ReNEW: A Practical Module for Reliable Routing in Networks of Energy-Harvesting Wireless Sensors

Rao, Vijay S.; Prasad, Ramjee; Sarkar, Chayan; Niemegeers, Ignas

doi:10.1109/globecom38437.2019.9013219

Cited by 3 publications

(3 citation statements)

References 29 publications

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“…k is the number of the data packet being stored in queue j , which encourages agents to send the task to the device that does not have a heavier transmission burden. The Er is a bonus reward if the agent transmits data to Sink node, given as (5). 5) Queue overflow: The storage of destination node j reaches its capacity Ψ.…”

Section: Gap Settingsmentioning

confidence: 99%

“…Existing studies [5], [6] have made efforts to improve the reliability and energy efficiency of routing selection by * Corresponding Author either finding the shortest routing path or minimizing the transmission time for the small-scale IoT systems powered by EH. However, the packet loss and energy depletion brought by the power intermittency are still yet to be solved.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Energy Harvesting Aware Multi-hop Routing Policy in Distributed IoT System Based on Multi-agent Reinforcement Learning

Zhang¹,

Liu²,

Xie³

et al. 2022

Preprint

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Energy harvesting technologies offer a promising solution to sustainably power an ever-growing number of Internet of Things (IoT) devices. However, due to the weak and transient natures of energy harvesting, IoT devices have to work intermittently rendering conventional routing policies and energy allocation strategies impractical. To this end, this paper, for the very first time, developed a distributed multi-agent reinforcement algorithm known as global actor-critic policy (GAP) to address the problem of routing policy and energy allocation together for the energy harvesting powered IoT system. At the training stage, each IoT device is treated as an agent and one universal model is trained for all agents to save computing resources. At the inference stage, packet delivery rate can be maximized. The experimental results show that the proposed GAP algorithm achieves ∼ 1.28× and ∼ 1.24× data transmission rate than that of the Q-table and ESDSRAA algorithm, respectively.

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Section: Gap Settingsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Energy Harvesting Aware Multi-hop Routing Policy in Distributed IoT System Based on Multi-agent Reinforcement Learning

Zhang¹,

Liu²,

Xie³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…• To the best of our knowledge, this is the first work (apart from our conference publication [14]) that attempts to provide guarantees on latency and improves reliability considerably for data collection and dissemination in EH-WSNs. This practically important aspect is novel and has not yet received its due attention.…”

mentioning

confidence: 99%