The integration of wireless power transfer (WPT) and mobile edge computing (MEC) provides an effective solution for overcoming the energy and computational limitations of Internet of Things (IoT) devices by enabling them to harvest energy from radio frequency signals and offload data to edge servers. A crucial challenge in wireless powered MEC (WP-MEC) networks is how to efficiently optimize offloading decisions and resource allocation to enhance overall system performance. In this paper, we investigate the partial offloading strategy within a WP-MEC network consisting of multiple HAPs. The optimization problem is formulated as a Mixed-Integer Non-linear Programming (MINLP) problem with variables of WPT duration, offloading decisions and energy allocation. To solve this problem, we propose a deep reinforcement learning (DRL)-based framework, which employs a neural network architecture combining convolutional and fully connected layers to output offloading decisions. Additionally, we design an optimization algorithm for joint optimization of WPT duration and offloading proportions. Numerical results demonstrate the proposed method achieves better performance than the existing DRL methods, which demonstrates the efficiency of the proposed method.

As symbiotic radio (SR) is an effective technique to address the issue of spectrum scarcity, and the simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) that provides full-space service is a flexible technique to boost data transmission, we study the STAR-RIS-aided SR network (SSRN). In the SSRN, primary users receive data from a base station under the hybrid time division multiple access and non-orthogonal multiple access protocol, and an Internet of Things (IoT) receiver receives data from the STAR-RIS with the data source. Existing studies about the SSRN focus on one of three STAR-RIS operation protocols, i.e., energy splitting (ES), mode switching (MS), and time switching (TS). We are curious about how to select the protocol in the SSRN in terms of throughput performance. Therefore, we formulate the throughput maximization of IoT transmission problem with primary throughput constraint by jointly optimizing active beamforming, passive beamforming, primary user pairing scheme, and decoding order in each protocol. To solve each formulated non-convex mixed integer programming problem, we decompose it into three subproblems: active beamforming optimization subproblem, passive beamforming subproblem, and pairing scheme with decoding order optimization subproblem. We propose alternating optimization (AO)-based algorithms for the formulated problems in ES and TS, and AO-based penalty algorithm for the formulated problem in MS. Numerical results validate the superiority of the proposed algorithms, and provide the comparison results of three protocols in terms of the throughput of IoT transmission.

Federated learning (FL) has emerged as a promising framework for the training of decentralized models on multiple devices without the need to share local data. When integrated with mobile edge computing (MEC), FL enables wireless user equipment (UE) to upload locally trained models to a cellular base station (BS) for aggregation, thereby significantly reducing response latency and alleviating network congestion. However, the energy constraints of mobile devices present significant challenges to system sustainability. In this work, we incorporate wireless power transfer (WPT) technology at the BS to replenish energy for both local training and model uploading of UEs with limited battery capacity. To optimize overall system performance, we propose an efficient Federated Twin Delayed Deep Deterministic Policy Gradient (EF-TD3) algorithm that jointly manages transmit power, computational resources, wireless bandwidth allocation, and training accuracy in dynamic mobile environments. The main objective is to minimize the overall system cost by balancing energy consumption, latency, and FL convergence delay. Numerical experiments validate that EF-TD3 substantially lowers system cost while maintaining efficient and stable federated learning operations in MEC scenarios.

Wireless-powered mobile edge computing (WPMEC) has gained significant attention for enabling energy-sustainable and low-latency Internet of Things (IoT) applications in domains such as smart cities and industrial automation. Heterogeneous computing resources, which encompass devices with diverse computational capabilities, are essential to support adaptive task offloading and resource-efficient service provisioning. However, integrating wireless power transfer (WPT) with mobile edge computing (MEC) in dynamic environments remains challenging, particularly in the efficient coordination of heterogeneous resources. This paper investigates a distributed optimization problem in a wireless-powered heterogeneous MEC network, where base station association, binary offloading decisions, and distributed resource allocation are closely intertwined. We formulate the problem as a mixed-integer nonlinear programming (MINLP) problem and propose a novel decentralized solution framework that decomposes it into two subproblems: deep reinforcement learning is employed to optimize base station selection and task offloading strategies, while convex optimization techniques, including the Lagrangian method, are used to allocate bandwidth and computational resources at the base stations. Simulation results demonstrate that the proposed approach significantly enhances the energy efficiency and computational performance of wireless edge computing compared to existing benchmarks, confirming its practical value for resource-constrained MEC systems.

Benefiting from smartphones' powerful computing and sensing capabilities, biometric authentication is widely applied to them for conveniently verifying users' identities. However, most biometric features can be easily acquired or reproduced, making them vulnerable to replay and impersonation attacks. To address this issue, we propose DBreathLock, a non-contact deep breath-based authentication system that utilizes a smartphone emitting inaudible frequency-modulated continuous waves (FMCW)-based sonar signals and synchronously records breath sounds and sonar echoes of chest-abdominal-joint (C-A-joint) movements. Then, we implement a dual-protection barrier to defend against advanced replay attacks (ARAs). First, by analyzing the energy features of C-A-joint movements, we develop a Deep Breath Activity Detection method to detect deep breath fragments alongside the capability of resisting ARAs. Second, we take C-A-joint movements and smartphone vibrations caused by holding a smartphone as features and design a liveness detection mechanism to further fortify the resistance to ARAs. Furthermore, a multi-stream identity authentication model is designed to verify legitimate users by fusing biometric features from C-A-joint movements, deep breath sounds, and correlation sequences of both. Extensive real-world experiments with 40 users demonstrate DBreathLock's authentication accuracy of 95.97%. Additionally, it successfully defends against advanced replay, impersonation, simple hybrid, and advanced hybrid attacks, achieving the AUC of 0.9792, and FPRs of 2.17%, 2%, and 4.17%, respectively.

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.

Wireless power transfer enhanced mobile edge computing (WPT-MEC) has emerged as a key technology to support low-latency and energy-efficient computation in wireless networks. With increasing network density, multi-base-station architectures emerge where wireless devices (WDs) offload tasks to distributed base stations (BSs), creating challenges in maintaining quality-of-service fairness during complex resource coordination in multi-BS WPT-MEC networks. To address these challenges, we investigate a non-orthogonal multiple access (NOMA)-enhanced WPT-MEC network comprising multiple WDs and BSs with finite computational capacities. For ensuring fairness, we formulate a max-min problem to maximize the minimum task computation amount by jointly optimizing offloading decisions, NOMA decoding orders, offloading powers and time resource allocation, which results in a challenging mixed integer, sequence and nonlinear programming (MISNLP). To tackle this problem, we propose a two-stage distributed multi-agent algorithm. In the first stage, each BS agent generates offloading preferences based on partial observations, guiding WDs' offloading decisions. In the second stage, given these offloading decisions, we develop an efficient convex-based algorithm to solve the per-BS resource allocation subproblem, jointly optimizing NOMA decoding order, offloading powers and time resource allocation. For effective training, we leverage off-policy training and the centralized training with decentralized execution (CTDE) paradigm with two key innovations: (1) a convex-based critic that evaluates the joint action without bias, and (2) a counterfactual baseline that isolates individual agent credit assignment. The proposed C3MA algorithm achieves six times faster convergence and at least 20% performance improvement when serving more than 20 WDs, compared with existing multi-agent schemes, while maintaining a near-optimal Jain's fairness index of 0.97. Moreover, it sustains an ultra-low execution delay below 5 milliseconds even with 40 WDs, confirming its efficiency and scalability.

The distributed edge computing network has been proposed as a promising approach to accelerate task computation. Incorporating parallel edge computing, we investigate a parallel distributed edge computing network where each edge device is allowed to receive one task and compute another task simultaneously, and meanwhile, edge devices are allowed to compute their respectively received tasks simultaneously. We minimize the total task completion time (TCT) of source nodes by jointly optimizing offloading selections of source nodes and scheduling order of task offloading, i.e., MTOS problem, which is proved to be NP-hard. To tackle it, we first study the MTOS problem with one edge device (MTOS-1), establish three task offloading rules to minimize the total TCT, and propose a priority-based scheduling order of task offloading algorithm. Based on the established task offloading rules for the MTOS-1 problem, we study the MTOS problem withMedge devices and additional offloading-adjacency constraint (MTOSO-M), establish another two task offloading rules for scheduling order, and propose an automatic adjustment-based joint offloading selections and scheduling order algorithm. By relaxing the offloading-adjacency constraint of the MTOSO-Mproblem, we further study the general MTOS problem withMedge devices (MTOS-M), derive a lower bound of the total TCT, and propose a queue jumping-based joint offloading selections and scheduling order algorithm. Extensive numerical results are conducted to discuss impacts of vital network parameters on the total TCT, verify the superiority of the proposed algorithms, and show that the communication-computation parallelism for edge devices and the computation parallelism among edge devices further reduce the total TCT.

As resource-intensive and latency-sensitive applications continue to expand, the integration of unmanned aerial vehicles (UAVs) with mobile edge computing (MEC) has emerged as a viable solution, offering flexible, on-demand services for mobile users (MUs) without reliance on terrestrial infrastructure. The adoption of non-orthogonal multiple access (NOMA) further reduces latency by allowing MUs to offload tasks simultaneously over a single subchannel. However, many existing offloading methods do not explicitly incorporate a priority-based task scheduling mechanism and instead optimize task execution based on system constraints such as latency or energy consumption. To bridge this gap, we propose a QoS-oriented task offloading scheme that systematically optimizes task scheduling. We formulate an average system utility maximization problem that jointly optimizes UAVs’ 3D trajectories, MU association, task offloading ratios, and resource allocation. The optimization problem is inherently complex due to its non-convex nature and multiple constraints. To address this, we first employ Lagrange duality to decouple constraints, reducing computational complexity. Subsequently, we propose a novel improved soft actor-critic (ISAC) algorithm, which incorporates a perturbation term into the loss function to guide the training process away from local minima and toward globally optimal solutions. Through extensive simulation, we demonstrate that the ISAC algorithm guarantees convergence and significantly outperforms benchmark methods on offloading transmission rates, task completion rates, and overall system utility.

Existing studies have shown that merging sensory data, especially visual signals, into Radio Frequency (RF) networks can enhance their perception and adaptability to hidden disruptive environmental factors (e.g., blockages). A majority of these methods assume tight integration of the RF and non-RF components within a wireless system, making them rely heavily on prior network deployment knowledge and applicable to only specific deployments. Aiming at ubiquitous physically assisted intelligent communications, this article presents a new LOOSELY COUPLED approach that incorporates the videos from widely installed external surveillance cameras to aid the operation of existing networks. To enable adaptive integration across individual sensing and communication systems in various unknown deployments, we devise a novelobject-centric hierarchical learningscheme that can combine decoupled visual and RF signals to autonomously discover the compositional structure of the blockage scene and analyze its spatiotemporal development for inferring the future blockage condition changes. We demonstrate the application of these structural representations in building a generalized Vision-Perceptive Link Quality Predictor (VP-LQP). To support the training and testing of VP-LQP, we construct a new real-world multi-scenario dataset, and the experimental results validate the superiority of our design.

