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LPGA: Location Privacy-Guaranteed Offloading Algorithm in Cache-Enabled Edge Clouds
DOI:10.1109/TCC.2020.3030817.png)
摘要
En 中文
The computation offloading, where Internet of Things (IoT) devices transfers their task to an external cloud, has several advantages such as low energy consumption of IoT devices and fast response time. To maximize these advantages, IoT devices can exploit the nearest edge cloud. However, frequent offloadings to the nearest edge cloud can cause a location privacy vulnerability due to the proximity of the edge cloud from IoT devices, which is a critical issue in smart city IoT applications. To address this problem, we propose a location privacy-guaranteed offloading algorithm (LPGA) in cache-enabled edge cloud environments. In LPGA, an IoT device decides where to offload the task (i.e., edge cloud or central cloud) with the consideration of the privacy level on its location and the cache hit probability. To minimize the generated traffic volume while maintaining low energy outage probability and providing a sufficient level of location privacy, a constrained Markov decision process (CMDP) problem is developed and it is converted into an equivalent linear programming (LP) model to achieve the optimal policy for offloading. Evaluation results demonstrate LPGA can reduce the traffic volume up to 39 percent compared to a central cloud-based offloading scheme while maintaining the energy outage probability below a certain level and providing required location privacy level.
Keyword:
Offloading
edge cloud
privacy
constrained Markov decision process (CMDP)
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期刊
I
IF:
5
论文数:
1.8K
被引数:
4.3K
机构
引用论文
Jointly Optimized Energy-Minimal Resource Allocation in Cache-Enhanced Mobile Edge Computing Systems
IEEE ACCESS
IF3.6
Energy-Latency Tradeoff for Energy-Aware Offloading in Mobile Edge Computing Networks移动边缘计算网络中能量感知卸载的能量-延迟权衡

