Abstract
The active field of research is to achieve fast answers with minimal energy usage in mobile edge computing. The latest development of mobile edge computing (MEC) is a new model for allowing access to advanced computing resources at the edge of the network, near-end devices, enabling an abundance of latency-sensitive services needed for Internet of Things (IoT) devices. IoT devices associatedwith the Internet has enlarged intense and the data created by these devices. This would need offloading IoT tasks to dischargehigh computation and storage to resource-rich nodeslikeCloud Computing and Mobile Edge Computing. One of the considerations of cloud-based IoT environments is resource management, which generally revolves around workload balance, resource allocation, resource provisioning, task scheduling, and quality of service to attain performance enhancements. Effective resource management is a challenging problem in mobile edge computing and can deliver high-quality services to mobile devices. When a user demands (tasks) run on a local mobile device, energy consumption rises instead of in the cloud. In the server environment, latency becomes a problem where the task is done rather than on the handheld device. Hence in this paper, the mobile Edge computing-based resource management and task scheduling (MEC-RMTS) framework has been proposed for efficient task-offloading strategies, low latency, and scalable IoT network computing for smartphone devices. Thus, the suggestedmethodhelps the resource manager in the mobile edge computing system concerning scheduling the offloading tasks to reduce the total service time and enhance the effectiveness of mobile edge computing resources. First, the proposed system uses Power Usage (PU) to decrease energy consumption, a Transmission problem for smartphone phones. Using the almost convex technique, designers answer PU and present a gradient-dependent Game Model (GM). Then, as a mixed-intelligent, non-linear program model, the issue of Common Needs and Resource Utilization (CNRU) is used to minimize request delays. The simulation results show that MEC-RMTSF is implemented to save energy usage to 7.1% and has a delayed 0.95sfor allocating resourcesand scheduling management.