With the introduction of 5G and the Internet of Things, Multi-access Edge Computing (MEC) has become an evolving distributed compute and storage capability at the network edge. MEC will support task offloading, mobility, resource allocation, and management of inter-server communications to improve quality of service and satisfy real-time applications that require low latency. Thus, MEC is regarded as a crucial capability that will provide computation and storage at the network edge as an extension of the more traditional cloud. By placing MEC at the network edge, a broad range of new applications and services can be offered in close proximity to users, including support for vehicular networks. This paper provides a current and comprehensive review of MEC-enabled vehicular networks. It first introduces MEC by providing a definition, architecture, applications, and challenges. The paper then investigates MEC support for vehicular network applications and services and identifies current research and future challenges.
In this paper, we study the social properties, including community, friendship, and individual selfishness of cognitive radio networks and analyze the effect of these social properties on the performance of routing protocols. We first introduce the concept of cooperation willingness considering social relations and individual selfishness. Then, we model the impact of cooperation willingness on the routing performance in cognitive radio networks. After that, we perform extensive simulations to evaluate the accuracy of our model. The simulation results show that our model matched the simulation results well. Finally, our results suggest that community members, friends, and nodes with lower individual selfishness should be selected in the forwarding routes.
One critical issue for routing in cognitive radio ad hoc networks (CRAHNs) is how to select a reliable path for forwarding traffic. This is because mobility may cause radio links to break frequently. The reliability of a path depends on the availability of those links that constitutes the path. In this letter, we present a novel approach to predict the probability of the availability of the link between two cognitive radio nodes. The prediction is achieved by estimating the link activation and spectrum activation probabilities. Our prediction is verified by simulation and proved to be accurate. This study can provide reliability assurance on dynamic routing for cognitive radio ad hoc networks.
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