Leveraging breakthroughs in wireless power transfer technology, deploying Mobile Chargers (MCs) to charge sensor nodes can significantly prolong the operational lifetime of wireless sensor networks. Directional Mobile Chargers (DMCs), which integrate directional antennas, can focus radio frequency energy along designated directions, thereby boosting charging efficiency. Most existing works assume that DMCs emit a single energy beam; however, in practice, some DMC platforms can generate multiple sectorial- like energy beams, enabling a novel Multi-Beam DMC (MB-DMC)-based charging paradigm for Wireless Rechargeable Sensor Networks (WRSNs). This paradigm introduces a critical challenge: determining a minimum-size antenna direction set for the DMC while assuring that it is functionally equivalent to the infinite$[0,2\pi )$direction space in maintaining charging schedules with minimal energy loss. This paper systematically investigates the Multi-Sectorial-Beam DMC Charging Scheduling (MSBDCS) problem in MB-DMC-enhanced WRSNs (MB-WRSNs) and proves its NP-hardness. Furthermore, to address this challenge, we propose a Multi-Sectorial Beam Antenna Direction Selection (MSBADS) algorithm by efficiently exploiting geometric properties of local node set distribution, and theoretically prove its optimality in generating a minimum-size charging direction set that is functionally equivalent to the$[0,2\pi )$space. By integrating MSBADS into a four-step charging scheduling framework, we develop our MSBADS-based four-step Charging Scheduling (MSB-4S) algorithm to address the MSBDCS problem in MB-WRSNs. Extensive simulations and testbed experiments demonstrate that MSB-4S outperforms state-of-the-art baseline schemes by over 22% in energy efficiency improvement and more than 14% in scheduling time reduction.

In wireless sensor networks (WSNs), enabling nodes to harvest energy from the environment and facilitating energy sharing among nodes through wireless power transfer (WPT) technology, known as energy cooperation, can alleviate energy scarcity issues and effectively prolong the lifespan of WSNs. Although previous research has investigated various forms of energy cooperation, recent developments have underscored the potential of Multicast Energy Cooperation (M-EC) in supporting efficient multinode energy sharing. This approach leverages the broadcast nature of wireless signals, potentially offering greater efficiency compared to traditional point-to-point style Unicast Energy Cooperation (U-EC). In this article, We focus on the M-EC Assisted Data Collection paradigm for energy harvesting-WSNs (EH-WSNs) and investigate the underlying M-EC assisted data collection scheduling (MECADCS) problem, aiming to minimize the data collection completion time by jointly optimizing the schedule decisions for energy cooperation and data collection. We formulate the MECADCS problem as a mixed integer nonlinear programming (MINLP) problem and establish its NP-hardness. We also simplified the MECADCS problem into a mixed integer linear programming (MILP) formulation via piecewise linear approximation, yet solving it using existing mature MILP solvers is still computationally expensive. To promptly return good solutions, we propose an efficient greedy-based data transmission scheduling algorithm (GDTS),heuristically determines energy cooperation and data transmission schedules and achieves a computational speedup of$10^{4}$times compared to exact solvers. Simulation results demonstrate that GDTS significantly reduces the data collection completion time compared to both algorithms without energy cooperation and those utilizing U-EC.

Mobile edge computing enhances the performance of low-capability end devices by offloading tasks to nearby edge servers, enabling timely responses for delay-sensitive, computation-intensive tasks. However, the rapid and continuous growth of such tasks may soon exceed the capacity of the initially deployed edge server system. This calls for deploying new servers while re-using deployed ones for saving investment, leading to the emergence of a novel paradigm named as Edge Server Expansion Deployment (ESED) here. For this ESED paradigm, aiming to simultaneously minimize the average access delay between end devices and edge servers and the workload deviation among servers, we studied the Budget-Constrained ESED (BC-ESED) problem under the condition of a specified budget constraint. We formulate the problem as a multi-objective optimization problem and prove its NP-hardness. We then propose an algorithm, by combining Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), named GA-PSO. GA-PSO utilizes a four-step iteration framework of selection, crossover, mutation, and correction, where a novel three-party globalbest-localbest-individual crossover operation, inspired by PSO, complements the traditional two-party crossover operation in the crossover step. The convergence and time complexity of GA-PSO are established and analyzed. Simulation results, based on realistic network topologies and workload data from the Shanghai Telecom base station dataset, demonstrate that GA-PSO outperforms other benchmark algorithms in terms of average access delay and workload deviation.

