Mobodexter

MoboDexter is a company based out of U.S.A., Singapore & India that develops IOT products & solutions; Offers technology services to enterprise clients.

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Kubernetes Edge Computing - Offers Highly Scalable and Flexible Edge Compute Capabilities

Mobodexter | Mobodexter

Kubernetes Edge Computing: Chick-fil-A, known for its addictive poultry sandwiches and also waffle french fries, is reported to become the third-largest U.S. fast-food chain behind McDonald’s and Starbucks. Behind the scenes, their business remains at the forefront of embracing a powerful modern technology like Edge computing and Kubernetes.

Chick-fil-A published a Medium post that it will undoubtedly be running Kubernetes on the Edge of 6,000 IoT devices in all 2,000 of its dining restaurants. Part of the chain’s internet of things (IoT) strategy to gather and examine more information to enhance customer service and also functional effectiveness. For example, they can forecast how many waffle french fries need to be cooked every minute of the day.

This case study shows why Kubernetes has quickly ended up being a crucial active ingredient in edge computing. A tried and tested and effective runtime system to help address particular challenges across telecommunications, media, transportation, logistics, farming, retail, and also other market sections.

The telco industry particularly has much to acquire from edge computing. As competitors among operators intensify, telco firms must distinguish themselves with new use-cases such as commercial automation, virtual reality, connected cars and trucks, sensor networks, and smart cities. Telcos significantly are using edge computing to ensure these applications function flawlessly while also driving down the prices of deploying and handling the network framework.

Just what is edge computing? 

Edge computing is a variation of cloud computing, with your infrastructure like compute, storage, as well as networking physically closer to the devices that create information. Edge computing enables you to position applications and solutions closer to the source of the data. This placement gives you the twin advantage of reduced latency as well as lower web traffic. Reduced latency boosts the performance of field devices by enabling them not just to react quicker, yet to also respond to even more events. Lowering web traffic helps in reducing prices as well as boosts overall throughput. Whether an application or service will be in the edge cloud or the core datacenter will rely on the specific use-case.

Intel Edge Computing Framework

How can you build an Edge cloud? 

Edge clouds need to be built with the very least two layers. Both layers will certainly maximize operational efficiency as well as designer performance, and each layer is created differently.

The initial layer is the Infrastructure-as-a-Service (IaaS) layer. In addition to giving compute and storage space sources, the IaaS layer needs to address the network performance needs of ultra-low latency and also high data transfer. IaaS is the layer where blade systems from HP, Dell, IBM, and Lenovo, and specialized systems like Lenovo Think system SE350 come in the picture.

The 2nd layer is the Kubernetes layer, which gives an ideal platform to run your applications and services. Whereas making use of Kubernetes for this layer is optional, it has confirmed to be a reliable system for those organizations leveraging edge computers today. You can release Kubernetes to field tools, edge clouds, core data centers, and the general public cloud. This multi-cloud implementation ability provides you total flexibility to deploy your applications anywhere in the field, Edge, or cloud. Kubernetes deals with your developers the capability to simplify their DevOps practices and minimize time spent integrating with heterogeneous operating environments.

Benefits of Kubernetes on Edge

In addition to very low downtime and outstanding performance, Kubernetes offers numerous built-in advantages to resolve Edge compute obstacles, including versatility, resource performance, performance and reliability, scalability, and observability.

1) Flexibility

Kubernetes decreases the complexities associated with running computing power across various geographically dispersed points of existence and varied architecture by supplying versatile tooling that allows designers to connect with the Edge seamlessly.

With Kubernetes behind our Edge platform, users can run application containers at scale through an agnostic network of distributed computing infrastructure, which in turn, extends complete flexibility to the users to be able to run the application anywhere along with the Edge computing.

2) Resource Efficiency

Containers are light-weight by nature and enable you to use the underlying infrastructure in an extremely effective method. However, managing thousands or, in many cases, countless containers throughout a distributed architecture gets complex very rapidly. Kubernetes supplies the underlying tools to effectively manage container workflows through automated networking, storage, and event logs.

