Multiregional secure localization using compressive sensing in wireless sensor networks

Liu, Chang; Yao, Xiangju; Luo, Juan

doi:10.4218/etrij.2017-0116

Cited by 8 publications

(3 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Compressive sensing is a popular signal processing technique to efficiently acquire and reconstruct signals. This technique is used in wireless-based indoor positioning systems [100,101] to recover sparse signals efficiently. Many of the existing compressive sensing techniques are intended to solve the issues for a single application and it lacks dynamic adaptability.…”

Section: Communication Technology Based Indoor Positioning and Wayfinding Systemsmentioning

confidence: 99%

Indoor positioning and wayfinding systems: a survey

Kunhoth

Karkar

Al‐Maadeed

et al. 2020

Hum. Cent. Comput. Inf. Sci.

174

View full text Add to dashboard Cite

Navigation systems help users access unfamiliar environments. Current technological advancements enable users to encapsulate these systems in handheld devices, which effectively increases the popularity of navigation systems and the number of users. In indoor environments, lack of Global Positioning System (GPS) signals and line of sight with orbiting satellites makes navigation more challenging compared to outdoor environments. Radio frequency (RF) signals, computer vision, and sensor-based solutions are more suitable for tracking the users in indoor environments. This article provides a comprehensive summary of evolution in indoor navigation and indoor positioning technologies. In particular, the paper reviews different computer vision-based indoor navigation and positioning systems along with indoor scene recognition methods that can aid the indoor navigation. Navigation and positioning systems that utilize pedestrian dead reckoning (PDR) methods and various communication technologies, such as Wi-Fi, Radio Frequency Identification (RFID) visible light, Bluetooth and ultra-wide band (UWB), are detailed as well. Moreover, this article investigates and contrasts the different navigation systems in each category. Various evaluation criteria for indoor navigation systems are proposed in this work. The article concludes with a brief insight into future directions in indoor positioning and navigation systems.

show abstract

Section: Communication Technology Based Indoor Positioning and Wayfinding Systemsmentioning

confidence: 99%

Indoor positioning and wayfinding systems: a survey

Kunhoth

Karkar

Al‐Maadeed

et al. 2020

Hum. Cent. Comput. Inf. Sci.

174

View full text Add to dashboard Cite

show abstract

“…Secure localization is getting sufficient attention from the researchers. In [30], Liu et al propose a compressive-sensingbased localization approach to minimize the effect of hostile nodes on secure localization. Their approach uses a multiregion offline technique to detect hostile nodes and further employs a preprocessing method to weigh and balance the roles of anchors.…”

Section: Related Workmentioning

confidence: 99%

“…Secure localization is getting sufficient attention from the researchers. In [30], Liu et al. propose a compressive‐sensing‐based localization approach to minimize the effect of hostile nodes on secure localization.…”

Section: Related Workmentioning

confidence: 99%

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

Khan

Nguang

2021

IET Wireless Sensor Systems

View full text Add to dashboard Cite

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.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

show abstract