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• W2531644154 abstract "Resource-constrained nodes in unattended wireless sensor network (UWSN) operate in a hostile environment with less human intervention. Achieving the optimal quality of service (QoS) in terms of packet delivery ratio, delay, energy, and throughput is crucial. In this paper, we propose a topology control and data dissemination protocol that uses multi-agent reinforcement learning (MRL) and energy-aware convex-hull algorithm, for effective self-configuration and self-optimization (SCSO) in UWSN, called MRL-SCSO. MRL-SCSO maintains a reliable topology in which the effective active neighbor nodes are selected using MRL. The network boundary is determined using convex-hull algorithm to maintain the connectivity and coverage of the network. The boundary nodes transmit data under high traffic load conditions. The performance of MRL-SCSO is evaluated for various nodes count and under different load conditions by using the Contiki’s Cooja simulator. The results showed that MRL-SCSO stabilizes the performance and improves QoS." @default.
• W2531644154 created "2016-10-21" @default.
• W2531644154 creator A5062445099 @default.
• W2531644154 creator A5075572378 @default.
• W2531644154 date "2016-10-25" @default.
• W2531644154 modified "2023-10-16" @default.
• W2531644154 title "Erratum to: MRL-SCSO: Multi-agent Reinforcement Learning-Based Self-Configuration and Self-Optimization Protocol for Unattended Wireless Sensor Networks" @default.
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• W2531644154 doi "https://doi.org/10.1007/s11277-016-3832-5" @default.
• W2531644154 hasPublicationYear "2016" @default.
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