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What is the Rugged Edge and Why It's Shaping Edge Computing

Page Contents:

Advancing Technology Trends

The industrial world has gone under significant technological changes in the past decades through a digitalization of all its operations. The introduction of computers into traditional processes have accelerated innovation to help automate and optimize industries for efficient growth. In order to continue increased productivity and performance, new advents of complex smart sensors, artificial intelligence, big data, and advanced robotics combined with improved data communication is paving the way for a new industrial era.

Initially, the ubiquity of cloud computing was a primary driver for all new innovations by providing heightened performance infrastructure that could be accessed by any device with an internet connection. In fact, 73% of organizations have at least one application or a portion of their enterprise computing infrastructure in the cloud.1 Industries were able to realize greater cost reductions and maximize growth by migrating IT environments to the cloud.

This is also where big data established its dominance by generating new insights on business operations through incorporating smart Internet of Things (IoT) sensors and digitalizing machines to record every bit of data from the shop floor to the top floor creating valuable information for decision making.

However, industries today have quickly realized that a centralized cloud platform may not be a universal solution for all future applications to come. The sheer amount of data being generated from IoT sensors and devices cannot always funnel through a bandwidth pipeline to the distant cloud. Given current bandwidth limitations and network latency, applications that need actual real-time analysis or the ability to process larger complex datasets, face potential bottlenecks and would otherwise be unachievable. Aiming to break through this barrier for increased performance and at the same time provide immediate insights, data processing and communication needs to be located closer to the source, or also being termed the “edge.”

Shifting to the Edge

Moving away from the centralized aspect of cloud computing is where most new opportunities are starting to materialize. The rapid growth of advanced IoT will require more flexibility and performance in an effort to expedite applications in automation, machine-to-machine communication, and computer vision just to name a few. This can be achieved by integrating distributed edge computing platforms that are physically located closer to the source of data.

 

The “edge” in relation to a network layout sits right in-between the cloud and devices that generate data. While edge computing is not a new concept by any means, the advancement and proliferation of smarter devices is transforming the way we interact with the world through digital platforms. The migration of data processing is shifting further away from the data center through these devices as more and more of them are interconnected through a shared peer-to-peer network.

Edge computing deployments are an ideal solution for a variety of circumstances. In contrast to having a collection of IoT devices connected to a central point on the cloud, an edge platform can offer better efficiency that doesn’t constantly siphon bandwidth and instead only transfer meaningful processed data.


For an example, consider a building’s smart security system utilizing motion-sensor IP cameras. In a strictly cloud based system, these cameras will continuously record video data and stream it to a server located elsewhere in a data center. The motion-sensor application reviews the recorded video data to only archive footage that featured motion activity. In this scenario, the security system puts a constant strain on the building’s internet infrastructure and substantial bandwidth gets consumed by the continuous stream of raw video data.

Alternatively, an intelligent edge computer or server can alleviate the amount of stress on the infrastructure by moving the motion-sensor computation onto a separate platform located in-between the camera system and the cloud network. The cameras will be connected to a nearby edge computer that analyzes all video data feeds for motion activity, and in turn, only transfers meaningful data packaged into a smaller size for archival on the cloud as needed. In this method, the security system minimizes network bandwidth and resources freeing up the server for other tasks as well as delivering significant cost savings.

In addition, an edge computer that handles all the local processing could in theory detach from the cloud network entirely giving businesses more flexibility in remote locations that might not have reliable internet connectivity. It could also work in a hybrid model that manages computational tasks at the edge for non-time sensitive data that can be compiled at the end of the day in the form of a daily report to a central cloud server.

The versatility of edge computers open up new opportunities for innovation given its ability to be deployed in varying environments. It is estimated by Gartner that connected edge devices will grow up to 25 billion units by 2021.2 Edge computing is a not a type of technology looking to satisfy market demands but a topology and framework that is being fulfilled with IoT technologies and cloud access to push forth new solutions. As the number of smart devices grow, a mediator in edge computing will facilitate the placement of proper processing hubs along the entire continuum that ranges from the core cloud at one end to the end device closer to source of data generation.

The progressive edge computing paradigm is made possible by several key factors bringing the concept into modern reality. The growth and enhancements of the platform is thanks to a range of smart sensors, actuators and interconnected devices each with their own memory, processors, storage and networking capabilities factored in with a trend of increased compatibility and ease of market access.

  1. Lower costs on compute and the Internet of Things
  2. Increased processing performance in smaller footprints
  3. Exponential growth of big data and bandwidth limitations
  4. Connectivity and interoperability between machines-to-machines
  5. Advancement in machine learning towards federated learning

Until recently, edge computing was not considered necessary due the strength of the cloud and its economies of scale. The centralized environment allowed data centers to maximize processing capabilities by operating at hyperscale and offered impressive computational proficiency. However, implementation realities must consider the limitations of bandwidth speeds that bottleneck applications needing real-time computing and minimal latency. Factored in with the economic costs of backhauling massive amounts of data and cloud ingress/egress expenses, enterprises are quickly realizing that both centralized cloud and distributed edge environments are necessary for future growth.

