A k -nearest neighbor query method based on trust and location privacy protection

2020 ◽  
pp. e5766
Author(s):  
Liangmin Guo ◽  
Ying Zhu ◽  
Hao Yang ◽  
Yonglong Luo ◽  
Liping Sun ◽  
...  
Keyword(s):  
Privacy Protection ◽  
Location Privacy ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Query ◽  
Location Privacy Protection

Toward Oblivious Location-Based k-Nearest Neighbor Query in Smart Cities

2021 ◽  
pp. 1-1
Author(s):  
Yunguo Guan ◽  
Rongxing Lu ◽  
Yandong Zheng ◽  
Jun Shao ◽  
Guiyi Wei
Keyword(s):  
Nearest Neighbor ◽  
Smart Cities ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Query

GPU-aided Edge Computing for Processing the k Nearest-Neighbor Query on SSD-resident Data

2021 ◽  
pp. 100428
Author(s):  
Polychronis Velentzas ◽  
Michael Vassilakopoulos ◽  
Antonio Corral
Keyword(s):  
Nearest Neighbor ◽  
Edge Computing ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Query

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.

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