MoteTrack:
A Robust, Decentralized Approach to RF-Based Location Tracking

Harvard University
Spring 2006

 

Introduction  |  People  |  Papers and Talks  |  Results  |  Download  |  Resources

 

Introduction
Wireless sensor networks deployed throughput an indoor environment offer the opportunity for accurate location tracking of mobile users. Using radio signal information alone, it is possible to determine the location of a roaming node at close to meter-level accuracy.  We are particularly concerned with applications in which the robustness of the location-tracking infrastructure is at stake.  For example, firefighters and rescuers entering a building can use a heads-up display to track their location and monitor safe exit routes. Likewise, an incident commander could track the location of multiple rescuers in the building from the command post.

We are developing a robust, decentralized approach to RF-based location tracking. Our system, called MoteTrack, is based on low-power radio transceivers coupled with a modest amount of computation and storage capabilities. MoteTrack does not rely upon any back-end server or network infrastructure: the location of each mobile node is computed using a received radio signal strength signature from numerous beacon nodes to a database of signatures that is replicated across the beacon nodes themselves. This design allows the system to function despite significant failures of the radio beacon infrastructure.

In our deployment of MoteTrack, consisting of 25 beacon nodes distributed across our Computer Science building, we achieve a 50th-percentile and 80th-percentile location-tracking accuracy of 1 meter and 1.7 meters respectively when diversifying the radio signal over 16 frequencies. In addition, MoteTrack can tolerate the failure of up to 60% of the beacon nodes without severely degrading accuracy, making the system suitable for deployment in highly volatile conditions. We investigate in detail MoteTrack's performance under a wide range of conditions, including variance in the number of obstructions, beacon node failure, radio signature perturbations, receiver sensitivity, and beacon node density.

People
Papers and Talks

Papers:

Talks:

Results

The following data was collected in the 2nd floor of Maxwell Dworkin, the EECS Building at Harvard.

Training Data

Testing Data

Accuracy of Location Estimates Benefits of Multiple Frequencies Robustness to Beacon Node Failure
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