Розробка критерію оптимальності розміщення антен при реалізації комбінованого методу просторової двовимірної RFID-локалізації

Розроблено критерій оптимальності розміщення антен для комбінованого методу просторової локалізації на базі технології радіочастотної ідентифікації (RFID). Критерій заснований на мінімізації усередненої величини фактору горизонтального зниження точності (HDOP), максимізації коефіцієнта покриття області локалізації зонами дії антен та мінімізації середньої помилки локалізації алгоритму перетинів.This paper presents an optimality criterion of an antenna deployment for the hybrid spatial localization method based on the radio frequency identification (RFID) technology. The criterion implies minimization of the average value of horizontal dilution of precision (HDOP), maximization of the localization field coverage coefficient, and minimization of the average localization error of the intersectional algorithm.Разработан критерий оптимальности размещения антенн для комбинированного метода пространственной локализации на базе технологии радиочастотной идентификации (RFID). Критерий основан на минимизации усредненной величины фактора горизонтального снижения точности (HDOP), максимизации коэффициента покрытия области локализации зонами действия антенн и минимизации средней ошибки локализации алгоритма пересечений

Group-In: Group Inference from Wireless Traces of Mobile Devices

This paper proposes Group-In, a wireless scanning system to detect static or mobile people groups in indoor or outdoor environments. Group-In collects only wireless traces from the Bluetooth-enabled mobile devices for group inference. The key problem addressed in this work is to detect not only static groups but also moving groups with a multi-phased approach based only noisy wireless Received Signal Strength Indicator (RSSIs) observed by multiple wireless scanners without localization support. We propose new centralized and decentralized schemes to process the sparse and noisy wireless data, and leverage graph-based clustering techniques for group detection from short-term and long-term aspects. Group-In provides two outcomes: 1) group detection in short time intervals such as two minutes and 2) long-term linkages such as a month. To verify the performance, we conduct two experimental studies. One consists of 27 controlled scenarios in the lab environments. The other is a real-world scenario where we place Bluetooth scanners in an office environment, and employees carry beacons for more than one month. Both the controlled and real-world experiments result in high accuracy group detection in short time intervals and sampling liberties in terms of the Jaccard index and pairwise similarity coefficient.Comment: This work has been funded by the EU Horizon 2020 Programme under Grant Agreements No. 731993 AUTOPILOT and No.871249 LOCUS projects. The content of this paper does not reflect the official opinion of the EU. Responsibility for the information and views expressed therein lies entirely with the authors. Proc. of ACM/IEEE IPSN'20, 202

RSS Indoor Localization Based on a Single Access Point

This research work investigates how RSS information fusion from a single, multi-antenna access point (AP) can be used to perform device localization in indoor RSS based localization systems. The proposed approach demonstrates that different RSS values can be obtained by carefully modifying each AP antenna orientation and polarization, allowing the generation of unique, low correlation fingerprints, for the area of interest. Each AP antenna can be used to generate a set of fingerprint radiomaps for different antenna orientations and/or polarization. The RSS fingerprints generated from all antennas of the single AP can be then combined to create a multi-layer fingerprint radiomap. In order to select the optimum fingerprint layers in the multilayer radiomap the proposed methodology evaluates the obtained localization accuracy, for each fingerprint radio map combination, for various well-known deterministic and probabilistic algorithms (Weighted k-Nearest-Neighbor-WKNN and Minimum Mean Square Error-MMSE). The optimum candidate multi-layer radiomap is then examined by calculating the correlation level of each fingerprint pair by using the "Tolerance Based-Normal Probability Distribution (TBNPD)" algorithm. Both steps take place during the offline phase, and it is demonstrated that this approach results in selecting the optimum multi-layer fingerprint radiomap combination. The proposed approach can be used to provide localisation services in areas served only by a single AP