A new routing mechanism for energy-efficient in bluetooth mesh-low power nodes based on wireless sensor network

• Main Author: Almuslehi, Hussein Saad Mohammed
• Format: Thesis
• Language: English; English
• Published: 2023
• Subjects: T Technology (General)
• Online Access: https://etd.uum.edu.my/11469/1/s904149_01.pdf; https://etd.uum.edu.my/11469/2/s904149_02.pdf; https://etd.uum.edu.my/11469/

The escalating energy consumption in wireless sensor networks (WSNs) necessitates immediate action for conservation. As WSNs are deployed across various applications, increased energy demands have adversely impacted operational efficiency, network lifetime, and sustainability. Thus, it is crucial to explore strategies to minimize energy consumption and optimize WSN performance. This study focuses on analyzing and enhancing routing algorithms in Bluetooth Mesh Low Power Node (M-LPN) and WSN, with an emphasis on energy efficiency. The study also explores the criteria for node formation and selection, proposing a multi-criterion, energyefficient routing mechanism that takes into account security and sets directions for future research. This research assesses the efficacy of routing algorithms within the Bluetooth M-LPNWSN architecture. It evaluates system formation methods, hierarchical structures, and leader selection criteria, comparing various parameters to identify factors influencing node formation and routing. The multi-criterion energyefficient routing mechanism uses the ACO algorithm, inspired by ants' foraging behaviour. In Bluetooth M-LPNWSN systems, virtual ants explore paths based on signal robustness, distances, and energy consumption. Pheromone trails mark efficient routes, reinforcing paths with higher pheromone concentrations. This optimizes data transmission, enhances energy efficiency, and extends network longevity. The ACO algorithm integrates multiple criteria, ensuring routes are short and energy-efficient. The ACO algorithm in the Bluetooth M-LPNWSN system reduces energy consumption by 60% over 500 iterations and significantly improves data delivery rates by adapting dynamically to network topology and traffic. Compared to the Ant Colony Optimization-Genetic Algorithm (ACO-GA) and Ant Colony Optimization- Hierarchical Clustering Mechanism (ACOHCM), the ACO algorithm shows superior power savings and efficiency. Future research will focus on developing a Software Defined Networking (SDN) management framework, optimizing power management strategies, and exploring diverse network environments to enhance the adaptability and performance of wireless sensor networks.