams for different tasks under joint supervision. The polymorphic concept here means that the trained network with parallel designs can be slimmed as a single-branch structure for inference. By this design strategy, we can not only efficiently extract spatial-temporal features during the training stage but also largely improve the response speed for simultaneously testing human activities and identities. Specifically, we design triple-view (TRIview) video-like data as the input by successively concatenating the range-velocity and range-angle matrices. Then a PolyLite module with linear and lightweight designs in each branch is integrated into our RadarNet framework to learn discriminative representations. Experimental results demonstrate that our approach is able to reach the accuracy over 98${\%}$within 0.21ms inference time. Especially, untrained intruders can also be successfully identified by a simple matching computation. Our code is available athttps://github.com/MagicalLiHua/PolyLite-RadarNet.

Despite the widespread application of radio frequency (RF) signal-based human action recognition, traditional solutions can only recognize seen categories and the perception scope is restrained by the limited activity classes. When a novel category emerges, the model needs to be optimized again on additionally collected samples at the cost of computation and labor burden. To address this challenge, we develop the mmZeAR system, which learns semantic knowledge from available vision data as class attributes and then transforms the classification into a matching problem. Specifically, we build the attribute space by fusing the coarse-grained video classification features and fine-grained angle change features of 3D joint skeletons. Then we design an efficient feature extraction backbone named TriSqN, which integrates triple radar heatmaps into the final representations by sufficiently exploring the heterogeneous and complementary characteristics. Finally, a projection network is developed between semantic attributes and radar features to construct indirect relationships between samples and labels. By implementing mmZeAR on millimeter wave (mmWave) radar signal datasets, our extensive experiments have demonstrated its remarkable recognition accuracy in novel category recognition with zero effort and achieved state-of-the-art performance.

Authentication in millimeter-Wave (mmWave) Multiple-Input Multiple-Output (MIMO) systems is a critical issue due to the unique characteristics of mmWave communication, such as highly directional beamforming and the ability to support massive device connectivity. To address this challenge, this paper proposes a novel low-complexity decision-level-based Distributed Physical Layer Authentication (DPLA) framework to combat identity-based impersonation attacks in mmWave MIMO systems. The DPLA framework leverages Beam Pattern (BP) deviation, which arises from hardware-specific gain errors, as a key authentication feature. A fusion center is introduced to make the final authentication decision by aggregating local decisions from multiple collaborative nodes, enabling multi-directional perception. Specifically, a low-complexity hybrid combining fusion rule is carefully designed to accommodate the fully connected structure of mmWave MIMO systems, balancing computational efficiency and authentication performance. A rigorous performance analysis is conducted by deriving closed-form analytical expressions for the probabilities of correct detection and false alarm. Furthermore, the asymptotic detection and discrimination performance are systematically analyzed in the large-scale antenna regime. To further enhance authentication accuracy, digital signaling matrices are designed using the deflection coefficient maximization principle. The feasibility of the proposed framework is validated through a comprehensive evaluation, demonstrating its superior robustness and efficiency compared to benchmark methods.

As the demand for global computing coverage continues to surge, satellite edge computing emerges as a pivotal technology for the next generation of networks. Unlike ground-based edge computing, Low Earth Orbit (LEO) satellites face distinctive challenges, including high-speed mobility and resource limitations, etc. Therefore, effectively utilizing LEO satellites for global coverage services is crucial but challenging due to their dynamic coverage areas and diverse task requirements. To address these challenges, we introduce a novel dual-cloud edge collaborative task offloading architecture in the satellite network-assisted edge computing environment, namely,Satellite-GroundTaskOffloading (SGTO). The architecture employs a Geostationary Earth Orbit (GEO) satellite and a ground cloud computing center as satellite cloud and ground cloud, respectively, and LEO satellites as edge nodes. We formally define the task offloading problem in theSGTOwith the aim of minimizing the average latency and average energy consumption. We then propose an adaptive approach namedSGTO-Afrom the perspective of satellites to adaptively solve the problem leveraging deep reinforcement learning. Specifically, we transform the task offloading problem into a Markov decision process and adopt the generalized proximal policy optimization (GePPO) algorithm to solve the problem. Finally, experimental results demonstrate thatSGTOarchitecture andSGTO-Aoutperform the representative approaches in terms of average latency, average energy consumption and running time.

In edge computing, a user prefers offloading his/her task to nearby edge servers to maximize the offloading utility. However, this inevitably exposes the user's location privacy information when suffering from the side-channel attacks based on offloading decision behaviors and Received Signal Strength Indicators (RSSI). Existing works only consider the scenario with one untrusted edge server or defend only against one of the attacks. In this paper, we first study the edge task offloading problem with comprehensive privacy protection against these side-channel attacks from multiple edge servers. To address this problem while ensuring satisfactory offloading utility, we develop aLocationImitation-based EdgeTaskOffloading approachLITO. Specifically, we first determine a suitable perturbation region centered at the user's real location for a balance between offloading utility and privacy protection, and then propose a modified Laplace mechanism to generate a fake location meeting geo-indistinguishability within the region. Subsequently, to mislead the side-channel attacks to the fake location, we design an approximate algorithm and a transmit power control strategy to imitate the offloading decisions and RSSIs at the fake location, respectively. Theoretical analysis and experimental evaluations demonstrate the performance ofLITOin improving privacy protection and guaranteeing offloading utility.

In the field of multi-view multi-label learning, the challenges of incomplete views and missing labels are prevalent due to the complexity of manual labeling and data acquisition errors. These challenges significantly reduce the quality of latent representations and hinder prediction by multi-label classification. To address this issue, we propose a novel Category-driven Semi-supervised Contrastive Recovery (CSCR) framework in this study. Our framework aims to fully integrate existing label information into incomplete representation learning and classification. Specifically, to address the limitations posed by incomplete views and labels, we construct a label coincidence matrix based on existing labels, which serves as a similarity matrix in subsequent semi-supervised contrastive learning and multi-view classification. By leveraging this matrix, we design a semi-supervised multi-view contrastive learning module, which constructs sample pairs on the basis of inter-view correspondences and label similarity. It learns discriminative latent representations without the need for data augmentation. A weighted multi-label classification module is subsequently employed to integrate the predictions from each view to obtain the final classification result. Experimental evaluations on five challenging datasets demonstrate the superiority of our model over existing state-of-the-art methods.

The rapid development of the metaverse has brought about numerous security challenges. Virtual Reality (VR) , as one of the core technologies, plays a crucial role in the metaverse. The security of VR devices directly impacts user authentication and privacy. Currently, no attention has been paid to the vulnerabilities and security risks of VR devices. This article employs a bi-layer BiLSTM neural network to conduct a root cause analysis for user authentication and scene interaction when users enter metaverse environment using VR devices. By establishing the mapping between vulnerable VR firmware file attributes and metaverse interaction scenarios, we implement a vulnerability discovery and verification prototype called VRVul-Discovery, based on the concept of vulnerability discovery. Experiment results demonstrate that VRVul-Discovery provides high-accuracy determinations of firmware vulnerability attributes and scenarios susceptible to hijacking. In the end, the prototype system discovers seven unknown vulnerabilities, all of which are authenticated.

With the rapid increase in user demand for business traffic, the deployment and migration of virtual service function chains (VSFC) considering network load balancing has become a fascinating research topic. In this paper, we delve into the cascading failures in virtual network function (VNF) migration and design a topology-awareness fault-tolerant migration mechanism for VSFC. Firstly, we design a measurement model for the evolution of network nodes under cascading faults, which provides an evaluation method for node importance. Secondly, we present aTopology andResourceAware VNF fault-tolerant migration framework (TRA) under cascading faults. Finally, we investigate a topology-awareness energy consumption optimization algorithm based on the cascading failure. The proposed algorithm can reduce network energy consumption and alleviate network link congestion while reasonably migrating virtual machines to meet system performance requirements. Through extensive simulation experiments and real testbed evaluations,TRAreduced migration time and average latency by 28.6% and 24.7% compared with the average performance of VVi, TeaVisor, and MiOvnm, respectively. In addition,TRAhas improved revenue to expense ratio and load balancing index by 19.6% and 26.5%, respectively.

While the flexibility of programmable switches brings opportunities, it also introduces security risks. Hence, it is vital to conduct effective troubleshooting in the programmable switch to mitigate frequent network failures. However, troubleshooting programmable switch failures is challenging due to their enhanced flexibility and functionality compared to regular switches, posing increased difficulty in debugging, particularly with limited debugging tools and information. To address this problem, we propose an efficient troubleshooting method that records real-time information about packets in the data plane, including the tables involved in packet processing. Unfortunately, due to hardware limitations, it is infeasible to record all tables’ information in the data plane. Thus, the key is to find the table set reflecting the execution path a packet goes through while minimizing the resource overhead. We first represent P4 programs as a probabilistic transition directed acyclic graph (DAG) and employ information entropy to quantify the information within a set of tracked tables. Then, we adopt a two-step approach and design algorithms to find both optimal and approximately optimal table record plans. The evaluation results show the efficacy of the proposed method, including achieving the same path recovery rate as the related works with less than one-third of the resource consumption.

Mobile crowdsourcing (MCS) has attracted widespread attention by recruiting users with mobile devices to collect crowdsourcing data. Existing research on MCS assumes that the platform has sufficient users. However, platforms in their early stages of development face the cold start problem, which can lead to their inability to grow or even result in bankruptcy. While some studies try to solve it by recruiting users through social networks to participate in crowdsourcing tasks, they only focus on how to recruit more users without addressing the issue of user retention. This can lead to an increasing proportion of users losing interest in the platform and dropping out and thus it fails to solve the cold start problem truly. In light of this, we present a task recommendation-based method to recruit new users via the social network and keep registered users active on the platform. Specifically, we first use an extended independent cascade model to describe the recruitment of users through social networks. Secondly, we use a task acceptance model to describe user decisions. Finally, we utilize a fuzzy control system that incorporates spatiotemporal crowdsourcing information to predict user behaviour and recommend tasks to users most likely to complete them. Extensive experiments on large-scale real datasets were conducted to evaluate the proposed solution. The results indicate that compared to existing methods such as SocialRecruiter, our solution reduces the 30-day average user churn rate by 23.90% while significantly boosting user retention and task completion rates by up to 23.73% and 48.7%, respectively.

Congestion control protocols play a vital role in enhancing the performance of various applications within datacenter networks. While reactive congestion control (RCC) protocols are widely deployed in commercial datacenters, the research community has actively explored token-based proactive congestion control (TCC) protocols to further push the boundaries of performance. However, despite the emergence of numerous TCC variants, there has been a lack of systematic exploration in the design space of TCC. This paper aims to bridge this gap by proposing a framework for understanding the design choices within the TCC approach. In this study, we systematically analyze different design choices of TCC approaches and leverage this understanding to develop a novel TCC protocol called ToCC. To implement ToCC, we address a set of challenges and deploy it in NP-based smart NICs. We compare ToCC with state-of-the-art TCC and RCC protocols through extensive large-scale simulations and testbed evaluations. The results demonstrate that ToCC exhibits robustness in achieving low latency across various scenarios. Additionally, ToCC effectively reduces buffer occupancy by 4.8 times compared to existing approaches, and under incast scenarios, it significantly shortens flow completion time by up to 90%. Congestion control protocols are crucial for optimizing the performance of datacenter network applications. Although reactive congestion control (RCC) protocols are commonly used in commercial datacenters, researchers have been exploring token-based proactive congestion control (TCC) protocols to further enhance network performance. Despite the development of numerous TCC variants, there has not been a thorough examination of the design space of TCC protocols until now. This paper aims to address this gap by introducing a framework for understanding the design choices within the TCC approach for TCC protocols. By analyzing various design aspects of TCC approaches, we create a novel TCC protocol called ToCC. At the central of ToCC design is that it leverages congestion control mechanisms over tokens. To implement ToCC, we tackle several challenges and integrate it into NP-based smart NICs. Comparing ToCC with state-of-the-art TCC and RCC protocols through extensive large-scale simulations and testbed evaluations, we find that ToCC consistently achieves low latency across different scenarios. Moreover, ToCC significantly reduces buffer occupancy by 4.8 times compared to existing methods, and during incast scenarios, it decreases flow completion time by up to 90%.

The state-of-the-art storage structure relies on the NVMe devices and SmartNICs to provide high IO performance and low CPU overhead. In data centers, the existing data transmission control and storage methods are not ideal, resulting in long flow completion time, especially for small IO, which directly affects the performance of disaggregated storage systems. In this paper, we present Thunder, a disaggregated storage solution designed to minimize tail latency. Firstly, Thunder achieves the minimum I/O tail latency for disaggregated storage via packet-level write-through, and has an ingenious mechanism for precise semantic conversion from message level to packet level. It refers to the process of converting message level data into packet level data and ensuring the integrity and reliability of data transmission. This process involves steps such as message segmentation, addressing, acknowledgment, and reassembly. Secondly, we present a novel optimization approach for end-to-end and information transmission processes, aiming to address a range of issues such as user usage, congestion control, and system compatibility. Finally, we conducted both testbed and large-scale simulations to verify the performance of Thunder. The results show that Thunder reduced the average latency and tail latency by 71.6% and 59.7%, respectively compared to Gimbal and Timely. Furthermore, it effectively avoids queue head blocking and congestion diffusion in PFC, increasing throughput by 2.5X and reducing tail latency by an average of 49.7%.

Wireless power transmission has been widely used to replenish energy for wireless sensor networks, where the energy consumption rate of sensor nodes is usually time varying and indefinite. However, few works have investigated the problem of long-term charging scheduling with random variable. This article designs an optimization model for the long-term scheduling of chargers to maximize the time-averaged charging utility while ensuring its time-averaged constraints of budget and response rate. The Lyapunov optimization technique is adopted to transform the stochastic optimization problem into a deterministic optimization problem, which remains NP-hard. Thus, an approximation algorithm following greedy approach is proposed to solve the deterministic optimization problem. We further provide the theoretical analysis of feasibility and performance guarantee of the proposed scheduling algorithm. The simulation results show that our algorithm outperforms three comparison algorithms by 6.53%, 20.04%, and 19.97% in terms of time-averaged charging utility, as well as by 11.25%, 4.42%, and 3.73% in terms of time-averaged response rate on average.

Accurate geographical information of nodes is crucial for network applications. However, many existing positioning algorithms face challenges in achieving efficient, accurate, and robust performance when applied to irregular networks with holes or obstacles. Therefore, we introduce a new algorithm, named Network Division Positioning (NDP), to tackle this issue. In NDP, we use a similarity function to derive the distance between neighboring nodes and explore routing paths concurrently, facilitating efficient distance measurement. Next, we analyze measurement errors between landmark nodes to define a threshold that filters out incorrect distances, ensuring measuring and positioning accuracy. To enhance robustness, we first identify collinearity issues by examining the positional relationship between unpositioned nodes and their nearest landmark. Subsequently, we addressed the poor positioning results and built the subnetwork utilizing the nearest landmark node and its associated measurement distance, seeking the most accurate and robust estimated position within this subnetwork. The simulation results demonstrate that NDP outperforms state-of-the-art algorithms in terms of efficiency, accuracy, and robustness when dealing with various irregular networks. Specifically, NDP enhances positioning accuracy by at least 40.82% in terms of the median.

In the application of the Internet of Things (IoT), existing cloud edge collaboration technologies face the problem of poor coordination of heterogeneous resources. In this article, we proposeCFEMC, which is a novelCloud-Fog-EdgeMulti-layerCollaboration resource scheduling framework for IoT. First, we design a collaborative resource scheduling framework based on semi-distributed artificial intelligence. It can achieve collaborative optimization of cloud/edge computing resource allocation under the constraints of high reliability and low latency. Second, we present a workflow applications scheduling strategy based on the proposed collaborative resource scheduling framework. This can solve the problem of unstable computing performance and transmission bandwidth during the scheduling process. Finally, the extensive and real data supported simulation results show thatCFEMChas advantages in terms of energy consumption, delay and throughput compared with other benchmark strategies. Against CEC Hu et al. 2023 and PSO Zeng et al. 2022, the average throughput increases by 16.37% and 24.21%, and the total queuing delay decreases by 54.23% and 58.12%, respectively.

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% .

Edge computing enables low-latency services by deploying application instances near users. Application vendors tend to serve more users with higher service satisfaction and a limited budget. This raises a critical yet open problem - optimally deploying application instances and allocating users to edge servers to maximize service utility while fulfilling multiple constraints, including user latency requirements, budget limitations, and edge resource constraints. To address this problem, we formulate it as a constrained optimization problem that jointly solves two tightly coupled sub-problems:ApplicationDeployment andUserAllocation (named as ADUA problem). The objective is to maximize overall service utility by optimizing resource utilization and service satisfaction under latency, budget, and resource constraints. We reformulate the ADUA problem as a multi-armed bandit (MAB) problem and then propose a novel approach namedUMESPbased on penalized MAB framework. Specifically, we design a marginal utility–aware reward function to align bandit learning with the optimization objective. We introduce a penalty mechanism to effectively handle constraint violations. By further integrating the upper confidence bound policy,UMESPachieves adaptive and constraint-aware balance between exploration and exploitation. Theoretical analysis demonstrates thatUMESPachieves bounded regret. Extensive experiments verify thatUMESPexhibits stable convergence in all tests and, on average, outperforms six representative approaches in overall service utility.

Unmanned aerial vehicles (UAVs) are increasingly utilized in smart city applications, particularly for urban surveillance. UAVs can be provisioned as mobile surveillance to avoid various difficulties in ground operations and reduce extensive labor cost. However, their limited energy capacity restricts flight time and coverage, making it difficult to build a large-scale, long-term city-wide monitoring network. To address this problem, an ubiquitous public transportation network is introduced for UAVs to periodically recharge by landing on public transportation buses. We propose a novel public transportation-assisted UAV scheduling framework that leverages the existing bus network to enable recharging of UAVs. Two intertwined sub-problems are addressed: the UAV trajectory planning and the surveillance task offloading problems. The trajectory planning is modeled as a Traveling Salesman Problem (TSP) and it is solved via the Lin-Kernighan heuristic (LKH), decomposing the bus station network into sub-graphs for efficient routing. For task offloading, a time-slot-based scheduling method is proposed that dynamically assigns UAVs to monitor points of interest (PoIs) while ensuring energy constraints and full coverage. Experimental results demonstrate that the proposal outperforms baseline algorithms, achieving a 1.72%-3.46% average extension in system lifetime compared to state-of-the-art baselines, while maintaining computational efficiency (average runtime: 277.57 ms). The robustness of the proposal is further validated across diverse testing instances with various parameter settings.

Current RF-based solutions have demonstrated that human heartbeat activity induces millimeter-scale chest displacements, changing RF reflection paths and making contact-free heartbeat monitoring possible. However, human heartbeat activity is very weak and can be hidden by out by the body movements or breathing, which poses significant challenges in accurately extracting heartbeat information. To solve this, in this paper we propose a contact-free heartbeat rate estimation system based on the periodic variation feature, namely UWHeart. UWHeart utilizes Impulse Radio-Ultra Wideband (IR-UWB) to capture reflection signals from the monitoring target and outputs the heartbeat rate. This system first filters out the noise unrelated to heartbeat through band-pass filtering. Then, we leverage the periodicity of heartbeat activity to construct autocorrelation matrix features of the denoised signal. Finally, we develop a one-dimensional Temporal Convolutional Network (TCN) model to realize the mapping between the heartbeat autocorrelation features and actual heartbeats, and accurately estimate the heartbeat rate of the monitoring target. We also implement the UWHeart system on commercial IR-UWB equipment to verify our method through a series of experiments, and the experimental results prove the effectiveness of our method.

Alzheimer’s Disease (AD) is the primary cause of dementia in the elderly. Utilizing advanced Graph Neural Networks (GNNs) to map the relationships between various brain regions and the AD using structural magnetic resonance imaging (sMRI) is one of the effective diagnostic methods. However, current GNN-based methods suffer from significant data redundancy, resulting in high computational costs and overlooking the critical role of connectivity between different brain regions. To solve this, in this paper we propose a lightweight Alzheimer’s disease diagnosis method based on Gaussian Graphical Lasso (GLasso) estimation. Our method first compresses features from traditional MRI and maps them to corresponding Regions of Interest (ROIs) using the Automated Anatomical Labeling (AAL) template. Subsequently, a Gaussian Graphical Model (GGM) is employed to construct the graph network, and iterative GLasso estimation is applied to retain significant ROI connections while removing redundant and irrelevant connections. Finally, a graph transformer network with edge features is used to achieve efficient diagnosis of AD symptoms. Extensive testing on the open-source dataset ADNI demonstrates that our method achieves precise diagnostic results, outperforming state-of-the-art approaches, and significantly reduces training costs. Furthermore, it effectively identifies critical region connections associated with AD, providing a feasible solution for the early diagnosis of Alzheimer’s disease.

Malicious attackers frequently aim to partition the network into disjointed segments to facilitate specific attacks. Consequently, enhancing network reliability stands as an effective preventive measure. Connectivity serves as a crucial metric for gauging network reliability, yet classical connectivity inadequately captures a network's fault tolerance in the face of such attacks. To address this, cluster connectivity has been proposed, considering the faults within clusters to improve fault tolerance assessment. In this paper, we establish the cluster connectivity of the half hypercube network HHn. In detail, we show that the K1,1-cluster connectivity of HHnis $\left\lfloor {n/2} \right\rfloor + 1$, where n ≥ 3, and the K1,r- cluster connectivity of HHnis $\left\lceil {\frac{{\left\lceil {n/2} \right\rceil }}{2}} \right\rceil + 1$, where n ≥ 5 and 2 ≤ r ≤ 4, which is almost r times the classical connectivity. This indicates that the network possesses an enhanced capacity to accommodate a greater number of faulty nodes, potentially enabling more effective orchestration of attacks.

As a centralized location for computer systems, data centers provide high-performance computing hardware, storage devices, and network facilities for collaborative computing. The node-disjoint paths can be used to implement multi-path transmission in data center networks, which provide multiple high-quality transmission paths and improve the performance of the network. Moreover, the disjoint paths can also provide redundant transmission paths, which enhance the fault tolerance of the network. The EHDC network is a novel server-centric and highly scalable data center network based on exchanged hypercube, and its logical structure is ED(s, t). In this paper, we propose the algorithm NDPath to construct the node-disjoint paths between two distinct nodes when the two nodes are in the same EDs in ED(s, t). Moreover, we analyze the maximum length of the disjoint paths. Experimental results show that our proposed algorithm performs better than the classical algorithm Dijkstra in the Average Running Time (ART) and is very close to that in the Average Path Length (APL).

In the era of information explosion, the scale of data center networks (DCNs) has expanded exponentially, consequently leading to an inevitable increase in server failures. Therefore, how to ensure the efficient and secure operation of the network has emerged as a critically important research topic. WaveCube is a scalable, fault-tolerant, high-performance optical DCN architecture. In this paper, we first propose a local secure model (LS model) of WaveCube. This model segments fault-free nodes within sub-Wavecube by imposing specific constraints, thereby adeptly circumventing potential communication impediments that could arise due to faulty nodes. Secondly, based on this model, we design a protection routing with secure mechanism to ensure stable communication within WaveCube. Finally, we perform a series of experiments, and the results show that when the number of faulty nodes is less than half of the number of total nodes, the hit rate can reach nearly 100%, while the shortest path rate can achieve up to 90%.

