Special Issue "Artificial Intelligence and Adversarial Deep Learning in Wearable Indoor Localization"

Submission Deadline: 31 March 2022 (closed)
Guest Editors
Dr. Abdulraqeb Alhammadi, Universiti Teknologi Malaysia, Malaysia.
Dr. Ayman El-Saleh, A’Sharqiyah University (ASU), Oman.
Dr. Ibraheem Shayea, Istanbul Technical University (ITU), Turkey.

Summary

The technologies of Internet of Things (IoT) connectivity focus on supporting the needs of multiple applications or use cases across different sectors, such as agricultural production, asset management or smart cities, logistics , and healthcare. However, several challenges require addressing each of the aforementioned applications, such as low and ultra-low latency requirements, dealing with large or massive networks and long-battery life requirements, authentication, security, and real-time location monitoring of particular nodes. Therefore, choosing a connectivity solution to a specific application depends entirely on how well its features address the end application's specific needs. One of the main features required for future IoT networks is providing location-based services for large-scale IoT applications. Using IoT wearables technology opens a new opportunity for location-based services in today's context-awareness applications. Wearable indoor localization is an emerging technology that lacks theoretical and analytical background in terms of authentication and authorization based on short or long communication modes. For this reason, wearable indoor technology should be designed in such a way more secure to ensure that personal data are not shared with third parties. Besides, the high accuracy of wearable device/human recognition is another critical issue that needs to be addressed to provide a remarkable location-based service. The objective of this Special Issue is to provide the state-of-the-arts in the field of wearable indoor localization in terms of identification and authorization that are able to solve the issues in next-generation location-based service with secure authentication.

The special issue is targeted to serve the industrial researchers and academia and to present their state-of-art ideas and contributions towards the perspective scope and challenges.

 

Topics of interest include, but are not limited to, the following areas:

 

- Localization services

- Indoor wearable identification and authorization

- Adversarial deep learning in indoor localization

- Machine learning in indoor localization

- Multiple target localization

- Adaptive and hybrid algorithms for localization

- Signal processing for indoor localization

- Security and vulnerability analysis for indoor localization

- Privacy-preserving indoor localization

- News trends on indoor localization.



Keywords
IoT in localization
Indoor localization
Wearable identification and authorization
Artificial Intelligence in localization
Adversarial deep learning
Machine learning
Wireless senor networks
Privacy and Security in indoor localization