Complete Targets Coverage in Energy-Harvesting IoT Networks With Dual Imperfect Batteries

Zhang, Longji; Chin, Kwan-Wu; Wang, Luyao; Yang, Chang‐Lin

doi:10.1109/jiot.2021.3109148

Cited by 5 publications

(2 citation statements)

References 37 publications

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“…Alternatively, ambient energy can be harvested to charge low-power IoT devices. The harvested energy can be used either to extend the lifespan of batteries or to completely eliminate batteries [2][3][4][5][6]. Solar energy is a clear candidate because it is the most powerful source of ambient energy, and the relevant industry is quite mature [7].…”

Section: Introductionmentioning

confidence: 99%

“…Solar energy is a clear candidate because it is the most powerful source of ambient energy, and the relevant industry is quite mature [7]. However, it has certain limitations: (1) its full potential is not globally available; there are multiple places with a short sunshine duration, (2) solar panels generate energy for a maximum of only 12 h a day, (3) the efficiency of solar panels significantly deteriorates in multiple settings, such as indoor environments, hidden/buried underground areas, and places where panels are shaded or covered. Therefore, depending only on solar energy for powering IoT sensor nodes is risky.…”

Section: Introductionmentioning

confidence: 99%

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Synergistic multi-source ambient RF and thermal energy harvester for green IoT applications

et al. 2023

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Synergistic multi-source ambient RF and thermal energy harvester for green IoT applications

et al. 2023

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Distributed dynamic scheduling algorithm of target coverage for wireless sensor networks with hybrid energy harvesting system

Bao,

Jiang,

Han

et al. 2024

Sci Rep

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The integration of energy harvesting techniques has the potential to significantly prolong target monitoring in wireless sensor networks (WSNs). However, the stochastic nature of hybrid solar-wind energy arrivals poses a significant challenge to optimizing energy utilization for target coverage. To address this issue, we propose a dynamic and distributed node scheduling algorithm based on Lyapunov optimization for hybrid energy-harvesting WSNs (HEH-WSNs). By formulating the maximum long-term average coverage utility subject to peak power constraints, we utilize Lyapunov optimization theory to develop a dynamic potential game framework for target coverage optimization in HEH-WSNs. The proposed distributed dynamic target-coverage node scheduling algorithm (DTNSA) is then derived from the potential game. We present a comprehensive performance analysis of the distributed implementation and evaluate its efficiency through extensive simulations. The results demonstrate that in two distinct scenarios, specifically with different numbers of sensor nodes and target nodes, the average coverage utility of our proposed DTNSA exceeds that of existing algorithms by and , respectively. The performance of the average number of active sensor nodes decreased by and compared to existing algorithms, while the average coverage redundancy decreased by and relative to existing algorithms. Furthermore, our algorithm adapts effectively to dynamic changes in hybrid harvested energy and exhibits lower computational complexity compared to existing target coverage algorithms.

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Solar Powered IoT Sensors to Increase the Network Longevity

Sharanya,

Brahma Rao,

Rohith Bhat

et al. 2023

Electric Power Components and Systems

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Complete Targets Coverage in Energy-Harvesting IoT Networks With Dual Imperfect Batteries

Cited by 5 publications

References 37 publications

Synergistic multi-source ambient RF and thermal energy harvester for green IoT applications

Synergistic multi-source ambient RF and thermal energy harvester for green IoT applications

Distributed dynamic scheduling algorithm of target coverage for wireless sensor networks with hybrid energy harvesting system

Solar Powered IoT Sensors to Increase the Network Longevity

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