An Autonomic Spatial Query Processing Model for Urban Sensor Networks

Wireless Sensor Networks (WSN) in urban environments manage a large amount of sensoring data. The deployment of spatial query processing in a decentralized and autonomous large-scale WSN is a major challenge due to the network resources constraints. This paper proposes ASQPM, a scalable and autonomous model for data storage and spatial query processing. Scalability is provided by grouping sensors into clusters based on the spatial similarity of their readings. The query processing efficiency relies on the concept of repositories, which are regions in the monitored area that concentrate information, storing the readings of a set of clusters. The experimental results show that it is more effective for query processing than classical approaches.