# Toward Collaborative Intelligence in IoV Systems: Recent Advances and Open Issues
## 4 Collaborative Computing in IoV
### Big-Data-Oriented Task Offloading  
In MEC architecture, the computing tasks of nodes such as vehicles and RSUs are offloaded to nearby devices with idle resources as much as possible. [PERSON] et al. [82] proposed a collaborative method based on MEC and cloud computing to offload services to cars in vehicle network, which means using cloud resources to provide more computing power to vehicles. By co-optimizing computing offloading decisions and computing resource allocation, the cloud-MEC collaborative computing offloading problem is established. In the Collaborative Computing Offloading and Resource Allocation Optimization scheme, a distributed computing offloading and resource allocation algorithm is designed to achieve the optimal solution, which allows users to request CC resources through their MEC links instead of letting edge servers make decisions. While it can respond quickly when resources are scarce, it is not well suited to tasks with high computational or storage requirements. The framework proposed by [PERSON] et al. [83] can share the intelligence of different MEC servers to improve their performance. By using MEC and knowledge transfer, the processing speed and accuracy of object detection in different vehicle network scenarios can be significantly improved. In Ref. [84], it was stated that collaboration among network users can allow them to pool their sensor data and compute results. This eliminates the need for them to manually perform various tasks and increases the efficiency of their networks. One of the most important factors that affects the performance of computation and caching processes is the availability of bandwidth. This allows them to handle large amounts of content efficiently. In order to effectively manage their networks, both cloud and users need to have the necessary edge servers. These servers can help them with content update policy management and broadcast planning. By simultaneously optimizing service caching, request scheduling, and resource-allocation policies, an online distributed method combining edge caching and computing management is studied in [85]. It cost-effectively minimizes the time-average service response latency of randomly arriving service requests, leveraging its online and distributed nature to investigate real-time queue sizes and link states. To address the dynamic and unpredictable challenges of virtual cars, the authors utilize Lyapunov optimization, matching theory, and multiplier consensus alternating directions to solve the problem in an online and distributed manner. Specifically, by using fresh data at each time point, the selection algorithm saves critical storage space and maintains generality, but at the cost of additional resource usage. For collaborative intelligent optimization in the IoV, energy consumption is another issue that needs to be taken into account. [PERSON] et al. [86] proposed the energy efficiency cost-reduction issue to carry out computational offloading in the vehicle edge computing network in order to balance delay and energy usage. In addition, a distributed iterative method is created to deal with multivariable and time-varying channel conditions. Transmission power, offloading and uploading times, decision-making, and processor frequencies are concurrently adjusted to establish a non-local efficient offloading method, which increases computation flexibility while considering the impact of vehicle motion on communication quality. In order to strike a balance between energy consumption and delay, it is necessary to reasonably schedule the computation tasks requested by users in the vehicle-edge-cloud cooperative system. [PERSON] et al. [89] studied the problem of task scheduling and energy consumption minimization among MECs when MEC devices are deployed on RSUs, and provided a processing model. Considering the power consumption of RSUs and EVs, task offloading based on the connection between the remaining power and the application is the method proposed by [PERSON] et al. [90].