Elastic Information Management for Air Pollution Monitoring in Large-Scale M2M Sensor Networks

Abstract

In large-scale machine-to-machine sensor networks, the applications such as urban air pollution monitoring require information management over widely distributed sensors under restricted power, processing, storage, and communication resources. The continual increases in size, data generating rates, and connectivity of sensor networks present significant scale and complexity challenges. Traditional schemes of information management are no longer applicable in such a scenario. Hence, an elastic resource allocation strategy is introduced which is a novel management technique based on elastic computing. With the discussion of the challenges of implementing real-time and high-performance information management in an elastic manner, an air pollution monitoring system, called EIMAP, was designed with a four-layer hierarchical structure. The core technique of EIMAP is the elastic resource provision scheduler, which models the constraint satisfaction problem by minimizing the use of resources for collecting information for a defined quality threshold. Simulation results show that the EIMAP system has high performance in resource provision and scalability. The experiment of pollution cloud dispersion tracking presents a case study of the system implementation.