Efficient and accurate in-network processing for monitoring applications in wireless sensor networks

In-network processing has long been considered a fundamental mechanism to improve energy efficiency in various monitoring applications in wireless sensor networks. This thesis proposes in-network processing algorithms which leverage the spatial and temporal correlations in sensor data and the geometric properties in the network topologies to enable monitoring applications to efficiently produce accurate results. We demonstrate this by designing and implementing three application-specific in-network processing techniques. First, we investigate the efficient and approximate in-network aggregation algorithm called Clustered AGgregation (CAG) for environmental monitoring. CAG reduces the number of transmissions by leveraging both spatial and temporal correlations of sensor data to perform in-network aggregation. The CAG algorithm forms clusters with sensor nodes sensing similar values by exploiting spatial correlation of sensor readings, and efficiently adjust those clusters over time under the data or network dynamics. CAG is a novel algorithm in that it provides approximate results with bounded error performing in-network aggregation. Second, we design autonomous detection, identification, and localization algorithms which reduce false alarms in applications that monitor steam and water pipeline in oilfields. We formulate detection and identification problems in oilfield as a decision making problem based on information with uncertain errors and build a decision tree to capture the salient pressure and flow characteristics of each problem and distinguish them from false alarms. The proposed Steam and WAter Tracking System (SWATS) utilizes multi-modal sensing and multi-sensor collaboration and exploits spatial and temporal patterns of the sensed phenomena. Third, we introduce spatial skyline operation to wireless sensor networks and suggest potential applications which can benefit from this operation. Subsequently, we design and implement three flavors of Distributed Spatial Skyline (DSS) algorithms in wireless sensor networks based on different partitioning strategies: (1) TDSS: Triangulation-based DSS, (2) RDSS: Rendezvous-based DSS, and (3) TRDSS: Triangulation and Rendezvous-based DSS. Our algorithms utilize geometric properties in two layers of the protocol stack: (1) geographic routing techniques such as GPSR, GHT-based routing, and Voronoi-based geographic flooding in the routing layer, and (2) geometric notions such as convex hull and Voronoi diagram to compute spatial skyline in the application layer. The proposed DSS algorithms are novel in that they provide accurate and progressive spatial skyline results with modest delay while performing in-network processing efficiently.