The Security and Privacy Protection of Mobile Computing Based on Edge Computing for Internet of Vehicles

2021 ◽  
pp. 1779-1785
Author(s):  
Duan Xue
Keyword(s):  
Mobile Computing ◽  
Privacy Protection ◽  
Edge Computing ◽  
Security And Privacy ◽  
Internet Of Vehicles

An Edge-Computing Based Task-Unloading Technique with Privacy Protection for Internet of Connected Vehicles

2021 ◽  
Author(s):  
Hasnain Ali Almashhadani ◽  
Xiaoheng Deng ◽  
Suhaib Najeh Abdul Latif ◽  
Mohammed Mohsin Ibrahim ◽  
Ali Hussien Alshammari
Keyword(s):  
Privacy Protection ◽  
Edge Computing ◽  
Connected Vehicles

scholarly journals Editorial for special issue on ”security and privacy protection in the era of IoT devices”

2021 ◽  
Vol 7 (2) ◽  
pp. 245-246
Author(s):  
Weizhi Meng ◽  
Daniel Xiapu Luo ◽  
Chunhua Su ◽  
Debiao He ◽  
Marios Anagnostopoulos ◽  
...  
Keyword(s):  
Privacy Protection ◽  
Security And Privacy ◽  
Special Issue ◽  
Iot Devices

Dynamic server placement in edge computing toward Internet of Vehicles

2021 ◽  
Author(s):  
Bowen Shen ◽  
Xiaolong Xu ◽  
Lianyong Qi ◽  
Xuyun Zhang ◽  
Gautam Srivastava
Keyword(s):  
Edge Computing ◽  
Internet Of Vehicles ◽  
Server Placement

Biometrics Security and Privacy Protection [From the Guest Editors]

2015 ◽  
Vol 32 (5) ◽  
pp. 17-18 ◽  
Author(s):  
Nicholas Evans ◽  
Sebastien Marcel ◽  
Arun Ross ◽  
Andrew Beng Jin Teoh
Keyword(s):  
Privacy Protection ◽  
Security And Privacy

Electric Energy Data Storage and Privacy Protection in Edge Computing Mode

2020 ◽  
Author(s):  
Yixin Jiang ◽  
Yunan Zhang ◽  
Aidong Xu ◽  
Xiaoyun Kuang ◽  
Jiaxiao Meng ◽  
...  
Keyword(s):  
Data Storage ◽  
Privacy Protection ◽  
Electric Energy ◽  
Edge Computing ◽  
Energy Data

scholarly journals A collaborative scheduling strategy for IoV computing resources considering location privacy protection in mobile edge computing environment

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Meiyu Pang ◽  
Li Wang ◽  
Ningsheng Fang
Keyword(s):  
Privacy Protection ◽  
Location Privacy ◽  
Completion Rate ◽  
Task Completion ◽  
Mobile Edge Computing ◽  
Computing Environment ◽  
Internet Of Vehicles ◽  
Scheduling Strategy ◽  
Collaborative Scheduling ◽  
Location Privacy Protection

Abstract This paper proposes a collaborative scheduling strategy for computing resources of the Internet of vehicles considering location privacy protection in the mobile edge computing environment. Firstly, a multi area multi-user multi MEC server system is designed, in which a MEC server is deployed in each area, and multiple vehicle user equipment in an area can offload computing tasks to MEC servers in different areas by a wireless channel. Then, considering the mobility of users in Internet of vehicles, a vehicle distance prediction based on Kalman filter is proposed to improve the accuracy of vehicle-to-vehicle distance. However, when the vehicle performs the task, it needs to submit the real location, which causes the problem of the location privacy disclosure of vehicle users. Finally, the total cost of communication delay, location privacy of vehicles and energy consumption of all users is formulated as the optimization goal, which take into account the system state, action strategy, reward and punishment function and other factors. Moreover, Double DQN algorithm is used to solve the optimal scheduling strategy for minimizing the total consumption cost of system. Simulation results show that proposed algorithm has the highest computing task completion rate and converges to about 80% after 8000 iterations, and its performance is more ideal compared with other algorithms in terms of system energy cost and task completion rate, which demonstrates the effectiveness of our proposed scheduling strategy.

Multi-Source Temporal Knowledge Graph Embedding for Edge Computing Enabled Internet of Vehicles

2021 ◽  
Author(s):  
Haoyang Shi ◽  
Yulan Zhang ◽  
Zhanyang Xu ◽  
Xiaolong Xu ◽  
Lianyong Qi
Keyword(s):  
Graph Embedding ◽  
Edge Computing ◽  
Knowledge Graph ◽  
Internet Of Vehicles

scholarly journals Cost-Effective Resource Sharing in an Internet of Vehicles-Employed Mobile Edge Computing Environment

Symmetry ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 594 ◽  
Author(s):  
Tri Nguyen ◽  
Tien-Dung Nguyen ◽  
Van Nguyen ◽  
Xuan-Qui Pham ◽  
Eui-Nam Huh
Keyword(s):  
Video Streaming ◽  
Resource Sharing ◽  
Vehicular Network ◽  
Edge Computing ◽  
Mixed Integer ◽  
Mobile Users ◽  
Vehicle Speed ◽  
Mobile Edge Computing ◽  
Internet Of Vehicles ◽  
The Cost

By bringing the computation and storage resources close proximity to the mobile network edge, mobile edge computing (MEC) is a key enabling technology for satisfying the Internet of Vehicles (IoV) infotainment applications’ requirements, e.g., video streaming service (VSA). However, the explosive growth of mobile video traffic brings challenges for video streaming providers (VSPs). One known issue is that a huge traffic burden on the vehicular network leads to increasing VSP costs for providing VSA to mobile users (i.e., autonomous vehicles). To address this issue, an efficient resource sharing scheme between underutilized vehicular resources is a promising solution to reduce the cost of serving VSA in the vehicular network. Therefore, we propose a new VSA model based on the lower cost of obtaining data from vehicles and then minimize the VSP’s cost. By using existing data resources from nearby vehicles, our proposal can reduce the cost of providing video service to mobile users. Specifically, we formulate our problem as mixed integer nonlinear programming (MINP) in order to calculate the total payment of the VSP. In addition, we introduce an incentive mechanism to encourage users to rent its resources. Our solution represents a strategy to optimize the VSP serving cost under the quality of service (QoS) requirements. Simulation results demonstrate that our proposed mechanism is possible to achieve up to 21% and 11% cost-savings in terms of the request arrival rate and vehicle speed, in comparison with other existing schemes, respectively.

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