Cardiovascular disease (CVD) is one of the foremost causes of mortality globally, and cardiac arrhythmias constitute a major contributing factor. Continuous monitoring of cardiac signals plays a vital role for early detection and prevention. However, traditional electrocardiogram (ECG) devices require skin contact, which can be uncomfortable and inconvenient for long-term usage. In contrast, contactless cardiac health monitoring technologies, such as Wi-Fi and millimeter-wave (mmWave) radar, present a promising alternative. Millimete-rwave radar provides high range resolution, strong immunity to ambient light, and high sensitivity to small vibrations, making it ideal for contactless monitoring. However, mmWave radar primarily captures cardiac mechanical vibrations, known as radar cardiography (RCG) signals, which differ from the electrical activity recorded by clinical ECGs. To bridge this gap, we propose a contactless framework that uses mmWave radar and a conditional diffusion model to reconstruct ECG signals and then utilizes a deep learning model to classify arrhythmias. Specifically, mmWave radar captures RCG signals associated with cardiac activities. Leveraging the nonlinear relationship between cardiac mechanics and electrical activities, we design a Residual Network (ResNet)-based conditional diffusion model to convert these RCG signals into ECG signals. Finally, we develop a CNN-BiLSTM-SE network for arrhythmia classification. Experimental findings demonstrate the efficacy of the proposed approach for signal conversion as well as arrhythmia classification, offering a promising pathway toward contactless cardiac health monitoring.

With the increasing proliferation of Ultra-High-Definition (UHD) videos, the demand for efficient video transmission schemes to alleviate network congestion is growing. In this paper, we propose a bi-directional motion enhanced semantic communication (SemCom) system for efficient and robust video transmission. In particular, we introduce a bi-directional motion estimation module to capture inter-frame differences caused by camera movements, where the obtained forward and backward motion vectors are combined with the residual information to generate motion-compensated frames. We also introduce a predicted feature module to discard semantically redundant features, prioritizing crucial semantic-related content. Leveraging information from previously reconstructed frames, the frame prediction module refines predicted frames with the assistance of the motion compensation module. To enhance the system’s robustness to channel noise, we propose a noise attention module that assigns varying importance weights to the extracted features under different channel conditions. Experimental results show that our proposed method outperforms existing deep learning (DL)-based approaches in terms of transmission efficiency, achieving about 33.3% reduction in the number of transmitted symbols while improving the peak signal-to-noise ratio (PSNR) and multi-scale structural similarity index measure (MS-SSIM) performance by an average of 0.56 dB and 0.0024 over an additive white Gaussian noise channel for different schemes. When employing the same compression ratio, our method achieves an average gain of 0.637 dB in PSNR and 0.0038 in MS-SSIM over the slow Rayleigh fading channel.

As semantic communication (SemCom) attracts growing attention as a novel communication paradigm, ensuring the security of transmitted semantic information over open wireless channels has become a critical issue. However, traditional encryption methods often introduce significant additional communication overhead to maintain stability, and conventional learning-based secure SemCom methods typically rely on a channel capacity advantage for the legitimate receiver, which is challenging to guarantee in real-world scenarios. In this paper, we propose a coding-enhanced jamming method that eliminates the need to transmit a secret key by utilizing shared knowledge–potentially part of the training set of the SemCom system–between the legitimate receiver and the transmitter. Specifically, we leverage the shared private knowledge base to generate a set of private digital codebooks in advance using neural network (NN)-based encoders. For each transmission, we encode the transmitted data into digital sequence Y1and associate Y1with a sequence randomly picked from the private codebook, denoted as Y2, through superposition coding. Here, Y1serves as the outer code and Y2as the inner code. By optimizing the power allocation between the inner and outer codes, the legitimate receiver can reconstruct the transmitted data using successive decoding with the index of Y2shared, while the eavesdropper’s decoding performance is severely degraded, potentially to the point of random guessing. Experimental results demonstrate that our method achieves security comparable to state-of-the-art approaches while significantly improving the reconstruction performance of the legitimate receiver by more than 1 dB across varying channel signal-to-noise ratios (SNRs) and compression ratios.

Accurate channel prediction is essential in massive multiple-input multiple-output (m-MIMO) systems to improve precoding effectiveness and reduce the overhead of channel state information (CSI) feedback. However, existing methods often suffer from accumulated prediction errors and poor generalization to dynamic wireless environments, making it challenging to maintain high prediction accuracy. Large language models (LLMs) have demonstrated remarkable modeling and generalization capabilities in tasks such as time series prediction, making them a promising solution. Nevertheless, a significant modality gap exists between the linguistic knowledge embedded in pretrained LLMs and the intrinsic characteristics of CSI, posing substantial challenges for their direct application to channel prediction. Moreover, the large parameter size of LLMs hinders their practical deployment in real-world communication systems with stringent latency constraints. To address these challenges, we propose a novel channel prediction framework based on semantically aligned large models, referred to as CSI-ALM, which bridges the modality gap between natural language and channel information. Specifically, we design a cross-modal fusion module that aligns CSI representations with the language feature space using a pretrained corpus. Additionally, we maximize the cosine similarity between word embeddings and CSI embeddings to construct semantic cues, effectively leveraging the latent knowledge in LLMs. To reduce complexity and enable practical implementation, we further introduce a lightweight version of the proposed approach, called CSI-ALM-Light. This variant is derived via a knowledge distillation strategy based on attention matrices, which extracts essential features from the teacher model, CSI-ALM, and transfers them to a compact, efficient student model, CSI-ALM-Light. Extensive experimental results demonstrate that CSI-ALM consistently outperforms state-of-the-art deep learning methods across various communication scenarios, achieving substantial performance gains. Moreover, under limited training data conditions—where all models are trained using only 10% of the original training dataset—CSI-ALM-Light, with only 0.34M parameters, attains performance comparable to CSI-ALM and significantly outperforms conventional deep learning approaches. These validate the effectiveness of the proposed approach for accurate and efficient channel prediction in m-MIMO systems.

LoRa has emerged as a crucial technology that provides ubiquitous connectivity for geographically distributed Internet of Things devices. LoRa employs linear Chirp Spread Spectrum (CSS) modulation in its physical layer to enable long-range communication. Recently, nonlinear chirps have been proposed to replace linear chirps in CSS, to enhance the capacity and scalability of LoRa technology. However, the characteristics and advantages of nonlinear chirps are not yet fully understood. To unleash the potential of nonlinear CSS modulation, this paper presents a comprehensive theoretical and experimental analysis of the nonlinear chirp, focusing on key aspects such as noise resilience, error correction, curvature selection, and bandwidth expansion. Our findings reveal that nonlinear chirps are intrinsically more resilient to noise than linear chirps. Moreover, nonlinear chirps typically exhibit a single-place demodulation error under strong noise conditions, which has informed our development of a simple yet effective error correction design. Additionally, users should carefully manage nonlinear chirp curvatures and signal power compensation to avoid distortion while benefiting from the bandwidth expansion offered by nonlinear chirps. Building on these insights, we further refine nonlinear CSS modulation and implement our optimized design on US-RPs. Field experiments demonstrate that our design achieves significant performance improvements, marking a step forward in the practical application of nonlinear CSS modulation in LoRa networks.

The digital twin (DT) assisted mobile edge computing (MEC), which adopts DT to bridge cyber and physical systems by generating digital replicas of real entities, is proposed as an effective solution to manage the proliferating Internet of Things (IoT) networks. However, it is challenging to continuously maintain the digital representation of physical system because of the limited battery and computation capabilities of IoT devices. In this paper, we present a new paradigm of DT assisted wireless powered MEC (WP-MEC) network, where IoT devices can harvest energy from hybrid access points (HAPs) and upload sensing data to maintain DT. The DT, in turn, is responsible for efficiently making offloading decisions and resource allocations to maximize the computing data volume of the WP-MEC. To maximize the computation rate and maintain the DT assisted WP-MEC network, we formulate a mixed-integer nonlinear programming (MINLP) problem that addresses task offloading, wireless power transfer (WPT) duration, energy allocation, DT uploading duration, and data processing duration. Initially, we simplify the original problem through mathematical derivation. Subsequently, we design a mixed deep reinforcement learning (mixed-DRL) framework comprising two deep neural networks (DNNs) that output discrete offloading decisions and continuous resource allocations to achieve a higher volume of processed task data. Extensive experiments demonstrate that the proposed mixed-DRL framework with the self-designed DRL algorithms can improve the processed data volume by 30% compared to the advanced algorithm in the literature.

Edge computing has emerged as a promising paradigm to enable low-latency and high-bandwidth Internet of Things (IoT) applications. However, owing to the intrinsic characteristics of IoT devices, this emerging paradigm still faces bottlenecks such as energy shortages and security vulnerabilities. In this paper, we consider a physical layer security empowered wireless powered mobile edge computing (WP-MEC) system where wireless devices (WDs) can harvest energy from radio frequency signals, and the delivered data is protected against eavesdropping by using the well-known Wyner's wiretap encoding scheme. We focus on maximizing the secure computation rate by jointly optimizing artificial noise power, resource allocation, and offloading decisions for a multi-antenna scenario with wireless devices and a potential eavesdropper. We formulate this sum secure computation rate (SSCR) maximization problem as a mixed-integer programming problem and design an integrated deep reinforcement learning framework consisting of a discrete policy network module, a continuous policy network module, and an optimization module to derive binary offloading decisions, continuous time allocation, and an artificial noise covariance matrix. We evaluate the proposed approach through extensive simulations, and the results demonstrate that our approach can significantly enhance the security, energy efficiency, and computing capacity of wireless edge computing compared with existing works.

An analytical and optimization framework on rate-Cramér-Rao bound (CRB) tradeoff is proposed in this paper for the continuous-aperture array (CAPA)-based integrated sensing and communication (ISAC) system. To evaluate the dual-functional performance, the sensing CRB and communication rate are analyzed concerning the induced electromagnetic (EM) waves of CAPAs. For rate-CRB region characterization, the spatially continuous beamforming of transmit CAPA is optimized under three cases: i) A novel closed-form expression for the optimal CAPA beamformer is derived under the single-user single-target scenario, proven to be aligned within the space spanned by the EM-based sensing and communication channels; ii) A general subspace-based beamforming design approach is proposed to address the intractable continuity, converting the continuous beamforming design in spatial domain to discrete weight design in subspace domain and resorting to the semidefinite relaxation for the globally optimal solution; iii) Moreover, the general beamforming design is specialized to both the low-complexity zero-forcing (ZF) and the conventional spatially discrete array (SPDA)-based designs. Numerical results demonstrate that: i) The proposed subspace-based approach can realize efficient and effective beamforming design for reduced mutual interference, enhanced sensing performance, and guaranteed communication rate; ii) The general CAPA beamforming design achieves broader rate-CRB region than the ZF-oriented design and reaches the ultimate performance of the SPDA-based system.

This paper proposes a novel type of random finite set (RFS), namely augmented RFS, to address the problem of resolvable group target tracking, which integrates the information of both the group attributes and the dynamic state of group targets into random finite sets. Specifically, we initially introduce an augmented random finite set framework, incorporating group labels and group cardinality to estimate both the trajectories and states of group targets. Then, a new multi-target filter based on the augmented RFS is proposed to achieve the process of group target tracking. Finally, simulation experiments are conducted to demonstrate the effectiveness of the proposed filter in group target tracking scenarios.

Enabled by metamaterials, continuous aperture array (CAPA) has been proven to play a crucial role in communication performance enhancement, while its potentials in integrated sensing and communication (ISAC) systems have not been investigated. This paper investigates the performance analysis and optimization of CAPA-based ISAC systems for simultaneous user communication and target sensing. To be specific, communication and sensing rates are evaluated based on electromagnetic channels and a Pareto-optimal problem is formulated for beamforming optimization. Closed-form solutions to CAPA-oriented beamforming are derived under communication-, sensing-, and Pareto-optimal cases, and the attainable ISAC rate region is obtained. Numerical results verify that CAPA-based systems can achieve the ultimate sensing and communication performance of spatially discrete array (SPDA)-based systems and significantly expand the ISAC rate region for Pareto optimality.

Electro-thermal coupling has become a concerning issue in 3D integrated circuit (IC) designs. Conventional electro-thermal co-simulation methods rely on iterative solutions of electrical and thermal partial differential equations (PDEs) using numerical techniques, which are computationally expensive and time-consuming. To address this, in this paper, we propose a novel electro-thermal co-analysis framework based on physics-informed neural networks (PINNs) with coarse-to-fine models. The coarse-grained models first predict the electrical potential and temperature distributions of the entire circuit under various boundary conditions, while the fine-grained models provide enhanced resolution for regions of interest. Additionally, we introduce an efficient training strategy that accelerates convergence. Experimental results show that the proposed framework achieves high accuracy with 0.10-0.19% mean relative error and 3-4 orders of magnitude improvements in efficiency compared to the commercial tool.

Large language models (LLMs) have emerged as a cornerstone for advancing AI technologies. It revolutionizes the way we interact with devices, websites, and information, and paves the way for the development of highly intuitive and capable virtual assistants. Training of today's LLMs happens in cloud data centers due to the requirement of enormous data and a significant amount of computing power. Despite extensive research in mobile edge computing, fine-tuning pre-trained LLMs using resource-constrained devices like commodity smartphones remains highly under-explored. In this paper, we propose Confidant, a practical collaborative training framework that allows modern LLMs to be fine-tuned across multiple off-the-shelf mobile devices. To this end, Confidant partitions an LLM into several sub-models, allowing each of them to fit in the memory of a mobile device. Multiple mobile devices then collaborate to train the LLM by employing a novel pipeline parallel training approach. In specific, Confidant encompasses a memory-aware dynamic model partitioning and intra-device multi-processor scheduler to minimize the training time across heterogeneous platforms. To ensure resilient distributed training, a hybrid fault tolerance mechanism is devised to proactively manage potential device and network failures. We fully implemented Confidant in C++/Python, and built a cross-framework adapter, enabling collaborative training on a variety of mobile platforms. Experimental results show that Confidant excels in achieving computation-, memory-efficient, and robust customization of LLMs - it manages to train state-of-the-art billion-sized LLMs including BERT, GPT-2, Phi2, and LLaMA3, and fine-tunes Phi2-2.7B on Alpaca in just 40.1 hours using three consumer-grade mobile devices.

Despite large language models (LLMs) being an essential part of our lives, training of LLMs still needs to be done in cloud data centers due to the large requirements of data and computing power, leaving fine-tuning pre-trained LLMs on resource-constrained mobile devices remains highly under-explored. In this demo, we present Confidant, a practical collaborative training system that allows modern LLMs to be fine-tuned across multiple off-the-shelf mobile devices. Confidant partitions an LLM into several sub-models, deploying each of them to a mobile device. Multiple mobile devices then collaborate to train the LLM by employing a novel pipeline parallel training approach. Specifically, Confidant encompasses a memory-aware dynamic model partitioning and intra-device multi-processor scheduler to minimize the training time across heterogeneous platforms. A hybrid fault tolerance mechanism is also devised to proactively manage potential device and network failures. By building a cross-framework adapter and fully implementing Confidant on smartphones and laptops, we present the demo of collaborative training on a variety of mobile platforms.

