Volume no :
9 |
Issue no :
1
Article Type :
Scholarly Article
Author :
Dr.P.Meenalochini
Published Date :
May, 2025
Publisher :
Journal of Artificial Intelligence and Cyber Security (JAICS)
Page No: 1 - 11
Abstract : Wireless Sensor Networks (WSNs) are critical for various applications, including environmental monitoring, healthcare, smart cities, and industrial automation. However, WSNs face several challenges, such as limited energy resources, network scalability, data transmission reliability, and the efficient allocation of network resources. To address these challenges, optimization techniques are essential for improving the overall performance of WSNs. Evolutionary algorithms (EAs) have emerged as powerful tools for optimizing the parameters and operation of WSNs. This paper explores the application of EAs, particularly Genetic Algorithms (GAs), Particle Swarm Optimization (PSO), and Differential Evolution (DE), in optimizing key aspects of WSNs to enhance their efficiency and performance. In WSNs, energy efficiency is a primary concern due to the limited battery life of sensor nodes. EAs can be used to optimize energy consumption by selecting optimal routing paths, determining the best sleep-wake schedules, and managing the power usage of individual nodes. Additionally, these algorithms can help in optimizing the placement of sensor nodes to improve coverage while minimizing energy usage. The adaptability of EAs allows them to dynamically respond to changing network conditions, such as node failures or varying environmental factors, further enhancing network resilience and longevity. The scalability of WSNs is another challenge, especially when dealing with large numbers of sensor nodes. EAs can be utilized to design scalable network architectures, optimizing cluster formation, node grouping, and data aggregation strategies. By using evolutionary approaches, the network can be scaled efficiently without compromising performance, ensuring that large-scale deployments maintain optimal performance while reducing the overhead. Another critical aspect of WSN optimization is data transmission. EAs help to enhance the reliability of data transmission by optimizing routing protocols, load balancing, and fault tolerance mechanisms. By finding the best trade-offs between routing cost and data reliability, EAs can significantly reduce latency and improve the overall throughput of the network. Moreover, these algorithms can be integrated with machine learning techniques to predict network congestion and adjust routing decisions in real time. In conclusion, the application of evolutionary algorithms offers a promising approach to solving the complex optimization problems in WSNs. The flexibility, adaptability, and efficiency of EAs make them well-suited for enhancing the performance of WSNs, ensuring energy efficiency, scalability, and reliable data transmission across diverse applications. Future research should focus on hybridizing evolutionary algorithms with other intelligent optimization techniques to further enhance WSN capabilities.
Keyword Wireless sensor networks, evolutionary algorithms, network optimization, performance enhancement, energy efficiency, sensor node deployment, data routing optimization, genetic algorithms, swarm intelligence, metaheuristic techniques, WSN scalability, adaptive optimization, network lifetime improvement, intelligent routing, heuristic-based optimization.
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