Frequency Modulated Continuous Wave (FMCW)-based mmWave radar has attracted widespread attention due to its non-contact and high spatial resolution for multi-target vital signs monitoring. However, current works mostly focus on how to improve detection performance under the steady scenarios with a single target, while unsteady scenarios and physical mutual interference from multiple targets are rarely considered. In this work, we propose an innovative method for multi-target vital signs for unsteady scenarios, such as aerobic exercise. The method automatically distinguishes between steady state and motion state, and completes a best-effort vital signs detection during unsteady state. To differentiate multiple targets, we design a weight vector enhancement method combined with target space localization, and then, apply the variational mode decomposition (VMD) algorithm to extract the respiratory and heart rates of a single target. Moreover, for evaluating the efforts of exercise, we propose Multi-target Motion Recognition (MMR) based on MobileNet-V2 network to recognize the motion states of multiple targets. The experimental results showed that the mean absolute error of respiratory rate and heart rate decreased to 1.37 bpm and 2.56 bpm, respectively. Meanwhile, the MMR algorithm achieves close to 98.1% accuracy in recognizing motion states.

The rise of smart applications in wireless devices increasingly relies on mobile edge computing (MEC), where longterm system energy efficiency holds crucial significance for both green computing and application vendors. This paper focuses on long-term energy efficiency in a wireless power transferenabled MEC system. This system faces the challenges of timevarying channel states and stochastic task arrivals. We first formulate this problem to simultaneously optimize offloading, power transfer duration, and energy consumption, while ensuring device queue stability. We then introduce a novel algorithm based on Lyapunov-guided deep reinforcement learning, referred to as LyCNN-DRL. This approach efficiently handles the mixed integer non-linear programming problem by transforming it into a deterministic per-slot problem for online optimization, without needing prior knowledge of future conditions. Specifically, we tackle the problem by dividing it into resource allocation and binary offloading components, applying a convolutional neural network model for near-optimal offloading decisions, and obtaining the optimal solution for resource allocation. Simulation results show that LyCNN-DRL outperforms baseline algorithms, stabilizing MEC network task queues. Furthermore, we quantitatively derive the trade-off between energy efficiency and queue length, represented as$[O(1/V), O(V)]$with the variable$V$.

Mobile-edge computing (MEC) and wireless power transfer (WPT) are pivotal for enhancing computational power and battery life in 5G/6G networks. However, their performance declines in remote or disaster-stricken areas due to the lack of access points and energy sources. This paper proposes a wirelessly powered unmanned aerial vehicle enabled MEC (UAV-MEC) system to address this issue, focusing on nodes with ignorable computing capabilities and randomly arriving, size-varying tasks. We aim to maximize the long-term average computation rate under constraints such as UAV coverage, time resources, energy, and task causality, formulating a non-convex problem with dynamic states and complex actions. To solve this problem, we introduce an exploration-enhanced deep reinforcement learning (EDRL) algorithm with a bi-layered structure: the main problem determines the UAV’s flying actions, while the sub-problem allocates time resources given these actions. EDRL employs a deep neural network to analyze real-time UAV positions and task demands, determining optimal flight paths. Upon path determination, an efficient algorithm utilizing bisection and golden section search methods allocates WPT and computational offloading durations. Simulations reveal that EDRL achieves an execution latency of just 11.5 ms in thirty-node networks, outperforming baseline DRL algorithms and predetermined trajectory schemes by 20% and 25% in long-term computation rates, respectively. These results highlight EDRL’s effectiveness and low computational complexity, making it a robust solution for challenging environments.

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.

Symbiotic radio (SR), combining the advantages of cognitive radio and ambient backscatter communication (AmBC), stands as a promising solution for spectrum-and-energy-efficient wireless communications. In an SR network, backscatter devices (BDs) share the spectrum resources with the primary transmitter (PT) by utilizing the incident radio frequency (RF) signal from PT for uplink non-orthogonal multiple access (NOMA) transmission. The primary receiver (PR) decodes the signals of PT and BDs via the successive interference cancellation (SIC) technique. Our goal is to establish a long-term commensalistic relationship between PT and BDs. We address the problem of maximizing the long-term average sum rate of BDs while ensuring a minimum average rate for the PT by optimizing the power reflection coefficients of the BDs. We explicitly consider practical constraints such as the required power difference among signals for SIC decoding and the unknown future channel state information (CSI). We prove the NP-hardness of the offline version of the problem and subsequently employ the Lyapunov optimization technique to convert the original problem into a series of sub-problems in each individual time slot that can be solved in an online manner without relying on future CSI. We then utilize the successive convex optimization (SCO) technique to solve the non-convex sub-problems. Extensive simulations validate that our proposed Lyapunov-SCO algorithm achieves superior performance in terms of the average sum rate of BDs while ensuring PT’s required average rate. In addition, we provide discussions on extending the proposed solution to SR networks with multiple PT-PR pairs, high-mobility BDs, and enhancing fairness among BDs.

Virtual reality (VR) applications have revolutionized digital interaction by providing immersive experiences. 360$^{\circ }$VR video streaming has experienced significant growth and popularity as a pivotal VR application. However, the combination of limited network bandwidth and the demand for high-quality videos frequently hinders the achievement of a satisfactory quality of experience (QoE). Although prior methods have enhanced QoE, the effects of decoding latency have been poorly studied. It is technically challenging to design a quality adaptation algorithm that can balance the pursuit of high-quality videos and the limitation of limited bandwidth resources. To address this challenge, we propose an edge-end architecture for 360$^{\circ }$VR video streaming and aim to enhance overall QoE by solving a performance optimization problem. Specifically, our experiments on commercial mobile devices in real-world situations reveal that decoding latency significantly influences QoE. First, decoding latency plays a major role in contributing to end-to-end latency, which exceeds the transmission latency. Second, decoding latency can differ considerably between devices with varying computational capabilities. Building on this insight, we propose a novellatency-awarequalityadaptation (LAQA) algorithm. LAQA lies in developing a solution that can allocate video quality in real-time and enhance overall QoE. LAQA involves not only the quality of the received content, the transmission latency and the quality variance, but also the decoding latency and the fairness of the user quality. Subsequently, we formulate a combinatorial optimization problem to maximize overall QoE. Through extensive validation with experimental data from real-world situations, LAQA offers a promising approach to enhance QoE and ensure fairness performance in different devices. In particular, LAQA achieves 16.77% and 10.66% enhancement over the state-of-the-art combinatorial optimization and reinforcement learning algorithm, respectively, in terms of QoE at 4K resolution. Furthermore, LAQA ensures excellent scalability by simulating the number of users ranging from 15 to 60, making it a robust solution for diverse and growing user scales.

Non-orthogonal multiple access (NOMA) emerges as a superior technology for enhancing spectral efficiency, reducing latency, and improving connectivity compared to orthogonal multiple access. In NOMA networks, successive interference cancellation (SIC) plays a crucial role in decoding user signals sequentially. The challenge lies in the joint optimization of SIC ordering and power allocation, a task complicated by the factorial nature of ordering combinations. This study introduces an innovative solution, the Attention-based SIC Ordering and Power Allocation (ASOPA) framework, targeting an uplink NOMA network with dynamic SIC ordering. ASOPA aims to maximize weighted proportional fairness by employing deep reinforcement learning, strategically decomposing the problem into two manageable subproblems: SIC ordering optimization and optimal power allocation. We use an attention-based neural network to process real-time channel gains and user weights, determining the SIC decoding order for each user. A baseline network, serving as a mimic model, aids in the reinforcement learning process. Once the SIC ordering is established, the power allocation subproblem transforms into a convex optimization problem, enabling efficient calculation of optimal transmit power for all users. Extensive simulations validate ASOPA’s efficacy, demonstrating a performance closely paralleling the exhaustive method, with over 97% confidence in normalized network utility. Compared to the current state-of-the-art implementation, i.e., Tabu search, ASOPA achieves over 97.5% network utility of Tabu search. Furthermore, ASOPA has two orders of magnitude less execution latency than Tabu search when$N=10$and even three orders magnitude less execution latency less than Tabu search when$N=20$. Notably, ASOPA maintains a low execution latency of approximately 50 milliseconds in a ten-user NOMA network, aligning with static SIC ordering algorithms. Furthermore, ASOPA demonstrates superior performance over baseline algorithms besides Tabu search in various NOMA network configurations, including scenarios with imperfect channel state information, multiple base stations, and multiple-antenna setups. These results underscore the robustness and effectiveness of ASOPA, demonstrating its ability to ability to achieve good performance across various NOMA network environments.

This paper investigates a wireless powered mobile edge computing (WP-MEC) network with multiple hybrid access points (HAPs) in a dynamic environment, where wireless devices (WDs) harvest energy from radio frequency (RF) signals of HAPs, and then compute their computation data locally (i.e., local computing mode) or offload it to the chosen HAPs (i.e., edge computing mode). In order to pursue a green computing design, we formulate an optimization problem that minimizes the long-term energy provision of the WP-MEC network subject to the energy, computing delay and computation data demand constraints. The transmit power of HAPs, the duration of the wireless power transfer (WPT) phase, the offloading decisions of WDs, the time allocation for offloading and the CPU frequency for local computing are jointly optimized adapting to the time-varying generated computation data and wireless channels of WDs. To efficiently address the formulated non-convex mixed integer programming (MIP) problem in a distributed manner, we propose aTwo-stageMulti-Agent deep reinforcement learning-basedDistributed computationOffloading (TMADO) framework, which consists of a high-level agent and multiple low-level agents. The high-level agent residing in all HAPs optimizes the transmit power of HAPs and the duration of the WPT phase, while each low-level agent residing in each WD optimizes its offloading decision, time allocation for offloading and CPU frequency for local computing. Simulation results show the superiority of the proposed TMADO framework in terms of the energy provision minimization.

