| Abstract |
In the present digital era, the Internet of Things (IoT) has gained significant attraction in divergent fields of constructing smart cities, agriculture, medical, handheld devices and traffic monitoring systems. The IoT is been used to inculcate the objects with intelligence in order to deceive, recognize and assist the machines and humans present in the environment. In the present IoT network many physical entities like humans and devices have been connected. When the network scales up there will a huge rise in volume and velocity, and different types of data would be generated from the connected devices in the network. Due to environmental factors and dynamic factors of IoT systems present in the network, designing a secure and robust IoT network becomes a hectic problem for the researchers. In that case, the data security will be at very high risk because privacy data of the users, medical data and government’s data are put at a very high risk. On conducting a literature survey, and based on the analysis, there have been potential drawbacks on the methodologies present, and it is quite evident that meta-heuristic approaches are more suitable in designing a robust IoT network. The meta-heuristic algorithms are Bio Inspired, mimicking the behavior of the species, and it has promising results in the search space deployed. It is also problem-independent and, irrespective of problems, it can be applied and always attains global optima. The proposed system HECB_OA is compared with other benchmark systems such as SFLA, ITACS and BPSO_BGWO. © 2025 Taylor & Francis Group, LLC. |
