Computational Modelling and Smart Integration of Bio-Inspired Systems for Enhanced Wireless Localization Using Wearable Sensors

Abstract

In green buildings, computational intelligence is progressively applied to improve energy efficiency and sustainability, with special attention to passive cooling systems inspired by natural thermoregulatory systems. In this paper, we present a bio-inspired passive cooling framework that is optimized computationally for smart built environments. Inspired by biological methods-such as evaporative cooling in desert beetles, convective ventilation in termite mounds, and radiative dissipation in elephant ears-the system uses physics-based modeling and algorithmic design to reduce energy use. In addition to sustainable architecture, the same computational and sensor-driven paradigm is directly comparable to the wearable-assisted wireless localization systems in WSNs. Accurate localisation consists of distributed sensing, adaptive modeling, and feedback optimisation - all of which structurally resemble thermal regulation in bio-inspired systems. By shifting the approach to wearable sensor fusion, signal propagation modeling, and machine-learning based refinement, the framework can improve localization precision in dynamic IoT environments. Experimental validation combines sensor-enabled prototypes with data-driven surrogate models and machine learning feedback loops for adaptation of parameters in real-time. Experimental testing shows greater thermal stability, increased air flow (~3.2 m/s) and temperature reductions of up to 5degC in the interior. Applied to WSNs, these results point to the possibility of bio-inspired computational optimization to provide scalable, low cost, and adaptive solutions for smart cities, healthcare monitoring, and industrial IoT applications.