This paper introduces a novel robotic path planning method that integrates Recursive Segmentation Point Migration Optimization (RSPMO) with Progressive Inverse Kinematics (PIK). RSPMO continuously refines the path by detecting collisions on a direct trajectory, adjusting segmentation points to avoid obstacles, and simplifying the path by removing redundant points, thereby rapidly generating an optimal and efficient trajectory for the manipulator’s end-effector. PIK utilizes second derivative information to iteratively compute joint angles, employing dynamic interpolation and damping factor adjustments near anomalous configurations to enhance system stability. Extensive experimental validation confirms the efficacy of both methods. In a multi-obstacle 2D scenario, RSPMO can plan paths of 789.4516 units in merely 0.0170 seconds, significantly outperforming comparative methods. Additionally, PIK achieves robust performance, with average iteration times of 0.0041 seconds near singular points over 8.45 iterations. Real-world tests on a 6-DOF robotic arm further confirm that the integration of RSPMO and PIK provides an efficient solution for path planning in complex scenarios.

Adaptive traffic signal control (ATSC) is a critical component of intelligent transportation systems, with the capability to significantly alleviate urban traffic congestion. Reinforcement learning (RL)-based methods have demonstrated promising performance in achieving ATSC. However, due to the excessive trust of RL policies and the ineffectiveness of traffic movement representation, the existing methods are prone to making unreasonable policies. Such policies originate from instability in convergence or entrapment in local optima during training, and are reflected in poor decision-making performance. Therefore, this paper proposes a novel Bayesian Critique-Tune-based reinforcement learning with Adaptive Pressure (BCT-APLight) for multi-intersection signal control. In BCT-APLight, the Critique-Tune (CT) framework, a two-layer Bayesian structure is designed to refine the excessive trust of RL policies. Specifically, the Bayesian inference-based Critique Layer (CL) provides effective evaluations of the credibility of policies. When these evaluations are negative, the Bayesian decision-based Tune Layer (TL) fine-tunes the policies by minimizing the posterior risks. Meanwhile, an attention-based Adaptive Pressure (AP) mechanism is designed to effectively weight the effect of upstream lanes, thereby enhancing the rationality of traffic movement representation within the network. Extensive experiments conducted with a simulator over multi-intersection demonstrate that BCT-APLight is superior to other state-of-the-art (SOTA) methods on nine real-world datasets. Specifically, BCT-APLight decreases average waiting time (AWT) by$\boldsymbol{12.92\%}$over all tested datasets on average. Codes are open-sourced.

For a simultaneous wireless information and power transfer-enabled single amplify-and-forward relay communication system, we investigate the relay’s transmission power management problem for maximizing the long-term average (LTA) system communication capacity while guaranteeing some constraints on LTA battery energy for the relay and destination. Because LTA expressions are involved in this problem, the Lyapunov optimization framework is adopted. We first formulate the problem, and then we construct two virtual queues for the battery energy constraints of the relay and the destination. Next, by constructing a drift-plus-penalty function that combines the original objective function and the LTA constraints, the original problem is transformed into a global optimization problem without the LTA constraints. The new global optimization problem is further transformed into a slotwise local optimization problem by replacing the objective function with an upper bound expression involving only the current time slot. We propose an algorithm named LTA communication capacity optimization based on Lyapunov optimization (LTCOL), which runs online to determine the relay’s transmission power slot by slot through solving slotwise local problems. Some important properties of LTCOL, including guaranteed LTA constraints and an assured controllable lower bound on system communication capacity, are provided and proved. Simulation results demonstrate the superiority of LTCOL over existing algorithms.

