Genetic Algorithm Based Optimization to Improve the Cluster Lifetime by Optimal Sensor Placement in WSN’s
T.Ganesan1, Pothuraju Rajarajeswari2
1T.Ganesan, Assistant Professor, Department of CSE, Koneru Lakshmaiah Education Foundation, Guntur, (A.P), India.
2Pothuraju Rajarajeswari, Professor, Department of CSE, Koneru Lakshmaiah Education Foundation, Guntur, (A.P), India.
Manuscript received on 02 June 2019 | Revised Manuscript received on 10 June 2019 | Manuscript published on 30 June 2019 | PP: 3400-3408 | Volume-8 Issue-8, June 2019 | Retrieval Number: H7547068819/19©BEIESP
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© 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: Wireless sensor network are known for its numerous application and such variety demands improvement of the currently available protocols and its parameter. Certain specific parameter is energy consumption for routing which plays a key role in every application. Genetic algorithm is one of the optimization methods to improve its efficiency for large scale application. Over observation shows that while no of algorithm try to select the best cluster head based some metric, the process normally introduces over heads in communication which in term leads to more energy dissipation. The primary approach in cluster-based routing protocols are to maintain network- cluster lifetime because node displacement and network failure (node energy level). Due to the cluster lifetime maximization, in order to cover the target, sensor node placement is playing the main role to cover maximum target and minimum node connectivity with limited nodes. The simulation work is performed primarily to generate genetic algorithm-based sensor position with different population. The work has been compared with random deployment, genetic algorithm.
Keyword: Wireless Sensor Networks, Cluster lifetime, genetic algorithm and optimization.
Scope of the Article: Clustering.