Multiaccess edge computing (MEC) enables autonomous vehicles to handle time-critical and data-intensive computational tasks for emerging Internet-of-Vehicles (IoV) applications via computation offloading. However, a massive amount of data generated by colocated vehicles is typically redundant, introducing a critical issue due to limited network bandwidth. Moreover, on the edge server side, these computation-intensive tasks further impose severe pressure on the resource-finite MEC server, resulting in low-performance efficiency of applications. To solve these challenges, we model the data redundancy and collaborative task computing scheme to efficiently reduce the redundant data and utilize the idle resources in nearby MEC servers. First, the data redundancy problem is formulated as a set-covering problem according to the spatiotemporal coverage of captured images. Next, we exploit the submodular optimization technique to design an efficient algorithm to minimize the number of images transferred to the MEC servers without degrading the quality of IoV applications. To facilitate the task execution in the MEC server, we then propose a collaborative task computing scheme, where an MEC server intentionally encourages nearby resource-rich MEC servers to participate in a collaborative computing group. Accordingly, a cost model is formulated as an optimization problem, the objective of which is to prompt the MEC server to judiciously allocate computing tasks to nearby MEC servers with the goal of achieving the minimal cost while the latency of tasks is guaranteed. Experimental results show that the proposed scheme can efficiently mitigate data redundancy, conserve network bandwidth consumption, and achieve the lowest cost for processing tasks.

Modeling Data Redundancy and Cost-Aware Task Allocation in MEC-Enabled Internet-of-Vehicles Applications

Distributed network function virtualization management and orchestration (NFV-MANO) offers a flexible way to manage and orchestrate diversified network services in large-scale Internet of vehicles (IoV). However, it is challenging to manage different services and resources in distributed NFV due to the difficulties of reliable message synchronization among multiple MANO systems. Recently, blockchain technology has emerged to solve the trust and security problems for the interconnections of multiple MANO systems. Moreover, multi-access edge computing (MEC) has become a prospective paradigm shift from the centralized cloud due to its advantages of completing tasks near users. In this work, we propose a blockchain-enabled distributed NFV framework to reach consensus among multiple MANO systems where the computation tasks of the blockchain are processed with MEC. The consensus procedures of MANO systems and blockchain nodes are explained in detail and the representation of the blockchain throughput is given. The blockchain throughput is the number of transactions a blockchain system can handle per second, which is an important evaluation indicator for the performance of a blockchain system. We make decisions for the primary node selection, the MANO system selection and the edge server selection for reaching consensus. Moreover, the blockchain throughput, the processing delay of computation tasks of blockchain and operational costs are jointly considered in the problem formulation. A dueling deep reinforcement learning approach is applied to solve this problem. Simulation results show the effectiveness of the proposed scheme.

Performance Optimization for Blockchain-Enabled Distributed Network Function Virtualization Management and Orchestration

Automated driving is expected to improve road safety and traffic efficiency. Host vehicle onboard sensing systems typically sense the environment up to 250 m ahead of the vehicle. Today&#x0027;s LiDARs can see approximately 120 m, and recognition of small objects, such as animals or dropped cargo, however, today reliably drop when range is more than 50m. Connected driving adds an electronic horizon to the onboard sensing system which could extend the sensing range and greatly improves the efficiency. Therefore, collaborative sensing in which the vehicle exchanges not only status messages but also real data has recently been intensively discussed. Current cellular 3G&#x002F;4G networks have enhanced the downlink capacity for sharing large data blocks. However, uplink is limited and therefore vehicles are unable to share point clouds of what they see in front. This article investigates the opportunities of 5G-based cellular vehicle-to-everything (C-V2X) collaborative sensing based on the results of trials conducted at test sites in China and Finland. The results indicate that the round-trip is stable (&#x003C; 60 ms) even when exchanging 1 MB&#x002F;s between vehicles. Finally, the automotive industry perspective is taken into account in identifying and prioritizing potential use case scenarios for utilizing 5G based connected driving applications.

C-V2X Supported Automated Driving

That alleviating the heavy computing task, improving spectral efficiency and prolonging battery lifetime have been the key design challenges in Internet of Things (IoT) and intelligent connected vehicles (ICV). This paper studies the optimization of communication, computation and energy resource to minimize the energy consumption in the mobile terminal, where some superior technologies are included, such as Full-Duplex (FD), Simultaneous Wireless Information and Power Transfer (SWIPT), Mobile-Edge Computing (MEC) and Multi-input Multi-output (MIMO). In this model, the MEC-assisted Base station (BS) works in FD mode, then it can transmit and receive signals in the same frequency and time. Moreover, the mobile devices offload some computation tasks to the BS and complete local computations at the same time. Besides, the mobile device harvests the energy from the BS to support its energy consumption. And, our target is to minimize the energy consumption of mobile devices. Since the problem is non-convex, we propose an iterative solving algorithm including a multi-step optimization. First, we obtain the closed-form solution of the CPU frequency. And then, we transform the remain problem into a convex one to solve it by the interior point algorithm. Finally, we obtain the approximate solution by multiple iterations. Simulation results show that the proposed algorithm is superior to the compared schemes.

Optimization of Energy Consumption in the MEC-Assisted Multi-User FD-SWIPT System

The Multi-access Edge Computing (MEC) paradigm allows several automotive applications to be offloaded from the vehicles to the edge. Besides a higher computation capability, compared to the on-board vehicle, and the shorter latency, compared to the remote cloud, the edge offers additional (context) information that is not directly available at the vehicle, e.g., via data fusion from multiple sources. In this paper we propose a high-level architecture for MEC-assisted platooning control. Within the architecture, the longitudinal controller is conceived as a virtualized application running on an edge server, and aligned with the European Telecommunications Standard Institute (ETSI) MEC reference framework. Performance assessment conducted through a realistic simulation framework, coupling a vehicular mobility simulator and Docker containers, showcases the feasibility and effectiveness of our proposal.

A MEC-assisted Vehicle Platooning Control through Docker Containers

Multi-access Edge Computing (MEC) is an emerging technology aimed to improve the communication latency and scalability e.g. of cloud-based connected vehicle applications and services. This is accomplished by bringing services closer to the end users, that is, to the network edge. In a high-mobility scenario, such as one involving vehicles, there will be a need for handovers between different MEC servers in order to maintain the required communication latency. Part of the application data may also be relevant to multiple MEC servers, covering overlapping geographical areas or being hosted by different network operators. This paper focuses on these situations and studies the problem of maintaining service continuity and synchronization of relevant data among multiple MEC servers to support vehicular applications. The analysis is conducted in the context of two example applications with different requirements, namely platoon management and shared world model, with promising results regarding their suitability for future implementation.

Miodrag Djurica

Papers

Application Synchronization Among Multiple MEC Servers in Connected Vehicle Scenarios