The Kubernetes Horizontal Pod Autoscaler is one key feature that naturally provides itself to edge computing performances. It immediately scales the variety of pods up or down in a replication controller, release, or replica set based upon latency or volume thresholds. Think about a point of existence in an edge location that requires to manage abrupt traffic increases, like, in the case of a local sporting event. Kubernetes can auto-detect traffic from event logs and provide resources to scale to the fluctuating demand. This kind of auto-scaling takes the uncertainty out of forecasting and preparing for infrastructure needs. It also makes sure that you’re only provisioning for what your application requires in any given period.

3) Performance

Modern applications require lower latency than standard cloud computing designs can provide. By running workloads better to end-users, applications can recuperate vital milliseconds to give a much better user experience. As mentioned above, Kubernetes’ capability to respond to latency and volume limits (utilizing the Horizontal Pod Autoscaler) implies that traffic can be routed to the most optimal edge places to reduce latency.

4) Dependability & Scalability

Among the significant advantages of Kubernetes is that it is self-healing. In Kubernetes, you can restart containers that stop working, replace, and reschedule containers when nodes fail and eliminate containers that do not respond to your user-defined health check.

 In Kubernetes, services abstract this process, allows you to begin and stop containers behind the service. At the same time, Kubernetes carries on managing traffic reroutes to the right containers to avoid service disturbances.

Besides, since Kubernetes’ control plane can handle tens of countless containers running over numerous nodes, it allows applications to scale as needed, especially fitting the management of distributed edge workloads.

5) Observability

Knowing where and how to run edge work to make the most of efficiency, security, and performance needs observability. However, observability in a microservices architecture is complex.

Kubernetes offers a full view of production work, enabling optimization of efficiency and efficiency. The built-in monitoring system allows real-time insights (consisting of transaction traces, logging, and aggregated metrics) with instant provisioning figured out by configuration settings. 

Another point of observability is of specific interest when it concerns edge computing is dispersed tracing. Distributed tracing permits you to collect and construct an extensive view of requests throughout the chain of API calls made, all the way from user requests to interactions in between numerous services. With this info, you can determine traffic bottlenecks and opportunities for optimization.

Interest in edge computing is being driven by rapid data increases from smart tools in the IoT, the coming influence of 5G networks as well as the growing importance of performing artificial intelligence jobs at the Edge. All of which need the ability to deal with flexible needs as well as shifting workloads. Kubernetes Edge Computing is one of the rapidly growing technology that companies like Mobodexter are continually innovating and help customers to become productive and efficient with their IoT strategy. 

Footnotes:

Mobodexter, Inc., based in Redmond- WA, builds internet of things solutions for enterprise applications with Highly scalable Kubernetes Edge Clusters that works seamlessly with AWS IoT, Azure IoT & Google Cloud IoT.

Want to build your Kubernetes Edge Solution – Email us at [email protected]

Check our Edge Marketplace for our Edge Innovation. 

Join our newly launched marketing partner affiliate program to earn a commission here.

We publish weekly blogs on IoT & Edge Computing: Read all our blogs or subscribe to get our blogs in your Emails. 

https://blogs.mobodexter.com/kubernetes-edge-computing-offers-highly-scalable-and-flexible-edge-compute-capabilities/

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Real-World Examples of IOT edge computing

Mobodexter | Mobodexter

Real-World Examples of IOT Edge: IOT Edge computing is a framework that brings computing in the internet of things closer to the devices, which are the endpoint of the IOT network. By bringing the data processing closer to the Edge, it accelerates the processing, reduces the time taken for making decisions, and thus drives real-time action. IOT edge computing has gained enormous frenzy in recent years. Different versions of edge computing like fog computing have also gained momentum. Various applications, solutions, hardware, software have come into the market to enhance and propagate the adoption of edge computing among the IOT implementers. There are going be to be several advantages for the early adopters of the technology. Edge computing has also advanced to include AI and machine learning customized such that it is adaptable to edge computing. One of the significant benefits of Edge computing is the reduction of latency.

There are several early movers with IOT edge adoption. We will look at some of the real-world examples of where edge computing has been implemented and where it is bringing in operational efficiency.

