Is Localization of Wireless Sensor Networks in Irregular Fields a Challenge?

Bhat, Soumya J; Santhosh, K. V.

doi:10.1007/s11277-020-07460-6

Cited by 29 publications

(19 citation statements)

References 93 publications

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“…However, overlapping CI between the most stringent distance and RSS filters indicated that the difference in localization error was small (CI RSS −80: 47.16–77.29 m; CI Distance 1.25×: 68.20–97.19 m). The reduced advantage of distance‐based filters for node networks with uneven spacing may be due to the optimization of the trilateration algorithm, where uneven spacing of nodes adds additional noise to the system, thus reducing the accuracy of location estimates (Bhat & Santhosh, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

Optimizing trilateration estimates for tracking fine‐scale movement of wildlife using automated radio telemetry networks

Paxton

Baker

Crytser

et al. 2022

Ecology and Evolution

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A major advancement in the use of radio telemetry has been the development of automated radio tracking systems (ARTS), which allow animal movements to be tracked continuously. A new ARTS approach is the use of a network of simple radio receivers (nodes) that collect radio signal strength (RSS) values from animal‐borne radio transmitters. However, the use of RSS‐based localization methods in wildlife tracking research is new, and analytical approaches critical for determining high‐quality location data have lagged behind technological developments. We present an analytical approach to optimize RSS‐based localization estimates for a node network designed to track fine‐scale animal movements in a localized area. Specifically, we test the application of analytical filters (signal strength, distance among nodes) to data from real and simulated node networks that differ in the density and configuration of nodes. We evaluate how different filters and network configurations (density and regularity of node spacing) may influence the accuracy of RSS‐based localization estimates. Overall, the use of signal strength and distance‐based filters resulted in a 3‐ to 9‐fold increase in median accuracy of location estimates over unfiltered estimates, with the most stringent filters providing location estimates with a median accuracy ranging from 28 to 73 m depending on the configuration and spacing of the node network. We found that distance filters performed significantly better than RSS filters for networks with evenly spaced nodes, but the advantage diminished when nodes were less uniformly spaced within a network. Our results not only provide analytical approaches to greatly increase the accuracy of RSS‐based localization estimates, as well as the computer code to do so, but also provide guidance on how to best configure node networks to maximize the accuracy and capabilities of such systems for wildlife tracking studies.

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Section: Discussionmentioning

confidence: 99%

Optimizing trilateration estimates for tracking fine‐scale movement of wildlife using automated radio telemetry networks

Paxton

Baker

Crytser

et al. 2022

Ecology and Evolution

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“…Several localization techniques have been proposed for WSN which make use of distance measurements based on RSS, TOA, AOA, hop lengths, etc [11]. Among these, hop based localization techniques have gained the interest of researchers for their simplicity, ease of use, and reduced hardware requirements.…”

Section: Related Workmentioning

confidence: 99%

“…The developed localization algorithms use few reference nodes with location information, and locations of unknown nodes are estimated with respect to these reference nodes [10]. Various measurement techniques such as Time of Arrival (TOA), Angle of Arrival (AOA), Received Signal Strength (RSS), connectivity, etc are used for this purpose [11], [12]. Among the available techniques, connectivity based localization does not need any additional hardware and hence is popularly used for WSN applications [13], [14].…”

Section: Introductionmentioning

confidence: 99%

A Method for Fault Tolerant Localization of Heterogeneous Wireless Sensor Networks

Bhat

Santhosh

2021

IEEE Access

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Location information of nodes in Wireless Sensor Networks (WSN) is essential to identify the origins of events and to act on them. Several localization algorithms are developed for this purpose. In this work, we have considered hop based localization algorithms, which are popularly used in WSN applications. These algorithms use a few reference nodes with location information and localize other nodes with reference to these nodes. But, in practical scenarios, some reference nodes may turn faulty and report incorrect location information to other nodes. This reduces the localization accuracy of the entire network. Therefore, it is essential to identify and filter out faulty reference nodes from the localization process. But, in Heterogeneous Wireless Sensor Networks (HWSN), since both faulty nodes and heterogeneous nodes modify hop distances, it becomes even more challenging to identify only faulty nodes among a set of heterogeneous nodes. In this work, we have reported a fault filtering method that can be used with any of the existing hop based localization algorithms for fault-tolerant localization. This method first normalizes the distance estimations using the communication radius of nodes and then uses the Jenks Natural Breaks algorithm for filtering out the nodes producing inconsistent distance estimations. The reported method is incorporated into existing localization algorithms and tested in 2D/3D, isotropic/anisotropic environments. The results show an improvement of 14%, 52%, and 51% in localization accuracy when tested with DV-Hop, Weighted DV-Hop, and HHO-AM algorithms, respectively.

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“…Some applications like air quality monitoring systems require the deployment of sensor nodes in 2D plain fields, and some applications like military surveillance and intelligent transportation systems demand the deployment of nodes in anisotropic and irregular shaped 3-Dimensional (3D) fields. The localization of sensor nodes in different types of fields is a challenge [29]. The boundary condition of WSN in 3D fields becomes more complicated and the connectivity of the network can be more diverse compared to the 2D scenario [30].…”

Section: Introductionmentioning

confidence: 99%

Localization of isotropic and anisotropic wireless sensor networks in 2D and 3D fields

Bhat

Santhosh

2021

Telecommun Syst

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Internet of Things (IoT) has changed the way people live by transforming everything into smart systems. Wireless Sensor Network (WSN) forms an important part of IoT. This is a network of sensor nodes that is used in a vast range of applications. WSN is formed by the random deployment of sensor nodes in various fields of interest. The practical fields of deployment can be 2D or 3D, isotropic or anisotropic depending on the application. The localization algorithms must provide accurate localization irrespective of the type of field. In this paper, we have reported a localization algorithm called Range Reduction Based Localization (RRBL). This algorithm utilizes the properties of hop-based and centroid methods to improve the localization accuracy in various types of fields. In this algorithm, the location unknown nodes identify the close-by neighboring nodes within a predefined threshold and localize themselves by identifying and reducing the probable range of existence from these neighboring nodes. The nodes which do not have enough neighbors are localized using the least squares method. The algorithm is tested in various irregular and heterogeneous conditions. The results are compared with a few state-of-the-art hop-based and centroid-based localization techniques. RRBL has shown an improvement in localization accuracy of 28% at 10% reference node ratio and 26% at 20% reference node ratio when compared with other localization algorithms.

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Is Localization of Wireless Sensor Networks in Irregular Fields a Challenge?

Cited by 29 publications

References 93 publications

Optimizing trilateration estimates for tracking fine‐scale movement of wildlife using automated radio telemetry networks

Optimizing trilateration estimates for tracking fine‐scale movement of wildlife using automated radio telemetry networks

A Method for Fault Tolerant Localization of Heterogeneous Wireless Sensor Networks

Localization of isotropic and anisotropic wireless sensor networks in 2D and 3D fields

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