Online and coverage aware charging method in Wireless Rechargeable Sensor Networks

In a wireless sensor network, the sensing and data transmission for sensors will cause energy depletion, which will lead to the inability to complete the tasks. To solve this problem, wireless rechargeable sensor networks (WRSNs) have been developed to extend the lifetime of the entire network. In WRSNs, a mobile charging robot (MR) is responsible for wireless charging each sensor battery and collecting sensory data from the sensor simultaneously. Thereby, MR needs to traverse along a designed path for all sensors in the WRSNs. In this paper, dual-side charging strategies are proposed for MR traversal planning, which minimize the MR traversal path length, energy consumption, and completion time. Based on MR dual-side charging, neighboring sensors in both sides of a designated path can be wirelessly charged by MR and sensory data sent to MR simultaneously. The constructed path is based on the power diagram according to the remaining power of sensors and distances among sensors in a WRSN. While the power diagram is built, charging strategies with dual-side charging capability are determined accordingly. In addition, a clustering-based approach is proposed to improve minimizing MR moving total distance, saving charging energy and total completion time in a round. Moreover, integrated strategies that apply a clustering-based approach on the dual-side charging strategies are presented in WRSNs. The simulation results show that, no matter with or without clustering, the performances of proposed strategies outperform the baseline strategies in three respects, energy saving, total distance reduced, and completion time reduced for MR in WSRNs.

Battery power limitation of sensor nodes (SNs) is a major challenge for wireless sensor networks (WSNs) which affects network survival. Thus, optimizing the energy consumption of the SNs as well as increasing the lifetime of the SNs and thus, extending the lifetime of WSNs are of crucial importance in these types of networks. Mobile chargers (MCs) and wireless power transfer (WPT) technologies have played an important long role in WSNs, and much research has been done on how to use the MC to enhance the performance of WSNs in recent decades. In this paper, we first review the application of MCs and WPT technologies in WSNs. Then, forwarding issues the MC has been considered in the role of power transmitter in WSNs and the existing approaches are categorized, with the purposes and limitations of MC dispatching studied. Then an overview of the existing articles is presented and to better understand the contents, tables and figures are offered that summarize the existing methods. We examine them in different dimensions such as advantages and disadvantages etc. Finally, the future prospects of MC are discussed.

Wireless rechargeable sensor network (WRSN) has become the most effective solution to the energy problem in wireless sensor networks (WSNs) by introducing mobile chargers (MCs) to replenish energy for energy‐starved sensor nodes. In the studies of WRSN, there has been growing interest in using lightweight unmanned aerial vehicles (UAVs) as MCs to overcome geographical constraints. Recently, automatic landing pads (PADs) have been introduced in the UAV‐based WRSN, enabling the UAV to automatically replenish energy during the charging flight. Consequently, the UAV’s service range is enlarged, and the UAV can now perform large‐scale charging services. In this paper, we investigate the periodic charging scheduling problem in this novel form of WRSN with the goal of minimizing the charging delay. We present the problem’s definition and model and prove it is NP‐hard. To address this problem, we propose two periodic charging schemes: SNBC (sensor‐node‐based charging scheme) and PBC (PAD‐based charging scheme). We extensively simulated our proposed schemes in a UAV‐based WRSN with PADs, evaluating them based on two key metrics: charging delay and UAV replenishment frequency. The results indicate the efficiency of our approaches. SNBC averaged a 7.2 × 104 s charging delay, while PBC outperformed it with a 14.01% reduction in charging delay and a 13.66% decrease in replenishment frequency.

