IoT use cases are expanding as more businesses are moving from proof of concept to large scale deployments. Across all industries, IoT is becoming an enabler of new business opportunities, streamlining processes that were previously labor and time intensive.
Insights generated from sensor data collected by drones, robots, self-driving cars and other platforms are being increasingly used to support smarter, automated processes. In many of these processes, speed is the key. The ability to collect and analyze huge amounts of sensor data and deliver insights to edge devices and end users in a timely manner can drive unprecedented operational efficiencies and improved decision-making. However, setting up an IT environment that can support these initiatives with low latency, high availability, scalability, real-time analytics and other capabilities involves significant technological challenges.
The complexity of IoT deployments along these lines can be illustrated by looking into the computing requirements of large-scale drone operations. Over the last few years, the use of drones has expanded to cover various use cases spanning a range of industries. Search and rescue and first aid missions, disaster management, crime scene investigation, firefighting, terrain modeling, soil analysis, site surveying, cargo delivery – these are just a few examples of current drone use cases.
Oil and gas is one of the industries where drones are a game-changer. Way before the COVID-19 pandemic, the industry has been dealing with long periods of low oil and gas prices and decreasing cash flows, resulting in budget cuts, continuous decline in stock value and major layoffs. The pandemic has exacerbated these pressures. According to the Wall Street Journal, 2020 alone saw $145 billion in write-downs of oil reserves and related assets.
Amidst volatile market conditions, oil and gas companies are undergoing accelerated digital transformation to optimize operations and implement new business processes enabled by advanced technologies. One of the main goals of these initiatives is to mitigate inefficiencies that stem from the difficulty of correlating and analyzing data coming from multiple technology silos and to obtain a comprehensive, real-time operational view, which is a common challenge for oil and gas companies.
IoT plays a major role here. An IoT environment that supports collection and analysis of sensor data from edge “things” or devices can help oil and gas companies optimize processes, improve decision-making, respond faster to situations as well as identify and capitalize on new business opportunities. However, as oil and gas operations often involve remote assets located in hazardous environments, achieving this goal is a complex task.
This is where drones can make a big difference. Used as flying sensor platforms, drones are ideal for providing a 360-degree view of offshore drilling rigs, wellheads, production platforms, powerlines, wind turbines, transmission gear, tankers and various other assets. By using drones to monitor these mission-critical assets, oil and gas companies can significantly reduce the time and cost involved in manual inspection, and improve their ability to rapidly detect malfunctions, predict issues, take predictive maintenance actions accordingly and reduce asset downtime. In some cases, drones allow for monitoring areas that were otherwise inaccessible.
The benefits of using drones for inspection and surveillance are far-reaching and go beyond improving operational efficiency. For example, one of the biggest concerns for oil and gas companies are accidents, whether due to human error or natural disaster, which may result in environmental catastrophes such as massive oil spills, air and water pollution, fires and more. The combination of real-time drone imagery and advanced video analytics and AI technologies can provide oil and gas companies with early alert on incidents. Drones can then be used for rapid, comprehensive mapping of disaster-affected areas that can be acted upon immediately to minimize the potential damage.
The benefits of using drones in oil and gas operations are compelling. In accordance, in its Worldwide Oil and Gas 2020 Predictions report, research firm IDC estimated that “by 2021, driven by safety, efficiency, data accuracy, and integration in real time with ERP and asset management systems, 50% of midstream operators will deploy drones to collect pipeline asset data. By 2023, 40% of new offshore production facilities will be unmanned”.
To realize the advantages of advanced, drone-enabled operations, oil and gas companies must make sure that their IT infrastructure can support the unique computing requirements of this use case. This undertaking could be challenging due to the need to balance conflicting needs for performance, scalability as well as flexibility in customizing the environment to the unique needs of oil and gas companies.
Consider, for example, an oil and gas use case where drones continuously collect massive amounts of imagery and other sensor data such as temperature, humidity, gas emissions, pressure, etc. This data can be correlated with inspection data from autonomous inspection gear and operational systems, as well as environmental, weather, geospatial, seismic and other data collected from external systems to obtain a complete picture of the situation. The data is then typically streamed to a central location where it is analyzed and translated into actionable insights that are pushed to edge devices and end users.
In theory, public cloud is a perfect fit for this scenario as it can be used to route drone data to a central cloud server. A public cloud strategy can help oil and gas companies break legacy data silos, providing an effective way of moving data across previously disconnected systems, and enabling them to extract full value from their data assets. Moreover, public cloud offers benefits such as unlimited scalability and access to the latest technologies and applications that otherwise might be out of the reach of companies due to lack of adequate resources and expertise.
However, public cloud is not an ideal solution for drone use cases that are latency-sensitive, which means that they are dependent on the ability to send and/or receive data at speeds of less than 10 milliseconds. Moving large amounts of data from connected devices scattered across multiple remote locations to the public cloud for processing and analysis may therefore result in latency issues. Another drawback of using a public cloud located far from the edge is the cost of network traffic, which increases dramatically as more data needs to move to and from the cloud.
In addition, as depicted above, drones are often used by oil and gas companies as part of a broader framework that is aimed to provide a comprehensive, accurate operational picture. These environments tend to be very complex. The oil and gas industry has been relatively lagging in terms of technology adoption. Many large companies in this space are still reliant on disparate legacy systems and application silos that use a variety of platforms, protocols, network topologies, etc. Hence, oil and gas companies require flexibility in adjusting their IT infrastructure to meet their requirements. Such flexibility is typically lacking in public cloud deployments as the infrastructure is owned and managed by the cloud provider.
To minimize latency, drone operators use other methods that enable them to place computing resources closer to edge devices. While advanced drones can process sensor data, they are equipped with limited compute resources and are not flexible enough to be configured to support data-intensive, high-performance workloads such as AI, machine learning, video analytics and others. Hence, it is a common practice to combine local processing with a method of transferring data from drones to a nearby central location for further processing and analysis.
Many drone operators use an edge computing architecture that is based on locating a self-contained version of a traditional enterprise data center in proximity to edge devices for improved performance. An edge data center has its own shortcomings though. Most notably, there is a limit to the amount of compute resources that can be placed in a single edge data center. Hence, given the huge volume of sensor data collected by drones, scalability may quickly become an issue. In theory, setting up additional edge data centers could help address this challenge, but at the expense of mounting operational costs and complexities due to the need to manage multiple sites.
In times when oil and gas companies are challenged with continuous budget cuts due to the low price of oil, the implementation of advanced IoT technologies such as drones could be critical for improving business performance and achieving competitive advantages.
To capitalize on the benefits of drones as an enabler of oil and gas IoT initiatives, companies should implement an IT infrastructure that can effectively address the challenges involved. As depicted above, neither distributed approaches such as edge computing nor centralized computing models like public cloud or colocation can fully support complex IoT environments consisting of drones without making painful trade-offs. Hence, digitally transformed oil and gas companies require a new computing approach that can natively provide advanced public cloud capabilities at the point of need, in proximity to where data is generated.
An emerging computing model that could help IoT players combine the benefits of edge and public cloud is offered by Ridge. Ridge’s distributed cloud enables application developers to deliver modern workloads locally from globally distributed networks of data centers and local cloud providers. Ridge federates data centers across the globe into a unified network hosting its platform, which can be leveraged to support the delivery of cloud services where data is generated, in proximity to end-users and end-devices. Using this global network, IoT developers can instantly deploy their workloads across disparate geographic locations without investing in new infrastructure.
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