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Enhancing Sensor Networks with In-Network Processing and Data Aggregation Techniques

This study presents innovative modeling techniques for in-network processing and data aggregation in sensor networks. The focus is on optimizing energy consumption, which is crucial for the longevity and efficiency of these resource-constrained networks. Key approaches reviewed include Directed Diffusion, LEACH, PEGASIS, and PEDAP. These methods aim to enhance data collection through clustering and efficient routing, thereby improving the network's lifetime. This research is essential for applications such as battlefield surveillance, traffic monitoring, and environmental sensing, where efficient data management is critical.

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Enhancing Sensor Networks with In-Network Processing and Data Aggregation Techniques

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  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

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