Advances in wireless charging technology give great new opportunities for extending the lifetime of a wireless sensor network (WSN) which is an important infrastructure of IoTs. However, the existing greedy algorithms lacked learning from the experiences of energy dissipation trends. Unlike the existing studies, this paper proposes a reinforcement learning approach, called RLR, for mobile charger to learn the trends of WSNs, including the energy consumption of the sensors, the recharging cost as well as the coverage benefit, aiming to maximize the coverage contribution of the recharged WSN. The proposed RLR mainly consists of three modules, including Sensor Energy Management, Charger Location Update and Charger Reinforcement Learning modules. In the Sensor Energy Management module, each sensor manages its energy and calculates its threshold for the recharging request in a distributed manner. The Charger Location Update module adopts the quorum system to ensure effective communication between sensors and the mobile charger. Meanwhile, the Charger Reinforcement Learning module employs attraction rewards to reflect the coverage benefit and penalties of waiting time raised due to charger movement and recharging other sensors. As a result, the charger accumulates the learning experiences from the Q-Table such that it is able to execute the appropriate actions of charging or moving in a manner of state management. Performance results show that the proposed RLR outperforms the existing recharging mechanisms in terms of charging waiting time of sensors, the energy usage efficiency of the mobile charger, as well as the coverage contribution of the given sensor network.

The limited energy of the sensor nodes (SNs) has always been a major hindrance for wireless sensor networks (WSNs). Wireless charging of the SNs is a promising alternative to solve the energy constraint problem in WSNs. The charging paradigm with such rechargeable SNs is known as wireless rechargeable sensor network (WRSN). While plenty research efforts have been made to improve the charging performance in a WRSN, little has been done to address the scheduling problem of the MCs having limited capacity. By scheduling problem of the MCs, we want to mean that how efficiently the capacity of multiple MCs can be utilized so that minimum number of MCs are used in order to fulfill the energy demands of the SNs. To fill this research lacuna, we propose three novel on-demand charging schemes which are termed as Pcharge, Bcharge, and Fcharge in this paper. The effectiveness of the proposed schemes is validated by simulation results which reveal that the proposed schemes can achieve promising performance in terms of tour length and capacity utilization of the MCs as compared to a state-of-the-art scheme.

The rapid development of wireless power transfer technology brings forth innovative vehicle energy solutions and breakthroughs utilizing wireless sensor networks (WSNs). In most existing schemes, wireless rechargeable sensor networks (WRSNs) are generally equipped with one or more wireless charging vehicles (vehicles) to serve sensor nodes (SNs). These schemes solve the energy issue to some extent; however, due to off-road and speed limitations of vehicles, some SNs still cannot be charged in time, negatively affecting the lifetime of the networks. Our work proposes a new WRSN model equipped with one wireless charging drone (drone) with a constrained flight distance coupled with several wireless charging pads (pads) deployed to charge the drone when it cannot reach the subsequent stop. Our model solves this charging issues effectively and overcomes energy capacity limitations of the drone. Thus, a wireless charging pad deployment problem is formulated, which aims to apply the minimum number of pads so that at least one feasible routing path can be established for the drone to reach every SN in a given WRSN. Four feasible heuristics, three based on graph theory and one on geometry, are proposed for this problem. In addition, a novel drone scheduling algorithm, the shortest multi-hop path algorithm, is developed for the drone to serve charging requests with the assistance of pads. We examine the proposed schemes through extensive simulations. The results compare and demonstrate the effectiveness of the proposed schemes in terms of network density, region size and maximum flight distance.

The growing awareness of the strategic value of service along with higher product performance expectations has directed many industries to adopt service centric competitive strategies in managing their supply chains. Service innovation based on currently available advanced technologies has become critical in improving enterprises’ competitive strengths. Wireless sensor network (WSN) technology, which can provide a mobile, scalable, and reliable monitoring solution, is emerging as a tool for service innovation in supply chain management. This paper provides an overview of wireless sensor network technology and discusses how it can benefit modern industries. The fundamental theories of WSN are introduced as well as an overview of the development. The impact of information technologies on supply chain management and service innovation is then briefly discussed. Much emphasis is placed on the feasibility, procedures, and critical challenges of implementing WSN in supply chain management innovation. Current and future applications of WSN are also provided, followed by a case study demonstrating the application potentials of WSN for service innovation in the healthcare industry.

