Wireless sensor network-assisted fuzzy sink based model

The exponential growth of technology has increased device connectivity in the Internet of Things (IoT), enabling sophisticated networks of spatially distributed sensors to monitor and record environmental conditions. These sensors collect high-quality data and transmit it via a wireless sensor network (WSN) for processing and analysis, aiding in detecting injection attacks. However, the high computational demands lead to excessive power consumption and rapid battery depletion. This study proposes a novel fuzzy mobile sink model to optimize WSN energy usage. A real-time fuzzy decision-making framework dynamically determines the optimal mobile sink placement based on network conditions, enhancing data representation, prediction accuracy, and energy efficiency compared to traditional WSN models. The intelligent system autonomously analyzes IoT and WSN conditions, extending network lifespan through adaptive clustering and sink mobility. It mitigates fabricated attacks and sensor energy depletion while introducing a new statistical distribution to model WSN lifespan. The proposed model was evaluated against existing approaches, including the expectation-maximization (EEM) algorithm and the K-Conid probabilistic distributed clustering scheme. Integrating fuzzy logic with clustering reduces rule complexity and improves decision-making transparency. NS-3 simulations demonstrated that the fuzzy mobile sink model significantly outperforms existing methods in extending network life expectancy.