Center for Edge Computing and 5G

At its core, edge computing is the practice of deploying distributed devices that are capable of performing data processing, computation, and decision making across a network. Businesses place these computational devices as close to the “edge” – the location within the network where data is generated by IoT devices – as possible, to minimize the space and time between ingestion and the analysis of data.

These edge and IoT devices come in a variety of forms, depending on the application and the computational power necessary. They can be offshore wind turbines, industrial controllers, a smart light bulb, security cameras, gas turbines, autonomous vehicles, cameras phones, point-of-sale systems, and much more. Sometimes, they’re as inconspicuous as a single industrial sensor on an oil rig or in a factory. They can also be small “data centers” of servers located in strategic areas around a business’ network or embedded within distributed facilities (such as warehouses or distribution centers) rather than centralized either on-premises at company headquarters or in a cloud computing data center.

Regardless of the size, edge devices often take on the responsibility of processing real-time streaming data themselves, rather than relying on a centralized cloud computing service. They perform analytics, make decisions, and even run artificial intelligence or machine learning models on the data, without additional support. An autonomous car is an excellent example of the types of considerations required for a good edge architecture – many focus on video-based object detection and recognition, but consider the simple use case of smart air bag, and the requirements this may impose for analysis and response. The many variables of passenger height/weight/position in car (especially proximity to the dash), and the sensors which need to work in concert to ensure accuracy and determine whether or not to deploy on impact (and based on severity of impact, location of impact, etc.). There is no time to take the data from an impact to a car to a data center or a cloud, and back again, in time to instruct the air bag to deploy – this analysis and response must happen at the edge – in milliseconds.

An autonomous vehicle relies heavily on sensors that monitor the vehicle’s current state and observe its surroundings. These sensors are connected, through the vehicle’s operating system, to essential mechanical functions like steering and braking. In order to respond rapidly to hazards, shifting lanes, other vehicles, pedestrians, cyclists, and stop signs, the vehicle needs to ingest data from sensors, analyze that against the current speed and other state data, make a decision of its next move, and apply that decision in a fraction of a second.

The increasing power of edge devices and infrastructure is one of the primary reasons autonomous vehicles exist today. If the vehicle needed to send sensor data back to a cloud computing data center for analysis, it may have crashed by the time a decision was transferred back. By placing computational power in the vehicle itself, and thus the fleet’s physical edge, an entirely new industry is born.

This is where the value of 5G networks becomes apparent. The telecom edge, also known as Multi-access Edge Computing (MEC), allows for distributed computing that enables low-latency and high-bandwidth uses cases that otherwise wouldn’t be feasible on centralized cloud architecture³. Autonomous vehicles are one of the ideal use cases for MEC. Beyond transportation use cases, industries in every sector are adopting edge computing and 5G.

Consumers see edge computing in action in so many ways today. A few of the most recognizable are in voice-recognition hardware, smart thermostats, internet-connected TVs, and even the smartphone in their pockets. At the same time, industrial organizations are pushing edge infrastructure and edge analytics to empower their digital innovation and create smart factories using truly intelligent machines and automated processes. By applying computational power, artificial intelligence, machine learning, and advanced analytics at the machine’s edge, businesses can tap into yet-undiscovered value from machine data.

Benefits of edge computing

• Dramatically reduced latency. Without the edge, data transfer between an IoT device and a cloud computing infrastructure adds latency and reduces value; and isn’t fast enough for applications that require instant action, such as autonomous vehicles, customer experiences or industrial applications in a closed-loop system where insights from machine data directly affect the following actions.
• Value gained instantly from data. Edge computing helps to prioritize data as it reduces the signal to noise ratio. Often data is most valuable at precisely the second it is created, and then its value diminishes over time. Through reduced latency – the time gap between the acquisition and analysis of data – organizations can respond to, or learn from, the data and save lives, perform robotic surgeries, create experiences, deliver outcomes and drive disruptive innovation. Edge Computing can reduce the cycle to just a few milliseconds.
• Streamlined analysis process. Traditional IoT & Cloud infrastructures transfer all data from dozens, hundreds, or thousands of devices to and from a centralized computing environment. But, if edge devices are capable of making critical decisions locally, they can choose when and how to push that data to the cloud – if it’s necessary at all.
• Bandwidth reduction. Because high volumes of bandwidth incur a heavy cost for businesses, any cut can make a meaningful impact on the bottom line. Edge computing presents an agile opportunity to keep data close to home whenever possible.
• No single point of failure. A centralized cloud computing environment, despite all its benefits, creates a single point of failure for making mission-critical decisions on IoT machine data. Edge computing spreads out the risk – an edge platform can still operate – ingesting data and making key decisions even if the central cloud experiences downtime.
• On-device encryption and security. By reducing the need for data in transfer (and through embedded or on-device data encryption itself), edge computing can provide security benefits to industrial applications requiring the tightest security measures. By minimizing reliance on a single cloud environment to store all company data, there is also less vulnerability.
• Operations maintained where data connectivity is difficult. Remote or challenging locations, such as offshore oil rigs or deep mining sites, often struggle to maintain strong network connectivity. Edge computing can allow these rough operations to function self-sufficiently using onboard decision-making capabilities.
• Onboard data streaming made easy. Instant analysis of streaming data plays an important role in the future of smart manufacturing and challenges around IT/OT convergence across industries. Edge computing can open the door to complex analysis on high-velocity streaming data.

