Dual Cyber-Physical Network-Based Optimization of Cloudlet Formation for the Internet of Vehicles

The vehicular cloudlet (VC), capable of leveraging synergies to support emerging cooperative services and task offloading among neighboring vehicles, will be adopted in the Internet of Vehicles (IoV). Compared with traditional clustering methods based on link connectivity, VCs require higher transmission capacity and stability. Three major challenges must be addressed when optimally establishing the VC structure. First, the transmission capacity is affected by inherent stochastic characteristics, including channel fading and interference. Second, the mobility of vehicles introduces instability. Third, a comprehensive optimization model is required to jointly improve the stability and transmission capacity of the established VC. Therefore, this paper proposes a dual cyber-physical network (DCP) model to represent the dynamic physical network and the coupled transmission network. Generalized analytical expressions for channel quality are derived using moment-generating functions based on a Nakagami-m small-scale fading model to handle stochastic characteristics. Furthermore, a DCP association density optimization model is proposed that considers the stability of the physical topology and the transmission capacity of the channel. Symmetric non-negative matrix factorization is used to solve the optimization problem with low complexity. Simulation results confirm that our proposed method achieves higher transmission capacity and stability compared existing link connectivity-based clustering methods.