Q-learning-based algorithms for dynamic transmission control in IoT equipment

Malekijou, Hanieh; Hakami, Vesal; Javan, Nastooh Taheri; Malekijoo, Amirhossein

doi:10.1007/s11227-022-04643-9

Cited by 2 publications

(2 citation statements)

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“…The Markov decision process, Q‐learning, 18 actor‐critic algorithm, 19 and other RL‐based algorithms are used in energy harvesting, routing, decision making and packet transmission 20 . In particular, it plays an important role in determining the shortest path in WSN‐based IoT systems.…”

Section: Introductionmentioning

confidence: 99%

“…14 Also, modern learning methods that are based on artificial intelligence (AI) are proposed for solving complex optimization problems in a dynamic way. 17 The Markov decision process, Q-learning, 18 actor-critic algorithm, 19 and other RL-based algorithms are used in energy harvesting, routing, decision making and packet transmission. 20 In particular, it plays an important role in determining the shortest path in WSN-based IoT systems.…”

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confidence: 99%

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Double Q‐learning‐based adaptive trajectory selection for energy‐efficient data collection in wireless sensor networks

Rajagopal

Bhanumathi

Sakthivel

2023

Int J Communication

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This paper proposes a novel distributed stochastic routing strategy using mobile sink based on double Q-learning algorithm to improve the network performance in wireless sensor network with uncertain communication links. Furthermore, in order to extend network lifetime, a modified leach-based clustering technique is proposed. To balance the energy dissipation between nodes, the selected cluster head nodes are then rotated based on the newly suggested threshold energy value. The simulation results demonstrate that the proposed algorithms outperform the QWRP, QLMS, ESRP and HACDC in terms of network lifetime by 18.33%, 35.1%, 39.7% and 44.7%, respectively. Moreover, the proposed algorithms considerably enhances the learning rate and hence reduces the data collection latency.

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Section: Introductionmentioning

confidence: 99%