In this paper, we consider the problem of scheduling mobile chargers (MCs) in an on-demand way to maximize the covering utility (CU) in wireless rechargeable sensor networks (WRSNs), while nearly all previous related works assume the MCs move along predefined paths with perfect priori information. And the CU is defined to quantify the effectiveness of event monitoring. We propose three heuristics for this problem after proving its NP-Completeness. Finally we evaluate our solutions through extensive trace-driven simulations.KeywordsOn-demand chargingEvent monitoring

The breakthrough of wireless energy transmission (WET) technology has greatly promoted the wireless rechargeable sensor networks (WRSNs). A promising method to overcome the energy constraint problem in WRSNs is mobile charging by employing a mobile charger to charge sensors via WET. Recently, more and more studies have been conducted for mobile charging scheduling under dynamic charging environments, ignoring the consideration of the joint charging sequence scheduling and charging ratio control (JSSRC) optimal design. This paper will propose a novel attention-shared multi-agent actor–critic-based deep reinforcement learning approach for JSSRC (AMADRL-JSSRC). In AMADRL-JSSRC, we employ two heterogeneous agents named charging sequence scheduler and charging ratio controller with an independent actor network and critic network. Meanwhile, we design the reward function for them, respectively, by considering the tour length and the number of dead sensors. The AMADRL-JSSRC trains decentralized policies in multi-agent environments, using a centralized computing critic network to share an attention mechanism, and it selects relevant policy information for each agent at every charging decision. Simulation results demonstrate that the proposed AMADRL-JSSRC can efficiently prolong the lifetime of the network and reduce the number of death sensors compared with the baseline algorithms.

The energy problem is one of the most important challenges in the application of sensor networks. With the development of wireless charging technology and intelligent mobile charger (MC), the energy problem can be solved by the wireless charging strategy. In the practical application of wireless rechargeable sensor networks (WRSNs), the energy consumption rate of nodes is dynamically changed due to many uncertainties, such as the death and different transmission tasks of sensor nodes. However, existing works focus on on-demand schemes, which not fully consider real-time global charging scheduling. In this article, a novel dynamic charging scheme (DCS) in WRSN based on the actor-critic reinforcement learning (ACRL) algorithm is proposed. In the ACRL, we introduce gated recurrent units (GRUs) to capture the relationships of charging actions in time sequence. Using the actor network with one GRU layer, we can pick up an optimal or near-optimal sensor node from candidates as the next charging target more quickly and speed up the training of the model. Meanwhile, we take the tour length and the number of dead nodes as the reward signal. Actor and critic networks are updated by the error criterion function of R and V. Compared with current on-demand charging scheduling algorithms, extensive simulations show that the proposed ACRL algorithm surpasses heuristic algorithms, such as the Greedy, DP, nearest job next with preemption, and TSCA in the average lifetime and tour length, especially against the size and complexity increasing of WRSNs.

In Wireless Sensor Networks (WSNs) localization -the assigning of sufficiently accurate positions to nodes -is frequently required to meaningfully identify the data collected. In Wireless Rechargeable Sensor Networks (WRSNs) -in which nodes are recharged by some external energy source -high accuracy (e.g. smaller than the mean internode distance) is often required to enable charging in a reasonably efficient manner with practical charger power.

SummaryCharging path planning in wireless sensor networks (WSNs) refers to designing an efficient charging path for sensor nodes in the network. However, most charging schemes mainly consider the planning of charging paths and pay little attention to the impact of uncertainties, such as road conditions and environment on the planning of charging paths, as well as ignoring the charging problem of new nodes in need of charging. Road conditions and the environment directly affect the energy consumption of wireless charging vehicles (WCVs) during traveling. To address the aforementioned challenges, this article proposes an interval many‐objective charging path scheme model, the WCV consumption is an uncertain value, it will change according to the environment, and road conditions, so we represent it as an interval parameter with upper and lower bounds. An interval high‐dimensional multi‐objective model with target energy consumption, path distance, number of dead nodes, and communication delay is constructed. Second, to implement this model, an interval SPEA2 algorithm (I‐SPEA2) that introduces an environmental response mechanism is proposed. I‐SPEA2 treats individual target interval values as ranges of values on a two‐dimensional coordinate axis, forming a quadrilateral, calculates individual size probabilities based on the area to determine the dominant relationship, and combines fixed distance and interval overlap to eliminate redundant individuals. The simulation results show that the interval dynamic model is effective in prolonging the lifecycle of WSN as well as the proposed algorithm reduces the mortality rate of the nodes by 15%, 28%, 13%, 16%, and 21% compared with other algorithms.

