Mobile Localization and Navigation in Wireless Sensor Networks
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".
Deadline for manuscript submissions: closed (30 August 2023) | Viewed by 1798
Special Issue Editors
Interests: signal processing; blind channel estimation; localization; FBMC waveforms
Interests: indoor localization; machine learning; signal processing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The evolution of mobile communication standards from 2G to 5G was accompanied by the emergence of many new services and uses. It currently appears that in the main areas of 5G, such as Internet of Things (IoT), Industrial Internet of Things (IIoT), Machine to Machine type communications (mMTC), Ultra-Reliable Low Latency Communications (URLLC), and enhanced Mobile Broad-Band communications (eMBB), it is necessary to have precise, reliable, and fast localization algorithms. Despite the fact that the up-to date Global Navigation Satellite System (GNSS) can already provide precise location-aware information in the outdoor space, it is functionally ineligible indoors due to the signal blocking of architectures. Thus, Indoor Location-Based Services (ILBS) have drawn tremendous attention due to their huge potential value for wide-scale commercial and industrial applications such as in tracking products through manufacturing lines, shop advertising for target customers, security surveillance in banking system, first-responder navigation at medical centers, etc.
In general, indoor localization is commonly conducted via the geometric mapping or the location fingerprinting-based methods. For geometric mapping, intermediate spatial parameters such as distance or direction, with regard to the Reference Points (RPs), are first derived from certain physical measurements. Typical parameters include Time of Flight (ToF), Angle of Arrival (AoA), Received Signal Strength (RSS), and Channel State Information (CSI). A target’s physical location can be further inferred using geometric algorithms (e.g., trilateration or triangulation). As an alternative to analyzing sophisticated signal propagation, location fingerprinting adopts a pattern-matching approach. The main idea is to collect signal features from predefined RP locations in the area of interest to construct a fingerprint radio map in the offline phase. Subsequently, in the online phase, localization can be simply accomplished by matching the measured fingerprint at an unknown location with those in the offline database to return the best-fitted location estimation.
Due to the harsh nature of indoor propagation environments, the performance of these solutions can be improved using data preprocessing to extract relevant location information or include environment information from different sensors (data fusion) or by proposing original algorithms to deal with the different problems that are faced in indoor localization.
For the proposed Special Issue, authors from academia and industry are encouraged to submit new research results regarding technological innovations for the indoor positioning and navigation of sensor networks.
Dr. Michel Terre
Dr. Iness Ahriz
Dr. Dany Merhej
Guest Editors
Manuscript Submission Information
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Keywords
- indoor localization
- position estimation
- learning
- mapping
- fingerprinting
- data fusion for localization
