Wireless Rechargeable Sensor Networks Research Articles

An Efficient Scheduling Scheme for Semi-On-Demand Charging in Wireless Rechargeable Sensor Networks

An Efficient Scheduling Scheme for Semi-On-Demand Charging in Wireless Rechargeable Sensor Networks

A Hybrid Charging Scheme for Minimizing the Number of Energy-Exhausted Nodes in Wireless Rechargeable Sensor Networks

A Hybrid Charging Scheme for Minimizing the Number of Energy-Exhausted Nodes in Wireless Rechargeable Sensor Networks

A DRL-based Partial Charging Algorithm for Wireless Rechargeable Sensor Networks

Breakthroughs in Wireless Energy Transfer (WET) technologies have revitalized Wireless Rechargeable Sensor Networks (WRSNs). However, how to schedule mobile chargers rationally has been quite a tricky problem. Most of the current work does not consider the variability of scenarios and how many mobile chargers should be scheduled as the most appropriate for each dispatch. At the same time, the focus of most work on the mobile charger scheduling problem has always been on reducing the number of dead nodes, and the most critical metric of network performance, packet arrival rate, is relatively neglected. In this paper, we develop a DRL-based Partial Charging (DPC) algorithm. Based on the number and urgency of charging requests, we classify charging requests into four scenarios. And for each scenario, we design a corresponding request allocation algorithm. Then, a Deep Reinforcement Learning (DRL) algorithm is employed to train a decision model using environmental information to select which request allocation algorithm is optimal for the current scenario. After the allocation of charging requests is confirmed, to improve the Quality of Service (QoS), i.e., the packet arrival rate of the entire network, a partial charging scheduling algorithm is designed to maximize the total charging duration of nodes in the ideal state while ensuring that all charging requests are completed. In addition, we analyze the traffic information of the nodes and use the Analytic Hierarchy Process (AHP) to determine the importance of the nodes to compensate for the inaccurate estimation of the node’s remaining lifetime in realistic scenarios. Simulation results show that our proposed algorithm outperforms the existing algorithms regarding the number of alive nodes and packet arrival rate.

Asynchronous Multi-Agent Reinforcement Learning for Collaborative Partial Charging in Wireless Rechargeable Sensor Networks

Asynchronous Multi-Agent Reinforcement Learning for Collaborative Partial Charging in Wireless Rechargeable Sensor Networks

Deep Reinforcement Learning-Based Dynamic Charging–Recycling Scheme for Wireless Rechargeable Sensor Networks

Deep Reinforcement Learning-Based Dynamic Charging–Recycling Scheme for Wireless Rechargeable Sensor Networks

An adaptive charging scheme for large-scale wireless rechargeable sensor networks inspired by deep Q-network

An adaptive charging scheme for large-scale wireless rechargeable sensor networks inspired by deep Q-network

Deep Reinforcement Learning-Based Joint Sequence Scheduling and Trajectory Planning in Wireless Rechargeable Sensor Networks

Deep Reinforcement Learning-Based Joint Sequence Scheduling and Trajectory Planning in Wireless Rechargeable Sensor Networks

An efficient partial charging and data gathering strategy using multiple mobile vehicles in wireless rechargeable sensor networks

An efficient partial charging and data gathering strategy using multiple mobile vehicles in wireless rechargeable sensor networks

Deep-Reinforcement-Learning-Based Joint Energy Replenishment and Data Collection Scheme for WRSN.

With the emergence of wireless rechargeable sensor networks (WRSNs), the possibility of wirelessly recharging nodes using mobile charging vehicles (MCVs) has become a reality. However, existing approaches overlook the effective integration of node energy replenishment and mobile data collection processes. In this paper, we propose a joint energy replenishment and data collection scheme (D-JERDG) for WRSNs based on deep reinforcement learning. By capitalizing on the high mobility of unmanned aerial vehicles (UAVs), D-JERDG enables continuous visits to the cluster head nodes in each cluster, facilitating data collection and range-based charging. First, D-JERDG utilizes the K-means algorithm to partition the network into multiple clusters, and a cluster head selection algorithm is proposed based on an improved dynamic routing protocol, which elects cluster head nodes based on the remaining energy and geographical location of the cluster member nodes. Afterward, the simulated annealing (SA) algorithm determines the shortest flight path. Subsequently, the DRL model multiobjective deep deterministic policy gradient (MODDPG) is employed to control and optimize the UAV instantaneous heading and speed, effectively planning UAV hover points. By redesigning the reward function, joint optimization of multiple objectives such as node death rate, UAV throughput, and average flight energy consumption is achieved. Extensive simulation results show that the proposed D-JERDG achieves joint optimization of multiple objectives and exhibits significant advantages over the baseline in terms of throughput, time utilization, and charging cost, among other indicators.

