Tekoälytekniikat energiatehokkuuden optimointiin langattomissa anturiverkoissa - Pihlajisto, Mikko (2025)
The application of artificial intelligence (AI) techniques for optimizing energy efficiency in wireless sensor networks (WSNs) is a rapidly growing research area, with particular relevance for managing distributed and resource-constrained environments. This master's thesis presents a systematic mapping study aimed at identifying which AI methods are currently used to promote energy efficiency in WSN contexts, what challenges hinder their implementation, and what research gaps and future directions can be identified. The study analyzed 75 peer-reviewed articles to form a comprehensive overview of the connections between AI techniques, application areas, and energy-related constraints. The findings show that the most commonly applied methods were machine learning and reinforcement learning, particularly in the optimization of routing and energy management. The most significant challenges identified were limitations in computational capacity, real-time requirements, and the dynamic structure of networks. This mapping study provides a structured foundation for the further development of AI-based solutions and the strategic orientation of future research on energy efficiency in WSNs.