Distributed localization algorithm for wireless sensor networks using range lookup and subregion stitching

IF 1.5 Q3 TELECOMMUNICATIONS
Farhan Khan, Sing Kiong Nguang
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引用次数: 2

Abstract

One of the ways in which localization algorithms in wireless sensor networks (WSNs) have been categorized is whether they are range-based or range-free. Range-based algorithms use expensive hardware to measure one or more physical quantities and, in turn, use them to localize nodes with greater precision. In contrast, range-free algorithms use coarse-grained quantities like connectivity to localize nodes with limited precision. A middle way between these two approaches can be called a partial range-based approach that can utilize the existing received signal strength indicator (RSSI) readings from sensor nodes to improve the already existing coarse-grained localization methods. Another important consideration in WSNs is that a distributed localization algorithm is more computationally feasible as compared to its centralized counterpart. Keeping these two considerations in mind, a distributed localization algorithm is proposed here which falls in the aforementioned partial range-based category. The proposed algorithm called RangeLookup-MDS first creates subregions using connectivity information only. This is followed by the collection of RSSI readings from individual sensor nodes that are used to perform range lookup for inter-node distance estimates in a lookup table. After that, relative localization in every subregion is performed using multidimensional scaling, and then the relative maps are stitched together to create a consistent (but relative) coordinate system. The algorithm also has the capability to compute absolute coordinates in two-dimensional if the stitching step is executed with at least three non-collinear anchor nodes with known locations. Simulation results on uniform as well as irregular networks of various sizes show that the proposed algorithm provides improved localization accuracy and reduces localization error up to 25% in comparison to a previous partial range-based localization algorithm.

Abstract Image

基于距离查找和子区域拼接的无线传感器网络分布式定位算法
无线传感器网络定位算法的分类方法之一是基于距离的定位算法和无距离的定位算法。基于范围的算法使用昂贵的硬件来测量一个或多个物理量,然后使用它们以更高的精度定位节点。相比之下,无距离算法使用连接等粗粒度量来定位精度有限的节点。在这两种方法之间的一种中间方法可以称为部分基于距离的方法,它可以利用来自传感器节点的现有接收信号强度指示器(RSSI)读数来改进现有的粗粒度定位方法。无线传感器网络的另一个重要考虑因素是,与集中式定位算法相比,分布式定位算法在计算上更可行。考虑到这两点,本文提出了一种分布式定位算法,它属于上述部分基于范围的类别。提出的rangellookup - mds算法首先仅使用连接信息创建子区域。然后收集来自各个传感器节点的RSSI读数,这些读数用于在查找表中执行节点间距离估计的范围查找。之后,在每个子区域中使用多维缩放执行相对定位,然后将相对地图拼接在一起以创建一致(但相对)的坐标系统。该算法还具有计算二维绝对坐标的能力,如果拼接步骤执行至少三个已知位置的非共线锚节点。在不同大小的均匀和不规则网络上的仿真结果表明,与先前基于部分距离的定位算法相比,该算法可以提高定位精度,将定位误差降低25%。
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来源期刊
IET Wireless Sensor Systems
IET Wireless Sensor Systems TELECOMMUNICATIONS-
CiteScore
4.90
自引率
5.30%
发文量
13
审稿时长
33 weeks
期刊介绍: IET Wireless Sensor Systems is aimed at the growing field of wireless sensor networks and distributed systems, which has been expanding rapidly in recent years and is evolving into a multi-billion dollar industry. The Journal has been launched to give a platform to researchers and academics in the field and is intended to cover the research, engineering, technological developments, innovative deployment of distributed sensor and actuator systems. Topics covered include, but are not limited to theoretical developments of: Innovative Architectures for Smart Sensors;Nano Sensors and Actuators Unstructured Networking; Cooperative and Clustering Distributed Sensors; Data Fusion for Distributed Sensors; Distributed Intelligence in Distributed Sensors; Energy Harvesting for and Lifetime of Smart Sensors and Actuators; Cross-Layer Design and Layer Optimisation in Distributed Sensors; Security, Trust and Dependability of Distributed Sensors. The Journal also covers; Innovative Services and Applications for: Monitoring: Health, Traffic, Weather and Toxins; Surveillance: Target Tracking and Localization; Observation: Global Resources and Geological Activities (Earth, Forest, Mines, Underwater); Industrial Applications of Distributed Sensors in Green and Agile Manufacturing; Sensor and RFID Applications of the Internet-of-Things ("IoT"); Smart Metering; Machine-to-Machine Communications.
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