Published Paper Details:

The Industrial Internet of Things (IIoT) is transforming industrial operations by embedding intelligent sensors and devices into systems, generating large volumes of data that drive operational improvements. While cloud computing has traditionally been employed for data processing, its drawbacks--such as latency, limited bandwidth, and security concerns--create challenges for time-sensitive industrial tasks. Edge computing overcomes these limitations by processing data near its source, which reduces latency and supports quicker decision-making. This paper examines the role of edge computing within IIoT ecosystems, focusing on how it can enhance operational efficiency, improve predictive maintenance, and enable smarter automation. We explore the key architectural elements of edge computing, the advantages it brings to IIoT, and the challenges it faces in terms of hardware limitations, security vulnerabilities, and interoperability issues. Additionally, we discuss emerging trends like federated learning, digital twins, and autonomous edge systems, exploring their potential to influence the future of industrial computing. The paper concludes by underlining the significant role of edge computing in transforming industrial infrastructures, providing a platform for more resilient, intelligent, and scalable IIoT systems.