1. Modeling In-Network Processing and Aggregation in Sensor Networks Ajay Mahimkar mahimkar@ece.utexas.edu The University of Texas at Austin March 24, 2004

2. Sensor Networks – Goals & Challenges Distributed Sensing of physical phenomena Establish paths between point(s) of interest & observer(s) Base Station / Aggregators Sensor Networks are extremely resource-constrained Energy – the most critical Lifetime & utility of sensor network – determined by energy usage Computational and Communication Capabilities Communication Pattern Data-centric Applications Battlefield Surveillance, Nuclear Attack Detection, Real-time Traffic Monitoring, Wireless Meter Reading

3. Problem Statement Energy consumption occurs due to Sensing Data processing and communication Protocols that extend network lifetime are useful Query Dissemination and Information Aggregation in an energy-efficient way

4. Existing Approaches Directed Diffusion [C. Intanagonwiwat, 2003] The base station / end user queries the network by broadcasting interest message Sensors possessing the information respond via multi-hop communication Information aggregated at each hop

5. Existing Approaches (contd….) Power Efficient Algorithms LEACH (Low Energy Adaptive Clustering Hierarchy) [W. Heinzelman, 2000] Clusters formed in a self-organized manner in each round of data collection Cluster-Head responsible for data aggregation PEGASIS (Power-Efficient Gathering in Sensor Information Systems) [S. Lindsey, 2002] Instead of multiple cluster-heads (as in LEACH), only one designated node sends the aggregated data to base station Key idea – form a chain among sensor nodes PEDAP (Power-Efficient Data gathering and Aggregation Protocol) [H. O. Tan, 2003] MST based routing scheme using energy as the metric

6. Evaluation PEGASIS outperforms LEACH by avoiding the overhead of dynamic cluster-head formation PEDAP better than both LEACH & PEGASIS Balances the energy consumption among the nodes

7. Project Plan Model sensors Radio Battery Model Model communication paradigm Communication schedule Sleep/wake-up nodes Asynchronous triggering of sensors Performance Model In-network Processing and Data Aggregation Integrating with network simulators NS-2, TinyOS (TOSSIM), OPNET, Ptolemy-II