Collaborative-Filtering Privacy-Preserving Vehicular Edge Computation Offloading in Green Smart Cities
Jiaxin Fan, Jun Wu, Shahid Mumtaz · 2023
Nowadays, vehicle edge computation supports a novel computing resource provisioning roadmap for green smart cities, which benefits the distributed intelligent applications, such as unmanned vehicle. Despite the fact that vehicle edge computation can better offload computing resource, there are still certain problems in implementing vehicle edge computation in green smart cities. To begin, the geographical imbalance in computing resource results in a time latency when computation offloading. Second, the security of computing resource is an issue that cannot be disregarded. This is because the loss of some sensitive data in computing resource may result in repercussions that cannot be undone. To address aforementioned challenges, we present a collaborative-filtering privacy-preserving vehicular edge computation offloading approach (CVECO). By utilizing collaborative filtering, the CVECO algorithm is able to reduce the latency of the computation offloading. Meanwhile, the CVECO algorithm is able to efficiently provide high security and protect computing resource privacy by applying multiple privacy mechanisms. Finally, the results of the simulation indicate that the CVECO algorithm is capable of lowering the latency associated with the computation offloading while simultaneously preserving a high degree of safety regarding the computing resource. To the best of our knowledge, our proposed approach is capable of performing vehicle edge computation offloading well, which permits a rational use of electricity in green smart cities, further lowering greenhouse gas emissions.