This article presentsInsight, an AR system for visualizing the IoT-enabled smart environment without relying on the unique appearances, barcodes, world coordinates, or wireless signals of IoT infrastructures. The system analyzes the camera video and motion data taken by mobile AR equipment to extract the self and cross visual geometries describing the poses and geographic distribution of nearby IoT devices. To recognize IoT devices using the extracted geometries,Insightoperates in two phases. At deployment time, it learns pairwise mappings from the visual geometries to the corresponding device identities. After that, it leverages the geometries scanned at run time to look for a partial assignment to the recorded geometries, allowing it to automatically recognize the IoT devices in AR view. As such, our system turns the IoT device recognition task into a geometry matching problem, which is further formalized as to perform Subset, Incomplete, and Duplicated Point Cloud Registration (SID-PCR) in this work. We design a deep neural network paying specific edge- and spectral-wise graph attention to solve SID-PCR, and implement a prototype that adaptively requests visual geometry scan and registration operations for accurate recognition. The performance ofInsightis validated using both synthetic data and a real-world testbed.

Unmanned Aerial Vehicle (UAV)-enabled wirelessly powered Mobile Edge Computing (MEC) is emerging as a powerful technology for boosting computational capability and energy supplementation in Internet of Things (IoT). This work addresses the long-term task completion ratio maximization problem in UAV-enabled wirelessly powered MEC systems. Besides the large number of optimization parameters, the environment can only be partially observed as the UAVs cannot cover the whole network area. Then, it is very challenging to obtain good solutions due to the lack of global information. We introduce a novel distributed Multi-Agent Deep Reinforcement Learning (MADRL) framework for optimizing UAVs’ actions and resource allocation, considering the constraints of tasks that vary in size, arrival times, and required computation completion time. To decouple the complicated parameters, we divide the problem into two manageable subproblems—UAVs’ action decision and resource allocation under a given UAV’s action. We employ a distributed Deep Reinforcement Learning (DRL) scheme for the former subproblem to cope with the partially observable nature. By revealing some important properties of the later subproblem, we design an efficient two-stage optimal algorithm to minimize the total consumed energy of nodes while maximizing the task-completing number. Extensive simulations validate the effectiveness of the proposed framework, achieving over a 50% improvement in task completion ratio compared to baseline schemes in some scenarios.

With the development of the Internet of Things (IoT), substantial research efforts have been devoted to extending the sensing capability of commercial off-the-shelf (COTS) radio-frequency identification (RFID) tags. State-of-the-art approaches either demand sophisticated hardware redesign or are constrained to specific sensing capability, leading to increased costs and limited scalability. In this paper, we present the design of a generic sensor tag (GS-Tag) based on RF switches and COTS RFID system for transmission of generic sensor data. RF switches are connected in parallel with the RFID chip, and the GS-Tag modulates the sensor data by controlling the RF switches. Specifically, with the RF switches turned on, the tag’s chip is short-circuited, rendering it unreadable; conversely, turning off the RF switches makes the tag readable. The reader demodulates the data through the compatible electronic product code (EPC) protocol. A subtle dual RF switch scheme is adopted to extend the communication range. In addition to the battery-powered solution, we integrate an RF energy-harvesting module, develop a high-efficiency energy management circuit, and design an efficient task scheduling strategy to enable the GS-Tag to operate in a battery-free mode. We implement a prototype of GS-Tag with COTS RFID devices. Comprehensive experiments demonstrate that our designed GS-Tag can achieve an average packet reception rate (PRR) exceeding 99%, exhibits robustness to environmental disturbance, and facilitates coexistence of six GS-Tags with an average PRR of over 91%. Due to the dual RF switch scheme, the communication range of GS-Tag extends to 12 m. Besides, GS-Tag has an extremely low power consumption of just 3.98 μ W. A practical application is developed to accurately monitor temperature and ambient light intensity in an office environment while maintaining low power consumption. Our designed GS-Tag presents a cost-effective and compatible solution for expanding the sensing capabilities of COTS RFID system.

This paper addresses long-term energy efficiency in a wireless power transfer-enabled mobile-edge computing (MEC) system, facing challenges from time-varying channels and stochastic task arrivals. We formulate the problem to optimize offloading, power transfer duration, and energy consumption while ensuring queue stability. We propose a novel Lyapunov-guided deep reinforcement learning (LyCNN-DRL) algorithm to efficiently solve the long-term mixed integer non-linear programming problem without prior knowledge of future conditions. The approach decomposes the problem into resource allocation and binary offloading components, using a convolutional neural network for near-optimal offloading decisions and the Lagrange dual function for optimal resource allocation. Extensive simulations show that LyCNN-DRL outperforms benchmark algorithms in energy efficiency and latency, achieving over 97% of the optimal utility while reducing execution latency to approximately 50 milliseconds in ten-WD networks. Additionally, we derive the trade-off between energy efficiency and queue length as [O(1/V),O(V)], where V is the Lyapunov control parameter.

Deep neural network (DNN) has emerged as a superior technology in mobile applications. However, due to the computation-intensive nature of DNN’s execution, it is challenging for mobile devices with limited computational power and energy to deploy them and meet real-time requirements. This paper proposes an adaptive edge-device collaborative (AEC) framework for DNN inference optimization. Different from previous works, our approach dynamically optimizes model segmentation and feature compression based on the current environmental state, ensuring that accuracy requirements are met. Specifically, the problem is divided into two subproblems: segmentation and feature compression. A policy-based deep reinforcement learning (DRL) model determines the optimal segmentation layer, while an attention-based compression algorithm maximizes feature compression. We conducted the experiments in an edge computing scenario consisting of an NVIDIA Jetson Nano and an edge server equipped with RTX 3090. Experiments show that the scheme significantly optimizes DNN inference in time-varying network environments. The proposed adaptive dynamic segmentation outperforms the local scheme, edge-only scheme, and the static split computing scheme, which can effectively reduce the latency and energy consumption while satisfying the accuracy requirements with varying network environments. Based on the measured results, the inference performance of AEC for different network environments is improved by more than 30% on average compared to all the baseline algorithms in this paper.

The Internet of Nano Things (IoNT) as an innovative technology which integrates nanotechnology and communication technology involves the use of nanomachines to create systems that can interact with each other. IoNT provides possibilities for applications in many fields, i.e., drug delivery, healthcare, and environment monitoring. It promotes the development of molecular communication (MC) and nanonetworks. In this article, a mobile multiuser MC system where multiple transmitters and one receiver move in the anomalous diffusion channel is studied. First, the molecular division multiple access technique where multiple transmitters convey information by delivering different types of information molecules is employed. The mathematical expression of the average bit error rate (BER) of this MC system is derived. Then we formulate a multiobjective optimization problem with the constraint that the detection interval has lower and upper bounds in order to achieve minimum average BER of this system. Furthermore, we use the alternative search algorithm with gradient projection to solve this optimization problem and obtain the optimal detection interval. Finally, numerical results show this algorithm has good convergence and the optimization results can be approximate to the optimal solutions obtained by the exhaustive search.

To enable resource-constrained wireless devices (WDs) to process the computation-intensive and latency-sensitive computation tasks, the wireless powered mobile edge computing (WP-MEC) network has been proposed as a promising approach. Incorporating mobile cloud computing (CC) in the WP-MEC network, we investigate the wireless powered mobile edge-CC (WP-MECC) network, where the WDs first harvest energy from a hybrid access point (HAP), and then consume the harvested energy to compute the tasks locally, offload them to the HAP for computation, or offload them to the cloud server (CS) via the relaying of the HAP. To pursue fairness among the WDs, we minimize the maximum task completion time (TCT) of WDs by jointly optimizing the time resources, computing mode selection, and computation resources. We prove the minimization problem is NP-hard. To tackle the problem, we decompose it into the subproblem and top problem, and propose an alternate optimization-based resource allocation and the computing mode selection (ARACM) algorithm with low computational complexity, which achieves a comparable performance with the exhaustive search method in terms of the minimal maximum TCT of WDs. Moreover, we propose a deep reinforcement learning (DRL)-based resource allocation and the computing mode selection (DRACM) algorithm with less execution latency than the ARACM algorithm. Numerical results show that the two proposed algorithms achieve satisfactory performance in terms of the minimal maximum TCT of WDs and execution latency.

Wireless powered mobile edge computing (WP-MEC), which combines mobile edge computing (MEC) and wireless power transfer (WPT), is a promising paradigm for coping with the computing power and energy constraints of wireless devices. However, how to realize the online optimal offloading decision and resource allocation in the WP-MEC system is very challenging. This paper studies the system computation completion time (SCCT) minimization problems for WP-MEC networks using non-orthogonal multiple access (NOMA) communication under binary and partial offloading modes. Due to the complexity of the optimization problems and the time-varying nature of the channel state information, we decouple the original problems into a top-problem of optimizing WPT duration and a sub-problem of optimizing resource allocation, and then propose a convolutional deep reinforcement learning online (CDRO) algorithm. For the top-problem, a deep reinforcement learning framework is used to obtain the near-optimal WPT duration, and an incremental exploration policy is designed to balance the exploration accuracy and exploration range to improve the convergence performance of the CDRO algorithm. For the sub-problems, we propose their corresponding low-complexity algorithms based on in-depth analysis and derivation of the optimal offloading decision’s properties. Finally, numerical results show that the proposed CDRO algorithm achieves near-optimal SCCT with low computational complexity, enabling online decision-making in time-varying channel environments.

In the ongoing 5G and upcoming 6G eras, the intelligent Internet of Things (IoT) network will take increasingly important responsibility for industrial production, daily life and so on. The IoT devices with limited battery size and computing ability cannot meet many applications brought out by the data-driven artificial intelligence technique. The combination of wireless power transfer (WPT) and edge computing is regarded as an effective solution to this dilemma. IoT devices can collect radio frequency energy provided by hybrid access points (HAPs) to process data locally or offload data to the edge servers of HAPs. However, how to efficiently make offloading decisions and allocate resource is challenging, especially for the networks with multiple HAPs. In this paper, we consider the sum computation rate maximization problem for a WPT empowered IoT network with multiple HAPs and IoT devices. The problem is formulated as a mixed-integer nonlinear programming problem. To solve this problem efficiently, we decompose it into a top-problem of optimizing offloading decisions and a sub-problem of optimizing time allocation under the given offloading decisions. We propose a deep reinforcement learning (DRL) based algorithm to output the near-optimal offloading decision and design an efficient algorithm based on Lagrangian duality method to obtain the consequent optimal time allocation. Simulations verified that the proposed DRL-based algorithm can achieve more than 95 percent of the maximal computation rate with low complexity. Compared with the common actor-critic algorithm, the proposed algorithm has the substantial advantage in convergence speed, achieved computation rate and running time.

