Optimizing the Workload Scheduling in MEC-Assisted Intelligent Transportation Systems

Autonomous driving (AD) is rising as an efficient solution to a wide range of transportation problems. With all the capabilities utilized (sensors, 5G communication technologies, computation units), intelligent vehicles can interact with the surroundings and cooperate in instantaneously maneuvering safely and effectively. Incorporating a central agent that supports this on-the-road interaction represents a critical enabling idea that will elevate the Cooperative Autonomous Driving (CAD) performance. Multi-access Edge Computing (MEC) recently attracted a considerable focus, specifically in vehicular networks, as it provides a reliable and online response to service demands arriving from vehicles. In the context of CAD, MEC can reduce the usage of wireless communications, orchestrate the activities on the road and provide massive computation capabilities to the vehicles. In this dissertation, we investigate the potential of MEC in the context of supporting autonomous driving and managing the radio and computation resources available. We propose adequate solutions for various problems that MEC should continuously resolve as an essential component of a complete intelligent transportation system. First, we examine the capability of MEC by formulating the problem of scheduling vehicular computational tasks over the resources as an optimization problem and solve it via integer linear programming (ILP) and Lagrangian relaxation. We prove the complexity of the problem, and thus we develop a scalable solution that reaches near-optimal solutions and around 90% speedup compared with branch-and-cut. Second, to consolidate our work veracity, we tighten the system capacity by limiting the wireless communication resources. Also, we propose a system model that harvests the computational resources available on the vehicles' onboard units via a fog computing scheme and utilizes them along with the infrastructure edge resources. The system aims to jointly allocate the radio and computational resources to maximize the number of admitted tasks. We provide a formal definition of the problem as multi-stage scheduling and, due to its complexity, propose a Dantzing-Wolfe decomposition method to solve the problem. We compare the performance of the proposed method with CPLEX and show that the solution is only 20% far from the optimal solution while achieving 94% speedup. After demonstrating the merit of deploying/utilizing edge servers in a vehicular network, the third contribution particularizes more the system model to an AD environment by applying two significant modifications. First, we accurately represent an AD scenario by modeling the computational load as long-term processes that continuously receive data from multiple sources, process them together, and inform multiple destinations with decisions supporting cooperative autonomous driving applications. Such processes work as assistants to on-the-road activities such as changing lanes, taking turns, or establishing/maintaining platoons. Second, we adopt a sophisticated, more suitable metric that quantifies the freshness of the information received called Age of Information (AoI). We aim to minimize AoI of the information continuously received in the destinations. The problem turned to be NP-hard. We propose a novel Benders decomposition technique that divides the problem into several subproblems and one integer master problem. We developed a scalable solution for each of these problems and compared the overall method with the optimal solution. The method proposed showed high scalability and efficiency in terms of the objective and computation time. We conclude with a discussion on the outcomes of this thesis and the directions we intend to take in our future work