Distributed Quad-Tree for Spatial Querying in Wireless Sensor Networks (WSNs) Murat Demirbas, Xuming Lu Dept of Computer Science and Engineering, University at Buffalo, SUNY, NY {demirbas, URL: iComp

2 In-network querying in WSNs In contrast to traditional WSN applications that perform only data collection, new generation of WSN applications require in-network information querying –Disaster relief applications –Battlefield applications Where is the nearest enemy tank? A soldier queries the WSN via a palm device. Energy-efficiency and latency suffers drastically if queries are always routed to a centralized basestation over many hops In-network querying should satisfy Distance sensitivity Cost of querying for nearby events should be lower Low-cost construction Costly bottom-up constructions (via flooding) should be avoided Graceful resilience Node failures should not impact performance disproportionately

3 Our contributions We present an in-network querying infrastructure that satisfies all these requirements for event querying –Distance sensitivity: the cost is at most (stretch factor) times the distance d to the nearest event –Low-cost maintenance: stateless, minimalist infrastructure, bottom-up construction is avoided –Graceful resilience: single mote failures are masked and performance degrades proportionately wrt the severity of holes (failures of motes in a region)

4 Distributed Quad-Tree (DQT) We embed a DQT over the WSN : DQT is a multi-resolution structure, but for a lightweight representation of DQT we use an encoding trick –Based on the DQT node id, a node determines which level it is at, which children, neighbor, parent it has To achieve low-cost construction, we exploit location info at the nodes –Nodes know the boundary coordinates of deployment, and calculate which DQT node id they fall into using their coordinates –We chose the clusterheads closer to the basestation to avoid backward links during querying and data collection

5 Event Indexing & Querying Indexing of event information A node at level i maintains the event information of its cluster, as well as the event information of its neighbors Event querying algorithm If query point is not current location, query is routed to the query point via GPSR If matching answer is not found, query is sent to next parent progressively (until root is reached) The result is returned to the initiator Query Propagation Path

6 Graceful resilience DQT achieves resilience via: –its stateless nature, and –using GPSR for routing More specifically : –Mote failures do not often lead to failure of level 1 node & are masked –GPSR routes around coverage holes, & delivers the message to a boundary node if destination is inside hole –Since DQT is stateless, any node can act as a proxy node for another Simulations show that s ’ increases slowly wrt failure rate Stretch factor under different failure rate s = ratio of DQT querying cost to dist (q, p) s’ =ratio of DQT querying cost to GPSR cost (q, p)

7 Simulation (ns2) Querying success rate Stretch factor with node failure: Case 1Stretch factor with node failure: Case 2 Settings: 16x16 nodes, unit distance 200m, transmission range 250m Case 1: Failures happen before the event advertisement. Case 2: The event has already been published in the structure before the failure happens.

8 Related work Distance Sensitive Information Brokerage protocol ( Funke et al[2006].) –achieves distance-sensitivity via hierarchically partitions –relies on communication protocol and needs costly construction DIMENSIONS ( Estrin et. al. [2002]) –provides a unified view of data processing and in-network querying via wavelet compression and waveRoute routing protocol –sacrifices flexibility space to achieve multi-resolution capability Geographic Hash Tables ( Ratnasamy et al. [2002] ) –stores and retrieves information using a geographic hash function –querying is not distance sensitive Distributed Index for Features in Sensor Networks (Greenstein et al. [2003]) –addresses complex querying, not restricted to event querying –costly to construct and update –special purposed routing

9 Our current work: Model-based querying in DQT –Only supporting event type of querying is very limited –Complex querying is very costly without prior knowledge of data –We use modeling to capture the correlations of sensor values and reduce the cost of querying We use multi-resolution modeling, and a query is answered with approximate values accompanied by confidence levels The original temperature data Our modeling of the data at high levels