TY - GEN
T1 - Collaborative-Filtering Privacy-Preserving Vehicular Edge Computation Offloading in Green Smart Cities
AU - Fan, Jiaxin
AU - Wu, Jun
AU - Mumtaz, Shahid
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Collaborative Filtering
KW - Different Pri-vacy
KW - Edge Computation Offloading
UR - http://www.scopus.com/inward/record.url?scp=85178304620&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85178304620&partnerID=8YFLogxK
U2 - 10.1109/ICC45041.2023.10279728
DO - 10.1109/ICC45041.2023.10279728
M3 - Conference contribution
AN - SCOPUS:85178304620
T3 - IEEE International Conference on Communications
SP - 2455
EP - 2460
BT - ICC 2023 - IEEE International Conference on Communications
A2 - Zorzi, Michele
A2 - Tao, Meixia
A2 - Saad, Walid
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Conference on Communications, ICC 2023
Y2 - 28 May 2023 through 1 June 2023
ER -