International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 11 Issue: 07 | July 2024
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p-ISSN: 2395-0072
Energy Optimization Approaches for CH in WSNs: A Review Eshtiag Jahalrasool Ahmed1,3, Abdalla Akod Osman2, Sally Dfaallah Awadalkareem1 1Department of Computer Engineering, University of Gezira, Wad Madani, Sudan 2Department of Computer Sciences, University of Elahlia, Wad Madani, Sudan.
3Department of Information Technology, College of Computing, and Information Technology Khulais, University of
Jeddah, Jeddah 21959, Saudi Arabia ---------------------------------------------------------------------------***--------------------------------------------------------------------------Abstract In recent years, wireless sensor networks (WSNs) have grown in importance as a technology for various uses, including healthcare systems, industrial automation, and environmental monitoring. This research thoroughly analyzes methods for optimizing energy consumption in WSN Cluster Heads (CHs). Energy efficiency methods and technologies for Wireless Sensor Networks are examined in the research. Due to predetermined protocols and static factors, traditional approaches are successful yet ineffective in dynamic contexts. By forecasting energy use and improving CH selection and data routing, machine learning systems may circumvent these restrictions. The research also illustrates the advantages of hybrid models that mix classical optimization with modern machine learning methods. Hybrid energy management methods include incorporating heuristic algorithms with machine learning to improve CH election and load balancing decisions are more resilient. The study shows that these improved technologies may enhance energy efficiency by 30% and network lifetime by using simulations and tests. Machine learning models provide more dependable data transfer, reduce packet loss, and maintain network performance. However, the research admits some obstacles and limits, including the requirement for significant computing resources and specialized expertise, which might increase the complexity and expense of network administration. The study concludes that WSNs need creative energy optimization methodologies, improving algorithms for varied WSN applications and exploring new innovation paths. Addressing present limits and pursuing new innovation routes will help academics and practitioners develop sustainable, high-performance WSNs that match contemporary application needs.
Keywords: WSNs, CH, Energy Optimization, Clustering Algorithms, ML, Nature-Inspired Optimization, Energy Efficiency. I. Introduction WSNs are decentralized, self-operating sensors that track and report various physical and environmental variables [1]. They have evolved due to advancements in sensor technologies, wireless communication, and microelectronics. Standard WSNs consist of sensing nodes, data sinks, and communication networks, which gather, interpret, and exchange information wirelessly [2]. These networks are built with scalability, energy efficiency, and environmental adaptability, making them useful in various fields such as healthcare, agriculture, smart cities, and disaster management [3]. Energy efficiency is crucial for WSNs, as it allows for a longer network lifetime, fewer maintenance costs, more reliability and data continuity, better resource utilization, environmental sustainability, energy-harvesting solutions, adaptability to changing environments, and less network overhead [4]. WSNs with energy-efficient designs make the most of scarce resources like processing power, bandwidth, and battery life. These are particularly important when several sensor nodes work together to acquire data [5]. Cluster Heads (CHs) play a crucial role in improving network efficiency, communication, and energy consumption. CHs work together to compile information from all cluster nodes and send it to the central location or sink [6]. They also play an essential role in energy management, helping WSNs stay organized in a hierarchical structure and allowing them to scale more easily by splitting the network into smaller units called clusters, each with its own CH [7]. CHs oversee and coordinate the actions of other nodes in their cluster, improving data management, communication, and energy usage. They aggregate data from sensor nodes inside their cluster by eliminating duplicate transmissions and preserving energy [8]. They ensure the network lasts a long time by controlling energy usage and ensuring nodes do not use too much. By facilitating the transfer of aggregated data from sensor nodes to the sink, they improve communication efficiency and reduce power consumption [9]. Chase Heads also
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