With the growing use of unmanned aerial vehicles (UAVs), UAV recognition has become crucial due to emerging risks in many scenarios. Various radio frequency (RF)-based recognition methods are already in use. However, they still struggle to recognize unknown UAVs and rely heavily on bundant features derived from large-scale labeled datasets. To address this limitation, a few-shot open-set UAV recognition method is proposed. The method employs a multi-domain network and self-attention mechanism to extract and fuse features from time and frequency domains. Moreover, the extracted features are integrated into few-shot open-set recognition under the Euclidean distance criterion, incorporated with a threshold. Furthermore, experimental results demonstrate that the accuracy of proposed method achieves 85.13%, which is about 10% higher than existing open-set UAV recognition methods in few-shot scenarios, offering a solution for real-time spectrum monitoring and countermeasures against unauthorized UAVs.

In pursuit of reliable performance for high-mobility integrated sensing and communication (ISAC) scenarios, the orthogonal delay-Doppler division multiplexing (ODDM) modulation can be leveraged to exploit the inherent channel sparsity in the delay-Doppler (DD) domain. In this letter, we propose a joint target localization and channel estimation method for ODDM-ISAC systems that employs a multi-pilot training frame to enhance parameter estimation with accumulated signal energy. By exploiting the block-circulant-like structure of the equivalent sampled DD domain (ESDD) channel matrix, multiple pilots are strategically arranged within a training frame. To facilitate effective energy accumulation from different pilots, a phase compensation strategy is devised, which improves the accuracy of parameter estimation for target localization and channel reconstruction. Moreover, the feasibility of the proposed method is theoretically analyzed when the actual delay and Doppler shift are on or off the quantized DD grid, respectively. Simulation results validate the effectiveness of the proposed method for both target localization and channel estimation.

Employing a single moving receiver for emitter localization offers numerous advantages, including low cost, ease of implementation, and elimination of clock synchronization. A novel localization method is proposed for a stationary emitter with an unknown Signal Repetition Interval (SRI). We first construct a localization model based on time-of-arrival, taking into account potential missed detections, and then derive the Cramér-Rao Lower Bound. Given that the established model is non-convex, we reformulate the problem into a convex form and convert it to Second-order Cone Programming (SOCP) format. However, the SOCP formulation encounters convex hull issues, which may hinder far-field localization. To address this challenge, we introduce a penalty term to alleviate the convex hull problem for the first time. Simulation results demonstrate the effectiveness of the proposed approach in comparison to existing methods, and the validity of the penalty term is also confirmed.

Chiplet technology has recently emerged as a promising solution to improving chip performance through the modularization of complex designs and communication facilitated by high-speed interchiplet serial links. However, the increasing on-package routing density and data rates of these links introduce complex signal and power integrity challenges, surpassing those encountered in traditional large monolithic chips. Addressing these complexities with efficient analysis and design tools is crucial for maintaining design robustness. In this article, we propose SPIRAL+: signal-power integrity co-analysis framework for high-speed interchiplet serial links validation. The framework employs machine learning (ML) to construct transmitter models and utilizes an impulse response extraction method for modeling the channel and receiver. It then performs signal-power integrity co-analysis through a novel double-edge response-based method, leveraging the developed equivalent models. Additionally, an efficient ML model is crafted to accurately predict eye diagram metrics. The analysis provides valuable insights for design optimization. Experimental results show that SPIRAL+ achieves eye diagrams with a mean relative error of 0.07%–7.47%, while realizing a speedup of$31\times $–$326\times $over traditional commercial tools.

Recent rapid advancements in intelligent vehicular systems and deep learning techniques have led to the emergence of diverse applications utilizing high-quality automotive videos in the Internet-of-Vehicles (IoV), often assisted by uncrewed aerial vehicles (UAVs). These applications aim to provide convenience and security for users. However, transmitting automotive videos with high-quality and low-bit-rate poses a challenge due to the inherent lossiness of traditional compression codecs in current UAV-assisted IoV systems, thereby affecting the performance of subsequent tasks. To address this, we propose a spatial-temporal hybrid video compression framework (STHVC), which integrates Space-Time Super-Resolution (STSR) with conventional codecs to enhance the compression efficiency on automotive videos. In our hybrid design, the encoder generates a low-frame-rate and low-resolution version of the source video, which is then compressed using a traditional codec. During the decoding stage, an effective STSR network is developed to increase both the resolution and the frame rate, and mitigate compression artifacts for automotive videos simultaneously. Additionally, we introduce a rectified intermediate flow estimation technique (RecIFE) within the proposed STSR network to address the challenge of noisy and inaccurate motions during the compression pipeline. Extensive experiments on various benchmark datasets demonstrate that our approach achieves bit-rate reductions of 29.97% compared to H.265 (slow) and 31.27% compared to H.266, while also exhibiting superior restoration performance compared to other state-of-the-art learning-based approaches.

As the electromagnetic spectrum becomes increasingly crowded in recent years, synthetic aperture radar (SAR) is confronted with an escalating amount of radio-frequency interference (RFI). In civilian SAR satellite data, narrowband pulsed RFI (PRFI) is a prevalent interference type that significantly degrades the interpretability of SAR images. Among most approaches for suppressing PRFI, notch filtering methods face significant challenges in threshold selection of signal intensity. Conversely, low-rank and sparse decomposition (LRSD) algorithms, though free of threshold selection, often struggle to satisfy the required low-rank conditions. These limitations underscore the necessity of developing more robust interference suppression methods. In this article, we propose a joint framework of wavelet filtering and fast generalized singular value thresholding-based LRSD (FGSVT-LRSD) method to suppress narrowband PRFI in range–frequency and azimuth–time domain of SAR single-look complex (SLC) data. First, a wavelet domain notch filtering (WNF) method is employed to extract the strong spectral components that are primarily composed of PRFI in the 2-D range spectrum of SAR SLC data, while simultaneously protecting the spectrum of low-energy useful signals. Then, the FGSVT-LRSD method is applied to the extracted strong spectral components to efficiently separate the PRFI from the useful signals. By strategically integrating these two approaches, the proposed framework simultaneously exploits the high-intensity and low-rank properties of PRFI for more precise separation, significantly reduces the sensitivity of threshold selection in the WNF process and facilitates the low-rank conditions required by the FGSVT-LRSD method. Finally, the separated PRFI spectrum is subtracted from the original 2-D range spectrum, resulting in the RFI-suppressed SAR image. Experimental results based on both simulated and measured spaceborne SAR data will be presented to demonstrate that, compared with existing methods, the proposed approach exhibits superior PRFI suppression capabilities and effectively preserves useful signals.

The rapid advancement of sixth-generation (6G) networks and artificial intelligence technologies is leading to the emergence of collaborative intelligent transportation systems (C-ITS), which is regarded as an essential trend in the future of transportation. Integrating Internet of Things (IoT) with C-ITS is an efficient solution to provide real-time data collection and status monitoring for vehicles and infrastructures to improve the intelligence and reliability of C-ITS. In order to address the challenges of limited battery energy and low computing power of IoT nodes, integrating wireless power transfer (WPT) with mobile edge computing (MEC) is considered as a promising solution to improve their lifetime and computational capability for IoT nodes. In this paper, we investigate a distributed dynamic computing offloading model for an MEC-enabled C-ITS, where multiple roadside units (RSUs) collaborate to provide offloading services to wireless devices (WDs). We formulate the task offloading and bandwidth resource allocation as a distributed Stackelberg game. The WDs act as leaders, aiming to maximize their computing rate by offloading tasks to RSU or performing local computing. The RSUs act as followers, optimizing their bandwidth allocation based on the WDs’ offloading decisions, thereby improving the overall system computing rate. We prove the existence of a Stackelberg equilibrium (SE) and propose a multi-agent reinforcement learning algorithm to enable WDs to select offloading decisions and help RSUs optimize bandwidth allocation. Numerical simulation results demonstrate that the proposed scheme offers significant performance improvements over existing methods.

A continuous-aperture array (CAPA)-based integrated sensing and communication (ISAC) framework is proposed in this paper, where CAPA transceivers are optimized to enhance both target sensing and user communication performance. Novel expressions for achievable communication and sensing rates are derived and CAPA-oriented beamforming is designed to balance the dual-functional Pareto-optimal tradeoff in two scenarios: i) For the single-user single-target case, closed-form continuous beamformers are derived based on communication-, sensing-, and Pareto-optimal criteria to reveal the interrelation of the ISAC rate region with the antenna aperture and channel gains; ii) For the multi-user multi-target case, a general CAPA-ISAC beamforming design algorithm is developed to achieve the Pareto optimality. Beamformer design in the continuous spatial domain is transformed into weight design in the discrete wavenumber domain using Fourier series expansions. Furthermore, alternating optimization, successive convex approximation, and difference of convex techniques are employed to tackle the coupling and non-convexity issues. Numerical results demonstrate that: i) The proposed CAPA-ISAC framework significantly improves both sensing and communication performance and expands the ISAC Pareto rate region; ii) CAPAs exhibit superior beamforming capabilities and reach the ultimate performance limits of spatially discrete arrays (SPDAs).

Integrated sensing and communication (ISAC) enables the next-generation network to possess networked sensing capability, propelling the proliferation of various intelligent applications but introducing complex sensing and communication interference. To this end, this paper studies the cooperative transceiver beamforming design for a multi-cell anti-unmanned aerial vehicle (UAV) ISAC system, where multiple base stations (BSs) collaboratively perform joint UAV sensing. Specifically, to ensure reliable detection, we jointly optimize the ISAC transmit and receive beamformers at BSs and downlink users via maximizing the signal-to-clutter-plus-noise ratio of sensing, taking into account the communication requirements and power constraints. To handle the nonconvex fractional problem, we first propose a centralized beamforming algorithm resorting to alternating optimization, successive convex approximation, and Dinkelbach methods. Then, to alleviate heavy backhaul overhead, a distributed algorithm is put forward, adopting the primal decomposition technique to decouple the inter-cell interference. Numerical results verify that: i) Compared with the standalone sensing by a single BS, the proposed cooperative beamforming design achieves notable enhancement in sensing performance; ii) The designed transceiver beamforming is constructive for interference and clutter suppression in multi-cell ISAC systems.

Internet of Things (IoT) devices face an escalating threat from code reuse attacks (CRAs) as they can reuse existing code for malicious purpose. Thus a practical cost-effective Control-Flow Integrity (CFI) mechanism for IoT devices is urgently needed. However, existing CFI solutions suffer from impractical-ities, including high performance overhead and a heavy reliance on offline perfect Control-Flow Graph (CFG) generation. To tackle these challenges, we propose a fine-grained dependable CFI scheme for IoT devices that real-time updates the CFG of devices. We evaluate the implementation on RISC-V architectures and the results show that our CFI scheme provides both backward- and forward-edge protection with almost no performance overhead in the case of fixed CFG, negligible power overhead, and low hardware overhead. Compared to the current hardware-assisted CFI designs, our design eliminates the dependence on the offline perfect CFG generation and performs real-time CFG updating for better practicality.

Resolvable group target tracking is of great challenge due to the complex motion interaction between group targets, which leads to tracking performance degradation. To solve this problem, a cardinality-balanced multi-target multi-Bernoulli filter based on the graph theory and leader-follower model is proposed. In the proposed filter, firstly, the group targets are divided into leaders and followers by mean of the leader-follower model. Furthermore, the graph theory is used to establish the state transition equations between those divided group targets. Lastly, the process of state prediction is given, and its corresponding implementation is derived by Gaussian mixture approximations. Simulation experiments verify the superiority and effectiveness of the proposed filter.

As three-dimensional acquisition technologies like LiDAR cameras advance, the need for efficient transmission of 3D point clouds is becoming increasingly important. In this paper, we present a novel semantic communication (SemCom) approach for efficient 3D point cloud transmission. Different from existing methods that rely on downsampling and feature extraction for compression, our approach utilizes a parallel structure to separately extract both global and local information from point clouds. This system is composed of five key components: local semantic encoder, global semantic encoder, channel encoder, channel decoder, and semantic decoder. Our numerical results indicate that this approach surpasses both the traditional Octree compression methodology and alternative deep learning-based strategies in terms of reconstruction quality. Moreover, our system is capable of achieving high-quality point cloud reconstruction under adverse channel conditions, specifically maintaining a reconstruction quality of over 37dB even with severe channel noise.

The interference-plus-noise covariance matrix (INCM) reconstruction-based adaptive beamforming methods have been successful in preventing signal self-nulling. However, their computational complexity is generally high, which cannot be neglected. In this paper, we propose a data-driven adaptive beamforming method named Deep-Reconstruction, which utilizes deep learning to establish a direct mapping from the sample covariance matrix to the inverse of the INCM. Specifically, we devise a Unet-based fully convolutional network to extract the low-dimensional representations of interferences and noise from the sample covariance matrix. Meanwhile, a conjugate symmetrization layer is designed to maintain a Hermitian structure of the network output. As a result, an accurate estimation of the inverse of the INCM can be obtained for the beamformer design. Simulation results demonstrate that the proposed method can effectively avoid signal self-nulling, while achieving a higher computational efficiency as compared to the traditional methods.

In the integrated sensing and communication system, sensing functionalities are expected to benefit the communication instead of compromising its performance. In this paper, a sensing-aided communication channel estimation method is proposed, where the non-cooperative moving targets are localized and the associated propagation paths are excluded from the channel. Specifically, the received signal is formulated as a high-order tensor and then decomposed for channel parameter estimation. The parameters including velocity and angles of each path are automatically paired in the decomposed tensor factors, which enables identification of the high-velocity paths of moving targets. Then, by excluding the parameters of moving targets, a stable communication channel can be constructed. According to simulation, the proposed method contributes to enhanced data transmission performance while accurately localizing the moving targets.

Compared with the commonly used Cramér-Rao bound, the Ziv-Zakai bound (ZZB) is a global tight lower bound for evaluating the performance of parameter estimators. However, the existing ZZB for multi-source direction-of-arrival (DOA) estimation is derived under an ideal array assumption, where the gain-phase error is not taken into account. Hence, when the gain-phase error exists, the existing ZZB cannot provide a global effective bound. To address this problem, we incorporate the gain-phase error term into the ZZB derivation, and formulate the ZZB as an explicit function of the gain-phase error, which reveals that the gain-phase error affects the ZZB by introducing an extra signal-to-noise ratio gain/loss to the received signals. Simulation results demonstrate that the derived ZZB is global effective and tighter than the existing ZZB for multi-source DOA estimation in gain-phase error scenarios.

