1 / 7

Modeling In-Network Processing and Aggregation in Sensor Networks

Modeling In-Network Processing and Aggregation in Sensor Networks. Ajay Mahimkar mahimkar@ece.utexas.edu The University of Texas at Austin March 24, 2004. Sensor Networks – Goals & Challenges. Distributed Sensing of physical phenomena

liona
Télécharger la présentation

Modeling In-Network Processing and Aggregation in Sensor Networks

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related