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Against the backdrop of multiple challenges faced by the industry in recent years, the industrial internet of things (IoT) may provide needed hybrid cloud and fog computing to analyze huge amounts of sensitive data from sensors and actuators to monitor oil rigs and wells closely, thereby better managing global oil production. Improved quality of service is possible with the fog computing because it can alleviate challenges that a standard isolated cloud cannot handle.

Recently, the petroleum industry has faced critical challenges including oil price volatility, dramatically increased environmental regulations, the COVID-19 outbreak, and digital solutions to cybersecurity challenges. Oil and gas deals with a huge amount of data requiring an immediate response, with the flow of data generated by the IoT in all domains: upstream, midstream, and downstream. The vast amount of data and different types of IoT will create a barrier to holistic evaluation that does not depend on machine learning and artificial intelligence. Currently, the cloud solves that problem by processing data and applying artificial intelligence and machine learning, along with other applications. However, the cloud cannot work efficiently with such enormous sets of data and produce actions in a limited time because of the physical distance between the IoT and a centralized cloud will increase latency. In addition, cyberattack challenges to the IoTs and the degree of damage to the environment and equipment can halt operations, which can be circumvented using the fog and cloud. These challenges demonstrate that the current architecture cannot handle the large number of endpoint devices and data in a way that effectively provides significant protection to the system.

The fog model has several advantages over the cloud model, including close proximity to endpoints. The fog connects cyberphysical social systems to cloud computing centers and has the potential to lower the bandwidth and strain of industrial clouds. Cloud computing is a critical paradigm for managing all types of calculations, including those that were previously considered insignificant. However, when the task must be completed in real time with a very low latency, the cloud can become ineffective. As a result, fog computing was created to supplement the cloud. Traditional and fog computing are employed to increase the performance of industrial IoT-based applications. When the fog is unable to complete an operation independently because of capacity constraints, heavy computations must be offloaded to the cloud.

Fog computing is thought to be more cost-effective than cloud computing in time-critical applications such as health care because of its decreased latency, and, in some situations, the spare capacity of locally accessible resources.

Fog computing is a novel paradigm for computation that can be represented as the link between the cloud and the networks edge, where it provides computing, communication, control, and storage. The decentralized platform differs from previous traditional, architectural computational methods because the fog computing environment connects resources, including people, to improve the quality of life by running cyberphysical social applications on network edge processing resources.

Fog Nodes or Microdata Centers (MDCs). Such applications can gather and analyze data from local microdata centers by fog computing. Local data processing and analysis are carried out by the MDC to limit the amount of data transferred to a centralized cloud, reduce network latency, and enhance overall performance, especially for time-sensitive services such as connected cars and health monitoring. In order to reduce network congestion, bandwidth consumption, and delay for user requests, MDCs typically are placed between data sources and the cloud data center. The MDC handles most user requests instead of forwarding them to centralized and remote cloud data centers.

Smart Gateway. Because it permits communication between the network layer and the ubiquitous sensor network layer, a smart gateway is an important component of industrial IoT applications. IoT gateways are communication points that connect lower-end users who operate in influential data centers, connect the many devices in use, and perform a variety of functions to complete the computing purpose. The gate is solely used to receive sensor data, incorporate it, and then send it to the cloud for processing.

Virtual Servers. Fog computing has developed as a response to the massive amounts of data being transmitted to cloud servers and the severe latency and bandwidth limits that come with it. As an intermediary computing layer between cloud servers and IoT devices, it distributes numerous heterogeneous fog servers. Fog servers contain fewer resources than cloud servers, but, because they can be accessible over a local area network, they offer better bandwidth and reduced latency for industrial IoT devices.

Management Subsystem. Fog computing optimizes task execution and management system by achieving a balance of attention between resources and tasks. Load balancing is an important resource-management method that can be used in conjunction with task management to produce a reliable system.

Storage Subsystem. The fundamental objective of the industrial IoT is to acquire correct data in real time and then respond quickly and appropriately to provide desired results. Fog and edge computing have been used to help solve these problems and enhance service quality and user experience by effectively distributing data storage and processing across multiple locations physically close to the data source.

Fog computing architectures are built on fog clusters that combine the processing of several fog devices. Data centers, on the other hand, are the clouds primary physical components, with high operational costs and energy usage. The fog-computing paradigm consumes less energy and has lower operating expenses. Because the fog is closer to the user, the distance between users and fog devices could be one or a few hops.

The clouds communication latency is always higher than the fogs because of distance. The fog relies upon a more-distributed strategy based on geographical orchestration, whereas the cloud represents a more-centralized approach. Because of its high latency, the cloud does not allow for real-time contact; however, fog computing can alleviate this problem easily. On the other hand, the fog has a high failure rate because of its dependence upon wireless connectivity, decentralized management, and power outages. When the software is not managed properly, these devices can fail.

Drawbacks of the cloud-based model include the following:

The fogs dispersed design safeguards linked systems from the cloud to the device by placing computing, storage, networking, and communications closer to the services and data sources, offering an extra layer of security. Fog nodes protect cloud-based industrial IoT and fog-based services by executing a variety of security tasks on any number of networked devices, even the tiniest and most resource-constrained ones. For managing and upgrading security credentials, malware detection, and timely software patch distribution at scale, the fog provides a trusted distributed platform and execution environment for applications and services, as shown in Fig. 1.

By detecting, verifying, and reporting assaults, the fog provides reliable communication and enhanced security. If a security breach is discovered, the fog can detect and isolate risks quickly by monitoring the security status of surrounding devices. Blockchain deployments to low-cost IoT endpoints are possible using the fog. If multiple power generators are attacked using malware, the fogs node-based root-of-trust capabilities allows operations managers to remotely isolate and shut down affected generators. This ensures that service interruptions are minimized. If hackers attempt to take control of a smart factory by exploiting a vulnerability in assembly-line equipment, the domains are protected by fog nodes. Traffic is monitored from the internet into the distributed fog network and uses machine learning in the local environment to detect a potential assault once it has been recognized.

Based on a literature review of the use of the fog paradigm in health-care, smart cities, industrial automation, and smart-connected vehicles, great potential for fog computing exists in the petroleum industry. It is an open-architecture methodology that allows industrial IoT 5G and artificial-intelligence advancement. Fog nodes protect cloud-based IoT and fog-based services by executing a variety of security tasks on any number of networked devices. The benefits of using fog computing in the petroleum industry are latency reduction, improved response time, enhanced compliance, increased security, greater data privacy, reduced cost of bandwidth, overall increase in speed and efficiency, less reliance on wide-area-network services, greater up-time of critical systems, and enhanced services for remote locations.

Fig. 1Securing industrial IoT through fog computing.

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 206067, The Role of Hybrid IoT With Cloud Computing and Fog Computing in Helping the Oil and Gas Industry Recover From COVID-19 and Face Future Challenges, by Ethar H.K. Alkamil, SPE, University of Basrah; Ammar A. Mutlag, Universiti Teknikal Malaysia Melaka; and Haider W. Alsaffar, SPE, Halliburton, et al. The paper has not been peer reviewed.

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IoT With Cloud and Fog Computing Can Help Industry Recovery, Advancement - Journal of Petroleum Technology