On lifetime enhancement of dynamic wireless sensor networks with energy-harvesting sensors

Lin, Chun-Cheng; Deng, Der-Jiunn; Shu, Lei; Wang, Kun; Wang, Shang-Bin; Tsai, I-Hsin

doi:10.1109/iccchina.2016.7636837

Cited by 5 publications

(2 citation statements)

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“…Meanwhile, other states change independently of each other. Therefore, for the chosen action A, the transition probability from the current state of IoT device i, (10) We assume that the inter-task occurrence time of IoT device i follows an exponential distribution with mean 1/λ i . Then, the probability that the task occurs in IoT device i during a decision epoch can be calculated as λ i τ [27,31].…”

Section: Transition Probabilitymentioning

confidence: 99%

“…In this situation, if an IoT device cannot harvest energy for a long time and it processes lots of tasks requiring high computing power, its energy can be depleted. To mitigate this problem and improve the energy efficiency of harvesting IoT devices, a number of works (e.g., sleep scheduling, CPU cycle adjustment, and so on) have been investigated in the literature [5][6][7][8][9][10]. One of the possible solutions is offloading tasks to nearby IoT devices [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

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Energy Efficient Cooperative Computation Algorithm in Energy Harvesting Internet of Things

Lee

Jang

et al. 2019

Energies

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The limited battery capacity of Internet of Things (IoT) devices is a major deployment barrier for IoT-based computing systems. In this paper, we propose an energy efficient cooperative computation algorithm (EE-CCA). In an EE-CCA, a pair of IoT devices decide whether to offload some parts of the task to the opponent by considering their energy levels and the task deadline. To minimize the energy outage probability while completing most of tasks before their deadlines, we formulate a constraint Markov decision process (CMDP) problem and the optimal offloading strategy is obtained by linear programming (LP). Meanwhile, an optimization problem of finding pairs of IoT devices (i.e., IoT device pairing problem) is formulated under the optimal offloading strategy. Evaluation results demonstrate that the EE-CCA can reduce the energy outage probability up to 78% compared with the random offloading scheme while completing tasks before their deadlines with high probability.

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Section: Transition Probabilitymentioning

confidence: 99%