Smart Wireless Indoor Localization

Dear Colleagues,

The use of wireless signals has shown great potential in solving localization problems, especially in GPS-denied indoor environments. However, a number of practical issues, including multipath, NLOS, the large scale of the data, heterogeneous data, and the presence of measurement error, still need to be properly addressed in order to achieve reliable localization in real-world scenarios. With the adaptation of artificial intelligence (in the context of machine learning, transfer learning, reinforcement learning, deep learning, etc.), the focus has been on making wireless indoor localization smarter and more effective. In addition, the recent breakthrough in wireless communication technologies (in the context of IoT, crowdsensing, massive MIMO, intelligent surfaces, etc.) provides a transformative means of turning the wireless environment into a programmable smart entity. This link between the intelligent control of the indoor environment and self-learning artificial intelligence poses many challenges that call for novel approaches and rethinking of the entire localization architecture to meet requirements in accuracy, reliability, budget, and more.

We invite authors from both industry and academia to submit original research and review articles that cover the design, implementation, and optimization, with a specific focus on waveforms, protocols, and positioning algorithms in the following topics (not an exhaustive list):

• Waveform and protocol design for indoor localization;
• MIMO, massive MIMO, and intelligent reflecting surface (IRS) for indoor localization;
• Indoor localization for IoT environment;
• Crowdsourcing and sensing approaches for indoor localization;
• Artificial-intelligence-enhanced indoor localization;
• Multi-sensor fusion for indoor localization;
• Transfer learning solutions in indoor localization;
• Multi-agent systems for indoor localization;
• Novel location-based services and applications;
• Multi-object localization in indoors;
• Indoor rigid body localization;
• Evolutionary computing.

Prof. Dr. Xiansheng Guo
Prof. Dr. Nirwan Ansari
Prof. Dr. Gang Wang
Guest Editors

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