Title: Prolonging the lifetime of wireless sensor models with red panda CAViaR optimisation

Authors: Sunil Kumar Yadav; Ruchi Sharma; Kiran Mayee Adavala

Addresses: Department of Computer Science and Engineering, Madhyanchal Professional University Ratibad, Bhopal, Madhya Pradesh, 462044, India ' Department of Computer Science and Engineering, Madhyanchal Professional University Ratibad, Bhopal, Madhya Pradesh, 462044, India ' CSE (AI and ML), Kakatiya institute of Technology and Sciences Warangal, Telangana, 506015, India

Abstract: The basic issue in WSN is the maximisation of network lifespan relying upon specific constraints. Clustering is an important technique to enhance network lifespan. Here, RPCO is devised for CH selection in WSN. Initially, the WSN system model is simulated, and a deep recurrent neural network (DRNN) is employed to predict the energy. After that, CH selection is carried out employing RPCO that is designed newly by joining red panda optimisation (RPO) with conditional autoregressive value at risk (CAViaR). Furthermore, fitness parameters considered to perform CH selection are distance, residual energy, link life time (LLT), delay, trust and predicted energy. At last, routing is accomplished by the low energy adaptive clustering hierarchy (LEACH) protocol to determine the best route for the transmission of data. Furthermore, RPCO acquired maximum LLT, throughput and trust about 0.912 sec, 12.123 Mbps and 90.112, as well as minimum delay and energy consumption about 0.569 sec and 0.021 J.

Keywords: wireless sensor network; WSN; cluster head; CH; red panda optimisation; RPO; conditional autoregressive value at risk; CAViaR; low energy adaptive clustering hierarchy; LEACH.

DOI: 10.1504/IJAMECHS.2026.155375

International Journal of Advanced Mechatronic Systems, 2026 Vol.13 No.3, pp.188 - 201

Received: 16 Dec 2024
Accepted: 03 Feb 2026
Published online: 30 Jul 2026 *

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