Performance analysis of clustering-based fingerprinting localization systems

Sadhukhan, Pampa

doi:10.1007/s11276-018-1682-7

Cited by 16 publications

(16 citation statements)

References 21 publications

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“…Here, the cluster-head is determined on the coarse localization, and Wk-NN is used for fine localization. In addition to APC and K-means, other clustering methods in IPS include fuzzy c-means and hierarchical clustering strategy (HCS) [ 168 , 169 , 170 , 171 ]. APC has been a widely used clustering technique in IPS owing to its initialization-independent and better cluster head selection characteristics.…”

Section: Positioning Algorithms and Survey Of Available Solutionsmentioning

confidence: 99%

A Survey of Smartphone-Based Indoor Positioning System Using RF-Based Wireless Technologies

Subedi

Pyun

2020

Sensors

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In recent times, social and commercial interests in location-based services (LBS) are significantly increasing due to the rise in smart devices and technologies. The global navigation satellite systems (GNSS) have long been employed for LBS to navigate and determine accurate and reliable location information in outdoor environments. However, the GNSS signals are too weak to penetrate buildings and unable to provide reliable indoor LBS. Hence, GNSS’s incompetence in the indoor environment invites extensive research and development of an indoor positioning system (IPS). Various technologies and techniques have been studied for IPS development. This paper provides an overview of the available smartphone-based indoor localization solutions that rely on radio frequency technologies. As fingerprinting localization is mostly accepted for IPS development owing to its good localization accuracy, we discuss fingerprinting localization in detail. In particular, our analysis is more focused on practical IPS that are realized using a smartphone and Wi-Fi/Bluetooth Low Energy (BLE) as a signal source. Furthermore, we elaborate on the challenges of practical IPS, the available solutions and comprehensive performance comparison, and present some future trends in IPS development.

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Section: Positioning Algorithms and Survey Of Available Solutionsmentioning

confidence: 99%

A Survey of Smartphone-Based Indoor Positioning System Using RF-Based Wireless Technologies

Subedi

Pyun

2020

Sensors

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“…In addition to that, Ma et al [15] presents a multi-dimension algorithm to provide cellular network security. However, Sadhukhan et al [16] analyses the performance of clustering based fingerprint for smartphones devices.…”

Section: 1fingerprint Biometric Authenticationmentioning

confidence: 99%

A survey of biometric approaches of authentication

Yusuf¹,

Marafa²,

Shehu³

et al. 2020

IJACR

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Information security refers to a means of preventing unauthorized users from access to information. Risk management usually adopts in information security to provide solutions to security challenges by minimizing risks.Risk can be minimized by administrative control and defense mechanisms. Access control can also enforce the user right access and thereby minimizing risks. Authentication is one of the techniques used in access control systems to protect unauthorized access. Many approaches for authentication have been proposed to address security challenges. These approaches have a conflict with the system usability and therefore required the modification of tradition password techniques for better solutions. Information Authentication is the common method used by security experts to verify the users' identities before getting access right into the system. Access controls are enforced for all users, irrespective of categories they belong. Traditional authentication methods [1] were enough to protect the unauthorized access right as many security breaches were reported. Therefore, advanced security methods that are based on human features required.

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“…The APC creates the centers and the corresponding clusters depending on message exchange of RSS readings similarities between the RPs. In addition to APC and K-means, fuzzy c-means and hierarchical clustering strategy (HCS) are some representative methods of clustering [25]- [28]. Reference [29] utilizes K-means and fuzzy c-means clustering in wireless sensor networks (WSN) to divide the fingerprinting database into clusters.…”

Section: Related Workmentioning

confidence: 99%

“…The final location is estimated by comparing the online signal strength measurements with the prototype signal strength vector that was determined for each cluster. HCS has been put forward in [28] where it groups the training locations (RPs) into a cluster if those training locations detect strongest RSS from one particular AP. The HCS starts with assigning total number of APs as the number of clusters and later it multiplies to more clusters depending on second highest RSS from neighboring APs.…”

Section: Related Workmentioning

confidence: 99%

Improving Indoor Fingerprinting Positioning With Affinity Propagation Clustering and Weighted Centroid Fingerprint

Subedi

Gang

et al. 2019

IEEE Access

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Nowadays, research and development of various indoor positioning systems (IPS) have been increasing owing to flourishing social and commercial interest in location-based services (LBSs). Among LBS technologies, we used the Bluetooth low energy beacon in our system, which consumes less energy and is embedded in many current smartphones and tablets. In particular, the fingerprinting method has become a prime choice in the design of IPS owing to its good location estimation and the fact that a line-of-sight from access points is not required. We propose an improved two-step fingerprinting localization using multiple fingerprint features to enhance the localization accuracy. The proposed system uses a propagation model to convert RSS of beacons to distance and estimate the weighted centroid (WC) of nearby beacons. The estimated WCs along with signal strength and rank of the nearby beacons are stored in the server database for localization instead of RSS from all the deployed beacons. First, the proposed system makes use of diverse fingerprinting features to increase localization accuracy that also reduces both the physical size of the database and the amount of data communication with the server in the execution phase; second, affinity propagation clustering minimizes the searching space of RPs and reduces the computational cost; third, exponential averaging is introduced to smooth the noisy RSS. The experimental results obtained by real field deployment show that the proposed method significantly improves the performance of the positioning system in both the positioning accuracy and radio-map database size. INDEX TERMS Affinity propagation clustering, BLE, Exponential averaging, RSS, Weighted centroid.

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Performance analysis of clustering-based fingerprinting localization systems

Cited by 16 publications

References 21 publications

A Survey of Smartphone-Based Indoor Positioning System Using RF-Based Wireless Technologies

A Survey of Smartphone-Based Indoor Positioning System Using RF-Based Wireless Technologies

A survey of biometric approaches of authentication

Improving Indoor Fingerprinting Positioning With Affinity Propagation Clustering and Weighted Centroid Fingerprint

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