Neural attention guided stochastic fractal search for energy efficient localization in mobile wireless sensor networks

Wireless Sensor Networks (WSNs) require novel approaches to optimize localization accuracy and minimize energy usage due to their decentralized structure and the resource constraints of mobile sensor nodes. This research proposes LEE-NAM-SFS, a neural attention guided stochastic fractal search framework that equips sensor nodes with cognitive capabilities to adapt their movement patterns, sensing behavior, and communication strategies. The neuronal attention model selectively focuses on high-value measurements, while stochastic fractal search explores the high-dimensional search space of node trajectories and routing choices to jointly optimize localization and energy efficiency. Extensive simulations on a 100-node mobile WSN scenario show that, compared to FLA-RTWOA, DASUL, RDEANTN, MANAL, and QLAMSR, LEE-NAM-SFS improves localization accuracy by approximately 2–5 %, enhances energy efficiency by 5–10 %, increases data delivery rate by 2–5 %, expands effective coverage area by 5–10 %, and prolongs network lifetime by 5–10 %. These gains are achieved without compromising connectivity or data reliability.