Elephant Herding Robustness Evolution Algorithm With Multi-Clan Co-Evolution Against Cyber Attacks for Scale-Free Internet of Things in Smart Cities

A large number of sensors are deployed for performing various tasks in the smart cities. The sensors are connected with each other through the Internet that leads to the emergence of Internet of Things (IoT). As the time passes, the number of deployed sensors is exponentially increasing. Not only this, the enhancement of sensors has also laid the base of automation. However, the increased number of sensors make the IoT networks more complex and scaled. Due to the increasing size and complexity, IoT networks of scale-free nature are found highly prone to attacks. In order to maintain the functionality of crucial applications, it is mandatory to increase the robustness of IoT networks. Additionally, it has been found that scale-free networks are resistant to random attacks. However, they are highly vulnerable to intentional, malicious, deliberate, targeted and cyber attacks where nodes are destroyed based on preference. Moreover, sensors of IoT network have limited communication, processing and energy resources. Hence, they cannot bear the load of computationally extensive robustness algorithms. A communication model is proposed in this paper to save the sensors from computational overhead of robustness algorithms by migrating the computational load to back-end high power processing clusters. Elephant Herding Robustness Evolution (EHRE) algorithm is proposed based on an enhanced communication model. In the proposed work, 6 phases of operations are used: initialization, sorting, clan updating, clan separating,selection and formation, and filtration. These process collectively increase the robustness of the scale-free IoT networks. EHRE is compared with well-known previous algorithms and is proven to be robust with a remarkable lead in performance. Moreover, EHRE is capable to achieve global optimum results in less number of iterations. EHRE achieves 95% efficiency after 60 iterations and 99% efficiency after 70 iterations. Moreover, EHRE performs 58.77% better than Enhanced Differential Evolution (EDE) algorithm, 65.22% better than Genetic Algorithm (GA), 86.35% better than Simulating Annealing (SA) and 94.77% better than Hill climbing Algorithm (HA).