Acknowledgement

❑ Motivations

▪ To allow indoor location services since GPS fails to do so

▪ To enhance the experience of museums, malls, and corporations, through the use of interactive directories and venue maps

▪ To enable indoor tracking of employees such as security personnel

❑ Existing Solutions

▪ Apple’s indoor tracking requires use of Wi-Fi and motion data from the phone

▪ Trilateration measures distance from a device using RSSI and Link Quality signal parameters

▪ Dead Reckoning uses triangulation with the accelerometer and compass between signal strength updates

▪ Fingerprinting uses RF signals to find a unique location map offline and uses location estimation online

❑ Objectives

▪ A scalable passive system (requires an IOS application)

▪ Portable & low energy (regulated battery power)

▪ Incorporation of the magnetometer for physical phone’s direction and error checking

Michael Feinstein, Chris Gary, Kyle Bailey, Arinze Umenyiora

{m.feinstein, chris.gary, kyle.bailey, aeu10}@rutgers.edu SP16-038Advisor: Prof. Waheed Bajwa

Goal Beacons are asymmetrically distributed in the building and the floor plan is subdivided into grid squares Samples are taken at each grid square to create a mapping of grid square to signal strength intensity A database of RF samples from each grid square is used in the location estimation algorithm Tracking starts by taking a sample of the current RF environment and comparing this against the database The comparison returns an estimated location and the app updates the location on screen

Develop a low-cost Bluetooth indoor positioning system to track a user's location indoors. To supplement the location and tracking functionality of GPS to enable a more immersive indoor experience.

Methodology

References[1] Bekkelien, A. (2012). Bluetooth indoor positioning. (Master’s Thesis). University of Geneva. [2] F. Subhan, H. Hasbullah, A. Rozyyev and S. T. Bakhsh, "Analysis of Bluetooth signal parameters for indoor positioning systems," Computer & Information Science (ICCIS), 2012 International Conference on, Kuala Lumpeu, 2012, pp. 784-789.[3] J. s. Jeon, Y. Kong, Y. Nam and K. Yim, "An Indoor Positioning System Using Bluetooth RSSI with an Accelerometer and a Barometer on a Smartphone," 2015 10th International Conference on Broadband and Wireless Computing, Communication and Applications (BWCCA), Krakow, 2015, pp. 528-531. [4] L. H. Chen, E. H. K. Wu, M. H. Jin and G. H. Chen, "Intelligent Fusion of Wi-Fi and Inertial Sensor-Based Positioning Systems for Indoor Pedestrian Navigation," in IEEE Sensors Journal, vol. 14, no. 11, pp. 4034-4042, Nov. 2014.[5] "Taking Core Location Indoors - WWDC 2014 - Videos - Apple Developer." Taking Core Location Indoors - WWDC 2014 - Videos - Apple Developer. Apple. Web. 25 Apr. 2016.

We would like to thank professor Waheed Bajwa for the supportand guided instruction for our project.

Indoor Positioning System (IPS) with Bluetooth Beacons

Research Challenges❑ UUID identification of Bluetooth beacons ❑ Location estimation algorithm

▪ Nearest neighbor implementation▪ Use of onboard phone sensors for error checking

Motivations and Objectives

Hardware▪ Purchase Bluetooth modules▪ Voltage-Regulator Circuit for modules ▪ Build circuit packaging to hang▪ Mount asymmetrically on floor plan▪ Grid the floor for sample taking

Software▪ RSSI [dBm] sampling app▪ Online RF sample database

▪ Device model, heading, name of user, UUID & 5 RSSI, timestamp

▪ Location estimation algorithm

Cost breakdown – 5 Beacons▪ Cost per device: $40.10▪ Total cost of system: $200.50

❑ Theoretical tracking should produce a maximum error 0.57 meters (1.9 feet)

❑ Experimental tracking produces an error of1.5 meters (4.9 feet)

❑ 20+ Hours of battery life for beacons

Business analytics (track many user’s locations, property, and statistics of traffic flow -> possible areas of higher revenue stream & energy efficiency of building)

3D printed enclosures for better portability, simpler installation, and a sleeker form factor

Geo-fenced push notification to phone if near a beacon

Estimation Algorithm: supplement with a Naïve Bayes adaptive decision method

Implementation Results

Future Works