Paper Info
Title
Enhancing The Accuracy Of Ibeacons For Indoor Proximity-Based Services
Abstract
Proximity-based Services (PBS) require high detection accuracy, energy efficiency, wide reception range, low cost and availability. However, most existing technologies cannot satisfy all these requirements. Apple's Bluetooth Low Energy (BLE), named iBeacon, has emerged as a leading candidate in this domain and has become an almost industry standard for PBS. However, it has several limitations. It suffers from poor proximity detection accuracy due to its reliance on Received Signal Strength Indicator (RSSI). To improve proximity detection accuracy of iBeacons, we present two algorithms that address the inherent flaws in iBeacon's current proximity detection approach. Our first algorithm, Server-side Running Average (SRA), uses the path-loss model-based estimated distance for proximity classification. Our second algorithm, Server-side Kalman Filter (SKF), uses a Kalman filter in conjunction with SRA. Our experimental results show that SRA and SKF perform better than the current moving average approach utilized by iBeacons. SRA results in about a 29% improvement while SKF results in about a 32% improvement over the current approach in proximity detection accuracy.
Year
Venue
Keywords
2017
2017 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC)
Location Based Services, iBeacons, Internet of Things, Proximity Detection, Kalman Filter
Field
DocType
ISSN
Noise measurement,Efficient energy use,Computer science,Server,Measurement uncertainty,iBeacon,Real-time computing,Kalman filter,Moving average,Industry standard
Conference
1550-3607
Citations 
PageRank 
References 
4
0.42
7
Authors
4
Name
Order
Citations
PageRank
Faheem Zafari113110.64
Ioannis Papapanagiotou213815.43
Michael Devetsikiotis387193.90
Thomas J. Hacker433832.29