Semantic communication is an emerging communication approach that aims to enhance efficient transmission by conveying the essential semantic meaning of the information while eliminating redundancy. In the current deep learning (DL)-based semantic communication systems, the encoder and decoder at the sender and receiver persist without modification after deployment, irrespective of variations in device computing power and channel bandwidth. This lack of adaptability may result in a decline in performance. To overcome this issue, we introduce an adaptive semantic communication approach aimed at minimizing end-to-end latency by leveraging a dynamic model partitioning mechanism. This mechanism dynamically splits the overall model into the encoder and decoder components, with the partitioning points adapting to changing communication and computing resources. Furthermore, we present a training method referred to as scheduled random partition point training to ensure that changes in the partitioning points do not adversely impact the performance of downstream tasks. Our experimental results affirm the effectiveness of these methods in terms of reducing latency and improving task performance.

The design of Low Power Wide Area Network (LPWAN) protocols has attracted increasing attention in recent years, particularly within the LoRa research community. However, NB-IoT, another critical LPWAN technology, has not seen similar growth in its research community due to the lack of a functional and flexible software-defined radio (SDR) implementation. To address this gap, we present SoftNB, the first fully functional physical layer SDR implementation for NB-IoT. SoftNB conforms to the latest 3GPP standards and features an efficient and effective signal processing pipeline to mitigate time, frequency, and phase offsets during transmission and reception. Additionally, SoftNB is compatible with various SDR platforms, including USRP, HackRF One, and RTL-SDR Dongle. Extensive evaluations of SoftNB demonstrate its superior performance. Compared to the state-of-the-art baseline, SoftNB achieves an$8\times$reduction in Block Error Rate (BLER) when the number of repetitions is set to 4 at a distance of 450 meters.

Time-Sensitive Networking (TSN) serves as a one-size-fits-all solution for mixed-criticality communication, in which flow scheduling is vital to guarantee real-time transmissions. Traditional approaches statically assign priorities to flows based on their associated applications, resulting in significant queuing delays. In this paper, we observe that assigning different priorities to a flow leads to varying delays due to different shaping mechanisms applied to different flow types. Leveraging this insight, we introduce a new scheduling method in mixed-criticality TSN that incorporates a priority adjustment scheme among diverse flow types to mitigate queuing delays and enhance schedulability. Specifically, we propose dependency-aware priority adjustment algorithms tailored to different link-overlapping conditions. Experiments in various settings validate the effectiveness of the proposed method, which enhances the schedulability by 20.57% compared with the SOTA method.

Facial recognition is an important user authentication function in many systems. Traditional vision-based approaches are vulnerable to spoofing attacks and are affected by lighting conditions. In this work, we propose a human facial recognition system using a single millimeter-wave (mmWave) radio. Our system actively transmits the mmWave signal toward the user's face and receives the echoes that contain the geometric face information. We design a novel signal processing pipeline to defend against spoofing attacks and extract facial features. The extracted features are fed into a CNN-based classifier to recognize user identities. We implement and evaluate our system on a tiny and low-cost mmWave radar (25$). The experimental results show that the system can detect the spoofing samples with an average accuracy of 99.12% and achieves an average recognition accuracy of 97.5% over 21 volunteers in different environments.

In this paper, we propose a semantic communication approach based on probabilistic graphical model (PGM). The proposed approach involves constructing a PGM from a training dataset, which is then shared as common knowledge between the transmitter and receiver. We evaluate the importance of various semantic features and present a PGM-based compression algorithm designed to eliminate predictable portions of semantic information. Furthermore, we introduce a technique to reconstruct the discarded semantic information at the receiver end, generating approximate results based on the PGM. Simulation results indicate a significant improvement in transmission efficiency over existing methods, while maintaining the quality of the transmitted images.

Integrated sensing and communication (ISAC) provides a spectrum-efficient approach for simultaneously enabling reliable data transmission and high-quality sensing. This paper investigates an ISAC-enabled multi-device cooperative sensing system in which the devices perform cooperative sensing towards multiple targets in a time-division manner. Within the allocated time, each device senses the targets and transmits data to the base station simultaneously via ISAC. To investigate this problem, we formulate a joint optimization of the beamforming for both sensing and transmission as well as the time allocation for different devices, aiming at maximizing the total throughput of the devices while guaranteeing the multi-target sensing quality, the cooperative sensing requirement and the fairness in data transmission. To tackle the non-convexity of the formulated problem, we first decompose the problem into a beamforming subproblem and a time allocation subproblem. Subsequently, we transform the beamforming subproblem into a tractable form. We then analyze the feature of the optimal time allocation in the time allocation subproblem while providing its semi-analytical expression, based on which we further propose an efficient algorithm to solve the original problem. Simulation results validate the effectiveness of our algorithm and the performance advantages of our fairness-aware ISAC-enabled cooperative sensing in improving both throughput and cooperative sensing accuracy.

Time-sensitive software-defined networking (TSSDN) is an emerging technology that combines the real-time network configuration capabilities of software-defined networking (SDN) with the deterministic flow delivery capabilities of time-sensitive networking (TSN), making it ideal for use in the Industrial Internet of Things (IIoT). However, as data flows generated by industrial applications grow exponentially, it is challenging to achieve low-latency and reliable data flow transmission at the same time in TSSDN due to the limited network resources. To address this issue, we propose the adoption of the frame replication and elimination for reliability (FRER) mechanism in TSSDN-based IIoT systems. However, it is important to acknowledge that the FRER mechanism introduces stress on the already restricted network resources by generating redundant paths. In light of this concern, we construct an end-to-end delay bound model and a reliability model to analyze this issue. To mitigate the stress imposed on the network, we formulate an optimization problem for maximizing the overall system utility while adhering to the transmission requirements of business flows and the limitations of hardware resources. Consequently, we devise an algorithm for reliability-enhanced flow routing and scheduling, which effectively solves the aforementioned optimization problem. To validate the effectiveness and performance of our proposed algorithm, we conduct numerical simulations on four data sets. The results demonstrate the superior performance of our approach compared to existing methods.

The abuse of drones has raised critical concerns about public security and personal privacy, bringing an urgent requirement for drone recognition. Existing radio frequency (RF)-based recognition methods follow the assumption of the closed set, resulting in the unknown signals being misclassified as known classes. To address this problem, we propose a Signal Semantic-based open Set Recognition (S3R) method in this paper. First, the short-time Fourier transform is introduced to construct the signal spectra, decoupling the drone signals with other interference signals. Then, we design a texture extractor and a position extractor to extract the texture features and position features from the spectra, respectively. The extracted features are further fused and structurally optimized to construct distinguishable signal semantics. Based on the structural characteristics of signal semantics, an outlier analysis-based semantic classifier is proposed, which searches the outliers of each known class in the closed set as the bounding thresholds to detect unknown instances. Finally, the detected unknown instances are further classified into their exact classes by implementing clustering in a new semantic space, where semantics are augmented by introducing basic features from the intermediate layers of the texture extractor. Besides, a real-world spectrogram dataset of commonly-used drones is released, which includes 24 classes and covers 7 brands. Extensive experiments demonstrate that the proposed S3R method outperforms the state-of-the-art methods in terms of accuracy and generalizability for both the closed set and the open set.

Effectively predicting the evolution of COVID-19 is of great significance to contain the pandemic. Extensive previous studies proposed a great number of SIR variants, which are efficient to capture the transmission characteristics of COVID-19. However, the parameter estimation methods in previous studies are based on data from epidemiological investigations, which inevitably have caused a large delay. The popularity of digital trajectory data world-wide makes it possible to understand epidemic spreading from human mobility perspective. The major advantage of digital trajectory data lies in that the co-location level of a population is reflected at every moment, making it possible to forecast the evolution in advance. We showed that the mobility data contributed by mobile phone users could be exploited to estimate the contact probability between individuals, thus revealing the dynamic transmission of COVID-19. Specifically, we developed an estimation method to obtain human co-location levels and quantified the variations of human mobility during the epidemic. Then, we extended the infection rate with a real-time co-location level to further forecast the transmission of an epidemic, predicting the epidemic size much more accurately than conventional methods. Finally, the proposed method was applied to evaluate the quantitative effect of different non-pharmacological interventions by predicting the epidemic situations with various mobility characteristics. The empirical results and simulations corroborated our theoretical analysis, providing effective guidance to contain the pandemic.

With the increasing proliferation of Internet-of-Things (IoT) devices, there is a growing trend towards distributing the power of deep learning (DL) among edge devices rather than centralizing it at the cloud. To deploy deep and complex models at edge devices with limited resources, model partitioning of deep neural network (DNN) models has been widely studied. However, most of the existing literature only considers distributing the inference model while still training the model at the cloud. In this paper, we propose FTPipeHD, a novel DNN training approach that trains DNN models across distributed heterogeneous devices with the fault-tolerance mechanism. To accelerate the training with the time-varying computing power of each device, we optimize the partition points dynamically according to real-time computing capacities. We also propose a novel weight redistribution approach that replicates the weights to both the neighboring nodes and the central node periodically, which combats the failure of multiple devices during training while incurring limited communication costs. Our numerical results demonstrate that FTPipeHD is 6.8 times faster in training than the state-of-the-art method when the computing capacity of the best device is 10 times greater than the worst one. It is also shown that the proposed method is able to accelerate the training even with the existence of device failures.

It is usually infeasible to fit and train an entire large deep neural network (DNN) model using a single edge device due to the limited resources. To facilitate intelligent applications across edge devices, researchers have proposed partitioning a large model into several sub-models, and deploying each of them to a different edge device to collaboratively train a DNN model. However, the communication overhead caused by the large amount of data transmitted from one device to another during training, as well as the sub-optimal partition point due to the inaccurate latency prediction of computation at each edge device can significantly slow down training. In this paper, we propose AccEPT, an acceleration scheme for accelerating the edge collaborative pipeline-parallel training. In particular, we propose a light-weight adaptive latency predictor to accurately estimate the computation latency of each layer at different devices, which also adapts to unseen devices through continuous learning. Therefore, the proposed latency predictor leads to better model partitioning which balances the computation loads across participating devices. Moreover, we propose a bit-level computation-efficient data compression scheme to compress the data to be transmitted between devices during training. Our numerical results demonstrate that our proposed acceleration approach is able to significantly speed up edge pipeline parallel training up to 3 times faster in the considered experimental settings

The next-generation wireless network is perceived to integrate with sensing capability and evolve into the perceptive mobile network (PMN), enabling massive sensing-intensive applications. However, the sensing function will affect the communication performance in cellular networks. To study the sensing and communication performance of PMNs and their interactions, this paper investigates a millimeter-wave PMN with dual-functional base stations (BSs) for simultaneous detection of unauthorized unmanned aerial vehicles (UAVs) and user communication via the unified transmit signal and beamforming. We develop a system-level theoretical framework to investigate the sensing and communication performance of PMNs based on stochastic geometry, which captures the mutual interference and resource contention between the two functions and builds a foundation for the optimization of network configurations. In addition, by leveraging the collaboration of multiple BSs in PMNs, we propose a cooperative sensing strategy combining the monostatic and bistatic sensing processes to enhance the reliability of UAV surveillance. Simulation results verify the effectiveness of the proposed theoretical framework and demonstrate the benefits of cooperative sensing in UAV detection and communication performance, as compared with the standalone sensing by individual BSs.

In recent years, semantic communication has been a popular research topic for its superiority in communication efficiency. As semantic communication relies on deep learning to extract meaning from raw messages, it is vulnerable to attacks targeting deep learning models. In this paper, we introduce the model inversion eavesdropping attack (MIEA) to reveal the risk of privacy leaks in the semantic communication system. In MIEA, the attacker first eavesdrops the signal being transmitted by the semantic communication system and then performs model inversion attack to reconstruct the raw message, where both the white-box and black-box settings are considered. Evaluation results show that MIEA can successfully reconstruct the raw message with good quality under different channel conditions. We then propose a defense method based on random permutation and substitution to defend against MIEA in order to achieve secure semantic communication. Our experimental results demonstrate the effectiveness of the proposed defense method in preventing MIEA.

As three-dimensional (3D) data acquisition devices become increasingly prevalent, the demand for 3D point cloud transmission is growing. In this study, we introduce a semanticaware communication system for robust point cloud classification that capitalizes on the advantages of pre-trained Point-BERT models. Our proposed method comprises four main components: the semantic encoder, channel encoder, channel decoder, and semantic decoder. By employing a two-stage training strategy, our system facilitates efficient and adaptable learning tailored to the specific classification tasks. The results show that the proposed system achieves classification accuracy of over 89% when SNR is higher than 10 dB and still maintains accuracy above 66.6% even at SNR of 4 dB. Compared to the existing method, our approach performs at 0.8% to 48% better across different SNR values, demonstrating robustness to channel noise. Our system also achieves a balance between accuracy and speed, being computationally efficient while maintaining high classification performance under noisy channel conditions. This adaptable and resilient approach holds considerable promise for a wide array of 3D scene understanding applications, effectively addressing the challenges posed by channel noise.

Deep learning (DL) based semantic communication methods have been explored to transmit images efficiently in recent years. In this paper, we propose a generative model based semantic communication to further improve the efficiency of image transmission and protect private information. In particular, the transmitter extracts the interpretable latent representation from the original image by a generative model exploiting the GAN inversion method. We also employ a privacy filter and a knowledge base to erase private information and replace it with natural features in the knowledge base. The simulation results indicate that our proposed method achieves comparable quality of received images while significantly reducing communication costs compared to the existing methods.

Gait recognition is widely used in diversified practical applications. Currently, the most prevalent approach is to recognize human gait from RGB images, owing to the progress of computer vision technologies. Nevertheless, the perception capability of RGB cameras deteriorates in rough circumstances, and visual surveillance may cause privacy invasion. Due to the robustness and non-invasive feature of millimeter wave (mmWave) radar, radar-based gait recognition has attracted increasing attention in recent years. In this research, we propose a Hierarchical Dynamic Network (HDNet) for gait recognition using mmWave radar. In order to explore more dynamic information, we propose point flow as a novel point clouds descriptor. We also devise a dynamic frame sampling module to promote the efficiency of computation without deteriorating performance noticeably. To prove the superiority of our methods, we perform extensive experiments on two public mmWave radar-based gait recognition datasets, and the results demonstrate that our model is superior to existing state-of-the-art methods.

The rapid development of unmanned aerial vehicle (UAV) market presents potential threats to public security and personal privacy, and the vision sensors are widely deployed to detect the invasive UAVs because of the intuitivity and accessibility of the video. However, the small pixel area and weak morphological characteristics of distant invasive UAVs pose a considerable challenge to the detection precision. Prior work on UAV detection simply focuses on the information fusion between different feature layers, but ignoring the feature information inside each layer. In addition, to detect small UAV in video streams, the motion information of the target is also a noteworthy feature. In this regard, we propose a feature super-resolution-based UAV detector with motion information extractor. The proposed network fully utilizes the motion information of UAVs between temporal frames and the spatial invariant features between different resolution frames to pursue a high-accuracy small UAV detection performance. Experiments on Drone vs Birds dataset are carried out, and it is demonstrated that a higher detection accuracy on small UAVs is achieved compared with the baseline.

