FLPSO-AMPS : an optimized WSN model for air quality monitoring in tier-2 smart cities

Abstract

Wireless Sensor Networks (WSNs) are composed of small, cost-effective sensing nodes that are primarily employed for the collection of environmental data. These networks are integral to various applications including industrial pollution monitoring, disaster management, and air quality regulation. However, WSNs encounter significant challenges, such as energy efficiency, end-to-end delay, and packet loss during data transmission. Existing methodologies often fall short in optimizing the network lifespan while ensuring reliable data delivery. To address these limitations, this study introduces FLPSO-AMPS, a novel Fuzzy Logic-based Particle Swarm Optimization (FLPSO) approach aimed at enhancing energy-efficient routing in WSN-based Air Pollution Monitoring Systems (APMS) for Tier-2 smart cities. The proposed approach leverages fuzzy logic principles combined with PSO to intelligently select optimal routing paths, thereby ensuring minimal energy consumption and enhanced network longevity. Unlike conventional methodologies, FLPSO-AMPS incorporates realtime pollutant data collection and mobility-aware optimization to improve network performance. The effectiveness of FLPSO-AMPS was validated through extensive simulations, demonstrating superior performance over existing approaches, particularly with improvements of 10% in energy efficiency, 15% in task delay, 24.5% in packet delivery ratio (PDR), 11.5% in packet loss ratio (PLR), and 20.1% in throughput. These findings underscore the potential of FLPSO-AMPS in establishing an intelligent, resource-efficient air quality monitoring framework for smart cities. Future research will explore security enhancements to safeguard data transmissions in APMS networks.