Indoor Farming

Vertical Indoor farming has been an exciting field that benefits significantly from IOT. In indoor agriculture, the plants are mounted with sensors that track the water needed for the plants and the environmental conditions of the plants. The data from these sensors helps to adjust the operating conditions for these plants to achieve the maximum nutrients, flavor, and avoid wastages. These data also provide information when the vegetables can be picked and thus avoid wastage. Building an edge solution in this environment helps in the timelines of the decision and improve the growing environment.

Autonomous Vehicles 

Autonomous Vehicles is another interesting domain that can immensely benefit from edge IOT computing. Driverless vehicles need to analyze much information regarding their surroundings and communicate with the other cars on the road. An autonomous vehicle can send a large volume of data from all its sensors for it to perform optimally and efficiently. And as more and more self-driving cars are introduced, the data being sent by these sensors from all cars could put an enormous strain on the network bandwidth. If all these data needs to go to the cloud for processing, it will be killing the network. So these self-driving cars will benefit significantly if these have edge computing implemented. Some of the decisions by the vehicles can be local if the processing and AI are locally available.

Healthcare

The healthcare industry is another use case that can immensely benefit from edge implementation. It helps to localize the data processing for patients in the hospitals. Rural areas would be most beneficial in diagnosing and administering the action. 

Industrial Automation

Many factories are now adopting the internet of things, and edge computing will be one of the enablers for smart manufacturing. Sensors implemented in the factory gather data on the machine and factory conditions. Localized processing enable action on these data in real-time. For example, predicting a machine failure in real-time can help in taking corrective action to fix the machine in time and thus save a lot of time and cost. Similarly, data gathered can also help to determine the quality of the product in the factory and can drive any fine-tuning necessary to make changes in real-time.

Smart City

A smart city project includes traffic management, smart buildings, smart parking, smart streetlights. Data from a smart city could be voluminous. Sending this amount of data to the cloud could strain the bandwidth of the network, and getting a real-time response for the data could be not as quick as needed. Implementing edge computing can help some of these entities to make an autonomous decision and in real-time.

Real-World Examples of IOT Edge: When large amounts of data needed to process in a time-critical way, Edge computing would be the way to go. Edge computing can handle a large volume of data and improves the response to events by processing the sensor data locally. This process removes the need to send a large amount of data to the cloud and reduces the time to respond to the event. Also, lowering the cost involved in storing large volumes of data in the cloud. It also reduces the network bandwidth utilized in sending large amounts of data to the cloud. It also helps in securing the data by maintaining the data locally.

Footnotes:

Mobodexter, Inc., based in Redmond- WA, builds Internet of things solutions for enterprise applications with Highly scalable Kubernetes Clusters on the Edge. 

Check our Edge Marketplace for our Edge Innovation. 

We publish weekly blogs on IoT & Edge Computing: Read all our other blogs or subscribe to get our blogs in your Emails. 

https://blogs.mobodexter.com/real-world-examples-of-iot-edge-computing/

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New Features in Raspberry Pi 4

Mobodexter | Mobodexter

New Features in Raspberry Pi 4: Raspberry Pi, the learning computing kit, became a standard in low-cost Single Board Computing (SBC). Since the launch, Raspberry Pi has been powering everything from robots, smart home devices, and even digital kiosks. The new Raspberry Pi 4 takes Pi to another level. Its performance is that’s good enough to use as a desktop P.C. with the ability to output video at 4K 60 Hz resolution or use with dual monitors. Hence it can be used for more complex applications. 

Major Differences

The most important new features are :

a) the faster processor and GPU.

b) the faster RAM.

c) the addition of USB 3 ports.

d) Dual micro HDMI ports and 

e) Support for 4K HDMI output. 

On-board Ethernet port run like a real Gigabit connection with 125 MBps. The microSD card slot is also 2X as fast, offering a theoretical maximum speed of 50 MBps versus 25 MBps on the 3B+.

Because the new processor needs more power, the Raspberry Pi 4 B charges over USB Type-C unlike micro USB used by its predecessors. It also requires a newer power adapter that can deliver at least three amps of power and 5 volts. USB Type-C connectors are useful as they are reversible and more comfortable to plug in.