In direction-of-arrival (DOA) estimation, Cramér-Rao bound is widely used to lower bound the mean square error (MSE), which, however, is a local bound. As a global bound, existing Ziv-Zakai bound (ZZB) is restricted by the single source assumption and has not considered the effect of ordering process during MSE calculation. In this paper, we derive an explicit ZZB for multiple sources DOA estimation, where the ZZB derivation framework is first extended to multiple sources case. Further, order statistics are introduced to describe the effect the ordering process on the change of a priori distribution of DOAs, which finally makes the derived ZZB tight over a wide range of signal-to-noise ratio. The derived ZZB reveals the relationship between the number of sources and the convergence performance in the a priori performance region. Simulation results demonstrate the global tightness of the derived ZZB.

Existing matrix-based neural network for direction-of-arrival (DOA) estimation has to train a large amount of parameters proportional to the length of vectorized signal statistics, resulting in a heavy system overload. To address the problem, a tensorized neural layer decomposition-based neural network is proposed for 2-D DOA estimation. In particular, the covariance tensor of tensor signals is propagated to hidden state tensors. The feedforward propagation is formulated as an inverse Tucker decomposition, such that parameters in the tensorized neural layers are compressed into inverse Tucker factors. Accordingly, the tensorized backpropagation procedure is designed for network training. It is proved that the number of parameters is significantly reduced, which leads to a faster training process. Simulation results demonstrate that the proposed method reduces the number of trained parameters by more than 122,000 times compared to the matrix-based neural network while maintaining a moderate accuracy.

Multi-object tracking (MOT) aims to build moving trajectories for number-agnostic objects. Modern multi-object trackers commonly follow the tracking-by-detection strategy. Therefore, fooling detectors can be an effective solution but it usually requires attacks in multiple successive frames, resulting in low efficiency. Attacking association processes improves efficiency but may require model-specific design, leading to poor generalization. In this paper, we propose a novel False negative and False positive attack (F&F attack) mechanism: it perturbs the input image to erase original detections and to inject deceptive false alarms around original ones while integrating the association attack implicitly. The mechanism can produce effective identity switches against multi-object trackers by only fooling detectors in a few frames. To demonstrate the flexibility of the mechanism, we deploy it to three multi-object trackers (ByteTrack, SORT, and CenterTrack) which are enabled by two representative detectors (YOLOX and CenterNet). Comprehensive experiments on MOT17 and MOT20 datasets show that our method significantly outperforms existing attackers, revealing the vulnerability of the tracking-by-detection paradigm to detection attacks.

Human pose estimation is a crucial problem in computer vision, and it has numerous applications in diverse fields such as virtual reality, surveillance, human-computer interaction, and action assistance. With the advent of edge computing, it is a promising paradigm to perform real-time artificial intelligence (AI)-assisted action based on pose estimation at the edge. However, task scheduling optimization for human pose estimation in edge computing is a challenging problem, due to the limited computing resources. In this paper, we propose a novel framework for task scheduling optimization in human pose estimation at the edge. Our framework takes computing resources scheduling and task scheduling decision into account, with the objective of maximizing the quality of service (QoS) of the system. We use multiple depth cameras at different locations to build three-dimensional (3D) poses to maintain the accuracy of estimation and to assist in guiding action. We evaluate our proposed framework on a real-world dataset. The results demonstrate its effectiveness in improving system delay and estimation accuracy in comparison with benchmark methods. We also verify the sensitivity of our proposed framework, which can provide insights into optimal parameter settings for different scenarios.

The previous robust adaptive beamformer for sparse array enjoys performance improvement due to enhanced degrees-of-freedom (DOFs), while the polarization diversity of signals is not considered. The polarization diversity is an important factor that could be exploited to design a more functional beamformer. Specifically, in this paper, a cascaded sparse array (CSA) composed of diversely polarized antennas, which is polarization sensitive and has reduced mutual coupling, is proposed with a closed-form expression for the array geometry. Then, a polarimetric sparse reconstruction beamformer operating in the joint spatial and polarization domain is proposed for the CSA, and it is capable of suppressing the interferences using the information in the additional polarization domain with enhanced DOFs. As a result, it not only offers reduced costs but also improved functionality, demonstrating its potential value in future wireless communications. The polynomial rooting based joint direction-of-arrival and polarization estimation procedures are then given to support the power distribution estimation in a closed-form manner. Subsequently, the estimated steering vector of the desired signal and the reconstructed interference-plus-noise covariance matrix are combined to calculate the proposed beamformer. Numerical simulations are included to verify the potential advantages of the proposed CSA as well as the superior performance and robustness of the proposed beamformer.

Nowadays, wireless charging has become one of the most popular technologies in Internet of Things (IoT), which makes electric devices battery free and flexible. The electromagnetic coupling in antenna design is important to push the range limits beyond the near field. Previous studies did not consider the coupling among coils and they do not work for the far-field scenarios. In this article, we design an antenna for wireless charging that can expand the charging distance from 5 to 55 cm, a ten-fold improvement compared to the near-field commercial communication distance according to the simulation results. Specifically, it is practical to take into account the coupling of every two coils when we analyze the transmit power. Therefore, we first model the near-field signal propagation with higher-order factors, and establish the relationship between the geometry parameters and the transmit power. Then, since our problem is extremely nonlinear, we design the greedy search algorithm and narrow down the feasible region to obtain the optimal parameters of the coils that can maximize the charging distance. An Ansoft-High Frequency Structure Simulator is adopted to simulate the performance of the coil models, and the results show that square coils achieve the best performance. We also discuss the best arrangement of multiple coil antennas. The impact of the signal phase is also introduced and the best combination of signal phases from different transmit coils is analyzed.

The problem of multiple integrity attacks (attackers) detection and identification (MIADI) in cyber–physical systems (CPSs) is still a challenging problem to date. The goal of this article is to develop a knowledge-based method capable of simultaneously detecting and identifying multiple integrity attacks aiming at different sensors in a CPS. In this article, with the help of labeled random finite set (RFS) theory, a new solution to solve the MIADI problem is proposed. The main contributions of this article lie in the following two aspects, the first is the novel formulation of the MIADI problem, in which labeled RFSs are used to model the behaviors of multiple integrity attackers for the first time, and the second is the proposed labeled RFS-based solution, which provides an elegant framework to cope with the MIADI problem. Numerical experiments are conducted and experimental results demonstrate the effectiveness of the proposed solution. This proposed solution further extends the feasibility of the labeled RFS theory in the context of CPSs cybersecurity.

Deep learning (DL) based semantic communication methods have been explored for the efficient transmission of images, text, and speech in recent years. In contrast to traditional wireless communication methods that focus on the transmission of abstract symbols, semantic communication approaches attempt to achieve better transmission efficiency by only sending the semantic-related information of the source data. In this paper, we consider semantic-oriented speech transmission which transmits only the semantic-relevant information over the channel for the speech recognition task, and a compact additional set of semantic-irrelevant information for the speech reconstruction task. We propose a novel end-to-end DL-based transceiver which extracts and encodes the semantic information from the input speech spectrums at the transmitter and outputs the corresponding transcriptions from the decoded semantic information at the receiver. In particular, we employ a soft alignment module and a redundancy removal module to extract only the text-related semantic features while dropping semantically redundant content, greatly reducing the amount of semantic redundancy compared to existing methods. We also propose a semantic correction module to further correct the predicted transcription with semantic knowledge by leveraging a pretrained language model. For the speech to speech transmission, we further include a CTC alignment module that extracts a small number of additional semantic-irrelevant but speech-related information, such as duration, pitch, power and speaker identification of the speech for the better reconstruction of the original speech signals at the receiver. We also introduce a two-stage training scheme which speeds up the training of the proposed DL model. The simulation results confirm that our proposed method outperforms current methods in terms of the accuracy of the predicted text for the speech to text transmission and the quality of the recovered speech signals for the speech to speech transmission, and significantly improves transmission efficiency. More specifically, the proposed method only sends 16% of the amount of the transmitted symbols required by the existing methods while achieving about a 10% reduction in WER for the speech to text transmission. For the speech to speech transmission, it results in an even more remarkable improvement in terms of transmission efficiency with only 0.2% of the amount of the transmitted symbols required by the existing method while preserving the comparable quality of the reconstructed speech signals.

Existing stochastic Ziv-Zakai bound (ZZB) for compressive time delay estimation from compressed measurement relies on a Gaussian approximation, which makes it inaccurate in the asymptotic region when the stochastic component dominates the received signals. In this letter, we apply different random projections on zero-mean Gaussian received signal to obtain multiple compressed measurements, based on which the log-likelihood ratio test is exactly formulated as the difference of two generalized integer Gamma variables. Accordingly, we further derive the exact expression of the stochastic ZZB for compressive time delay estimation from zero-mean Gaussian signal. Simulation results show that the derived ZZB is globally tight to accurately predict the estimation performance regardless of the number of compressed measurements, and it can also accurately predict the threshold signal-to-noise ratio for the estimator when the number of compressed measurements is large.

Direction-of-arrival (DOA) estimation using sub-Nyquist tensor signals benefits from enhanced performance by extracting structural angular information with multi-dimensional sparse arrays. Although convolutional neural network (CNN) has been employed to achieve efficient DOA estimation in challenging conditions, conventional methods demand excessive memory storage and computation power to process sub-Nyquist tensor statistics. In this letter, we propose a decomposed CNN for sub-Nyquist tensor-based 2-D DOA estimation, where an augmented coarray tensor is derived and used as the network input. To compress convolution kernels for efficient coarray tensor propagation, we develop a convolution kernel decomposition approach. This enables the acquisition of canonical polyadic (CP) factors containing compressed parameters. Performing decomposable convolution between the coarray tensor and the CP factors leads to resource-efficient DOA estimation. Our simulation results indicate that the proposed method conserves system resources while maintaining competitive performance.

Most previous studies mainly have focused on the analysis of structural properties of individual neuronal networks from C. elegans. In recent years, an increasing number of synapse-level neural maps, also known as biological neural networks, have been reconstructed. However, it is not clear whether there are intrinsic similarities of structural properties of biological neural networks from different brain compartments or species. To explore this issue, we collected nine connectomes at synaptic resolution including C. elegans, and analyzed their structural properties. We found that these biological neural networks possess small-world properties and modules. Excluding the Drosophila larval visual system, these networks have rich clubs. The distributions of synaptic connection strength for these networks can be fitted by the truncated pow-law distributions. Additionally, compared with the power-law model, a log-normal distribution is a better model to fit the complementary cumulative distribution function (CCDF) of degree for these neuronal networks. Moreover, we also observed that these neural networks belong to the same superfamily based on the significance profile (SP) of small subgraphs in the network. Taken together, these findings suggest that biological neural networks share intrinsic similarities in their topological structure, revealing some principles underlying the formation of biological neural networks within and across species.

Time-sensitive networking (TSN), an emerging network technology, requires high-performance scheduling mechanisms to deliver deterministic service in Industry 5.0. Cyclic queuing and forwarding (CQF) is launched to simplify the configuration complexity of the early stage mechanism time-aware shaper in TSN flow scheduling. Previous CQF studies adopt an inflexible incremental flow scheduling scheme, which consists of flow sorting, offset search, and resource judgment. However, we observe that flow sorting and offset search are mutually interdependent. The offset of a flow helps determine the resource status on the flow path, which can guide flow sorting. By utilizing the interaction between flow and offset, we design a novel scheduling approach that achieves high scheduling performance and time efficiency. Specifically, the proposed approach combines flow sorting and offset search together to select flow and its offset (i.e., (flow, offset)) simultaneously. To effectively determine the selecting priority and select the potential optimal flow-offset combination, we define a unified metric,$mapping\,score$, to quantify the schedulability of different flow and offset combinations. The extensive experiments demonstrate that the scheduling success rate of our proposed approach is on average 31.69% higher than the baseline and 4.57% higher than the state-of-the-art flow judgement approach (FLJ) method. Moreover, it outperforms the state-of-art FLJ method by 7.62% in large-scale linear topologies, indicating its great scalability in different network scales and complex topologies.

For improving the performance of industrial cyber-physical systems (ICPS), the joint design of control and transmission has been demonstrated as an efficient mechanism. However, the existing metrics in recent joint design works lack completeness and accuracy, which leads to the challenge of improving the stability and network resource utilization of ICPS. This article proposes a full-loop age of information (FL-AoI)-based control and transmission joint design architecture for multisubsystem ICPS integrating multihop network. The FL-AoI depicts the timeliness of information by state delay, input delay, and event-triggered status in full-loop ICPS. To avoid the instability caused by the input delay, we design a linear-quadratic regulator (LQR)-based controller by the FL-AoI and derive a feasible region of the FL-AoI for guaranteeing the stability of control systems. To ensure the accuracy of FL-AoI, we propose a routing and scheduling policy based on time-sensitive networking (TSN) for the deterministic bound of delay. Finally, we propose an optimal event-triggered policy based on FL-AoI for minimizing the data transmission amount while ensuring the system stability. The evaluation results show that our strategy improves the nonlinear and nonscalar control systems' stability while reducing the network burden of TSN compared with the traditional joint design strategies.

Power integrity analysis is an essential step in power distribution network (PDN) sign-off to ensure the performance and reliability of chips. However, with the growing PDN size and increasing scenarios to be validated, it becomes very time- and resource-consuming to conduct full-stack PDN simulation to check the power integrity for different test vectors. Recently, various works have proposed machine learning–based methods for PDN power integrity prediction, many of which still suffer from large training overhead, inefficiency, or non-scalability. Thus, this article proposed an efficient and scalable framework for the worst-case power integrity prediction, which can handle general tasks including dynamic noise prediction and bump current prediction. The framework first reduces the spatial and temporal redundancy in the PDN and input current vector and then employs efficient feature extraction as well as a novel convolutional neural network architecture to predict the worst-case power integrity. Experimental results show that the proposed framework consistently outperforms the commercial tool and the state-of-the-art machine learning method with only 0.63–1.02% mean relative error and 25–69× speedup for noise prediction and 0.22–1.06% mean relative error and 24–64× speedup for bump current prediction.

In this paper, a coarray tensor completion-based direction-of-arrival (DOA) estimation method is proposed for coprime planar array. To perform Nyquist-matched coarray signal processing, the completion of the coarray tensor corresponding to an augmented discontinuous virtual array is pursued. However, it is difficult to impose a low-rank regularization on the incomplete coarray tensor with slices of missing elements for its completion. To solve this problem, a structural tensorization approach is designed to reshape the incomplete coarray tensor into one with distributed missing elements. As such, a coarray tensor completion problem based on tensor nuclear norm minimization is formulated to complete these missing elements. By exploiting the filled virtual array obtained from the completed coarray tensor, a super-resolution DOA estimation can be achieved in closed-form.

The shaking table has been used extensively in the structure test field to verify the structure’s performance against various vibrations, e.g., earthquakes. In order to replicate the vibrations, which are measured by the acceleration signal specifically, the model of the shaking table should be thoroughly constructed to design the controller. However, parametric uncertainty and strong nonlinearity, such as the nonlinear friction, make it an obstacle to obtaining an accurate model. A neural network-based controller is designed in this paper to address this issue, and the nonlinear systems are estimated by the neural network’s universal approximation characteristics. Furthermore, a robust sliding mode controller is utilized to compensate for the residual error of the neural network and other uncertainties. The semi-global asymptotic stability of the controller is proved by Lyapunov analysis. Comparative experimental results indicate the superiority of the proposed controller.

