A Survey on Wireless Power Transfer based Charging Scheduling Schemes in Wireless Rechargeable Sensor Networks

Zhang, Fan; Zhang, Jie; Qian, Yujie

doi:10.1109/ccsse.2018.8724809

Cited by 14 publications

(6 citation statements)

References 17 publications

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“…However, complicated charging route planning and little scalability reduced the excellent feasibility and increased charging efficiency in low-density WRSNs. The Advantages of PTP charging schemes can be summarized as: (1) Suitable for sparse networks, (2) Reduce the charging frequency and enhance its efficiency, (3) Minimize the total power consumption of the sensor network, and the charging method, (4) Maximize the vacation time ratio of the WCVs in each cycle time, and (5) Maximize the travel route, the charging period in each node and discharge guidance techniques, and the optimal travel path of WCVs rotated out to be the quickest Hamiltonian cycle passing via all sensor nodes [21]. On the other hand, the main disadvantage of such schemes is lowering the charging scalability and inefficiency, especially in dense networks.…”

Section: B the Charging Model Of Wcvsmentioning

confidence: 99%

Wireless Power Transfer Technologies, Applications, and Future Trends: A Review

Alabsi,

Hawbani,

Wang

et al. 2024

IEEE Trans. Sustain. Comput.

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Wireless Power Transfer (WPT) is a disruptive technology that allows wireless energy provisioning for energylimited IoT devices, thus decreasing the over-reliance on batteries and wires. WPT could replace conventional energy provisioning (e.g., energy harvesting) and expand for deployment in many of our daily-life applications, including but not limited to healthcare, transportation, automation, and smart cities. As a new rising technology, WPT has attracted many researchers from academia and industry in terms of technologies and charging scheduling within a plethora of services and applications. Thus, in this paper, we review the most recent studies related to WPT, including the classifications, advantages, disadvantages, and main application domains. Furthermore, we review the recently designed wireless charging scheduling algorithms and schemesfor wireless sensor networks. Our study provides a detailed survey of wireless charging scheduling schemes covering the main scheme classifications, evaluation metrics, application domains, advantages, and disadvantages of each charging scheme. We further summarize trends and opportunities for applying WPT at some intersections.Index Terms-wireless power transfer, wireless sensor networks, smart homes, healthcare, industrial, and charging schemes. I. INTRODUCTIONS IXTH Generation (6G) wireless networks seek to enhance the dependability, speed, and bandwidth of their forerunners, the 5G networks, in order to meet the expanding demands of the Internet of Everything (IoE) applications and accommodate cutting-edge technological trends like decentralized and pervasive artificial intelligence (AI). Energy efficiency and power provisioning are critical problems in future wireless networking, including Wireless Sensor Networks (WSN), IoE, Beyond 5G (B5G), and 6G [70], [144]. Many power-saving (energy conservation) solutions are used to improve the power usage in wireless communications, such as in [44], dynamic routing techniques [80], [146], [145], efficient construction of multi-cast trees [27], [25], mobile data collection, low power hardware architecture [61, 33], and resource allocation. Despite the fact that these solutions

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Section: B the Charging Model Of Wcvsmentioning

confidence: 99%

Wireless Power Transfer Technologies, Applications, and Future Trends: A Review

Alabsi,

Hawbani,

Wang

et al. 2024

IEEE Trans. Sustain. Comput.

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“…Another key study proposes a methodology of distributing many MCs to sustain performance levels of R-WSN of life-critical sensor nodes [16]. It is also proposed a novel idea called "shuttling," as well as an optimal charging mechanism that used charger collaboration to reduce the number of chargers and increase the sensing range [17].…”

Section: Related Workmentioning

confidence: 99%

Energy‐Efficient Enhancement for the Prediction‐Based Scheduling Algorithm for the Improvement of Network Lifetime in WSNs

Mohiddin

Kohli

Dutt

et al. 2021

Wireless Communications and Mobile Computing

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In wireless sensor networks, due to the restricted battery capabilities of sensor nodes, the energy issue plays a critical role in network efficiency and lifespan. In our work, an upgraded long short-term memory is executed by the base station to frequently predict the forecast positions of the node with the help of load-adaptive beaconing scheduling algorithm. In recent years, new technologies for wireless charging have offered a feasible technique in overcoming the WSN energy dilemma. Researchers are deploying rechargeable wireless sensor networks that introduce high-capacity smartphone chargers for sensor nodes for charging. Nearly all R-WSN research has focused on charging static nodes with relativistic routes or mobile nodes. In this work, it is analysed how to charge nondeterministic mobility nodes in this work. In this scenario, a new mechanism is recommended, called predicting-based scheduling algorithm, to implement charging activities. In the suggested technique, it directs them to pursue the mobile charger and recharge the sensor, which is unique for the present work. The mobile charger will then choose a suitable node, utilizing a scheduling algorithm, as the charging object. A tracking algorithm based on the Kalman filter is preferred during energy transfer to determine the distance needed for charging between the destination node & mobile charger. Here, the collecting & processing of data are performed through the big data collection in WSNs. The R-WSN charging operations of nondeterministic mobility nodes will be accomplished using the proposed charging strategy.

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“…One-hop communication from each sensor to MR can be useful to extend their lifetime. On the other hand, both energy recharging and sensory data transmitting can be done by means of MR visiting [ 10 , 11 ]. Each sensor can obtain energy from MR and transfer sensory data to MR for storage.…”

Section: Introductionmentioning

confidence: 99%

Mobile Charging Strategy for Wireless Rechargeable Sensor Networks

Chen

Gao

et al. 2022

Sensors

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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.

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A Survey on Wireless Power Transfer based Charging Scheduling Schemes in Wireless Rechargeable Sensor Networks

Cited by 14 publications

References 17 publications

Wireless Power Transfer Technologies, Applications, and Future Trends: A Review

Wireless Power Transfer Technologies, Applications, and Future Trends: A Review

Energy‐Efficient Enhancement for the Prediction‐Based Scheduling Algorithm for the Improvement of Network Lifetime in WSNs

Mobile Charging Strategy for Wireless Rechargeable Sensor Networks

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