Industrial Environment


Much of the current attention on edge computing originate from the growing interest in developing IoT systems with the ability to deliver smarter data and valuable predictive insights. While edge computing affects nearly every corner of the IT landscape, the Industrial Internet of Things (IIoT) is at the forefront of the trend.

Information Technology (IT) and Operational Technology (OT) are converging in numerous industry sectors including healthcare, transportation, manufacturing, defense, aviation, mining, energy, utilities and telecommunications. Increasingly, connected systems featuring wireless sensor and actuator networks are being integrated into the management of industrial environments, such as those for water treatment, electric power and factories. The combination of automation, communications and networking is an integral part of the growing IIoT. Edge computing provides the platform capacity to efficiently filter and analyze the large quantities of data generated from IIoT devices for local real-time actions.

The modernization of operations through innovative IT solutions enables more direct control and cohesive monitoring to infer more value from machine-driven data. As the adoption of IIoT steadily rises, industrial organizations are finding ways to help them further monetize the value of their assets and achieve meaningful results.

The Edge Computing Case for 5G

One prime example that will be the result from the culmination of edge computing is none other than 5G networks, the fifth generation of cellular network technology. Just like its predecessor, 4G, the track towards 5G was developed in direct response to the continued exponential growth of smart devices needing improved internet network connections to manage large amounts of data. In comparison, 4G broadband can only support up to 2,000 active devices in a square kilometer while 5G standards are designed to support up to 1 million connected devices in the same space paving the way for massive IoT networks.3

Processing higher volumes of data at speeds 10 times faster compared to 4G will require an entirely new landscape of systems starting from the network infrastructure all the way to every single connected device. The evolution to 5G will drive a necessity for edge computing platforms given the complexities of real-time inference for massive IoT density. The reason being that 5G works with extremely high bandwidth frequencies that allow for faster speeds but reduces overall range, providing a structure for real-time processing power.


So in order to distribute 5G capabilities reasonably, small cell base stations need to be integrated throughout dense locations to provide ample 5G coverage. This is exactly what defines edge computing, a system that stands as close to the end-user as possible to deliver faster processing speeds to support real-time computing. 5G technology will have to rely on edge computing hubs deployed at each base station to enable time-critical applications such as autonomous vehicles, high-frequency trading, augmented reality and industrial automation.

These emerging technologies require massive amounts of real-time computation to deliver content or instructions to users with reliability and availability anywhere at any given time. This digital transformation will help reduce back-haul traffic by keeping data content at the edge and provide increased quality of service. Edge computing plays a pivotal role in the arrival of 5G, solving a variety of challenges for latency, management, security and monitoring.

However, some obstacles lay in the way for edge computers to fulfill the need for a successful IIoT and 5G integration. When implemented in rigorous industrial environments, sensitive computer components face a risk of breaking down under extreme conditions where stability and continued operability are the standard for traditional machinery. For edge computers to thrive, industrial ruggedization is necessary to deliver new computing functionality in situations where typical computers are unsustainable.

The Rugged Edge

Often referred to as Industry 4.0 or the Fourth Industrial Revolution, the digitalization of heavy industry is a larger than life transformation of equipment and processes to build an end-to-end ecosystem that leverages the advantages of edge computing and IIoT. This ecosystem involves every facet throughout enterprise resource planning (ERP) from internal functions including sales, procurement, engineering and R&D, all the way to external players in suppliers, logistics and end-users.

The application topology of edge computing moving data, applications, service and power closer to the source of data generation can attain valuable insights for industrial factories looking to make better informed strategic decisions. The benefits of digitalization does not only stop at increasing production efficiency or expanding tactical regionalization, it also opens the possibilities for developing new products and technology for new untapped verticals.

The benefits of incorporating edge computing are clear, however, when deployed in real-world environments that are harmful to electronic components, a specialized rugged industrial computer is required to bring mobile edge computing capabilities to new frontiers.

For instance, heavy machinery used in deep underground mining operations are exploring the integration of smart IIoT sensors and rugged edge computers to enable remote control and driverless equipment. Human operators can safely control heavy drilling jumbos and loaders to further increase safety, productivity, and efficiency. Given the nature of underground mining, any sensitive computer component would be severely affected by the high amount of shock, vibration, hot temperatures and dust. To combat this issue, the edge computer needs to be fortified and validated to operate reliably under strict conditions. The addition of IoT data and edge computing also provides the mining industry better insight through process optimization to boost asset utilization, predictive maintenance, energy management and real-time analytics for advanced modelling when making strategic decisions.

Rugged edge computers are specifically developed to withstand the rigors of harsh usage conditions such as the industrial mining example and are able to achieve a high level of durability through incorporating ruggedized features throughout its entire product design. From the external enclosure to the internal components, every piece of a rugged edge computer is purpose-built through a combination of mechanical and thermal engineering to address the issues of strong vibrations, extreme temperatures and wet or dusty situations.

5 Rugged Edge Computer Key Characteristics to consider for edge deployments:

1. Wireless Connectivity

2. Mobility and Remote Deployment

    3. Performance Accelerators

    4. Variety of I/O Ports and PCIe/PCI Expansion Slots

      5. Ruggedness and Security

       

      Learn more about the 5 Must Have Hardware Requirements for Rugged Edge Computing (Infographic Available)