Autonomous Load Regulation Based Energy Balanced Routing in Rechargeable Wireless Sensor Networks

Wu, Runze; Guo, Haobo; Tang, Liangrui; Fan, Bing

doi:10.3390/app9163251

Cited by 5 publications

(2 citation statements)

References 25 publications

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“…Further. Wu et al [94] have proposed an efficient energy-balanced routing framework that is based on an autonomous load regulating scheme for rechargeable WSN. In this framework, significant steps are taken to control the relay radius, and consequently, nodes become capable of adjusting their load.…”

Section: Schemes Based On Load Balancingmentioning

confidence: 99%

Green Communication for Next‐Generation Wireless Systems: Optimization Strategies, Challenges, Solutions, and Future Aspects

Rathore

Sangwan

Kaiwartya

et al. 2021

Wireless Communications and Mobile Computing

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Wireless sensor networks (WSNs) have emerged as a backbone technology for the wireless communication era. The demand for WSN is rapidly increasing due to their major role in various applications with a wider deployment and omnipresent nature. The WSN is rapidly integrated into a large number of applications such as industrial, security, monitoring, tracking, and applications in home automation. The widespread use in many different areas attracts research interest in WSNs. Therefore, researchers are taking initiatives in exploring innovation day by day particularly towards the Internet of Things (IoT). But, WSN is having lots of challenging issues that need to be addressed, and the inherent characteristics of WSN severely affect the performance. Energy constraints are one of the primary issues that require urgent attention from the research community. Optimal energy optimization strategies are needed to counter the issue of energy constraints. Although one of the most appropriate schemes for handling energy constraints issues is the appropriate energy harvesting technique, the optimal energy optimization strategies should be coupled together for effectively utilizing the harvested energy. In this high-level systematic and taxonomical survey, we have organized the energy optimization strategies for EH-WSNs into eleven factors, namely, radio optimization schemes, optimizing the energy harvesting process, data reduction schemes, schemes based on cross-layer optimization, schemes based on cross-layer optimization, sleep/wake-up policies, schemes based on load balancing, schemes based on optimization of power requirement, optimization of communication mechanism, schemes based on optimization of battery operations, mobility-based schemes, and finally energy balancing schemes. We have also prepared the summarized view of various protocols/algorithms with their remarkable details. This systematic and taxonomy survey also provides a progressive detailed overview and classification of various optimization challenges for the EH-WSNs that require attention from the researcher followed by a survey of corresponding solutions for corresponding optimization issues. Further, this systematic and taxonomical survey also provides a deep analysis of various emerging energy harvesting technologies in the last twenty years of the era.

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Section: Schemes Based On Load Balancingmentioning

confidence: 99%

Green Communication for Next‐Generation Wireless Systems: Optimization Strategies, Challenges, Solutions, and Future Aspects

Rathore

Sangwan

Kaiwartya

et al. 2021

Wireless Communications and Mobile Computing

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show abstract

“…In addition, some works were aimed at introducing wireless charging technology into traditional multi-hop routing algorithms for networks with a single static sink. The autonomous load regulation mechanism-based routing (ALRMR) [18] was an efficient framework of joint wireless energy transfer and multi-hop routing where the routing strategy was adapted to the charging scheme. Aslam [19] attempted an approach based on the shortest path algorithm and grid clustering to save and renew power in a way that minimized energy consumption and prolonged the overall network lifetime of WSNs.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Dual-Mode Routing-Based Mobile Data Gathering Algorithm in Rechargeable Wireless Sensor Networks for Internet of Things

Tang

Guo

et al. 2020

Applied Sciences

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Great improvement recently appeared in terms of efficient service delivery in wireless sensor networks (WSNs) for Internet of things (IoT). The IoT is mainly dependent on optimal routing of energy-aware WSNs for gathering data. In addition, as the wireless charging technology develops in leaps and bounds, the performance of rechargeable wireless sensor networks (RWSNs) is greatly ameliorated. Many researches integrated wireless energy transfer into data gathering to prolong network lifetime. However, the mobile collector cannot visit all nodes under the constraints of charging efficiency and gathering delay. Thus, energy consumption differences caused by different upload distances to collectors impose a great challenge in balancing energy. In this paper, we propose an adaptive dual-mode routing-based mobile data gathering algorithm (ADRMDGA) in RWSNs for IoT. The energy replenishment capability is reasonably allocated to low-energy nodes according to our objective function. Furthermore, the innovative adaptive dual-mode routing allows nodes to choose direct or multi-hop upload modes according to their relative upload distances. The empirical study confirms that ADRMDGA has excellent energy equilibrium and effectively extends the network lifetime.

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Adaptive SSO based node selection for partial charging in wireless sensor network

Prasannababu

Amgoth

2022

Peer-to-Peer Netw. Appl.

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Autonomous Load Regulation Based Energy Balanced Routing in Rechargeable Wireless Sensor Networks

Cited by 5 publications

References 25 publications

Green Communication for Next‐Generation Wireless Systems: Optimization Strategies, Challenges, Solutions, and Future Aspects

Green Communication for Next‐Generation Wireless Systems: Optimization Strategies, Challenges, Solutions, and Future Aspects

Adaptive Dual-Mode Routing-Based Mobile Data Gathering Algorithm in Rechargeable Wireless Sensor Networks for Internet of Things

Adaptive SSO based node selection for partial charging in wireless sensor network

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