Modeling In-Network Processing and Aggregation in Sensor Networks

Ajay Mahimkarmahimkar@ece.utexas.edu

The University of Texas at AustinMarch 24, 2004

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

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

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

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

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

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