Discrete-event simulation (DES) is a prevalent tool for evaluating network designs. Although DES offers full fidelity and generality, its slow performance limits its application. To speed up DES, many network simulators employ parallel discrete-event simulation (PDES). However, adapting existing network simulation models to PDES requires complex reconfigurations and often yields limited performance improvement. In this paper, we address this gap by proposing a parallel-efficient and user-transparent network simulation kernel, Unison, that adopts fine-grained partition and load-adaptive scheduling optimized for network scenarios. We prototype Unison based on ns-3. Existing network simulation models of ns-3 can be seamlessly transitioned to Unison. Testbed experiments on commodity servers demonstrate that Unison can achieve a 40× speedup over DES using 24 CPU cores, and a 10× speedup compared with existing PDES algorithms under the same CPU cores.

Integrated Sensing and Communication (ISAC) is gaining a tremendous amount of attention from both academia and industry. Recent work has brought communication capability to sensing-oriented mmWave radars, enabling more innovative applications. These solutions, however, either require hardware modifications or suffer from limited data rates. This paper presents Talk2Radar, which builds a faster communication channel between smartphone speakers and mmWave radars, without any hardware modification to either commodity smartphones or off-the-shelf radars. In Talk2Radar, a smartphone speaker sends messages by playing carefully designed sounds. A mmWave radar acting as a data receiver captures the emitted sounds by detecting the sound-induced smartphone vibrations, and then decodes the messages. Talk2Radar characterizes smartphone speakers for speaker-to-mmWave radar communication and addresses a series of technical challenges, including modulation and demodulation of extremely weak sound-induced vibrations, multi-speaker concurrent communication and human motion suppression. We implement and evaluate Talk2Radar in various practical settings. Experimental results show that Talk2Radar can achieve a data rate of up to 400bps with an average BER of less than 5%, outperforming the state-of-the-art by approximately 33×.

New Wi-Fi systems have leveraged beamforming to manage a significant portion of traffic for achieving high throughput and reliability. Unfortunately, this has amplified certain security risks since beamforming critically relies on the clear-text beamforming feedback information (BFI): though similar risks have been exposed using emulation platforms (e.g., USRP), they have never proven realistic till this day. In this paper, we propose BeamCraft, the first attack to manipulate traffic in commodity Wi-Fi systems; it differs significantly from existing attacks either staying only on emulation platforms with limited real-world applicability or jamming communications by brute force. The core idea of BeamCraft involves corrupting beamforming decisions by injecting crafted BFIs that feed an access point (AP) with erroneous information on channel states. To mount a covert yet purposeful attack, we develop i) a joint location and transmit power selection strategy to evade detection by victims and ii) a novel BFI forgery method to effectively manipulate AP's beamforming decisions. We implement BeamCraft using commodity Wi-Fi devices and perform extensive evaluations with it; the results reveal that BeamCraft effectively manipulates Wi-Fi traffic while maintaining a low exposure rate.

Continuous authentication is increasingly critical for cyber security. However, existing approaches are time-inefficient due to their simple signal modulation with low-effective feature extraction throughput. In this paper, we propose a continuous authentication technique, OnePiece. OnePiece is free from the requirement of in-ear microphones, which are necessary for existing earphone authentication systems. It exploits out-ear microphones for biometrics extraction, which are ubiquitous on off-the-shelf earphones. We analyze the acoustic response model of ears towards out-ear microphones via the air, which is different from that towards in-ear microphones. A frequency-varying ultrasonic modulation scheme is proposed to characterize in-depth ear biometrics in user-friendly, error-free, and time-efficient ways. Therefore, OnePiece enables quick-pass authentication once users wear the earphones, followed by continuous authentication covering the whole course. Moreover, we propose a wake-up mechanism to reduce the consumed power, which addresses the key power consumption issue in ultrasonic sensing techniques. Particularly, OnePiece can be smoothly deployed on off-the-shelf wired and wireless earphones. It performs good cross-device performance in which users just register only once. Extensive evaluations are conducted to validate its effectiveness under real-world scenarios.

Traditional indoor location data sharing methods using centralized servers face issues like safe and reliable transmission, personal privacy leaks, location information tampering, and computing and storage loads, hampering the growth of personalized indoor services. In this paper, a novel mobile blockchain-enabled federated learning (MBFL) data sharing framework for indoor positioning is presented. Then, we derive training latency and reward of the individual user, and formulate latency-limited resource allocation as a non-cooperative game. We propose an efficient alternating iterative algorithm to achieve the Nash equilibrium of this game. Numerical results demon-strate that the proposed alternating iterative algorithm achieves rapid convergence. Furthermore, when confronted with model poisoning attacks, the MBFL method exhibits superior security performance compared to the traditional FL method.

Summary With the continuous expansion scale of data center networks (DCNs), the probability of network failures becomes high. Trustworthy fault‐tolerant routing is extremely significant for reliable communication in data centers. In this article, we tackle the challenge by proposing a novel fault‐tolerant routing scheme for a torus‐based DCN. First, we present a multipath information transmission model based on the trust degree of reachable paths and propose a novel Hamiltonian odd–even turning model without deadlock. Second, we design an efficient deadlock‐free fault‐routing algorithm by constructing the longest fault‐free path between any two fault‐free nodes in DCN. Extensive simulation results show that the proposed fault‐tolerant routing outperforms the previous algorithms. Compared with the most advanced fault‐tolerant routing algorithms, the proposed algorithm has a 21.5% to 25.3% increase in throughput and packet arrival rate. Moreover, it can reduce the average delay of 18.6% and the maximum delay of 23.7% in the network respectively.

The purpose of malicious traffic detection and identification in the Internet of Things (IoT) is to detect the intrusion of malicious traffic within the IoT network into IoT devices. Detection and identification play a key role in ensuring the security of the IoT. At this time, great success has been achieved with deep learning in the field of malicious traffic detection and identification. However, due to resource limitations, such as computation weaknesses and low-edge network node storage capacity in the IoT, a high-complexity model based on deep learning cannot be deployed and applied. In this article, we propose a lightweight malicious traffic detection and recognition model named lightweight knowledge distillation space time neural network (LKD-STNN) based on knowledge distillation (KD) deep learning for the IoT. We use KD to build a lightweight student model by depthwise separable convolution and bidirectional long short-term memory (BiLSTM) to realize a lightweight student model and obtain multidimensional characteristic information. According to the characteristics of KD, we propose an adaptive temperature function that can adaptively and dynamically change the temperature during the process of knowledge transfer so that different softening characteristics can be obtained during the training process. Then, the weight is updated by combining loss functions to improve the performance of the student model. The experimental results show that with the publicly available malicious traffic data sets for the IoT, the ToN- IoT and IoT-23, our model not only reduces the complexity of the model and the number of model parameters to less than 1% of the teacher model but also reaches an accuracy of more than 98%, indicating that our model can be applied to the multiclassification identification of malicious traffic in the IoT.

Abstract Public transportation system is one of the most effective ways to conserve energy and reduce carbon emissions. However, the traditional public transportation system does not provide customized service and cannot guarantee the arrival time to destination. To address these issues, we formulate the minimum shared bus scheduling problem to minimize the number of shared buses such that all orders can be completed under constraints of deadlines and capacity of shared bus. We propose the approximation algorithms, S‐MBSA for the shared bus with strong endurance and E‐MBSA for the large‐scale order scenario, to solve the minimum shared bus scheduling problem. We further formulate the constrained maximum revenue shared bus scheduling problem to maximize the revenue under the limited number of shared buses, and propose an approximation algorithm, CMRBSA, to find the shared bus route schedules. Through the extensive simulations, we demonstrate the significant superiority of S‐MBSA and E‐MBSA in terms of number of shared buses. Furthermore, CMRBSA outperforms the benchmark algorithms significantly in terms of revenue.

3D tori are significant interconnection architectures in building supercomputers and parallel computing systems. Due to the rapid growth of edge faults and the crucial role of path structures in large-scale distributed systems, fault-tolerant path embedding and correlated issues have drawn widespread researches. However, existing path embedding methods are based on traditional fault models, allowing all faults to be near the same node, so they usually only focus on theoretical proof and generate linear fault-tolerance related to dimension$n$. In order to improve the fault-tolerance of 3D torus, we first propose a novel conditional fault model called the Locally Faulty Block model (LFB model). On the basis of this model, the Hamiltonian paths with large-scale edge defects in torus are investigated. After that, we construct an Hamiltonian path embedding algorithm HP-LFB into torus with$O(N)$under the LFB model, where$N$is the number of nodes in torus. Furthermore, we present an adaptive routing algorithm HoeFA, which is based on the method of distance vector to limit the use of virtual channels (VCs). We also make a comparison with state-of-the-art schemes, indicating that our scheme enhance other comprehensive results. The experiment indicated that HP-LFB can sustain the dynamic degradation of the batting average of establishing Hamiltonian paths, with the added faulty edges exceeding fault-tolerance.

With the development of the smart traffic, the traditional vehicular Ad hoc Networks (VANETs) and Traffic Estimation and Prediction System (TrEPS) do not satisfy the growing safety requirement, due to the network delay, transmit price and privacy security. In this paper, we propose a group-vehicles oriented reputation assessment scheme for edge VANETs. Firstly, based on edge computing, we build a reputation assessment framework for Group-Vehicles, to validate the correctness of message for other vehicles rapidly. Secondly, through filtering the malicious feedback and faulty message, our scheme can effectively defend against the Bad-mouth attack and Zigzag attack to assure the security of VANETs. Thirdly, the message isolation is implemented by the group-vehicles management, to enhance the privacy security of scheme. In the end, we validate the effectiveness of our scheme through experiments. In other words, even though the proportion of Bad-mouth attack vehicles is about 40%, the precision is 92.12%, and the recall is 88.25%. Also, the proportion of Zigzag attack vehicles is about 40%, the precision is 88.52%, and the recall is 86.75%.

This paper exploits two intrinsic hardware-specific fingerprints in terms of carrier frequency offset (CFO) and phase noise (PHN) to propose a two-dimensional physical layer authentication (PLA) scheme in the unmanned aerial vehicle (UAV)-aided communication systems. By leveraging expectation conditional maximization (ECM), extended Kalman filtering (EKF) algorithms and binary hypothesis testing, we first extract the inherent hardware impairments of UAV-aided systems including CFO and PHN as PHY-layer fingerprints to establish an authentication framework. To accurately characterize authentication performance, we examine the hybrid Cramér-Rao lower bound (HCRLB) for individual estimators of CFO and PHN, and then theoretically derive the analytical expressions for the false alarm and detection probabilities by utilizing tools from statistical signal processing. Finally, extensive numerical results are provided to validate the correctness of the developed theoretical models and to illustrate the authentication performance of the proposed scheme under various system parameters.

Human-machine interactions (HMIs), e.g., touchscreens, are essential for users to interact with mobile devices. They are also beneficial in resisting emerging active attacks, which aim at maliciously controlling mobile devices, e.g., smartphones and tablets. With touchscreen-like HMIs, users can notice and interrupt malicious actions conducted by the attackers timely and perform necessary countermeasures, e.g., tapping the ‘Quit’ button on the touchscreen. However, the effect of HMI-oriented active attacks has not been investigated yet. In this paper, we present a practical attack towards touch-based devices, namely Expelliarmus. It reveals a new attack surface of active attacks for hijacking users’ operations and thus taking full control over victim devices. Expelliarmus neutralizes users’ touch commands by producing a reverse current via electromagnetic interference (EMI). Since the reverse current offsets the current change caused by a touch, the touchscreen detects no current change and thus ignores users’ commands. Besides this basic denial-of-service attack, we also realize a target cancellation attack, which can neutralize target commands, e.g., ‘Quit’ without interference in irrelevant operations. Thus, the active attack can be completely performed without interruption from users, even if they are alerted by the abnormal events. Extensive evaluations demonstrate the effectiveness of Expelliarmus on 29 off-the-shelf devices.

Cooperative jamming (CJ) is a promising technique for enhancing the physical-layer security in wireless powered sensor networks. The secrecy performance of CJ-aided wireless powered sensor networks is affected by three issues including disguised eavesdropper as cooperative node, estimation error of the channel between sink node and each sensor node, and distance-related limitation on the transmit power of cooperative nodes. To address the above issues, this paper proposes a CJ-aided secure communication scheme for wireless powered sensor networks with disguised eavesdropper and imperfect channel estimation. Since each cooperative node could be the disguised eavesdropper, the proposed scheme incorporates all possible cases of the eavesdropper disguising itself as unfixed cooperative jamming node. The maximization of secrecy rate over all possible cases is formulated for designing secrecy optimization problem. The imperfect channel estimation and distance-related jamming power limitation are both integrated as constraints into the secrecy rate maximization problem. Since the original optimization problem is complex and non-convex, a two-level optimization algorithm is proposed to solve it. Simulation results show that the proposed scheme achieves significant secrecy rate improvement over typical existing schemes.

Edge-cloud networks face security threats during data collection, data routing, and service construction, resulting in data tampering, stealing, and communication interruption. Trust mechanism can predict data quality and cooperation probability of nodes before purchasing data or establishing cooperation, so as to select trusted participants for data perception and interaction. However, there are some problems with existing trust methods, such as limited evaluation scope, incomplete trust evidence, and inaccurate evaluation results. To address these issues, a Trust mechanism-based Multi-Tier Computing system (TMTC) is proposed in this paper. Specifically, we propose a two-tier trust evaluation model. At the data collection layer, it conducts trust evaluation on data reporters based on data submission and communication interactions. At the network layer, it evaluates trust of routers through path backtracking verification, multi-service analysis and coincident path analysis. Then, based on evaluation results, a differentiated trust detection is initiated for normal and abnormal nodes. And high-frequency detection tasks are initiated for malicious nodes to improve accuracy, sparse detection tasks are initiated for normal nodes to reduce costs. Finally, extensive experiments conducted on the synthetic and real-world datasets demonstrate that, TMTC can resist data tampering and good-bad mouth attacks effectively. And whether in a dense or uniform scene, it outperforms two benchmark methods by increasing malicious node detection rate by 13.37%-21.87% and reducing cost by 18.8%-50.32%.

Machine Learning (ML) techniques can facilitate the automation ofmalicious software(malware for short) detection, but suffer from evasion attacks. Many studies counter such attacks in heuristic manners, lacking theoretical guarantees and defense effectiveness. In this article, we propose a new adversarial training framework, termedPrincipledAdversarial MalwareDetection (PAD), which offers convergence guarantees for robust optimization methods. PAD lays on a learnable convex measurement that quantifies distribution-wise discrete perturbations to protect malware detectors from adversaries, whereby for smooth detectors, adversarial training can be performed with theoretical treatments. To promote defense effectiveness, we propose a new mixture of attacks to instantiate PAD to enhance deep neural network-based measurements and malware detectors. Experimental results on two Android malware datasets demonstrate: (i) the proposed method significantly outperforms the state-of-the-art defenses; (ii) it can harden ML-based malware detection against 27 evasion attacks with detection accuracies greater than 83.45%, at the price of suffering an accuracy decrease smaller than 2.16% in the absence of attacks; (iii) it matches or outperforms many anti-malware scanners in VirusTotal against realistic adversarial malware.

The widespread use of smartphones has yielded a wealth of behavioral sequence data from user interactions. These interactions offer insights into user preferences and patterns for personalized behavior prediction. However, there are some challenges in current privacy-preserving works for analyzing these data. These approaches have suboptimal service quality with smaller but longer datasets and insufficient emphasis on secure pattern storage and retrieval in real-world applications. To handle these challenges on smartphones, we propose a novel Privacy-enhanced Frequent Sequence Mining and Retrieval (PrivFSMR) framework for this scenario. Specifically, we first introduce a dynamic sequence truncation to anonymize the maximum sequence length of datasets. Following this, we design a privacy-enhanced FSM algorithm to uncover patterns, effectively reducing the privacy budget by integrating differential privacy and the Markov assumption. During the secure pattern storage, PrivFSMR employs symmetric encryption for protection and constructs an encrypted index forest for retrieval. Lastly, future behavior retrieval leverages current device information and the index forest to search similar patterns, thereby predicting potential user behaviors in the future. A comprehensive security analysis proves the PrivFSMR framework guarantees differential privacy and maintains storage and retrieval confidentiality in the lifecycle. In addition to using two publicly available datasets, we also collected a real behavior dataset within 2-4 weeks from 30 users for evaluation. Experimental results on three datasets demonstrate that PrivFSMR excels in mining frequent patterns and predicting future behaviors compared to existing approaches.

Advancements in quantum computing pose a threat to public-key cryptosystems, leading to the development of post-quantum cryptography. NIST is standardizing candidate algorithms, with BIKE, a code-based key encapsulation mechanism, among those under consideration. Performance is crucial in NIST PQC standardization process, and researchers have introduced a range of optimization techniques for BIKE across various platforms. To the best of our knowledge, our Efficient CryptOgraphy BIKE (ECO-BIKE) represents the first attempt at optimizing the implementation of BIKE on GPU architecture. In this paper, we introduce a comprehensive construction of a 3-threading parallel architecture tailored for the BIKE cryptosystem. This architecture covers a range of computational tasks, addressing operations from low-level to high-level computations. These include a parallel dense polynomial multiplication scheme with a better memory access pattern and a better XOR calculation, which forms the basis for a comprehensive parallel execution framework for the entire BIKE algorithm. Targeted optimizations are implemented for specific modules (KEYGEN, ENCAPS, DECAPS), which collectively enhance the overall efficiency of the algorithm. Our ECO-BIKE exhibits exceptional throughput performance on the NVIDIA GeForce RTX 4090. In the 3-thread mode, the throughput of the KEYGEN, ENCAPS, and DECAPS modules reaches 24.033 kops/s, 277.789 kops/s, and 5.817 kops/s, respectively. Our proposed optimal parallel multiplication scheme achieves a significantly higher overall throughput of 481.302 kops/s. These results highlight the substantial computational advantages our approach provides for cryptographic workloads.

This paper exploits the efficient channel modeling and channel sparsity to propose a novel Physical (PHY)-layer authentication framework for a Millimeter Wave (mmWave) Multiple-Input Multiple-Output (MIMO) Unmanned Aerial Vehicle (UAV)-ground system. Inspired by the Image Processing theory, we first explore a new Laplace prior approach for the efficient modeling of angular-domain mmWave MIMO channels. With the help of the new channel model, we then reveal the channel sparsity in the concerned system exhibiting a nice spatial correlation property. By a joint use of channel sparsity correlation, efficient sparsity feature extraction with Expectation Maximization (EM)/Generalized Approximate Message Passing (GAMP) algorithms and hypothesis testing, we thus devise a new authentication framework for the concerned system. Theoretical performance analysis is also carried out by deriving the closed-form expressions for false alarm and detection probabilities. Finally, extensive numerical results are provided to validate the feasibility of the proposed channel model and the theoretical analysis, as well as to demonstrate the efficiency of the new authentication framework under various scenarios.

Recently, Hyun et al. have utilized simplicial complexes to construct several infinite families of binary minimal and optimal linear codes. Building upon their work, we draw inspiration and extend their research by constructing codes over the ring$\mathbb {Z}_{4}$with the aid of simplicial complexes. In this paper, we present two infinite families of quaternary codes, one of which is linear while the other is nonlinear. We analyze the Lee weight distributions of the resulting quaternary codes and compare them with the existing database of$\mathbb {Z}_{4}$codes. Our findings reveal the discovery of several new quaternary codes. Furthermore, we also provide two classes of binary codes that can be obtained from these quaternary codes using the Gray map.

Recently, simplicial complexes are used in constructions of several infinite families of minimal and optimal linear codes by Hyunet al. Building upon their research, in this paper more linear codes over the ring Z4are constructed by simplicial complexes. Specifically, the Lee weight distributions of the resulting quaternary codes are determined and two infinite families of four-Lee-weight quaternary codes are obtained. Compared to the databases of Z4codes by Aydinet al., at least nine new quaternary codes are found. Thanks to the special structure of the defining sets, we have the ability to determine whether the Gray images of certain obtained quaternary codes are linear or not. This allows us to obtain two infinite families of binary nonlinear codes and one infinite family of binary minimal linear codes. Furthermore, utilizing these minimal binary codes, some secret sharing schemes as a byproduct also are established.

The Industrial Internet of Things (IIoT) integrates diverse wireless and heterogeneous devices to enable time-sensitive applications. Multi-access edge computing (MEC) offers computing services for nearby tasks to meet their time requirements. However, offloading a large number of tasks to servers with minimal time is a challenging issue. Existing approaches typically allocate tasks into equal-length timeslots for offloading based on optimization or heuristic methods, overlooking the time-varying nature of task arrival density. This neglect significantly increases task execution time. To address this problem, we propose an Adaptive Task Offloading scheme with two-stage hybrid Matching (ATOM). In ATOM, a global buffer with an adjustable threshold is employed to store task information, enabling it to adapt to the time-varying arrival density and execute different offloading stages accordingly. In the online matching stage, if the threshold is not reached, tasks in the buffer are promptly offloaded to the most suitable server. In the offline matching stage, when the threshold is exceeded, all tasks in the buffer are optimally matched with servers and offloaded in batches. Experimental results demonstrate that ATOM outperforms state-of-the-art schemes in terms of average execution time and timeout rate, achieving reductions of 23.3% and 10.4%, respectively.

In the Industrial Internet of Things (IIoT), Multi-access Edge Computing (MEC) emerges as a transformative paradigm for managing computation-intensive tasks, where task offloading plays an important role. However, due to the complex environment of IIoT, existing deep reinforcement learning-based schemes suffer from significant shortcomings in accuracy and convergence speed during model training when addressing the issue of task offloading. In this paper, to solve this problem, we propose an online task offloading scheme based on reinforcement learning, leveraging the double deep Q network (DQN) and dueling DQN with a prioritized experience replay mechanism, called thePrioritized experience-basedDoubleDuelingDQNtask offloading scheme (P-D3QN). P-D3QN enhances action selection accuracy using double DQN and mitigates Q-value overestimation by decomposing state and advantage using dueling DQN. Additionally, we adopt the prioritized experience replay mechanism to enhance the convergence speed of model training by selecting transitions that induce a higher training error between the evaluation network and the target network. Experimental results demonstrate that P-D3QN outperforms several state-of-the-art schemes, achieving a reduction of 21.0% in the average cost of the task and improving the completion rate of the task by 19.5%.

Integrated Sensing and Communication (ISAC) has emerged as a promising technology for next-generation mobile networks. Towards ISAC, we developmmRipplethat empowers commodity mmWave radars with communication capabilities through smartphone vibrations. InmmRipple, a smartphone (transmitter) sends messages by modulating smartphone vibrations, while a mmWave radar (receiver) receives the messages by detecting and decoding the smartphone vibrations. By doing so, a smartphone user can not only be passively sensed by a mmWave radar, but also actively send messages to the radar without any hardware modifications. Although promising, the data rate ofmmRippleis limited by Morse-style communication. To address this, we presentmmRipple+, which leverages the Pulse Width and Amplitude Modulation (PWAM) technique and suppresses inter-symbol interference to enable faster communication. We prototypemmRippleandmmRipple+on commodity mmWave radars and different types of smartphones. Experimental results show thatmmRippleachieves an average vibration pattern recognition accuracy of 98.60% within a$ 2$m communication range, and 97.74% within$ 3$m. The maximum communication range extends to$ 5$m. Meanwhile,mmRipple+achieves a bit rate of 100 bps with a BER of less than 3%, improving the data rate by 4× overmmRippewith the same symbol duration. This work pioneers smartphone-to-COTS mmWave radar communication via vibrations, unlocking diverse applications.

