Optimization of Processing Sequence and Computation Mode in IoT for Mobile Edge Computing: A Comprehensive Analysis

Emerging Materials, Technologies, and Solutions for Energy Harvesting

1. Introduction

Developing IoT systems' capabilities depends on optimizing their processing order and computation mode for MEC. By incorporating edge computing capabilities into IoT frameworks, this strategy attempts to solve the particular difficulties brought by the dispersed nature of IoT networks while reducing latency, improving responsiveness and streamlining data processing (Zhang et al., 2022). MEC transforms computer architecture by bringing computational resources closer to the site of data creation, principally IoT devices, avoiding the need to send every piece of information to faraway cloud data centers (Liu et al., 2019). A complex range of approaches and factors are orchestrated to optimize resource consumption, boost system efficiency and boost overall performance in the processing sequence and computation mode (Feng et al., 2021). IoT systems need a careful task offloading and computation allocation. This entails determining which jobs can be completed locally on the IoT devices and which ones are better suited for execution at the edge servers. Decision-making algorithms are crucial because they consider resource availability, computational complexity and job priority when determining a task that assign to the edge servers (Miao et al., 2020). The optimal technique choice reduces latency that enables a real-time processing and manages the use of computer resources distributed across the network. Furthermore, to maintain workload balance and resource optimization, the effective distribution of computing workloads across edge servers becomes more crucial (Chu et al., 2020, Mehta et al, 2024). Fault tolerance tactics are important components that help to manage fluctuating workloads and possible server outages, which enhance the system's overall dependability and efficiency. Optimizing data storage and minimizing duplicate data transmission are the major benefits of data management in the MEC architecture (Du et al., 2020). Reducing the frequency of data retrievals from the cloud can save bandwidth and increase the overall efficiency. This can be achieved by putting effective data caching, storage methods, as well as data compression and reduplication techniques into practice. The choice of suitable calculation modes further defines the efficiency of the system (Zhou et al., 2021). The distinctive needs of the applications and the resources at the edge are considered when making decisions about batch, real-time, or event-triggered processing. Through deliberate selection of the processing mode appropriate for each activity, the system can optimize performance and responsiveness, matching the exceptional requirements of different applications (Zhao et al., 2021). To provide the best possible job performance, effective management of edge resources is essential. For optimal use, it is necessary to monitor and control resources like CPU, memory and storage in the edge servers (Wan et al., 2019). Strategies for allocating and de-allocating resources are essential for maximizing resource utilization and guaranteeing the dependability along with the consistency of system. A crucial component of improving IoT systems for MEC is refining network communication protocols and transmission techniques to lower overhead and latency (Nduwayezu et al., 2020). The main objective of the investigation is to optimize the computation mode and processing order in IoT for MEC. This means enhancing the effectiveness and efficiency of data processing and analysis at the network edge to lower latency and enhances real-time decision-making, leading to more resource-efficient and responsive IoT services and applications.