Cost-Effective Industrial IoT Model using Cloud Environment

Abstract

The integration of Industrial Internet of Things (IIoT) technologies into industrial processes has revolutionized manufacturing and production sectors by enabling real-time data collection, analysis, and decision-making. However, the scalability and cost considerations associated with cloud-based IIoT implementations remain significant challenges for industries seeking to maximize the benefits of this transformative technology. This paper presents a comprehensive analysis of a cost-effective cloud model designed specifically for Industrial IoT applications. This paper proposes an approach that optimizes resource utilization, reduces operational expenses, and maintains high-performance standards, ensuring the seamless functioning of IIoT systems within budget constraints. The study begins by examining the current state of IIoT cloud deployments, highlighting the prevalent cost-related challenges faced by industries. Subsequently, a cost-effective cloud model is introduced, which leverages advanced technologies such as containerization, edge computing, and server less computing to optimize resource allocation while minimizing infrastructure overheads. To evaluate the proposed model, a series of experiments are conducted in a simulated industrial environment, measuring performance metrics, scalability, and cost-effectiveness. The results demonstrate that the cost-effective cloud model significantly reduces both capital and operational expenditures, making IIoT implementations more accessible to a wider range of industries.