Passive human tracking plays a critical role in the field of ubiquitous sensing, offering customized services such as real-time location tracking for vital sign monitoring and motion detection. Traditional contact-free tracking systems are primarily designed for Line-of-Sight (LOS) scenarios, requiring a direct path between the radio device and the target. However, in Non-Line-of-Sight (NLOS) scenarios, where obstacles obstruct this direct path, these systems suffer from sensing failures and are unable to accurately obtain the motion trajectory of the sensing target. In this paper, we propose the UWTracking system, which utilizes the Commercial Off-the-Shelf (COTS) Impulse Radio-Ultra Wideband (IR-UWB) radars to enable precise indoor passive human tracking in both LOS and NLOS scenarios. To effectively capture the motion information of a moving target in NLOS scenarios, we present the Reconstructed Distributed- Doppler Frequency Shift (RD-DFS) features. We then binarize the RD-DFS features and design the Distance Extraction Algorithm (DEA) to obtain the target's distance in both scenarios. Subsequently, the Circle Intersection Method with Distance Stretching (CIM-DS) algorithm is developed to determine the indoor position of the sensing target, and the Scanning Angle and Velocity Particle Filter (SAV-PF) algorithm facilitates high-precision trajectory tracking. We implement a prototype of UWTracking system and conduct extensive evaluations to showcase its trajectory tracking performance under various scenarios. The results demonstrate that UWTracking achieves effective real-time tracking, with a median tracking error of 17.65 cm in the LOS scenario and 23.34 cm in the NLOS scenario, outperforming the state-of-the-art trajectory tracking systems based on COTS IR-UWB Radars.

Sedentary behaviors are shown to be hazardous to human health. Detecting sedentary behaviors in a ubiquitous way can be realized by the promising WiFi sensing technique. The accurate detection of sedentary behaviors is determined by the accurate recognition of sit-stand postural transition (SPT). However, according to our findings, SPT recognition errors are inevitable even with advanced machine-learning methods, because different SPTs may result in a similar change in WiFi channel state information (CSI). To effectively reduce SPT recognition errors, in this paper we propose RoSeFi, a robust sedentary behavior monitoring system. We first classify the errors in SPT recognition results into two categories: the errors violating SPT's consistency and the errors violating SPTs' symmetry. To correct the above errors, we reveal two inherent features in the CSI data of SPTs, i.e., contextual association and waveform mirror symmetry. Then a novel metric named WMSF is defined to quantify the degree of waveform mirror symmetry between two SPTs' CSI data. Integrating the above features, the problem of recognition error correction can be modeled as a constrained nonlinear optimization problem (CNOP). To solve the problem, we design a unified error detection/correction scheme, named UEDC, which converts the CNOP into a sequence decoding problem in Hidden Markov Model (HMM). A tailored Viterbi algorithm combined with WMSF is proposed to detect and correct the errors simultaneously. The experimental results show that RoseFi reduces 60-82% SPT recognition errors, gains 15-20% relative improvement in the accuracy of SPT recognition, and eventually reduces the sedentary time estimation errors by 10%-20%, compared with typical existing systems. In addition, our error correction method can be adapted to most existing machine learning based human action recognition methods, effectively improving their performance.

Contactless WiFi based human action recognition exhibits remarkable prospects in the fields such as human-computer interaction and smart home. However, domain dependency restricts its generalization into the real-world deployment. Since it is expensive to label enough new data for retaining a model, it is beneficial to explore few-shot learning for cross-domain sensing with limited target labels. Nevertheless, there are two challenges to be addressed. The first challenge is how to select a suitable dataset from a series of available source domains to prevent negative transfer. The second is to mine action-related characteristics by the feature learning model for the following effective knowledge transfer. In order to tackle the above challenges, we present a cross-domain sensing framework named CDFi, which consists of Nearest Neighbor based Domain Selector (NNDS) and Fine-to-Coarse-Grained Transformer Network (FCGTN). NNDS is proposed to evaluate the source-target domain similarities by measurements among local and global feature distributions. Besides, FCGTN embeds convolution map based hierarchical transformer structures and the modified linear layer into an end-to-end deep network, which can quickly adapt to the unseen domain by few samples. Comprehensive experiments show that CDFi can effectively realize cross-domain action recognition, and achieve about 4 cross-scene cases, respectively, compared to the state-of-the-art.

In the paper, the Unmanned Aerial Vehicle (UAV) path planning and task offloading problem in UAV-assisted mobile edge computing (MEC) systems is investigated. A bi-criterion ant colony optimization (bi-ACO) framework is proposed for the considered problem with the objectives of minimizing the total cost and the completion time, meanwhile satisfying the energy, deadline, location, and priority constraints. In the bi-ACO framework, multiple heterogeneous colonies are introduced with different preferences of objectives. Each colony maintains five pairs of pheromone matrices for constructing feasible solutions. Besides the colony settings, three key components of bi-ACO are delicately designed: feasible solution generation method (FSGM) to construct a feasible solution, solution division method (SDM) to improve obtained solutions of good quality, and pheromone update method (PUM) to updates pheromone matrices by pheromone evaporation operation and pheromone enhancement operation based on the preferences of colonies. Four Pareto-based metrics are introduced to evaluate the performance of the compared algorithms. Experimental results show that the proposal outperforms the compared baseline algorithms in effectiveness and robustness.

Wireless Power Transfer (WPT) technology has been developed rapidly in recent years. The cooperative charging model and corresponding scheduling methods have been proposed to save the charging cost in paid charging service. However, the state-of-the-art methods ignore the spatial occupation issue of rechargeable devices. Moreover, the cooperative charging scheduling in directional wireless charging has not been studied yet. This paper studies the cooperative scheduling for directional wireless charging with spatial occupation. We formulate the Cooperative Charging Scheduling with Spatial occupation (CCSS) problem of Mobile Rechargeable Sensor Devices (MRSDs) for optimizing the total cost of whole charging system. We first investigate the properties of optimal arrangement of MRSDs in charging group and calculate the tight intervals of charging angles of MRSDs. We show that it is sufficient to bound the error by conducting angle discretization for only two MRSDs in each charging group. Then, a$(\ln n+1)(1+\varepsilon)$-approximation algorithm of the CCSS problem is proposed based on greedy approach, where$n$is the number of MRSDs, and$\varepsilon$is the discretization error. The results of extensive simulations and field experiments demonstrate that our algorithm can reduce at most 42.5% total cost comparing with the benchmark algorithms.

Current facial authentication (FA) systems are mostly based on the images of human faces, thus suffering from privacy leakage and spoofing attacks. Mainstream systems utilize facial geometry features for spoofing mitigation, but they are still vulnerable to feature manipulation, e.g., 3D-printed human faces. In this article, we propose a novel privacy-preserving anti-spoofing FA system, named RFace, which extracts both the 3D geometry and inner biomaterial features of faces using a COTS RFID tag array. These features are difficult to obtain and forge, hence are resistant to spoofing attacks. Unlike images, RF signals are not perceptible to human eyes, so RFace protects user's privacy. We build a theoretical model to rigorously prove the feasibility of feature acquisition and the correlation between facial features and RF signals. To enhance the security of RFace, we specify the tag reading order for each authentication to defend against the signal replay attack. For practicality, we design an effective algorithm to mitigate the impact of unstable distance and angle deflection from the face to the array. Extensive experiments with 30 participants and three types of spoofing attacks show that RFace achieves an average authentication success rate of over 95.7$\%$and an EER of 4.4$\%$. More importantly, no replay attack or spoofing attack succeeds in deceiving RFace in the experiments.

Satellite edge computing, as an extension of ground edge computing, is a key technology for achieving seamless global computing coverage. However, the low earth orbit (LEO) satellites have limited computing resources and are moving at a high speed. This naturally poses a challenge to find more suitable computation offloading strategies with minimum network latency and energy consumption, especially when a large number of co-existing users are to offload their tasks. In this paper, therefore, we mainly focus on computation offloading in the satellite edge computing network (SECN) by jointly considering LEO satellites' mobility and SECN's heterogeneous resource constraints to explore more practical computation offloading strategies. We first formulate the problem ofMobility-awareComputationOffloading (MCO) in the SECN via specifying the effect of LEO satellites' high-speed movement on the computation offloading, aiming to minimize the network latency and energy consumption. Considering the MCO problem is discrete and non-convex as the objective function and constraints are associated with the binary decision variables. We then convert the original non-convex problem into a continuous convex problem which is proved to be feasible. To avoid a high computational complexity incurred by the extensive co-existing user offloading, we designMCO-A, a distributed algorithm based on ADMM (alternating direction method of multipliers) to solve the MCO problem efficiently. Finally, the performance ofMCO-Ais evaluated via extensive experiments including small-scale and large-scale scenarios. The experimental results show that MCO-A can achieve a lower network latency and energy consumption in an efficient way compared with the baseline and state-of-the-art approaches.

Traditional indoor location information management methods based on centralized servers have problems such as safe and reliable transmission, personal privacy leaks, location information tampering, and computing and storage loads. These problems have seriously affected the development of personalized services based on indoor location information. In this paper, a novel mobile blockchain-enabled federated learning (MBFL) information management framework for indoor positioning is presented, comprising the mobile blockchain model, the federated learning (FL) model, and the InterPlanetary file storage model. Then, we design the MBFL algorithm, establishing a robust foundation for collaborative model training, efficient block mining, and secure data storage. Moreover, we derive training and mining latency as well as the individual user rewards, and formulate latency-limited resource allocation strategies as a non-cooperative game. We propose an efficient alternating iterative algorithm to achieve the Nash equilibrium of this game. Numerical results demonstrate that the proposed alternating iterative algorithm achieves rapid convergence and strikes an effective balance between economic and time efficiency. Furthermore, when confronted with model poisoning attacks, the MBFL algorithm exhibits superior security performance compared to the traditional FL algorithm. Future work will focus on adapting the MBFL framework for various indoor environments and enhancing consumption and computational efficiency with hybrid consensus mechanisms.

The existing personalized text-to-image generation models face issues such as repeated training and insufficient generalization capabilities. We present an adaptive Style-Guided Diffusion Model (SGDM). When provided with a set of stylistically consistent images and prompts as inputs, SGDM can generate images that align with the prompts while maintaining style consistency with the input images. SGDM first extracts features from the input style image and then combines style features from different depths. Last, style features are injected into the noise generation process of the original Stable Diffusion (SD) model by the style-guided module we propose. This strategy fully leverages the generative and generalization capabilities of the pre-trained text-to-image model to ensure the accuracy of the generated image's content. We present a dataset construction method suitable for style personalized generation tasks of this kind, enabling the trained model to generate stylized images adaptively instead of re-training for each style. We also present an evaluation metric, StySim, to measure the style similarity between two images, and this metric shows that the style personalization capability of SGDM is the best. And metrics such as FID, KID, and CLIPSIM indicate that SGDM maintains good performance in text-to-image generation.

Edge servers, which are located in close proximity to mobile users, have become key components for providing augmented computation and bandwidth. As the resources of edge servers are limited and shared, it is critical for the decentralized mobile users to determine the amount of offloaded workload, to avoid competition or waste of the public resources at the edge servers. Reinforcement learning (RL) methods, which are sequential and model-free, have been widely considered as a promising approach. However, directly deploying RL in edge computing remains elusive, since arbitrary exploration in real online environments often leads to poor user experience. To avoid the costly interactions, in this paper, we propose an offline RL framework which can be optimized by using a static offline dataset only. In essence, our method first trains a supervised offline model to simulate the edge computing environment dynamics, and then optimize the offloading policy in the offline environment with cost-free interactions. As the offloading requests are mostly asynchronous, we adopt a mean-field approach that treats all neighboring users as a single agent. The problem can then be simplified and reduced to a game between only two players. Moreover, we limit the length of the offline model rollout to ensure the simulated trajectories are accurate, so that the trained offloading policies can be generalized to unseen online environments. Theoretical analyses are conducted to validate the accuracy and convergence of our algorithm. In the experiments, we first train the offline simulation environment with a real historical data set, and then optimize the offloading policy in this environment model. The results show that our algorithm can converge very fast during training. In the execution, the algorithm still achieves high performance in the online environment.

Impulsive noise is always present in real-world image Compressive Sensing (CS) acquisition systems, where existing CS reconstruction performance may seriously deteriorate. In this article, we propose a robust CS formulation for image reconstruction to suppress outliers in the presence of impulsive noise. To address this issue, we consider a novel truncated-Cauchy loss function as the metric of residual error to elevate the reconstruction robustness. Specifically, we design a complementary priors model to incorporate nonconvex nonlocal low-rank prior and deep denoiser prior for high-accuracy image reconstruction. By means of the half-quadratic optimization theory and generalized soft-thresholding technique, we also develop an alternative optimization algorithm for solving the induced nonconvex optimization problem. Numerical simulations demonstrate the robustness and accuracy of the proposed robust CS method compared to some recent CS methods for image reconstruction in impulsive noise.

Multi-user delay-constrained scheduling is a crucial challenge in various real-world applications, such as wireless communication, live streaming, and cloud computing. The scheduler must make real-time decisions to guarantee both delay and resource constraints simultaneously, without prior information on system dynamics that can be time-varying and challenging to estimate. Additionally, many practical scenarios suffer from partial observability issues due to sensing noise or hidden correlation. To address these challenges, we propose a deep reinforcement learning (DRL) algorithm called Recurrent Softmax Delayed Deep Double Deterministic Policy Gradient ($\mathtt{RSD4}$) (https://github.com/hupihe/RSD4), which is a data-driven method based on a Partially Observed Markov Decision Process (POMDP) formulation.$\mathtt{RSD4}$guarantees resource and delay constraints by Lagrangian dual and delay-sensitive queues, respectively. It also efficiently handles partial observability with a memory mechanism enabled by the recurrent neural network (RNN). Moreover, it introduces user-level decomposition and node-level merging to support large-scale multihop scenarios. Extensive experiments on simulated and real-world datasets demonstrate that$\mathtt{RSD4}$is robust to system dynamics and partially observable environments and achieves superior performance over existing methods.

As two important contents in WiFi-based action perception, detection and recognition require localizing motion regions from the entire temporal sequences and classifying the corresponding categories. Existing approaches, though yielding reasonably acceptable performances, are suffering from two major drawbacks: heavy empirical dependency and large computational complexity. In order to solve these issues, we develop LiteWiSys in this article, a lightweight system in an end-to-end deep learning manner to simultaneously detect and recognize WiFi-based human actions. Specifically, we assign different attentions on sub-carriers, which are then compressed to reduce noise and information redundancy. Then, LiteWiSys integrates deep separable convolution and a channel shuffle mechanism into a multi-scale convolutional backbone structure. By feature channel split, two network branches are obtained and further trained with a joint loss function for dual tasks. We collect different datasets at multi-scenes and conduct experiments to evaluate the performance of LiteWiSys. In comparison to existing WiFi sensing systems, LiteWiSys achieves promising precision with lower complexity.

CRYSTALS-Kyber, as the only public key encryption (PKE) algorithm selected by the National Institute of Standards and Technology (NIST) in the third round, is considered one of the most promising post-quantum cryptography (PQC) schemes. Lattice-based cryptography uses complex discrete algorithm problems on lattices to build secure encryption and decryption systems to resist attacks from quantum computing. Performance is an important bottleneck affecting the promotion of post quantum cryptography. In this paper, we present a High-performance Implementation of Kyber (named HI-Kyber) on the NVIDIA GPUs, which can increase the key-exchange performance of Kyber to the million-level. Firstly, we propose a lattice-based PQC implementation architecture based on kernel fusion, which can avoid redundant global-memory access operations. Secondly, We optimize and implement the core operations of CRYSTALS-Kyber, including Number Theoretic Transform (NTT), inverse NTT (INTT), pointwise multiplication, etc. Especially for the calculation bottleneck NTT operation, three novel methods are proposed to explore extreme performance: the sliced layer merging (SLM), the sliced depth-first search (SDFS-NTT) and the entire depth-first search (EDFS-NTT), which achieve a speedup of 7.5%, 28.5%, and 41.6% compared to the native implementation. Thirdly, we conduct comprehensive performance experiments with different parallel dimensions based on the above optimization. Finally, our key exchange performance reaches 1,664 kops/s. Specifically, based on the same platform, our HI-Kyber is 3.52× that of the GPU implementation based on the same instruction set and 1.78× that of the state-of-the-art one based on AI-accelerated tensor core.

Multi-access edge computing (MEC) has emerged as a promising computing paradigm to push computing resources and services to the network edge. It allows applications/services to be deployed on edge servers for provisioning low-latency services to nearby users. However, in the MEC environment, edge servers may suffer from failures while the app vendor has to guarantee continuously available services to its users, thereby securing its revenue for application instances deployed. In this paper, we focus on available service provisioning when cost-effectively deploying application instances on edge servers. We first formulate a novelAvailability-awareRevenue-effectiveApplicationDeployment (ARAD) problem in the MEC environment with the aim to maximize the overall revenue by considering both service availability benefit and deployment cost. We prove that the ARAD problem is$\mathcal {NP}$-hard. Then, we propose an approximation algorithm namedARAD-Ato find the ARAD solution efficiently with a constant approximation ratio of$\frac{1}{2}$. We extensively evaluate the performance ofARAD-Aagainst five representative approaches. Experimental results demonstrate that ourARAD-Acan achieve the best performance in securing the app vendor's overall revenue.

Edge computing (EC), as an emerging technology, allows app vendors to deliver low-latency services by allocating users to edge servers nearby. Unfortunately, these heterogeneous and resource-constrained edge servers struggle to serve all the co-existing users with ever-growing service requirements, especially during peak periods. Existing approaches for the user allocation problem insisting on fulfilling prescribed service requirements often fail to tackle this issue and consequently result in a tremendous user loss that significantly impairs app vendors' service profit. We observe that a certain degree of violation of users' requirements can help spare resources to serve more users. Meanwhile, such a violation, which is also called service degradation, can be tolerated by users if they receive appropriate compensation. However, abusive degradation may introduce huge compensation, ultimately leading to reduced service profit. As a result, the service degradation and compensation must be carefully considered by app vendors. In this paper, we study theServiceDegradation-ToleratedOnlineUserAllocation (SD-OUA) problem in EC environment, aiming to maximize app vendors' service profit. We prove the$\mathcal {NP}$-hardness of the SD-OUA problem. Then, we propose theDegradation-awareUserAllocation (DUA) approach based on problem reformulation and primal-dual optimization to find its solutions in polynomial time. The performance ofDUAis theoretically guaranteed and evaluated against three representative approaches via extensive experiments.

Unsupervised Video Anomaly Detection (UVAD) utilizes completely unlabeled videos for training without any human intervention. Due to the existence of unlabeled abnormal videos in the training data, the performance of UVAD has a large gap compared with semi-supervised VAD, which only uses normal videos for training. To address the problem of insufficient ability of the existing UVAD methods to learn normality and reduce the negative impact of abnormal events, this paper proposes a novel Enhanced Spatio-temporal Self-selective Learning (ESSL) framework for UVAD. This framework is designed for capturing both the appearance and motion features through effective network structures by solving the spatial and temporal jigsaw puzzles. Specially, we develop a Self-selective Learning Module (SLM) for UVAD, which prevents the model learning abnormal features and enhances the model by selecting normal features. Experimental results on three benchmark datasets show that the proposed method not only surpasses the state-of-the-art UVAD works, but also achieves the performance comparable to the classic semi-supervised methods for video anomaly detection that needs normal videos selected manually. Code is available at: https://github.com/xusuger/ESSL.

Edge computing (EC) provides low-latency services by deploying edge servers close to users. However, these servers are prone to failures that can invalidate any predefined user allocation strategies. To ensure continuous services and maintain users' payments, affected users who are disconnected from the failed edge servers need to be reallocated. Unfortunately, due to the strict latency requirements of users and the limited resources on edge servers, many of them fail to be reallocated. Thus, we propose to migrate unaffected users from affected users' nearby edge servers to free up more resources for reallocation. In this paper, with the aim of maximizing the overall revenue and ensuring continuous service provisioning for users, we formulate the problem of Migration-Assisted _User _Reallocation (MUR) upon edge server failures and prove its NP-hardness. We then introduce an Integer Programming-based approach named MUR-O to find the optimal solution and a heuristic approach named MUR-H to efficiently find sub-optimal solutions. Experimental results on real-world datasets demonstrate that our approaches are superior to three representative approaches.

Millimeter-wave radar based human activity recognition (RADHAR) exhibits remarkable prospects in the field of device-free sensing. However, most existing RADHAR systems only focus on performance improvement, failing to simultaneously lighten the network parameters. In this paper, we propose a dynamic lightweight SlowFast network named DyLiteRADHAR, which can efficiently extract spatial-temporal features and largely reduce the resource consumption for human activity recognition. Specifically, we design triple-view signal maps (TRIview) as the input by successively concatenating the range-velocity, range-azimuth and range-elevation matrices. Then dynamic lightweight network is presented to learn discriminative representations which integrates dynamic convolution and lightweight shuffle net structure into the SlowFast framework. Experimental results demonstrate that the proposed approach DyLiteRADHAR is able to achieve superiority performance with limited computation complexity.

Remote Direct Memory Access has been widely adopted in distributed storage systems. However, it only supports unicast operations, which degrades the performance significantly for data replication because of bandwidth waste and CPU overhead. To address the problem, we propose MC-RDMA, a distributed and reliable multicast RDMA. It is compatible with existing unicast RDMA but supports lazy packet replication with reliable RDMA multicasting. The key idea of MC-RDMA is utilizing in-network programmable switches to build a NIC-transparent reliable multicast protocol for RDMA. MC-RDMA combines the address information of the IP and RoCEv2 into a sender-initialized multicast routing protocol. Besides, it synchronizes the hardware transmission states of multiple receivers by merging ACKs and NAKs. To verify the effectiveness of MC-RDMA, we implement it with Mellanox ConnectX-6 commodity RNICs and Intel Tofino P4 programmable switches. Experimental results show that MC-RDMA can double the sender bandwidth utilization and reduce the CPU overhead significantly compared to unicast-based RDMA replications. Moreover, it reduces the storage request latency by -30% with realistic workloads and decreases the training time by -50% in the distributed training system.

Human-machine interactions (HMIs), e.g., touchscreens, are essential for users to interact with mobile devices. They are also beneficial in resisting emerging active attacks, which aim at maliciously controlling mobile devices, e.g., smartphones and tablets. With touchscreen-like HMIs, users can notice and interrupt malicious actions conducted by the attackers timely and perform necessary countermeasures, e.g., tapping the ‘Quit’ button on the touchscreen. However, the effect of HMI-oriented active attacks has not been investigated yet. In this paper, we present a practical attack towards touch-based devices, namely Expelliarmus. It reveals a new attack surface of active attacks for hijacking users’ operations and thus taking full control over victim devices. Expelliarmus neutralizes users’ touch commands by producing a reverse current via electromagnetic interference (EMI). Since the reverse current offsets the current change caused by a touch, the touchscreen detects no current change and thus ignores users’ commands. Besides this basic denial-of-service attack, we also realize a target cancellation attack, which can neutralize target commands, e.g., ‘Quit’ without interference in irrelevant operations. Thus, the active attack can be completely performed without interruption from users, even if they are alerted by the abnormal events. Extensive evaluations demonstrate the effectiveness of Expelliarmus on 29 off-the-shelf devices.

