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- W4360996888 abstract "Wireless sensor networks (WSNs) have been widely used as a potential network paradigm for the ubiquitous interconnection of devices. However, due to the property of limited energy resources, an energy-efficient routing algorithm is urgently needed to prolong the network lifetime. To tackle this issue, we propose MARTO, a Multi-Agent Deep Reinforcement Learning (MADRL) based scheme to provide online topology optimization, after modeling the WSN lifetime maximization problem as a Decentralized Partially Observable Markov Decision Process (Dec-POMDP). Additionally, a loop-free topology generation algorithm is given which can completely avoid routing loops to assist the decision-making of RL agents. Experimental results show that MARTO outperforms several heuristic algorithms and has high energy efficiency which can effectively prolong the lifetime of WSNs." @default.
- W4360996888 created "2023-03-30" @default.
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- W4360996888 date "2022-11-11" @default.
- W4360996888 modified "2023-09-24" @default.
- W4360996888 title "MARTO: A Multi-agent Deep Reinforcement Learning Based Online Topology Optimization Scheme for Wireless Sensor Network" @default.
- W4360996888 doi "https://doi.org/10.1109/icct56141.2022.10073203" @default.
- W4360996888 hasPublicationYear "2022" @default.
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