Abstract
Thanks to IEEE 802.15.4 defining the operation of low-rate wireless personal area networks (LR-WPANs), the door is open for localizing sensor nodes using tiny, low power digital radios such as Zigbee. In this paper, we propose a three-dimensional (3D) localization scheme based on well-known loop invariant for division algorithm. Parametric points are proposed by using the reference anchor points bounded in an outer region named as Parametric Loop Division (PLD) algorithm. Similar to other range-based localization methods, PLD is often influenced by measurement noise which greatly degrades the performance of PLD algorithm. We propose to adopt extended Kalman filtering (EKF) to refine node coordinates to mitigate the measurement noise. We provide an analytical framework for the proposed scheme and find the lower bound for its localization accuracy. Simulation results show that compared with the existing PLD algorithm, our technique always achieves better positioning accuracy regardless of network topology, communication radius, noise statistics, and the node degree of the network. The proposed scheme PLD-EKF provides an average localization accuracy of 0.42 m with a standard deviation of 0.26 m.
Highlights
Advancement in wireless communication technologies and electronic systems leads to the implementation of wireless sensor networks (WSN) which plays an important role in Internet of Things (IoT)
A WSN consists of several nodes, which possess low power and low cost devices armed with a processor, one or more sensors, a power [1]
We proposed a noise-reduced parametric loop division (PLD) algorithm with extended Kalman filtering (EKF)
Summary
Advancement in wireless communication technologies and electronic systems leads to the implementation of wireless sensor networks (WSN) which plays an important role in Internet of Things (IoT). Practically it is hard to use GPS due to the following factors: (1) line of sight between a sensor node and GPS satellites is not always available It does not work indoor, under water or in a subway. In second phase (offline phase), by means of a sample RSS collected at a particular node and an estimation algorithm with the RSSI database, the node location is determined. In this group, several different techniques and approaches, such as the ray tracing model [17], support vector machine [18], data mining techniques [19], probabilistic methods [20], and some others based on Kalman filtering [21] were reported.
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