New CPU & Memory

The Raspberry Pi 4 has similar dimensions and design to all previous models. However, it is an all-new platform, powered by a new processor from Broadcom BCM2711B0. Since the first Pi launch in 2012, all Pis have used 40nm SoCs. BCM2711B0 is a new chip based on a 28nm process. It uses ARM Cortex-A72 instead of the older Cortex-A53 microarchitecture. The BCM2711B0 chip has four cores and is clocked at 1.5 GHz. The ARM Cortex-A72 processors are significantly faster and use more power than their A53-powered ancestors.

The RAM is also a bit quicker. Pi 3B+ had 1GB of DDR2 RAM. On the Pi 4 B, it goes up to 4GB of DDR4 RAM. In addition to the increased bandwidth, the additional memory option is a huge deal, particularly for web surfing.

New Video Processor – GPU

The GPU got a nice performance boost too. GPU moves from a Broadcom VideoCore IV that operated at a clock speed of 400 MHz to a VideoCore VI that operates at 500 MHz. The new GPU allows it to output to a display at up to 4K resolution with a refresh rate of 60 fps or to support dual monitors at up to 4K resolutions with a refresh rate of 30 Hz.

Storage

Storage is critical, no matter how fast are your processor, RAM, and GPU. If your storage is slow in an SBC, everyday tasks like opening apps and files will be slow. Raspberry Pi 4 B’s primary storage device is its microSD card reader. MicroSD is convenient but a bit constrained. The Raspberry Pi 4 B has a top transfer rate of 50 MBps, and that is double the speed of the card reader on the 3 B+.

A speedy USB drive or an external SSD can get you a far better storage performance out of the Raspberry Pi 4 B. Also the Pi 4 B comes with USB 3 ports of speed at 625 MBps.

GPIO Pins

The great thing on any Raspberry Pi is the GPIO (General Input / Output) at 40 pins. The pin count and pin layout remain unchanged from all the prior models. Hence any “hats,” sensors or LED screens that were made to attach to a Pi 2 or 3 will be compatible.

Networking

The Raspberry Pi 4 supports 802.11ac Wi-Fi as its predecessor. However, it has Bluetooth 5.0 support, an improvement over the Bluetooth 4 on prior models. The Ethernet port now has more bandwidth, that allows it to offer a full gigabit throughput while previous models could only achieve about 330 megabits.

Power and Heat

Pi 4 has A more power-hungry processor and the need for at least a 5-volt, 3-amp power adapter. Hence it is be expected to consume more power than its predecessors. Under load, that power number jumps to 7.6 watts, but that’s still only 19 percent more juice than its direct predecessor.

A.I. and Machine Learning

The most compelling new use case for the Raspberry Pi 4 is for A.I. & machine learning. With the earlier Pis, you could use a camera to do simple object detection at low frame rates. However, the added performance and I/O from this new model should open up a whole new world of use cases.

With an optimized A.I. framework, Pi 4 should be able to do real-time facial and object recognition. Moreover, because it has USB 3, an A.I. accelerator like the Google TPU USB dongle will have much more bandwidth to send data back to the SoC.

Conclusion

New Features in Raspberry Pi 4: The Raspberry Pi 4 represents a significant improvement, not only for the Raspberry Pi but for single-board computing. For the first time, it is realistic to use your Pi as a secondary P.C. However, the more substantial real benefit will come not from folks who use Raspberry Pi 4s instead of x86 P.C. Also from all ll the innovators who harness the system’s enhanced performance, I/O and graphics to create new IOT devices, media servers, and robots. 

Try Raspberry Pi 4 to enjoy these New Features in Raspberry Pi 4. Available now at a special 20% discount on our Edge Marketplace here. 

Footnotes:

Mobodexter, Inc based in Redmond, WA builds Internet of things Edge solutions for enterprises applications on Kubernetes Edge Clusters. 

Check our Edge Marketplace. Special 6th Anniversary Discount sale of 20% on all products. 

IoT Developers can try Edge Platform here. 

