During the past few decades, the Internet of Things (IoT) has made remarkable progress in many real-world applications including healthcare, military, transportation, etc. Multiple sensor nodes are deployed in these _elds to get the required data. Different network topologies are used in IoT and scale-free is one of them. It is mostly preferred due to its robust behavior against random node removal, however, the network collapsed because of malicious attacks. Therefore, in this thesis, robustness of the scale-free networks is enhanced against malicious attacks through optimization. To achieve this, the edge's degree and nodes' distance based edge swap operations are used in the proposed Improved Scale-Free Networks (ISFNs) scheme. In the edge's degree based operation, nodes of similar degrees are linked. Moreover, the connections of the nearest nodes are made in distance based edge swap. These operations help to achieve a better onion-like structure without changing the degree distribution of the network. Therefore, the network becomes robust against malicious attacks. Moreover, no new links or nodes are added in the optimization process, therefore, no extra cost is incurred. Furthermore, to make the network more robust against realistic attacks, the variable attacks are considered. Simulation results of the proposed scheme are compared with ROSE and Simulated Annealing (SA) for different number of nodes. The proposed scheme outperforms the existing techniques for different numbers of nodes and against the low degree, high degree and random attacks. Moreover, ISFNs has 13% and 23% better network robustness as compared to ROSE and SA, respectively. Network Topology Evolution Scheme (NTES) is proposed to prevent the scale-free networks from random and malicious attacks. In this scheme, the network field is divided into two parts with uniformly distributed nodes. After the network's evolution, the nodes are linked with each other through one-to-many correspondence. The division of the network field is made by considering that a network is robust if its size is small. Moreover, to study the hierarchical changes in the degree of nodes, k-core decomposition is used. In addition, nodes' degrees and core based attacks are performed on the network to evaluate the performance of the proposed scheme. Furthermore, the network robustness is analyzed using three optimization techniques: Artificial Bee Colony (ABC), Bacterial Foraging Optimization (BFO) and Genetic Algorithm (GA). The techniques are compared with each other and a technique that efficiently optimizes the network to increase the robustness is selected. In the optimization process, we make use of three edge swap methods. Due to the edge swap, the network robustness is enhanced without changing the degree distribution, so the addition of nodes/links is not required to increase the robustness. Furthermore, NTES is compared with Barabasi Albert (BA) model and Hill Climbing (HC) algorithm against random and malicious attacks. The simulation results show that the proposed NTES optimized using GA outperforms BA and HC by 46.90% and 57.08%, respectively, in terms of robustness. In addition, the network robustness of Scale Free Networks (SFNs) is enhanced against the malicious attacks. For that purpose, initially, a parameterless optimization algorithm JAYA is used because it requires less computational efforts as compared to the heuristic techniques. Then, as the edge swap plays an important role to enhance the robustness of SFNs, therefore, the edge swaps are classified into three categories. For each category, effects on the network's topological parameters such as average shortest path length, assortativity and clustering coefficient are analyzed. Next, the robustness is enhanced with the addition of nodes in the maximum connected subgraphs and the protection of bridge edges maintain the network connectivity. Moreover, optimized network is analyzed for different attack strengths. In simulations, the comparison of JAYA is made with two existing algorithms: ROSE and Simulated Annealing (SA). The network optimized by JAYA has a better robustness against random and malicious attacks, as compared to the existing algorithms. Furthermore, among the edge swap categories, the degree dependent edge swap is better to increase the robustness of SFNs. Moreover, the addition of nodes into the maximum connected subgraphs enhances the robustness and the protection of bridge edges ensures the network connectivity in all the algorithms. Furthermore, the robustness against different attack strengths are analyzed and the results show that high attacks strength paralyzed the network more efficiently.