Comparing the effects of various smartphones on the accuracy of Wi-Fi based proximity estimation approaches in an outdoor setting
Main Article Content
Abstract
Proximity estimation is a task that estimates the distance between a reference point and a selected object. It allows for the possibility of creating applications that are based on proximity from a reference point. With increased penetration of multiple sensor smartphones, it is possible to utilize one of the many built-in sensors for proximity estimation. Usable approaches include the Global Positioning System (GPS), Bluetooth Low-Energy (BLE), and Wi-Fi approaches in proximity estimation. The paper explores current approaches before expanding on utilization of Wi-Fi approaches to proximity estimation in a practical outdoor field test using multiple smartphones and mobile devices to examine their suitability and issues that may arise when utilizing these methods.
Article Details
References
Bluetooth Low Energy specification adopted document [Internet] 2014 Dec 12 [cited 2016 Jun 9]. Available from: https://www.bluetooth.com/specifications/adopted-specifications
Yang C, Shao H R, Wifi-based indoor positioning. IEEE Communication Magazine. 2015;53(3): 150-157.
Sinnott R W, Vitues of the haversine. Sky and Telescope. 1984;68(2).
Lee L, Jones M, Ridenour G S, Testa M P, Wilson M J, Investigating and Comparing Spatial Accuracy and Precision of GPS Enabled Devices in Middle Tennessee. Springer Berlin Heidelberg. 2015: 215-224.
Park M, Gao Y, Error and performance analysis of mens-based inertial sensors with a low-cost gps receiver. Sensors. 2008;8(4): 2240-2261.
Zeimpekis V, Giggles G M, Lekakos G, A Taxonomy of indoor and outdoor positioning techniques for mobile location services. SIGecom Exch. 2002;3(4): 19-27.
Newman N, Apple beacon technology briefing. J Direct Data Digit Mark Pract. 2014;15(3): 222-225.
Liu S, Jiang Y, Striegel A, Face-to-face proximity estimation using bluetooth on smartphones. IEEE Transactions on Mobile Computing. 2014;13(4): 811-823.
Deepest P C, Rath R, Tiwari A, Rao V N, Kanakalata N, Experience with using beacons for indoor positioning. Proceedings of the 9th India Software Engineering Conference; 2016 184-189; New York, USA: ACM: 2016.
Lim C H, Wan Y, Ng B P, See C M S, A real-time indoor WiFi localization system utilizing smart antennas. IEEE Transactions on Consumer Electronics. 2007;52(2): 618-622.
Liu E E L, Lee B G, Lee S C, Chung W Y, Enhanced RSSI based high accuracy real-time user location tracking system for indoor and outdoor environments. International Journal on Smart Sensing and Intelligent System. 2008;1(2): 534-548.
Sklar B, Digital Communications: Fundamentals and Applications. 2nd ed. Prentice Hall; 2001.
Apple Developer Program Terms and Agreement [Internet] 2014 [updated 2016 May 18; cited 2016 Jun 9]. Available from: https://developer.apple.com/terms
Device Networking Information Class (WP 8.1) [Internet] 2015 [cited 2016 Jun 9]. Available from: https://goo.gl/FnTESB
GSM Arena [Internet] 2016 [cited 2016 Jun 9]. Available from: https://www.gsmarena.com/
Halepovic E, Williamson C, Grader M, Wireless data traffic: a decade of change. IEEE Transaction on Networking. 2009;23(2): 20-26.
Alippi C, Panini G, A RSSI-based and Calibrated Centralized Localization Technique for Wireless Sensor Networks. In: PERCOMW 06. Proceedings of the 4th Annual IEEE International Conference on Pervasive Computing and Communications Workshops; 2006 Mar; 2006. P. 305