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Area Coverage Strategy in IoT Networks Based on Redeployment, Descriptive Statistics, Correlation and Regression Parameters

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Abstract

Both minimal set cover and maximum coverage are known to be NP-Hard when using homogeneous and heterogeneous wireless sensor networks due to the lack of knowledge of the minimum set that can cover the area of interest, limited power reserves, monitoring and communication ranges, and so...To avoid these issues, this paper proposes to measure the dispersion of sensor nodes in the large-scale area and construct a decision algorithm based on the descriptive statistics methods to maximize the coverage. The descriptive statistics methods (positional parameters as the arithmetic mean and dispersion parameters as variance or standard deviation) are necessaries to measure the dispersion of sensor nodes in a geographical area. A decision algorithm will be executed when we need redeployment stages of sensor nodes in area of low dispersion, especially on wide geographical areas where the large distances between the sensors nodes and the base station cause a rapid depletion of sensor nodes power. We hope by applying this approach to maintain: (a) area coverage, (b) connectivity, (c) lifetime and (d) ensuring a better quality of service (QoS) of a wide geographical areas. We evaluate the proposed approach by an mathematical proof, followed by a simulation step using Matlab simulator. We demonstrate through extensive simulations, correlation coefficient and regression function that the capability of the proposed approach guarantee full coverage with minimal energy consumption for a period as long as possible.

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This article is part of the topical collection “Sensor Networks” guest edited by César Benavente-Peces and Nirwan Ansari.

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Boualem, A., De Runz, C. & Ayaida, M. Area Coverage Strategy in IoT Networks Based on Redeployment, Descriptive Statistics, Correlation and Regression Parameters. SN COMPUT. SCI. 3, 343 (2022). https://doi.org/10.1007/s42979-022-01235-5

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