Adaptive landslide monitoring in wireless sensor networks using FLPSO-based MIP systems

Landslides are one of the most significant natural geological hazards, capable of causing extensive damage to lives, infrastructure, and property. These events are often triggered by specific geological and environmental conditions that can be monitored utilizing advanced technologies such as Wireless Sensor Networks (WSNs). This study introduces a novel itinerary planning approach for WSNs, employing the Fuzzy Logic-based Particle Swarm Optimization (FLPSO) technique, which integrates Fuzzy Logic and Particle Swarm Optimization methodologies. The primary objective of this approach is to minimize the energy consumption in large-scale WSNs, thereby enhancing their efficiency for landslide detection systems. The proposed method improves on traditional network grouping methods by optimizing energy usage across sensor nodes. A case study was conducted in Shiradi village, Mangalore, India, an area characterized by high annual rainfall and changing climatic patterns. Over a year, data was collected and analyzed to evaluate the system's potential for accurate landslide hazard predictions. The soil suction stress was calculated using laboratory tests, incorporating various geotechnical and unsaturated soil parameters specific to the study area. The experimental results demonstrated that energy-efficient nodes not only have a longer operational lifespan and greater adaptability to environmental changes, but also exhibit superior performance compared to current methods, with improvements of 14.15% in Packet Delivery Ratio (PDR), 11.15% in Energy Delay Product (EDP), 10.15% in Packet Loss Ratio (PLR), 22.1% in task delay, and 20.1% in throughput.