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Internet of Vehicles (IoV) enables numerous in-vehicle applications for smart cities, driving increasing service demands for processing various contents (e.g., videos). Generally, for efficient service delivery, the contents from the service providers are processed on the edge servers (ESs), as edge computing offers vehicular applications low-latency services. However, due to the reusability of the same contents required by different distributed vehicular users, processing the copies of the same contents repeatedly in an edge server leads to a waste of resources (e.g., storage, computation, and bandwidth) in ESs. Therefore, it is a challenge to provide high-quality services while guaranteeing the resource efficiency with edge content caching. To address the challenge, an edge content caching method for smart cities with service requirement prediction, named E-Cache, is proposed. First, the future service requirements from the vehicles are predicted based on the deep spatiotemporal residual network (ST-ResNet). Then, preliminary content caching schemes are elaborated based on the predicted service requirements, which are further adjusted by a many-objective optimization aiming at minimizing the execution time and the energy consumption of the vehicular services. Eventually, experimental evaluations prove the efficiency and effectiveness of E-Cache with spatiotemporal traffic trajectory big data.

The Internet of Vehicles (IoV) connects vehicles, roadside units (RSUs) and other intelligent objects, enabling data sharing among them, thereby improving the efficiency of urban traffic and safety. Currently, collections of multimedia content, generated by multimedia surveillance equipment, vehicles, and so on, are transmitted to edge servers for implementation, because edge computing is a formidable paradigm for accommodating multimedia services with low-latency resource provisioning. However, the uneven or discrete distribution of the traffic flow covered by edge servers negatively affects the service performance (e.g., overload and underload) of edge servers in multimedia IoV systems. Therefore, how to accurately schedule and dynamically reserve proper numbers of resources for multimedia services in edge servers is still challenging. To address this challenge, a traffic flow prediction driven resource reservation method, called TripRes, is developed in this article. Specifically, the city map is divided into different regions, and the edge servers in a region are treated as a “big edge server” to simplify the complex distribution of edge servers. Then, future traffic flows are predicted using the deep spatiotemporal residual network (ST-ResNet), and future traffic flows are used to estimate the amount of multimedia services each region needs to offload to the edge servers. With the number of services to be offloaded in each region, their offloading destinations are determined through latency-sensitive transmission path selection. Finally, the performance of TripRes is evaluated using real-world big data with over 100M multimedia surveillance records from RSUs in Nanjing China.

The development of the Internet of Vehicles (IoV) enables numerous emerging in-vehicle applications to accommodate users with various contents, thus enhancing their traveling experiences. In IoV, content decoding tasks are typically offloaded to edge servers for implementation, as edge computing is an admirable paradigm to provide low-latency services. However, as different vehicular users may request the same contents, processing these contents repeatedly leads to the waste of storage, computation and bandwidth resources. Therefore, fine-grained computation offloading and content caching are demanded in IoV. In this paper, a joint optimization method for computation offloading and content caching based on traffic flow prediction, named COC, is proposed. Firstly, traffic flow covered by each edge server is predicted by a modified deep spatiotemporal residual network (ST-ResNet). Secondly, the non-dominated sorting genetic algorithm III (NSGA-III) is leveraged to realize the many-objective optimization to shorten the execution time and reduce the energy consumption of computation and transmission in IoV. Finally, evaluated by real-world big data from Nanjing China, COC shows a great reduction in execution time and energy consumption of transmission and computation compared to other methods.