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A Novel Non-Line-of-Sight Indoor Localization Method for Wireless Sensor Networks 时间:2018-10-05 22:04   来源:未知   作者:admin   点击: 次 Abstract:The localization technology is the essential requirement of constructing a smart building and smart city. It is one of the most important technologies for wireless sensor networks (WSNs). However, when WSNs are deployed in harsh indoor environments, obstacles can result in non-line-of-sight (NLOS) propagation. In addition, NLOS propagation can seriously reduce localization accuracy. In this paper, we propose a NLOS localization method based on residual analysis to reduce the influence of NLOS error. The time of arrival (TOA) measurement model is used to estimate the distance. Then, the NLOS measurement is identified through the residual analysis method. Finally, this paper uses the LOS measurements to establish the localization objective function and proposes the particle swarm optimization with a constriction factor (PSO-C) method to compute the position of an unknown node. Simulation results show that the proposed method not only effectively identifies the LOS/NLOS propagation condition but also reduces the influence of NLOS error.
1. Introduction
  The rapid development of microelectromechanical system (MEMS) technology, sensor technology, wireless communication, and low-power embedded technology promotes the progress and development of wireless sensor networks (WSNs). WSNs consist of a large number of inexpensive microsensor nodes deployed in a monitored region. The sensor nodes are connected to each other by self-organization and multihop communications [1]. Sensor nodes consist of sensors, digital processing units, a wireless communication module, and a power module. They can collaboratively sense, gather, and process the information of the perceived objects in a monitored region and then send the information to the sink node. WSNs are widely used in traffic management, environmental monitoring, medical care networks, logistics management, and other fields and profoundly influence the social life of people [2].
  One of the most important issues for WSNs is localization technology [3]. The localization technology is the essential requirement of constructing a smart building and smart city. WSN-based localization methods can be categorized as range-based localization methods and range-free localization methods. In range-based localization methods, different measurement techniques for localization can be classified as time of arrival (TOA), time difference on arrival (TDOA), received signal strength (RSS), and angle of arrival (AOA). The range-free localization methods do not need to measure the distance or angle between the nodes [4]. These methods can estimate position based on the network connectivity and the distribution of the history measurements. The range-free localization methods can be divided into multihop estimation-based localization and pattern matching-based localization.
  For the TOA-based localization method, the signal velocity is known in advance. It measures the travel time of the signal from the beacon node to the unknown node, and the distance between two nodes is equal to the product of the signal velocity and the travel time. However, this method requires high-precision time synchronization between two nodes. As light synchronization error can significantly affect the ranging error. Therefore, the TOA method requires additional hardware to ensure the time synchronization. The TDOA method requires two different transceivers on a node so that the node can transmit two signals with different velocities at the same time. It estimates the distance by measuring the two signals’ arrival time difference between the beacon node and the unknown node. The requirement of time accuracy of the TDOA method is lower than the TOA method, but it still has high requirements for hardware. The RSS method is one of the least expensive ways to locate an unknown node because it does not need additional hardware. The RSS method measures the signal power loss value from a beacon node to an unknown node, and it converts the power loss value to the distance through a signal propagation model. The AOA method measures and calculates the angles between beacon nodes and an unknown node and then estimates the position of the unknown node based on the angle between two nodes.