Commodity RFID tags backscatter stored electronic product code (EPC) to the reader, but do not have sensing capability. Existing works have made much effort on designing RFID-based sensing platform. But most of them either need intricate hardware design or rely on modification of the tag, which increases the cost or constrains the sensing capability. In this paper, we design a self-jamming identification and sensing platform (SJISP) consisting of SJISP nodes and a commodity RFID reader. A subtle design of the SJISP node is the adoption of a jammer radio module with the same frequency as the reader, controlled by the micro control unit (MCU) to decide whether to interfere with the query process of the RFID reader. The RFID tag is not readable if the jammer is turned on to generate interference signals. Otherwise, it is readable when the jammer is turned off. The sensing data is thus modulated by switching the jammer on and off for transmitting bit 0 and bit 1, respectively. The reader demodulates the data through the compatible EPC UHF Gen2 air interface protocol. To further save the energy of the SJISP node, we propose a prefix codebook based data delivery scheme, which leverages the difference of energy consumption (DEC) between transmitting bit 0 and bit 1. Our proposed scheme can save more than 50$\%$of the energy than common communication without codebook. Experimental results based on our prototyped system show that the designed SJISP can achieve an average packet reception rate (PRR) of over 99$\%$and is quite robust to environmental disturbance. Our designed platform provides a low-cost and compatible solution to extend the sensing capability of RFID system. A demo application with a temperature sensor and a light sensor embedded in two SJISP nodes respectively are developed to demonstrate how SJISP applies in real world scenario.

In recent years, wireless powered mobile edge computing (WP-MEC) is one of solutions to the problem of insufficient computing power and battery capacity of current edge devices (EDs). In this paper, we consider a WP-MEC network with multiple EDs and study the problem of maximizing the long-term computation rate of the system under the premise of maintaining the stability of the system data queue. Specifically, the objective function is described as a complex non-convex problem, we used Lyapunov optimization theory to decouple the multi-stage continuous random problem into sub-problems of deterministic frames. The problem of determining frames requires joint optimization of wireless power transfer (WPT) duration, local computing frequency, transmission duration, and energy required for offloading. In order to efficiently optimize these variables, we design a DRL-based algorithm combined with the CVX solver, the DRL algorithm learns the WPT duration, and the convex optimization algorithm obtains the system offloading strategy. From the simulation results, our algorithm can achieve performance close to that of the one-dimension exhaustive search algorithm while ensuring the stability of the data queue.

Abstract The 0–1 mixed integer programming problem of binary offloading on wireless powered mobile edge computing (WP‐MEC) networks requires joint optimization of binary and continuous variables, which is computationally expensive for traditional techniques and difficult to solve within the channel coherence time under time‐varying condition. Using machine learning models to output variable values is also challenging. Hence, designing efficient and low‐complexity algorithms is crucial for optimal network performance. This paper focuses on the computation latency of the FDMA‐based WP‐MEC network and proposes a task‐offloading algorithm to minimize the total completion delay (TCD). The TCD minimization is modelled as a 0–1 MIP problem and is decomposed into a master problem of optimizing the offloading decision and the sub‐problem of optimizing other parameters under a given offloading decision. The sub‐problem is solved using optimization method, while the master problem is solved using a deep reinforcement learning algorithm. Simulation results show that the proposed algorithm can achieve almost minimal TCD with low complexity.

Abstract In terms of spectrum reuse and lifetime prolongation, the energy‐harvesting cognitive radio networks (EH‐CRNs) have been a hot issue in the wireless networking research community. While satisfying the minimal throughput demand of primary users (PUs), it is aimed to maximize the throughput of secondary users (SUs) in the EH‐CRN with multiple SUs. Specifically, the problem of secondary throughput maximization (STM) is first formulated as a non‐linear optimization problem, then its convexity is proven, and finally an efficient algorithm is proposed that jointly uses the Fibonacci search method and equal interval search method to obtain the optimal time allocation among primary transmitter (PT)'s energy transfer and each SU's packet transmission, and the optimal transmit power of PT. Furthermore, for the scenarios where the circuit power is negligible, the convex problem is first proven, and a more efficient algorithm is presented for the problem of STM. Simulation results demonstrate that, with the increase of the minimal throughput demand of PUs, the reduction rate of the maximal secondary throughput increases.

Abstract This paper considers the wireless powered mobile edge computing combining wireless power transmission (WPT) and mobile edge computing, where the hybrid access point (HAP) uses multiple radio beams to charge multiple wireless devices (WDs) and WD adopts the binary offloading mode to offload computation workload to HAP via FDMA. It is aimed to maximize the sum computation rate (SCR) of WDs by jointly optimizing the binary offloading decision, transmit power of each radio beam, and WPT duration. Due to the strong coupling between the offloading decision and other optimization variables, the SCR maximization is formulated as a mixed integer nonlinear programming problem. To address this challenging problem, an online DRL‐based decoupling optimization algorithm is proposed. Specifically, the original problem is first split into a top‐problem of optimizing binary offloading decision and a sub‐problem of optimizing transmit powers and WPT duration under given offloading decision. Then a self‐learning DRL framework is designed to output the near‐optimal offloading decisions. Finally, for the sub‐problem, based on the Lagrangian dual theory, an efficient approach to fast obtain the closed‐form expression of the optimal solution is proposed. The simulation results show that the proposed DRL‐based algorithm can achieve the near‐maximal SCR with low computational complexity.

As one widely applied wireless technique, WiFi has the potential to execute noncontact monitoring of vital signs based on channel state information (CSI). However, due to the dynamic of the surrounding environment, the bandwidth of the WiFi channel is not enough to identify the respiration-induced path from other movement-induced paths and this seriously limits the accuracy of respiration rate detection. In this article, we propose ExRadio, a system that can monitor respiration in high-dynamic environments via combining multiple WiFi channels. Specifically, the receiver synchronously switches the channels with the transmitter and samples CSI at multiple channels, and then the CSI data are combined and regarded as CSI data of one extended-bandwidth channel. However, the hardware-related noises from multiple channels are also accumulated. Eliminating these noises causes too heavy computation overhead to be afforded by the embedded devices and affects the real-time performance of respiration monitoring. To address this problem, we propose an effective approach that employs the ratio of CSI readings from the wireless channel and wire direct connection channel to shorten the time of eliminating the hardware-related noise. We deploy the ExRadio in commercial off-the-shelf embedded devices and conduct a series of experiments. The experimental results demonstrate that reducing the execution time is beneficial to respiration rate detection under high-dynamic environments, and the overall detection error of ExRadio is less than 0.5 bpm even when multiple persons who are 1.5-m away from the monitored person are fast walking.

Currently, powerful and ubiquitous mobile devices provide an opportunity to map physical conditions to cyberspace and realize Digital Twins enabled Healthcare (DTeH). Especially, the impact of the COVID-19 epidemic renders it necessary to keep an eye on the changing trend of respiration. Long-term respiration monitoring helps to assess personal health status and thus becomes an important issue in DTeH. However, previous mobile device-assistant methods mostly implement the monitoring via short-time detection in a best-effort way and with less consideration of identity recognition, the only mean to bind physical vital signs into personal profiles in digital twins space. Thus, it is necessary to introduce the identification to complete string multiple short-time detections and form long-term personal monitoring. To this end, we propose IDRes, an identity-based respiration monitoring system for DTeH. This system employs mobile devices to generate a high-frequency sonar signal to complete respiration detection and identity recognition. As well as it also estimates the respiration rate by tracking the phase change of the sonar signal and recognizes identity via the Doppler frequency shift of the signal to capture characteristics of chest movement. Moreover, via band-pass filtering to remove the low-frequency voice component of the received signals, the usage of the high-frequency sonar signal also enhances security at the physical level. At last, we conduct a series of experiments under different conditions. Experimental results illustrate that IDRes achieves the mean detection error of 0.49bpm with over 93.3% recognition accuracy, and manifest that IDRes can satisfy the requirements of mapping the accurate vital sign data to the personal profile of DTeH.

Ambient backscatter (AB) communications and radio frequency (RF)-powered cognitive radio networks (CRNs) address the concerns of energy and spectrum scarcities from different perspectives, and the integration of them has potential benefits for throughput. Motivated by this fact, we study the RF-powered AB-assisted hybrid underlay CRN (ABHU-CRN), and optimize the long-term secondary throughput. Based on the channel states, secondary users choose to perform different actions, and two action spaces are accordingly designed. Due to dynamic and unpredictable environment states, we jointly control the time scheduling and energy management of secondary users, and propose two algorithms, i.e., adjusted-deep deterministic policy gradient (A-DDPG) and combination of A-DDPG and convex optimization (C-ADCO), for the long-term secondary throughput. A-DDPG, a deep reinforcement learning algorithm with continuous spaces, is extended from DDPG to adapt to the design of two action spaces. C-ADCO utilizes the convex optimization that can find the optimal solution to assist A-DDPG to accelerate the convergence. In simulations, the ABHU-CRN under A-DDPG and C-ADCO achieves higher throughput than the optimal throughput of AB-assisted overlay CRN and AB-assisted underlay CRN, which indicates the advantage of the hybrid transmission mode in the ABHU-CRN.

Wireless power transfer (WPT) and edge computing have been validated as effective ways to solve the energy-limited problem and computation-capacity-limited problem of wireless devices (WDs), respectively. This paper studies the wireless-powered multi-access edge computing (WP-MEC) network, where WDs conduct either local computing or task offloading for their individable computation tasks. We aim to minimize total computation delay (TCD) when each WD has a computation task to execute, referred to as the total computation delay minimization (TCDM) problem, by jointly optimizing the offloading-decision, WPT duration, and transmission durations of offloading WDs. The TCDM problem is a mixed integer programming (MIP) problem that is challenging to efficiently obtain the optimal or near-optimal solution. To tackle this challenge, we decompose the TCDM problem into the sub-problem of optimizing the WPT duration and transmission durations, and the top-problem of optimizing the offloading decision. For the nonconvex sub-problem, we design a worst-WD-adjusting (WDA) algorithm to efficiently obtain its optimal solution. For the top-problem, under the time-varying channel conditions, traditional optimization methods are hard to determine the optimal or near-optimal offloading decision within the channel coherence duration. To fast obtain the near-optimal offloading decision, we propose a deep neural networks (DNN)-based deep reinforcement learning (DRL) model, which takes the sub-problem solving as one component for utility evaluation. Finally, numerical results demonstrate that the proposed online DRL-based offloading algorithm achieves the near-minimal TCD with low computational complexity, and is suitable for the fast-fading WP-MEC network.

