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Nowadays, the Internet of Things (IoT) provides benefits to humans in numerous domains by empowering the projects of smart cities, healthcare, industrial enhancement and so forth. The IoT networks include nodes, which deliver the data towards their destination. However, the removal of nodes due to malicious attacks affects the connectivity of the nodes in the networks. The ideal plan is to construct a topology, which maintains the nodes' connectivity after the attacks and subsequently increases the network robustness. Therefore, in this thesis, werst adopt two different mechanisms for the construction of a robust scale-free network. Initially, a Multi-Population Genetic Algorithm (MPGA) is used to overcome the premature convergence in GA. Then, an entropy based mechanism is used, which replaces the first solution of high entropy population with the best solution of low entropy population to improve the network robustness. Second, two types of edge swap mechanisms are introduced. The Efficiency based Edge Swap Mechanism (EESM) selects the pair of edges with high efficiency to increase the network robustness. The second edge swap mechanism named EESM-Assortativity transforms the network topology into an onion-like structure to achieve maximum connectivity between similar degree nodes in the network. The optimization of the network robustness is performed using Hill Climbing (HC) and Simulated Annealing (SA) methods. The simulation results show that the proposed MPGA Entropy has 9% better network robustness as compared to MPGA. Moreover, the proposed ESMs effectively increase the network robustness with an average of 15% better robustness as compared to HC and SA. Furthermore, they also increase the graph density as well as network's connectivity with high computational cost. Furthermore, we design a robust network to support the nodes' functionality for the topology optimization in the scale-free IoT networks. It is because the computational complexity of an optimization process increases the cost of the network. Therefore, in this thesis, the main objective is to reduce the computational cost of the network with the aim of constructing a robust network topology. Thus, four solutions are presented to reduce the computational cost of the network. First, a Smart Edge Swap Mechanism (SESM) is proposed to overcome the excessive randomness of the standard Random Edge Swap Mechanism (RESM). Second, a threshold based node removal method is introduced to reduce the operation of the edge swap mechanism when an objective function converges at a point. Third, multiple attacks are performed in the network to find the correlation among the measures, which are degree, betweenness and closeness centralities. Fourth, based on the third solution, the Heat Map Centrality (HMC) is introduced that finds the set of most important nodes from the network. The HMC damages the network by utilizing the information of two positively correlated measures. It helps to provide a good attack strategy for robust optimization. The simulation results demonstrate the efficacy of the proposed SESM mechanism. It outperforms the existing RESM mechanism by almost 4% better network robustness and 10% less number of swaps. Moreover, 64% removal of nodes helps to reduce the computational cost of the network. In addition, we also perform topology optimization using a new heuristic algorithm, named as Great Deluge Algorithm (GDA). Afterwards, four rewiring strategies are designed. The first strategy is based on the degree dissortativity, which performs rewiring if maximum connectivity among similar degree nodes is achieved. In second strategy, we propose a degree difference operation using degree dissortativity to make sure that the connected edges possess low dissortativity and degree difference. Whereas the other two strategies consider nodes' load capacity as well as improved GDA to maximize the network robustness. The effectiveness of the proposed rewiring strategies is evaluated through simulations. The results prove that the proposed strategies increase the network robustness up to 25% as compared to HC and SA algorithms. Besides, the strategies are also very effective in increasing the graph density and network connectivity. However, their computational time is high as compared to HC and SA.

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