This paper proposes a scalable DNN processor that can be flexibly reconfigured to maximize inference efficiency on a wide range of DNN models. The processor consists of 18 computing nodes with various precision modes support. To improve the computation throughput, we propose a sub-image parallelization strategy, where the original input image is divided into multiple sub-images and computed on multiple nodes in parallel. In addition, the cross-layer pipeline is implemented to improve resource utilization. The proposed processor is implemented in 28nm CMOS technology and achieves a peak performance of 4.17 TOPS and an energy efficiency of 2.08 TOPS/W.

Heart rate provides aln important reference for human physical conditions and psychological changes. MmWave-based heart rate estimation has increasingly attracted attention in recent years due to its non-intrusiveness and cost-effectiveness. However, when the subject locates far away from the mmWave radar and also deviates from it, the low accuracy of heart rate estimation becomes a major concern. This paper presents MMHeart, a new heart rate estimation and heartbeat waveform reconstruction system based on MIMO mmWave radar. In order to effectively improve the accuracy of heart rate estimation, MMHeart first calculates appropriate range bins based on positioning results, then estimates candidate heart rates in all channels, removes abnormal candidates based on spectrum kurtosis, and finally estimates the heart rate by clustering the remaining candidates. Then in order to reconstruct more accurate heartbeat waveform, MMHeart first segments the signal by trough detection, then resamples heartbeat waveform template, and finally fine-tunes the start and end points of each segment. Our extensive experiments show that in long-range and large-deviation scenarios, MMHeart can improve the accuracy of heart rate estimation by at least 54.1% compared to mmEGC and PiVimo, while it can improve the accuracy of cardiac cycle duration by 52.2% compared to mmEGC.

The trading of social media data has attracted wide research interests over years. In particular, the trading for Web browsing histories, when being applied to targeted advertising, produces tremendous economic value for data consumers. However, the disclosure of entire browsing histories, even in form of anonymous data sets, poses a huge threat to user privacy. Although some existing solutions have investigated privacy-preserving outsourcing of social media data, unfortunately, they neglected the impact on the data consumer’s utility. In this article, we propose CEATSE, a correlated data trading framework for various kinds of private Web browsing histories. CEATSE first models the correlation among multiple dimensional features, and then generates the optimal feature clustering scheme. Combined with this scheme, CEATSE next incorporates a correlated data perturbation strategy on each feature cluster, in order to balance the privacy-utility tradeoff. It then quantifies each chosen data contributor’s privacy loss on optimal feature clusters. Through real-data-based experiments, our analysis and evaluation results demonstrate CEATSE indeed achieves user privacy protection, the data consumer’s accuracy requirement, and truthfulness, individual rationality as well as budget balance.

The integration of Internet of Things (IoT) with cloud–edge computing in cyber–physical systems has revolutionized the way healthcare enterprises manage electronic health records (EHRs). With more healthcare enterprises outsourcing encrypted EHRs to the cloud, searchable encryption (SE) is utilized to retrieve encrypted data, especially attribute-based SE (ABSE) can achieve fine-grained access control. However, ABSE usually requires a lot of computation, which imposes a serious burden on resource-limited devices. Moreover, ensuring fairness in data access is crucial in the healthcare domain, where both data users and owners may have conflicting interests. In order to overcome these problems, this article proposes an SE scheme with fine-grained access control for cloud-based EHRs sharing assisted by blockchain. It transfers computing tasks to edge servers and enables users to control who has access to their EHRs. The adoption of blockchain and smart contracts guarantees data integrity and transaction fairness. Moreover, a consensus algorithm is designed for the higher efficiency of the proposed scheme. Finally, security analysis proves that the proposed scheme resists adaptive chosen keyword attacks (CKAs). Performance analysis further confirms that it has more functionalities and is efficient for smart healthcare.

With the growing popularity of Internet of Things (IoT) systems, device-free action recognition begins to attract extensive attention due to its friendly feasibility in broad applications, such as human–computer interaction and smart elderly care. Considering abundant information in the vision modality, existing methods adopt the cross-model methods for performance enhancement. However, the dependency of synchronous multimodal data in the collection and recognition stage brings into the vision weaknesses, such as sensitivity to occlusion and privacy invasion. In this article, we integrate multi-attention structure and auxiliary video information into a novel end-to-end deep learning framework named MuAt-Va, in which video soft labels learned in advance are utilized to teach the multi-attention WiFi feature training process without vision information involved during the test. Specifically, in order to enlarge the application scope and reduce the data cost, we beforehand acquire videos under a satisfactory condition only once, and then leverage teacher–student mechanism to guide the WiFi stream. Instead of straightforwardly concatenating multiantenna channel state information (CSI) from homogeneous wireless signals as previous works, we design a CSI subcarrier-wise, temporal-wise, and view-wise attention module to assign different weights on the basis of data characteristics for the sensing task. Our experiments with multiple subjects data in two scenes demonstrate that MuAt-Va can accurately recognize human actions with more superior performances.

Edge servers, which are located in close proximity to mobile users, have become emerging components for computation offloading in multiple Internet of Things (IoT) applications. As the edge resources are limited and shared among multiple mobile users, it is crucial for the users to choose appropriate edge server for task offloading, so that their cumulative utility can be maximized. Reinforcement learning (RL) algorithms, which are sequential and model-free, have been widely considered. However, it is still a critical challenge to coordinate the mobile users in a decentralized way. In this work, we propose a novel framework of Multiagent RL by learning to coordinate. The main idea is to introduce an additional “virtual” agent at the edge, which learns to broadcast public messages to the mobile users at each interval. We then enforce positive correlation between each user’s offloading policy and the message. The underlying intuition is that the message can contain information of edge resources and other users’ policies. Therefore, it is expected that the decentralized users can make coordinated decisions. Theoretical analysis shows that our algorithm can converge to equilibrium points under certain mild assumptions. In the experiments, our approach outperforms other baselines significantly in different scenarios. In addition, the results show that the broadcast message plays a very important role in coordinating the mobile users.

With the increasing types and number of Internet of Things (IoT) devices and malicious programs and the popularization of encryption technology in the communication process between the Internet and the IoT, a large amount of encrypted abnormal traffic among devices endangers IoT cybersecurity. How to identify abnormal encrypted traffic of the IoT has become the premise of cybersecurity. Presently, most of the detection methods for traffic in the IoT have problems, such as simple data set processing, imperfect feature extraction, data imbalance, and low multiclassification accuracy. In this article, we propose a multiclassification deep learning model named the cost matrix time–space neural network (CMTSNN) for abnormal and encrypted IoT traffic. The CMTSNN is divided into three parts. The first part is the preprocessing stage of the data set, which needs to retain the timing relation between two data packets in the stream and create a cost penalty matrix according to the sample distribution. Aimed at the robustness of feature extraction in network flow, the second part extracts time series features and then space features to ensure the robustness of feature extraction. The third part is aimed at the problem of data imbalance. The cost penalty matrix is applied to the cost penalty layer in the training process, and then the improved cross-entropy loss function is used to calculate the loss to improve the classification accuracy of minority categories and increase the overall multiclassification performance of the model. Experiments were carried out with the ToN-IoT, BoT-IoT, and ISCX VPN-NonVPN data sets. Compared with current methods, the proposed method shows better performances, including accuracy, precision, recall, F1 Score, and false alarm rate.

BCube is a promising structure of data center network, as it can significantly improve the performance of typical applications. With the expansion of network scale and increasement of complexity, reliability and stability of networks have become more essential. In this paper, we study the fault-tolerant routings in BCube. First, we design a fault-tolerant routing algorithm based on node disjoint multi-paths. The proposed multi-path routing has stronger fault tolerance, since each path has no other common nodes except the source node and the destination node. Second, we investigate an effective fault-tolerant routing based on routing capabilities algorithm for BCube. The proposed algorithm has higher fault tolerance and success rate of finding feasible routes, since it does not limit the faults number. Third, we present an adaptive path finding algorithm for establishing virtual links between any two nodes in BCube, which can shorten the diameter of BCube. Extensive simulation results show that the proposed routing scheme outperforms the existing popular algorithms. Compared with the state-of-the-art fault-tolerant routing algorithms, the proposed algorithm has a 21.5% to 25.3% improvement on both throughput and packet arrival rate. Meanwhile, it reduces the average latency of 18.6% and the maximum latency of 23.7% in networks.

Electrocardiogram (ECG) is a common diagnostic indicator of heart disease in hospitals. Because of the low price and noninvasiveness of ECG diagnosis, it is widely used for prescreening and physical examination of heart diseases. In several studies on ECG analysis, only rough diagnoses are made to determine whether ECGs are abnormal or on a few kinds of ECG. In actual scenarios, doctors must analyze ECG samples in detail, which is a multilabel classification problem. Herein, we propose Hygeia, a multilabel deep learning-based ECG classification method that can analyze and classify 55 types of ECG. First, a guidance model is constructed to transform the multilabel classification problem into multiple interrelated two-classification models. This method ensures the good performance of each ECG analysis model, and the relationship between various types of ECG can be used in the analysis. The imbalance of samples in ECG datasets makes it difficult to analyze abnormal ECGs with high sensitivity and accuracy. We used data generation and mixed sampling methods for 11 ECG types with imbalanced problems to improve the average accuracy, sensitivity, F1 value, and accuracy from 87.74%, 43.11%, 0.3929, and 0.3929, to 92.68%, 96.92, 0.9287, and 99.47%, respectively. The average accuracy, sensitivity, F1 value, and accuracy of 44 of the 55 tags of the abnormal ECG analysis model are 99.69%, 95.81%, 0.9758, and 99.72%, respectively.

With the increasing scale of parallel computer interconnection network, the possibility of processor failure or link failure between processors in the network is also increasing. In the design of supercomputers, not only link overhead and communication delay should be taken into account, but also fault-tolerant performance of networks should be emphasized. Locally exchanged twisted cube ($LeTQ$) is a newly proposed interconnection network with lower link overhead and shorter diameter. With the increasing scale of supercomputers, fault-tolerant routing is indispensable. In this article, we propose a new load balancing fault-tolerant routing algorithm based on node contraction for$LeTQ$networks. The proposed algorithm uses the node shrinkage method to evaluate the priority of nodes. The sending node adaptively adjusts the probability of forwarding packets to the neighbor node according to the priority of the neighbor node and the state of the network. The path can be adapted to the load state of the network. The simulation results show that the fault-tolerant routing algorithm has good performance in throughput and delay.

The CAN FD emerges as a promising CAN technology inside the ACPS due to its advantages of high data-phase bit-rate and message payload. HSM based security solution is recommended by auto industry to protect CAN FD from potential security attacks, but it induces new challenges on timing analysis of CAN FD messages, which is left open in the literature. This article develops the first security-aware system model to describe the processing of CAN FD messages, and presents a new WCRT analysis to bound the interference induced by security-critical messages. We give the theoretical proof that our WCRT analysis can upper bound the response time of CAN FD messages. Using a small message set, we show that the WCRT computed by our new analysis is only 14% percent higher than the true WCRT obtained from an exhaustive search based simulator. By comparing with existing method, the number of impacted messages increases along with the increasing number of security critical messages, and for the two typical CAN FD systems, the percentage of WCRT increase varies from 12.43% to 14.57% and 7.0% to 10.89%, respectively; the percentage of WCRT decrease varies from 3.29% to 6.04% and 4.13% to 7.93%, respectively.

This article proposes a tag-based approach for physical (PHY)-layer authentication in a reconfigurable intelligent surface (RIS) communication system. We first extract the intrinsic PHY-layer features of RIS communication systems in terms of channel gain and background noise, and then apply these PHY-layer features, a random signal as well as the private key of the transmitter to construct a robust cover tag signal against the impersonation attack. We adopt an asymmetric cryptography technique to encrypt tagged signals and to resist against unauthorized detection and tampering attacks during the transmission process. The receiver then applies the maximum a-posteriori (MAP) ratio test to conduct authentication based on the received tag signal, a reference tag signal transmitted in training phase and the knowledge of distributions of the channel gain, background noise and the random signal. We also provide security analysis to demonstrate how the proposed scheme can resist unauthorized detection, tampering attacks, etc. With the help of tools of the MAP ratio test, maximum likelihood estimation, we further analyze the distribution of the test statistics and derive analytical models for the false alarm and detection probabilities. Finally, extensive simulations are conducted to verify the theoretical results and to illustrate the performance of the proposed scheme.

With the widespread deployment of commercial wireless devices, researchers begin to focus on device-free sensing tasks. In the field of action perception, existing WiFi-based sensing works mostly follow the framework in which action instances of channel state information (CSI) are first extracted and then classified. As for the part of human action detection, a majority of works adopt threshold based sliding window or frame-by-frame detection methods. However, it is hard for the former approach to set a reasonable threshold for all samples. As for the latter, it costs a relatively substantial amount of labor to label each moment of the time sequences. In order to overcome the above problems, we design an end-to-end context-aware faster region-based convolutional neural networks (RCNN) framework named Wisense to simultaneously detect the temporal boundaries as well as classify the actions. More specifically, Wisense consists of backbone net, region proposal net (RPN), pooling layer, and the prediction net, which directly regresses the action location along the time axis and classifies the action types. For the sake of wireless signal temporal detection, we transform the input into 1-D feature map and extract multiscale 1-D anchors. Besides, in order to sufficiently mine the context information, we extend the boundaries of region proposals and further establish the temporal pyramid features. Experimental results conducted in three indoor scenes validate the effectiveness of our proposed Wisense.

The Augmented Intelligence of Things enables many edge or end devices in the Internet of Things (IoT) to perform machine reasoning to make decisions, thus become more intelligent. For healthcare enterprises, huge physical data generated by smart devices facilitate to iterate their products. However, traditional data sharing models based on cloud outsourcing meet many security challenges, such as data confidentiality, reliability, and privacy protection, and most existing schemes have high computational complexity for the utilization of time-consuming cryptographic operations, such as bilinear pairing, which is not suitable for those resource-constrained IoT devices. To tackle the abovementioned issues, we present Double Rainbows, a promising data sharing scheme based on pairing-free searchable encryption. It is constructed on the cloud-edge-end architecture, supporting computing task transfer and reliable data storage and retrieval. The experimental results show that it outperforms in efficiency and exhibit more security functionalities.

With the continuous development of Industry 4.0 technology, the embedded devices in Industrial Internet of Things (IIoT) are showing explosive growth, and large-scale cyber attacks or related security incidents continue to sound the alarm bell of information security. IIoT has strict requirements on computing performance and energy consumption, which poses severe challenges to cryptographic algorithms, especially public key cryptographic algorithms with high computational complexity. Embedded graphic processing unit (GPU) devices, always as edge computing nodes or AI accelerators, are widely deployed in IIoT applications. In this article, we propose an embedded GPU-based Four$\mathbb {Q}$(EG-Four$\mathbb {Q}$) elliptic curve public key cryptographic acceleration scheme. As far as we know, EG-Four$\mathbb {Q}$is the first work to completely implement Four$\mathbb {Q}$on the GPU platforms, including finite field operations, point arithmetic, and scalar multiplication. Relying only on 36-W power consumption, our scalar multiplication performance reaches 1717 kops/s with the latency of 2.38 ms. In terms of the energy-efficiency ratio, EG-Four$\mathbb {Q}$has significant advantages over other platforms such as advanced RISC machines (ARM) CPU, Intel CPU, field programmable gate array (FPGA), and desktop GPUs. The throughput of EG-Four$\mathbb {Q}$is 1.75 times that of the fastest elliptic curve cryptography implementation based on the same platform and even exceeds the performance of Intel top server CPU E5-2699v3 (18-core). Based on the embedded GPU Xavier, EG-Four$\mathbb {Q}$can act as a cryptographic edge computing module or even a cloud cryptographic accelerator, providing more efficient elliptic curve cryptographic services for IIoT.

Blockchain can help edge IoT-enabled Maritime Transportation Systems (MTS) in solving its privacy and security problems. In this paper, a lightweight blockchain called LBlockchainE is designed for edge IoT-enabled MTS to guarantee the security of sensor data stored in an edge computing environment. To save the resources of edge servers on ship, a data placement strategy is proposed. To encourage edge servers to positively contribute to storing data generated by sensor devices, storage resource consumption is employed as an influencing parameter, and servers with abundant resources are selected for priority storage. The data placement strategy also takes care of the access delay between servers and selects the nodes with the least access and storage costs as the priority storage choice. LBlockchainE applies the low-energy-consumption characteristics of Proof of Stake to determine the ownership of bookkeeping rights through a small number of competitive calculations and the resources of the node. Experimental results indicate that compared with Ethereum, the consensus mechanism of LBlockchainE consumes less energy and occupies less storage space. On average, the new system uses 1.6% less time and consumes 78% less battery power compared with traditional blockchain systems. In comparison to the random storage, the best storage, and the optimal data storage strategies, the proposed strategy maintains the same message costs.

GCN-based clustering schemes cannot interactively fuse feature information of nodes and topological structure information of graphs, leading to insufficient accuracy of clustering results. Moreover, the deep clustering model based on graph structure is vulnerable to the attack of adversarial samples leading to the reduced robustness of the model. To solve the above two problems, this paper proposes a robust clustering model based on attention mechanism and graph convolutional network (GCN), named AG-cluster. This model firstly uses graph attention network and GCN to learn the feature information of nodes and the topological structure information of graphs, respectively. Then the representation results of the above two learning modules are interactively fused by the interlayer transfer operator. Finally, the model is trained end-to-end using a self-supervised training module to optimize the clustering results of the model. In particular, an efficient graph purification defense mechanism (GPDM) is designed to resist adversarial attacks on graph data to improve the robustness of the model. Experimental results show that AG-cluster outperforms the other four benchmark methods, specifically, AG-cluster improves 7.6% in Accuracy and 11.5% in NMI compared to the best benchmark method. Besides, the new model still shows higher robustness and stronger transferability under multiple attacks.

We develop WiDE, a WiFi-distance estimation based group profiling system using LightGBM. Given the uploaded WiFi information by users, WiDE can automatically learn powerful hidden features from the proposed features for between-user distance estimation, and infer group membership with the estimated distance. For each group, WiDE classifies the mobility level, and recognizes the group structure by applying the multi-dimensional scaling technique on the matrix of distance between pairwise users within the same group. We first validate the performance of between-user distance estimation via conducting extensive experiments in a three-floor campus building and a shopping center, and the results show that WiDE outperforms other machine learning based approaches for between-user distance estimation, with the average absolute error (AAE) of 0.69m and 1.14m for the campus building and shopping center, respectively, and the corridor identification accuracy for the campus building is over 99 percent. In addition, the experiments in the shopping center show that our approach can accurately detect groups, classify group mobility into fine-grained level and recognize the group structure.

Although crowdsensing has emerged as a popular information collection paradigm, its security and privacy vulnerabilities have come to the forefront in recent years. However, one big limitation of previous research is that the security domain and the privacy domain are typically considered separately. Therefore, it is unclear whether the defense methods in the privacy domain will have unexpected impact on the security domain. To bridge this gap, in this paper, we propose a novel Disguise-based Data Poisoning Attack (DDPA) against the differentially private crowdsensing systems empowered with the truth discovery method. Specifically, we propose a novel stealth strategy, i.e., disguising the malicious behavior as privacy behavior, to avoid being detected by truth discovery methods. With this stealth strategy, the shortcoming of failing to maximize the attack effectiveness is avoided naturally through structuring a bi-level optimization problem, which can be solved with the alternating optimization algorithm. Moreover, we show that the differentially private crowdsensing systems are vulnerable to data poisoning attacks, and enhancing the level of privacy will bring more serious security threats. Finally, the evaluation results on the real-world dataset Emotion and the synthetic dataset SynData demonstrate that DDPA can not only achieve maximum utility damage but also remain undetected.

The multi-hop wireless charging technology can largely extend the charging service range of chargers, thus has promising prospect in sustainable energy replenishment for wireless rechargeable sensor network. This paper proposes a new cost criterion, termed comprehensive cost consisting of energy cost and deployment cost, to measure the actual expenditure of wireless charging. We present a multi-hop wireless charging model and formulate the problem of minimizing the comprehensive cost such that the energy demand of all sensor nodes can be fulfilled by the energy capacitated chargers. We propose a (ln n+1)-approximation algorithm for the optimization problem, where n is the number of sensor nodes. Then, we propose a straightforward cost sharing mechanism, which ensures that no subset of sensor nodes can benefit by breaking away from the current charging tree for any fixed charger position, to realize the paid charging service of multi-hop wireless charging. Furthermore, to keep the magnetic fields of transmitters from the interfering, the conflict avoidance schemes are proposed in both central and distributed situations. Finally, we discuss the distributed scheme for minimizing the comprehensive cost without support of central server. Through extensive simulations, we demonstrate the significant superiority of the proposed algorithms in terms of comprehensive cost.

Occlusion handling in crowded scenes is an intractable challenge for human pose estimation. To address this problem, we propose two novel feed-forward network structures named Global Feed-Forward Network (GFFN) and Dynamic Feed-Forward Network (DFFN), which are specifically designed for image-based tasks to capture both local and global contextual information within intermediate features and update feature representations with high adaptability for occlusions. By exploiting the context modeling ability of the proposed GFFN and DFFN, we present a novel backbone network, namely High-Resolution Context Network (HRNeXt), which learns high-resolution representations with abundant contextual information to better estimate poses of occluded human bodies. Compared to state-of-the-art pose estimation networks, our HRNeXt absorbs advantages of convolution operation and attention mechanism, and it is more efficient in terms of training data sizes, network parameters and computational costs. Experimental results show that our HRNeXt significantly outperforms state-of-the-art backbone networks on challenging pose estimation datasets with high occurrence of crowds and occlusions.

Many end-to-end deep learning algorithms seeking to classify malicious traffic and encrypted traffic have been proposed in recent years. End-to-end deep learning algorithms require a large number of samples to train a model. However, it is hard for existing methods fully utilizing the heterogeneous multimodal input. To this end, we propose the SHAPE model (simultaneous header and payload encoding), which mainly consists of two autoencoders and a transformer layer, to improve model performance. The two auto encoders extract features from heterogeneous inputs—the statistical information of each packet and byte-form payloads—and convert them into a unified format; then, a lightweight Transformers layer further extracts the relationship hidden in simultaneous input. In particular, the autoencoder for payload feature extraction contains several depthwise separable residual convolution layers for efficient feature extraction and a token squeeze layer to reduce the computing overhead of the Transformers layer. Moreover, we train the SHAPE model using deep metric learning, which pulls samples with the same class label together and separates samples from different classes in the low-dimensional embedding space. Thus, the SHAPE model can naturally handle multitask classification, and its performance is approximately 5.43% better than the current SOTA on the traffic type classification of the ISCX-VPN2016 dataset, at the cost of 9.31 times the training time, and 1.45 times the inference time.

