Privacy and Energy Co-Aware Data Aggregation Computation Offloading for Fog-Assisted IoT Networks
Siguang Chen, Zihui You, Xiukai Ruan
Abstract
With the exponential growth of the data generated by Internet of Things (IoT) devices, computation offloading becomes a promising method to alleviate the computation burden of local IoT device and improve processing latency. In order to address the bottleneck problem of limited resources in IoT device more efficiently and provide security guarantee in data processing and forwarding process, in this paper, we propose a privacy and energy co-aware data aggregation computation offloading for fog-assisted IoT networks. Specifically, a fog-assisted three-layer security computing architecture is developed to counteract security threats and enable the aggregation operation can be performed in ciphertext. Meanwhile, a momentum gradient descent based energy-efficient offloading decision algorithm is developed to minimize the total energy consumption of computation tasks, which can achieve the optimal value with fast convergence rate. Finally, the security and performance evaluations reveal that the developed data aggregation offloading scheme is a secure data processing scheme and achieves significant performance advantage in energy consumption. For example, the total energy consumption can be reduced by an average of 23.1% compared with benchmark PGCO solution.