Written by: Mirko Grabel

Edge computing brings a number of benefits to the Internet of Things. Reduced latency, improved resiliency and availability, lower costs, and local data storage (to assist with regulatory compliance) to name a few. In my last blog post I examined some of these benefits as a means of defining exactly where is the edge. Now let’s take a closer look at how edge computing benefits play out in real-world IoT use cases.

Benefit No. 1: Reduced latency

Many applications have strict latency requirements, but when it comes to safety and security applications, latency can be a matter of life or death. Consider, for example, an autonomous vehicle applying brakes or roadside signs warning drivers of upcoming hazards. By the time data is sent to the cloud and analyzed, and a response is returned to the car or sign, lives can be endangered. But let’s crunch some numbers just for fun.

Say a Department of Transportation in Florida is considering a cloud service to host the apps for its roadside signs. One of the vendors on the DoT’s shortlist is a cloud in California. The DoT’s latency requirement is less than 15ms. The light speed in fiber is about 5 μs/km. The distance from the U.S. east coast to the west coast is about 5,000 km. Do the math and the resulting round-trip latency is 50ms. It’s pure physics. If the DoT requires a real-time response, it must move the compute closer to the devices.

Benefit No. 2: Improved resiliency/availability

Critical infrastructure requires the highest level of availability and resiliency to ensure safety and continuity of services. Consider a refinery gas leakage detection system. It must be able to operate without Internet access. If the system goes offline and there’s a leakage, that’s an issue. Compute must be done at the edge. In this case, the edge may be on the system itself.

While it’s not a life-threatening use case, retail operations can also benefit from the availability provided by edge compute. Retailers want their Point of Sale (PoS) systems to be available 100% of the time to service customers. But some retail stores are in remote locations with unreliable WAN connections. Moving the PoS systems onto their edge compute enables retailers to maintain high availability.

Benefit No. 3: Reduced costs

Bandwidth is almost infinite, but it comes at a cost. Edge computing allows organizations to reduce bandwidth costs by processing data before it crosses the WAN. This benefit applies to any use case, but here are two example use-cases where this is very evident: video surveillance and preventive maintenance. For example, a single city-deployed HD video camera may generate 1,296GB a month. Streaming that data over LTE easily becomes cost prohibitive. Adding edge compute to pre-aggregate the data significantly reduces those costs.

Manufacturers use edge computing for preventive maintenance of remote machinery. Sensors are used to monitor temperatures and vibrations. The currency of this data is critical, as the slightest variation can indicate a problem. To ensure that issues are caught as early as possible, the application requires high-resolution data (for example, 1000 per second). Rather than sending all of this data over the Internet to be analyzed, edge compute is used to filter the data and only averages, anomalies and threshold violations are sent to the cloud.

Benefit No. 4: Comply with government regulations

Countries are increasingly instituting privacy and data retention laws. The European Union’s General Data Protection Regulation (GDPR) is a prime example. Any organization that has data belonging to an EU citizen is required to meet the GDPR’s requirements, which includes an obligation to report leaks of personal data. Edge computing can help these organizations comply with GDPR. For example, instead of storing and backhauling surveillance video, a smart city can evaluate the footage at the edge and only backhaul the meta data.

Canada’s Water Act: National Hydrometric Program is another edge computing use case that delivers regulatory compliance benefits. As part of the program, about 3,000 measurement stations have been implemented nationwide. Any missing data requires justification. However, storing data at the edge ensures data retention.

Bonus Benefit: “Because I want to…”

Finally, some users simply prefer to have full control. By implementing compute at the edge rather than the cloud, users have greater flexibility. We have seen this in manufacturing. Technicians want to have full control over the machinery. Edge computing gives them this control as well as independence from IT. The technicians know the machinery best and security and availability remain top of mind.

Summary

By reducing latency and costs, improving resiliency and availability, and keeping data local, edge computing opens up a new world of IoT use cases. Those described here are just the beginning. It will be exciting to see where we see edge computing turn up next. 

Originaly posted: here

Connected devices are becoming essential components for enterprises as they can drive significant connectivity and integration between systems and data. The increasing number of devices getting connected to each other generates a huge amount of data.

However, when it comes to leveraging the full potential of these connected devices and data, it is necessary to have a scalable and robust environment which allows faster processing of data between systems.

The fundamental concern is on how to efficiently manage this data, as any data loss or delay in processing of data from a connected ecosystem can cause critical damage to an enterprise’s workflow.

Role of IoT gateway edge analytics in data processing & management

IoT Gateway is the key to any IoT deployment. It is a bridge between IoT devices and cloud that enables remote control of the devices and machines. The increasing number of devices propels the requirement for IoT gateways to solve the data management issues with Edge Analytics.

