Modeling In-Network Processing and Aggregation in Sensor Networks

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 interestmessage • 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