Energy Efficient CH Selection Scheme Based on ABC and Q-Learning Approaches for IoUT Applications

• Published in: Systems (Basel) Vol. 11; no. 11; p. 529
• Main Authors: Sayed Ali, Elmustafa, Saeed, Rashid A., Eltahir, Ibrahim Khider, Abdelhaq, Maha, Alsaqour, Raed, Mokhtar, Rania A.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2023
• Subjects: ABC; ACO; Algorithms; Ant colony optimization; CH selection optimization; Clustering; Communication; Energy consumption; Energy efficiency; Energy levels; Energy resources; Engineering research; heterogeneous IoUT; Internet of Things; Localization; Mathematical optimization; Methods; Nodes; Optimization; Particle swarm optimization; PSO; Residual energy; Sensors; Signal quality; Swarm intelligence; Underwater acoustics; Underwater communication; Underwater construction; Saudi Arabia
• ISSN: 2079-8954; 2079-8954
• DOI: 10.3390/systems11110529

Nowadays, the Internet of Underwater Things (IoUT) provides many marine 5G applications. However, it has some issues with energy efficiency and network lifetime. The network clustering approach is efficient for optimizing energy consumption, especially for underwater acoustic communications. Recently, many algorithms have been developed related to clustering-based underwater communications for energy efficiency. However, these algorithms have drawbacks when considered for heterogeneous IoUT applications. Clustering efficiency in heterogeneous IoUT is influenced by the uniform distribution of cluster heads (CHs). As a result, conventional schemes are inefficient when CHs are arranged in large and dense nodes since they are unable to optimize the right number of CHs. Consequently, the clustering approach cannot improve the IoUT network, and many underwater nodes will rapidly consume their energies and be exhausted because of the large number of clusters. In this paper, we developed an efficient clustering scheme to effectively select the best CHs based on artificial bee colony (ABC) and Q-learning optimization approaches. The proposed scheme enables an effective selection of the CHs based on four factors, the residual energy level, the depth and the distance from the base station, and the signal quality. We first evaluate the most suitable swarm algorithms and their impact on improving the CH selection mechanism. The evaluated algorithms are generic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO), and ABC. Then, the ABC algorithm process is improved by using the Q-learning approach to improve the process of ABC and its fitness function to optimize the CH selection. We observed from the simulation performance result that an improved ABC-QL scheme enables efficient selection of the best CHs to increase the network lifetime and reduce average energy consumption by 40% compared to the conventional ABC.