With the emergence of Industry 5.0, it is significant to enable efficient cooperation between humans and machines in the Industrial Internet of Things (IIoT). However, achieving real-time and reliable transmission of data flows deriving from time-sensitive applications in IIoT remains an open challenge. In this paper, we propose a three-layer software-defined IIoT (SDIIoT) architecture to enable multiple industrial services and flexible network configuration. In particular, when network services change frequently in SDIIoT, the delay of the control plane has a great influence on the end-to-end delay of data flows. To address this issue, we portray two different service curves of OpenFlow switches to adapt to dynamic network status based on Network Calculus (NC). To elevate resource efficiency and comply with friendly environments, we minimize the total worst-case network cost under strict resource constraints and transmission requirements by exploiting the joint flow routing and scheduling algorithm (JFRSA). Our numerical simulation results demonstrate the effectiveness and efficiency of our solution.

Intelligent transformation for traditional factories is a widely discussed topic. The key to this transformation is ensuring the integration between information technology and operational technology. However, it is a challenging task in industry owing to the communication heterogeneity of the underlying production equipment (horizontal communication), and inefficient interactions between the equipment and information decision center (vertical communication). In this paper, we explore asset administration shell (AAS), an asset virtualization technology, shielding heterogeneous physical communication protocol of production equipment. Besides, to promote inefficient communication between the equipment and information decision center, we adapt OPC UA protocol as the communication protocol of AAS for vertical communication. In addition, time-sensitive networking (TSN) is applied to ensure communication between the AAS and the corresponding physical device. Above operations ensure devices interconnection and interoperability. On this basis, we propose an AAS-based production management platform (AASPMP), which aims at the coverage from the demand side to the production side. Such an intelligent system characterizes three layers to decompose complicated system functionalities, and a visible client is provided for the convenience of remote operation and maintenance. We deploy our system on the actual production system and demonstrate the effectiveness of our design.

Multi-object tracking (MOT) in the scenario of low-frame-rate videos is a promising solution for deploying MOT methods on edge devices with limited computing, storage, power, and transmitting bandwidth. Tracking with a low frame rate poses particular challenges in the association stage as objects in two successive frames typically exhibit much quicker variations in locations, velocities, appearances, and visibilities than those in normal frame rates. In this paper, we observe severe performance degeneration of many existing association strategies caused by such variations. Though optical-flow-based methods like CenterTrack can handle the large displacement to some extent due to their large receptive field, the temporally local nature makes them fail to give correct displacement estimations of objects whose visibility flip within adjacent frames. To overcome the local nature of optical-flow-based methods, we propose an online tracking method by extending the CenterTrack architecture with a new head, named APP, to recognize unreliable displacement estimations. Then we design a two-stage association policy where displacement estimations or historical motion cues are leveraged in the corresponding stage according to APP predictions. Our method, with little additional computational overhead, shows robustness in preserving identities in low-frame-rate video sequences. Experimental results on public datasets in various low-frame-rate settings demonstrate the advantages of the proposed method.

In-depth analysis of medical data through machine learning to achieve disease prediction is beneficial to the early detection and treatment of diseases. However, medical data involves mass patient privacy, and datasets of different medical institutions cannot be directly shared due to privacy protection. So medical data often exists in the form of data islands, which makes it difficult for most existing prediction models to complete disease prediction. In this paper, a digital twin assisted efficient clustering Federated Learning (FL) algorithm for disease prediction is proposed. It can break data islands to predict diseases on the premise of privacy security. Firstly, we design an efficient clustering Federated Learning with Client Selection (FLCS) protocol based on heterogeneity and contribution to improve the training efficiency and prediction accuracy. Secondly, we use digital twin to assist the FLCS protocol to carry out large-scale prediction. In addition, the shapley value introduced in the calculation of client contribution makes the model interpretable and enhances the reliability of prediction results. Finally, the evaluation results show that compared with the common prediction models and FedAvg algorithm, the FLCS protocol assisted by digital twin has better efficiency and accuracy in binary classification prediction.

This paper presents a deep learning-based image features extraction and compression for multi-tasks, which can be applied to various intelligent tasks. We explore the multilevel features of the source by designing different information extraction networks, which contain text semantics, image segmentation, and pixel information. We propose a coarse-to-fine architecture to excavate the plentiful semantic information received from the encoder. The coarse module recovers the multi-granularity image according to the receiving symbols, and the fine module fuse different quality image to improve the reconstruction performance. In particular, we use multi-attention networks to extract and recover the image features at pixel levels. To overcome the artifact blocks phenomenon during the image reconstruction process that lacks necessary information, we design a dual features block that can mitigate the problem. Meanwhile, the system can accomplish different tasks by changing the last layers of the model.

Conventional tensor direction-of-arrival (DOA) estimation methods for sparse arrays apply canonical polyadic decomposition (CPD) to the high-order coarray covariance tensor for retrieving angle information. However, due to the low convergence rate of CPD-based algorithms for high-order tensors, these methods suffer from a high computation cost. To address this issue, a sub-Nyquist tensor train decomposition (SubTTD)-based DOA estimation method is proposed for a three-dimensional (3-D) sparse array, where an augmented virtual array is derived from the sub-Nyquist tensor statistics. To reduce computational complexity of processing the 6-D coarray covariance tensor, the proposed SubTTD model efficiently decomposes it into a train of head matrix, 3-D core tensors, and tail matrix. Based on that, a core tensor decomposition and a change-of-basis transformation for the head matrix are designed to retrieve canonical polyadic factors of the coarray covariance tensor for DOA estimation. The computational efficiency of the proposed method is theoretically analyzed, and its effectiveness is verified via simulations.

Existing tensor-based coherent direction-of-arrival (DOA) estimation methods adopting spatial smoothing to decorrelate the coherent tensor statistics usually lead to a poor decorrelation performance. In this letter, we propose a structured tensor reconstruction method for two-dimensional coherent DOA estimation, which then avoids the inefficient spatial smoothing. In particular, after investigating the structural property of the four-dimensional incoherent covariance tensor, we propose a tensorial Hermitian Toeplitz mapping rule to reconstruct a structured covariance tensor from the rank-deficient coherent covariance tensor statistics. It is theoretically proved that, the reconstructed covariance tensor admits a decorrelated canonical polyadic model with a tensorial Hermitian Toeplitz structure, whose decomposition ensures a closed-form coherent DOA estimation. The effectiveness of the proposed method is verified by simulations.

Detecting small low-contrast targets in the airspace is an essential and challenging task. This article proposes a simple and effective data-driven support vector machine (SVM)-based spatiotemporal feature fusion detection method for small low-contrast targets. We design a novel pixel-level feature, called a spatiotemporal profile, to depict the discontinuity of each pixel in the spatial and temporal domains The spatiotemporal profile is a local patch of the spatiotemporal feature maps concatenated by the spatial feature maps and temporal feature maps in channelwise, which are generated by the morphological black-hat filter and a ghost-free dark-focusing frame difference methods, respectively. Instead of the handcrafted feature fusion mechanisms in previous works, we use the labeled spatiotemporal profiles to train an SVM classifier to learn the spatiotemporal feature fusion mechanism automatically. To speed up detection for high-resolution videos, the serial SVM classification process on central processing units (CPUs) is reformed as parallel convolution operations on graphics processing unit (GPUs), which exhibits over 1000+ times speedup in our real experiments. Finally, blob analysis is applied to generate final detection results. Elaborate experiments are conducted, and experimental results demonstrate that the proposed method performs better than 12 baseline methods for the small low-contrast target detection. The field tests manifest that the parallel implementation of the proposed method can realize real-time detection at 15.3 FPS for videos at a resolution of 2048×1536 and the maximum detection distance can reach 1 km for drones in sunny weather.

As an emerging and prospective paradigm, the industrial Internet of Things (IIoT) enable intelligent manufacturing through the interconnection and interaction of industrial production elements. The traditional approach that transmits data in a single physical network is undesirable because such a scheme cannot meet the network requirements of different industrial applications. To address this problem, in this article, we propose a network slicing orchestration system for remote adaptation and configuration in smart factories. We exploit software-defined networking and network functions virtualization to slice the physical network into multiple virtual networks. Different applications can use a dedicated network that meets its requirements with limited network resources with this scheme. To optimize network resource allocation and adapt to the dynamic network environments, we propose two heuristic algorithms with the assistance of artificial intelligence and the theoretical analysis of the network slicing system. We conduct numerical simulations to learn the performance of the proposed algorithms. Our experimental results show the effectiveness and efficiency of our proposed algorithms when multiple network services are concurrently running in the IIoT. Finally, we use a case study to verify the feasibility of the proposed network slicing orchestration system on a real smart manufacturing testbed.

Nowadays, accurate and real-time vehicle tracking is critical to ensure the safety of intelligent vehicles. However, tracking in the complex traffic environments still remains a challenging issue. In this article, we present a road-map aided Gaussian mixture probability hypothesis density (RA-GMPHD) filter for multivehicle tracking with automotive radar. Since the road-map is commonly available in traffic scenarios, we focus on leveraging road-map information to enhance the tracking performance. We first model the vehicle dynamics in a 2-D road coordinates, then approximatively map it onto ground coordinates considering map errors. Additionally, we integrate the variable structure interacting multiple model into the RA-GMPHD filter considering both the dynamic uncertainty of targets and the road geographic constraints. Furthermore, we perform extensive simulations and conduct physical testings to demonstrate the superiority of our approaches compared with state-of-the-art method. Experimental results show our methods enhance both the tracking quality and tracking continuity.

Incentive mechanism design and quality control are two key challenges in data crowdsourcing, because of the need for recruitment of crowd users and their limited capabilities. Without considering users’ social influences, existing mechanisms often result in low efficiency in terms of the platform’s cost. In this paper, we exploit social influences among users as incentives to motivate users’ participation, in order to reduce the cost of recruiting users. Based on social influences, we design incentive mechanisms with the goal of achieving high quality of crowdsourced data and low cost of incentivizing users’ participation. Specifically, we consider three scenarios. In the full information scenario, we design task assignment and user recruitment mechanisms to optimize the data quality while reducing the incentive cost. In the partial information scenario, users’ qualities and costs are unknown. We exploit the correlation between tasks to overcome the information asymmetry, for both cases of opportunistic crowdsourcing and participatory crowdsourcing. Further, in the dynamic social influence scenario, we investigate the dynamics of users’ social influences and design extra rewards for users to make full use of the social influence and achieve maximum cost saving. We evaluate the incentive mechanisms using numerical results, which demonstrate their effectiveness.

Real-time dense SLAM techniques aim to reconstruct the dense three-dimensional geometry of a scene in real time with an RGB or RGB-D sensor. An indoor scene is an important type of working environment for these techniques. The planar prior can be used in this scenario to improve the reconstruction quality, especially for large low-texture regions that commonly occur in an indoor scene. This article fully explores the planar prior in a dense SLAM pipeline. First, we propose a novel plane detection and segmentation method that runs at 200 Hz on a modern graphics processing unit. Our algorithm for constructing global plane constraints is very efficient; hence, we use it in the process of each input frame for the camera pose estimation while maintaining the real-time performance. Second, we propose herein a plane-based map representation that greatly reduces the memory footprint of plane regions while keeping the geometric details on planes. The experiments reveal that our system yields superior reconstruction results with planar information running at more than 30 fps. Aside from speed and storage improvements, our technique also handles the low-texture problem in plane regions.

Equipped with machine learning (ML) models, unmanned aerial vehicle (UAV) swarms can execute various applications like surveillance and target detection. However, the connections between UAVs and cloud servers cannot be guaranteed, especially when executing massive data. Thus, traditional cloud-centric approach will not be suitable, since it may cause high latency and significant bandwidth consumption. In this work, we propose a federated learning (FL) framework via non-orthogonal multiple access (NOMA) for a UAV swarm which is composed of a leader-UAV and a group of follower-UAVs. Specifically, each follower-UAV updates its local model by using its collected data, and then all follower-UAVs form a NOMA-group to send their respectively trained FL parameters (i.e., the local FL models) to the leader-UAV simultaneously. We formulate a joint optimization of the uplink NOMA-transmission durations, downlink broadcasting duration, as well as the computation-rates of the leader-UAV and all follower-UAVs, aiming at minimizing the latency in executing the FL iterations until reaching a specified accuracy. Numerical results are presented to verify the effectiveness of our proposed algorithm, and demonstrate that the proposed algorithm can outperform some baseline strategies.

The deep penetration of electric vehicles (EVs) into the transportation section and the associated charging management has yielded a critical issue, namely, how to efficiently store the generated charging records. In this article, we investigate the cost-efficient charging-record storage scheme by exploiting blockchain (BC). Accounting for the operational cost due to the consensus process via the practical Byzantine fault tolerance (PBFT) protocol, we model the associated cost for storing the charging records via an ideal multiblockchain system and formulate a joint optimization of the storage selection (i.e., either storing the charging record locally or selecting one of the BCs for storing the charging record) and server-node allocation for each BC, with the objective of minimizing a systemwise cost. Despite the nature of the complicated mixed binary and integer programming problem, we exploit the decomposition structure and propose a layered algorithm (i.e., the bottom subproblem for determining the optimal storage selection and the top problem for finding the server-node allocation) to solve it. For the bottom subproblem, we exploit the nature of minimum weighted matching of the problem and propose a distributed auction-based algorithm for computing the optimal storage selection. With the optimal solution from the subproblem, we further propose an annealing-based algorithm to determine the server-node allocation for each BC. Numerical results are provided to validate the effectiveness of our proposed algorithms and the performance of our cost-efficient charging-record storage scheme via BC.

Hybrid long-range (LoRa) network is a promising approach to overcome the half-duplex issue in traditional LoRa networks, increasing the network efficiency and confidence for today's fast-developing smart city services. Gateways (GWs) in hybrid LoRa networks link the end devices (EDs) and the Netserver and have a great impact on the system performance. Previous results on GW planning cannot be directly applied to hybrid LoRa networks since the heterogeneous EDs require different redundancy of coverage. Furthermore, the spreading factors (SFs) which determine the system performance should be considered concurrently. In this article, in order to find the optimal planning scheme, i.e., deciding the number and locations of GWs in the hybrid LoRa network, we propose a heterogeneous redundant coverage solution to meet the requirements of the heterogeneous EDs using the same or different frequencies for uplink and downlink. Specifically, we formulate this problem as a point coverage problem that meets the requirements of EDs. The deleted greedy algorithm (DGA) and the nondeleted greedy algorithm (NDGA) are designed to solve this problem, in which the DGA shows better performance when compared to NDGA. Furthermore, we build models of system performance and analyze the system throughput and energy efficiency based on SFs. The simulation results show that our solution gains more system throughput and energy efficiency than a one-coverage solution.