Respiration is a vital indicator of the state of the human body. Monitoring human respiration enables the realization of a variety of intelligent applications, including smart medical and sleep monitoring. Traditional methods that are dependent upon wearable devices are more costly and inconvenient for users. Recent studies have evidenced that low-cost commodity WiFi devices can be used to accomplish contactless respiration monitoring. In this article, we present BreatheBand, a fine-grained and robust respiration monitoring system based on commercial WiFi signals. We first remove the time-varying phase shift in the channel state information (CSI) by developing the Multi-antenna CSI–Subpopulation Genetic algorithm. Then we separate human respiratory components from WiFi signals by employing subcarrier selection and Independent Component Analysis. Next, applying a Mixed Cluster Gaussian–Hidden Markov Model, we generate a respiration signal resembling that of wearable devices. Finally, we integrate the BreatheBand system into commercial WiFi infrastructure. The results show that the BreatheBand’s respiration signal is remarkably identical to the signal collected by the wearable device in various scenarios. In particular, the mean absolute error of the BreatheBand’s respiration rate is approximately 0.1 bpm, outperforming state-of-the-art algorithms.

The multi-hop wireless charging technology has attracted a lot of attention, as it largely extends the charging range of chargers. Different from the existing work with single cost optimization, the objective of this article is to optimize the comprehensive cost, which is the combination of energy cost and deployment cost. We decompose the target problem into two sub-problems. The first sub-problem aims to minimize the deployment cost with energy capacity constraints. The proposed algorithm follows the greedy strategy, where the subset of sensor nodes for any charger is determined by finding the capacitated minimum spanning tree. The second sub-problem, which aims to maximize the reduction of comprehensive cost by adding chargers to the solution of the first sub-problem, is proved to be an unconstrained submodular set function maximization problem and can be solved by a 1/2-approximation randomized linear time algorithm for its equivalent problem. Through extensive simulations, we demonstrate that the proposed solution can reduce the comprehensive cost by 57.55% comparing with the benchmark algorithms.

The commoditization of private data has become an attractive research topic with the emergence of Big Data era. In this paper, we study the trading of high-dimensional private data with differential privacy guarantee. We proposeCheap, which is a novel Correlated data trading framework for High-dimEnsionAl Private data.Cheapfirst models data correlations among high-dimensional user attributes, and builds an initial attribute clustering scheme. Combined with this scheme,Cheapdevises a novel data perturbation mechanism by solving optimal attribute clustering (OAC) problem, in order to improve data utility of traded data and further generate a privacy-preserving high-dimensional dataset with close joint distribution with the original one. It then quantifies privacy loss based on near-optimal attribute cluster scheme due to the NP-hardness of theOACproblem, and further compensates data owners by running auction in a cost-effective way. We evaluate the performance ofCheaponUserBehaviordataset andObesitydataset, respectively. Our evaluation and analysis demonstrate thatCheapwell balances data utility and privacy protection, and achieves all desired economic properties of budget balance, individual rationality and truthfulness.

Network virtualization (NV) has extensive and significant applications in cloud computing and parallel and distributed systems. Virtual network embedding (VNE) is a key issue in NV, which is an effective means to advance systems’ performance. While existing VNE research lacks resource allocation coordination between mappings of different virtual network requests, resulting in insufficient resource utilization and high overhead. In this article, we propose a novel node essentiality evaluation model for data center networks (DCNs), and design an efficient distributed collaborative virtual network embedding. Firstly, we propose a node essentiality evaluation scheme based on dynamic model, which combines the characteristics of network topology and nodes to make the evaluation results more comprehensive. Secondly, we establish the two-stage node importance evaluation criteria for the deviation mean of the data center dynamic model and the variance based on the deviation mean. Furthermore, we investigate a nodal importance assessment method based on the data center dynamic model for perturbation testing. Finally, we design a distributed coordinated VNE algorithm (CNI-VNE) which calculates the importance index of physical nodes through topology awareness. The proposed algorithm can increase the coordination between different request mappings, thereby reducing the mapping cost of physical node resources and minimizing the cost of VNE. We use the real Fat-tree DCN of 128 servers and 80 switches as testbed, and evaluate them from indicators such as average reliability, average bandwidth consumption, average energy consumption, and average mapping time. Massive simulation results in different scenarios show that our algorithm achieves the best performance on most indicators compared with the existing state-of-the-art proposals, mapping acceptance and average revenue increased by 19.4% and 21.3%, respectively, and DCN reduced bandwidth consumption by about 30%.

Hash is one of the most important algorithms of cryptography, it is widely used in cryptographic primitives, such as digital signature, key exchange and so on. Further, hash cryptography is also the core operation of blockchain technology. With the explosive growth of the number of IoT devices and the rapid development of blockchain technology, the computing performance of hash has received widespread attention. The GPU high-performance computing platforms with a number of arithmetic cores are widely used in cryptographic optimization and acceleration. In this paper, we propose an efficient parallel accelerated framework of SM3 cryptography hash function based on GPU parallel computing devices, short for GPU-based SM3 (G-SM3). Our G-SM3 optimizes the implementation of the hash cryptographic algorithm from three aspects: parallelism, memory access and instructions. On the desktop GPU NVIDIA Titan V, the peak performance of G-SM3 reaches 23 GB/s, which is more than 7.5 times the performance of OpenSSL on a top-level server CPU (E5-2699V3) with 16 cores. On the embedded GPU which consumes less than 40 W, the SM3 throughput reaches 3.8 GB/s, which is even better than the performance of the serverlevel CPU. Based on the same GTX 1080, our performance is 1.12 times that of the fastest known GPU implementation, and the latency is reduced by more than 95%. Compared to other platforms, our G-SM3 has a huge advantage.

Mobile edge computing (MEC) is emerging as a novel computing paradigm that pushes network resources (such as computation and storage resources) away from the centralized data center to distributed edge servers. By hiring various resources of nearby edge servers, the MEC provides high-bandwidth and low-latency network services for mobile users. As numerous mobile users may compete for limited edge servers’ resources, to improve the resource utilization of the MEC system, it is very critical to investigate an effective user allocation policy. Previous studies mainly focus on investigating offline user allocation policies. However, mobile users may arrive online, and the MEC should be able to allocate these users online too. In a real-world MEC environment, online allocation decisions should not be made entirely in the dark. The historical user requests which may contain powerful hints about future user requests, can be adopted to assist in making allocation decisions. In this paper, we take the historical data into account and study the history-assisted online user allocation strategy. Specifically, we formulate the user allocation problem with a comprehensive model and show its hardness. Then, we present an online algorithm named HOUA to allocate mobile users according to both the online arrived user requests and the historical user requests. The competitive ratio of HOUA is proved. To further verify the effectiveness of HOUA, we conduct experiments on a widely-used real-world dataset. We show that HOUA can allocate more mobile users and achieve high resource rental revenue compared with the other approaches.

A high-resolution network exhibits remarkable capability in extracting multi-scale features for human pose estimation, but fails to capture long-range interactions between joints and has high computational complexity. To address these problems, we present a Dynamic lightweight High-Resolution Network (Dite-HRNet), which can efficiently extract multi-scale contextual information and model long-range spatial dependency for human pose estimation. Specifically, we propose two methods, dynamic split convolution and adaptive context modeling, and embed them into two novel lightweight blocks, which are named dynamic multi-scale context block and dynamic global context block. These two blocks, as the basic component units of our Dite-HRNet, are specially designed for the high-resolution networks to make full use of the parallel multi-resolution architecture. Experimental results show that the proposed network achieves superior performance on both COCO and MPII human pose estimation datasets, surpassing the state-of-the-art lightweight networks. Code is available at: https://github.com/ZiyiZhang27/Dite-HRNet.

As a key component of most machines, the status of the rotation shaft is a crucial issue in the factories, which affects both the industrial safety and the product quality. Tracking the rotation angle can efficiently monitor the status of the rotation shaft, but traditional solutions either rely on the specialized sensors, suffering from intrusive transformation, or use the computer vision-based solutions, suffering from poor light conditions. In this paper, we present a non-contacting low-cost angle tracking solution, RF-Protractor, to track the rotation shaft based on the surrounding RFID tags. Particularly, instead of directly attaching the tags to the shaft, which may lead to serious miss reading problems due to metal interference, we deploy the tags beside the shaft and leverage the polarization effect of the reflection signal from the shaft for angle tracking. To improve the polarization effect, we exploit the linear polarization feature by using the linear shaft turntable or placing a light aluminum foil on the shaft turntable, which requires no transformation of the shaft. We firstly build a polarization model to quantify the relationship between the rotation angle and the reflection signal. To extract the accurate reflection signal, we then propose to combine the signals of multiple tags to cancel the reflection effect and then estimate the environment-related parameter to calibrate the model. Finally, we propose to leverage both the power trend and the IQ signal to estimate the rotation direction and the rotation angle. We have implemented a real system and the extensive experiments in the real environment confirm the effectiveness of RF-Protractor, which achieves an average error of about 3.1° in angle tracking.

Video surveillance systems have demonstrated their great importance in security protection these years. However, due to limited budget, installing cameras in every place of surveillance region is not practical and then blind areas become inevitable. As a result, eliminating blind areas with the lowest cost has become a tough challenge. To the best of our knowledge, this is the first work that fixes the blind spots of video surveillance systems based on WiFi sensing technique. By taking existing WiFi infrastructure as the sensing device, this paper attempts to eliminate blind spots with tiny hardware cost. Moreover, in order to completely fix blind area with minimum loss of video communication QoS, the WiFi sensing device’s location boundary that satisfies the above objective is modeled and estimated. To that end, a visitor-disturbed channel model is first derived for precisely describing the inherent relation between the appearance of visitor and the change of wireless channel. Then a location boundary model satisfying both blind-area elimination and QoS maximization is further derived. Based on the derived model, a practical system is designed to estimate the real location boundary. The simulation and experiment results have not only verified the correctness of our derived location boundary model, but also showed its good performance on both blind-area elimination and communication QoS optimization.

Industrial Internet of Things (IIoT) has strict requirements on the performance and security of devices. Public-key cryptography, as a kind of computing resource-consuming algorithm, is widely used in the digital signature, key exchange, and so on. The embedded graphics processing units (GPUs) are now rapidly achieving extraordinary computing power, such as NVIDIA Tegra K1/X1/X2/Xavier, which are also treated as edge computing devices. They are widely used in IIoT environments, such as intelligent manufacturing, smart cities, and vehicle-mounted systems. The performance advantages endow embedded GPUs with the possibility of accelerating cryptography that also requires high-density computing. This article implements an efficient Tegra-based embedded GPU RSA acceleration server-oriented IIoT, named TEGRAS. Various optimization methods are employed to promote efficiency, including multithreaded Montgomery multiplication and Chinese Remainder Theorem implementation on the resource-constricted embedded GPUs. With about 40–50 W of power consumption, TEGRAS can deliver 34 kops/s of RSA2048 signature generation and 1007 kops/s of RSA signature verification, which outperforms implementations in the desktop GPUs and embedded CPUs in the perspective of performance-to-power ratio. To evaluate TEGRAS in real-world scenarios, we additionally build a network stack to deliver digital signature services, which can provide more than 34 and 978 kops of signature generation and signature verification, respectively. In a word, based on the embedded GPU, we provide a high-throughput, low-latency, and ready-to-use RSA accelerator-oriented IIoT.

As a novel computing paradigm, fog computing can provide more efficient and high-quality medical services for patients by deploying the smart healthcare system. Smart healthcare needs to store and share patient’s physiological data in fog nodes for online diagnoses. However, if fog nodes of smart healthcare lack effective security mechanisms, user’s privacy data might be stolen by malicious users. Moreover, fog computing needs to confront some brand-new challenges, such as resource-constrained Internet of Things (IoT) and insider attacks. To overcome these challenges, we propose an software-defined networking (SDN)-based security authentication scheme for fog nodes of smart healthcare. In our scheme, an authentication algorithm is deployed in SDN gateway to validate the creditability of fog node, and IoT only need send the privacy and functional attributes to the SDN gateway to decrease the computing of the IoT. To validate the effectiveness and feasibility of our scheme, we implement an experimental system with POX controller and Mininet emulator. The experimental results show that, compared with other similar work, our scheme is more effective, and the computing overhead (${2}{n}{T}_{m}$) and storage overhead [$({n}{+}{2}){\cdot }{1024}{+}{256}$] is lower.

Angle-of-Arrival (AoA) estimation is an important problem in passive radio-frequency identification (RFID) systems. Affixing an RFID tag array to an object enables to acquire its orientation information. However, the electromagnetic interaction between the tags can induce mutual coupling interference, distorting the RFID fingerprint measurements used for AoA estimation. Moreover, RFID reader modes with radio-frequency (RF) noise-tolerant Miller encoding can induce$\pi $-radians phase jump. In this article, we propose a scheme called RF-Mirror that can resolve the mutual coupling and phase jump problems and achieve accurate AoA estimation for an array with two or more tags. First, we characterize the impact of mutual coupling on a tag’s signal fingerprint and develop novel RSSI/phase-distance models. We then develop new experimental methods and signal processing techniques to verify the effectiveness of the proposed models. Based on the validated models, we develop new AoA estimation algorithms for tag arrays that deal with the mutual coupling effect explicitly. We provide extensive experimental results, which demonstrate that RF-Mirror can achieve significantly improved performance compared to baseline schemes, with median AoA estimation errors of 11.65° and 6.29° for two- and four-tag arrays, respectively.

As a typical Internet of Things application, network traffic prediction (NTP) plays a decisive role in congestion control, resource allocation, and anomaly detection. The trend of network traffic is different at different scales, so multiscale is an important characteristic of network traffic. In addition, the network traffic is nonlinear on each scale and dependent between scales. The existing NTP methods cannot comprehensively consider these characteristics, which limits their performance. In view of the characteristics of network traffic, such as multiscale, nonlinearity, and scale dependence, this article proposes a new multiscale NTP method based on a deep echo-state network (ESN). First, a multiscale parallel layered structure based on deep ESN is designed to fully consider the influence of each scale on the prediction result and then reduce the prediction error. Second, a feature extraction algorithm is proposed to improve the nonlinear approximation ability by extracting more abundant dynamic features with multiple reservoirs. Third, an NTP model based on scale dependence is proposed to reduce the influence from partial scale missing and then improve the prediction accuracy. Finally, simulation results demonstrate that compared with the state-of-the-art NTP methods, the proposed method significantly improves the prediction performance of network traffic with a slight increase in running time.

Traditional searchable encryption schemes based on the Term Frequency-Inverse Document Frequency (TF-IDF) model adopt the presence of keywords to measure the relevance of documents to queries, which ignores the latent semantic meanings that are concealed in the context. Latent Dirichlet Allocation (LDA) topic model can be utilized for modeling the semantics among texts to achieve semantic-aware multi-keyword search. However, the LDA topic model treats queries and documents from the perspective of topics, and the keywords information is ignored. In this article, we propose a privacy-preserving searchable encryption scheme based on the LDA topic model and the query likelihood model. We extract the feature keywords from the document using the LDA-based Information Gain (IG) and Topic Frequency-Inverse Topic Frequency (TF-ITF) model. With feature keyword extraction and the query likelihood model, our scheme can achieve a more accurate semantic-aware keyword search. A special index tree is used to enhance search efficiency. The secure inner product operation is utilized to implement the privacy-preserving ranked search. The experiments on real-world datasets demonstrate the effectiveness of our scheme.

In this paper, we consider deploying an intelligent reflecting surface (IRS) to enhance the downlink (DL) energy transfer and uplink (UL) information transmission efficiency for secure multiple-input multiple-output (MIMO) wireless powered communication networks (WPCNs). We aim to maximize the secrecy throughput of all users by jointly optimizing the DL/UL time allocation, the energy transmit covariance matrix of hybrid access point (AP), the information transmit beamforming matrix of users and the phase shifts of IRS in DL/UL, subject to constraints of energy/information transmit power at the hybrid AP/users and that of unit-modulus IRS phase shifts for DL/UL. To tackle the non-convex problem, we first transform the original problem into an equivalent form based on the mean-square error (MSE) method given time allocation, and then apply the alternating algorithm to update the optimization variables iteratively. Specifically, the energy covariance matrix and the information beamforming matrix are obtained based on the dual subgradient method. For the IRS phase shifts, we investigate two IRS beamforming reflection setups, namely different DL/UL IRS beamforming and identical DL/UL IRS beamforming. For the former case, the second-order cone programming technique and the Majorization-Minimization algorithm/element by element iterative algorithm are applied to obtain the DL and UL IRS phase shifts, respectively. For the latter case, the IRS phase shifts are obtained by the successive convex approximation technique. To further reduce the computational complexity of the single-user system, we derive the closed-form solutions of IRS phase shifts in each iteration for the two different reflection setups. Simulation results show that all the proposed algorithms can greatly improve the secrecy throughput compared to the conventional system without IRS.

Driven by artificial intelligence and computer vision industries, Graphics Processing Units (GPUs) are now rapidly achieving extraordinary computing power. In particular, the NVIDIA Tegra K1/X1/X2 embedded GPU platforms, which are also treated as edge computing devices, are now widely used in embedded environments such as mobile phones, game consoles, and vehicle-mounted systems to support high-dimension display, auto-pilot, and so on. Meanwhile, with the rise of the Internet of Things (IoT), the demand for cryptographic operations for secure communications and authentications between edge computing nodes and IoT devices is also expanding. In this contribution, instead of the conventional implementations based on FPGA, ASIC, and ARM CPUs, we provide an alternative solution for cryptographic implementation on embedded GPU devices. Targeting the new cipher suite added in TLS 1.3, we implement Edwards25519/448 and Curve25519/448 on an edge computing platform, embedded GPU NVIDIA Tegra X2, where various performance optimizations are customized for the target platform, including a novel parallel method for the register-limited embedded GPUs. With about 15 W of power consumption, it can provide 210k/31k ops/s of Curve25519/448 scalar multiplication, 834k/123k ops/s of fixed-point Edwards25519/448 scalar multiplication, and 150k/22k ops/s of unknown-point one, which are respectively the primitives and main workloads of key agreement, signature generation, and verification of the TLS 1.3 protocol. Our implementations achieve 8 to 26 times speedup of OpenSSL running in the very powerful ARM CPU of the same platform and outperform the state-of-the-art implementations in FPGA by a wide margin with better power efficiency.

The reduction in the number of queries to the object model is a hot topic in the current research of black-box adversarial attack methods. To solve this problem, in this article, we propose generating adversarial examples with shadow model (GASM) that shifts the number of queries to the object model to the shadow model. The method first determines the shadow model based on the robustness and transferability of classifiers and fine-tunes the decision boundary of the shadow model by constructing adversarial datasets. Second, accesses the shadow model and constructs adversarial examples by maximizing the output probability of the targeted class (any class other than the current one) to modify the image gradient information. Finally, the results show that GASM has the strongest transferability and outperforms white-box attacks when AlexNet (MNIST), VGG-19 (CIFAR10), and MobileNet v2 (Tiny ImageNet) are selected as shadow models.

In this paper, we mainly study quaternary linear codes and their binary subfield codes. First we obtain a general explicit relationship between quaternary linear codes and their binary subfield codes in terms of generator matrices and defining sets. Second, we construct quaternary linear codes via simplicial complexes and determine the weight distributions of these codes. Third, the weight distributions of the binary subfield codes of these quaternary codes are also computed by employing the general characterization. Furthermore, we present two infinite families of optimal linear codes with respect to the Griesmer Bound, and a class of binary almost optimal codes with respect to the Sphere Packing Bound. We also need to emphasize that we obtain at least 9 new quaternary linear codes.

Accurate forecast of citywide passenger demand helps online car-hailing service providers to better schedule driver supplies. Previous research either uses only passenger order history and fails to capture the deep dependency of passenger demand, or is restricted on grid region partition that loses physical context. Recent advance in mobile traffic analysis has fostered understanding of city functions. In this article, we propose FlowFlexDP, a demand prediction model that integrates regional crowd flow and applies to flexible region partition. Analysis on a cellular dataset covering 1.5 million users in a major city in China reveals strong correlation between passenger demand and crowd flow. FlowFlexDP extracts both order history and crowd flow from cellular data, and adopts Graph Convolutional Neural Network to adapt prediction for regions of arbitrary shapes and sizes in a city. Evaluation on a large scale data set of 6 online car-hailing applications from cellular data shows that FlowFlexDP accurately predicts passenger demand and outperforms the state-of-the-art demand prediction methods.

The UHF radio-frequency identification (RFID) has gained growing attention for tagged object localization in smart storage systems. Due to Non-Line-Of-Sight (NLOS) condition, it is challenging to accurately locate the position of tags inside closed spaces. In this paper, we propose a precise and cost-effective solution for tagged object localization in closed spaces, using only received signal strength (RSS) information. We establish a RSS profile for each tag and discover some important features of RSS profiles including uniqueness, time-variation, column-dependence and waveform-similarity. Based on these features, we propose a reference-free RSS-profile (RFRP) localization scheme. The advantage of our propose scheme is to accurately localize multiple tags in closed spaces by overcoming the challenges including the lack of pre-deployed reference tags, NLOS propagation, multi-path propagation and coupling effect. The RFRP scheme first roughly estimates tags’ coordinates based on Peak Asymmetry Factor, then acquires reference-tag substitutes through the similarity of RSS sequences. Subsequently, our scheme refines the relative positions of all tags by these substitutes. Finally all tags’ absolute positions are estimated through a RSS-ranging model. Extensive experiment results demonstrate that our approach can achieve high ordering accuracy and localization accuracy for the tags inside closed spaces.

This paper considers the fundamental problem of deployment of Unmanned AerialVehIcles for aniSotropic monItoringTasks (VISIT), that is, given a set of objects with determined coordinates and directions in 2D area, deploy a fixed number of UAVs by adjusting their coordinates and orientations such that the overall monitoring utility for all objects is maximized. We develop a theoretical framework to address VISIT problem. First, we establish monitoring model whose quality of monitoring (QoM is anisotropic with monitoring angle and varying with various monitoring distance. To the best of our knowledge, we are the first considering the anisotropy of monitoring angle. Then, we propose a framework consisting of area discretization and Monitoring Dominating Set (MDS) extraction to reduce the infinite solution space of VISIT to a limited one with performance bound. Finally, we model the reformulated problem as maximizing a monotone submodular function subject to a matroid constraint, and present a greedy algorithm with$1-1/e-\epsilon$approximation ratio. We conduct both simulations and field experiments to evaluate our framework, and the results show that our algorithm outperforms comparison algorithms by at least 41.3 percent.

In medical scenarios supported by edge clouds, it is difficult for patients to truly gain ownership of their electronic health records (EHRs). However, it is easy for doctors to modify hospital data to deny incorrect treatment records, which makes it difficult to protect the rights of patients. To improve patient control over EHRs, an attribute-based encryption protection scheme named CEC-ABE for EHRs combined with a blockchain is proposed to protect EHRs in edge cloud environments. In this scheme, the agreement process between the patient and the hospital is completed before the ABE stage, and the treatment information, including the treatment time, treatment doctor and additional information, is confidentially transmitted through an encryption algorithm. By storing the uploaded encrypted data in the blockchain in the form of transaction records, the integrity of the data is guaranteed, which facilitates the traceability of EHR generation. Access to EHRs is controlled by the ABE scheme of the outsourced ciphertext policy, and fine-grained attribute revocation can be employed to ensure the security of the ciphertext. The CEC-ABE algorithm, CP-ABE algorithm and other algorithms are experimentally tested, and the computational cost of each stage of the algorithms and the computational delay of each role are compared. CEC-ABE can significantly improve performance in key generation, outsourced decryption and other stages. Compared with the algorithm whose performance is second only to CEC-ABE, it reduces the computational overhead by 1.73% and 5.2%. The results show that the overall comprehensive performance of the CEC-ABE algorithm is better than that of the other algorithms.

