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Edge computing is hot right now, but not everyone understands why so many people are so focused on keeping their data in gateways or on on-premise services instead of sending it to the cloud. While it may seem like a huge shift to bring processing to the edge of the networks as opposed to sending all of the data to the cloud, for many IoT use cases, the cloud was never going to be a viable solution.
There are five primary reasons why the edge is winning when it comes to the internet of things. Three of them are technical limitations on cloud data transfers and two are dependent on business culture and the perception of cloud security. Let’s cover them.
1. Security – This is one of the favored reasons for big industrial companies. They don’t want to connect their processes to the internet because it exposes their operations to hackers and data breaches. For example, at the Honeywell User Group meeting I attended last year, most of the customers of Honeywell’s industrial automation products were loath to even put wireless infrastructure in their plants for fear of security breaches. Some of this is perception of risk, but thanks to a variety of hacks from Target’s breach — which began in its HVAC system and ended up compromising customers’ credit cards, raising concerns over hackers targeting infrastructure — this is a legitimate fear, given certain types of industrial processes.
2. IP – Related to the issue of security are concerns over proprietary data and intellectual property. High-quality sensors can be used to derive important information, such as a refinery process that counts as a trade secret. Jaganath Rao, SVP of IoT Strategy at Siemens, says that food companies are particularly sensitive to these sorts of issues. Imagine if the recipe for Coke could be inferred through its industrial data, for example.
3. Latency and resiliency – Latency is a measure of how fast information can travel over a network. Whether you are waiting for a Netflix movie to load or playing “Call of Duty,” latency matters. And when you translate digital bits into electrons or machinery, latency matters even more. In the home, for example, cloud-to-cloud services can lead to a second or two of delay when I’m turning on my lights using an app. That’s irritating. But in an industrial process, sending data from a machine to the cloud and then back again can cost a lot of money or even lives.
One of the more popular arguments for edge computing is autonomous cars. The idea is that a car going 60 miles an hour needs to be able to identify a threat and stop the car instantly, not wait a few seconds to make a round trip to the cloud. In the industrial world, a machine that is in danger of failing might only have a few seconds or a minute of warning. A sensor might pick up the new vibration signature that signals a failure and then send that to a local gateway for processing. The gateway needs to have the ability to recognize the failure and either alert someone or send back instructions to shut off the machine within milliseconds or seconds.
This also ties into resiliency. Network coverage can falter and the internet can go down. When that happens, cars, heavy industrial machinery, and manufacturing operations still need to work. Edge computing enables them to do that.
4. Bandwidth costs – Some connected sensors, such as cameras or aggregated sensors working in an engine, produce a lot of data. As in multiple gigabytes of data every hour or, in some cases, every minute. In those cases, sending all of that information to the cloud would take a long time and be prohibitively expensive. That’s why local image processing or using local analytics to detect patterns makes so much sense. Instead of sending terabytes of raw image data from a connected streetlight, a local gateway can process that data and then send the relevant information.
5. Autonomy – The problems of latency and resiliency bring us to the final reason the edge will flourish in the internet of things: autonomous decision-making can’t rely on the cloud. For many, the promise of connected plants or offices is that a large number of processes can become automated. If a machine can monitor itself and the process it’s performing, then it can eventually be programmed to take the right action when problems occur. So for example if a sensor detects a pressure buildup, it can release a valve further down the line to relieve that pressure. But once a process relies on a particular level of automation, it’s imperative that it can rely on that level to be enacted in time and all the time.
Most of these are fairly common sense, but what many in the traditional IT world miss is that when you start moving real-world machinery around instead of just bits, it’s no longer good enough to provide 99.99% reliability or millisecond latency. When challenges in the digital world meet the physical world they are magnified; real people’s lives or production processes are on the line, with real-world consequences.
It’s not to say that the cloud won’t pick up more IoT work over time, but right now, it’s a pretty scary proposition for a lot of IoT use cases.
Stacey on IoT | Internet of Things news and analysis
This story continues at The Next Web
The Next Web
OPINION James Wickes, CEO and co-founder of Cloudview, explains why visual data is an untapped resource for smart analytics within many IoT projects.
