Data Analytics-backed Vehicular Crowd-sensing for GPS-less Tracking in Public Transportation [article]

Cem Kaptan, University, My, University, My
The widespread availability of sensors, improved computing, and storage capabilities, and ubiquity of networking services have led to the transformation of the conventional transportation services. Achieving the smart transportation goal can be either via dedicated or non-dedicated methods. The former denotes utilization of sensors that are explicitly deployed and configured for pre-defined sensing tasks whereas the latter exploits opportunistic and participatory sensing paradigms. With that
more » ... ivation in mind, the contributions of this thesis are three-fold: \textit{(i)} Sensor emulation, \textit{(ii)} Crowd-sensing based GPS-less tracking, and \textit{(iii)} Reliable data acquisition in crowd-sensed GPS-less tracking. Emulating non-dedicated sensors in a simulation environment enables us to perform large-scale crowd-sensing tasks. We introduce a variety of vehicular crowd-sensing-based frameworks to track public transportation vehicles that move over static routes in a smart city setting without \textit{GPS}-enabled devices because of the major downsides of \textit{GPS} (e.g. high energy consumption, inaccurate localization in certain environments such as indoor, and privacy violation due to direct location sharing). To this end, we propose a novel framework, in our initial approach, to emulate the functionality of a sensor by using multiple available soft sensors and machine intelligence algorithms. As a case study, the localization of city buses in a smart city setting is investigated by using the accelerometer and microphones of the passengers and supervised machine intelligence running in the cloud. In this application, the \textit{GPS} functionality is emulated by using these two soft sensors. We evaluate our proposed scheme through simulations and show that the proposed framework can operate with more than 90\% accuracy in estimating the location of public buses while preserving the actual location privacy of the smartphone users. This approach results in smartphone battery energy savings of 38--46\% (as [...]
doi:10.20381/ruor-22219 fatcat:ty2qcvktqvcidjzy625jnz7oda