Regional medical consortium systems facilitates medical information sharing. However, many security issues exposed by the dominant centralized architectures, such as single points of failure, unauthorized operations and illegal access, are increasingly apparent constraints on the security and efficiency of data sharing across domains. Even more, any malicious operation detected, effective measures should be executed promptly for identity tracing. In this paper, we propose a secure and efficient cross-domain authentication scheme based on two cooperative blockchains (BCs) for medical consortium systems. Specifically, an intra-domain BC records any legal users’ registration and authentication information while an inter-domain BC is responsible for writing users’ cross-domain authentication information. In each domain, the general hospital acts as a trusted third service provider to achieve cross-chain interactions. For the entire cross-domain authentication procedure, anonymity mechanism is utilized to enhance security, and to trace malicious users, the improved chameleon hash is used in the intra-domain BC to redact the state of the user, and blacklist merkle tree is extended in the inter-domain BC to protect different domains’ services from illegal accessing. In addition, security analysis and performance evaluation are completely given to prove the superior security features and performance compared with other schemes.

Visual Question Answering (VQA) is a research topic of significant interest at the intersection of computer vision and natural language understanding. Recent research indicates that attributes and knowledge can effectively improve performance for both image captioning and VQA. In this article, an inner knowledge-based Img2Doc algorithm for VQA is presented. The inner knowledge is characterized as the inner attribute relationship in visual images. In addition to using an attribute network for inner knowledge-based image representation, VQA scheme is associated with a question-guided Doc2Vec method for question–answering. The attribute network generates inner knowledge-based features for visual images, while a novel question-guided Doc2Vec method aims at converting natural language text to vector features. After the vector features are extracted, they are combined with visual image features into a classifier to provide an answer. Based on our model, the VQA problem is resolved by textual question answering. The experimental results demonstrate that the proposed method achieves superior performance on multiple benchmark datasets.

Nowadays, detecting and evaluating the internal structure of packages becomes a crucial task for logistics systems to guarantee reliability and security. However, prior solutions such as X-ray diffraction and WiFi-based detection are not suitable for this purpose. X-ray-based methods usually require manual analysis or image processing algorithms with high complexity, while WiFi-based solutions may fail to detect complex structures due to the significant error of the RF-signal features. In this article, we propose RF-Detector, a low-cost RFID solution for performing three-dimensional (3D) structure detection of items contained in the packages, including the item orientations and relative locations. We thoroughly investigate a brand-new sensing model for RFID-based 3D structure detection, i.e., revolving scanning. We propose not only the fundamental revolving model but also a novel calibration method for the undesired deployments. We have implemented a prototype system to evaluate the performance of RF-Detector. Extensive evaluations in real settings show the effectiveness of RF-Detector, achieving very high accuracy of the internal 3D structure detection.

With the commoditization of private data, data trading in consideration of user privacy protection has become a fascinating research topic. The trading for private web browsing histories brings huge economic value to data consumers when leveraged by targeted advertising. And the online pricing of these private data further helps achieve more realistic data trading. In this paper, we study the trading and pricing of multiple correlated queries on private web browsing history data at the same time. We propose CTRADE, which is a novel online data CommodiTization fRamework for trAding multiple correlateD queriEs over private data. CTRADE first devises a modified matrix mechanism to perturb query answers. It especially quantifies privacy loss under the relaxation of classical differential privacy and a newly devised mechanism with relaxed matrix sensitivity, and further compensates data owners for their diverse privacy losses in a satisfying manner. CTRADE then proposes an ellipsoid-based query pricing mechanism according to a given linear market value model, which exploits the features of the ellipsoid to explore and exploit the close-optimal dynamic price at each round. In particular, the proposed mechanism produces a low cumulative regret, which is quadratic in the dimension of the feature vector and logarithmic in the number of total rounds. Through real-data based experiments, our analysis and evaluation results demonstrate that CTRADE balances total error and privacy preferences well within acceptable running time, indeed produces a convergent cumulative regret with more rounds, and also achieves all desired economic properties of budget balance, individual rationality, and truthfulness.

Once started, existing hash tables cannot change their pre-defined hash functions, even if the incoming data cannot be evenly distributed to the hash table buckets. In this paper, we presentDHash, a type of hash table for shared memory systems, that can change its hash function and rebuild the hash table on the fly, without noticeably degrading its service. The major technical novelty ofDHashstems from an efficient distributing mechanism that can atomically distribute every node when rebuilding, without locking the corresponding hash table buckets. This not only enables non-blocking lookup, insert, and delete operations, but more importantly, makesDHashindependent of the implementation of hash table buckets, such thatDHashallows programmers to select the set algorithms that meet their requirements best from a variety of existing lock-free and wait-free set algorithms. Evaluations show thatDHashcan efficiently change its hash function on the fly. Moreover, when rebuilding,DHashconsistently outperforms the state-of-the-art hash tables in terms of throughput and response time of concurrent operations, at different concurrency levels, and with different operation mixes and average load factors.

In the fog computing, it is difficult to satisfy the security and privacy requirement for traditional access control system, such as attribute-based access control system. Risk-based access control system can adapt to the dynamic fog environment. However, the existing risk schemes are modeled for users in majority, not for fog nodes, and the context and privacy sensitivity are rarely considered. In this article, we propose a risk assessment scheme for fog nodes in access control system. Firstly, to improve the accuracy of risk score, the risk assessment is modeled with the subject, object, and context for fog nodes; Secondly, we address the risk assessment computing module for every component. Moreover, we depict system model for risk assessment and implement its prototype system risk assessment model. In the end, the reasonability and correctness of computing model are analyzed by proving and simulation. According to the experiments, the accuracy of risk score is higher than that of the work-based access control and dynamic access control for IoT. Therefore, the feasibility and effectiveness of this scheme are proved through the experiments.

To meet user's functional requirements, cloud computing and big data have become the most commonly used computing and data resources. Based on analysis, conversion, extraction and refinement for the big data, a disease can be prevented and group behavior can be predicted. However, each user's private data is also an element in big data. Users must provide private data to the service providers to meet their functional requirements. To gain economic benefits, some SaaS service providers have not been authorized to collect and analyze the user's sensitive private data, as a result, the user's private data is disclosed. In this paper, we propose a private data chain disclosure discovery method, to prevent a user's sensitive privacy information from being illegally disclosed. First, we measure the similarity degree and cost of the disclosure of the private data. Second, according to the similarity degree and cost of disclosure, the disclosure chain and key private data are detected in the process of interaction between user and SaaS service. Third, we propose a discovery framework for the private data chain and demonstrate its feasibility and effectiveness by experiments.

The implementation of immunity passport has been hampered by the controversies over vaccines in various countries, the privacy of vaccinators, and the forgery of passports. While some existing schemes have been devoted to accelerating this effort, the problems above are not well solved in existing schemes. In this paper, we present an immunity passport scheme based on the dual‐blockchain architecture, which frees people from the cumbersome epidemic prevention process while traveling abroad. Specially, the dual‐blockchain architecture is established to fit with the scenarios of immunity passport. Searchable encryption and anonymous authentication are utilized to ensure users’ privacy. In addition, the performance and security evaluations show that our scheme achieves the proposed security goals and surpasses other authentication schemes in communicational and computational overheads.

State-of-the-art battery-free RFID systems attach multiple RFID tags to an object and exploit their RF phase to estimate its three-dimensional (3D) orientation. However, the measured RF phase may be inaccurate because each tag's signal fingerprint (i.e., RSSI and RF Phase) is distorted by multipath interference and electromagnetic interaction between neighboring tags. In this paper, we propose RF-Orien3D that minimizes these interferences for accurate 3D orientation recognition only using two RFID tags. The electromagnet interference modifies the radiation pattern and modulation factor of each tag in the two-element tag array, which can be estimated to compensate for the distortion in RFID fingerprints. To deal with the multipath impact, we simulate multipath noise to generate huge amounts of RFID fingerprints and use them to pre-train a convolutional neural network (CNN). Then we only collect dozens of actual samples to fine-tune the CNN for multipath-tolerant orientation recognition. The experiments show RF-Orien3D recognizes a two-tag labeled object's 2D orientation with the angular error of about 16° and its 3D orientation (azimuth and elevation) with the errors of about 29° and 11° in low/rich multipath scenarios.

Current facial authentication (FA) systems are mostly based on the images of human faces, thus suffering from privacy leakage and spoofing attacks. Mainstream systems utilize facial geometry features for spoofing mitigation, which are still easy to deceive with the feature manipulation, e.g., 3D-printed human faces. In this paper, we propose a novel privacy-preserving anti-spoofing FA system, named RFace, which extracts both the 3D geometry and inner biomaterial features of faces using a COTS RFID tag array. These features are difficult to obtain and forge, hence are resistant to spoofing attacks. RFace only requires users to pose their faces in front of a tag array for a few seconds, without leaking their visual facial information. We build a theoretical model to rigorously prove the feasibility of feature acquisition and the correlation between the facial features and RF signals. For practicality, we design an effective algorithm to mitigate the impact of unstable distance and angle deflection from the face to the array. Extensive experiments with 30 participants and three types of spoofing attacks show that RFace achieves an average authentication success rate of over 95.7% and an EER of 4.4%. More importantly, no spoofing attack succeeds in deceiving RFace in the experiments.

Since Deep Convolutional Neural Network (DCNN) training involves complex computations and data transmissions, the previous DCNN processors hard to achieve ideal energy efficiency. This paper proposed a DCNN processor supports both inference and training for the embedded system. The processor contains three heterogeneous cores to provide distinct computation patterns and dataflow for different training phases. In addition, since inference takes up more than 90% of the workload of the DCNN application, the three cores of the processor can be reconfigured to efficiently support the inference to achieve leading resources Utilization. The processor is fabricated in 55nm CMOS technology, post-layout simulation shows the processor achieving 1.36 Tops/w energy efficiency for training and 2.44 Tops/w for the inference.

Temperature sensing plays a significant role in upholding quality assurance and meeting regulatory compliance in a wide variety of applications, such as fire safety and cold chain monitoring. However, existing temperature measurement devices are bulky, cost-prohibitive, or battery-powered, making item-level sensing and intelligence costly. In this paper, we present a novel tag-based thermometer called Thermotag, which uses a common passive RFID tag to sense the temperature with competitive advantages of being low-cost, battery-free, and robust to environmental conditions. The basic idea of Thermotag is that the resistance of a semiconductor diode in a tag's chip is temperature-sensitive. By measuring the discharging period through the reverse-polarized diode, we can estimate the temperature indirectly. We propose a standards-compliant measurement scheme of the discharging period by using a tag's volatile memory and build a mapping model between the discharging period and temperature for accurate and reliable temperature sensing. We implement Thermotag using a commercial off-the-shelf RFID system, with no need for any firmware or hardware modifications. Extensive experiments show that the temperature measurement has a large span ranging from 0 °C to 85 °C and a mean error of 2.7 °C.

Abstract The ever‐evolving trip mode of human being leads the automobiles moving toward connected, autonomous, sharing, and electrified vehicles rapidly. But the connection introduces new cybersecurity problems on in‐vehicle networks, which poses great challenges for safety guarantee of distributed automotive cyber‐physical systems. This article first analyzes the cybersecurity vulnerabilities and defines the security requirements for in‐vehicle networks, and then introduces the architecture evolution of in‐vehicle network. Based on the definition on architecture of in‐vehicle networks, this article defines a security protection framework for it. And then, it surveys the state‐of‐the‐art works for availability protection, integrity protection, and confidentiality protection of in‐vehicle networks, respectively, and detailed analysis and comparisons are given about the proposed cybersecurity protection mechanisms. Finally, it summarizes the future challenges for cybersecurity protection of in‐vehicle networks, and proposes possible solutions for these challenges.

Abstract Network traffic prediction is of great significance to resource management in cyber‐physical systems (CPSs). In particular, network traffic is a nonlinear time series. Echo state network (ESN) is a new neural network with strong nonlinear processing capacity and short‐term memory capacity, and thus can achieve good performance in predicting nonlinear time series. However, network traffic has various characteristics such as self‐similarity, chaos, mutability. As the core of ESN, the reservoir will be fixed rather than adjustable once it is generated, which limits the prediction performance of ESN in different network traffic. To achieve universal excellent prediction performance, this paper proposes a new network traffic prediction method based on ESN with adaptive reservoir (ESN‐AR). First, the framework of ESN‐AR is constructed for network traffic prediction, in which the idea of generative adversarial network (GAN) is incorporated into ESN to adaptively adjust the reservoir. Specifically, ESN is used as the generative model to predict network traffic and feedforward neural network (FNN) is used as the discriminative model to distinguish between the real network traffic and the predicted network traffic. Second, the adversarial training algorithm of ESN‐AR is proposed to obtain the appropriate reservoir depending on the network traffic characteristics. Finally, ESN‐AR is applied to the prediction of three actual network traffic with different characteristics. Simulation results show that compared with the state‐of‐the‐art models, the proposed method achieves more accurate and stable prediction performance.

Named data networking (NDN) is an emerging information-centric networking paradigm, in which the Internet of Things (IoT) achieves excellent scalability. Recent literature proposes the concept of NDN-IoT, which maximizes the expansion of IoT applications by deploying NDN in the IoT. In the NDN, the security is built into the network by embedding a public signature in each data package to verify the authenticity and integrity of the content. However, signature schemes in the NDN-IoT environment are facing several challenges, such as signing security challenge for resource-constrained IoT end devices (EDs) and verification efficiency challenge for NDN routers. This article mainly studies the data package authentication scheme in the package-level security mechanism. Based on mobile edge computing (MEC), an efficient certificateless group signature scheme featured with anonymity, unforgeability, traceability, and key escrow resilience is proposed. The regional and edge architecture is utilized to solve the device management problem of IoT, reducing the risks of content pollution attacks from the data source. By offloading signature pressure to MEC servers, the contradiction between heavy overhead and shortage of ED resources is avoided. Moreover, the verification efficiency in NDN router is much improved via batch verification in the proposed scheme. Both security analysis and experimental simulations show that the proposed MEC-based certificateless group signature scheme is provably secure and practical.

Extensibility is an important optimization objective for the E/E architecture of automotive cyber-physical systems (ACPS), while little attention has paid to the extensibility-aware design of in-vehicle network. To address this problem, this paper formulates a trade-off problem that balances the bandwidth utilization and the extensibility from the initial design of CAN FD. We firstly propose a new extensibility model and the related evaluation metric, and then two optimization algorithms, namely, the mixed integer linear programming (MILP) approach and the simulated annealing (SA) based heuristic approach, are proposed to resolve the trade-off problem for mid-sized and industry sized signal sets, respectively. The experiment results show the efficiency of the proposed extensibility metric and the optimization algorithms. By comparing with state-of-the-art algorithm, the MILP reduces the increase range of the bandwidth utilization of the extended signal set by 18.17% to 57.64% averagely, and 49.22% to 89.40% maximally, with only 0.06% to 0.79% bandwidth utilization overhead; the SA approach can reduces the increase range of the bandwidth utilization of the extended signal set by 12.71% to 58.33% averagely, and 40.08% to 89.40% maximally, with only 0.06% to 0.8% bandwidth utilization overhead.

In most RFID localization systems, acquiring a reader antenna's position at each sampling time is challenging, especially for those antenna-carrying robot or drone systems with unpredictable trajectories. In this article, we present RF-MVO that fuses RFID and computer vision for stationary RFID localization in 3D space by attaching a light-weight 2D monocular camera to two reader antennas in parallel. First, the existing monocular visual odometry only recovers a camera/antenna trajectory in the camera view from 2D images. By combining it with RF phase, we design a model to estimate a scale factor for real-world trajectory transformation, along with spatial directions of an RFID tag relative to a virtual antenna array due to the mobility of each antenna. Then we propose a novel RFID localization algorithm that does not require exhaustively searching all possible positions within the pre-specified region. Second, to speed up the searching process and improve localization accuracy, we propose a coarse-to-fine optimization algorithm. Third, we introduce the concept of horizontal dilution of precision (HDOP) to measure the confidence level of localization results. Our experiments demonstrate the effectiveness of proposed algorithms and show RF-MVO can achieve 6.23 cm localization error.

The demand for data center bandwidth has exploded due to the continuous development of cloud computing, causing the use of network resources close to saturation. Optical network has become an encouraging technology for many burgeoning networks and parallel/distributed computing applications because of its huge bandwidth. This article focuses on efficient embedding of data centers into optical networks, which aims to reduce complexity of the network topology by using the parallel transmission characteristics of optical fiber. We first present a novel virtual network embedding (VNE) mathematical model used for optical data center networks. Then we derive a priority of location VNE algorithm according to node proximity sensing and path comprehensive evaluation. Furthermore, we propose routing and wavelength assignment for DCNs into optical networks, and identify the lower bound of the required number of wavelengths. Extensive evaluations show that the proposed embedding algorithm can reduce the average waiting time of virtual network requests by 20 percent, increase the request acceptance rate and revenue-overhead ratio by 13 percent, as compared to the latest VNE algorithm.

Due to robustness against multi-path effect, channel state information (CSI) of Orthogonal Frequency Division Multiplexing (OFDM) systems is supposed to provide accurate distance measurement for indoor localization. However, we find that the original CSI ranging model is biased, so the model cannot be used to directly derive Cramer-Rao lower bound (CRLB) of positioning error for CSI-ranging based localization scheme. In this paper we first analyze the estimation bias of the original CSI ranging model according to indoor wireless channel model. Then we propose a negative power summation ranging model which can be used as an unbiased ranging model for both Line-Of-Sight (LOS) and Non-LOS scenarios. Subsequently, based on the proposed model, we derive both the CRLB of ranging error and the CRLB of positioning error for CSI-ranging localization scheme. Through simulation we validate the bias of the original ranging model and the approximately zero bias of our proposed ranging model. Through comprehensive experiments in different indoor scenarios, localization errors by different ranging models are compared to the CRLB, meanwhile our proposed ranging model is demonstrated to have better ranging and localization accuracy than the original ranging model.

Recent RFID systems start attaching a tag array consisting of two or more tags on an object to deal with polarization mismatch and RF phase periodicity for battery-free sensing and localization. The multi-tag solution can also provide target orientation estimation. However, when these tags are closely spaced apart, mutual coupling will be induced, producing the unexpected changes in reported RSSI and RF phase. In this paper, we present RF-Mirror that enables compensating the distortion in a two-tag array labeled RFID system. The system would output the accurate difference in tag-to-antenna distances between two tags, which is a fundamental parameter in previous works for use. Firstly, we model the backscatter signal of a responding tag in a two-tag scenario, and then formulate novel RSSI- and RF phase-distance models with coupling terms. Secondly, we design an algorithm to characterize the coupling effect on tag gain by fusing RSSI and RF phase. Thirdly, we design a decoupling algorithm based on an observation that tag mutual coupling is independent of the position of a tag array relative to a reader antenna. Our experiments show the effectiveness of our models and RF-Mirror achieves the decoupling error of 0.197 cm in calculating the tag-to-antenna distance difference.

In recent years, with the rapid development of the Internet-of-Things (IoT) technologies, many intelligent sensing applications have emerged, which realize contactless sensing and human-computer interaction (HCI). Handwriting recognition is the communication link between the human and computer. Previous handwriting recognition applications are usually founded on images and sensors, which require significant device overhead and are device dependent. Recently, the revolution of the wireless signal sensing technology has laid the foundation for the intelligent handwriting recognition technology without devices. In this article, we propose WiReader, an adaptive air handwriting recognition system based on wireless signals. WiReader utilizes ubiquitous commercial WiFi devices to process the collected channel state information (CSI), segments the data in combination with activity factors, and then transforms the original signal using the CSI-Ratio model. In order to address the problem of feature extraction caused by handwriting, we utilize the cumulative principal components and multilayer wavelet transform for the transformed signal. Finally, the energy feature matrix is generated and combines with long short-term memory (LSTM) to realize the recognition of different handwriting actions. Extensive real-world experiments show that WiReader achieves an average recognition accuracy of 90.64% leading other applications in three scenarios and has strong robustness to user location, user diversity, and different scenarios.

As the infrastructure of the intelligent transportation system, vehicular ad hoc networks (VANETs) have greatly improved traffic efficiency. However, due to the openness characteristics of VANETs, trust and privacy are still two challenging issues in building a more secure network environment: it is difficult to protect the privacy of vehicles and meanwhile to determine whether the message sent by the vehicle is credible. In this article, a blockchain-based trust management model, combined with conditional privacy-preserving announcement scheme (BTCPS), is proposed for VANETs. First, an anonymous aggregate vehicular announcement protocol is designed to allow vehicles to send messages anonymously in the nonfully trusted environment to guarantee the privacy of the vehicle. Second, a blockchain-based trust management model is present to realize the message synchronization and credibility. Roadside units (RSUs) are able to calculate message reliability based on vehicles' reputation values which are safely stored in the blockchain. In addition, BTCPS also achieves conditional privacy since trusted authority can trace malicious vehicles' identities in anonymous announcements with the related public addresses. Finally, a mixed consensus algorithm based on proof-of-work and practical Byzantine fault tolerates algorithm is suggested for better efficiency. Security analysis and performance evaluation demonstrate that the proposed scheme is secure and effective in VANETs.

With the popularization of Internet-of-Things (IoT) systems, passive action recognition on channel state information (CSI) has attracted much attention. Most conventional work under the machine-learning framework utilizes handcrafted features (e.g., statistic features) that are unable to sufficiently describe the sequence data and heavily rely on designers' experiences. Therefore, how to automatically learn abundant spatial-temporal information from CSI data is a topic worthy of study. In this article, we propose a deep learning framework that integrates spatial features learned from the convolutional neural network (CNN) into the temporal model multilayer bidirectional long short-term memory (Bi-LSTM). Specifically, CSI streams are segmented into a series of patches, from which spatial features are extracted by our designed CNN structure. Considering long-term dependencies between adjacent sequences, the fully connected layer of CNN for each patch is taken as the Bi-LSTM sequential input to further capture temporal features. Our model is appealing in that it can simultaneously learn temporal dynamics and convolutional perceptual representations. To the best of our knowledge, this is the first work to explore deep spatial-temporal features for CSI-based action recognition. Furthermore, in order to solve the problem that the trained model fully fails with environmental changes, we use the off-the-shelf model as the pretrained model and fine-tune it in the new scenario. The transfer method is able to realize cross-scene action recognition with low computational consumption and satisfactory accuracy. We carry out experiments on indoor data and the experimental results validate the effectiveness of our algorithm.

This article studies multiple unmanned aerial vehicles (multi-UAVs)-enabled wireless-powered Internet of Things (IoT), where a group of UAVs is dispatched as mobile power sources to charge a set of ground IoT devices. Different from the conventional radio-frequency (RF) wireless power transfer (WPT) systems, magnetic resonance-coupled (MRC) WPT systems can guarantee high power transfer efficiency without the complete alignment, which is remarkable. In this article, we extend the charging range by the wired connection between the energy receiving systems and IoT devices. Due to the restriction of carriable energy on the UAVs, designing the shortest possible trajectory for each UAV is necessary. We formulate it as a multidepots multi-UAVs trajectory optimization problem, jointly with constraints of the UAV's energy capacity and the area of the target region, to maximize the resource utilization of UAVs. To tackle this nonconvex problem, we decompose it into two subproblems, i.e., hovering locations selection and multi-UAVs trajectory optimization. For the first subproblem, we propose two approximation algorithms to obtain the near-optimal solution in the sparse networks. Then, we adopt a heuristic algorithm, a memetic algorithm-based variable neighborhood search (MAVNS), to achieve the quasioptimal trajectory rapidly. Finally, extensive numerical results are provided to evaluate the performance of the proposed algorithms. New insights are investigated on the estimation of feasibility that whether the given UAVs with energy capacity constraint can fully charge ground IoT devices within open areas.