Imagine when you line up in a store, the person in front of you can make you pay her bill by using a passive wearable device that forces a scan of your credit card or mobile phones without your awareness. An important assumption of today's near-field communication (NFC)-enabled cards is the limited communication range between the commercial reader and the NFC cards. Previous approaches effectively used mobile phones and active relays to break the range limit of NFC propagation for the NFC attack. However, these approaches require a power supply and protocol modification when mobile phones or active relays transmit NFC signals. We propose ReCoil, a system that uses passive relays to attack NFC-enabled mobile phones or cards by expanding the communication range of NFC to 49.6 cm, an obvious improvement over its intended commercial distance. ReCoil is a magnetically coupled resonant wireless power transfer system, which optimizes the energy transfer by searching the optimal geometry parameters. Specifically, we first narrow down the feasible area reasonably and design the ReCoil-greedy algorithm such that the relays absorb the maximum energy from the reader. In order to reroute the signal to pass over the surface of the human body, we then design a half waistband by carefully analyzing the impact of the distance and orientation between two coils on the mutual inductance. Then, three more coils are added to the system to keep enlarging the communication range. Finally, extensive experiment results validate our analysis, showing that our passive relays consisting of common copper wires and tunable capacitors can expand the range of NFC to 49.6 centimeters.

Conventional canonical polyadic decomposition (CPD) approach for tensor-based sparse array direction-of-arrival (DOA) estimation typically partitions the coarray statistics to generate a full-rank coarray tensor for decomposition. However, such an operation ignores the spatial relevance among the partitioned coarray statistics. In this letter, we propose a coupled coarray tensor CPD-based two-dimensional DOA estimation method for a specially designed coprime L-shaped array. In particular, a shifting coarray concatenation approach is developed to factorize the partitioned fourth-order coarray statistics into multiple coupled coarray tensors. To make full use of the inherent spatial relevance among these coarray tensors, a coupled coarray tensor CPD approach is proposed to jointly decompose them for high-accuracy DOA estimation in a closed-form manner. According to the uniqueness condition analysis on the coupled coarray tensor CPD, an increased number of degrees-of-freedom for the proposed method is guaranteed.

Information barrier coverage has been widely adopted to prevent unauthorized invasion of important areas in Internet of Things. As sensors are typically placed outdoors, they are susceptible to getting faulty. Previous works assumed that faulty sensors are easy to recognize, e.g., they may stop functioning or output apparently deviant sensory data. In practice, there exist multiple types of fault that sensors may have during operation. It is, thereby, difficult to recognize faulty sensors as well as their invalid output and attain accurate intrusion detection. We, in this paper, propose a novel fault-tolerant intrusion detection algorithm (TrusDet) based on trust management to address this challenging issue. TrusDet comprises of three steps: i) sensor-level detection, ii) sink-level decision by collective voting, and iii) trust management and fault determination. In the Step i) and ii), TrusDet divides the surveillance area into a set of fine-grained subareas and exploits temporal and spatial correlation of sensory output among sensors in different subareas to yield a more accurate and robust performance of information barrier coverage. In the Step iii), TrusDet builds a trust management based framework to determine the confidence level of sensors being faulty. We implement TrusDet on HC-SR501 infrared sensors, and design hardware and software to build a practical detection system. Extensive experimental results and simulation results validate the information coverage model and demonstrate that TrusDet has a very low false alarm rate.

Coprime arrays can cope with the underdetermined case for direction-of-arrival (DOA) estimation. However, the popular matrix-based coarray signal processing approaches suffer performance loss on the underlying characteristics among the multi-dimensional signals. To address this problem, we propose a novel coarray tensor-based two-dimensional underdetermined DOA estimation method for coprime planar array in this paper, where both the multi-dimensional information of the received signals and the augmented coarray are effectively utilized. The received signal tensors of the coprime planar array are constructed by concatenating each snapshot, which are then transformed to an augmented uniform rectangular array statistics for extending the effective array aperture. Subsequently, the tensorization technique is adopted to increase the degrees-of-freedom of the proposed DOA estimation method, and a structured coarray tensor is optimized for super-resolution DOA estimation. The effectiveness of the proposed method is verified via simulation results.

In this paper, we investigate localizing multiple wire-less devices in the context of Internet of Things (IoTs). Considering the massive amount of IoT devices deployed in an IoT system, we establish an economical and lightweight Moving-Anchor multitar-Get IoT loCalization system (MAGIC), which is capable of localizing multiple targets simultaneously in a low-power and low-cost manner. To be specific, MAGIC utilizes a single mobile anchor with GPS and LoRa gateway to localize multiple IoT devices embedded with LoRa tags. The single anchor moves to different positions and makes location estimations at each step based on the distance measurements between each target and itself. To find the optimal moving strategy of the anchor, we formulate an optimal path problem aiming to minimize the total length of the path with guaranteed localization accuracy. Since solving the optimal path problem requires the localization information at all the positions, which is unavailable during the moving process, we instead solve an optimal step problem in each step that minimizes the length of the next step without relying on future information. We address the non-convex issue of the problem by decomposing it into a set of convex subproblems by partitioning the feasible domain, which yields a suboptimal solution. Simulation results and practical experiments validate the effectiveness of the proposed MAGIC system and demonstrate its practical value in realistic IoT scenarios.

Industrial Internet of Things (IIoT) as an emerging and prospective paradigm, has great potential to significantly improve production efficiency of manufacturing systems. An open data exchange standard, namely OPCUA, has been proposed for the IIoT systems to provide semantic interoperability over heterogeneous devices. However, a mass of traditional devices that do not support OPC UA, are still operating in legacy automation systems. To address this problem, in this paper, we propose a three-layer IIoT architecture for manufacturing system, which combines OPC UA-based gateways and Time-Sensitive Software-Defined Networking (TSSDN) switches to realize the efficient and reliable communication. The OPC UA is adopted to realize the interoperability of heterogeneous devices and the TSSDN achieves centralized control of network resources and flexible configuration in real-time industrial networks. Finally, we design a smart factory test bed, to evaluate the applicability of the proposed system architecture, in which we implement the information model and data transmission based on OPC UA.

Mobile edge computing (MEC) has been considered as a promising approach for enabling computation-intensive Internet services in future wireless systems. In this paper, we investigate non-orthogonal multiple access (NOMA) assisted MEC, in which edge-computing users (EUs) adopt NOMA to simultaneously offload part of their computation-workloads to the edge-server (ES). To improve the spectrum-efficiency, we consider a paradigm of underlaying device-to-device (D2D) communications, namely, the EUs reuse a cellular user's (CU's) licensed channel for offloading transmission. We firstly characterize the transmit-powers of EUs and CU in this D2D approach, and then formulate a joint optimization of the EUs' computation- workloads offloading and the ES's computation-resource allocation, with the objective of minimizing the latency in completing the EUs' tasks. In spite of the non-convexity of the formulated problem, we exploit its layered structure and propose an efficient algorithm for computing the optimal solution. Numerical results are provided to validate the effectiveness and efficiency of our proposed NOMA assisted MEC via the D2D sharing1.

The frequency diverse array (FDA) radar has been extensively studied due to its unique range‐angle dependent beampattern. Time‐invariant spatial patterns have been reported for FDAs proposed so far. However, some recent studies indicate that the patterns obtained by using these methodologies neglect the time‐range or frequency‐phase relationship, and the definition of time in some equations are misinterpreted which results in erroneous conclusions. Taking the time‐variant property of FDA beampatterns into consideration, in this study, the authors propose a short range FDA radar to generate a time‐variant focused range‐angle dependent transmit beampattern, where chirp waveform with multi‐carrier FDA architecture is used. Both the fixed and time‐modulated frequency offsets are considered to analyse the proposed FDA radar. The frequency offset employed across each element is generated by chaos sequence and sine function. By compensating the propagation delays of signals, the transmitted signals sum up constructively to focus on the desired range‐angle sector only at a specific instant of time. Numerical results are implemented to verify the validity of the proposed schemes.

The recent technology advancement on unmanned aerial vehicle (UAV) has enabled diverse applications in vision-related outdoor tasks. Visual object detection is a crucial task among them. However, it is difficult to actually deploy detectors on embedded devices due to the challenges among energy consumption, accuracy, and speed. In this article, we address a few key challenges from the platform, application to the system, and propose an energy-efficient system for real-time UAV object detection on an embedded platform. The proposed system can achieve speed of 28.5 FPS and 2.7-FPS/W energy efficiency on the data set from 2018 low-power object detection challenges (LPODCs).

Low power system-on-chips (SoCs) are now at the heart of Internet-of-Things (IoT) devices, which are well-known for their bursty workloads and limited energy storage-usually in the form of tiny batteries. To ensure battery lifetime, dynamic voltage frequency scaling (DVFS) has become an essential technique in such SoC chips. With continuously decreasing supply level, noise margins in these devices are already being squeezed. During DVFS transition, large current that accompanies the clock speed transition runs into or out of clock networks in a few clock cycles, induces large Ldi/dt noise, thereby stressing the power delivery system (PDS). Due to the limited area and cost target, adding additional decoupling capacitance to mitigate such noise is usually challenging. A common approach is to gradually introduce/remove the additional clock cycles to increase/decrease the clock frequency in steps, also known as, clock skipping. However, such a technique may increase DVFS transition time, and still cannot guarantee minimal noise. In this paper, we propose a new noise-aware DVFS sequence optimization technique by formulating a mixed 0/1 programming to resolve the problems of clock skipping sequence optimization. Moreover, the method is also extended to schedule extensive wake-up activities on different clock domains for the same purpose. The experiments show that the optimized sequence is able to significantly mitigate noise within the desired transition time, thereby saving both power and energy.

To invade a cyber-physical system (CPS) successfully, hackers are prone to simultaneously launching multiple cyber attacks on different sensors in a CPS. However, little attention has been paid to the problem of detecting multiple cyber attacks up to now. Therefore, in this paper, we deal with the problem on how to efficiently detect multiple cyber attacks aiming at different sensors in CPSs. To achieve the goal of simultaneously detecting both the number of attacks and the attacked sensors, we formulate this problem via a random finite set (RFS) theory, and then apply an iterative RFS-based Bayesian filter and its approximation to solve the problem. Four numerical experiments with different attacks are provided, and the results have demonstrated the effectiveness of the RFS-based approach for the problem of multiple attacks detection in CPSs.

The increasing popularity of drones is bringing many public security and privacy breach issues, such as smuggling, intrusion, and illegal surveillance. Traditional approaches to detecting and localizing drones such as radar and computer vision incur high costs and hence are not desirable for large-scale applications. In this paper, we propose a cost-effective crowdsensing system named CEDAR to achieve such a goal. Specifically, we introduce a novel way of detecting drones by smartphones, exploiting the fact that most drones adopt Wi-Fi for communications with ground control stations. We design an efficient detection algorithm that takes advantage of historical Wi-Fi beacon information and MAC address encoding mechanisms used by drone manufacturers. Using received signal strength, we can also localize the detected drones. Further, to encourage participants' involvement, we design an incentive mechanism based on online auction that guarantees truthfulness and consumer sovereignty. CEDAR can be directly applied to multiple drone scenarios. We implement the system based on Android for the client and Spring, Spring MVC, and Mybatis (SSM) for the centralized platform that supports scalability and hierarchical structure, and enables the coordination between clients and the platform. We perform extensive experiments to validate our analysis. Particularly, the detection rate in the experiments reaches 86.7 percent even without any prior information about drones.

Given a wide spectrum of demands for indoor location-based service, great research effort has been devoted to developing indoor navigation systems. Nevertheless, due to high engineering complexity and expensive infrastructure and labor cost, scalable indoor navigation is still an unsolved problem. In this paper, we present SWiN, a Self-evolving WiFi-based Indoor Navigation system. SWiN provides plug-and-play and light-weight indoor navigation in a sharing manner. To alleviate the impact of the environmental change and device diversity, SWiN extracts both the static and dynamic properties of WiFi signals including scanned AP list, variations of signal strength, and AP's relative strength order. SWiN exploits the leader-follower structure, navigating following users by tracking their motion patterns to provide real-time navigation guidance. In specific, during navigation, SWiN utilizes a light-weight synchronization algorithm to synchronize multi-dimensional WiFi measurements between leader and follower traces. Furthermore, a trace updating mechanism is developed to guarantee the long-term utility of SWiN by extracting useful information in followers' traces. Consolidating these techniques, we implement SWiN on commodity smartphones, and evaluate its performance in a five-story office building and a newly opened two-story shopping mall with test areas over 8000 m2and 6000 m2, respectively. Our experimental results show that 95 percent of the tracking offsets during navigation are less than 2 m and 3.2 m in these two environments.

Node location information is very important to many novel applications of Internet of Things (IoT). Typically, IoT nodes are resource-constrained, and thus costly and energy-hungry localization techniques fall short. In this paper, we present iLoc, a low-cost, low-power and wide-area localization system for IoT applications. iLoc is built on the emerging LoRa technology and overcomes the disadvantage of many short-range localization techniques. Central to iLoc is a mobile anchor node comprising of a simplified LoRa gateway and a smartphone. To locate an IoT node, the anchor node moves around, during which the LoRa gateway receives its locations from the smartphone, and communicates with the IoT node for the information of time of flight (ToF) as well as received signal strength indication (RSSI). In order to obtain a better distance estimation, both RSSI and ToF are integrated in the regression analysis of distance between the anchor node and the IoT node. We further design an iterative localization algorithm by judiciously deciding the locations of the anchor node step by step. The LoRa gateway and tag we prototype cost less than 10 and 5 dollars, respectively. We conduct extensive experiments and the results demonstrate that iLoc achieves an average localization error of 1.33m and power consumption of 0.25mAh in an open environment.

Nonorthogonal multiple access (NOMA) and mobile edge computing (MEC) have been emerging as promising techniques in narrowband Internet of Things (NB-IoT) systems to provide ubiquitously connected IoT devices with efficient transmission and computation. However, the successive interference cancellation (SIC) ordering of NOMA has become the bottleneck limiting the performance improvement for the uplink transmission, which is the dominant traffic flow of NB-IoT communications. Also, in order to guarantee the fairness of task execution latency across NB-IoT devices, the computation resource of MEC units has to be fairly allocated to tasks from IoT devices according to the task size. For these reasons, we investigate the joint optimization of SIC ordering and computation resource allocation in this paper. Specifically, we formulate a combinatorial optimization problem with the objective to minimize the maximum task execution latency required per task bit across NB-IoT devices under the limitation of computation resource. We prove the NP-hardness of this joint optimization problem. To tackle this challenging problem, we first propose an optimal algorithm to obtain the optimal SIC ordering and computation resource allocation in two stages: the convex computation resource allocation optimization followed by the combinatorial SIC ordering optimization. To reduce the computational complexity, we design an efficient heuristic algorithm for the SIC ordering optimization. As a good feature, the proposed low-complexity algorithm suffers a negligible performance degradation in comparison with the optimal algorithm. Simulation results demonstrate the benefits of NOMA in reducing the task execution latency.

