Home-Explorer: Search, Localize and Manage the Physical Artifacts Indoors
AINA '07 Proceedings of the 21st International Conference on Advanced Networking and Applications
A Novel Distributed Sensor Positioning System Using the Dual of Target Tracking
IEEE Transactions on Computers
Incorporating Data from Multiple Sensors for Localizing Nodes in Mobile Ad Hoc Networks
IEEE Transactions on Mobile Computing
Building the Internet of Things Using RFID: The RFID Ecosystem Experience
IEEE Internet Computing
An O(N²) Square Root Unscented Kalman Filter for Visual Simultaneous Localization and Mapping
IEEE Transactions on Pattern Analysis and Machine Intelligence
Embedded Interaction: Interacting with the Internet of Things
IEEE Internet Computing
Robust Kalman filter based on a generalized maximum-likelihood-type estimator
IEEE Transactions on Signal Processing
Optimal Jamming Attack Strategies and Network Defense Policies in Wireless Sensor Networks
IEEE Transactions on Mobile Computing
Ubiquitous ID: Standards for Ubiquitous Computing and the Internet of Things
IEEE Pervasive Computing
What Can the Internet of Things Do for the Citizen? Workshop at Pervasive 2010
IEEE Pervasive Computing
IEEE Transactions on Services Computing
Guest editorial: the internet of things
IEEE Wireless Communications
SNAIL: an IP-based wireless sensor network approach to the internet of things
IEEE Wireless Communications
IEEE Wireless Communications
Relative location estimation in wireless sensor networks
IEEE Transactions on Signal Processing
Diffusion Recursive Least-Squares for Distributed Estimation Over Adaptive Networks
IEEE Transactions on Signal Processing
Adaptive Approximate Data Collection for Wireless Sensor Networks
IEEE Transactions on Parallel and Distributed Systems
IEEE Transactions on Fuzzy Systems
An implementation of wireless sensor network
IEEE Transactions on Consumer Electronics
Some Relations Between Extended and Unscented Kalman Filters
IEEE Transactions on Signal Processing
| Hi-index | 0.00 |
The Internet of Things (IoT), which is usually established over architectures of wireless sensor networks, provides an actual platform for various applications of personal and ubiquitous computing. Recently, moving target localization and tracking in an IoT environment have been paid more and more attention. This paper proposes a square-root unscented Kalman filtering (SR-UKF)-based algorithm to discover real-time location of a moving target in an IoT environment where there exist quantities of sensors. The data generated from wireless sensor nodes of the IoT make contributions to localization and tracking of the moving target. First, a least-square (LS) criterion-based mathematical model is proposed for localization initialization in an IoT scenario. Next, we employ an SR-UKF idea for the further localization and tracking. By using the data coming from sensor nodes near the target, real-time location of the moving target can be estimated by implementation of SR-UKF in an iterative fashion so as to achieve target status tracking. Simulation results show that the proposed algorithm achieves good performance in estimation of both position and velocity of the target with either uniform linear motion or variable-speed curve motion. Compared with some existing conventional extended Kalman filtering (EKF) or UKF-based methods, the proposed algorithm shows lower location/velocity estimation error under the same computational complexity, which demonstrates its potential significance in ubiquitous computing applications for an IoT environment.