Abstractive Text Summarization

Abstract

This study explores the application of Internet of Things (IoT) technologies for mon- itoring household energy consumption and proposes a framework for optimizing wireless network routing using algorithms inspired by natural processes. The initial chapters introduce the significance of IoT in enhancing energy efficiency, home safety, and sustain- ability through remote monitoring and optimal appliance maintenance. A comprehensive literature survey reviews key nature-inspired algorithms, including Particle Swarm Opti- mization (PSO), Artificial Bee Colony (ABC) optimization, and the Bacterial Foraging Algorithm (BFO), highlighting their mechanisms and applications. The proposed framework integrates these algorithms into a simulation setup, detailing the methodology for node creation, network configuration, mobility and energy models, and dynamic routing updates. The PSO algorithm is employed to optimize routing paths based on fitness metrics such as path length, energy consumption, link stability, and end-to-end delay. Simulation results indicate that increasing the number of nodes enhances routing success rates, while an optimal number of particles in the PSO is crucial for performance. A lower number of particles (20) yielded the best results, suggesting a balance between computational overhead and algorithm efficiency. Overall, the findings underscore the importance of parameter tuning in nature-inspired algorithms to achieve efficient and reliable routing in wireless networks, contributing to the advancement of IoT applications in energy management and beyond.