Mobile Charging Strategy for Wireless Rechargeable Sensor Networks

Chen, Tzung-Shi; Chen, Jen–Jee; Gao, Xiang-You; Chen, Tzung-Cheng

doi:10.3390/s22010359

Cited by 20 publications

(9 citation statements)

References 35 publications

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“…In addition, they introduced a linear programming framework for determining the optimal solution and proposed a method for minimizing the complexity based on a constant approximation method. The authors of [25] proposed a bidirectional charging strategy to minimize the MC traversal path length, energy consumption, and completion time. A clustering-based approach was also proposed to reduce the total MC travel distance and reduce the energy consumed by MC charging and the total single-round completion time.…”

Section: Offline Schemesmentioning

confidence: 99%

Deep Reinforcement Learning–Based Online One-to-Multiple Charging Scheme in Wireless Rechargeable Sensor Network

Gong

Feng

et al. 2023

Sensors

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Wireless rechargeable sensor networks (WRSN) have been emerging as an effective solution to the energy constraint problem of wireless sensor networks (WSN). However, most of the existing charging schemes use Mobile Charging (MC) to charge nodes one-to-one and do not optimize MC scheduling from a more comprehensive perspective, leading to difficulties in meeting the huge energy demand of large-scale WSNs; therefore, one-to-multiple charging which can charge multiple nodes simultaneously may be a more reasonable choice. To achieve timely and efficient energy replenishment for large-scale WSN, we propose an online one-to-multiple charging scheme based on Deep Reinforcement Learning, which utilizes Double Dueling DQN (3DQN) to jointly optimize the scheduling of both the charging sequence of MC and the charging amount of nodes. The scheme cellularizes the whole network based on the effective charging distance of MC and uses 3DQN to determine the optimal charging cell sequence with the objective of minimizing dead nodes and adjusting the charging amount of each cell being recharged according to the nodes’ energy demand in the cell, the network survival time, and MC’s residual energy. To obtain better performance and timeliness to adapt to the varying environments, our scheme further utilizes Dueling DQN to improve the stability of training and uses Double DQN to reduce overestimation. Extensive simulation experiments show that our proposed scheme achieves better charging performance compared with several existing typical works, and it has significant advantages in terms of reducing node dead ratio and charging latency.

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Section: Offline Schemesmentioning

confidence: 99%

Deep Reinforcement Learning–Based Online One-to-Multiple Charging Scheme in Wireless Rechargeable Sensor Network

Gong

Feng

et al. 2023

Sensors

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“…In this model, the purpose of optimization is to decrease node total energy consumption (i.e., ∑ i,i∈N p i ), which is the first portion of P total in Equation (6). Every node should indeed fulfill the fundamental flow balancing constraint in Equation ( 8) and the energy consumption model in Equation (9).…”

Section: Optimization With Flowing Rate and Data Routingmentioning

confidence: 99%

“…Optimal energy distribution in WSN has emerged as a new issue [2][3][4]; energy lifecycle management problem, i.e., how to transfer energy in an optimized or accurate way to control a network, is also a critical issue in wireless sensor networks [5]. Researchers have proposed effective methods to extend sensor life spans in recent decades, including wireless recharging strategies [6,7], sensor network energy consumption reduction techniques, and harvesting energy from the surrounding environment, such as solar [8,9]. Nonetheless, due to inefficiency in power conversion and environmental uncertainty, power conversion from a source of external energy, such as solar energy, into electrical energy is unreliable.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization with Mayfly Algorithm for Periodic Charging in Wireless Rechargeable Sensor Networks

Sandrine

Xia

2022

WEVJ

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Wireless energy transfer (WET) is a revolutionary method that has the power to tackle the energy and longevity challenges in wireless sensor networks (WSN). This paper uses a mobile charger (MC) to discover the procedure of WET based on a wireless sensor network (WSN) for a periodic charging technique to maintain the network operational. The goal of this work is to lower overall system energy consumption and total distance traveled while increasing the mobile charger device vacation time ratio. Based on an analysis of total energy consumption, a new metaheuristic called mayfly algorithm (MA) is used to achieve energy savings. Instead of charging all nodes at the same time in each cycle, in our strategy, the mobile charger charges only energy-hungry nodes due to their levels of energy. In this strategy, when the first node reaches the calculated minimum energy, it notifies the base station (BS), which computes all nodes that fall under threshold energy and sends the MC to charge all of them to the maximum energy level in the same cycle. Mathematical results show that the mayfly algorithm can considerably decrease the charging device’s total energy consumption and distance traveled while maintaining performance because it can keep the network operational with less complexity than other schemes.

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“…Energy storage systems are highly dependent on the size of the robot and intended use environment. It is therefore important to have a clear overview of what is available, and in which application specific storage can reach its maximum efficiency [5].…”

Section: Introductionmentioning

confidence: 99%

Materials for Batteries of Mobile Robot Power Systems: A Systematic Review and Comparison of Efficiency

Mikołajczyk¹,

Mikołajewski²,

Kłodowski³

et al. 2023

Preprint

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Mobile robots can perform tasks on the move, including exploring terrain, discovering landmark features or moving a load from one place to another. This group of robots is characterized by a certain level of intelligence allowing making decisions and responding to stimuli received from the environment. As part of Industry 5.0, such mobile robots and humans are expected to co-exist and work together in a shared environment to make human work less tiring, quicker, and safer. This can only be realized when clean, dense, and economical energy sources are available. The aim of the study is to analyze the state of the art and to identify the most important directions for future developments in robotic power systems based mainly on batteries. The efficiency and performance of the battery depends on the design using different materials. Work environments and performance requirements are considered in this systematic review to classify solutions that help developers choose the best suited power system for specific application. Indirectly, the aim of the work is to generate discussion within the scientific and engineering community. A narrative review of publications from six major bibliographic databases according to preset inclusion criteria was combined with a critical analysis of current and future technologies. The main findings of the review allow answering question what is the role of modern power source technologies, artificial intelligence and ground-breaking research work in global policies related to energy saving, green policies, and sustainable development. The main opportunities and threats are discussed, and a brief feasibility analysis is carried out. Novelty of the article relates not only to the analysis of technologies, but also to approaches and their use under conditions of limited resource availability, when resource usage must be minimized. The article provides an overview of batteries, their specifications, classifications, and their advantages and disadvantages. In addition, we propose: (1) an algorithm for selecting main energy source for robot application, and (2) an algorithm for selecting electrical system power supply. Current mobile robot batteries are, in most cases, their biggest limitation. Progress in batteries development is too slow to catch up with the demand for robot autonomy and range requirements, limiting the development of mobile robots. Further intensive research and implementation work is needed to avoid years of delay in this area.

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Mobile Charging Strategy for Wireless Rechargeable Sensor Networks

Cited by 20 publications

References 35 publications

Deep Reinforcement Learning–Based Online One-to-Multiple Charging Scheme in Wireless Rechargeable Sensor Network

Deep Reinforcement Learning–Based Online One-to-Multiple Charging Scheme in Wireless Rechargeable Sensor Network

Multi-Objective Optimization with Mayfly Algorithm for Periodic Charging in Wireless Rechargeable Sensor Networks

Materials for Batteries of Mobile Robot Power Systems: A Systematic Review and Comparison of Efficiency

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