Congratulations to SSRC for Winning the Best Paper Award at the Prestigious EWSN 2023

Nov 27, 2023
Dr. Michael Baddeley


Kudos to Dr. Michael Baddeley, Principal Researcher at our Secure Systems Research Cen-ter (SSRC), for winning the Best Paper Award at the 20th edition of the International Confer-ence on Embedded Wireless Systems and Networks (EWSN) in Calabria, Italy. The re-search paper, titled, “BLoB: Beating-based Localization for Single-antenna BLE Devices” proposes an ingenious method of concurrent transmissions to accurately localize Bluetooth tags with minimal channel occupancy in the crowded 2.4 GHz band.

Accurate localization of people and objects is a critical challenge for several IoT applications, such as asset tracking, smart manufacturing, and assisted living. Unfortunately, global navi-gation satellite systems do not work indoors due to the inability of satellites’ signals to pene-trate solid building structures.

Dr. Baddeley and his team tackle this problem by introducing BLoB, a system which can rap-idly localize devices by having multiple anchors transmit simultaneously then applying novel techniques to then determine position. The results demonstrated BLoB’s ability to increase the scalability of such indoor localization systems, while retaining sub-meter accuracy even in challenging indoor environments.

This work was supported by SSRC's "SPiDR" project, in collaboration with TU Graz in Aus-tria, and was co-authored with other prominent researchers in the field, including Jagdeep Singh, Aleksandar Stanoev, Tim Farnham, (Toshiba Europe Ltd., UK), Carlo Alberto Boano (Graz University of Technology, Austria), Zijian Chai (University of Bristol, UK), Qing Wang (Delft University of Technology, Netherlands), and Usman Raza (Waymap Ltd., UK).

Additionally, a second paper, also supported by "SPiDR", was a runner-up in the Best Paper category. The paper, titled “InSight: Enabling NLOS Classification, Error Correction, and An-chor Selection on Resource-Constrained UWB Devices”, examines the shrinking of machine learning models onto small, embedded devices to allow more accurate localization of ultra-wideband tags in non-line-of-sight environments.

To read the winning paper, click:

To read the nominated paper, click:

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