Abstract
Wireless sensor network (WSN) is a group of sensor nodes deployed and resource-constrained sensor nodes aware their surroundings and communicate the sensed data to the base station through sink node. Based on environmental conditions such as sound, humidity, temperature, wind, gas sensor can be clearly determined by WSN. In sensor node deployment model, Target COVerage (TCOV) and Network CONnectivity (NCON) are the basic issues in WSNs that have found important attention in Sensor Deployment Problem. In this viewpoint, Genetic Algorithm (GA) and Particle Swarm Optimisation (PSO) are conveyed to find optimal locations for sensor nodes. GA and PSO are evolutionary computation methods based optimisation scheme inspired from biology. The principal objective of WSN is to organise the whole sensor nodes in their related positions, thereby developing an effective network. In WSN, Many research works aspire the involvement of smart context awareness algorithm for sensor deployment issues in WSN. GA and PSO of the TCOV and NCON process are deployed as the minimisation problem.
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Acknowledgement
Author’s thanks to Dr. Baby Joseph Dean of Research, Dr. G. Ilavazhagan Director of Research, Head of Information Technology Dr. K. Ramesh Kumar and Head of Computer Science and Engineering Dr. Rajeswari Mukesh of Hindustan Institute of Technology and Science, Chennai for approval of topic and for their insightful comments, encouragement and love. Research scholar very thank full for guidance received from Dr. A. Ramesh Babu and express my sincere gratitude to Expert Panel Members.
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Puri, V., Ramesh Babu, A., Sudalai Muthu, T., Potdar, S. (2019). An Effective Optimisation Algorithm for Sensor Deployment Problem in Wireless Sensor Network. In: Prateek, M., Sharma, D., Tiwari, R., Sharma, R., Kumar, K., Kumar, N. (eds) Next Generation Computing Technologies on Computational Intelligence. NGCT 2018. Communications in Computer and Information Science, vol 922. Springer, Singapore. https://doi.org/10.1007/978-981-15-1718-1_21
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DOI: https://doi.org/10.1007/978-981-15-1718-1_21
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