Mobile edge computing (MEC), which enables smart terminals to actively offload computation workloads to computational servers deployed at the edge of networks, has provided an efficient approach to address the intensive computation requirement in mobile Internet applications. In this paper, we investigate the delay-aware computation offloading via MEC for Internet of Things (IoT) with secrecy provisioning. Specifically, we consider a scenario where a malicious eavesdropper intentionally overhears the IoT devices’ offloaded computational data. Taking into account the secrecy outage due to the eavesdropper’s overhearing, we formulate a joint optimization of the secrecy-provisioning, computation offloading, and radio resource allocation (including time and power allocations), with the objective of minimizing the overall delay in finishing the computation requirement of the IoT device. Despite the nonconvexity of the joint optimization problem, we propose an efficient algorithm to compute the optimal computation offloading solution. By exploiting the optimal offloading decision of each IoT device, we further consider the scenario of a group of IoT devices offloading computation workloads to the edge server, and investigate how the edge server optimally selects the devices for providing the computation offloading service while subject to the limited energy budget and the time-slot budget of the edge server. We propose an efficient algorithm to find the optimal selection of the devices. We present extensive numerical results to validate the effectiveness of our proposed algorithms and show the impact of the secrecy requirement.

Given the popularity of drones for leisure, commercial, and government (e.g., military) usage, there is increasing focus on drone regulation. For example, how can the city council or some government agency detect and track drones more efficiently and effectively, say, in a city, to ensure that the drones are not engaged in unauthorized activities? Therefore, in this article, we propose a crowdsensing-based cyber-physical system for drone surveillance. The proposed system, CSDrone, utilizes surveillance data captured and sent from citizens’ mobile devices (e.g., Android and iOS devices, as well as other image or video capturing devices) to facilitate jointly drone detection and tracking. Our system uses random finite set (RFS) theory and RFS-based Bayesian filter. We also evaluate CSDrone’s effectiveness in drone detection and tracking. The findings demonstrate that in comparison to existing drone surveillance systems, CSDrone has a lower cost, and is more flexible and scalable.

Narrow-band Internet of Things (NB-IoT), one of the emerging paradigms of low power wide area networks (LPWAN) for Internet of Things (IoT), has been envisioned as a promising solution to enable massive connectivity, cost-efficient, and highly reliable Internet of Thing (IoT) systems in future smart cities. In this work, we build up an indoor environment-temperature monitoring system based on NB-IoT. We present a detailed design of our system and illustrate the key technologies. Based on our system and the collected data (i.e., the temperature data), we further design an abnormality-detection mechanism based on the support vector machine (SVM). We provide experimental results to show the performance of our designed system and the proposed abnormality-detection mechanism.

Coprime arrays are capable of achieving an increased number of degrees-of-freedom by operating the coarray signals. However, their non-uniform coarrays prevent the full utilization of the available signals. To address this problem, a novel coarray interpolation-based direction-of-arrival (DOA) estimation algorithm via covariance matrix reconstruction is proposed in this paper. In particular, we formulate a gridless optimization problem to reconstruct the covariance matrix of the interpolated coarray, such that all the coarray observations are fully utilized. We also investigate the rotational invariance in the coarray domain to retrieve the DOAs. Neither spatial sampling nor spectrum searching is required in the proposed algorithm, indicating the capability of resolving off-grid DOAs. Simulation results demonstrate the effectiveness of the proposed DOA estimation algorithm.

The wide proliferation of drones has posed great threats to personal privacy and public security, which makes it urgent to monitor and locate intruding drones in sensitive areas. In Direction of Arrival (DOA) based localization, the estimation accuracy of DOA directly affects the localization accuracy. In this paper, we propose a novel algorithm to estimate the DOA of an intruding drone by exploiting its acoustic feature, which is mainly reflected in the strength distribution of the harmonics of the received acoustic signal. Specifically, this algorithm first estimates the harmonic frequencies of the drone's acoustic signal in frequency domain. Then, multiple signal classification is used to estimate the DOAs of all the selected harmonics. Furthermore, weighted sum of these DOA estimates will be taken as the drone's DOA estimate, where the weights are in proportional to the energy of the corresponding harmonics. The performance of the proposed algorithm is verified by both simulation and field experiments.

Recently, a considerable research interest has grown up in the millimetre wave wireless system as the most promising technologies in the next generation communication. Since high‐frequency channels of the millimetre wave are easily attenuated in space, beamforming technology relying on the massive multi‐input‐multi‐output system is introduced to transmit the millimetre wave in a very narrow directional beam, so as to greatly improve the transmit efficiency. Then a challenging problem lies in that how to optimise the overall throughput by allocating the antenna resources to different mobile users in the massive MIMO antenna system. In this study, the authors handle such a difficult problem in two different cases. They first begin with the one‐direction case, i.e. all sub‐arrays are deployed in several parallel rows along the edge of a rectangle antenna array. They decompose the problem and solve it gradually. Then they generalise the authors' result to the two‐dimensional case, where the sub‐arrays can be deployed in orthogonal directions. They apply the similar scheme, decompose the problem and solve each sub‐problem progressively. Both NP‐hard problems are solved with time efficient approximation algorithms. Simulation results demonstrate the efficiency of the proposed algorithms in different cases.

The emerging non‐orthogonal multiple access (NOMA) has been considered as a promising scheme to reach the goals of 5G cellular systems. By enabling a group of mobile users (MUs) to share a same frequency channel and adopting the successive interference cancellation to mitigate the co‐channel interference, NOMA can improve the spectrum efficiency compared with the orthogonal multiple access (OMA). This study proposes a joint optimisation scheme of the channel bandwidth and the transmit‐power allocations for the NOMA downlink transmission, which aims at minimising the overall resource consumption cost including both the spectrum consumption and the power consumption, while satisfying the MUs' traffic requirements. In spite of the non‐convexity nature of the joint optimisation problem, this study characterises the connection between the channel bandwidth and the associated transmit powers for the MUs. Based on this connection, this study transforms the joint optimisation problem into an equivalent bandwidth optimisation problem, and further proposes an efficient algorithm to compute the optimal bandwidth allocation (which enables us to derive the corresponding transmit powers for the MUs). Extensive numerical results are provided to validate the proposed algorithm and the advantage of the proposed joint channel bandwidth and power allocations for the NOMA transmission.

Recently, demands for low-power wide-area (LPWA) machine-type communications have increased dramatically. It is expected that LPWA connections will reach 2 billion in 2020, exceeding the number of traditional cellular users. Narrowband Internet of Things (NB-IoT), a new radio access technology, has been released by the Third Generation Partnership Project for such demands. NB-IoT supports super coverage extension, massive number of connections and long user lifetime with low power cost and low device complexity. With such prominent features, NB-IoT has become one of the dominating technologies in LPWA networks, applicable to a large range of IoT application scenarios such as smart meter, smart parking, smart home, smart tracking, e-health, etc. However, NB-loT is still in its infancy, needing deep theoretical investigation of modeling and optimizing system performance. Also, emerging applications that can be enabled by NB-loT and implementation challenges therein need further exploration.

In this letter, we propose a coprime array interpolation approach to provide an off-grid direction-of-arrival (DOA) estimation. Through array interpolation, a uniform linear array (ULA) with the same aperture is generated from the deterministic non-uniform coprime array. Taking the observed correlations calculated from the signals received at the coprime array, a gridless convex optimization problem is formulated to recover all the rows and columns of the unknown correlation matrix entries corresponding to the interpolated sensors. The optimized Hermitian positive semidefinite Toeplitz matrix functions as the covariance matrix of the interpolated ULA, which enables to resolve off-grid sources. Simulation results demonstrate that the proposed array interpolation-based DOA estimation algorithm achieves improved performance as compared to existing coarray-based DOA estimation algorithms in terms of the number of achievable degrees-of-freedom and estimation accuracy.

With a growing demand for mobile data usage, cellular networks are facing the challenge of severe traffic overload. Device-to-Device (D2D) multicast communication, a proximity communication technique that leverages the spatial-temporal locality of mobile data usage to achieve one-to-many simultaneous transmission, provides an efficient solution to offloading heavy traffic. However, D2D multicast communication relies on users' sharing behavior, and multicasting data incur costs such as energy, which prevents the popularity of such user-driven technique. Thus, in this paper, we study the problem of incentive design for promoting D2D multicast communication in cellular networks. Specifically, we propose a contract-based incentive mechanism to optimize the operator's expected profit from motivating D2D multicast communication for content sharing with guaranteed service quality. We consider both complete and incomplete information scenarios. The proposed mechanism can provide efficient incentives under information asymmetry by delivering contracts, which satisfy nice properties such as individual rationality and incentive compatibility. Greedy algorithms with low complexity are developed based on local optimization to obtain fast solutions for contract design. A Lagrange multiplier method based iterative algorithm that can be proved to obtain optimal contracts under information asymmetry is also proposed. Numerical results show that the proposed mechanism can handle information asymmetry better and has a better performance than linear and step pricing schemes, increasing the expected profit by up to 2.49 times and 1.8 times, respectively.

Wearable devices enable users to collect health data and share them with healthcare providers for improved health service. Since health data contain privacy-sensitive information, unprotected data release system may result in privacy leakage problem. Most of the existing work use differential privacy for private data release. However, they have limitations in healthcare scenarios because they do not consider the unique features of health data being collected from wearables, such as continuous real-time collection and pattern preservation. In this paper, we propose Re-DPoctor, a real-time health data releasing scheme with w-day differential privacy where the privacy of health data collected from any consecutive w days is preserved. We improve utility by using a specially-designed partition algorithm to protect the health data patterns. Meanwhile, we improve privacy preservation by applying newly proposed adaptive sampling tech- nique and budget allocation method. We prove that Re-DPoctor satisfies w-day differential privacy. Experiments on real health data demonstrates that our method achieves better utility with strong privacy guarantee than existing state-of-the-art methods.

A coprime array enables an increased number of degrees-of-freedom by deriving a non-uniform virtual array. However, existing work such as spatial smoothing fails to utilize all of the information provided by the coprime array, which results in performance loss. In this paper, we propose a novel coprime virtual array interpolation-based direction- of-arrival (DOA) estimation algorithm by Toeplitz matrix reconstruction. After investigating the challenges caused by the non-uniformity, we introduce the idea of array interpolation to construct a uniform linear virtual array, such that the information contained in the coprime virtual array can be fully utilized. According to the statistics of non-uniform coprime virtual array signal, we formulate a convex optimization problem for DOA estimation by reconstructing the covariance matrix of the equivalent received signals of the interpolated coprime virtual array under the Toeplitz constraint. Simulation results demonstrate the effectiveness of the proposed algorithm.

Barrier coverage has been widely adopted to prevent unauthorized invasion of important areas in sensor networks. As sensors are typically placed outdoors, they are susceptible to getting faulty. Previous works assumed that faulty sensors are easy to recognize, e.g., they may stop functioning or output apparently deviant sensory data. In practice, it is, however, extremely difficult to recognize faulty sensors as well as their invalid output. We, in this paper, propose a novel fault-tolerant intrusion detection algorithm (TrusDet) based on trust management to address this challenging issue. TrusDet comprises of three steps: i) sensor-level detection, ii) sink-level decision by collective voting, and iii) trust management and fault determination. In the Step i) and ii), TrusDet divides the surveillance area into a set of fine- grained subareas and exploits temporal and spatial correlation of sensory output among sensors in different subareas to yield a more accurate and robust performance of barrier coverage. In the Step iii), TrusDet builds a trust management based framework to determine the confidence level of sensors being faulty. We implement TrusDet on HC- SR501 infrared sensors and demonstrate that TrusDet has a desired performance.

A coprime array has a larger array aperture as well as increased degrees‐of‐freedom (DOFs), compared with a uniform linear array with the same number of physical sensors. Therefore, in a practical wireless communication system, it is capable to provide desirable performance with a low‐computational complexity. In this study, the authors focus on the problem of efficient direction‐of‐arrival (DOA) estimation, where a coprime array is incorporated with the idea of compressive sensing. Specifically, the authors first generate a random compressive sensing kernel to compress the received signals of coprime array to lower‐dimensional measurements, which can be viewed as a sketch of the original received signals. The compressed measurements are subsequently utilised to perform high‐resolution DOA estimation, where the large array aperture of the coprime array is maintained. Moreover, the authors also utilise the derived equivalent virtual array signal of the compressed measurements for DOA estimation, where the superiority of coprime array in achieving a higher number of DOFs can be retained. Theoretical analyses and simulation results verify the effectiveness of the proposed methods in terms of computational complexity, resolution, and the number of DOFs.

Recently, narrowband Internet of Things (NB-IoT), one of the most promising low power wide area (LPWA) technologies, has attracted much attention from both academia and industry. It has great potential to meet the huge demand for machine-type communications in the era of IoT. To facilitate research on and application of NB-IoT, in this paper, we design a system that includes NB devices, an IoT cloud platform, an application server, and a user app. The core component of the system is to build a development board that integrates an NB-IoT communication module and a subscriber identification module, a micro-controller unit and power management modules. We also provide a firmware design for NB device wake-up, data sensing, computing and communication, and the IoT cloud configuration for data storage and analysis. We further introduce a framework on how to apply the proposed system to specific applications. The proposed system provides an easy approach to academic research as well as commercial applications.

The past few years have witnessed the dramatic popularity of large-scale social networks where malicious nodes detection is one of the fundamental problems. Most existing works focus on actively detecting malicious nodes by verifying signal correlation or behavior consistency. It may not work well in large-scale social networks since the number of users is extremely large and the difference between normal users and malicious users is inconspicuous. In this paper, we propose a novel approach that leverages the power of users to perform the detection task. We design incentive mechanisms to encourage the participation of users under two scenarios: 1) full information and 2) partial information. In full information scenario, we design a specific incentive scheme for users according to their preferences, which can provide the desirable detection result and minimize overall cost. In partial information scenario, assuming that we only have statistical information about users, we first transform the incentive mechanism design to an optimization problem, and then design the optimal incentive scheme under different system parameters by solving the optimization problem. We perform extensive simulations to validate the analysis and demonstrate the impact of system factors on the overall cost.

Recently, numerous real-time, data-rich, and differentiated applications and services have appeared in the next-generation Heterogeneous Networks. As a result, the number of potentially “untrusted” connections to the mobile operator's core network is expected to dramatically increase. Therefore, the operators must provide adequate security, without significantly affecting the quality of service (QoS). Hence, joint consideration of QoS and security is a critical research issue. However, due to their difficult-to-model conflicting objectives, existing research works have often dealt with them separately. In this paper, we address this problem, formally formulate it, and envision GT-QoSec, a game-theoretic joint optimization of QoS and security. Using GT-QoSec, the mobile user equipment (UE) and their servicing base stations (eNBs) play games with each other. Thus, the UE obtains a balanced set of QoS and security levels while the eNBs maximize their bandwidth utilization. Extensive analysis and simulation results are presented to evaluate the performance of GT-QoSec in contrast with several conventional methods.

