Future Trends in Wireless Sensor Networks: Integration of Artificial Intelligence for Autonomous and Adaptive Sensor Networks
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Keywords: Wireless Sensor Network, Artificial Intelligence, Autonomous Network, Adaptive Network, Internet of ThingsAbstract
Wireless Sensor Networks (WSN) are an important component in the Internet of Things ecosystem and smart systems, but still face fundamental limitations related to energy efficiency, adaptability, scalability, and network reliability. The development of Artificial Intelligence (AI) offers new opportunities to transform WSN from conventional sensor networks into intelligent and autonomous networks. This study aims to examine future trends in WSN through the integration of AI in supporting autonomous and adaptive sensor networks. This study uses a systematic literature review method with conceptual analysis of reputable scientific literature discussing the application of AI in WSN, covering aspects of network architecture, routing, energy management, security, and autonomy mechanisms. The results of the study show that AI approaches, such as machine learning, deep learning, reinforcement learning, and federated learning, significantly improve energy efficiency, network adaptability, system reliability, and WSN scalability. In addition, AI integration enables the implementation of self-configuration, self-optimization, self-healing, and self-protection mechanisms, which are the main characteristics of autonomous sensor networks. However, a trade-off was found between increasing network intelligence and system complexity, particularly in terms of computational requirements and energy consumption. This study confirms that AI is a key element in the development of future autonomous and adaptive WSNs. Further research should focus on developing lightweight, energy-efficient, and secure AI models to support large-scale WSN implementation.
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