On-demand mobile sensing framework for traffic monitoring. (c2017)

Abstract

With the increased need for mobility and the overcrowding of cities, the area of Intelligent Transportation aims at improving the efficiency, safety, and productivity of transportation systems by relying on communication and sensing technologies. One of the main challenges faced in Intelligent Transportation Systems (ITS) pertains to the real time collection of traffic and road related data, in a cost effective, efficient, and scalable manner. The current approaches still suffer from problems related to the mobile devices energy consumption and overhead in terms of communications and processing. To tackle the aforementioned challenges, we propose in this thesis a novel infrastructure-less ondemand vehicular sensing framework that provides accurate road condition monitoring, while reducing the number of participating vehicles, energy consumption, and communication overhead. Our approach is adopting the concept of Mobile Sensing as a Service (MSaaS), in which mobile owners participate in the data collection activities and decide to offer the sensing capabilities of their phones as services to other users. Unlike existing approaches that rely on opportunistic continuous sensing from all available cars, this ability to offer sensory data to consumers on demand can bring significant benefits to ITS and can constitute an efficient and flexible solution to the problem of real-time traffic/road data collection. Moreover, we extend our approach by elaborating (1) cellular networks based model for selecting suitable set of mobile devices acting as data collectors and (2) inference rules based on deductive logic for traffic status classification inferred from both density and mean speed. A combination of prototyping and traffic simulation traces are used to realize the system, and a variety of test cases are used to evaluate its performance. When compared to the traditional continuous sensing, our proposed on-demand sensing approach provides comparable high traffic estimation accuracy while significantly reducing the resource consumption.This is achieved by selecting the smallest number of data collectors that can provided the best quality of sensed data, in order to maintain a good traffic estimation accuracy and an improved system performance (i.e., lower response time and network load). Other benefits of the proposed on-demand sensing approach include: an overall improved resource efficiency; a better quality of sensed information; more flexible and individual sensing as a service operations; and more users’ control over their devices related information.