NEW CONNECTIONSWe are constantly reading about IoT developments, but these rarely include visual data – which is strange, because sight is our most powerful sense and we are surrounded by digital cameras. However, much of the visual data currently collected is stored locally and only used for a single purpose, while a huge percentage is never used at all. Combining this with other IoT data streams and adding analytics would make it immensely valuable.
The volumes of visual data available are eye-watering. Looking at CCTV alone. In 2015, the British Security Industry Association estimated that there were between four and six million security cameras in the UK. Our own research suggests there are now around 8.2 million. Even six million cameras recording 12 hours a day would capture 72 million hours of footage every day, producing 7.5 petabytes of visual data every hour.
Applying analytics to visual data is complex. However, we now have the processing power, bandwidth, data storage capacity, and computing ability to enable fast, reliable analysis to a standard that makes it commercially viable. McKinsey expects video analytics to experience a compound annual growth rate (CAGR) of over 50 percent over the next five years.
Adding analytics and cloud storage to cameras provides the ability to spot anomalies that we are unable to identify with our own eyes. For example, in health and well-being alone there are many opportunities, such as:
• A camera trained on a patient in a hospital with the right analytics can now spot irregular breathing or an irregular pulse.
• Cameras are being used in care situations to monitor individuals to ensure they are being well-treated (with appropriate permissions).
• Qualified health and social care professionals are able to review footage for safeguarding purposes, and this can prove popular with both residents and staff.
The next step is to combine visual data with other data sets – from static data, such as grid references, to dynamic data, such as weather information.
This will create a vast new market – the Visual IoT (VIoT). In other words, the integration of visual data into a uniform, IP-based data stream, combined with the capabilities and functions of a network of physical objects and devices.
In this way, cameras can be turned into super-charged sensors providing data that can then be acted upon, such as identifying that a car with a certain numberplate is allowed to enter a given area, which automatically opens the gate.
The potential is huge, and could revolutionise traffic management, and the reporting of crimes or accidents. For example, when an individual with a VIoT device enters a certain area, by previous agreement their data could be aggregated with that of others to create an accurate picture of an event.
For a motorway accident, combining data from road cameras and in-vehicle routing systems would pinpoint the precise location and help first responders to arrive more quickly. Meanwhile, adding visual data from drivers’ dashcams (with permission) could add unique views of the area around an incident.
Combining visual data with analytics can provide insight into both what is happening and why things happen, together with the ability to anticipate what might happen next.
Consider the control centres used by emergency services to monitor cameras in city centres. Adding analytics and machine intelligence would enable them to identify impending problems and send resources to defuse a situation before it escalates. The same process could identify potential risky or suspicious behaviour at transport hubs and other public spaces.
There is also tremendous potential for smart city initiatives that use existing camera data to improve the local environment. For example, NVIDIA is developing an intelligent video analytics platform for smart cities, which will apply deep learning techniques to video streams. Applications include public safety, traffic management, and resource optimisation.
The big issue, of course, is privacy, but technologies such as facial and behaviour recognition can be used to reduce human involvement to a minimum. The General Data Protection Regulation (GDPR) provides additional protection, as it includes provisions for how visual data is collated and used in applications that apply AI, analytics, and deep learning techniques to that data. There are also applications in sectors such as the environment that will not involve individuals at all.
Provisions such as privacy by design, Privacy Impact Assessments, and the appointment of a data protection officer will be mandatory for public authorities and any organisation whose core activities require regular and systematic monitoring of data subjects on a large scale. There are also applications in sectors such as the environment that will not involve individuals at all.
By providing information that is not available in any other way, visual data will enable the IoT to bring even more benefits to all our lives. More information is available in the white paper Visual IoT: where the IoT, cloud and big data come together.
Internet of Business says: This opinion piece and the link to an external white paper have both been provided by Cloudview, and not by our independent editorial team.
The post Opinion: The Visual Internet of Things – why IoT needs visual data appeared first on Internet of Business.
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