Harnessing Bio-Inspired Optimization and Swarm Intelligence for Energy-Aware TinyML in IoT

This research investigates the integration of bio-inspired optimization and swarm intelligence principles with TinyML for the development of energy-aware Internet of Things (IoT) devices. A novel model algorithm, termed "BioSwarmML," is introduced and evaluated against existing algorithms through comprehensive simulation analyses employing suitable metrics. The proposed framework aims to enhance energy efficiency in IoT applications by leveraging the collective intelligence derived from bio-inspired optimization and swarm behaviors. The "BioSwarmML" algorithm is designed to draw inspiration from natural processes, incorporating bio-inspired optimization techniques such as genetic algorithms, simulated annealing, and evolutionary strategies. Concurrently, swarm intelligence principles are integrated to emulate decentralized and self-organizing behaviors observed in biological systems. This amalgamation aims to optimize the energy consumption of TinyML models on IoT devices, facilitating sustainable and adaptive learning processes. Simulation analyses involve a comparative study with established algorithms, evaluating BioSwarmML based on metrics such as energy consumption, accuracy, and latency. The results demonstrate the efficacy of the proposed algorithm in achieving superior energy efficiency while maintaining competitive performance in terms of accuracy and responsiveness. The comparison sheds light on the advantages of BioSwarmML in energy-aware TinyML applications, showcasing its potential for widespread adoption in IoT ecosystems. This research contributes to the advancement of energy-efficient IoT systems by introducing a novel algorithmic paradigm that aligns with international journal standards. The proposed "BioSwarmML" model showcases a promising avenue for enhancing sustainability in TinyML-driven IoT applications, offering a valuable addition to the existing body of knowledge in the field.