The introduction of a lightweight encryption scheme in the vehicular network has improved the reliability and security of data transmission substantially among vehicles. However, it is still not guaranteed that all vehicles comply with the encryption scheme throughout the whole operation period, considering the inherent selfishness of each participant. Apart from that, the data scheduling issue is another major concern in the vehicular network due to the high-speed mobility of vehicles. To tackle these issues, a multi-objective and multi-dimensional incentive mechanism is developed in this paper to achieve privacy-aware data scheduling for vehicles. This mechanism is designed to encourage vehicles to carry out different tasks by providing relevant incentives while maximizing the overall utility of the network. Additionally, security and privacy of data transmission between vehicles and cloud servers are realized through a data perturbation approach. Experimental evaluation shows that the proposed incentive mechanism is better than the traditional methods when it comes to maximizing the number of participants and completed key tasks.
|Name||Proceedings - 2020 IEEE 22nd International Conference on High Performance Computing and Communications, IEEE 18th International Conference on Smart City and IEEE 6th International Conference on Data Science and Systems, HPCC-SmartCity-DSS 2020|
|Conference||22nd International Conference on High Performance Computing and Communications; IEEE 18th International Conference on Smart City; IEEE 6th International Conference on Data Science and Systems|
|Abbreviated title||HPCC-SmartCity-DSS 2020|
|Period||14/12/20 → 16/12/20|
- Incentive Mechanism
- Key Task
- Vehicular Networks