A Hardware Architecture for GPS/INS-Enabled Wireless Sensor Networks
type: CreativeWork
description: Wireless sensor network technology has now been widely adopted. In many applications, distributed sensor nodes collect data at different locations and the location information of each node is required. The Global Positioning System is commonly used to identify the location of the nodes in such networks. Although GPS localization has consistent long-term accuracy, it is limited by the inherent dependency on a direct line of sight to 4 or more external satellites. The increasing demand for an embedded system providing reliable navigation solutions regardless of its operational environment has motivated investigations into the use of integrated systems that combine inertial sensors with GPS receivers. This research proposes a hardware architecture for location-based wireless sensor networks. In this architecture, each sensor node consists of a GPS receiver, a reduced set of low cost micro-electro-mechanical-system-based INS and a wireless transceiver. Sensor nodes in WSN are often equipped with irreplaceable batteries, which makes the power consumption crucial. To reduce the energy consumption, a microcontroller is used to control the power supply. Besides, a motion detection scheme is proposed by taking advantage of the ultra low-power wake-up function of the microcontroller. A low-power featured digital signal processor is used to accomplish the navigation computation using the Kalman filter for GPS/INS data fusion. Non-Holonomic Constraints derived velocity updates are applied to reduce the position errors. Field tests are conducted to verify the real-time performance of the proposed system with a positioning update rate of 20 Hz. The first test shows that the 2D INS/GPS integration can maintain the average system position error within 5 meters during a 60-second GPS outage. The second test used low cost inertial sensors. The average position error was 10.17 meters during a 20-second outage. The largest RMS value of position errors among these outages was within 14.…
abstract: Wireless sensor network technology has now been widely adopted. In many applications, distributed sensor nodes collect data at different locations and the location information of each node is required. The Global Positioning System is commonly used to identify the location of the nodes in such networks. Although GPS localization has consistent long-term accuracy, it is limited by the inherent dependency on a direct line of sight to 4 or more external satellites. The increasing demand for an embedded system providing reliable navigation solutions regardless of its operational environment has motivated investigations into the use of integrated systems that combine inertial sensors with GPS receivers. This research proposes a hardware architecture for location-based wireless sensor networks. In this architecture, each sensor node consists of a GPS receiver, a reduced set of low cost micro-electro-mechanical-system-based INS and a wireless transceiver. Sensor nodes in WSN are often equipped with irreplaceable batteries, which makes the power consumption crucial. To reduce the energy consumption, a microcontroller is used to control the power supply. Besides, a motion detection scheme is proposed by taking advantage of the ultra low-power wake-up function of the microcontroller. A low-power featured digital signal processor is used to accomplish the navigation computation using the Kalman filter for GPS/INS data fusion. Non-Holonomic Constraints derived velocity updates are applied to reduce the position errors. Field tests are conducted to verify the real-time performance of the proposed system with a positioning update rate of 20 Hz. The first test shows that the 2D INS/GPS integration can maintain the average system position error within 5 meters during a 60-second GPS outage. The second test used low cost inertial sensors. The average position error was 10.17 meters during a 20-second outage. The largest RMS value of position errors among these outages was within 14.…
author: Tang, Chun
language: eng
keywords: Embedded Systems, GPS/INS Integration, WSN, DSP
date published: 2012-01-13
date modified: 2026-07-27T20:35:17Z
provider: Queens University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Queens University

PropertyValue
property ID: Handle
value: http://hdl.handle.net/1974/6956