The risk of death and injury from traffic crashes has been universally recognized as one of the most serious threats to sustainable development. Among all the factors in traffic crashes, aggressive driving in which the driver usually makes excessive lane changes to overtake other vehicles is prevalent. As such, monitoring lane changes and providing real-time warnings is beneficial for improving transportation sustainability. This article presents BackWatch, a novel vehicle-mounted sensing system that uses a back view cabin camera monitoring the steering wheel rotations to track lane-change events. BackWatch consists of an encoder network to extract essential visual features of steering wheel rotations, and an inference network incorporating the visual and GPS speed features to recognize the resulting lane changes. Our system does not rely on precise coordinate alignment between the monitoring device and the vehicle, nor the wearables worn by the driver, and is robust against different drivers, vehicles, driving speeds, and environmental settings. We evaluate the system based on 16 hours of real-world on-road driving data collected from three pairs of cars and drivers under different traffic and environmental conditions. The results show that BackWatch achieves 0.952 of precision and 0.981 of recall on the detection of lane changes.

With the broadening of the radio spectrum to higher frequency bands, wireless links are more prone to blockages by nomadic obstacles. However, existing routing schemes mostly follow the network-oriented design principle, which makes it difficult to react quickly to sudden obstruction. This article proposes PAR, a novel physical-assisted routing scheme for the Internet of Things. PAR takes a physical-oriented viewpoint attempting to mitigate unexpected link blockages by leveraging sensor observations of obstacles at individual nodes. It analyzes the signatures of sensor measurements to directly estimate the positions and sizes of obstacles and to proactively infer remedial routing decisions even before the transmission failure occurs. During the network deployment time, PAR lets each node learn and calibrate the geographic distribution of neighbors with respect to its sensor measurements by exchanging the sensor observations of a mobile guidance obstacle. During the runtime, an obstacle bypassing algorithm is then developed to find the shortest detour routes by comparing the local sensor measurements with the calibrated positions of neighbors to immediately resume data forwarding. We evaluate the efficacy of PAR using simulation and a real-world testbed. It is observed that PAR significantly improves the success rate while reducing redundant hops and routing control overhead.

For real-time monitoring applications, the age of information (AoI) is used as a key metric to quantify the freshness of updated information. In this paper, we consider the wireless powered networks where multiple source nodes observe processes and send update packets to the base station. Time is divided into slots which are equal duration. At each slot, either wireless energy transfer or packet update via non-orthogonal multiple access (NOMA) communication is scheduled. We aim to minimize the long-term average weighted sum of AoI of processes at the base station. Particularly, we formulate the AoI minimization problem as a multi-stage stochastic non-linear integer programming subject to the battery energy constraints. By adopting the Lyapunov optimization, we decouple the multi-stage stochastic problem into perframe deterministic subproblems and solve it with a low computational complexity algorithm. Simulation results show that our proposed scheme can achieve much smaller average weighted AoI than the benchmark algorithm.

Nowadays, the Internet of Things (IoT) acts as a key enabler for smart cities, intelligent transportation systems, precision medicine, smart grids, etc. However, the computing power of IoT nodes is weak, and encryption algorithms cannot be used to ensure the secure transmission of data because of the high complexity. In recent years, the emerging technologies of wireless powered communication network (WPCN) and physical layer security (PLS) are regarded as potential solutions to allow IoT nodes to harvest energy from radio frequency (RF) and ensure secure data delivery. The secrecy rate, which is defined as the difference of main channel capacity and eavesdropping channel capacity, represents the secure data delivery ability. How to maximize the secrecy rate with the rather limited energy for IoT network is a challenging problem. In this paper, we formulate the secrecy rate maximization problem as an optimization problem. We solve this problem by two steps: using one-dimension research to find the optimal energy harvesting duration and find the closed-form solution for transmission power and transmission duration for every node. Compared with the heuristic algorithm, the proposed algorithm can obtain better performance from secrecy rate perspective.

Abstract Mobile edge computing is the network technology for providing computing resources of edge computing server to Internet of Things (IoT) applications. Additionally, wireless power transfer (WPT) technology can provide stable energy supplying to IIoT nodes and to overcome the limited node lifetime problem faced when using batteries. In this paper, the wireless powered mobile edge computing network is considered where the edge computing server transfers RF energy to IIoT nodes which use harvested energy to offload partial computation workload based on OFDMA and also conduct local computation. The aim is to maximise the weighted sum computation rate by jointly optimising the WPT duration and the amount of energy used for offloading at each node for each time frame. This paper proposes an offloading approach based on deep reinforcement learning which is able to quickly obtain the near‐optimal offloading solutions. Specifically, the original offloading problem is decomposed into the sub‐problem of optimising the energy allocated for offloading under a given WPT duration and the top‐problem of optimising the WPT duration. Simulation results demonstrate that the proposed algorithm can achieve the near‐optimal weighted sum computation rate with very low complexity, which is tailored for the practical dynamic‐channel environment.

The Internet of Things (IoT) is a highly integrated application of the advanced information technology, which is expected to bring convenience for daily life and improve the efficiency of industrial production. Owing to the limitation of battery capacity and the broadcast nature of IoT nodes, IoT networks face the bottleneck of energy shortage and security vulnerability. In recent years, the emerging technologies of wireless powered communication network (WPCN) and physical-layer security (PLS) are regarded as potential solutions to allow IoT nodes to harvest energy from radio frequency (RF) and ensure the secure data delivery. How to efficiently allocate energy for IoT devices to improve throughput while guaranteeing secure data transmission is a challenging problem. In this article, we consider a WPCN with the existence of an eavesdropper, who is trying to eavesdrop the data transmitted from a certain node to the hybrid sink ($H$-sink). In the proposed system, the nodes first harvest energy from the$H$-sink, then transmit the information to the$H$-sink and generate interference to the eavesdropper. We first formulate the sum-throughput maximization problem as the nonlinear optimization problem and find its closed-form solution by the Lagrangian method. We further design an efficient algorithm to obtain the optimal numerical results to make up for the defect that the closed-form solution may not meet the explicit constraints. Furthermore, we propose a simple and reasonable method, the ratio method (RM), based on the observation of the optimal solution and make a comparison between the proposed method and the most common method, the same interference power method (SIPM).

Multi-access edge computing (MEC) has been one promising solution to reduce the computation delay of wireless devices. Due to the high spectrum efficiency of non-orthogonal multiple access (NOMA), this paper studies the single-user multi-edge-server MEC system based on downlink NOMA, aiming to minimize task computation delay by jointly optimizing the NOMA-based transmission duration (TD) and workload offloading allocation (WOA) among edge computing servers. This task computation delay minimization (CDM) problem is formulated as a nonconvex optimization problem. To solve the CDM problem efficiently, we decompose it into the sub-problem of determining the optimal WOA with a given TD and the top-problem of optimizing the TD. For the sub-problem, we first derive its some important properties and then design an efficient channel quality ranking based algorithm to obtain the optimal WOA. We solve the top-problem for the static-channel and dynamic-channel scenarios, respectively. For the static-channel scenario, we design an optimal algorithm which only apply once the golden section search method to obtain the optimal TD of first task and directly obtain the optimal offloading solution for any consequently arrived task with different workloads. For the dynamic-channel scenario where the channel qualities from the wireless device to the edge-computing servers are varying, it is critical to quickly determine the current task’s offloading solution under the current channel state and task workload, which is very challenging for the traditional optimization methods. In order to conquer this challenge, we propose the deep reinforcement learning (DRL) based algorithm, which can obtain the near-optimal offloading solution instantly after enough learning. Finally, we validate through simulations the advantages of NOMA over frequency division multiple access (FDMA).

The advanced Internet of Things (IoT) enables more and more interactions between people and machines in the emerging applications, which rely on real-time communication and computing. However, the limited battery capacity and low computing capacity of IoT nodes can hardly support high-performance computing applications. The integration of wireless power transmission (WPT) and mobile edge computing (MEC) is a feasible and promising solution to address the energy shortage and computing capacity limitation of IoT nodes by harvesting radio frequency signal’s energy and offloading the nodes’ computation tasks to edge computing servers (ECSs). In this work, we focus on the wireless powered MEC network with an ECS and multiple edge devices (EDs), and study the joint optimization of WPT duration, transmission time allocation of each ED and partial offloading decision to maximize the sum computation rate. First, we formulate this as a non-convex problem which is hard to solve. Second, to conquer this problem, we decompose the original offloading problem into the sub-problem of optimizing the offloading time allocation among EDs and the proportion of harvested energy allocated for offloading at each ED under a given WPT duration and the top-problem of optimizing the WPT duration. Finally, we design an online DRL-based framework where one DNN together with its exploration strategy and training strategy is adopted to learn the near-optimal WPT duration and an efficient optimal algorithm is designed to solve the sub-problem. Numerical results show that the DRL-based offloading algorithm achieves the near-maximal sum computation rate while greatly reducing the processing time by at least three orders of magnitude compared with using the solver CVX for the sub-problem and the DNN for the top-problem.

Real-world, long-running Internet of Things (IoT) requires intense user-node interaction in the deployment, network operation, and maintenance stages. The rapidly increasing number of IoT nodes urges a new user-friendly interface to reduce the interaction complexity. This paper presents INSIGHT, an AR-enabled user interface for IoT nodes allowing the users to directly grasp perceptual node information from the surrounding videos shot by mobile AR devices such as smart glasses and phones. INSIGHT incorporates camera, magnetic, and IMU sensor data from an AR device to detect, locate, and recognize IoT nodes markerlessly. An intuitive user interface will then be overlaid on each recognized node for fetching/feeding information from/to the node. We have validated the performance of INSIGHT in terms of localization accuracy and the capability to distinguish nearby nodes. The results show that INSIGHT works well even in densely deployed networks.

Abstract Integrating backscatter communication (BackCom) into wireless powered communication networks (WPCNs) makes it possible to transmit information and harvest energy simultaneously, which is regarded as a promising method to enhance the throughput. A very important issue is how to allocate the charging time and transmitting time to efficiently utilize the energy in hybrid access point (HAP) so as to improve the network performance. This paper adopts convex optimization to handle the time allocation for both WPCNs and BackCom users. A two‐stage efficient algorithm is proposed to solve the problem of sum‐throughput maximization (STM) subject to the basic throughput requirements of BackCom users, where the Lagrange duality method is applied at the first‐stage, while golden section and bisection method are jointly used at the second‐stage. In order to solve the throughput unfairness among nodes in the STM problem, the common‐throughput maximization (CTM; i.e. the worst node's throughput) problem is further considered. This problem is decomposed into a master problem and a sub‐problem, which are solved in a progressive manner. Simulation results show that the proposed methods obtain substantial improvement compared to the benchmark scheme.

