About Beacons

This project focuses on exploring novel cross layer designs from the hardware level to the application layer involving Internet of Things (IoT) hardware, networking protocols, mobile middleware and social computing. They empower hands-on smart city applications in the integrated architecture where big data analytics technologies are built upon the IoT physical infrastructure. To overcome scalability challenges from a large number of IoT devices and a huge amount of social/mobile context, we address two-tier filtering to add a beacon-assisted filter and a social computing filter between the IoT and data analytics layers. These provide spatiotemporally restricted engagement between a user and relevant IoT devices as well as online and mobile data of the user. Such manageable context and device control could open the door to support real-time big data analytics for on-demand smartness. Furthermore, for sustainability and comprehensiveness beacon equipment is designed with energy harvesting modules, sensing modules, rechargeable battery and dynamic configuration protocol.

Hence, we develop smart city applications relying on IoT infrastructure and on-demand big data analytics to get people-awareness that can satisfy daily demands of each user. IoT infrastructure is established by not only sustainable beacons that contain rechargeable battery, energy harvesting modules, sensing modules and dynamic control protocol stacks but also cloud computing backup systems for social and mobile big data analytics.

Future Plan

After indoor and outdoor deployment of Beacons in the HKUST campus, the lab plans to develop people-aware smart city application framework based on the joint IoT-smartphone analytics for activity sensing. The framework involves sustainable beacon hardware, beacon-based IoT infrastructure and mobile/social big data analytics.



Powerpoint Presentation


Related Papers

R. Faragher and R. Harle, “Location Fingerprinting With Bluetooth Low Energy Beacons,” in IEEE Journal on Selected Areas in Communications, Vol.33, No.11, pp. 2418-2428, Nov. 2015.
S. Vigneshwaran, S. Sen, A. Misra, S. Chakraborti, and R.K. Balan, “Using infrastructure-provided context filters for efficient fine-grained activity sensing,” in Proc. of IEEE International Conference on Pervasive Computing and Communications (PerCom), pp. 87-94, Mar. 2015.
M. Radhakrishnan, A. Misra, R. K. Balan, and Y. Lee, “Smartphones & BLE Services: Empirical Insights,” in Proc. of IEEE International Conference on Mobile Ad hoc and Sensor Systems (MASS), pp. 226-234, Oct. 2015.