Computational Resource Allocation in IoT Fog Computing using Teaching–Learning-Based Optimization Algorithm

Because the Internet of Things (IoT) deals with large amounts of data, it is not easy to process and store this amount of data. However, many of its applications suffer from cloud computing challenges such as latency, location awareness and real-time mobility support. Fog calculations help provide solutions to these challenges. This paper includes an IoT network simulation for allocating optimal shared resources in fog computing to solve the mix integer nonlinear programming (MINLP) problem, which aims to maximize the profitability of cloud service providers through fog computing. The network architecture consists of three layers: cloud service providers, fog nodes, and users. In this paper, the three-layer network is simulated and the algorithm used in this problem is the Teaching–Learning-Based Optimization (TLBO) algorithm, which uses two phases of learning and teaching for the three parameters of cloud service providers’ revenue, average delay and user satisfaction for selecting the best node with the aim of allocating shared resources. This algorithm is implemented on the model and compared with a random method. This model and algorithm increases the profit of service providers compared to the algorithms used to solve similar models.