Air pollution has significant influence on the concentration of constituents in the atmosphere leading to effects like global warming and acid rains. The disproportion of the constituents in the air or atmosphere is monitored using the air pollution monitoring system. The classical air pollution monitoring system uses the costly instruments for monitoring the environment at fixed locations. Most of the traditional monitoring system is coarse- grained and costlier during the real time implementation. Some system have problems such as communication overhead, time and power consuming. The efficient clustering based data aggregation is proposed in this paper for reducing the communication overhead and efficiently monitoring the environment. The sensor nodes in the networks are grouped into clusters and the cluster head is selected using the optimization algorithm such as firefly algorithm. The data aggregation using the hybrid genetic algorithm is also proposed in this paper for efficient data transmission by reducing the communication overhead. The simulation results shows that the performance of the proposed methodology is better than the existing one and the proposed system collects the reliable source of real time fine-grain pollution data.
Development of Drone Real-time Air Pollution Monitoring for Mobile Smart Sensing in Areas with Poor Accessibility