Deep reinforcement learning approach with hybrid action space for mobile charging in wireless rechargeable sensor networks

Mobile charging is feasible to deal with the energy-constrained problem in wireless rechargeable sensor networks (WRSNs). The mobile chargers (MCs) are usually employed to charge the sensors sequentially according to the charging schemes. Existing studies assume that each sensor should be charged to its maximum energy capacity or to a fixed upper threshold before the next one can be charged. However, they neglect to control the charging time adaptively of each sensor according to the charging demand. Hence, in this paper, we assume that the charging time of each sensor can be controlled, and we study the joint optimization of charging sequence and charging time problem (JCSCT). Correspondingly, we propose a novel deep reinforcement learning with hybrid action space approach for JCSCT (DRLH-JCSCT), which utilizes deep q-network (DQN) to generate the charging sequence, and adopts deep deterministic policy gradient (DDPG) to determine the charging time. An attention-based encoder–decoder model is integrated in the actor network of DDPG, and a modified bi-directional gate recurrent unit network (MBGRU) is utilized as the decoder. We also design a novel reward function to evaluate the quality of the charging actions. Simulations demonstrate the improved charging performance of the proposed approach, with a longer network lifetime and fewer failed sensors compared with the existing mobile charging scheduling approaches.

Caddisfalcon optimization algorithm for on-demand energy transfer in wireless rechargeable sensors based IoT networks

Internet of things networks increase the demand for innovative applications due to their interdisciplinary research contributions. However, the internet source for remote applications still depends on the Wireless Sensor Networks (WSNs). Implementing the Wireless Rechargeable Sensor Networks (WRSN) with Wireless Energy Transfer (WET) can eradicate energy limitation issues and network lifetime in IoT applications. In recent years, WRSNs have steadily increased in popularity and have been responsible for significant advances in wireless communication. These accomplishments can be attributed to inexpensive and energy-efficient sensors. However, since WRSN nodes are battery-powered, they lose energy periodically. This energy constraint affects the network’s durability. To increase the network lifetime in WRSN, this research proposes an efficient energy transfer routing protocol based on Caddisfly and Falcon lifespan-based Optimization algorithms for transferring energy from the best node to the needy node through the optimal path. The caddisflies optimization algorithm is used to find the most energy-effective node among all the other nodes in the sensing region based on the residual energies. In contrast, the Falcon optimization algorithm finds the optimal path from the best node to the needy node. Simulated results are proved the improvement and contributions of the proposed algorithm and real-time implementation of the proposed model and its results are also compared with other related models to prove the performance.

Multi-Type Charging Scheduling Based on Area Requirement Difference for Wireless Rechargeable Sensor Networks

Multi-Type Charging Scheduling Based on Area Requirement Difference for Wireless Rechargeable Sensor Networks

An improved deep Q-network approach for charging sequence scheduling with optimal mobile charging cost and charging efficiency in wireless rechargeable sensor networks

Mobile charging is a practical solution to overcome energy constraints in wireless rechargeable sensor networks (WRSNs). It utilizes a mobile charger (MC) to charge sensors based on the wireless power transfer technique via a charging scheduling scheme. However, how to schedule MC to achieve higher charging efficiency with less mobile charging cost, while ensuring network functionality remains a challenge. Specifically, the mobile charging cost is the total energy consumed by the MC during the charging task. This paper studies the charging sequence scheduling problem with optimal mobile charging cost and charging efficiency (CSCE), considering dynamic changes in sensor energy consumption. Moreover, we model the CSCE as a multi-objective mixed integer nonlinear programming. To address this issue, we propose an improved deep Q network approach for CSCE (IDQN-CSCE), where the MC acts as an agent to explore the WRSN and determine a charging policy based on the charging demand of sensors. IDQN-CSCE employs Q-learning and deep Q-networks (DQN) to train the Q-table and DQN network simultaneously. Charging actions are selected based on probability, and the probability of using Q-learning decreases as iteration steps increase. Additionally, we design a novel reward function consisting of a weighted sum of rewards and penalties fed back by the environment after performing a charging action. Simulation results show that IDQN-CSCE achieves higher charging efficiency than other baseline approaches with less mobile charging cost.

