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

Indoor localization has been gradually improving over the past decade, utilizing emerging and proliferating wireless technologies. Since its inception, WiFi technology has been increasingly penetrating indoor environments, residential, and nonresidential. Becoming ubiquitous, WiFi was considered a fertile subject for use in indoor localization utilizing the wireless access points that predominantly provide network coverage in indoor environments. As the Internet of Things (IoT) technology is speeding towards pervasiveness, IoT devices can offer substantial aid to improve accuracy, which is at the heart of the indoor localization process. In this paper, we provide a preliminary study of the feasibility of using WiFi-enabled devices, including IoT devices, to improve location signature in indoor localization besides the traditionally used wireless access points. Through field experiments, we provide insights into the coverage and penetration of such devices in different domains characterized by the activities exercised in them. Our conclusions highlight the challenges of incorporating such devices into the fingerprint-based indoor localization. Additionally, we focus on the use of machine learning approaches that recently gained momentum in indoor localization research.

[1]  Zhu Xiao,et al.  WiFiMap+: High-Level Indoor Semantic Inference With WiFi Human Activity and Environment , 2019, IEEE Transactions on Vehicular Technology.

[2]  Mohammad Ali,et al.  An Improved Indoor Positioning Algorithm Based on RSSI-Trilateration Technique for Internet of Things (IOT) , 2016, 2016 International Conference on Computer and Communication Engineering (ICCCE).

[3]  Ram Dantu,et al.  LocateMe , 2013, ACM Trans. Intell. Syst. Technol..

[4]  Moustafa Youssef,et al.  CoSDEO 2016 Keynote: A decade later — Challenges: Device-free passive localization for wireless environments , 2016, 2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops).

[5]  Mohammad Bsoul,et al.  A Study on Threads Detection and Tracking Systems for Military Applications using WSNs , 2012 .

[6]  Samy El-Tawab,et al.  Localization of Health Center Assets Through an IoT Environment (LoCATE) , 2017, 2017 Systems and Information Engineering Design Symposium (SIEDS).

[7]  Carlos Parra,et al.  IoT-based system for indoor location using bluetooth low energy , 2017, 2017 IEEE Colombian Conference on Communications and Computing (COLCOM).

[8]  Jun'ichi Tsujii,et al.  Feature engineering combined with machine learning and rule-based methods for structured information extraction from narrative clinical discharge summaries , 2012, J. Am. Medical Informatics Assoc..

[9]  Shiwen Mao,et al.  CSI Phase Fingerprinting for Indoor Localization With a Deep Learning Approach , 2016, IEEE Internet of Things Journal.

[10]  Marimuthu Palaniswami,et al.  Internet of Things (IoT): A vision, architectural elements, and future directions , 2012, Future Gener. Comput. Syst..

[11]  Moustafa Youssef,et al.  Zero-Calibration Device-Free Localization for the IoT Based on Participatory Sensing , 2018, 2018 IEEE Global Communications Conference (GLOBECOM).

[12]  Samy El-Tawab,et al.  Improving the security of wireless sensor networks in an IoT environmental monitoring system , 2016, 2016 IEEE Systems and Information Engineering Design Symposium (SIEDS).

[13]  In Lee,et al.  The Internet of Things (IoT): Applications, investments, and challenges for enterprises , 2015 .

[14]  Hend Suliman Al-Khalifa,et al.  Ultra Wideband Indoor Positioning Technologies: Analysis and Recent Advances † , 2016, Sensors.

[15]  L. Mainetti,et al.  An Indoor Location-Aware System for an IoT-Based Smart Museum , 2016, IEEE Internet of Things Journal.

[16]  Feng Xia,et al.  Localization Technologies for Indoor Human Tracking , 2010, 2010 5th International Conference on Future Information Technology.

[17]  Lijun Jiang,et al.  Integrated UWB and GPS location sensing system in hospital environment , 2010, 2010 5th IEEE Conference on Industrial Electronics and Applications.

[18]  Florian Schweiger,et al.  TUMindoor: An extensive image and point cloud dataset for visual indoor localization and mapping , 2012, 2012 19th IEEE International Conference on Image Processing.

[19]  Samy El-Tawab,et al.  Indoor Localization Using 802.11 WiFi and IoT Edge Nodes , 2018, 2018 IEEE Global Conference on Internet of Things (GCIoT).

[20]  Samy El-Tawab,et al.  Origin-Destination Tracking Analysis of an Intelligent Transit Bus System using Internet of Things , 2019, 2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops).

[21]  Hao Jiang,et al.  Adaptive Localization in Dynamic Indoor Environments by Transfer Kernel Learning , 2017, 2017 IEEE Wireless Communications and Networking Conference (WCNC).

[22]  Günther Retscher,et al.  Location determination using WiFi fingerprinting versus WiFi trilateration , 2007, J. Locat. Based Serv..

[23]  Venkata N. Padmanabhan,et al.  Indoor localization without the pain , 2010, MobiCom.

[24]  Samy El-Tawab,et al.  Expanding Coverage of an Intelligent Transit Bus Monitoring System via ZigBee Radio Network , 2019 .

[25]  Erik C. Rye,et al.  A Study of MAC Address Randomization in Mobile Devices and When it Fails , 2017, Proc. Priv. Enhancing Technol..

[26]  Moustafa Youssef,et al.  CellinDeep: Robust and Accurate Cellular-Based Indoor Localization via Deep Learning , 2019, IEEE Sensors Journal.

[27]  Muhammad Aamir Cheema,et al.  Indoor location-based services: challenges and opportunities , 2018, SIGSPACIAL.

[28]  Stephan ten Brink,et al.  On Deep Learning-Based Massive MIMO Indoor User Localization , 2018, 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC).