The term “edge intelligence,” also referred to as “intelligence on the edge,” describes a new phase in edge computing. Organizations are using edge intelligence to develop smarter factory floors, retail experiences, workspaces, buildings, and cities. The edge has become “intelligent” by way of analytics that were formerly limited to the cloud or in-house data centers. In this process, an intelligent remote sensor node may make a decision on the spot or send the data to a gateway for further screening before sending it to the cloud or another storage system.
Mining big data for useful insights can be a major challenge. Searching through data is very much like panning for gold — a time consuming task with occasional rewards. Organizations are aware of the strategic importance of big data and analytics, but there are still hurdles to overcome.
While data can give a business a competitive edge, there is also the potential to swamp their storage systems with worthless information. There is simply an overwhelming amount of data being created on a daily basis, much of which is useless. Asha Keddy, Corporate Vice President and Manager of Next Generation and Standards at Intel, stated, “We’re generating too much data.”
Prior to edge computing, streams of data were sent straight from the internet of things (IoT) to a central data storage system. Early edge computing was an effort to provide a data screening process using micro-data stations (preferably within 100 square feet of the sensor nodes) to eliminate unnecessary or redundant data before sending it on. In simpler terms, early edge computing attempted to send leaner, more efficient data streams, with less data to store and process on the primary system.
Cities, buildings, and industrial systems start with an edge sensor node, which senses and measures a specific range of information that is then used in making key decisions. The edge nodes can process data intelligently, and can bundle, refine, or encrypt the data for transmission to a data storage system. Ideally, an edge node is small, unobtrusive, and can fit in environments with minimal amounts of space.
The Intelligence Aspect
There are a wide variety of sensing devices available for use at the edge that provide all kinds of data on such things as vibrations, sound, temperature, humidity, motion, pressure, pollutants, audio, and video. The screened data is then transmitted through a gateway to the cloud for storage and further analysis. These gateways are essentially small servers, and exist between an organization’s cloud or data center, or its cloud and the sensors being used.
Edge gateways have developed into architectural components that improve the performance of the internet of networks. These gateways are available as off-the-shelf devices that are adaptable enough to mix and match with the differing clouds and sensors. Different gateways are used for different tasks. Gateways needing to perform a real-time analysis of data from a factory floor will need to be more powerful than a gateway that simply tracks the location data of an automated fulfillment center.
Connected sensors provide a broad range of information that should be used in making key decisions. The edge node is the data source, and if recorded information is faulty and of poor quality, use of the data can do more damage than good.
Machine learning (ML) is an important aspect of edge intelligence, and chips designed for running ML models are commercially available. ML can detect patterns and anomalies in the data stream and initiate the appropriate response.
Machine learning provides support for factories, smart cities, smart grids, augmented and virtual reality, connected vehicles, and healthcare systems. ML models are trained in the cloud and then used to make the edge intelligent.
Machine learning is an effective way of creating a functional AI. Many ML techniques, such as decision trees, Bayesian networks, and K-means clustering have been developed to train the AI entity to make both classifications and predictions. Deep learning (a subdivision of the ML field) is one of the techniques, and uses an artificial neural network. Deep learning has resulted in impressive abilities to perform multiple tasks, classify images, and recognize faces.
While machine learning is becoming quite popular with sensor nodes in the manufacturing industry, artificial intelligence (AI) is being applied to the big data being gathered from such things as social media contents, business informatics, and online shopping records.
This data was generally sent to and stored in massive data centers. However, with the expansion of mobile computing and the internet of things, that trend is starting to reverse itself. Cisco has estimated that by 2021, nearly 850 ZB of data will be produced by all the people, machines, and things on the network edge.
Transporting bulk data from the IoT devices (smart phones and iPads) to the cloud for analytics can be expensive and inefficient. A recent solution uses on-device analytics that run AI applications to process IoT data locally. This situation, however, is not ideal. These AI applications require significant computational power (the kind not available on a smart phone), and often suffer from low performance and energy efficiency issues.
One proposal suggests dealing with these challenges by pushing cloud services from the network’s core, out to the network’s edges. An edge node sensor can be the smart phone or other mobile device. The sensor communicates with a network gateway, or a micro-data center. Physical proximity to data source devices is the most important characteristic in this situation. (Let’s say you have a smart phone. Its GPS would send a signal to a nearby 5G sensor on a telephone pole, which then sends it to a gateway that would determine your location, and then send the refined, finalized data to the cloud for storage or further analysis.)
Since 2009, Microsoft has been conducting continuous research on what applications should be shifted to the edge from the cloud. Their research ranges from voice command recognition to interactive cloud gaming to real-time video analytics.
Real-time video analytics is predicted to become a very popular application for edge computing. As an application built atop computer vision, real-time video analytics will continuously gather high-definition videos taken from surveillance cameras. These applications require high computation, high bandwidth, and low latency to analyze the videos. This is made possible by extending the cloud’s AI to gateways covering the edge.
The Smart Factory
One type of sensor that is fast gaining popularity measures the vibrations of equipment with mechanical components (rotating shafts or gears). These multi-axis sensors measure the vibrational displacement of the equipment in real time. The vibrational displacement can then be processed and compared with the acceptable range of displacement. In a factory, analyzing this information can increase efficiency, reduce down-time, and predict mechanical failures before they happen. In some cases, a piece of equipment with a disintegrating mechanical component, which will cause further damage, can be shut down immediately.
The time needed for sensor nodes to react can be dramatically reduced by including edge node analytics. A MEMS sensor, for example, will provide a warning when threshold limits are exceeded, and will immediately send out an alert. If data suggests the event is bad enough, the sensor may disable the equipment automatically, preventing a catastrophic breakdown.
In smart cities, some industrial IoT edge node sensors can be used, such as an industrial camera with embedded video analytics. The mission statement of smart cities typically include the desire to integrate and communicate useful information to its citizens and employees. A common application provides parking space availability. Cameras can be used to identify a wide variety of objects (such as parked cars) and identify motion. This can be used to analyze movement historically, as well.
Other sensors are designed specifically for smart cities, such as pollution sensors that warn city officials when a business has exceeded its allowable standards. A sensor for sound levels can be installed in some areas, or a sensor might be used to monitor vehicles and pedestrian traffic to optimize walking and driving routes. Citizens can have their energy and water consumption monitored to get advice on reducing their usage. The increasing use of automated decision-making in our devices, apps, and business processes makes AI essential to staying competitive.
The Future of Edge Computing
The intelligent edge continues to gain in popularity, connecting devices and systems to gather and analyze data. The number of IoT devices being used worldwide has exploded, and cloud computing is becoming overwhelmed with the volume of data being produced. The intelligent edge not only provides real-time insights on operational efficiency, such as improving maintenance for vital equipment before it breaks down, but also screens out useless data.
A seamless, synchronized user experience is basic goal of many internet organizations. For technology vendors, the intelligent edge and its connected devices provide opportunities for developing smarter, more integrated systems. These connected devices reduce the cloud’s burden by screening out useless data, and businesses ignoring the concept of edge computing will inevitably lose any competitive advantage they might have had in manufacturing or customer service.
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