Will edge computing overtake cloud computing?

The importance of a data-first strategy is to understand the importance of a multi-cloud environment. Every organization needs to be a digital organization – powered by data, running in a multi-cloud world. That means understanding the dynamics and significance of the edge, the core and the cloud.

Cloud computing immediately presents an opportunity for businesses to reduce up-front IT costs, scale as needed, and manage infrastructure more efficiently. Cloud computing providers have consistently improved both the performance of their product and its ease-of-use, which has resulted in enormous innovation and economic growth. Today, it’s possible for even the smallest of businesses to leverage the power of a supercomputer-strength data center.

Edge computing presents a paradigm shift away from computing architectures of the past, but there is no indication it will displace cloud computing environments. In fact, businesses are gaining the most value out of bridging their cloud and edge environments together and utilizing each to their strengths.

Cloud computing will continue to dominate in applications that require massive computational power or managing large quantities of data, such as deep learning training. That said, edge computing is more than powerful enough for even complex AI applications. An autonomous vehicle’s training may happen in the cloud, but its instantaneous decision-making and execution happens at the edge.

How is edge computing being used today?

By managing and analyzing the enormous volumes of data at the edge, the point of origin, and only transferring the most important results, edge computing has already delivered on the power of analytics, low latency, cost reductions and high flexibility capabilities to a variety of industries:

• Defense: Soldiers work in some of the most rugged environments imaginable, and require the ability to make split-second, informed and data-driven decisions, in real-time, using technologies that fit in a backpack or in the back seat of a Humvee. Edge computing, with approved military systems that include compute, storage, and networking capabilities, can assess critical situations, analyze communication spectrums being used on the battlefield, and enable real-time decisions that can save lives, divert critical maneuvers and protect nations.
• Energy: Across all energy use cases including utilities, natural resources, and oil and gas, edge computing is playing a critical role. From real-time workplace safety conditions in rugged environments like on oil rigs and in mines, to improving maintenance by remotely monitoring and tracking equipment conditions, to using smart meters to prevent theft and using smart grid technologies to reduce over consumption, the edge is delivering results. Edge devices eliminate latency and allow the most urgent decisions – such as shutting down operations due to a safety breach – to happen automatically, while also leveraging the raw power of the cloud to make wider analysis of efficiency and possible optimizations.
• Transportation: Aside from the enormous strides being made in autonomous vehicles, the transportation industry is embedding trains with edge platforms to help analyze the billions of data points per second that are already being collected by dozens of onboard sensors. Edge computing is being used to monitor the health of locomotive equipment in real time, and the industry is working toward autonomous operations applications, as well. For improved operational efficiencies, passenger recognition systems are being used to speed boarding for rail and air travel alike. We’re seeing edge computing use cases across land, sea and air.
• Healthcare:The healthcare industry is adopting edge solutions in so many ways. From hospitals, to ambulances, to pharmaceutical companies, and more, the industry has grown tremendously. From general biometric wearable medical devices, such as a wristwatch that would monitor motion and heart rate data to a similar wristwatch now monitoring seizure activity, blood sugar levels and the data that builds predictive algorithms in pacemakers, to using onboard computing power. Today these edge devices can detect episodes and notify caregivers or emergency services without the latency of cloud computing and without the risk of being momentarily disconnected from the network.
• Smart manufacturing: Smart manufacturing facilities can improve safety, efficiency, and quality all while reducing costs through automated processes driven by decisions made on edge computing devices. Edge, IoT, driving insights from machine-to-machine data, can detect when a machine is about to fail and automatically apply contingency plans, such as diverting product to alternative lines or notifying stakeholders.
• Retail: Infrared sensors create physical heatmaps of where retail customers gather in the store and how they move about, which gives retailers up-to-the-minute insights on performance. They can optimize store layouts, ensure associates are in the right places, and deploy promotions with guaranteed results. Edge use cases for retail include advanced loss prevention strategies, enhanced customer experience and improved inventory management.
• Smart cities: The future holds limitless possibilities, but to make them a reality, cities must become digital organizations, ecosystems of interconnectivity powered by data, running in a multi-cloud world. The city of the future uses Edge computer vision to protect citizens, sensors to improve traffic flow and smart grids to reduce the impact on the environment. As municipalities upgrade their aging gas or water infrastructures, they can install edge devices capable of monitoring for faults or anomalies. An intelligent water meter, for example, could detect a water main leak and reduce or shut off supply to minimize the impact before services can respond.
• Environmental monitoring: Warning the public about dangers from fires, volcanoes and earthquakes is difficult because of the speed at which it happens, and how remote the locations where they are most common. Seismic, vibration, air pressure, and temperature sensors, when placed in rugged conditions at the edge, can not only detect anomalies internally, but also know when it’s time to inform public safety organizations.

The amount of data being generated across the globe will continue to grow. Organizations must innovate to find ways of managing that data. Edge computing will play a key role in overseeing and prioritizing data while decreasing costs, lowering necessary bandwidth requirements and reducing latency for delivering insights that can save lives, drive businesses forward and transform the world in which we live.

¹ https://www.datanami.com/2018/11/27/global-datasphere-to-hit-175-zettabytes-by-2025-idc-says/

² https://www.gartner.com/smarterwithgartner/what-edge-computing-means-for-infrastructure-and-operations-leaders/