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National Safety Month: Ensuring Worker Safety With Edge Computing

There's no time like the present for organizations to reassess and evaluate the safety measures they have in place to protect personnel—and the IoT can help.
By Ramya Ravichandar
Jun 21, 2019

According to a recent Spiceworks survey, 86 percent of companies with more than 5,000 employees plan to adopt Internet of Things (IoT) solutions by 2020. Such solutions are driven by the need for operators to make real-time decisions in the field, especially in the context of mission-critical applications.

As the capability and availability of IoT technologies continue to expand, applications that can be delivered using these platforms are also emerging. The common thread that ties all of this together is the need for edge computing in order to manage the variety, velocity and volume of data generated by assets on the ground, as well as deliver low-latency response times.

For example, it is essential for IoT-connected sensors on drones, used for agriculture or infrastructure development and maintenance use cases, to be able to collect, process and infer insights quickly and effectively. As a result, organizations are exploring edge computing to drive critical use cases, such as task automation and condition-based maintenance across industries.

June is National Safety Month, and there's no time like the present for organizations to reassess and evaluate the safety measures they have in place to protect personnel. Modern technological innovations, such as edge computing, enable organizations to enhance safety while also better maintaining machinery and even improving outcomes.

Edge computing helps employees stay safer in dangerous situations, like exposure to extreme temperatures, toxic environments, open flames or heavy machinery. In such scenarios, connected sensors can monitor specific locations, situations or equipment in real time, enabling an opportunity to deploy preventive measures before perilous hazards can occur. In fact, the Bureau of Labor Statistics estimates that overexertion and falls account for more than $25 billion in workers' compensation costs. Beyond the importance of worker safety, organizations also risk significant fines if they don't operate plants and equipment appropriately.

This National Safety Month, organizations should look to key areas for safety improvements, including:

Real-time worker safety: Edge computing technology can play an essential role in the further deployment of audio and video data in commercial and industrial IoT systems—for example, the use of flare monitoring at oil and gas operations to track flare state remotely for large volumes of flare stack towers. Keeping tabs on every system and how they impact other systems or machinery downstream is nearly impossible without the use of streaming analytics. By monitoring systems with streaming analytics, operators can identify anomalies that are symptoms of catastrophic failures, possibly resulting in loss of life.

Predictive maintenance: Adopting or refining predictive maintenance models allows operations personnel to get ahead of potentially dangerous issues with machinery, and to ensure work environments meet the highest safety needs. For example, by analyzing sensor data at the source and in real time, oil and gas operations can determine maintenance needs well in advance, avoiding hazardous situations for maintenance teams. If a potential failure is detected, the system can automatically stop a piece of machinery to prevent damage, as well as alert operations to repair or replace the equipment.

By 2022, Gartner predicts 75 percent of enterprise-generated data will be created and processed outside the traditional, centralized data center or cloud, up from less than 10 percent in 2018, because of these reasons. Edge computing will unlock trillions of dollars of value creation for the IoT, while keeping the workforce safe.

Ramya Ravichandar, FogHorn's VP of products, brings a combination of technical expertise in real-time analytics, machine learning, artificial intelligence and Industrial IoT. She is a seasoned product leader who previously headed Cisco's Streaming Analytics platform for the Internet of Things. Ramya has a Ph.D. degree in computer science from Virginia Tech.

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