Abstract
Generally, wireless sensor network is a group of sensor nodes which is used to continuously monitor and record the various physical, environmental, and critical real time application data. Data traffic received by sink in WSN decreases the energy of nearby sensor nodes as compared to other sensor nodes. This problem is known as hot spot problem in wireless sensor network. In this research study, two novel algorithms are proposed based upon reinforcement learning to solve hot spot problem in wireless sensor network. The first proposed algorithm RLBCA, created cluster heads to reduce the energy consumption and save about 40% of battery power. In the second proposed algorithm ODMST, mobile sink is used to collect the data from cluster heads as per the demand/request generated from cluster heads. Here mobile sink is used to keep record of incoming request from cluster heads in a routing table and visits accordingly. These algorithms did not create the extra overhead on mobile sink and save the energy as well. Finally, the proposed algorithms are compared with existing algorithms like CLIQUE, TTDD, DBRkM, EPMS, RLLO, and RL-CRC to better prove this research study.
Highlights
This research study started with a valid question of how to enhance the network lifetime of Wireless sensor network (WSN) with better energy optimization of sensor nodes by using reinforcement learning
We have motivated towards research in sink mobility which has emerged in WSNs to properly handle the hot spot problem and to decrease the energy communication overheads [4]
To properly check the performance of our proposed reinforcement learning based clustering algorithm (RLBCA) and on-demand mobile sink traversal (ODMST) algorithm, we have compared these algorithms with other algorithms, namely, TTDD [13], DBRkM [3], EPMS [5], RLLO [15], and Reinforcement learning (RL)-CRC [16]
Summary
This research study started with a valid question of how to enhance the network lifetime of WSN with better energy optimization of sensor nodes by using reinforcement learning. Mobile sink [5, 6] needs to visit every cluster head [2] to collect the data, leading to longer mobile sink traversal path which in turns creates data delivery latency [7, 8] and higher energy consumption For this reason, we proposed RLBCA and ODMST algorithm upon reinforcement learning. Wireless Communications and Mobile Computing sink only to interested cluster heads by receiving a request message packet for collection of data Design of such on-demand mobile sink traversal path [3] is a challenging task as it highly depends upon coverage of network, data delivery, energy efficiency, and lifetime of network. (iii) Comparison of these above-mentioned algorithms with existing algorithm like CLIQUE [12], TTDD [13, 14], EPMS [5], DBRkM [3], RLLO [15], and RLCRC [16] to better prove our simulation results
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