MobileEdge: Enhancing OnBoard Vehicle Computing Units Using Mobile Edges for CAVs As the rapid growth of connected and autonomous vehicles (CAVs) and 5G intensifies, more thirdparty applications are increasingly being deployed on CAVs. They not only improve user experience but also provide more helpful services, for example, enhancing public safety by recognizing criminals in realtime videos. Current CAVs prefer to process collected data on the vehicle to avoid long transmission latency and extra network cost. However, due to the limitations of the onboard vehicle computing unit (VCU) and increasing use of computingintensive invehicle applications, the burden of onboard VCU has sharply increased, which may affect driving safety. In particular, for existing vehicles on the road, adding more computing devices is a challenge if not impossible due to cost concerns. Inspired by edge computing, we propose a novel platform, MobileEdge, to enhance the computing capability of the unchangeable onboard VCU, which leverages mobile devices as edge nodes, e.g., the passengersu0027 smartphones, by offloading computing tasks to them for collaboratively computing. Moreover, MobileEdge provides the dynamic management of mobile devices, monitoring device status and interfaces for customizable task offloading strategies and eventually achieves optimal task scheduling. We build a prototype to demonstrate the designed platform and evaluate three task offloading strategies which were implemented based on the developed interfaces. The results show that MobileEdge significantly reduces the application response latency. Compared with the baseline which does not employ task offloading, the response latency is almost near realtime when more computing resources are available. In addition, the proposed shortest response latency strategy outperforms the best overall task scheduling among the three strategies.

Task Offloading in Vehicular Edge Computing Networks: A LoadBalancing Solution Recently, the rapid advance of vehicular networks has led to the emergence of diverse delaysensitive vehicular applications such as automatic driving, auto navigation. Note that existing resourceconstrained vehicles cannot adequately meet these demands on low  ultralow latency. By offloading parts of the vehiclesxe2x80x99 computeintensive tasks to the edge servers in proximity, mobile edge computing is envisioned as a promising paradigm, giving rise to the vehicular edge computing networks (VECNs). However, most existing works on task offloading in VECNs did not take the load balancing of the computation resources at the edge servers into account. To address these issues and given the high dynamics of vehicular networks, we introduce fiberwireless (FiWi) technology to enhance VECNs, due to its advantages on centralized network management and supporting multiple communication techniques. Aiming to minimize the processing delay of the vehiclesxe2x80x99 computation tasks, we propose a softwaredefined networking (SDN) based loadbalancing task offloading scheme in FiWi enhanced VECNs, where SDN is introduced to provide supports for the centralized network and vehicle information management. Extensive analysis and numerical results corroborate that our proposed loadbalancing scheme can achieve superior performance on processing delay reduction by utilizing the edge serversxe2x80x99 computation resources more efficiently.