Energy harvesting wireless sensor networks (EH-WSNs) intend to support sustainable operations. It is important to design a high-throughput data delivery scheme that adapts to the fluctuation in harvested energy in the EH-WSN nodes. In this article, the optimal superframe and data buffer scheme (OSDBS) is investigated to improve the throughput of IEEE 802.15.4 beacon-enabled EH-WSNs. A stochastic model is developed for OSDBS, which leads to the characterization of network throughput and packet delay. The OSDBS achieves the maximum throughput through setting the optimal superframe and buffer sizes of the nodes, which are the solution of the formulated optimization problem that maximizes the network throughput with consideration of energy-harvesting rate and data arrival rate. The simulation results show the OSDBS significantly outperforms the existing schemes in terms of throughput.

Sink mobility is a significant technique to improve the performance of wireless sensor networks (WSNs). Generally a mobile sink visits several rendezvous points (RPs), forming a trip tour for data collection. However, the low movement speeds of mobile sinks tend to incur serious data delivery delays. In this article, we propose a quick convex hull-based rendezvous planning (QCHBRP) scheme, which aims to not only achieve full connectivity for disjoint WSNs but also construct a shorter trip tour and minimize the data delivery latency accordingly. The trajectory formation of the mobile sink is based on a path skeleton, i.e., a near-convex hull, which is created by the quick determination of several special locations as RPs. The benefits of QCHBRP are threefold. First, it is especially designed for disjoint WSNs where sensor nodes are deployed in multiple isolated segments and the network connectivity is lost in advance. Second, it is suitable for delay-harsh applications which require short paths of the mobile sink. Third, it is of much lower computational complexity compared with existing methods. The extensive analysis and experiments validate the effectiveness and advantages of this new scheme in terms of connectivity cost and data delivery delay.

Media access control (MAC) plays a pivotal role in ensuring proper operation in wireless body area networks (WBANs). However, current solutions still cannot satisfy the stringent requirements of low power and low delay for emergency data reporting. In this paper, we propose an energy-efficient and emergency-aware MAC (EEEA-MAC) protocol for meeting such a rigorous demand. First, we design a node-different channel access scheme, in which source nodes use the CSMA/CA pattern while relay nodes adopt the hybrid CSMA/CA-TDMA pattern. Second, we devise an emergency-first time-slot allocation scheme, in which channel sensing is performed and the emergency data is handled by relay nodes according to different cases. EEEA-MAC has two striking features. One is that, source nodes adopt the CSMA/CA scheme instead of the conventional CSMA/CA-TDMA scheme, ensuring the requirement because there are almost no collisions and no confirmation messages in this scheme. The other is that, relay nodes use a sensing-based emergency data handling mechanism instead of the traditional empty-slot occupying mechanism, further guaranteeing the requirement owing to the immediate handling of emergency data and the short time of channel sensing. Extensive simulations demonstrate the advantages of EEEA-MAC in terms of energy dissipation and latency.

So far, the research of wireless powered communication networks (WPCNs) mainly considers the scenarios with a single radio‐frequency (RF) energy transmitter (ET) and a single sink. However, in practice, there are many applications where multiple ETs and sinks need to be deployed. This study focuses on large‐scale WPCNs having multiple RF ETs and sinks. Specifically, the authors aim to minimise the total energy provision by optimising ETs' transmit powers with the node‐throughput demand and sum‐throughput demand, respectively. For the node‐throughput demand case, they firstly formulate it to be a convex optimisation problem, then transform it to be a linear programming (LP) problem, and finally present a distributed algorithm to obtain the optimal solution. For the sum‐throughput demand case, they firstly formulate it to be a non‐linear optimisation problem, then prove its convexity and finally propose an efficient dual subgradient algorithm to obtain the optimal solution. Simulation results demonstrate that compared to the sum‐throughput demand, imposing the node‐throughput demand can effectively alleviate the throughput unfairness at the cost of increased energy provision; the proposed optimal algorithms can substantially decrease the total energy provision of ETs; the energy provision reduction percentage achieved by their schemes increases as the number of ETs increases.

In the battery‐free wireless sensor network (BF‐WSN) that harvests radio signal energy, data delivery suffers from a longer delay arising from the energy‐harvesting period. It is significant to develop an energy‐efficient, low‐delay, and reliable data gathering scheme for the BF‐WSN. The goodput‐maximised data delivery scheme (GDDS) is proposed to reliably collect time‐constrained data in the IEEE 802.15.4‐based BF‐WSN. Under the GDDS, the sink's operation period consists of multiple data gathering cycles with each incorporating three phases: charging the nodes, assigning channel occupation time for the nodes, and receiving packets from the nodes. The scheme of accumulating correct data blocks (SACDB) is used in the third phase for the sink to gather data from the nodes. The authors develop an analytical model for the SACDB, from which they derive the time and the energy consumed in transmitting a packet. Then, they derive the goodput and the energy efficiency under the proposed GDDS. The GDDS aims at maximising the goodput by optimising the charging period, the number of data blocks, and the maximum number of transmission trials under the constraint on data gathering time. Simulation results show the GDDS outperforms the existing schemes in terms of the goodput and energy efficiency.

Synthetic aperture radar (SAR) multi‐target interactive motion recognition classifies the type of interactive motion and generates descriptions of the interactive motions at the semantic level by considering the relevance of multi‐target motions. A method for SAR multi‐target interactive motion recognition is proposed, which includes moving target detection, target type recognition, interactive motion feature extraction, and multi‐target interactive motion type recognition. Wavelet thresholding denoising combined with a convolutional neural network (CNN) is proposed for target type recognition. The method performs wavelet thresholding denoising on SAR target images and then uses an eight‐layer CNN named EilNet to achieve target recognition. After target type recognition, a multi‐target interactive motion type recognition method is proposed. A motion feature matrix is constructed for recognition and a four‐layer CNN named FolNet is designed to perform interactive motion type recognition. A motion simulation dataset based on the MSTAR dataset is built, which includes four kinds of interactive motions by two moving targets. The experimental results show that the recognition performance of the authors’ Wavelet + EilNet method for target type recognition and FolNet for multi‐target interactive motion type recognition are both better than other methods. Thus, the proposed method is an effective method for SAR multi‐target interactive motion recognition.

This article considers the unmanned-aerial-vehicle-enabled wireless powered communication networks (UAV-enabled WPCN) where one UAV plays the role of hybrid sink (H-sink) and coordinates the wireless energy/information transmissions to/from a set of nodes and aims to minimize the transmission completion time (TCT) of collecting a given number of bits per node. Due to its intractability, this article transforms this problem into a tractable one by using an area discretization technique so that the node's energy harvesting power can be approximated to be the same value under a given error tolerance $\varepsilon $ wherever the UAV is located inside one subregion. The optimal solution of the transformed problem has an approximation ratio of $1+\varepsilon $ to the theoretically minimum TCT. To solve the transformed problem, this article formulates it as a convex problem and decomposes it into the master problem and the slave linear programming problem. The master problem is solved by a subgradient-based algorithm. Furthermore, for the scenario where each node has the same amount of data to transmit, this article develops an algorithm with lower complexity. Specifically, this article first decomposes it into the master problem of determining the minimum TCT via the bisection search method and the slave feasibility problem under a given TCT. The slave problem is transformed to be a convex problem whose optimal solution is obtained by partially solving its Lagrange dual problem first and then solving a linear programming problem. The simulation results demonstrate that the UAV-enabled WPCN greatly outperforms the conventional WPCN with the fixed H-sink.

Barrier coverage problem is one of hot research topics in directional sensor networks (DSNs). Directional sensors could zoom their sensing ranges, within which the event detection probability decreases with the increase of the distance between the location of event and the sensor. Although the probability that an event is detected by a single sensor may be below the required criteria, the detection probability achieved jointly by two sensors can be above the required criteria. In this work, we study the barrier coverage problem of DSNs, taking into account the directional nodes' ability of adjusting their working directions and sensing ranges. We mainly propose a barrier construction scheme, which schedules the nodes to form multiple barriers by jointly determining which nodes jointly forming a barrier, and their respective working directions and sensing ranges. Comparing to existing works, the proposed scheme is able to form more barriers, leading to the increase of service lifetime.

This article focuses on the issue of cooperative spectrum sensing (CSS) in a mobile energy-harvesting cognitive radio network (EH-CRN), where secondary transmitters (STs) are powered by the radio-frequency (RF) signal emitted from primary transmissions. Only the STs with sufficient energy participate in CSS, and send their local sensing decisions to a fusion center (FC), which makes a final decision on the state of the spectrum by a general k-out-of-M(k) fusion rule. The target of this article is to develop an optimal CSS strategy in terms of final decision threshold k that maximizes the expected achievable throughput of the EH-CRN, subject to a collision constraint and an energy causality constraint. We first show that the EH-CRN can be divided into an energy-deficit state and a spectrum-deficit state depending on the final decision threshold. The final decision threshold has a negative correlation with the number of STs participating in CSS in the energy-deficit state, and has no impact on that in the spectrum-deficit state. We then derive the collision probability and the expected achievable throughput of the EH-CRN, both of which are indicated to be determined by the active probability of a ST, the state of the spectrum, and the spectrum access opportunity. By tackling the tradeoff between the active probability and spectrum access opportunity introduced by the final decision threshold, we derive the optimal final decision threshold that maximizes the expected achievable throughput of the EH-CRN while protecting primary transmissions to a predefined extent. Extensive numerical simulations are conducted to illustrate the performance versus the final decision threshold. One of the main findings indicates that the optimal range of final decision threshold in the energy-deficit state could be acquired by the number of reporting received at the FC.

Radio link quality is highly influenced by changes in the physical environment. To sustain reliable and efficient data delivery, link quality estimation is essential for Cyber-Physical Systems (CPSs) or Internet of Things (IoT). Network-based link quality estimation methods estimate the link quality by monitoring data transmissions. In a dynamic environment, the accuracy of link quality so estimated may become degraded because the accuracy must be balanced against the overhead of data transmissions. In this work, we propose to incorporate sensor readings available in a CPS/IoT system to augment existing link quality estimation. We call this a Physical-Assisted Channel Estimator (PACE). By analyzing sensor readings that are highly correlated to the link quality, PACE may detect the change of link quality in real-time. Evaluation conducted on a real intelligent parking system shows that compared to existing network-based methods, PACE reacts to persistent disturbances much more quickly without sacrificing robustness to transient fluctuations, and achieves higher accuracy even under a low data transmission rate. With PACE, the data delivery performance of routing protocols can be significantly improved. We expect PACE to be the first milestone towards Physical-Assisted Cyber Systems (PACSs) for fulfilling the vision of environment-aware computing and communication.

