Evolving landscape of wireless sensor networks: a survey of trends, timelines, and future perspectives
Amritesh Ojha, Bharat Gupta
Abstract
Wireless sensor networks (WSNs) are being transformed by the integration of artificial intelligence (AI) into mobile communication networks (MCNs), enabling innovative solutions for network optimization, energy efficiency and user experience. Developments in AI methodologies such as machine learning, deep learning, and reinforcement learning have led to rapid advancement in WSNs as a fundamental element of the Internet of Things (IoT). In this survey paper, we analyze contemporary research trends and legacy works in WSNs concentrating on AI driven advances to address the major issues found such as data routing, sensor node deployment, localization and time synchronization. The study highlights the role of AI to enhance energy efficiency, increase the lifetime of the network, and perform cross layer optimization and investigates how wireless sensor networks can be integrated with future technologies like Internet of Things, edge computing, and WSNs. As a source for academics to advance the integration of AI, WSNs, and MCNs, this study provides an in-depth review of significant studies, and underexplored domains. To pursue research in this area, one needs to have deep knowledge about the latest research trends. Using a PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) based systematic review methodology, we identified around 550 publications from 2010 to 2024, sourced from major databases including Springer, Elsevier, and IEEE Xplore. We compile a list of key studies in several WSN subdomains, ranging from achievements to corresponding challenges. In addition to that, we have identified an area that deserves more attention for future research. We also provide a comprehensive analysis of three major databases that focus on disseminating research pertaining to information and communication technology. Specific limitations recognized, including consideration of limited number of databases, potential biases in database coverage and the omission of non-English articles, which may influence the generalizability of our findings. Our study identifies new trends in the healthcare, utilization of artificial intelligence integrated with WSNs in diagnostics, and patient monitoring.