The Role of Fog Computing in Enabling Real-Time IoT Applications

Abstract

The emergence of the Internet of Things (IoT), which interconnects billions of devices and produces enormous quantities of data, has brought to light the deficiencies of existing cloud computing models. Challenges such as latency, security, data integrity, bandwidth expenses, and absence of operation independence hinder the ability to conduct real-time analysis and provide appropriate responses. As an emerging architecture, fog computing addresses the most significant challenges of cloud computing in IoT environments. Through distributed fog nodes, this paper investigates how fog computing extends the cloud to the perimeter of networks. A decentralized computing infrastructure that facilitates the exchange of computing, storage, and networking services between IoT devices and cloud data centers is referred to as fog computing. Fog nodes, as opposed to cloud-only systems, function locally, facilitating access to real-time device data with minimal latency. Fog nodes are accountable for data acquisition, analytics, transient storage, and transmission of filtered data to the cloud. Fog computing offers significant benefits to IoT systems due to the close proximity of fog nodes to endpoint devices. For time-sensitive decisions, latencies can be reduced from seconds to milliseconds through the processing and analysis of data at the periphery. Local processing of data also enhances its security and integrity in comparison to transmission to the cloud via a network. The utilization of fog computing can effectively mitigate the financial burden of data transfer by exclusively transmitting necessary summaries. In conclusion, the decentralized methodology enables autonomous operation in the event of a disconnect from cloud data centers. The desired characteristics and middleware platform for fog nodes that facilitate these benefits are described in this article. how modern fog computing can provide intelligence and realtime responsiveness to applications monitoring civil infrastructure and industrial control. Fog computing, as a result, surmounts intrinsic obstacles when it comes to the implementation of cloud architectures on IoT systems that are susceptible to latency. Through the integration of cloud and fog resources, stakeholders can optimize their operations in terms of security, scalability, and dependability by forming a hybrid ecosystem. Fog computing will dramatically accelerate the adoption of industry wide transformative IoT use cases by virtue of its decreased expenses and accelerated analytics velocity.