Cognitive radio and energy harvesting techniques have provided significant benefits in terms of spectrum reuse and lifetime prolongation for conventional wireless networks. We are thus motivated to consider the energy harvesting cognitive radio networks (CRNs) consisting of multiple primary users (PUs) and secondary users (SUs). We introduce two cooperation modes: the energy cooperation mode and joint cooperation mode. In the energy cooperation mode, there only exists energy cooperation between PUs and SUs, i.e., the SU transmits its own packets by using the energy harvested from primary signals. In the joint cooperation mode, the SU relays primary packets by using the energy harvested from primary signals. In each cooperation mode of three representational scenarios (the CRN with one pair of PUs and one pair of SUs, the CRN with two pairs of PUs and one pair of SUs, and the CRN with one pair of PUs and two pairs of SUs) and the general scenario, we exploit the optimal time allocation between PUs and SUs, and balance the tradeoff between energy harvesting and packet transmission to obtain the maximum total achievable throughput. To be specific, we first formulate the throughput maximization problems as non-linear optimization problems, and then prove them as convex problems by monotonicity analysis. Moreover, we obtain the closed-form optimal solution in the energy cooperation mode. We prove the existence of the optimal solution in the joint cooperation mode, obtain the upper and lower bounds, and provide numerical analysis for the optimal solution. Finally, we highlight the benefits of information cooperation and the impact of multi-user gain on the maximum of the total achievable throughput.

RF Energy harvesting technology has been proved one of the effective approaches for powering battery-free wearable devices in wireless body area networks. However, excessive electromagnetic radiation is harmful to human body. In this paper, we consider real-time healthcare scenario where wearable devices worn by mobile users collect their physiological data in real time and multiple wireless chargers are deployed for energy provision. Our goal is to minimize the maximal radiation degree among mobile users while maintaining normal work of wearable devices via adaptive power control of wireless chargers. We first discrete the users' moving trajectories and transform the stubborn problem into a docile one. Then we propose a distributed algorithm with interaction of wireless chargers, wearable devices and base station to solve it. Our proposed real-time power control scheme achieves an approximation ratio of (1+e) in general case. Furthermore, one special case is discussed. Simulation results reveal that our scheme is efficient and the maximal radiation degree among users can be reduced by almost 20\% as compared to the baseline scheme.

RF energy transfer, as the main far-field wireless energy transfer technology in wireless sensor networks, allows the relatively long charging distance from wireless charger to sensor nodes. Existing charging schemes based on a mobile RF energy charger neglect the energy consumption of the charger and its limited battery capacity. Motivated by this, we consider the practical charging scenario where energy-constrained mobile charger (MC) travels along a constrained long trajectory in the network area to wirelessly power the sensors, with multiple depots (for the energy provision of MC) deployed on the trajectory to achieve high energy efficiency. In this paper, we introduce VCEC, a Velocity-Control scheme of Energy-constrained mobile Charger to maximize the minimum charged energy in nodes after MC passes through the whole trajectory. Specifically, we first simplify the initial velocity-control problem to a tractable one by discretizing the trajectory into segments and propose a distributed algorithm to solve it. Then, we present a segment merging algorithm for the real-world applications. Our VCEC scheme achieves an approximation ratio of (1-θ)(1+ ε)-1. Simulations and test-bed experiments are conducted to show that VCEC promotes the bottleneck node's charged energy by at least 20% as compared to the baseline scheme where MC moves at a constant speed.

Radio‐frequency (RF) energy harvesting is one promising technology to power the nodes in wireless networks. This study focuses on large‐scale wireless powered communication networks having multiple RF energy transmitters (ETs) and sinks, which almost have not been investigated previously. The authors aim to optimise the throughput via optimizing the transmit power allocation of ETs subject to a total power budget. Specifically, for the sum‐throughput maximisation (STM) problem, they firstly formulate it to be a non‐linear optimisation problem, then prove its convexity and finally propose an efficient dual sub‐gradient algorithm to solve it. Owing to the throughput unfairness among nodes of the STM approach, they further consider the common‐throughput maximisation (CTM; i.e. the worst node's throughput) and propose a very efficient algorithm for it. This algorithm divides the CTM problem into a master problem and a subproblem. The subproblem of determining the feasibility of a given common‐throughput is solved by transforming it to a linear problem whose optimal solution indicates the feasibility. The master problem of determining the maximal common‐throughput is solved by using the bisection search method. Simulation results demonstrate the effectiveness of the CTM approach to mitigate the throughput unfairness problem at the cost of decreased sum‐throughput.

Recently, the newly emerging wireless powered communication network (WPCN) has drawn significant interests, where the network nodes are powered by the energy harvested from the radio-frequency (RF) signal. This paper focuses on the widely studied WPCN, where one hybrid sink (H-sink) coordinates the wireless energy/information transmissions to/from a set of one-hop nodes powered by the harvested RF energy only, and aims to minimize the network-throughput constrained H-sink's energy provision (EP). Specifically, we investigate the performance of two important MAC protocols: time-division multiple access (TDMA) and non-orthogonal multiple access (NOMA). For both the TDMA-based WPCN (T-WPCN) and NOMA-based WPCN (N-WPCN), we first formulate the EP minimization problems as the non-linear optimization problems, then transform them into convex problems, and finally propose an efficient algorithm, which jointly uses the golden-section search and bisection search methods to determine the optimal time allocation of H-sink's energy transfer and each node's information transmission as well as the optimal H-sink's transmit power. Furthermore, for the scenarios where the circuit power is negligible, we first prove that the optimal H-sink's transmit power is the maximum allowable value, then prove theoretically that the NOMA and TDMA achieve the same EP, and also present a more efficient algorithm for the EP minimization problem. Simulation results demonstrate that the TDMA outperforms NOMA when the circuit power is non-negligible because the circuit energy consumption of NOMA accounts for a large percentage of the total energy consumption.

In the Internet of Things, wireless sensor networks (WSNs) are widely deployed. In recent years, wirelessly powered WSNs or battery‐free WSNs (BF‐WSNs), in which the nodes harvest energy from radio signals in the environment, have been emerging to support sustainable operation for WSNs. It is significant to design an energy‐efficient data delivery scheme for the BF‐WSNs. In this study, the authors propose the prefix code based scheme (PCBS) to save energy in data delivery by making use of the energy consumption disparity (ECD) between transmitting/receiving bit 0 and bit 1 in the existing non‐modulation baseband transmission or carrier‐modulation based passband transmission. The authors formulate an optimisation problem and use genetic algorithm to find its solution so that the energy‐efficient prefix codebook is obtained. The codebook dilutes the ECD by containing the energy‐consuming bit as few as possible, and the PCBS maps each m ‐bit data block into a prefix codeword in the codebook to conduct energy‐efficient transmission at the transmitter and vice versa at the receiver. Both the experiments on wireless identification sensing platform and the simulations demonstrate that the proposed PCBS outperforms the existing schemes in terms of energy saving.

Narrowband Internet of Things (NB-IoT) is a new cellular technology introduced by the third generation partnership (3GPP) providing low-power and wide-area coverage for IoT. In this paper, we consider the scenario that NB-IoT is deployed in an heterogeneous network and the quality of the direct link from the NB-IoT user equipment (TIE) to the serving base station (BS) cannot satisfy the quality of service requirement for transmission of vital sensing data. Thereupon, device-to-device (D2D) communication is adopted as a routing extension to NB-IoT systems, and thus, enables two-hop routes between NB-IoT TIE and the serving BS via a set of D2D relays. As the candidate TIE relays work in duty cycle to save energy, we derive a model to select a set of TIE relays and perform opportunistic D2D communication according to a working schedule. Two optimization problems are formulated aiming at achieving optimal expected delivery ratio (EDR) and expected two-hop delay, respectively. Dynamic programming-based algorithms are proposed to solve the optimization problems and obtain the optimal working schedule of the relays. Simulation results demonstrate that our proposed maxEDR and minEED algorithms improves the system performance compared with other state-of-the-art algorithms.

Recently, the newly emerging wireless powered communication network (WPCN) has drawn significant interests, where network nodes are powered by the energy harvested from the radio-frequency (RF) signal. This paper studies the WPCN where one hybrid sink (H-sink) coordinates the wireless energy/information transmissions to/from a set of one-hop nodes powered by the harvested RF energy only. The transmission completion time (TCT) minimization for the uplink (UL) transmissions of a given number of bits per node is considered. First, we prove that the harvest-then-transmit (HTT) transmission strategy is one of the transmission strategies able to achieve the minimal TCT, where all nodes first harvest the RF energy broadcast by the H-sink in the downlink and then send their independent information to the H-sink in the UL by time-division multiple access. Then for the HTT transmission, we prove that in order to achieve the minimal TCT, each node must transmit with constant power and consume all available energy, which helps to simplify the considered TCT minimization problem to be the optimization of time allocated for the H-sink's wireless energy transfer and the nodes' wireless information transmissions, and we formulate the optimal time allocation problem as a nonlinear optimization problem. Finally, we prove that it is a convex optimization problem. Due to the inexistence of explicit closed-form expressions of optimal time allocations to minimize TCT, one efficient algorithm is presented to obtain the optimal time allocations. Simulation results show that, compared with the available transmission strategies, the designed TCT-minimized transmission achieves a significantly smaller TCT.

Battery-Free Wireless Sensor Networks (BF-WSNs) have become increasingly useful for many applications and how to ensure timely information exchange between nodes in IP networks and those in BF-WSNs is indispensable. The 6LoWPAN protocol is usually used to deliver IPv6 packets over IEEE 802.15.4 based WSNs, and has resolved the size mismatching problem between IPv6 packets and 802.15.4 Medium Access Control (MAC) frames by using packet fragmentation scheme to break an IPv6 packet into multiple small pieces with each fitted into a single 802.15.4 MAC frame. Unfortunately, IPv6 packets in BF-WSNs may suffer from intolerable delay for timely reassembling back to IPv6 packets. In this paper, we present a Latency Aware IPv6 Packet Delivery (LAID) scheme to reduce such IPv6 packet latency while maintaining high packet delivery ratio. Our LAID considers charging time, data rate, and the Maximum Number of Transmission Trials (MNTT) used in the IEEE 802.15.4 MAC layer so that the minimum latency can be achieved by optimizing the pairing of data rate and MNTT. In addition, we apply network coding to improve packet delivery reliability. Our analysis shows that the proposed LAID significantly outperforms existing schemes with fixed data rates in terms of IPv6 packet latency.

