An efficient on-demand charging schedule method in rechargeable sensor networks

Kumar, Naween; Dash, Dinesh; Kumar, M. N. Satheesh

doi:10.1007/s12652-020-02539-1

Cited by 25 publications

(4 citation statements)

References 45 publications

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“…Calculating the best sensor node charging sequence and energy restoration is critical to a mobile charger (MC) charging plan reason why the sensor node deployment and energy usage must be considered to maximize network longevity. An adaptive fuzzy model was used to construct a charging schedule that extends the WRSN sensor life [18]. The suggested technique uses multi-node mobile charging to charge many sensor nodes simultaneously and the MC received charging requests from low-energy sensors and had a restricted number of visiting spots.…”

Section: Preliminariesmentioning

confidence: 99%

Synthetic on-demand charging optimization with ADCMA for energy conservation in WRSN

Mukase,

Xia,

Owoola

2024

Preprint

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Recently, the research community has shown a growing interest in using mobile chargers to recharge the energy supply of sensor nodes in wireless rechargeable sensor network (WRSN). Mobile energy chargers offer a more dependable energy source compared to devices that harvest dynamic energy from the surrounding environment. This research introduces a synthetic on-demand charging optimization approach using Adaptive Crossover Mayfly Algorithm (ADCMA) to enhance the charging performance in WRSN. The genetic operator of roulette wheel selection is employed to choose possibly valuable solutions for crossover and the objective is to reduce the overall energy consumption of the system and the mobile charger's total distance traveled, while simultaneously enhancing its vacation time ratio. Unlike prior systems that required the mobile charger to visit and charge all nodes in every cycle, this strategy simply requires the mobile charger to visit a subset of nodes. The proposed ADCMA is also tested on 46 benchmark functions and applied to the on-demand charging model by comparing it with other well-known seven algorithms. The mathematical results demonstrate that the ADCMA effectively decreases the overall energy consumption and distance traveled by the mobile charging device, while still maintaining performance. This algorithm is particularly advantageous for real-world networks as it allows for the network to remain operational with reduced complexity compared to other approaches.

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Section: Preliminariesmentioning

confidence: 99%

Synthetic on-demand charging optimization with ADCMA for energy conservation in WRSN

Mukase,

Xia,

Owoola

2024

Preprint

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“…AI has been able to identify periodontal compromised premolars and molars with 90% and 95% accuracy, respectively [14]. This information is extremely valuable for establishing treatment protocols, leading to a comprehensive improvement in diagnostic accuracy and overall prosthetic treatment outcomes [15].…”

Section: Ai and Diagnosis In Field Of Prosthodonticsmentioning

confidence: 99%

Artificial intelligence in prosthodontics

Al Hendi,

Alyami,

Alkahtany

et al. 2024

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Artificial Intelligence (AI) is gaining popularity worldwide owing to its significant impact in science and innovation. The role of AI in prosthodontics has increased significantly in recent years. AI is used for diagnosis, decision-making, prognosis, treatment planning and prediction of outcomes. Integration of AI into prosthodontics can enhance the accuracy and precision of dental practice. However, limited datasets are a major constraint in its practical applications.

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“…However, the Mamdani FIS uses custom rules to create a knowledge base, which takes more time to execute and requires deep domain knowledge. The charging latency can be decreased by further optimizing the MC's travel path using the common overlapped charging regions of the requesting sensors, 26 suggested an on‐demand charging plan and used FIS to prioritize the overlapped charging regions. Due to the use of FIS and the full charging model, which are inappropriate for big networks, this strategy has a high computational overhead and increases the number of dead nodes.…”

Section: Literature Surveymentioning

confidence: 99%

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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An efficient on-demand charging schedule method in rechargeable sensor networks

Cited by 25 publications

References 45 publications

Synthetic on-demand charging optimization with ADCMA for energy conservation in WRSN

Synthetic on-demand charging optimization with ADCMA for energy conservation in WRSN

Artificial intelligence in prosthodontics

Genetic algorithm‐based partial charging schedule of rechargeable sensor networks

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