Collaborative mobile charging policy for perpetual operation in large-scale wireless rechargeable sensor networks

Wireless rechargeable sensor networks (WRSNs) have emerged as a critical infrastructure for monitoring and collecting data in large-scale and dynamic environments. The energy autonomy of sensor nodes is crucial for the sustained operation of WRSNs. This paper presents a comprehensive survey on the state-of-the-art approaches and technologies in on-demand energy provisioning in large-scale WRSNs. We explore various energy harvesting techniques, storage solutions, and energy management strategies tailored to the unique challenges posed by the dynamic and resource-constrained nature of WRSNs. This survey categorizes existing literature based on energy harvesting sources, including solar, kinetic, and ambient energy, and discusses advancements in energy storage technologies such as supercapacitors and rechargeable batteries. Furthermore, we investigate energy management techniques that adaptively balance energy consumption and harvesting, optimizing the overall network performance. In addition to providing a thorough overview of existing solutions, this paper identifies opportunities and challenges in the field of on-demand energy provisioning for large-scale WRSNs. By synthesizing current research efforts, this survey aims to provide insight to researchers and policymakers in understanding the landscape of on-demand energy provisioning in large-scale WRSNs. The insights gained from this study pave the way for future innovations and contribute to the development of sustainable and self-sufficient wireless sensor networks, critical for the advancement of applications such as environmental monitoring, precision agriculture, and smart cities.

As the technological breakthrough is made in wireless charging, the wireless rechargeable sensor networks (WRSNs) are finally proposed. In order to reduce the charging completion time, most existing works use the “mobile-then-charge” model—the Wireless charging vehicles (WCV) moves to the charging spot first and then charges nodes nearby. These works often aim to reduce the node’s movement delay or charging delay. However, the charging opportunities during the movement are overlooked in this model because WCV can charge nodes when it goes from one spot to the next. In order to use the charging opportunities, a speed grading method is proposed under the circumstance of variable WCV speed, which transformed the problem of final charging delay into a traveling salesman problem with speed grading. The problem was further solved by linear programming method. The simulation experiments show that, compared with the existing charging methods, the proposed method has a significant improvement in charging delay.

No abstract available.

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.

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.

The wireless power transfer technology in Wireless Sensor Networks (WSNs) lets the sensors replenish their energy through the Mobile Chargers (MCs). The new network is named Wireless Rechargeable Sensor Networks (WRSNs) in the literature. In these networks, the mobile charger scheduling is very important to retrieve the sensors' energy. In some of the proposed approaches, a deterministic and predetermined path is determined for MCs by using the information from the entire network. These approaches are not appropriate for the uncertain real networks in which the network changes over time. For this reason, we propose an online, grid-based and decentralized method. In this method, MCs select some sensors for renewing their energy by considering the status of the grid cells around them. The purpose of our method named Coverage Aware Charging (CAC) is to replenish the power of low-energy sensors as well as keeping a better network coverage. Finally, we evaluate CAC by using comprehensive simulation different situations.

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 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.

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.

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.

In this paper, we consider the scenario in which a mobile charger (MC) periodically travels within a sensor network to recharge the sensors wirelessly. We design joint charging and scheduling schemes to maximize the Quality of Monitoring (QoM) for stochastic events, which arrive and depart according to known probability distributions of time. Information is considered captured if it is sensed by at least one sensor. We focus on two closely related research issues, i.e., how to choose the sensors for charging and decide the charging time for each of them, and how to schedule the sensors’ activation schedules according to their received energy. We formulate our problem as the maximum QoM CHA rging and S ch E duling problem (CHASE). We first ignore the MC's travel time and study the resulting relaxed version of the problem, which we call CHASE-R. We show that both CHASE and CHASE-R are NP-hard. For CHASE-R, we prove that it can be formulated as a submodular function maximization problem, which allows two algorithms to achieve $1/6$ 1 / 6 - and $1/(4 + \epsilon)$ 1 / ( 4 + e ) -approximation ratios. Then, for CHASE, we propose approximation algorithms to solve it by extending the CHASE-R results. We conduct simulations to validate our algorithm design.

Sensor networks consist of multiple low-cost, autonomous, ad-hoc sensors, that periodically probe and react to the environment and communicate with other sensors or devices. A primary concern in the operation of sensor networks is the limited energy capacity per sensor. As a result, a common challenge is in setting the probing frequency, so as to compromise between the cost of frequent probing and the inaccuracy resulting from infrequent probing.

Wireless rechargeable sensor networks (WRSNs) have emerged as a potential solution to solve the challenge of prolonging battery-powered sensor networks’ lifetime. In a WRSN, a mobile charger moves around and charges the rechargeable sensors when stopping at charging spots. This paper newly considers a joint optimization of the charging location and the charging time to avoid node failure in WRSNs. We formulate the optimization and propose a solution for that. The proposal includes an algorithm to find a list of potential charging locations and their associated optimal charging time. Moreover, the Q-learning technique is adopted to determine the optimal next charging location among the candidates. We implement the algorithm and conduct experiments to compare it to the related works. The results show that the proposed algorithm outperforms the others in terms of network lifetime. Specifically, the highest performance gap of our proposal to the best algorithm among the others is 8.29 times.

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.