In the interest of privacy concerns, cloud service users choose to encrypt their personal data before outsourcing them to cloud. However, it is difficult to achieve efficient search over encrypted cloud data. Therefore, how to design an efficient and accurate search scheme over large-scale encrypted cloud data is a challenge. In this paper, we integrate bisecting k-means algorithm and multibranch tree structure and propose the α-filtering tree search scheme based on bisecting k-means clusters. The novel index tree is built from bottom-up, and a greedy depth first algorithm is used for filtering the nonrelevant document cluster by calculating the relevance score between the filtering vector and the query vector. The α-filtering tree can improve the efficiency without the loss of search accuracy. The experiment on a real-world dataset demonstrates the effectiveness of our scheme.

Broadcast authentication is a fundamental security primitive in wireless sensor networks (WSNs), which is a critical sensing component of IoT. Although symmetric-key-based μ TESLA protocol has been proposed, some concerns about the difficulty of predicting the network lifecycle in advance and the security problems caused by an overlong long hash chain still remain. This paper presents a scalable broadcast authentication scheme named DH- μ TESLA, which is an extension and improvement of μ TESLA and Multilevel μ TESLA, to achieve several vital properties, such as infinite lifecycle of hash chains, security authentication, scalability, and strong tolerance of message loss. The proposal consists of the t,n -threshold-based self-reinitializable hash chain scheme (SRHC-TD) and the d -left-counting-Bloom-filter-based authentication scheme (AdlCBF). In comparison to other broadcast authentication protocols, our proposal achieves more security properties such as fresh node’s participation and DoS resistance. Furthermore, the reinitializable hash chain constructed in SRHC-TD is proved to be secure and has less computation and communication overhead compared with typical solutions, and efficient storage is realized based on AdlCBF, which can also defend against DoS attacks.

Although deep features have achieved the state-of-the-art performance in action recognition recently, the hand-crafted shallow features still play a critical role in characterizing human actions for taking advantage of visual contents in an intuitive way such as edge features. Therefore, the shallow features can serve as auxiliary visual cues supplementary to deep representations. In this paper, we propose a discriminative subspace learning model (DSLM) to explore the complementary properties between the hand-crafted shallow feature representations and the deep features. As for the RGB action recognition, this is the first work attempting to mine multi-level feature complementaries by the multi-view subspace learning scheme. To sufficiently capture the complementary information among heterogeneous features, we construct the DSLM by integrating the multi-view reconstruction error and classification error into an unified objective function. To be specific, we first use Fisher Vector to encode improved dense trajectories (iDT+FV) for shallow representations and two-stream convolutional neural network models (T-CNN) for generating deep features. Moreover, the presented DSLM algorithm projects multi-level features onto a shared discriminative subspace with the complementary information and discriminating capacity simultaneously incorporated. Finally, the action types of test samples are identified by the margins from the learned compact representations to the decision boundary. The experimental results on three datasets demonstrate the effectiveness of the proposed method.

Infrared small target detection technology is one of the key technologies in the field of computer vision. In recent years, several methods have been proposed for detecting small infrared targets. However, the existing methods are highly sensitive to challenging heterogeneous backgrounds, which are mainly due to: 1) infrared images containing mostly heavy clouds and chaotic sea backgrounds and 2) the inefficiency of utilizing the structural prior knowledge of the target. In this article, we propose a novel approach for infrared small target detection in order to take both the structural prior knowledge of the target and the self-correlation of the background into account. First, we construct a tensor model for the high-dimensional structural characteristics of multiframe infrared images. Second, inspired by the low-rank background and morphological operator, a novel method based on low-rank tensor completion with top-hat regularization is proposed, which integrates low-rank tensor completion and a ring top-hat regularization into our model. Third, a closed solution to the optimization algorithm is given to solve the proposed tensor model. Furthermore, the experimental results from seven real infrared sequences demonstrate the superiority of the proposed small target detection method. Compared with traditional baseline methods, the proposed method can not only achieve an improvement in the signal-to-clutter ratio gain and background suppression factor but also provide a more robust detection model in situations with low false-positive rates.

Accurate forecast of citywide crowd flows on flexible region partition benefits urban planning, traffic management, and public safety. Previous research either fails to capture the complex spatiotemporal dependencies of crowd flows or is restricted on grid region partition that loses semantic context. In this paper, we propose DeepFlowFlex, a graph-based model to jointly predict inflows and outflows for each region of arbitrary shape and size in a city. Analysis on cellular datasets covering 2.4 million users in China reveals dependencies and distinctive patterns of crowd flows in not only the conventional space and time domains, but also the speed domain, due to the diverse transportation modes in the mobility data. DeepFlowFlex explicitly groups crowd flows with respect to speed and time, and combines graph convolutional long short-term memory networks and graph convolutional neural networks to extract complex spatiotemporal dependencies, especially long-term and long-distance inter-region dependencies. Evaluations on two big cellular datasets and public GPS trace datasets show that DeepFlowFlex outperforms the state-of-the-art deep learning and big-data-based methods on both grid and non-grid city map partition.

Currently, lots of real social relations in social networks force users to face the potential risk of privacy leakage. Consequently, data holders would like to disturbor anonymize their individual data before publishing them, for the purpose of privacy protection. Due to the characteristics of high sensitivity and large volume data of social network graph structure, it is difficult for privacy protection schemes to enable a reasonable allocation of noises while keeping desirable data availability and execution efficiency. On the basis of differential privacy model, combining with clustering and randomization algorithms, a privacy protection approach PBCN (Privacy Preserving Approach Based on Clustering and Noise) is proposed. This proposal is composed of five algorithms including random disturbance based on clustering, graph reconstruction after disturbing degree sequence and noise nodes generation, etc. Furthermore, a privacy measure algorithm based on adjacency degree is put forward in order to objectively evaluate the privacy-preserving strength of various schemes against graph structure and degree attacks. Simulation experiments are conducted to achieve performance comparisons between PBCN, Spctr Add/Del, Spctr Switch, DER and HPDP. The experimental results show that PBCN realizes more satisfactory data availability and execution efficiency. Finally, parameters utility analysis demonstrates PBCN can achieve a “trade-off” between data availability and privacy protection level.

The Underwater Sensor Network (USN) has great advantages in marine environmental monitoring. When collecting the perception information of sensor nodes, the mobile node can effectively compensate for the shortcomings of traditional multi-hop transmission modes. However, the complex marine environment causes the location uncertainty of nodes. Therefore, a path planning method based on the location uncertainty of water surface nodes in USN is proposed in this paper. Firstly, the structure of the USN based on the mooring model is introduced and the problem model of path planning is proposed. Secondly, the inevitable communication circle is obtained by analyzing the deviation range and communication range of water surface nodes. Thirdly, Convex Hull algorithm is used to plan the path in accordance with the inevitable communication circle of water surface nodes. Finally, simulation results show that the proposed method can obtain a shorter path under the premise of ensuring the completion of information collection.

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.

WSN-as-a-Service (WaaS) is a novel application model of wireless sensor networks (WSNs). Owners of WSNs provide data queries as services, while users pay for needed services as they use such services. The adoption of WaaS improves the usage of WSNs and reduces the cost of network deployment and maintenance. It is challenging to protect data from curious users while, at the same time, providing MAX/MIN query services. In this paper, we propose a privacy-preserving MAX/MIN query processing method for WaaS. To the best of our knowledge, this work is the first to discuss a privacy-preserving data query method in the WaaS environment. To implement privacy-preserving MAX/MIN queries, we propose a novel query protocol by adopting the idea of secure multiparty computation. The protocol consists of two cooperative query processing algorithms that are deployed in the aggregate sensor and normal sensors. During query processing, multiple rounds of secure interactions between sensors are performed. In each round, one bit of the query result is determined through cooperation of sensors, while the data of sensors participating in query processing remain private. Curious users cannot obtain any private data from the network even if a few compromised sensors collude with them. The analysis and evaluations indicate that the proposed protocol computes query results reliably, avoids the energy hole problem and is efficient in terms of communication cost.

This paper considers the fundamental problem of placement of Unmanned Aerial VehIcles for aniSotropic monItoring Tasks (VISIT). That is, given a set of objects on 2D area, place a fixed number of UAVs by adjusting their coordinates and orientations subject to Gaussian bias, such that the overall monitoring utility for all objects is maximized. We develop a theoretical framework to address VISIT. First, we establish the monitoring model whose quality of monitoring (QoM) is anisotropy with respect to monitoring angle and monitoring distance. To the best of our knowledge, we are the first to consider anisotropic QoM. Then, we propose an algorithm consisting of area discretization and Monitoring Dominating Set (MDS) extraction, to reduce the infinite solution space to a limited one without performance loss. Finally, we prove that the reformulated problem can be modeled as maximizing a monotone submodular function subject to a matroid constraint and present a greedy algorithm with 1−1/e−ϵ approximation ratio to address it. We conduct both simulations and field experiments to evaluate our algorithm, and the results show that our algorithm outperforms comparison algorithms by at least 41.3%.

Accurately estimating origin-destination (OD) network traffic is crucial for network management and capacity planning. However, the potential network anomaly and complex noise make this goal difficult to achieve. Existing network traffic estimation methods usually impute network traffic independent of anomaly detection, which ignores the potential relationship between the two tasks to help each other in achieving better performance. Moreover, these approaches can only be suitable for simple Gaussian or outlier noise assumptions, which cannot be applied to more complex noise distributions in practical applications. To address these issues, we propose a novel anomaly-tolerant network traffic estimation approach for simultaneously estimating network traffic and detecting network anomaly. Specifically, by utilizing the inherent low-rank property and temporal characteristic of traffic matrix, we formulate the network traffic estimation problem as a noise-immune temporal matrix completion (NiTMC) model, where the complex noise is fitted by mixture of Gaussian (MoG), and the network anomaly is smoothed by the L2,1-norm regularization. In addition, we also design a convergence-guaranteed optimization algorithm based on the expectation maximization (EM) and block coordinate update (BCU) methods to solve the proposed model. Furthermore, to deal with large-scale network problems, we develop a scalable and memory-efficient algorithm by employing stochastic proximal gradient descent (SPGD) method. Finally, the extensive experiments performed on real datasets demonstrate that our proposed NiTMC model outperforms the previously widely used network traffic estimation methods.

In recent years, rapid development of sensing and computing has led to very large datasets. There is an urgent demand for innovative data analysis and processing techniques that are secure, privacy-protected and sustainable. In this article, taking human activities and interactions with Cyber-Physical Systems (CPS) into consideration, we propose a human behavior learning system based on Channel State Information (CSI) utilizing a series of algorithms for data analysis and processing. Aiming to recognize a set of gestures, our system is designed based on the observation that different gestures have different effects on signals and specific gesture signals have a unique energy spectrum. Specifically, an improved Linear Discriminant Analysis Algorithm (I-LDA) is devised to reduce the dimension of human behavior signals. Additionally, behaviors are learned by Logistic Regression Algorithm (LRA). Bandwidth ratios in an energy spectrum are selected as features to eliminate the impact of speed differences on results. The system is based on commercial off-the-shelf WiFi devices and we conduct a large number of experiments in a typical indoor environment to evaluate its performance. Experimental results show that our system is robust with average recognition accuracy of up to 96%.

As the Industry 4.0 or Internet of Things (IoT) era begins, security plays a key role in the Industry Internet of Things (IIoT) due to various threats, which include escape or Distributed Denial of Service (DDoS) attackers in the virtualization layer and vulnerability exploiters in the device layer. A successful cross-VM escape attack in the virtualization layer combined with cross-layer penetration in the device layer, which we define as an Advanced Persistent Escaper (APE), poses a great threat. Therefore, the development of detection and rejection methods for APEs across multiple layers in IIoT is an open issue. To the best of our knowledge, less effective methods are established, especially for vulnerability exploitation in the virtualization layer and backdoor leverage in the device layer. On the basis of this, we propose Escaper Cops (EscaperCOP), a detection method for cross-VM escapers in the virtualization layer and cross-layer penetrators in the device layer. In particular, a new detection method for guest-to-host escapers is proposed for the virtualization layer. Finally, a novel encryption method based on Identity-based Broadcast Encryption (IBBE) is proposed to protect the critical components in EscaperCOP, detection library, and control command library. To verify our method, experimental tests are performed for a large number of APEs in an IIoT framework. The test results have demonstrated the proposed method is effective with an acceptable level of detection ratio.

Spectrum auction is an effective way to redistribute scarce spectrum resources. However, most spectrum auction designs only target at economic robustness, while neglecting the inherent privacy leakage problem. Existing secure spectrum auction mechanisms fail to provide adequate security, and they all neglect the online fashion of spectrum request arrival. In this paper, for the first time, we propose a Privacy-pReserving and truthful Online double auction mechanism for Spectrum allocaTion in wireless networks, PROST. Compared with the state-of-the-art solutions, PROST provides a comprehensive and strong protection for users' sensitive information, especially for location privacy and time dynamics. PROST is constructed based on our carefully designed security building blocks, which support various arithmetics over encrypted real numbers, and they are also well applicable in other spectrum auctions. Besides, we improve on the existing online spectrum auction mechanisms by designing a novel privacy-preserving buyer grouping protocol for spectrum reuse. We not only theoretically prove that PROST can realize an all-round security against semi-honest adversaries but also extensively evaluate its performance. Experimental results validate that PROST achieves nice spectrum allocation efficiency with light computation and communication costs.

In this paper, a distributed filtering scheme is presented to deal with the fault detection problem of nonlinear stochastic systems with wireless sensor networks (WSNs). The nonlinear stochastic systems, which are of discrete-time form, are represented by interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy models. Each sensor of the WSN can receive measurements from itself and its neighboring sensors subject to a deterministic interconnection topology. Independent random variables obeying the Bernoulli distribution are formulated to characterize the randomly occurred packet losses between the WSN and the filter unit. To generate residual signals for evaluation functions of the fault detection mechanism, a novel type of IT2 T-S fuzzy distributed fault detection filter is proposed corresponding to each sensor node. Additionally, a fault reference model is adopted for improving the performance of the fault detection system. A new overall fault detection system is formulated in an IT2 T-S fuzzy model framework. Applying Lyapunov functional approach, we concentrate on the analysis of stability and performance of the resulting fault detection system. New techniques are utilized to handle the decoupling problem in design procedure. The desired parametric matrices of the fuzzy filters are designed subject to a developed criterion, which is a sufficient condition of the robust mean-square asymptotic stability for the overall fault detection system with a disturbance attenuation performance. Finally, a truck-trailer system with a four-node WSN is established for simulation validation. In simulations, the mincx function of the MatLab 2017a in Windows 10 OS is used to optimize the level of the disturbance attenuation performance, and to obtain the filter gains for the established system. By comparing the different time instants when the residual evaluation functions exceed their respective thresholds, simulation results successfully validate the effectiveness and applicability of the presented distributed fault detection scheme.

Understanding and predicting cellular traffic at large-scale and fine-granularity is beneficial and valuable to mobile users, wireless carriers, and city authorities. Predicting cellular traffic in modern metropolis is particularly challenging because of the tremendous temporal and spatial dynamics introduced by diverse user Internet behaviors and frequent user mobility citywide. In this paper, we characterize and investigate the root causes of such dynamics in cellular traffic through a big cellular usage dataset covering 1.5 million users and 5,929 cell towers in a major city of China. We reveal intensive spatio-temporal dependency even among distant cell towers, which is largely overlooked in previous works. To explicitly characterize and effectively model the spatio-temporal dependency of urban cellular traffic, we propose a novel decomposition of in-cell and inter-cell data traffic, and apply a graph-based deep learning approach to accurate cellular traffic prediction. Experimental results demonstrate that our method consistently outperforms the state-of-the-art time-series based approaches and we also show through an example study how the decomposition of cellular traffic can be used for event inference.

Dictionary learning has produced state-of-the-art results in various classification tasks. However, if the training data have a different distribution than the testing data, the learned sparse representation might not be optimal. Recently, several domain-adaptive dictionary learning (DADL) methods and kernels have been proposed and have achieved impressive performance. However, the performance of these single kernel-based methods heavily depends heavily on the choice of the kernel, and the question of how to combine multiple kernel learning (MKL) with the DADL framework has not been well studied. Motivated by these concerns, in this paper, we propose a multi-kernel domain-adaptive sparse representation-based classification (MK-DASRC) and then use it as a criterion to design a multi-kernel sparse representation-based domain-adaptive discriminative projection method, in which the discriminative features of the data in the two domains are simultaneously learned with the dictionary. The purpose of this method is to maximize the between-class sparse reconstruction residuals of data from both domains, and minimize the within-class sparse reconstruction residuals of data in the low-dimensional subspace. Thus, the resulting representations can satisfactorily fit MK-DASRC and simultaneously display discriminability. Extensive experimental results on a series of benchmark databases show that our method performs better than the state-of-the-art methods.

Cross-modal hashing has attracted increasing research attention due to its efficiency for large-scale multimedia retrieval. With simultaneous feature representation and hash function learning, deep cross-modal hashing (DCMH) methods have shown superior performance. However, most existing methods on DCMH adopt binary quantization functions (e.g., [Formula: see text]) to generate hash codes, which limit the retrieval performance since binary quantization functions are sensitive to the variations of numeric values. Toward this end, we propose a novel end-to-end ranking-based hashing framework, in this paper, termed as deep semantic-preserving ordinal hashing (DSPOH), to learn hash functions with deep neural networks by exploring the ranking structure of feature dimensions. In DSPOH, the ordinal representation, which encodes the relative rank ordering of feature dimensions, is explored to generate hash codes. Such ordinal embedding benefits from the numeric stability of rank correlation measures. To make the hash codes discriminative, the ordinal representation is expected to well predict the class labels so that the ranking-based hash function learning is optimally compatible with the label predicting. Meanwhile, the intermodality similarity is preserved to guarantee that the hash codes of different modalities are consistent. Importantly, DSPOH can be effectively integrated with different types of network architectures, which demonstrates the flexibility and scalability of our proposed hashing framework. Extensive experiments on three widely used multimodal data sets show that DSPOH outperforms state of the art for cross-modal retrieval tasks.

Visual Question Answering (VQA) is a hot-spot in the intersection of computer vision and natural language processing research and its progress has enabled many in high-level applications. This work aims to describe a novel VQA model based on semantic concept network construction and deep walk. Extracting visual image semantic representation is a significant and effective method for spanning the semantic gap. Moreover, current research has shown that co-occurrence patterns of concepts can enhance semantic representation. This work is motivated by the challenge that semantic concepts have complex interrelations and the relationships are similar to a network. Therefore, we construct a semantic concept network adopted by leveraging Word Activation Forces (WAFs), and mine the co-occurrence patterns of semantic concepts using deep walk. Then the model performs polynomial logistic regression on the basis of the extracted deep walk vector along with the visual image feature and question feature. The proposed model effectively integrates visual and semantic features of the image and natural language question. The experimental results show that our algorithm outperforms competitive baselines on three benchmark image QA datasets. Furthermore, through experiments in image annotation refinement and semantic analysis on pre-labeled LabelMe dataset, we test and verify the effectiveness of our constructed concept network for mining concept co-occurrence patterns, sensible concept clusters, and hierarchies.

The ubiquitous luminaries provide a new dimension for indoor navigation, as they are often well-structured and the visible light is reliable for its multipath-free nature. However, existing visible light-based technologies, which are generally frequency-based, require the modulation on light sources, modification to the device, or mounting extra devices. The combination of the cost-extensive floor map and the localization system with constraints on customized hardwares for capturing the flashing frequencies, no doubt, hinders the deployment of indoor navigation systems at scale in, nowadays, smart cities. In this paper, we provide a new perspective of indoor navigation on top of the virtual graph representation. The main idea of our proposed navigation system, named PILOT, stems from exploiting the peak intensities of ubiquitous unmodulated luminaries. In PILOT, the pedestrian paths with enriched sensory data are organically integrated to derive a meaningful graph, where each vertex corresponds to a light source and pairwise adjacent vertices (or light sources) form an edge with a computed length and direction. The graph, then, serves as a global reference frame for indoor navigation while avoiding the usage of pre-deployed floor maps, localization systems, or additional hardwares. We have implemented a prototype of PILOT on the Android platform, and extensive experiments in typical indoor environments demonstrate its effectiveness and efficiency.

Echo state network (ESN), which was proposed as a novel recurrent neural network (RNN), has already been proved to exhibit better prediction ability than traditional neural networks in dealing with time series prediction. However, ESN's randomly generated reservoir structure is of high complexity and can not guarantee the stability of the prediction. In this paper, we propose a novel ESN with deterministic multiple loops reservoir structure (MLR) to avoid the randomness of the reservoir in the classic ESN. In addition, compared with the adjacent-feedback loop reservoir structure (ALR), the novel reservoir structure strengthens the connection of neurons in the reservoir and improves the nonlinear approximation ability of ESN. To test its performance, our MLR-based ESN is applied to network traffic prediction. Extensive simulation results regarding to different prediction steps demonstrate that MLR can achieve higher prediction accuracy, and outperform existing prediction models. Furthermore, we also analyze the influence of parameters of MLR on the prediction accuracy, such as the neuronal interval of multiple loops and the number of loops.

The past years have witnessed the rapid conceptualization and development of wireless sensing based on Channel State Information (CSI) with commodity WiFi devices.Many research efforts have been devoted to promote WiFi sensing by innovating applications, refining models and optimizing algorithms. A critical issue of Cross-Technology Interference (CTI), however, is surprisingly unnoticed and largely unexplored in the existing literature. In this paper, we demonstrate that CTI poses severe impacts on CSI measurements and further degrades the performance of CSI-based sensing. Based on in-depth understanding of such impacts, we present PERFIC to deal with CTI for CSI on commercial WiFi. We first exploit the inherent cyclostationarity property of different signals to detect CTI and further identify the specific distorted subcarriers on CSI. For each interfered CSI, we then propose to mitigate the impacts of CTI by amending the abnormal subcarriers. We conduct experiments on typical wireless sensing applications, including human detection and activity classification, using off-the-shelf WiFi devices. The results demonstrate that PERFIC yields a remarkable performance gain of >30% with high efficiency and outperforms existing robust classifiers.By providing interference-free CSI that is amendable to existing and emerging CSI-based sensing applications, PERFIC underpins new insights for improving the sensitivity and reliability of wireless sensing.

