Modeling In-Network Processing and Aggregation in Sensor Networks
Ajay [email protected]
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
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