By predicting a personalized ranking on a set of items, item recommendation helps users determine the information they need. While optimizing a ranking-focused loss is more in line with the objectives of item recommendation, previous studies have indicated that current sampling-based ranking methods don't always surpass non-sampling ones. This is because it is either inefficient to sample a pool of representative negatives for better generalization or challenging to gauge their contributions to ranking-focused losses accurately. To this end, we propose a novel weighted ranking loss, which weights each negative with the softmax probability based on model's predictive score. Our theoretical analysis suggests that optimizing this loss boosts the normalized discounted cumulative gain. Furthermore, it appears that this loss acts as an approximate analytic solution for adversarial training of personalized ranking. To improve optimization efficiency, we approximate the weighted ranking loss with self-normalized importance sampling and show that the loss has good generalization properties. To improve generalization, we further develop efficient cluster-based negative samplers based on clustering over item vectors, to decrease approximation error caused by the divergence between the proposal and the target distribution. Comprehensive evaluations on real-world datasets show that our methods remarkably outperform leading item recommendation algorithms.

For the capability of concentrating radiation energy along a direction, using Directional Mobile Chargers (DMCs) for charging the nodes in a Wireless Rechargeable Sensor Network (WRSN) via wireless power transfer has become a research hotspot. However, existing research neglect cooperative Energy ReDistribution (ERD) among nodes, handering energy charging efficiency. This motivated us to focus on the scenario of charging a WRSN using a DMC and address the underlying ERD-Assisted Directional Charging Schedule (ERADCS) problem. This problem involves determining a charging schedule with minimal energy loss and minimum time span. We first proved the NP-hardness of ERADCS and then proposed a Directional Charging Schedule algorithm Based on Greedy Strategy (DCSBGS) to solve it. In DCSBGS, to tackle the infinite charging directions, we created cMFRDS algorithm to determine a minimum-size representative direction set functionally equivalent to the original infinite directions, and proved its optimality. Subsequently, we assumed Virtual Mobile Chargers (VMCs) fixed to the representative directions, transformed ERADCS into a charging schedule problem using the VMCs, solving it using a two-step framework. We also established key properties of DCSBGS and its sub-algorithms. Furthermore, we extended DCSBGS for ERADCS involving a DMC with multiple charging beams. Our simulation results validate the superiority of DCSBGS over other typical algorithms.

Abstract The spatial and temporal uncertainty caused by large propagation delays is a fundamental feature of Underwater Acoustic Networks (UWANs), which seriously affects the performance of the UWANs and also brings challenges to the design of MAC protocols. In this paper, we develop an adaptive MAC protocol based on deep reinforcement learning for UWANs, called ARL-MAC protocol, to intelligently allocate time slots for nodes. Firstly, we design a reward mechanism based on the idea of Time-Domain Interference Alignment (TDIA). We determine the reward according to the combination of the node action and the feedback corresponding to the action. Then, we propose a flexible training mechanism to deal with the ever-changing underwater environment, which improves the fairness of time slot allocation. In addition, we introduce the Deep Recurrent Q-Network (DRQN) algorithm to solve the partially observable information issue. Finally, we evaluate the ARL-MAC protocol with the different number of nodes and changing network environment. Simulation results reveal that the ARL-MAC protocol outperforms other MAC protocols for UWANs in terms of throughput, collision rate and service fairness.

The use of mobile chargers (MCs) to charge the nodes in wireless rechargeable sensor networks via wireless power transfer (WPT) has attracted much research effort. Existing works mostly concentrate on path planning whereas neglecting the opportunities to improve charging coverage and efficiency by exploiting the energy redistribution (ERD) process among nodes and an MC’s capability of charging multiple nodes simultaneously via WPT. To exploit such opportunities, we study the underlying ERD-assisted MC charge scheduling (ERAMCCS) problem, i.e., to find a charging schedule satisfying the nodes’ energy demands with minimum energy loss and minimum time span. After proving that the problem is NP-hard, we propose a charge scheduling algorithm based on the greedy idea (CSBGI), which provides a solution by decoupling the problem into two subproblems: 1) ERAMCCS-Energy and 2) ERAMCCS-Time, to minimize the energy loss and the time span, respectively. By partitioning the energy loss into transmission energy loss and moving energy loss, we solve the ERAMCCS-Energy problem by minimizing the two parts, respectively, by formulating and solving some linear programming problems and traveling salesman problem problems based on the charging position set. The charging position set is iteratively refined by identifying and removing redundant charging positions. For the ERAMCCS-Time problem, concurrent energy transmission opportunities are exploited to try to minimize the time span of the schedule. We demonstrate some key properties of CSBGI, such as its approximation ratio in terms of energy loss and its time complexity. Testbed experiments and numerical simulations confirm the superiority of CSBGI over typical algorithms.

