Intelligent beacon location and fingerprinting

The complex way radio waves propagate indoors, leads to the derivation of location using fingerprinting techniques. In this cases, location is computed relying on WiFi signals strength mapping. Recent Bluetooth Low Energy (BLE) provides new opportunities to explore positioning. Indoor location identification plays a fundamental role as a business and personal level. At a business level, indoor location pinpointing where GPS signal is nonexistent is used to advise users and send push notifications (e.g., stores publicity, guide persons with special needs, or even for emergency evacuation). In this work is studied how BLE beacons radio signals can be used for indoor location scenarios, as well as their precision. The proposed study is performed inside the campus of Viseu Polytechnic Institute, using hundreds of students, each with his smart-phone, as proof of concept. Experimental results show that BLE allows having less than 1.5 meters error approximately 90% of the times.info:eu-repo/semantics/publishedVersio

Indoor Positioning for Monitoring Older Adults at Home: Wi-Fi and BLE Technologies in Real Scenarios

This paper presents our experience on a real case of applying an indoor localization system formonitoringolderadultsintheirownhomes. Sincethesystemisdesignedtobeusedbyrealusers, therearemanysituationsthatcannotbecontrolledbysystemdevelopersandcanbeasourceoferrors. This paper presents some of the problems that arise when real non-expert users use localization systems and discusses some strategies to deal with such situations. Two technologies were tested to provide indoor localization: Wi-Fi and Bluetooth Low Energy. The results shown in the paper suggest that the Bluetooth Low Energy based one is preferable in the proposed task

Distributed and adaptive location identification system for mobile devices

Indoor location identification and navigation need to be as simple, seamless, and ubiquitous as its outdoor GPS-based counterpart is. It would be of great convenience to the mobile user to be able to continue navigating seamlessly as he or she moves from a GPS-clear outdoor environment into an indoor environment or a GPS-obstructed outdoor environment such as a tunnel or forest. Existing infrastructure-based indoor localization systems lack such capability, on top of potentially facing several critical technical challenges such as increased cost of installation, centralization, lack of reliability, poor localization accuracy, poor adaptation to the dynamics of the surrounding environment, latency, system-level and computational complexities, repetitive labor-intensive parameter tuning, and user privacy. To this end, this paper presents a novel mechanism with the potential to overcome most (if not all) of the abovementioned challenges. The proposed mechanism is simple, distributed, adaptive, collaborative, and cost-effective. Based on the proposed algorithm, a mobile blind device can potentially utilize, as GPS-like reference nodes, either in-range location-aware compatible mobile devices or preinstalled low-cost infrastructure-less location-aware beacon nodes. The proposed approach is model-based and calibration-free that uses the received signal strength to periodically and collaboratively measure and update the radio frequency characteristics of the operating environment to estimate the distances to the reference nodes. Trilateration is then used by the blind device to identify its own location, similar to that used in the GPS-based system. Simulation and empirical testing ascertained that the proposed approach can potentially be the core of future indoor and GPS-obstructed environments

Multi-Sensor Localization and Navigation for Remote Manipulation in Smoky Areas

Abstract When localizing mobile sensors and actuators in indoor  environments  laser  meters,  ultrasonic  meters  or  even image processing techniques are usually used. On  the  other  hand,  in  smoky  conditions,  due  to  a  fire  or  building collapse, once the smoke or dust density grows,  optical  methods  are  not  efficient  anymore.  In  these  scenarios  other  type  of  sensors  must  be  used,  such  as  sonar,  radar  or  radiofrequency  signals.  Indoor  localization in low‐visibility  conditions due to  smoke is  one of the EU GUARDIANS [1] project goals.   The developed method aims to position a robot in front  of doors, fire extinguishers and other points of interest  with  enough  accuracy  to  allow  a  human  operator  to  manipulate the robot’s arm in order to actuate over the  element.  In  coarse‐grain  localization,  a  fingerprinting technique  based  on  ZigBee  and  WiFi  signals  is  used,  allowing  the robot  to  navigate  inside  the  building  in  order  to  get  near  the  point  of  interest  that  requires  manipulation.  In  fine‐grained  localization  a  remotely  controlled  programmable  high  intensity  LED  panel  is  used, which acts as a reference to the system in smoky  conditions.  Then,  smoke  detection  and  visual  fine‐ grained localization are used to position the robot with  precisely in the manipulation point (e.g., doors, valves,  etc.)