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Performance study of RSS-based location estimation techniques for wireless sensor networks
By: Xinrong Li;
2005 / IEEE / 0-7803-9393-7
Description
This item was taken from the IEEE Conference ' Performance study of RSS-based location estimation techniques for wireless sensor networks ' Most sensors are event-driven and wireless sensor networks are mostly used for monitoring purposes in environmental monitoring, structural monitoring, and military battleground and public safety applications. As a result, there is a need to quickly and accurately pin-point a sensor's location when it detects an emergent event. Since sensor networks are severely resource-constrained due to various physical and environmental constraints, including miniature size, limited battery power, and limited communicational and computational capacity, a low-complexity location estimation technique is needed. Several received-signal-strength (RSS) based techniques have been proposed as a low-cost, low-complexity solution for location estimation in wireless sensor networks, including the basic RSS location estimator and the RSS-UDPG location estimator in our earlier study, which jointly estimates location coordinates and the parameter of channel model, i.e., the distance-power gradient. In this paper we present a comparative study of these two location estimators based on computer simulations. It is shown that when the channel model is assumed known a priori, the two estimators have comparable performance, but RSS-UDPG is strongly preferred when the prior estimate of the channel model is inaccurate or when the channel characteristics tend to change, either accidentally or seasonally.
Related Topics
Monitoring
Channel Estimation
Wireless Sensor Networks
Received-signal-strength Based Techniques
Event-driven Sensors
Environmental Monitoring
Structural Monitoring
Military Battleground
Public Safety Applications
Low-complexity Location Estimation Technique
Wireless Sensor Networks
Monitoring
Military Computing
Safety
Event Detection
Capacitive Sensors
Batteries
Computer Networks
Physics Computing
Computer Simulation
Channel Estimation
Wireless Sensor Networks
Engineering