Edge analytics with IoT Gateway allows data processing before it is transmitted to the cloud. The gateway collects all the data from the connected devices and executes necessary algorithms or rule engine on it and sends actionable commands to connected devices. The actions allow for response to be taken in real-time and also helps in self-healing mechanism during faults/errors.

In large enterprises, having multiple geographical spread, there are a huge number of connected devices and generated data. This heterogeneous data, distributed at different levels (Devices and machines ) have high latency in cloud transferring due to the uncontrolled data flow. Here, distributed edge analytics is the solution as it allows faster data transfer and processing, resulting in the reduction of latency.

AWS Greengrass is the best example for the edge analytics setup. It allows enterprises to run local compute, messaging, data caching, sync, and ML inference capabilities for connected devices in a secure way. Greengrass ensures quick response of IoT devices at the time of local events, that reduces the cost of transmitting IoT data to the cloud.

How distributed edge analytics works in larger geographical areas

Let’s take an example of smart grids to understand the concept in-detail.

Smart grids are the combinations of smart meters, smart appliances, renewable energy resources, energy efficient resources, and substations. In a particular city area, the number of smart meters is equivalent to the number of households in that area. These AMI (Advanced Metering Infrastructure) continuously collects the energy consumption data and route it to the IoT gateways. The gateway enables edge analytics and then the processed data is rerouted to the cloud by the gateway.

As the number of AMI is high in a particular area, the number of gateways will be proportionately higher.

Merits of distributed edge analytics:

• Reduced data transfer latency
• Fast access to the faulty areas
• Quick functional recovery and self healing capabilities that brings resilience in the system

Distributed edge analytics also enables fast response to the cloud in case of faults and failures with Fog Computing so that the recovery time can be minimal. Let us understand how.

How fog computing works with smart grids for faster data processing

Fog computing is the combination of two key components of data processing, Edge and Cloud both. The idea of combining edge computing with more complex computing (cloud computing) results into more reliable and faster data processing.

As smart grid tech is increasing rapidly, fog computing is the best tool for the data and information processing between consumers, grid operators, and energy providers.

In the edge analytics concept, the gateways form a mesh network. The individual mesh network of a designated area creates Fog Nodes. Each fog node is connected to each other, resulting in a fog network of smart meters and IoT gateways in the larger setups. The combination of these fog nodes then allows distributed fog computing, which gives the benefit of fast and real-time data analysis in any large geographical area. This further enables faster fault response time.

Use case of smart grids in distributed edge analytics

eInfochips developed a solution in which gateways are being connected into a mesh network with peer-to-peer communication. Mesh and cluster of gateways enable high availability and reliabilityof the IoT deployment in smart grids. Clustering enables distributed edge analytics. These distributed edge nodes allow processing of data at the edge before transferring it to the cloud.

According to the market research data, fog computing market is growing with the attractive amount of cost annual growth rate (CAGR), 55.6% between 2017 and 2022 (MarketsandMarkets).

With our edge and fog computing expertise, we help the IoT solution providers to optimise their computing infrastructure by distributing load between the cloud and edge devices in an intelligent way through our ready-to-use dynamic rule engine or custom solutions.

How edge computing is poised to jump-start the next industrial revolution.

From travel to fitness to entertainment, we now have killer apps for many things we never knew we needed. Over the past decade, we’ve witnessed tremendous improvements in terms of democratizing data and productivity across the consumer world.

Building on that, we’re entering a new era of software-defined machines that will transform productivity, products and services in the industrial world. This is the critical link which will drive new scenarios at even faster rates of innovation. By 2020, the Industrial Internet of Things (IIoT) is expected to be a $225 billion market.

To jump-start the productivity engine of IIoT, real-time response is needed at the machine-level at scale and that requires an edge-plus-cloud architecture designed specifically for the Industrial Internet. From Google maps to weather apps, we’ve been experiencing the benefits of cloud and edge computing working together in our daily lives for quite some time.

But, what is edge? Edge is the physical location that allows computing closer to the source of data. Edge computing enables data analytics to occur and resulting insights to be gleaned closer to the machines. While edge computing isn’t new, it’s beginning to take hold in the industrial sector – and the opportunity is far greater than anything we’ve seen in the consumer sector, and here’s why:

Real-time data in a real-time world: The edge is not merely a way to collect data for transmission to the cloud. We are now able to process, analyze and act upon the collected data at the edge within milliseconds. It is the gateway for optimizing industrial data. And when millions of dollars and human lives are on the line, edge computing is essential for optimizing industrial data at every aspect of an operation.

Take windfarms for example. If wind direction changes, the edge software onsite would collect and analyze this data in real-time and then communicate to the wind turbine to adjust appropriately using an edge device, such as a field agent and connected control system, and successfully capture more kinetic energy. Because the data is not sent to the cloud, the processing time is significantly faster. This increases wind turbines’ production, and ultimately distributes more clean energy to our cities, increasing the value of the renewable energy space.

