Exploring the Use of IoT and WiFi-enabled Devices to Improve Fingerprinting in Indoor Localization

This study aims to investigate Wireless Local Area Network (WLAN) within the context of its applicability as a 21st-century business tool and its survivability in a security threat-infested cyber landscape. WLAN security leverages the Wardriving technique deployed within geolocation to scan for WLAN density and explore the associated security mechanisms. Specifically, the study adopts two approaches; the first part reviews relevant research articles in electronic libraries and databases on WLAN security based on wardriving techniques. The other part comprises a measurement campaign conducted in a mid-sized city in North Cyprus. The field measurement aims to underscore the claims from the literature to find out how the security encryption technologies are used. In particular, the goal is to determine the availability of WLAN infrastructure and monitor how the security measures are implemented in Northern Cyprus. The main objective is to determine the security state of WLAN in Cyprus and examine how it can be generalized for related environments. In order to completely grasp the research issue posed in this study, data analyses from several perspectives are analyzed and examined critically. The wardriving approach has been used in this work to crawl wider regions for examination. This study was conducted with security findings drawn only from publicly accessible information emitted by each investigated wireless access point. The channel usage, Service Set IDentifier (SSID) security, the Encryption type (Open, WEP, WPA, WPA2, WPA3, and Mixed mode), WPS usage statistics, geographical locations, detailed security statistics described in Wigle CSV format, and vendor statistics are highlighted. Generally, results indicate that 21,345 WLANs were detected. From the detected WLANs, 23 (0.1 percent) used WEP encryption, 18 (0.08 percent) used WPA-TKIP encryption, 5,359 (25.1 percent) were unencrypted, and a clear majority of 9,139 (42.82 percent) used the more secure WPA2 encryption, while 13 networks (0.06 percent) used the latest WPA3 encryption technique. The results imply that WLAN security in Cyprus can be said to be moderate. Thus, this study adds to the expanding corpus of research on WLAN security and Wardriving to all parties in the wireless security ecosystem. The current study examines WLAN operations in North Cyprus while pointing to future research directions on Wireless LAN security mechanisms. Overall, the dataset from the wardriving experiment is novel and would serve future research exploration in the wireless security systems domain.

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“…e growing popularity of the Internet of ings (IoT) in smart homes and cities is built around Wireless LAN or the other [5].…”

Section: Introductionmentioning

confidence: 99%

Assessment and Test-case Study of Wi-Fi Security through the Wardriving Technique

Etta

Imoize

Shukla

et al. 2022

Mobile Information Systems

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“…From these examples, it is undoubtedly clear that the RSS-based fingerprint method is widely used in the research community. This is due to improved localization and reduced computational complexity, as concluded by Amr et al [ 19 ]. A detailed comparison of technologies and algorithms implementing the fingerprint technique for IoT indoor positioning has been carried out by [ 15 , 21 , 22 , 23 ].…”

Section: Related Workmentioning

confidence: 85%

“…For instance, authors in [ 18 ] have studied how robust localization for robots and IoT can be achieved using RSS fingerprint. Additionally, another interesting approach has been introduced in [ 19 ] where the authors have focused on the use of IoT and Wifi-enabled devices to improve fingerprinting in an indoor environment. Recently, a new concept has been developed by Ali et al [ 20 ] using raster maps instead of traditional offline scene analysis.…”

Section: Related Workmentioning

confidence: 99%

Implementing Deep Learning Techniques in 5G IoT Networks for 3D Indoor Positioning: DELTA (DeEp Learning-Based Co-operaTive Architecture)

Boudani

Kanaris

Kokkinis

et al. 2020

Sensors

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In the near future, the fifth-generation wireless technology is expected to be rolled out, offering low latency, high bandwidth and multiple antennas deployed in a single access point. This ecosystem will help further enhance various location-based scenarios such as assets tracking in smart factories, precise smart management of hydroponic indoor vertical farms and indoor way-finding in smart hospitals. Such a system will also integrate existing technologies like the Internet of Things (IoT), WiFi and other network infrastructures. In this respect, 5G precise indoor localization using heterogeneous IoT technologies (Zigbee, Raspberry Pi, Arduino, BLE, etc.) is a challenging research area. In this work, an experimental 5G testbed has been designed integrating C-RAN and IoT networks. This testbed is used to improve both vertical and horizontal localization (3D Localization) in a 5G IoT environment. To achieve this, we propose the DEep Learning-based co-operaTive Architecture (DELTA) machine learning model implemented on a 3D multi-layered fingerprint radiomap. The DELTA begins by estimating the 2D location. Then, the output is recursively used to predict the 3D location of a mobile station. This approach is going to benefit use cases such as 3D indoor navigation in multi-floor smart factories or in large complex buildings. Finally, we have observed that the proposed model has outperformed traditional algorithms such as Support Vector Machine (SVM) and K-Nearest Neighbor (KNN).

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“…We were interested in all types of network devices that are either access points or acting as access points (e.g., printers, surveillance cameras, TVs, etc. ), which can be increasingly observed in indoor environments [24].…”

Section: A Data Collectionmentioning

confidence: 98%

DataLoc+: A Data Augmentation Technique for Machine Learning in Room-Level Indoor Localization

Hilal

Arai

El-Tawab

2021

2021 IEEE Wireless Communications and Networking Conference (WCNC)

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Exploring the Use of IoT and WiFi-enabled Devices to Improve Fingerprinting in Indoor Localization

Cited by 6 publications

References 27 publications

Assessment and Test-case Study of Wi-Fi Security through the Wardriving Technique

Assessment and Test-case Study of Wi-Fi Security through the Wardriving Technique

Implementing Deep Learning Techniques in 5G IoT Networks for 3D Indoor Positioning: DELTA (DeEp Learning-Based Co-operaTive Architecture)

DataLoc+: A Data Augmentation Technique for Machine Learning in Room-Level Indoor Localization

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