Abstract
A wireless sensor network uses sensor nodes with sensing, manipulating and communication abilities. The energy efficiency is one of the major challenges for WSN as it survives on batteries. Most of the energy is consumed by communication and data processing. Data aggregation is the best way to address such challenges. The in-network data aggregation mainly focuses on these problems which are energy constraint in the sensor networks. The main task in the data aggregation algorithms is to gather data and aggregate it in an energy-efficient manner so as to increase the network lifetime. In this paper, we have studied the random deployment of sensor nodes using eight different random distributions with and without clustering and their impact on the K-means and K-medoids clustering algorithms. Simulation results show that, for a dense WSN scenario, the K-medoids clustering algorithm gives better results for two sensor nodes distributions namely: Beta and Uniform distributions. Also, we carry out a brief survey on different data aggregation algorithms and their comparison on the basis of network lifetime, communication delay, data accuracy and energy efficiency. In the end, we conclude our work with possible future scope.
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Joshi, P., Gavel, S., Raghuvanshi, A.S. (2021). Estimating Energy Consumption for Various Sensor Node Distributions in Wireless Sensor Networks. In: Nath, V., Mandal, J. (eds) Nanoelectronics, Circuits and Communication Systems. Lecture Notes in Electrical Engineering, vol 692. Springer, Singapore. https://doi.org/10.1007/978-981-15-7486-3_28
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DOI: https://doi.org/10.1007/978-981-15-7486-3_28
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