Dependence-Aware Edge Intelligent Function Offloading for 6G-Based IoV

Using the increasingly wireless communication capacity of 5G/6G technology, edge intelligence (EI) enables modern vehicles to leverage the powerful computing resources of edge servers scattered aside the roads to implement intelligent transportation applications (ITA). The running of these intelligent applications is always accompanied by the calculating and transmitting of massive amounts of road situation data. For the protection of drivers and passengers, these complex processes should be handled in an ultra-reliable and low latent manner. Intelligent processing function offloading from terminals to edge servers is a promising approach to address these issues. However, the runtime environment specification of each intelligent processing function and the interdependence between two consecutive functions pose a challenge for the assignment of the offloaded functions among edge servers. In this paper, we propose a dependence-aware edge intelligent function offloading scheme for 6G-based Internet of Vehicle (IoV). All traditional ITAs are split into different chains of standard intelligent functions. Each edge server can provide some specific intelligent functional services. These services can receive data from cars and serve as different intelligent functions. Then, an intelligent application offloading scheme is changed into an embedding scheme of a service chain. An NP-hard objective function is constructed using a multi-winner committee selection model for this offloading service chain embedding problem. We design two algorithms to get the optimal assignment of intelligent functions using a greedy strategy and dynamic programming strategy separately. Finally, experiments show that when the proportion of vehicles meeting the constraint conditions is not in [9%,10%], our algorithms are fast.

Automated summary

In this paper, a dependence-aware edge intelligent function offloading scheme for 6G-based Internet of Vehicle (IoV) is proposed. The traditional intelligent transportation applications are split into different chains of intelligent functions, which can be offloaded to edge servers for processing. Two algorithms are designed to achieve the optimal assignment of intelligent functions, and experiments show that the algorithms are fast.