A Novel Approach to Network's Topology Evolution and Robustness Optimization of Scale Free Networks

Internet of Things (IoT) is rapidly increasing day by day due to its involvement in many applications such as electric grids, biological networks, transport networks, etc. In complex network theory, the model based on Scale Free Networks (SFNs) is more suitable for IoT. The SFNs are robust against random attacks; however, vulnerable to malicious attacks. Furthermore, as the size of a network increases, its robustness decreases. Therefore, in this paper, we propose a novel topology evolution approach to enhance the robustness of SFNs. Initially, we divide the network area into upper and lower parts. The nodes are deployed equally in both parts and connected via one-to-many correspondence. The distribution is made because small sized networks are more robust against malicious attacks. Moreover, we use k-core decomposition to calculate the hierarchical changes in the nodes' degree. In addition, the core-based and degree-based attacks are performed to analyze the robustness of SFNs. For the network optimization, we compare the Genetic Algorithm (GA) with Artificial Bee Colony (ABC) and Bacterial Foraging Algorithm (BFA). In the optimization process, the node's distance based edge swap is performed to draw long links in the network because these links make the network more robust.