Q-Learning Enabled Green Communication in Internet of Things

Limited energy capacity, physical distance between two nodes and the stochastic link quality are the major parameters in the selection of routing path in the internet of things network. To alleviate the problem of stochastic link quality as channel gain reinforcement based Q-learning energy balanced...

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Bibliographic Details
Published inNashrīyah-i mudīrīyat-i fannāvarī-i iṭṭilāʻāt Vol. 14; no. Security and Resource Management challenges for Internet of Things; pp. 103 - 117
Main Authors Mukesh Kumar, Sushil Kumar, Ankita Jaiswal, Pankaj Kumar Kashyap
Format Journal Article
LanguageEnglish
Published University of Tehran 01.03.2022
Subjects
energy balancing
internet of things
learning rate
link quality
qrl
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Summary:Limited energy capacity, physical distance between two nodes and the stochastic link quality are the major parameters in the selection of routing path in the internet of things network. To alleviate the problem of stochastic link quality as channel gain reinforcement based Q-learning energy balanced routing is presented in this paper. Using above mentioned parameter an optimization problem has been formulated termed as reward or utility of network. Further, formulated optimization problem converted into Markov decision problem (MDP) and their state, value, action and reward function are described. Finally, a QRL algorithm is presented and their time complexity is analyses. To show the effectiveness of proposed QRL algorithm extensive simulation is performed in terms of convergence property, energy consumption, residual energy and reward with respect to state-of-art-algorithms.
ISSN:2008-5893
2423-5059
DOI:10.22059/jitm.2022.86486