AI-enabled distributed energy conservative model for SDN based mobile IoT devices

The Internet of things (IoT) is an emerging technology for many smart applications due to its efficient resource utilization, scalability, and fast interaction with the physical world. Software-defined network (SDN), on the other hand, provides dynamic services for controlling and managing real-time systems. However, collected data are sent to a central location, which requires balancing energy resources with redundant channels to maximize the availability of smart functions. Furthermore, the IoT network faces numerous security vulnerabilities as a result of its open communication space, including malicious messages and privacy concerns. Thus, this paper presents a distributed and artificial intelligence-based energy-efficient model for IoT-SDN architecture, which aims to improve data aggregation and power distribution. It also provides security and authentication for smart communication systems. First, the proposed model introduces the heuristic evaluation using artificial intelligence and decreases the power consumption for sensor nodes in a real-time system. Moreover, it optimizes the paradigm of distributed processing and efficiently increases the green energy technology with nominal management costs using the mobile edges. Second, the aggregated data of the environment is secured using a centralized controller to attain the most trustworthy data availability. The experimental results show a comparative analysis of the proposed model in terms of energy efficiency, packet drop ratio, and waiting time by 22%, 23%, 40%, and 49% as compared to existing studies