An autonomic in-network query processing for urban sensor networks

The sensing of urban environments usually takes into account the deployment of a large number of devices to measure their environmental attributes, such as temperature, pressure, humidity, luminosity and pollution. In such applications, nearby sensors usually produce similar readings due to their spatial and temporal correlation. In the era of big data, management of collected data requires autonomous and scalable Wireless Sensor Network (WSN) structures. In this paper, we propose an in-network data storage model, called AQPM, that provides efficient processing of both spatial and value-based queries. AQPM is autonomous and scalable. That is, it does not rely on any central entity for neither managing data storage on sensor devices nor for processing queries. Scalability is achieved by grouping sensors with similar readings into clusters, while efficient query processing relies on the concept of repositories. Repositories are sensors that store readings of a set of clusters, and are the only ones that have to be contacted for answering queries. AQPM has been implemented on NS2 simulator and experimental results show that it is more effective than existing approaches.