Sheridan’s Dr Tarek El Salti, recent nominee for a Sheridan People Award in SRCA (Scholarship, Research and Creative Activities), Dr Edward Sykes, Director of the Centre for Mobile Innovation, and Nelson Shaw, former mobile computing degree student and Sheridan graduate, have recently been published in Procedia Computer Science for their work in localization algorithms for indoor environments.
Their research, with Telus’ Joseph Chun-Chung Cheung, was born out of Shaw’s Mobile Computing degree thesis, the concept of which stemmed from Shaw’s coop experience with TELUS working on the indoor research project; conditions in which GPS localization falls short.
Localization is important in fields like healthcare and security. But currently, GPS technologies are ill suited for indoor localization because the accuracy and precision of the users’ indoor locations are influenced by many factors. As a result, the methodologies and technologies for indoor localization services require continuous refinement.
Additionally, the time complexity of indoor localization methodologies impacting the performance of mobile phones’ limited resources is significant. To address these challenges, a new set of fingerprinting algorithms, called Fingerprinting Line-Based Nearest Neighbour, were designed and implemented.
The accuracy of the new set of fingerprinting algorithms has a theoretical upper limit for distance errors but had better accuracy than that of several existing Nearest Neighbour (NN)-based algorithms. The algorithms also improved the precision, and the new algorithm set has lower probabilities around positional errors than those of existing NN-based algorithms.
The new set of algorithms was deemed reliable and efficient for indoor location services and the paper was recently invited by the conference (MobiSPC 2021) to be submitted as a journal article to the International Journal of Ubiquitous Systems and Pervasive Networks (JUSPN), the writing of which is currently underway.
Find out more about ‘A New Set of Wi-Fi Dynamic Line-Based Localization Algorithms for Indoor Environments’.
