Smartphones and BLE Services: Empirical Insights

Driven by the rapid market growth of sensors and beacons that offer Bluetooth Low Energy (BLE) based connectivity, this paper empirically investigates the performance characteristics of the BLE interface on multiple Android smartphones, and the consequent impact on a proposed BLE-based service: continuous indoor location. We first use extensive measurement studies with multiple Android devices to establish that the BLE interface on current smartphones is not as "low-energy" as nominally expected, and establish that continuous use of such a BLE interface is not feasible unless we choose a moderately large scan interval and a low duty cycle. We then explore the implications of such constraints, on the parameters of a smart phone's BLE stack, on the accuracy of a BLE-based indoor localization techniques. We show that while RF-based indoor location can be highly accurate (80% of estimates have errors less than or equal to 4 meters) for stationary users only if the density of beacons is high, the combination of (large scan interval, low duty cycle) causes the location error to degrade significantly for moving users. These results provide practical insights into the use cases and limitations for future BLE-based mobile services.

[1]  Marco Zuniga,et al.  Incremental Wi-Fi scanning for energy-efficient localization , 2014, 2014 IEEE International Conference on Pervasive Computing and Communications (PerCom).

[2]  Paramvir Bahl,et al.  RADAR: an in-building RF-based user location and tracking system , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).

[3]  Yuwei Chen,et al.  Bayesian Fusion for Indoor Positioning Using Bluetooth Fingerprints , 2013, Wirel. Pers. Commun..

[4]  Archan Misra,et al.  LiveLabs: building an in-situ real-time mobile experimentation testbed , 2014, HotMobile.

[5]  Liviu Iftode,et al.  Context-aware Battery Management for Mobile Phones , 2008, 2008 Sixth Annual IEEE International Conference on Pervasive Computing and Communications (PerCom).

[6]  Shuangquan Wang,et al.  ContextSense: unobtrusive discovery of incremental social context using dynamic bluetooth data , 2014, UbiComp Adjunct.

[7]  Archan Misra,et al.  The challenge of continuous mobile context sensing , 2014, 2014 Sixth International Conference on Communication Systems and Networks (COMSNETS).

[8]  R. Faragher,et al.  An Analysis of the Accuracy of Bluetooth Low Energy for Indoor Positioning Applications , 2014 .

[9]  Paul Lukowicz,et al.  Bluetooth based collaborative crowd density estimation with mobile phones , 2013, 2013 IEEE International Conference on Pervasive Computing and Communications (PerCom).

[10]  Archan Misra,et al.  GruMon: fast and accurate group monitoring for heterogeneous urban spaces , 2014, SenSys.

[11]  Donatella Sciuto,et al.  BlueSentinel: a first approach using iBeacon for an energy efficient occupancy detection system , 2014, BuildSys@SenSys.

[12]  Alex Pentland,et al.  Reality mining: sensing complex social systems , 2006, Personal and Ubiquitous Computing.