This paper mainly introduces the application of multichannel communication technology in wireless sensor network. The corresponding solutions are proposed based on the analysis of the structure and characteristics of wireless sensor, and the multi-channel MAC protocol in wireless sensor network. In this paper, multi-channel communication technology in wireless sensor network (WSN) is studied, the existing problems and reasons are analysed, and some solutions to further promote the further development of the technology are put forward. With the wide application of WSN, the security of WSN has been paid more and more attention. In this paper, the existing time synchronization protocol, from the security point of view, and the fault tolerance design of the fits protocol. The protocol has an intrusion detection mechanism, which can detect the attack of malicious nodes in time synchronization. Aiming at the small deviation of slow attack and the problem of time synchronization error, information fusion is based on the quality principle, and according to the time synchronization information of different nodes, we make random weighted average correction for local time nodes. The simulation results show that the fits time synchronization protocol has good intrusion detection and fault tolerance capability.

Wireless energy transfer has emerged as a promising technology for wireless sensor networks to power sensors with controllable yet perpetual energy. In this paper, we study sensor energy replenishment by employing a mobile charger (charging vehicle) to charge sensors wirelessly in a rechargeable sensor network, so that the sum of charging rewards collected from all charged sensors by the mobile charger per tour is maximized, subject to the energy capacity of the mobile charger, where the amount of reward received from a charged sensor is proportional to the amount of energy charged to the sensor. The energy of the mobile charger will be spent on both its mechanical movement and sensor charging. We first show that this problem is NP-hard. We then propose approximation algorithms with constant approximation ratios under two different settings: one is that a sensor will be charged to its full energy capacity if it is charged; another is that a sensor can be charged multiple times per tour but the total amount of energy charged is no more than its energy demand prior to the tour. We finally evaluate the performance of the proposed algorithms through experimental simulations. The simulation results demonstrate that the proposed algorithms are very promising, and the solutions obtained are fractional of the optimum. To the best of our knowledge, the proposed algorithms are the very first approximation algorithms with guaranteed approximation ratios for the mobile charger scheduling in a rechargeable sensor network under the energy capacity constraint on the mobile charger.

Wireless sensor networks (WSNs) are key components in the emergent cyber physical systems (CPSs). They may include hundreds of spatially distributed sensors which interact to solve complex tasks going beyond their individual capabilities. Due to the limited capabilities of sensors, sensor actions cannot meet CPS requirements while controlling and coordinating the operations of physical and engineered systems. To overcome these constraints, we explore the ecosystem metaphor for WSNs with the aim of taking advantage of the efficient adaptation behavior and communication mechanisms of living organisms. By mapping these organisms onto sensors and ecosystems onto WSNs, we highlight shortcomings that prevent WSNs from delivering the capabilities of ecosystems at several levels, including structure, topology, goals, communications, and functions. We then propose an agent-based architecture that migrates complex processing tasks outside the physical sensor network while incorporating missing characteristics of autonomy, intelligence, and context awareness to the WSN. Unlike existing works, we use software agents to map WSNs to natural ecosystems and enhance WSN capabilities to take advantage of bioinspired algorithms. We extend our architecture and propose a new intelligent CPS framework where several control levels are embedded in the physical system, thereby allowing agents to support WSNs technologies in enabling CPSs.