ColLoc
Abstract
Localization in wireless sensor networks is an important functionality that is required for tracking personnel and assets in industrial environments, especially for emergency response. Current commercial localization systems such as GPS suffer from the limitations of either high cost or low availability in many situations (e.g., indoor environments that exclude direct line-of-sight signal reception). The development of industrial wireless sensor networks such as WirelessHART provides an alternative. In this article, we present the design and implementation of ColLoc: a collaborative location and tracking system on WirelessHART as an industrially viable solution. This solution is built upon several technological advances. First, ColLoc adds the roaming functionality to WirelessHART and thus provides a means for keeping mobile WirelessHART devices connected to the network. Second, ColLoc employs a collaborative framework to integrate different types of distance measurements into the location estimation algorithm by weighing them according to their precision levels. ColLoc adopts several novel techniques to improve distance estimation accuracy and decreases the RSSI presurvey cost. These techniques include introducing distance error range constraints to the measurements, judiciously selecting the initial point in location estimation and online updating the signal propagation models in the anchor nodes, integrating Extended Kalman Filter (EKF) with trilateration to track moving objects. Our implementation of ColLoc can be applied to any WirelessHART-conforming network because no modification is needed on the WirelessHART field devices. We have implemented a complete ColLoc system to validate both the design and the effectiveness of our localization algorithm. Our experiments show that the mobile device never drops out of the WirelessHART network while moving around; with the help of even one dependable anchor, using RSSI can yield at least 75% of distance errors below 5 meters, which is quite acceptable for many typical industrial automation applications.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Apr 01, 2014
- Source ID
- 10.1145/2584656
Entities
People
- Aloysius K. Mok
- Deji Chen
- Jianyong Meng
- Mark Nixon
- Pei-Chi Huang
- Song Han
- Xiuming Zhu
Organizations
- Emerson Electric
- National Science Foundation
- Office of Naval Research
- University of Texas at Austin