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IEESEP: an intelligent energy efficient stable election routing protocol in air pollution monitoring WSNs

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Abstract

Nowadays, wireless sensor network (WSN) consists of insignificant and low-priced sensing hops (nodes) focusing on gathering eco-friendly information. It may be used in a variation of control systems, environment monitoring such as industrial pollution, disaster management, indoor and outdoor temperature. The comprehensive series of uses of WSNs is constantly growing despite the limitations of sensor nodes (SNs) resources like capacity, a range of communication, etc. The major problems faced in WSNs are the maximum energy consumption (EC) and end-to-end delay (E2D) in relaying information to the destination node. This research work proposes an enhanced Stable election protocol that provides intelligent ways to form an optimal route in the network with the FFBPNN algorithm called IEESEP. In this method, the wireless air pollution monitoring (WAPM) System is proficient on a large dataset comprising all scenarios to create WAPMS reliability and adaptability to the environment. Moreover, it is used for varying cluster-based research methodology to improve the network lifetime. A feed-forward, back propagation (FFBPNN) gives to form an optimal path. It enhances network stability by using parameters like advanced and normal nodes. This protocol provides an effective threshold value for selecting an optimal route on the FFBPNN method. So, our research method is highly energy-efficient, proficient at maximizing SNs packet delivery rate and network lifetime. Experimental outperforms define that it results in an IEESEP protocol delivery rate by 78%, other protocols like SEP and ELDC Protocol by 50% and 27% delivery rate.

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  1. Department of Computer Science, Punjabi University, Patiala, Punjab, India

    Ekta Dixit

  2. PG Department of Computer Science, DAV College, Bathinda, Punjab, India

    Vandana Jindal

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Dixit, E., Jindal, V. IEESEP: an intelligent energy efficient stable election routing protocol in air pollution monitoring WSNs. Neural Comput & Applic 34, 10989–11013 (2022). https://doi.org/10.1007/s00521-022-07027-5

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