In this paper, we propose a novel hierarchical radio-frequency identification (RFID) tag-recognition method based on blind source separation (BSS), graph-based automatic modulation classification (AMC), and direct-sequence spread-spectrum (DSSS). In our proposed method, RFID tags can be modulated using different modulation schemes according to different scenarios (e.g., different users or different tag devices). For each modulation scheme, the direct-sequence spread-spectrum strategy is employed to allow simultaneous transmissions of multiple commands. In the signal separation phase, BSS is employed to separate different transmitted signals. Then in the first hierarchy of the recognition phase, different modulation types are adopted to distinguish different users, the graph-based AMC is built upon the periodicity of the modulated signals: the cyclic spectrum of the received signal is established; the graph representation is then constructed according to the cyclic spectrum. Ultimately, robust features are extracted from the graph representation. In the second hierarchy of the recognition phase, the DSSS scheme is utilized to differentiate the control or sensed data carried by individual tags; the signature sequence set with low cross-correlations can be generated from Kasami sequences. In the third hierarchy of the recognition phase, the information data are thus spread by these signature sequences. In our proposed new RFID framework, multiple tags can transmit signals simultaneously in the same frequency band where each tag signal can still be separated and identified and its carried information can be recovered. Monte Carlo simulation results demonstrate the promising performance of our proposed new RFID scheme.

Proper deployment of roadside units (RSUs) is of crucial importance to vehicular ad hoc networks (VANETs). However, our understandings to the simple one-dimensional RSU Deployment (D1RD) problem with nonuniform profit density is still seriously limited. In this paper, we analyze the D1RD problem and try to design optimal algorithms for it. We first analyze the properties of the optimal solutions of the D1RD problem involving a single RSU, and then extend to multiple RSUs. Next, we propose an efficient technique named Dynamic Limiting (DynLim), which reduces the solution search space size considerably by adjusting search space limits dynamically. Finally, an optimal algorithm named OptDynLim is proposed based on the DynLim technique, and its optimality is proved. Numerical simulations validate the correctness of our analyzes and show that DynLim can usually reduce solution search space size by more than 99%.

The capacity of IEEE 802.11p communication in vehicular ad hoc networks (VANETs) is widely sensitive to the tradeoff between control channel (CCH) and service channels (SCHs), which is particularly obvious in the different traffic flow condition. This paper proposes a hybrid multichannel scheduling algorithm with FPGA and traffic flow forecasting based on Kalman Filter (HMS-FFK) according to the extended SCH access mechanism mentioned in IEEE 1609.4 protocol. In HMS-FFK, a Random CCH Transmission Request Probability is defined to describe the CCH message congestion probability according to the local traffic flow density. Then, a hardware prototype of MAC sublayer management entities (MLME) based on HMS-FFK scheduling (MLME-HMS) is designed with FPGA, which is flexible to be integrated in the 802.11p communication system by the PCI interface. Theoretical analysis and simulation results show that the proposed scheme and hardware prototype of MLME are able to help IEEE 1609.4 MAC to optimize the throughput of SCHs and reduce the transmission delay of CCH in the different traffic flow condition.

A novel chaos‐based colour image encryption algorithm is proposed, which adopts a one‐time key mechanism based on message‐digest algorithm 5 ( MD5 ) value of the input plain image. The algorithm combines several key technologies including fractional Fourier transform (FrFT), MD5 , and global scrambling. Using fast digital discrete FrFT, the algorithm develops the complex data manipulating potentials of FrFT efficiently; meanwhile, keeps the size of the cipher image un‐changed, thus eliminates the requirement on double storage space to store complex values of cipher image. Exploiting the intrinsic robustness of FrFT, the algorithm achieves high robustness to pixel errors and noise attacks. Experimental results show that the algorithm achieves better pixel change rate and unified average change intensity, and is efficient, effective, and robust to attacks.

