Energy Harvesting Aware Multi-Hop Routing Policy in Distributed IoT System Based on Multi-Agent Reinforcement Learning

Zhang, Wen; Liu, Tao; Xie, Mimi; Li, Longzhuang; Kar, Dulal C.; Pan, Chen

doi:10.1109/asp-dac52403.2022.9712528

Cited by 4 publications

(1 citation statement)

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“…Furthermore, the system parameters of a node can be adjusted to improve node and network performance based on the periodicity and magnitude of harvestable energy by using Reinforcement Learning. A device can maximize its power use to achieve peak performance during the period that it has remaining because it is limited by the next harvesting chance (recharge cycle) [32].…”

Section: Introductionmentioning

confidence: 99%

Energy harvesting for devices in wireless sensor networks: A Review

Nguyen¹,

Nguyen²,

Tran³

et al. 2023

EAI Endorsed Trans IoT

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Recent years have witnessed several technological breakthroughs in wireless sensor networks (WSN), yet energy continues to be an indispensable resource despite these advancements. The amount of energy that is available in a WSN has a direct bearing on how well it functions, how well it performs, and how long it will continue to operate. Because of the limitations imposed on them by cost and size, sensor nodes almost always come outfitted with a constrained amount of energy. As a direct consequence of this, their batteries will need to be replaced at regular intervals. However, the replacement is only sometimes a viable alternative; in fact, there are some situations in which it is unlikely to be achievable and entirely improbable. Because of this, there is an urgent need for more feasible solutions, which include energy harvesting or wireless energy transfer, as well as the creation of power at the sensor nodes themselves or their delivery of power to them. These options are among the options that are now available. This study intends to accomplish the following three primary goals: In the first step of this process, we will investigate prospective renewable energy resources and information on their qualities and uses in wireless sensor networks (WSN). Second, this study examines various methods for charging batteries and the various ways each of these methods might be applied to wireless sensor networks (WSN).

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Section: Introductionmentioning

confidence: 99%