Vital signs such as heart rate and heartbeat interval are currently measured by electrocardiograms (ECG) or wearable physiological monitors. These techniques either require contact with the patient's skin or are usually uncomfortable to wear, rendering them too expensive and user-unfriendly for daily monitoring. In this paper, we propose a new noninvasive technology to generate an Acousticcardiogram (ACG) that precisely monitors heartbeats using inaudible acoustic signals. ACG uses only commodity microphones and speakers commonly equipped on ubiquitous off-the-shelf devices, such as smartphones and laptops. By transmitting an acoustic signal and analyzing its reflections off human body, ACG is capable of recognizing the heart rate as well as heartbeat rhythm. We employ frequency-modulated sound signals to separate reflection of heart from that of background motions and breath, and continuously track the phase changes of the acoustic data. To translate these acoustic data into heart and breath rates, we leverage the dual microphone design on COTS mobile devices to suppress direct echo from speaker to microphones, identify heart rate in frequency domain, and adopt an advanced algorithm to extract individual heartbeats. We implement ACG on commercial devices and validate its performance in real environments. Experimental results demonstrate ACG monitors user's heartbeat accurately, with median heart rate estimation error of 0.6 beat per minute (bpm), and median heartbeat interval estimation error of 19 ms.

We study a new problem, refined localization, in this paper. Refined localization calculates the location of an object in high precision, given that the object is in a relatively small region such as the surface of a table. Refined localization is useful in many cyber-physical systems such as industrial autonomous robots. Existing vision-based approaches suffer from several disadvantages, including good lighting conditions, line of sight, pre-learning process, and high computation overhead. Also vision-based approaches cannot differentiate objects with similar colors and shapes. This paper presents a new refined localization system, called Trio, which uses passive Radio Frequency Identification (RFID) tags for low cost and easy deployment. Trio provides a new angle to utilize RF interference for tag localization by modeling the equivalent circuits of coupled tags. We implement our prototype using commercial off-the-shelf RFID reader and tags. Extensive experiment results demonstrate that Trio effectively achieves high accuracy of refined localization, i.e., <; 1 cm errors for several types of main stream tags.

As the Ultra High Frequency (UHF) passive Radio Frequency IDentification (RFID) technology becomes increasingly deployed, it faces an array of new security attacks. In this paper, we consider a type of attack in which a malicious RFID reader could arbitrarily modify the tags via standard commands, e.g., IDs or other data in the memory. To deal with this type of attack, we propose a physical-layer RF signal based reader authentication solution, namely Arbitrator, that involves passively listening on RF channels, analyzing the communication signals, identifying unauthorized readers and jamming the commands from such readers. Our solution does not need to modify RFID devices or the underlying communication standards, hence fully compatible with the existing RFID infrastructure. In this study, we have implemented a prototype Arbitrator over the Universal Software Radio Peripheral (USRP) platform, and conducted extensive experiments to evaluate its performance. Our results show that Arbitrator can detect unauthorized RFID readers with high accuracy, and thus effectively diminish the unauthorized access attacks.

Data mining has heralded the major breakthrough in data analysis, serving as a “super cruncher” to discover hidden information and valuable knowledge in big data systems. For many applications, the collection of big data usually involves various parties who are interested in pooling their private data sets together to jointly train machine-learning models that yield more accurate prediction results. However, data owners may not be willing to disclose their own data due to privacy concerns, making it imperative to provide privacy guarantee in collaborative data mining over distributed data sets. In this paper, we focus on tree-based data mining. To begin with, we design novel privacy-preserving schemes for two most common tasks: regression and binary classification, where individual data owners can perform training locally in a differentially private manner. Then, for the first time, we design and implement a privacy-preserving system for gradient boosting decision tree (GBDT), where different regression trees trained by multiple data owners can be securely aggregated into an ensemble. We conduct extensive experiments to evaluate the performance of our system on multiple real-world data sets. The results demonstrate that our system can provide a strong privacy protection for individual data owners while maintaining the prediction accuracy of the original trained model.

Accurate forecast of citywide passenger demand helps online car-hailing service providers to better schedule driver supplies. Previous research either uses only passenger order history and fails to capture the deep dependency of passenger demand, or is restricted on grid region partition that loses physical context. Recent advance in mobile traffic analysis has fostered understanding of city functions. In this paper, we propose FlowFlexDP, a demand prediction model that integrates regional crowd flow and applies to flexible region partition. Analysis on a cellular dataset covering 1.5 million users in a major city in China reveals strong correlation between passenger demand and crowd flow. FlowFlexDP extracts both order history and crowd flow from cellular data, and adopts Graph Convolutional Neural Network to adapt prediction for regions of arbitrary shapes and sizes in a city. Evaluation on a large scale data set of DiDi Chuxing from cellular data shows that FlowFlexDP accurately predicts passenger demand and outperforms the state-of-the-art demand prediction methods.

With the increasing popularity of location-based services (LBSs), it is of paramount importance to preserve one's location privacy. The commonly used location privacy preserving approach, location k-anonymity, strives to aggregate the queries of k nearby users within a so-called cloaked region via a trusted third-party anonymizer. As such, the probability to identify the location of every user involved is no more than 1/k, thus offering privacy preservation for users. One inherent limitation of k-anonymity, however, is that all users involved are assumed to be trusted and report their real locations. When location injection attacks (LIAs) are conducted, where the untrusted users inject fake locations (along with fake queries) to the anonymizer, the probability of disclosing one's location privacy could be greatly more than 1/k, yielding a much higher risk of privacy leakage. To tackle this problem, in this paper we present ILLIA, the first work that enables k-anonymity-based privacy preservation against LIA in continuous LBS queries. Central to the ILLIA idea is to explore the pattern of the users' mobility in continuous LBS queries. With a thorough understanding of the users' mobility similarity, a credibility-based k-anonymity scheme is developed, such that ILLIA is able to defense against LIA without requiring in advance knowledge of how fake locations are manipulated while still maintaining high quality of services. Both the effectiveness and the efficiency of ILLIA are validated by extensive simulations on real world dataset loc-Gowalla.

Due to overshadow and channel fading, many mobile users are unable to receive the signal transmitted from satellite directly. Hence, some relay stations should be set to help this type of users to receive signals reliably. In this paper, we present a novel cognitive hybrid satellite-terrestrial model, where two cognitive relays forward their received signal for a mobile user successively. Furthermore, we address its achievable rate maximization. We first convert the co-channel interference threshold into transmit power constraints, and then formulate the maximization of the achievable rate as an optimization problem. Based on Karush-Kuhn-Tucker conditions, the optimization problem is decomposed into four cases, each of which is solved in closed form. Simulation study with different system settings is presented, and the efficiency of the proposed power allocation scheme is shown.

A see-through-wall system can be used in life detection, military fields, elderly people surveillance. and gaming. The existing systems are mainly based on military devices, customized signals or pre-deployed sensors inside the room, which are very expensive and inaccessible for general use. Recently, a low-cost RFID technology has gained a lot of attention in this field. Since phase estimates of a battery-free RFID tag collected by a commercial off-the-shelf (COTS) RFID reader are sensitive to external interference, the RFID tag could be regarded as a battery-free sensor that detects reflections off targeted objects. The existing RFID-based system, however, needs to first learn the environment of the empty room beforehand to separate reflections off the tracked target. Besides, it can only track low-speed metal objects with high-positioning accuracy. Since the human body with its complex surface has a weaker ability to reflect radio frequency (RF) signals than metal objects, a battery-free RFID tag can capture only a subset of the reflections off the human body. To address these challenges, a RFID-based human motion sensing technology, called RF-HMS, is presented to track device-free human motion through walls. At first, we construct transfer functions of multipath channel based on phase and RSSI measurements to eliminate device noise and reflections off static objects like walls and furniture without learning the environment of the empty room before. Then a tag planar array is grouped by many battery-free RFID tags to improve the sensing performance. RF-HMS combines reflections from each RFID tag into a reinforced result. On this basis, we extract phase shifts to detect the absence or presence of any moving persons and further derive the reflections off a single moving person to identify his/her forward or backward motion direction. The results show that RF-HMS can effectively detect the absence or presence of moving persons with 100% accuracy and keep a high accuracy of more than 90% to track human motion directions.

A large family of algorithms for unsupervised dimension reduction is based on both the local and global structures of the data. A fundamental step in these methods is to model the local geometrical structure of the data. However, the previous methods mainly ignore two facts in this step: 1) the dimensionality of the data is usually far larger than the number of local data, which is a typical ill-posed problem and 2) the data might be polluted by noise. These facts normally may lead to an inaccurate learned local structure and may degrade the final performance. In this paper, we propose a novel unsupervised dimension reduction method with the ability to address these problems effectively while also preserving the global information of the input data. Specifically, we first denoise the local data by preserving their principal components and we then apply a regularization term to the local modeling function to solve the illposed problem. Then, we use a linear regression model to capture the local geometrical structure, which is demonstrated to be insensitive to the parameters. Finally, we propose two criteria to simultaneously model both the local and the global information. Theoretical analyses for the relations between the proposed methods and some classical dimension-reduction methods are presented. The experimental results from various databases demonstrate the effectiveness of our methods.

Exploiting radio frequency signals is promising for locating and tracking objects. Prior works focus on per-tag localization, in which each object is attached with one tag. In this paper, we propose a comprehensive localization and tracking scheme by attaching two RFID tags to one object. Instead of using per-tag localization pattern, adding one-more RFID tag to the object exhibits several benefits: 1) providing rich freedom in RFID reader's antenna spacing and placement; 2) supporting accurate calibration of the reader's antenna location and spacing, and 3) enabling fine-grained calculation on the orientation of the tags. All of these advantages ultimately improve the localization/tracking accuracy. Our extensive experimental results demonstrate that the average errors of localization and orientation of target tags are 6.415 cm and 1.330°, respectively. Our results also verify that the reader's antenna geometry does have impact on tag positioning performance.

Indoor localization plays an important role as the basis for a variety of mobile applications, such as navigating, tracking, and monitoring in indoor environments. However, many such systems cause potential privacy leakage in data transmission between mobile users and the localization server (LS). Unfortunately, there has been little research done on privacy issue, and the existing privacy-preserving solutions are algorithm-driven, each designed for specific localization algorithms, which hinders their wide-scale adoption. Furthermore, they mainly focus on users' location privacy, while the LS's data privacy cannot be guaranteed. In this paper, we propose a Privacy-Preserving Paradigm-driven framework for indoor LOCalization (P3-LOC). P3-LOC takes the advantage that most indoor localization systems share a common two-stage localization paradigm: information measurement and location estimation. Based on this, P3-LOC carefully perturbs and cloaks the transmitted data in these two stages and employs specially designed “k -anonymity” and “differential privacy” techniques to achieve the provable privacy preservation. The key advantage is that P3-LOC does not rely on any prior knowledge of the underlying localization algorithms, and it guarantees both users' location privacy and the LS's data privacy. Our extensive experiments from the measured data have validated that P3-LOC provides privacy preservation for general indoor localization techniques. In addition, P3-LOC is comparable with the state-of-the-art algorithm-driven techniques in terms of localization error, computation, and communication overhead.

WiFi technology has fostered numerous mobile computing applications, e.g. indoor localization, gesture and activity recognition, device-free localization, etc., due to its ubiquity. The awareness of LOS and NLOS is a prerequisite for WiFi-based methods, since the WiFi signals received under NLOS conditions may contain a lot of noise and multipath effects, exerting great influences on the accuracy of location or identification. Traditional schemes based on commodity WiFi devices can achieve real-time LOS/NLOS identification. However, these methods face the challenges of limited bandwidth and coarse multipath resolution. In this work, we explore the amplitude feature of PHY layer information, and accordingly propose AmpN, a real-time LOS identification scheme based on commodity WiFi infrastructure that is applicable in both static and mobile scenarios. AmpN employs BP neural network algorithm in static scenario and K-Mean method in dynamic scenario, respectively. Experimental results demonstrate that AmpN outperforms existing approaches, achieving overall LOS and NLOS detection rates of 94.2% and 97.6% in static case, and above 97% LOS and NLOS detection rates in mobile context. In addition, the detection delay is less than 0.4s when the link state switches from LOS to NLOS.

Due to its indispensability for device-free passive (DfP) sensing, DfP human detection has attracted numerous research efforts during the past years. Although previous works have achieved considerable detection performance, they mainly focus on moving human detection, making mobility a prerequisite for reliable detection. Besides, existing static human detection systems usually require dense deployment or controlled settings. In this paper, we propose a robust respiration-rate-estimation-based passive static human detection system, R-PSHD. Specifically, different from recent works which leverage the amplitude of channel state information (CSI) for DfP sensing, we resort to the more sensitive phase information for minute respiration detection. To deal with the randomness of raw phase, R-PSHD exploits phase difference between antennas for feature extraction. Moreover, due to varying sensitivity of different subcarriers, R-PSHD tries to identify the useful subcarriers and only uses them for accurate estimation. Experimental results with different people during a week demonstrate that R-PSHD achieves great performance with both TP and TN rate higher than 90%.

Due to rapid developments of smart devices and mobile applications, there is an urgent need for a new human-in-the-loop architecture with better system efficiency and user experience. Compared with conventional device-based human-computer interactive (HCI) methods, device-free technology with WiFi provides a new HCI method and is promising for providing better user-perceived quality-of-experience. Being essential for device-free applications, device-free human detection has gained increasing interest, of which through-the-wall (TTW) human detection is of great challenge. Existing TTW detection systems either rely on massive deployment of transceivers or require specialized WiFi monitors, making them inapplicable for real-world applications. Recently, more and more researchers have tapped into the physical layer for more robust and reliable human detection, ever since channel state information (CSI) can be exported with commodity devices. Despite great progress achieved, there have been few works studying TTW detection. In this paper, we propose a novel scheme for robust device-free TTW detection (R-TTWD) of a moving human with commodity devices. Different from the time dimension-based features exploited in the previous works, R-TTWD takes advantage of the correlated changes over different subcarriers and extracts the first-order difference of eigenvector of CSI across different subcarriers for TTW human detection. Instead of direct feature extraction, we first perform a PCA-based filtering on the preprocessed data, since a simple low-pass filtering is insufficient for noise removal. Furthermore, the detection results across different transmit-receive antenna pairs are fused with a majority-vote-based scheme for more robust and accurate detection. We prototype R-TTWD on commodity WiFi devices and evaluate its performance both in different environments and over long test period, validating the robustness of R-TTWD with both detection rates for moving human and human absence over 99% regardless of different wall materials, dynamic moving speeds, and so on.

Wi-Fi has been widely deployed with the rapid development of wireless communication technique. Wi-Fi hotspots are popular in campus, making convenient wireless network access possible. However, during class teaching, in order to avoid students browsing the web based on Wi-Fi hotspots and distracting, we hope Wi-Fi hotspots adaptively shield network access to students in the classroom, while grant access to users outside the classroom. In this work, we prototype TA3C system, a teaching-oriented adaptive Wi-Fi authorized access control scheme, using Channel State Information (CSI) to locate instead of coarse-grained and temporally unstable Received Signal Strength Indication (RSSI). CSI can distinguish multipath signals, stay stable in the same propagation environment, and show different characteristics in different propagation environments, based on which, we can distinguish outdoor and indoor environments, and identify user's location to decide whether to offer him wireless network access or not. Experiment results show that TA3C effectively achieve adaptive Wi-Fi authorized access control, which provides a guarantee for the quality of class teaching. Compared to traditional indoor localization techniques, TA3C does not need accurate location information but simply recognizes user's location indoors or outdoors, which means it does not need dedicated hardware, realizing the low-cost indoor localization technique.

Due to continuous process scaling, process, voltage, and temperature (PVT) parameter variations have become one of the most problematic issues in circuit design. The resulting correlations among performance metrics lead to a significant parametric yield loss. Previous algorithms on parametric yield prediction are limited to predicting a single-parametric yield or performing balanced optimization for several single-parametric yields. Consequently, these methods fail to predict the multi-parametric yield that optimizes multiple performance metrics simultaneously, which may result in significant accuracy loss. In this paper we suggest an efficient multi-parametric yield prediction framework, in which multiple performance metrics are considered as simultaneous constraint conditions for parametric yield prediction, to maintain the correlations among metrics. First, the framework models the performance metrics in terms of PVT parameter variations by using the adaptive elastic net (AEN) method. Then the parametric yield for a single performance metric can be predicted through the computation of the cumulative distribution function (CDF) based on the multiplication theorem and the Markov chain Monte Carlo (MCMC) method. Finally, a copula-based parametric yield prediction procedure has been developed to solve the multi-parametric yield prediction problem, and to generate an accurate yield estimate. Experimental results demonstrate that the proposed multi-parametric yield prediction framework is able to provide the designer with either an accurate value for parametric yield under specific performance limits, or a multi-parametric yield surface under all ranges of performance limits.

With shrinking technology, the increase in variability of process, voltage, and temperature (PVT) parameters significantly impacts the yield analysis and optimization for chip designs. Previous yield estimation algorithms have been limited to predicting either timing or power yield. However, neglecting the correlation between power and delay will result in significant yield loss. Most of these approaches also suffer from high computational complexity and long runtime. We suggest a novel bi-objective optimization framework based on Chebyshev affine arithmetic (CAA) and the adaptive weighted sum (AWS) method. Both power and timing yield are set as objective functions in this framework. The two objectives are optimized simultaneously to maintain the correlation between them. The proposed method first predicts the guaranteed probability bounds for leakage and delay distributions under the assumption of arbitrary correlations. Then a power-delay bi-objective optimization model is formulated by computation of cumulative distribution function (CDF) bounds. Finally, the AWS method is applied for power-delay optimization to generate a well-distributed set of Pareto-optimal solutions. Experimental results on ISCAS benchmark circuits show that the proposed bi-objective framework is capable of providing sufficient trade-off information between power and timing yield.

Target detection plays an important role in the field of hyperspectral image (HSI) remote sensing. In this letter, a novel matched subspace detector based on low-rank regularized least squares (LRLS-MSD) is proposed for hyperspectral target detection. As pixels in an HSI have global correlation and can be represented in subspace, the low-rank regularization is introduced in the least squares model. An effective algorithm is presented to solve the problem. Then, the detection results are generated according to the generalized likelihood ratio test with statistical hypotheses. The experimental results suggest an advantage of the low-rank regularization over other classical target detection methods.

Received Signal Strength (RSS) maps provide fundamental information for mobile users, aiding the development of conflict graph and improving communication quality to cope with the complex and unstable wireless channels. In this paper, we present CARM: a scheme that exploits crowd-sensing to construct outdoor RSS maps using smartphone measurements. An alternative yet impractical approach in literature is to appeal to professionals with customized devices. Our work distinguishes itself from previous studies by supporting off-the-shelf smartphone devices, and more importantly, by mitigating the error-prone nature and inaccuracies of these devices to build RSS maps through crowd-sensing. The main challenges are that, we need to calibrate error-prone smartphone measurements with “inaccurate” and “incomplete” data. To address these challenges, we build the measurement error model of smartphone based on the experimental observations and analyses. Moreover, we propose an iterative method based on Davidon-Fletcher-Powell (DFP) algorithm, to estimate the parameters for the error models of each smartphone and the signal propagation models of each AP simultaneously. The key intuition is that, the calibrated measurements based on the error model are constrained by the physics of the signal propagation model. Finally, a model-driven RSS map construction scheme is built upon these two models with these estimated parameters. The theoretical analyses prove the optimality and convergence of this iterative method. Also, the crowd-sensing experiments show that, CARM can achieve an accurate RSS map, decreasing the average error from 19.8 to 8.5 dBm.

Accurate and sufficient range measurements are essential for range-based localization in wireless sensor networks. However, noise and data missing are inevitable in distance ranging, which may degrade localization accuracy drastically. Existing localization approaches often degrade in terms of accuracy in the co-existence of incomplete and corrupted range measurements. To address this challenge, a noise-tolerant localization algorithm called NLIRM is presented. By utilizing the natural low rank property of Euclidean distance matrix, the reconstruction of partially sampled and noisy distance matrix is formulated as a norm-regularized matrix completion problem, where Gaussian noises and outliers are smoothed by Frobenius-norm and L1norm regularization, respectively. As far as we are aware of, this is the first scheme that can recover the missing range measurements and explicitly sift Gaussian noise and outlier simultaneously. Simulation results demonstrate that, compared with traditional algorithms, NLIRM achieves better localization performance under the same experiment setting. In addition, our algorithm provides an accurate prediction of outlier positions, which is the prerequisite for malfunction diagnosis in WSN.

Robust Device-free passive (Dfp) detection is an essential primitive for a broad range of applications such as intrusion detection and smart space. Most recent works focus on finer-grained Channel State Information (CSI), instead of the variable Received Signal Strength (RSS). However, existing solutions have some limitations, being feasible only in the line of sight (LOS) or for more than one targeted entities. Moreover, space diversity supported by the MIMO systems hasn't been fully investigated. Motivated by this observation, we propose a novel scheme for Robust Passive Motion Detection (R-PMD). In our scheme, the variance of CSI amplitude feature is extracted as a new metric and the earth mover's distance (EMD) is utilized to determine the detection results. Besides, CSIs across multiantennas are further exploited to improve the detection precision and robustness. We prototype R-PMD on commercial WiFi devices and evaluate it in a typical indoor scenario. Experiment results show R-PMD can achieve great performance in terms of sensitivity and robustness.

We consider a capable and scalable network model named two-tiered sensor network which consists of regular resource-limited sensor nodes and powerful storage nodes with abundant resources. In such architecture, storage nodes are on charge of storing data collected and submitted by sensor nodes as well as processing queries from the base station. Owing to the importance role that storage nodes play, they are more vulnerable and attractive to adversaries in a hostile environment. A compromised storage node may inject fake data into and/or omit qualified data from its returned responses, which make the base station not able to obtain authentic and/or complete results. This paper proposes EVTQ, a novel and efficient verifiable top-k query processing which is capable of verifying the authentication and completeness of query result. To achieve such security features, sensor nodes are settled to submit their collected data items together with corresponding codes which embed ordered and adjacent relationships of the collect data items by a hashed message authentication coding function. Thus any attack that leads to unauthentic and incomplete query result will be detected. According to this basic idea, the data submission and query processing protocols are proposed to describe the details of EVTQ. Moreover, a hash based optimization is presented to save more communication cost. The simulation result shows that EVTQ is more efficient than the existing work in communication cost.

How to improve the design efficiency of complex product with meta-synthesis is a valuable research problem. The research achievements of complex product design based on metasynthesis, which has been made by the author's research group, is summarizd in this paper. First, the process model and the general framework of complex product design based on metasynthesis are established. Then, the architecture and the key technologies of HWME is presented, including group argumentation and group decision, project management, 3-d visualization, model and data management. Finally, a prototype system of HWME for complex product design is developed.

For the depth information of desired view is unknown, a per-pixel searching step is often inevitable in methods of inverse image warping. A novel approach is proposed in this paper called "cross-segment algorithm (CSA)". Different from other existing methods, CSA tickles the corresponding problem by solving the equations of crossed segments instead of searching per-pixel. CSA pays more attention to the relationship between the different reference images including depth information than that between the reference and desired images. Because of eliminating the searching cost, CSA is proved to be an accelerated method of inverse image warping by experiments.

One of the key problems in image-based rendering is to decide the visibility of objects in arbitrary viewplane. And reducing the need to compute depth for rendering has attracted a wide research interests. Most previous methods either work only on one reference image, which is unable to provide sufficient information for generating new views, or still rely partly on the depth information. In this paper, a novel approach is proposed to this problem based on multi-reference images, Where we deduce the warping function and a rendering order for image pixels onto the intermediary "aided-view", and in turn, to the target plane. By two-steps warping, it's proved a feasible method to compute visibility without depth in multi-reference images.