Mathematical Model and Genetic Algorithm in Computer Programming Optimization and Network Topology Structure

Abstract

Recent emphasis has been focused on the industrial Internet of Things (Ind-IoT) in the context of the Fourth Industrial Revolution (Industry 4.0). IoT devices are used in the Ind-IoT to increase manufacturing productivity. The problem, however, is that these instruments will create enormous volumes of data records that must be handled efficiently. Cloud computing (CC) is frequently cited as a viable option for providing effective support for Ind-IoT applications. However, the high-latency and unstable connectivity problem between the cloud and Ind-IoT endpoints continues to plague Ind-IoT operations. Fog computing (FC), which extends computation and storage to the edge of the network, is a possible answer to these problems. Cloud-fog integrated Internet of Things (CFI-Ind-IoT) is discussed in this study as an approach to integrating FC with cloud-based industrial Internet of Things (Ind-IoT). A constrained multiparent crossover genetic algorithm (CMPC-GA) for optimization of the load adjusting challenge in the distributed cloud-fog network is proposed in order to attain ultralow response latency in the CFI-Ind-IoT system. Furthermore, we develop a duty reallocation and retransmission method in order to lower the average delivery latency of the CFI-Ind-IoT architecture due to the unreliable scenario. Effectiveness measurements show that the CMPC-GA technique can deliver ultralow latency functionality in a typical Ind-IoT.