Artificial Intelligence Enabled Ad Hoc and Wireless Sensor Networks: Architectures, Challenges, and Applications

Ad hoc networks and wireless sensor networks (WSNs) have emerged as essential components of modern wireless communication systems due to their ability to operate without fixed infrastructure in dynamic and resource-constrained environments. These networks support a wide range of applications, including disaster recovery, military communication, smart cities, healthcare monitoring, and environmental sensing. However, their performance is significantly affected by challenges such as dynamic topology changes, limited energy resources, scalability constraints, and security vulnerabilities. Traditional rule-based networking approaches often struggle to address these issues effectively under real-world conditions. Recent advancements in artificial intelligence (AI) have introduced new opportunities to enhance the adaptability and intelligence of ad hoc networks and WSNs. AI-driven techniques, including machine learning, deep learning, and reinforcement learning, enable data-driven routing optimization, predictive energy management, intelligent fault detection, and adaptive security mechanisms. This review presents a comprehensive and critical analysis of recent research (2019-2025) on AI-enabled ad hoc networks and wireless sensor networks. The study systematically examines routing strategies, energy-efficient techniques, security solutions, and intelligent data processing approaches, while highlighting their advantages and limitations. Emerging trends such as edge intelligence and federated learning are also discussed. Finally, the review identifies open research challenges and future directions necessary for the practical deployment of scalable, secure, and intelligent wireless networking systems.