Design and Development of an Energy Efficient Algorithm for Data Aggregation in Wireless Sensor Network using Unsupervised Learning
Anitha C L1, R Sumathi2
1Anitha C L, Associate Professor, Department of CSE, Kalpataru Institute of Technology, Tiptur (Karnataka), India.
2Dr. R Sumathi, Professor & Head, Department of CSE, SIT, Tumkur (Karnataka), India.
Manuscript received on 03 December 2019 | Revised Manuscript received on 11 December 2019 | Manuscript Published on 31 December 2019 | PP: 124-128 | Volume-9 Issue-2S December 2019 | Retrieval Number: B10721292S19/2019©BEIESP | DOI: 10.35940/ijitee.B1072.1292S19
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: A wireless sensor network generally defined as the collection of sensors that are utilized to track and record the data in real-time on an ongoing basis from different applications. In comparison with other sensor nodes, data transmission obtained through sinks in WSN eliminates the energy in nearby nodes. This issue is identified as one of the major problems in a wireless sensor network. Two new algorithms were proposed in this research paper that mainly focused on the usage of machine learning algorithms to solve the data collection issue in the wireless sensor network. The algorithms proposed will able to create cluster heads to decrease energy usage, this will save about 50% of battery power consumption and mobile sinks are used to record the data from cluster heads in a network. Ultimately, current algorithms such as RLLO, DBRkM, CLIQUE, RL-CRC, and EPMS were compared.
Keywords: Agents, Cluster Head, Markov Decision Process, Sink Traversal, Reinforcement Learning.
Scope of the Article: Wireless ad hoc & Sensor Networks