An intelligent resource allocation strategy for machine type communication environment

Abstract

SummaryMobile communication has multiplied in many digital intelligent applications. The machine‐type communication (MTC) services have afforded flexible communication facilities considering those facilities. However, high energy consumption and poor data rate have made the MTC a complex system. So, the resource allocation strategy has been introduced by different procedures such as neural models, optimization, and mathematical models. But, in some cases, these algorithms have recorded high complexity and high resource requirement. Hence, the present research article has planned to develop a novel Chimp‐based Extreme Neural Model (CbENM) for allocating the desired optimal resource for each machine user. Moreover, the resources were assigned based on the task completion deadline. Before the resource allocation process, the active state users were predicted by analyzing their data rate and deadlines. Finally, the desired resources were allocated to each user, and the performance was validated. The presented model has a lower delay bond rate and a high data aggregate rate.