On-demand vehicle-assisted charging in wireless rechargeable sensor networks

Kumar, Rohit; Mukherjee, Joy Chandra

doi:10.1016/j.adhoc.2020.102389

Cited by 18 publications

(2 citation statements)

References 29 publications

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“…Based on the current charging intervals, there are two types of charging schemes: Periodic 6–9 and another on‐demand 10–12 . Due to the sensors' dynamic energy usage rate, periodic charging—where the MC charges the sensors in accordance with a predetermined charging schedule—is not appropriate for large‐scale WRSN.…”

Section: Introductionmentioning

confidence: 99%

“…5 Based on the current charging intervals, there are two types of charging schemes: Periodic [6][7][8][9] and another ondemand. [10][11][12] Due to the sensors' dynamic energy usage rate, periodic charging-where the MC charges the sensors in accordance with a predetermined charging schedule-is not appropriate for large-scale WRSN. However, with ondemand charging, the MC only supplies power to the sensors that have raised charging requests owing to low battery levels.…”

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confidence: 99%

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Genetic algorithm‐based partial charging schedule of rechargeable sensor networks

Dudyala

Dash

2023

Int J Communication

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Summary The use of rechargeable sensors is a promising solution for wireless sensor networks. On this type of network, mobile charging vehicles (MC) are used for charging sensors using wireless energy transfer (WET) technology. In on‐demand charging, a sensor transmits a charging request to the service station, and the MC visits the sensor to transfer energy. The key disadvantages of utilizing MC‐based WET are its high energy expenditure rate due to mobility, long service time, and slow charging rate. Because of these reasons, sensors deplete their energy and become dead before the MC reaches the requesting nodes to recharge. We have adapted a genetic algorithm‐based partial charging scheme to serve the charging requests. Our objective is to improve the survival ratio of the network. Using comprehensive simulations, we analyze the performance of our proposed method and compare it to two other existing approaches. The simulation results demonstrate that our proposed algorithm improves the survival ratio by up to 20 % by developing a dynamic energy threshold function for transmitting charging requests from the sensors and a partial charging schedule using a genetic algorithm.

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Section: Introductionmentioning

confidence: 99%