In Battery-Free Wireless Sensor Network (BF-WSN), nodes are powered by the energy harvested from ambience instead of batteries. The nodes may frequently suffer from insufficient energy so that they need to alternate normal operation (such as transmitting/receiving, etc.) with harvesting energy. This brings in extra packet delay for the nodes to deliver data to the sink(s). Therefore, delivering data with shorter delay is a critical concern for BF-WSN nodes. In this paper, we first define the weight of wireless link that takes into account interference among links, balancing data load among the subtrees of the data gathering tree, and energy harvesting rate (EHR) of the nodes. Then, using the defined weight, we present the heuristic algorithm to build data gathering tree by letting the wireless link with a smaller weight join the tree prior to the ones with greater weights so that the wireless links either tending towards interference with the other ones, bringing in unbalance data load in the subtrees, or having smaller EHR are deferred to join the tree, thus reducing packet delay. Simulation results show the proposed data gathering scheme outperforms the existing scheme in terms of the delay per packet reaching the sink.

It is critical to design energy-efficient communication technologies for wireless nanosensor networks (WNSNs) as nanosensors are highly energy-constrained. This paper focuses on WNSNs adopting the on-off keying (OOK) modulator. So far, some existing low-weight (LW) codes with low average codeword weight (ACW) map source symbols into different codewords with fewer high bits so as to greatly reduce the transmission energy at the transmitter. However, the transmission energy reduction is achieved at the price of large reception energy at the receiver as the codeword lengths of LW codes are large, incurring their ineffectiveness in most scenarios. To remedy the problem, we design the fixed-length minimum-communication-energy (F-MCE) code and variable-length minimum-communication-energy (V-MCE) code to minimize the total energy consumption at the transmitter and receiver for point-to-point communication in OOK-based WNSNs. Specifically, the code design problems are formulated as integer nonlinear programming (INLP) problems, and the F-MCE and V-MCE codes are obtained by solving the INLP problems. The F-MCE and V-MCE codes are applicable in more scenarios than the LW code. Extensive experimental results show that the V-MCE always outperforms the LW code regarding the energy saving, while the F-MCE code achieves energy saving no less than that of the existing LW codes.

IEEE 802.11 infrastructure wireless local area network (WLAN) is increasingly popular, in which access points (APs) are applied. In a WLAN with an AP connected to the Internet, the communication between any two nodes is relayed by the AP, i.e., the AP serves all the nodes in the WLAN, which degrades throughput. In this paper, we propose a novel network coding scheme called MPOF that is able to encode multiple packets from different data flows and take data rates of links into account so that throughput is improved.

It is significant to repair (or rebuilt) the damaged storage node in the distributed storage system (DSS). Recently, the network coding technique is applied to DSS to decrease the repair bandwidth. In practice, in addition to repair bandwidth, repair time is usually one of the key concerns in the DSS, which has not been investigated in the existing DSSs in which network coding is applied. In this paper, we propose the minimum-time repair (MTR) scheme, which is able to achieve the lower bound of repair time while considerably decreasing repair bandwidth. Numerical results show that MTR outperforms some available node recovery approaches in terms of repair time.

Wireless nano-sensor networks (WNSNs), which consist of nano-sensors a few hundred nanometers in size with the capability to detect and sense new types of events in nano-scale, are promising for many unique applications such as air pollution surveillance. The nano-sensors of WNSNs are highly energy-constrained, which makes it essential to develop energy-efficient communication techniques in such networks. In this paper, we focus on WNSNs employing on-off keying (OOK) modulation, whereby transmission energy minimization corresponds to the minimization of average codeword weight (ACW). We formulate an integer nonlinear programming problem to construct prefix-free codes with minimum ACW under the constraint of average codeword length (ACL) so as to minimize the transmission energy consumption while guaranteeing the throughput larger than a preset desired value. In addition, two efficient algorithms, called binary tree based weight decreasing (BT-WD) algorithm and binary tree based length decreasing (BT-LD) algorithm, are presented for constructing low-ACW prefix-free codes. The effectiveness of the proposed algorithms is verified through simulations and comparisons with the exhaustive search method. Compared with the available fixed-length low-weight codes, the designed prefix-free variable-length codes allow us to not only control the throughput more flexibly but also achieve lower transmission energy consumption in the scenarios with low or medium bit error rates.

Wireless nanosensor networks (WNSNs), which consist of a lot of nanosensors with size of just a few hundred nanometers and are able to detect and sense new types of events at the nanoscale, are promising for a lot of unique applications like intrabody drug delivery systems, air pollution surveillance, etc. One important feature of WNSNs is that the nanosensors are highly energy-constrained, which makes it essential to develop energy efficient protocols for different layers of such networks. This paper focuses on a WNSN with on-off keying (OOK) modulation and explores the problem of transmission energy minimization in it. We first propose a general minimum transmission energy (MTE) coding scheme, which maps m-bit symbols into n-bit codewords with the least number of high-bits and thus results in the lowest energy consumption per symbol for any given m and n. We further determine the optimal setting of symbol length m and codeword length n in the MTE coding scheme so as to achieve the minimum energy consumption per data bit, which serves as the lower bound of transmission energy consumption in such WNSNs. Numerical results are provided to demonstrate the efficiency of the MTE coding scheme.

In 6LoWPAN protocol, in order to deliver an IPv6 packet in the IEEE 802.15.4 based wireless personal area network (WPAN), an IPv6 packet is divided into multiple fragments, with each being incorporated in an IEEE 802.15.4 MAC frame, such that the size of each frame is no more than the Maximum Transmission Unit (MTU) of the frame. Usually, retransmission is used to reliably deliver an IPv6 packet to the destination node in the WPAN with lossy links. To improve the end-to-end packet delivery rate (PDR) of the mesh-under routing (MUR) presented in the 6LoWPAN protocol, we present a network coding based mesh-under routing (NC-MUR) scheme. The main idea underlying NC-MUR is that, the source node generates an encoded frame in addition to the M non-coded frames derived from the IPv6 packet, and then the M+1 fragments are delivered to the destination so that the destination can recover the IPv6 packet so long as any M fragments among them are received. Theoretical analyses show that NC-MUR outperforms MUR in terms of PDR and energy consumption in the cases where fragment error rate is moderate or high.

Reliable multicast, the lossless dissemination of data from one sender to a group of receivers, has a wide range of important applications like software update and dissemination of stock quotes. Recently, network coding has been applied to the reliable multicast in wireless networks, where the sender encodes multiple lost packets together into one packet and uses a single retransmission to potentially recover multiple packet losses, resulting in a significant reduction of band-width consumption. In this paper, we provide a review of recent research works in this area, examine their advantages and limitations, and also present some open research challenges need to be addressed in the future.

A new packet-forwarding architecture, COPE [1], was proposed recently to demonstrate that by properly exploiting the network coding and physical-layer broadcast properties, the throughput of multihop wireless networks may be significantly improved. However, the theoretical framework is not available yet for a rigorous throughput study of COPE. As the first step toward this direction, in this paper we conduct an analytical analysis on coding opportunities that COPE can create in a multihop wireless network. For a given node, we first derive the probability density function (pdf) of overhearing probability between its two neighbor nodes. Then, based on the pdf of overhearing probability we further analyze the probability of encoding multiple packets together at the node. This work not only provides a framework for the node-level performance analysis of COPE but also lays a foundation for the further network-level performance analysis of COPE.

It is anticipated that a large amount of multicast traffic needs to be supported in future communication networks. The network coding technique proposed recently is promising for establishing multicast connections with a significantly lower bandwidth requirement than that of traditional Steiner-tree-based multicast connections. How to design multicast network topologies with the consideration of efficiently supporting multicast by the network coding technique becomes an important issue now. It is notable, however, that the conventional algorithms for network topology design are mainly unicast-oriented, and they cannot be adopted directly for the efficient topology design of network-coding-based multicast networks by simply treating each multicast as multiple unicasts. In this paper, we consider for the first time the novel topology design problem of network-coding-based multicast networks. Based on the characteristics of multicast and network coding, we first formulate this problem as a mixed-integer nonlinear programming problem, which is NP-hard, and then propose two heuristic algorithms for it. The effectiveness of our heuristics is verified through simulation and comparison with the exhaustive search method. We demonstrate in this paper that, in the topology design of multicast networks, adopting the network coding technique to support multicast transmissions can significantly reduce the overall topology cost as compared to conventional unicast-oriented design and the Steiner-tree-based design.

Current implementations of multi-hop wireless networks suffer from a severe throughput limitation and do not scale well with an increasing number of nodes. A promising architecture COPE [12], which exploits the physical-layer broadcast property and network coding technique, was recently proposed to significantly improve the throughput of multi-hop wireless networks. In this paper, we will improve the packet coding scheme in COPE to further reduce the number of bytes transmitted by a network node for forwarding its incoming packets to the respective neighbors. We first propose a more general packet coding framework, which covers the one in COPE as a special case and can offer us more coding opportunities. We then formulate the optimal packet coding problem under this general coding framework as an integer programming problem, and prove that it is NP-complete. Finally, we present an efficient algorithm to find the optimal coding solution for the proposed general packet coding framework.

Future communication networks should be designed with the consideration of efficiently supporting intensive multicast applications. Network coding technique proposed recently is promising for implementing multicast transmissions with low bandwidth requirement. This paper concerns the novel topology design problem of network coding-based multicast networks, and proposes an improved heuristic algorithm for it by adopting a new cost efficiency (CE) metric for link removal and introducing a local optimization in the design process. The effectiveness of the new heuristic is demonstrated through extensive simulations and comparisons with both the available heuristic and the exhaustive search method. The results in this paper also clearly indicate that applying the novel network coding technique in a multicast network can significantly reduce the overall topology cost.

The future communication networks should have a good capability to support the rapidly growing multicast applications. It is notable, however, that the conventional algorithms for network design are mainly unicast-oriented, and they can not be adopted directly for the efficient topology design of multicast-capable networks by simply treating each multicast as multiple unicasts. The network coding technique proposed recently has the potential to efficiently support multicast transmissions with lower bandwidth requirement. In this paper we study the network-coding based network topology design problem with the consideration of efficiently supporting multicast traffic. Based on the characteristics of multicast and network coding, we first formulate this problem as a nonlinear integer programming problem, which is NP-hard. We then propose a heuristic algorithm for it. The efficiency of our algorithm is demonstrated by extensive simulation results under different traffic patterns. We conclude that our network-coding based topology design algorithm can be used to design multicast-capable networks with significantly lower cost than that of conventional unicast-oriented algorithms.