Security-Aware Resource Allocation in Fog Computing using a Meta-Heuristic Algorithm

In recent years, the number of smart devices and wireless data transmissions has increased worldwide. These emerging applications and services require not only extensive computing capabilities and high battery power, but also elevated data transmissions as well. Nonetheless, the computing capacity of this equipment is constrained, resulting in significant consequences on the performance and operating costs of services in 5th-generation wireless networks. Recent advantages of Fog computing have increased the use of this model to fulfill above requirements in the IoT context. A new Fog computing network model has been proposed in order to address these issues by providing cloud computing services at the network's edge. In Fog computing, mobile devices are not required to offload all their tasks to remote and central servers. However, since other users are exposed to offloaded tasks, they are vulnerable to malicious attacks and eavesdropping. In this paper, we investigate security-aware resource allocation in device-to-device based fog computing systems. In order to enhance task offloading, a novel multi-objective function is proposed to optimize delay and energy savings compared to local computing, as well as security breach costs. In order to address this issue, a modified version of the NSGA-II algorithm is proposed that employs sigma scaling. According to the results, the defined objective function is successful in optimizing objectives simultaneously. Using sigma scaling made the NSGA-II algorithm better at spreading out solutions. Moreover, the algorithm's exploration and exploitation capabilities are well controlled.