An optimal path selection in a clustered wireless sensor network environment with swarm intelligence-based data aggregation for air pollution monitoring system

Air pollution obtains a key concern in India owing to faster economic development, urbanisation and industrialisation connected with increased energy demands. But these methods are expensive and provide low resolution sensing data. Also the monitoring system has high communication overhead, power consuming and time. To solve the above problem a clustered wireless sensor network-based air pollution monitoring system with swarm intelligence is discussed. Initially, the sensor nodes in the networks are grouped into clusters and the cluster head is selected using the glowworm swarm optimisation (GSO) algorithm and Cuckoo search algorithm (CSA). Then the air quality index (AQI)-based fuzzy rule is formed using fuzzy inference system (FIS). Then the data aggregation is using the improved artificial fish swarm algorithm (IAFSA) and hybrid bat algorithm (HBA) to find the optimal path for efficient data transmission by reducing the communication overhead. The bat fitness function is calculated using differential evolution (DE). The result shows that the proposed method is improved than the obtainable one in stipulations of network energy utilisation, delay and throughput and aggregation latency.