Artificial Intelligence for Edge Service Optimization in Internet of Vehicles:A Survey

The Internet of Vehicles(IoV)plays a crucial role in providing diversified services because of its powerful capability of collecting real-time information.Generally,collected information is transmitted to a centralized resourceintensive cloud platform for service implementation.Edge Computing(EC)that deploys physical resources near road-side units is involved in IoV to support real-time services for vehicular users.Additionally,many measures are adopted to optimize the performance of EC-enabled IoV,but they hardly help make dynamic decisions according to real-time requests.Artificial Intelligence(AI)is capable of enhancing the learning capacity of edge devices and thus assists in allocating resources dynamically.Although extensive research has employed AI to optimize EC performance,summaries with relative concepts or prospects are quite few.To address this gap,we conduct an exhaustive survey about utilizing AI in edge service optimization in IoV.Firstly,we establish the general condition and relative concepts about IoV,EC,and AI.Secondly,we review the edge service frameworks for IoV and explore the use of AI in edge server placement and service offloading.Finally,we discuss a number of open issues in optimizing edge services with AI.

A Novel Intermediary Framework for Dynamic Edge Service Composition

Multimedia content,user mobility and heterogeneous client devices require novel systems that are able to support ubiquitous access to the Web resources.In this scenario,solutions that combine flexibility,efficiency and scalability in offering edge services for ubiquitous access are needed.We propose an original intermediary framework,namely Scalable Intermediary Software Infrastructure （SISI）,which is able to dynamically compose edge services on the basis of user preferences and device characteristics.The SISI framework exploits a per-user profiling mechanism,where each user can initially set his/her personal preferences through a simple Web interface,and the system is then able to compose at run-time the necessary components.The basic framework can be enriched through new edge services that can be easily implemented through a programming model based on APIs and internal functions.Our experiments demonstrate that flexibility and edge service composition do not affect the system performance.We show that this framework is able to chain multiple edge services and to guarantee stable performance.