| Abstract |
Fog computing (FC) as an extension of cloud computing provides a lot of smart devices at the network edge, which can store and process data near end users. Since FC reduces latency and power consumption, it is suitable for the Internet of Things (IoT) applications in smart cities. In FC, the Mobile Devices (MDs) can offload its heavy tasks to Fog Devices (FDs). The selection of best FD for offloading has serious challenges in the time and energy. In this paper, we present a Module Placement method by Classification and regression tree Algorithm (MPCA). We select the best FDs for modules by MPCA. Initially, if the total power consumption of CPU and memory in MDs is greater than Wi-Fi's power consumption, the offloading will be done. The MPCA's decision parameters for selecting of best FD include authentication, confidentiality, integrity, availability, capacity, speed, and cost. To evaluate our proposed approach, we simulate MPCA and compare it with First-Fit (FF) and local mobile processing methods in Cloud, fog, and MDs. The results show that the proposed method is superior to other compared methods in the average power consumption by 38.41%, response time by 37%, and 13.76% in performance. |