Adaptive Payoff Balance Among Mobile Wireless Chargers for Rechargeable Wireless Sensor Networks

Adaptive Payoff Balance Among Mobile Wireless Chargers for Rechargeable Wireless Sensor Networks

A deep reinforcement learning approach for online mobile charging scheduling with optimal quality of sensing coverage in wireless rechargeable sensor networks

Mobile charging provides a new energy replenishment technology for Wireless Rechargeable Sensor Network (WRSN), where the Mobile Charger (MC) is employed for charging nodes sequentially according to the mobile charging scheduling result, using node charging timeliness and quality of sensing coverage as the scheduling criteria. Sensing coverage is a critical network property and has received more interest in recent research studies in mobile charging scheduling in WRSN. As the network environment is usually uncertain and the charging demands may change dynamically from time to time, online mobile charging scheduling is crucial, but existing online approaches are mostly based on specific network models, which are difficult to obtain in practical applications. In this paper, we propose a novel model-free deep reinforcement learning algorithm for the Online Mobile Charging Scheduling with optimal Quality of Sensing Coverage (OMCS-QSC) problem in WRSN, Multistage Exploration Deep Q-Network (MEDQN), where MC is designed as an agent to explore the online charging schedules via a new multistage exploration strategy for maximizing the network QSC according to the real-time network state. In addition, we also design a novel reward function to evaluate the MC charging action via the real-time sensing coverage contributions of the nodes. Extensive simulations show that MEDQN can reach the convergence state stably and is superior to existing online algorithms, especially in large-scale WRSNs.

A Novel Data Collection and Partial Charging Scheme Using Multiple Mobile Vehicles in Wireless Rechargeable Sensor Networks

A Novel Data Collection and Partial Charging Scheme Using Multiple Mobile Vehicles in Wireless Rechargeable Sensor Networks

Joint Energy Replenishment and Data Collection Based on Deep Reinforcement Learning for Wireless Rechargeable Sensor Networks

Joint Energy Replenishment and Data Collection Based on Deep Reinforcement Learning for Wireless Rechargeable Sensor Networks

Retracted: Mixed Linear Programming for Charging Vehicle Scheduling in Large-Scale Rechargeable WSNs

Retracted: Mixed Linear Programming for Charging Vehicle Scheduling in Large-Scale Rechargeable WSNs

Charging non-deterministic mobile nodes in a transfer learning approach

Wireless rechargeable sensor networks (WRSNs) provide a solution to the energy problem in wireless sensor networks by introducing chargers to recharge the sensor nodes with rechargeable batteries. Most of the existing studies on WRSN focus on charging static nodes or nodes with certain mobility, while a few works investigate charging non-deterministic mobile nodes, where the movement pattern of nodes is unknown. The main challenge in the study of charging non-deterministic nodes is how to find the energy-hungry nodes, because the movement of the nodes is uncertain. The existing schemes assume that the historical trajectories of each node are known and predict the future locations of the nodes by analyzing the trajectory data. However, these schemes require a large amount of trajectory data to train the prediction model, and it takes a considerable amount of time for the network to run to obtain this trajectory data. If there is a node energy depletion during this time, these schemes cannot perform the energy replenishment to the nodes. To address this problem, we propose a novel charging scheme to provide charging services for non-deterministic nodes by introducing the transfer learning technology to predict the future locations of non-deterministic nodes. In the proposed scheme, a backbone Graph Convolutional Network (GCN) is pre-trained with other trajectory datasets, based on which only a small amount of trajectory data of nodes in the target network is needed to complete the location prediction task. In addition, a new scheduling algorithm is proposed, which considers multiple states of the nodes, including the energy consumption rate, the remaining energy state and the future locations, to select the charging target nodes. Simulation results show that the proposed scheme is able to achieve energy replenishment of non-deterministic mobile nodes when the network is just starting to operate.

Spatiotemporal Optimization for Charging Scheduling in Wireless Rechargeable Sensor Networks

Spatiotemporal Optimization for Charging Scheduling in Wireless Rechargeable Sensor Networks