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 Cross-Layer Cooperative Jamming Scheme for Social Internet of Things

In this paper,we design a friendly jammer selection scheme for the social Internet of Things(IoT).A typical social IoT is composed of a cellular network with underlaying Device-to-Device(D2D)communications.In our scheme,we consider signal characteristics over a physical layer and social attribute information of an application layer simultaneously.Using signal characteristics,one of the D2D gadgets is selected as a friendly jammer to improve the secrecy performance of a cellular device.In return,the selected D2D gadget is allowed to reuse spectrum resources of the cellular device.Using social relationship,we analyze and quantify the social intimacy degree among the nodes in IoT to design an adaptive communication time threshold.Applying an artificial intelligence forecasting model,we further forecast and update the intimacy degree,and then screen and filter potential devices to effectively reduce the detection and calculation costs.Finally,we propose an optimal scheme to integrate the virtual social relationship with actual communication systems.To select the optimal D2D gadget as a friendly jammer,we apply Kuhn-Munkres(KM)algorithm to solve the maximization problem of social intimacy and cooperative jamming.Comprehensive numerical results are presented to validate the performance of our scheme.