In this paper, we propose a novel adaptive beamforming algorithm for coprime array by compressive sensing the virtual uniform linear array signal. Based on the idea of coprime sampling, a much longer virtual uniform linear array can be generated from a coprime array. With a compressive sensing matrix, a connection can be built between the coprime array with fewer physical sensors and the virtual uniform linear array with much more virtual sensors. Hence, the proposed adaptive beamforming algorithm takes full advantage of the longer virtual array. The performance increment provided by the virtual array is much larger than the performance loss due to the introduced compressive sensing. Hence, the beam-former using the virtual array is expected to obtain much better performance than those using the coprime array directly. Simulation results demonstrate the effectiveness of the proposed adaptive beamforming algorithm.

In this paper, we propose a novel robust adaptive beamforming algorithm with direction-of-arrival (DOA) support for the coprime array. Specifically, by using the property of coprime number, we may estimate the DOAs of sources by matching two super-resolution spatial spectra of the pair of decomposed coprime subarrays. After that, the power of each source can be estimated via a covariance matrix joint estimation problem corresponding to the pair of decomposed coprime sub-arrays. Taking the estimated DOAs and their corresponding power as the support information, the interference-plus-noise covariance matrix for the coprime array can be reconstructed, from which the minimum variance distortionless response beamformer weight vector can be calculated. Simulation results show that the proposed adaptive beamforming algorithm is more robust to signal look direction mismatch than the existing algorithms.

Recently, millimeter wave wireless communications have emerged as one of the most promising technologies to significantly improve the throughput of massive multiple-input multiple-output (MIMO) system. Since high-frequency channels are quite easily attenuated in space, beamforming technology based on the massive MIMO is introduced to transmit the millimeter waves in a very narrow directional beam. One challenging problem in this is how to optimize the overall throughput by allocating the available antenna resources to different mobile users. In this paper, we tackle such a difficult problem and formulate it as an antenna selection combinatorial optimization, which is NP-hard. We first begin with the simplified one-dimension case, i.e., all antennas are deployed on a single line segment. We design a novel iterative greedy antenna selection algorithm (iGAS), that allocates antennas to different users in an iterative way, with each iteration maximizing the marginal increase of overall throughput. We then generalize our result to the two-dimension case. Simulation results are provided to demonstrate the efficiency of the proposed algorithms.

We present a system utilizing narrow band software defined radio to detect the moving human through walls, and give some motion details, such as motion orientation which includes relative moving direction. To achieve high accuracy, FindIt applies Short Time Fourier Transform (STFT) and statistical methods to received signals. In order to adapt to different environments, FindIt uses clustering and classification methods to determine thresholds. Moreover, FindIt provides user-friendly real-time detection results, which can be used as a trigger of high-level functions.

Electronic countermeasure ECM attack has been an emerging threat to radar network in recent years. It is necessary to design a secured radar network against ECM attack. In this paper, we prove that the radar network with conventional measurement fusion schemes is insecure to deceptive ECM DECM attack. Then, a new measurement fusion scheme is proposed, which shows better security performance when DECM attack happens. Numerical simulations are presented to demonstrate the effectiveness of the proposed measurement fusion scheme. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract Millimeter wave (mmWave) communication is a promising technology to support high‐rate (e.g., multi‐Gbps) multimedia applications because of its large available bandwidth. Multipacket reception is one of the important capabilities of mmWave networks to capture a few packets simultaneously. This capability has the potential to improve medium access control layer performance. Because of the severe propagation loss in mmWave band, traditional backoff mechanisms in carrier sensing multiple access/collision avoidance (CSMA/CA) designed for narrowband systems can result not only in unfairness but also in significant throughput reduction. This paper proposes a novel backoff mechanism in CSMA/CA by giving a higher transmission probability to the node with a transmission failure than that with a transmission success, aiming to improve the system throughput. The transmission probability is adjusted by changing the contention window size according to the congestion status of each node and the whole network. The analysis demonstrates the effectiveness of the proposed backoff mechanism on reducing transmission collisions and increasing network throughput. Extensive simulations show that the proposed backoff mechanism can efficiently utilize network resources and significantly improve the network performance on system throughput and fairness. Copyright © 2014 John Wiley & Sons, Ltd.

In this paper, we propose a direction-of-arrival estimation method by covariance matrix sparse reconstruction of coprime array. Specifically, source locations are estimated by solving a newly formulated convex optimization problem, where the difference between the spatially smoothed covariance matrix and the sparsely reconstructed one is minimized. Then, a sliding window scheme is designed for source enumeration. Finally, the power of each source is re-estimated as a least squares problem. Compared with existing methods, the proposed method achieves more accurate source localization and power estimation performance with full utilization of increased degrees of freedom provided by coprime array.

By taking advantage of active radio waves, radar sensors can provide high-accuracy target detection over traditional passive sensors. In this paper, we study barrier coverage in bistatic radar sensor networks (BRSNs), which consist of a set of transmitter radars and receiver radars. Barrier coverage in BRSNs is much more difficult than that in traditional sensor networks as the sensing area of a bistatic radar depends on the positions of both transmitter and receiver, and is typically a Cassini oval. Moreover, different transmitters and receivers can pair with each other by choosing the same frequency and thus the sensing network topology can be quite different in different time slots. To tackle this challenge, we first investigate the characteristic of the ε-covered area of a bistatic radar, then we represent a bistatic radar with a virtual point at the middle point of the line segment formed by the transmitter and receiver. With these representations, we formulate the barrier coverage problem in BRSNs as (k, ε)-Minimum Weight Barrier Coverage Problem ((k, ε)-MWBCP). By constructing a directed coverage graph, we transform the (k, ε)-MWBCP into finding k node-disjoint shortest paths and propose an energy-efficient algorithm called (k, ε)-MWBCA to solve the problem within polynomial time. Extensive simulations are conducted to demonstrate the performance of our proposed algorithm.

In this demo, we propose Phone meter which leverages the RF energy harvesting technologies to measure the strength of Electromagnetic Field (EMF). To this end, Phone meter combines EMF sensor with the smartphone through audio interface without any modifications to the phone. We fully implement the low-cost Phone meter and conduct extensive experiments to prove the functionality of Phone meter. Phone meter achieves about 13:7% relative error in average compared with the industrial-grade spectrum analyzer with significantly reduced the costs.

Device‐to‐device (D2D) communication has recently attracted much research attention because of its potential to increase the capacity of cellular networks. Most existing works aim to maximise the overall system throughput (system‐centric), which ignores the actual traffic demands of D2D users. In this study, the authors consider user‐centric relay assisted D2D communications where D2D users have different evaluations for the significance of every unit of increased data rate. By considering the traffic demands of D2D users, the authors propose a Vickrey–Clarke–Groves auction based relay allocation mechanism (ARM) in which every D2D user submits a bid to the basestation (BS). The submitted bids indicate D2D users’ valuation on every unit of the increased data rate. The BS then allocates relays to D2D users by maximising the social welfare of D2D users while maintaining a predefined data rate requirement for cellular users. A payment scheme to charge D2D users for using relays is designed, and the authors show that the auction is truthful. The authors also extend the results to a general case and provide a general ARM accordingly. Extensive simulation results are provided to demonstrate the performance of the proposed mechanisms.

Abstract In this paper, we propose a synchronization scheme based on an improved auxiliary particle filter (IAPF) for chaotic Colpitts circuit and conduct an experimental study on the synchronization performance with application to secure communications. Specifically, with the synchronization scheme, when the chaotic signals generated by an analog Colpitts circuit are transmitted through a nonideal channel, the distorted signals are processed digitally by the novelly designed IAPF at the receiver, in order to obtain the synchronized signals of the transmitter circuit. Experimental results indicate that synchronization can be achieved over both the additive white Gaussian noise channel and the multipath fading channel with low signal‐to‐noise ratio, even if there exist severe circuit parameter mismatches between the transmitter and the receiver. Furthermore, a chaos‐masking secure communication system is constructed and verified over both the additive white Gaussian noise channel and the multipath fading channel, and the bit error rate is evaluated versus different signal‐to‐noise ratios and symbol periods. It is shown that the achievable bit error rate can reach the order of magnitude of 10 − 4 without error correction coding techniques. In addition, security analysis demonstrates that the proposed chaotic secure communication system is resistant to the brute‐force attack. Copyright © 2013 John Wiley & Sons, Ltd.

In this paper, we propose a privacy-preserving framework, called PLAM, for local-area mobile social networks. The proposed PLAM framework employs a privacy-preserving request aggregation protocol with k-Anonymity and l-Diversity properties while without involving a trusted anonymizer server to keep user preference privacy when querying location-based service (LBS), and integrates unlinkable pseudo-ID technique to achieve user identity privacy, location privacy. Moreover, the proposed PLAM framework also introduces the privacy-preserving and verifiable polynomial computation to keep LBS provider's functions private while preventing the provider from cheating in computation. Detailed security analysis shows that the proposed PLAM framework can not only achieve desirable privacy requirements but also resist outside attacks on source authentication, data integrity and availability. In addition, extensive simulations are also conducted, and simulation results guide us on how to set proper thresholds for k-anonymity, l-diversity to make a tradeoff between the desirable user preference privacy level and the request delay in different scenarios.

Smart grid, as the next generation of power grid characterized with “two-way” communications, has been paid great attention to realize green, reliable and efficient electricity delivery for our future lives. In order to support the “two-way” communications in smart grid, a large number of smart meters should be deployed at customers to report their near real-time data to control center for monitoring. However, this kind of real-time report could disclose users' privacy, bringing down the users' willingness to participate in smart grid. In order to address the challenge, in this paper, we propose an efficient aggregation protocol with error detection, named APED, for secure smart grid communications. The proposed APED protocol employs a pairwise private stream aggregation scheme to not only achieve privacy-preserving aggregation but also perform error detection when some smart meters are malfunctioning. Detailed security analysis has shown that the proposed APED protocol can guarantee the security and privacy of smart grid communications. In addition, performance evaluation demonstrates its efficiency in terms of computation and communication cost.

Opportunistic network (OPPNET) is characterized by the intermittent connectivity among mobile nodes from their unpredictable mobility. Although it is promising, there still exist many security and privacy challenges. In this paper, we present an incentive and privacy-aware data dissemination (IPAD) scheme for OPPNETs, not only to exploit how to protect mobile node's identity privacy, location privacy and social profile privacy, but also to provide a secure incentive for privacy-aware data dissemination. Through extensive incentive analysis, we show that only if a source provides a secure incentive strategy, can a data packet be efficiently disseminated in OPPNETs.

Power dissipation and hardware cost are two major design concerns in the hardware implementation of particle probability hypothesis density (PHD) filters, wherein the weight update is a crucial design task due to its complicated operation and sequential nature. In this paper, we propose a novel mixed-mode implementation of the weight update in particle PHD filter, which outperforms its counterpart digital-form implementation in terms of power dissipation and hardware resource consumption. In specific, the mixed-mode implementation uses multiple-input translinear element (MITE) networks to realize the likelihood function of weight update in the analog domain. The MITE networks, which are operated in subthreshold region, contribute to the low-power implementation of the particle PHD filters, and can lead to parallel implementation of the weight update with lower hardware cost. Extensive simulations are conducted with circuit models and parameters from the Taiwan Semiconductor Manufacturing Company (TSMC) 0.18μm CMOS technology library for mixed-mode implementation of weight update, and the results show that the analog errors in this mixed mode implementation are negligible when used to support real-world multi-target tracking.

The increasing demands for improving transmission reliability and efficiency have brought us a wide interest in smart grid. In current smart grid research, one of challenges is its security issue. If the security is not well addressed, the concept of smart grid cannot be widely accepted. In this paper, in order to simultaneously resolve the security and efficiency challenges in smart grid communications, we propose an efficient aggregate authentication protocol, called EATH, which is characterized by eliminating the Map-To-Hash hash and reducing the pairing operations in aggregation and verification to improve the computational efficiency. Detailed security analysis has shown that the proposed EATH protocol is secure in terms of source authentication and data integrity in smart grid communications. In addition, performance evaluation also demonstrates its efficiency in terms of low computation and communication overheads.

The abundant bandwidth at 60 GHz band (around 7 GHz) offers the potential for multi-Gbps indoor wireless connections for bandwidth-intensive applications. However, 60 GHz millimeter wave (mmWave) links are highly susceptible to blockage since it is difficult to diffract around obstacles. In this paper, we propose multi-radio band integration framework to have 2.4/5 GHz band assist mmWave band to prevent drastic data rate reduction. Specifically, the problem of multi-radio band integration with TDMA-based MAC is formulated as an optimization problem. We decompose the problem into two sub-problems: radio band selection and space-time scheduling. Firstly, considering network load and mmWave channel status, we define start-integration threshold and stop-integration threshold to select an active radio band for data transmission. Secondly, a space-time scheduling scheme is proposed to allow multiple flows over different radio bands operate concurrently to exploit the spatial reuse. Simulation results of the proposed multi-radio band integration mechanism demonstrate significant improvements of network connectivity and the number of supported traffic flows.

In this paper, we propose a wormhole attack resistant secure neighbor discovery scheme, named SND, for 60 GHz directional wireless network with a centralized network controller (NC). In specific, the proposed SND scheme consists of three phases: the NC broadcasting phase, the network node response/authentication phase and the NC time-delay analysis phase. In the broadcasting phase and response/authentication phase, local time information and antenna direction information are elegantly exchanged with signature based authentication techniques between the NC and legislate network nodes, which can prevent most of the wormhole attacks. In the NC time-delay analysis phase, the NC can further detect the possible attack by using the time-delay information from the network node. To solve the transmission collision problem in the response/authentication phase, an RD-TDMA protocol is also proposed. Both simulation results and security analysis demonstrate that the proposed SND scheme can effectively resist wormhole attack for the 60 GHz communication network with directional antenna.

In this paper, we propose an efficient data-driven particle probability hypothesis density (PHD) filter for real-time multitarget tracking of nonlinear/non-Gaussian system in dense clutter environment. In specific, the input measurements are first classified into two sets, namely survival measurements and spontaneous birth measurements, after eliminating clutters by using existing historic state data of targets. Since most clutters do not participate in the complex weight computation of particle PHD filter, better real-time performance can be achieved. The tracking performance is also improved because the survival measurements are used for survival targets and the spontaneous birth measurements are used for spontaneous birth targets, resulting in less interference from each other and from clutters. Extensive simulations validate the improvement of both the real-time performance and tracking performance of the proposed data-driven particle PHD filter in comparison with the traditional particle PHD filter.

The linear frequency-modulated continuous-wave (LFMCW) millimeter-wave radar has been widely used in automotive industry applications. The motivation of this paper is to propose a segmented range detection (SRD) method on the foundation of plentiful mature research on the target detection of LFMCW radar. According to the SRD method, the detection range (about 150m in the scene of vehicular collision warning) is divided into several segments and triangular modulation waveform which consists of several different frequencies is designed. Compared with typical digital signal processing method, the SRD method can availably decrease the bandwidth of the IF signal, therefore the in-band noise will be significantly reduced and the signal to noise ratio (SNR) improved. Additionally, different range resolutions can be achieved and average range resolution improved. The SRD method is also validated through multiple outdoor experiments with the use of 60 GHz LFMCW millimeter-wave radar system designed by our research group. The efficiency of the SRD method is demonstrated in terms of ranging accuracy and range resolution after the analysis of experiment results.