Big data, big trade-offs: The harsh and remote conditions of many industrial sites make it challenging to connect and cost-effectively transmit large quantities of data in real-time. We are now able to add intelligence to machines at the edge of the network, in the plant or field. Through edge computing on the device, we’re bringing analytics capabilities closer to the machine and providing a less expensive option for optimizing asset performance.

Consider the thousands of terabytes of data from a gas turbine. Sending this data to the cloud to run advanced analytics maybe technologically possible, but certainly too cost prohibitive to do a daily basis. Through edge computing, we can capture streaming data from a turbine and use this data in real-time to prevent unplanned downtime and optimize production to extend the life of the machine.

What’s Next

Today, only 3% of data from industrial assets is useable. Connecting machines from the cloud to the edge will dramatically increase useable data by providing greater access to high powered, cost effective computing and analytics tools at the machine and plant level.

Consider the fact that for years traditional control systems were designed to keep a machine running the same way day in and day out for the lifecycle of the machine. At GE Energy Connections, we recently debuted the Industrial Internet Control System (IICS), which successfully allows machines to see, think and do and will enable machine learning at scale. To take IICS to the next level, we’re creating an ecosystem of edge offerings to accelerate widespread adoption across the industrial sector. We’re advancing this ecosystem and empowering app developers who want to play a role in driving the new industrial era. 

Currently, to add value to a software system, a developer writes the code, ports it into the legacy software stack, shuts down the devices and finally, updates it. That’s all going to change. We are working on creating an opportunity for any developer to create value-added edge applications. Customers will be able port the necessary apps to their machine without having to shut it down, just like we do on our phones today. Companies will be able to download apps for their needs and update frequently to ensure their business is running smoothly. While no one likes to run out of battery on their smart phone, an outage for a powerplant is far more costly, so the ability to port apps without shutting down devices and being able to detect issues before it occurs will be a game changer.

From wind turbines to autonomous cars, edge computing is poised to completely revolutionize our world. It’s forcing change in the way information is sent, stored and analyzed.  And there’s no sign of slowing down.

The Internet of Things (IoT) concept promises to improve our lives by embedding billions of cheap purpose-built sensors into devices, objects and structures that surround us (appliances, homes, clothing, wearables, vehicles, buildings, healthcare tech, industrial equipment, manufacturing, etc.).

IoT Market Map -- Goldman Sachs

What this means is that billions of sensors, machines and smart devices will simultaneously collect volumes of big data, while processing real-time fast data from almost everything and... almost everyone!!!

IoT vision is not net reality

Simply stated, the Internet of Things is all about the power of connections.

Consumers, for the moment anyway, seem satisfied to have access to gadgets, trendy devices and apps which they believe will make them more efficient (efficient doesn't necessarily mean productive), improve their lives and promote general well-being.

Corporations on the other hand, have a grand vision that convergence of cloud computing, mobility, low-cost sensors, smart devices, ubiquitous networks and fast-data will help them achieve competitive advantages, market dominance, unyielding brand power and shareholder riches.

Global Enterprises (and big venture capital firms) will spend billions on the race for IoT supremacy. These titans of business are chomping at the bit to develop IoT platforms, machine learning algorithms, AI software applications & advanced predictive analytics. The end-game of these initiatives is to deploy IoT platforms on a large scale for;

• real-time monitoring, control & tracking (retail, autonomous vehicles, digital health, industrial & manufacturing systems, etc.)
• assessment of consumers, their emotions & buying sentiment,
• managing smart systems and operational processes,
• reducing operating costs & increasing efficiencies,
• predicting outcomes, and equipment failures, and
• monetization of consumer & commercial big data, etc.

IoT reality is still just a vision

No technology vendor (hardware or software), service provider, consulting firm or self-proclaimed expert can fulfill the IoT vision alone.

Recent history with tech hype-cycles has proven time and again that 'industry experts' are not very accurate predicting the future... in life or in business!

Having said this, it only makes sense that fulfilling the promise of IoT demands close collaboration & communication among many stake-holders.

A tech ecosystem is born

IoT & Industrial IoT comprise a rapidly developing tech ecosystem. Momentum is building quickly and will drive sustainable future demand for;

• low-cost hardware platforms (sensors, smart devices, etc.),
• a stable base of suppliers, developers, vendors & distribution,
• interoperability & security (standards, encryption, API's, etc.),
• local to global telecom & wireless services,
• edge to cloud networks & data centers,
• professional services firms (and self-proclaimed experts),
• global strategic partnerships,
• education and STEM initiatives, and
• broad vertical market development.

I'll close with one final thought; "True IoT leaders and visionaries will first ask why, not how..!"