TY - JOUR ID - 86486 TI - Q-Learning Enabled Green Communication in Internet of Things JO - Journal of Information Technology Management JA - JITM LA - en SN - AU - Kumar, Mukesh AU - Kumar, Sushil AU - Jaiswal, Ankita AU - Kashyap, Pankaj Kumar AD - Ph.D. Scholar, School of Computer & Systems Sciences, Jawaharlal Nehru University, New Delhi-110067, AD - Assistant Professor, School of Computer & Systems Sciences, Jawaharlal Nehru University, New Delhi-110067. AD - Ph.D. Scholar, School of Computer & Systems Sciences, Jawaharlal Nehru University, New Delhi-110067. AD - Ph.D., School of Computer & Systems Sciences, Jawaharlal Nehru University, New Delhi-110067. Y1 - 2022 PY - 2022 VL - 14 IS - Special Issue: Security and Resource Management challenges for Internet of Things SP - 103 EP - 117 KW - Energy balancing KW - QRL KW - Link Quality KW - Learning rate KW - Internet of Things DO - 10.22059/jitm.2022.86486 N2 - 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. UR - https://jitm.ut.ac.ir/article_86486.html L1 - https://jitm.ut.ac.ir/article_86486_aecb312b47cc34d72adb686ae1fd659f.pdf ER -