%0 Journal Article
%T Elastic Information Management for Air Pollution Monitoring in Large-Scale M2M Sensor Networks
%A Yajie Ma
%A Yike Guo
%A Dilshan Silva
%A Orestis Tsinalis
%A Chao Wu
%J International Journal of Distributed Sensor Networks
%D 2013
%I Hindawi Publishing Corporation
%R 10.1155/2013/251374
%X 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 Traditional schemes of information management are no longer applicable in such a scenario.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. 1. Introduction Recently, an increasing amount of research interest has been drawn towards data management in large-scale machine-to-machine (M2M) sensor networks [1¨C3], where a large number of high-throughput autonomous sensor nodes communicate directly with each other without human intervention and can be distributed over wide areas. M2M sensor networks have found their applications ranging from home monitoring to industrial sensing, including environment and habitat monitoring, traffic control, and health care. Such networks are usually characterised by a large number of sensors, wide coverage areas, a huge amount of data, complicated connectivity, and increasingly stringent response-time requirements. Their applications normally require data management over widely distributed sensors under restricted power, processing, storage, and communication resources. The continual increases in size, data rates, and connectivity of sensor networks present significant scale and complexity challenges. This is especially true when the computational resources available are limited. Thus, efficient support from sensor data management for data acquisition, transmission, storage, and retrieval becomes critical [4]. 1.1. Motivation Current research on information management for sensor networks has increasingly focused on
%U http://www.hindawi.com/journals/ijdsn/2013/251374/