Modeling images/videos with covariance matrices has attracted increasing attentions in various vision tasks, especially in visual classification. For covariances-based visual classification, measuring the distances between covariances is one of the key issues and has been studied for decades. Since the space of covariances is a Riemannian manifold, the geometrical structure of covariances should be favorably considered when designing distance metrics. Although this problem has been widely studied, designing an effective and efficient metric between high-dimensional covariances (HDCOV) for scalable classification is still an open problem. In this paper, we present an information geometry-based distance (IGBD) to tackle this challenge from the perspective of information geometry. Our idea is based on the fact that each covariance can be viewed as a zero-mean Gaussian distribution, and thus the distances between covariances are measured by those between the corresponding Gaussian distributions. The core of our method is to project each distribution, in the form of a set of random samples, to a vector on the tangent space of a common, known distribution on the statistical manifold, based on Fisher information metric and maximum likelihood method. On the tangent space, the Euclidean norm can be used to measure the distances between those sets of projection vectors (or equivalently distributions). The proposed IGBD for HDCOV is computationally efficient and easily combined with a linear support vector machine, suitable for scalable visual classification. The experiments are conducted on various kinds and sizes of benchmarks, and results show the proposed method is efficient and the combination of HDCOV can achieve very competitive performance.

Abstract Although there have been many interesting works on outage performance analysis of cognitive AF relay networks, we have not found works taking into consideration all the following issues: multiple primary users (PUs), the existence of the direct link from secondary user (SU) source to SU destination, non‐identical, independent Rayleigh‐fading channels, non‐identical interference power limits of PUs, and non‐identical noise powers in signals. Additionally, in outage performance analysis for such networks, the correlation issue, which results from the channel gain of interference links from the SU nodes to the PU, requires elaborate treatments. Hence, analyzing outage performance of non‐identical‐parameter networks (where all channels are fully non‐identical Rayleigh‐fading channels, the PUs have different interference power limits, and received signals have different noise powers) from the beginning is highly complicated. To overcome this problem, we conduct the analysis in two steps. In the first step, expressions of both exact and asymptotic outage probability of identical‐parameter cognitive AF relay networks (where all channels are fully non‐identical Rayleigh‐fading channels but all other parameters are identical) are obtained. Then in the second step, we propose a method for transforming a network with all non‐identical parameters into a new identical‐parameter network, meanwhile guaranteeing that outage performance of the two networks before and after the transformation are the same. Hence, OP of the original non‐identical‐parameter network can be obtained indirectly by using the analysis results obtained in the first step. Our analysis results are validated through numerical simulations. The effects of the number of PUs and the diversity level of channel parameters (which means the range of the channel parameter values) are also inspected by simulations. The results show that taking these factors into consideration is of key importance in obtaining a more accurate estimation of outage performance of such networks. Copyright © 2014 John Wiley & Sons, Ltd.

ABSTRACT Solving wireless packet retransmission problems (WPRTPs) using network coding (NC) approach is increasingly attracting research efforts. However, existing researches are almost all focused on solutions in Galois field GF(2), and consequently, the solutions found by these schemes are usually less optimal. In this paper, we focus on optimal NC‐based scheme for perfect WPRTPs (P‐WPRTPs) where, with respect to each receiver, a packet is either requested by or already known to it. The number of retransmitted packets in optimal NC‐based solutions to P‐WPRTPs is firstly analyzed and proved. Then, random network coding‐based optimal scheme (RNCOPT) is proposed for P‐WRPTPs. RNCOPT is optimal in the sense that it guarantees to obtain a valid solution with minimum number of packet retransmissions. Furthermore, in RNCOPT, each coding vector is generated using a publicly known pseudorandom function with a randomly selected seed. The seed, instead of the coding vector, is used as decoding information to be retransmitted together with the coded packet. Thus, packet overhead of RNCOPT is reduced further. Extensive simulations show that RNCOPT distinctively outperforms some previous typical schemes for P‐WPRTPs in saving the number of retransmitted packets. Copyright © 2011 John Wiley & Sons, Ltd.

Based on the non-cooperative power control game (NPG) algorithm, the utility and pricing function are analyzed, then the combination pricing function is proposed and power control game algorithm suitable for cognitive radio (CR) networks is realized, the existence and uniqueness of the Nash equilibrium are proved for proposed algorithm. Simulation results show that the proposed algorithm can balance the interests of the CR users themselves, reduce the whole system of interference and improve the performance of cognitive radio system.