Distributed Quad-Tree for Spatial Querying in Wireless Sensor Networks (WSNs)

1. iComp 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 14260 {demirbas, xuminglu}@cse.buffalo.edu URL: http://www.cse.buffalo.edu/~demirbas/

2. 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 In-network querying in WSNs

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 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 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

6. Graceful resilience Stretch factor under different failure rate • 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 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 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. Stretch factor with node failure: Case 1 Stretch factor with node failure: Case 2

8. 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 Related work • 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