Mobodexter’s IoT Edge Blogs: Read

https://blogs.mobodexter.com/new-features-in-raspberry-pi-4/

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Mission-critical Edge Processing - A key requirement for IOT data

Mobodexter | Mobodexter

Mission-critical Edge: Internet of things is growing at phenomenal phase. The nucleus of IOT is the data, and as IOT is expanding, so is the data. With the growth in IOT, a sensor is attached to any, and all devices. These sensors keep churning out the data every second; what we are talking about is trillions of Gigabytes of data generated by IOT devices. Cisco predicts that by 2020, IOT will generate 600 trillion GB of data. 

These massive volumes of data will drive data management challenges for IOT. In the current architecture, the data collected by these sensors are all moved to the cloud. For example, in the case of traffic management, the data from different sensors across the city are being routed to multiple clouds sometimes to partner cloud. The data in the cloud are analyzed, and some anticipated events are driven based on the processing. The real potential of IOT is when these data can be used to create real-time actions. For instance, in the case of traffic accidents, the data collected by street sensors need to be processed in real-time. After that, only it can give live feedback in re-routing the traffic in avoiding congestions. In these scenarios does it mandate to loop the data to the cloud sometimes to partner cloud and back. The real value will be to process the data, analyze, and drive action at the Edge for quicker response time. In situations where the data changes spontaneously warrant an instinctual response. 

So the data management in mission-critical IOT application becomes crucial. Moreover, Edge is how data management can be made efficient in IOT mission-critical or not. Let’s take an example of non-mission critical use, an IOT smart home device like a thermostat does not need to send the data to the cloud every time. Edge has to make intelligent, the intuitive decision on data routing to the cloud. Edge has to determine whether collected data needs a real-time response. Hence data will have to be crunched immediately or is more of a storage and pattern collection data, thereby sending to the cloud. Also, if it is routing the data to the cloud where and which database does it go. Since only the necessary data travels outside the edge network, it is less likely to get into the hands of the wrong person.

So it is clear that for the best approach in edge-cloud architecture IOT data management tasks should be handled by Edge.

When talking about data management, we need to understand the type of data that is produced at the Edge. First this the data which requires a real-time action so let’s call these real-time data, these needs to crunched instantly and action-driven immediately. The second type of information is the data which needs to be stored in the Edge for a short time, and then the data can be sent in batch to cloud for later analysis.

For Edge to support data management, it should support storage which means it should support database. These databases have to be specifically designed for edge scenarios since these have to be efficient and will be built in a constrained architecture. Building data governance at the Edge helps in deciding which data needs to be stored and where it needs to stored and thus saving costs on storage. Building AI on the Edge helps to drive intelligent decision making at the Edge in real-time. However, at the same time, to better utilize the constrained environment, it is better to have the analytical engine on the cloud.

Mission-critical Edge: We are reaching a stage in the IOT and Edge revolution. In this revolution, data from the Edge to the cloud is refined and enriched. A robust working architecture of edge-cloud helps in improving the efficiency of the whole IOT platform.

Footnotes:

Mobodexter, Inc based in Redmond, WA builds IoT Edge solutions for enterprises applications on Kubernetes & Dockers.

Register now at developers.paasmer.co and try a free Edge trial license of Edge Intelligence software that runs on Raspberry Pi.

Follow our all our weekly IoT Blogs: https://blogs.mobodexter.com

Our IoT Newsletter reaches 3000+ subscribers– Subscribe Now.

Become our affiliate partner today.

https://blogs.mobodexter.com/mission-critical-edge-processing-a-key-requirement-for-iot-data/

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IoT Edge Analytics - Top 4 Industry UseCases

Mobodexter | Mobodexter

IoT Edge analytics is a method that includes the acquisition, analysis, and calculation of the gathered information at the information source itself or endpoints. Hence IoT Edge analytics removes the requirement to return the data to a central information repository.

The digital period is well and underway. The arrival of new-age innovations has changed the method of human interaction and performs its daily activities. Today, we see inanimate things around us obtaining a digital heartbeat, which enables them to run on their own, similar to us people. While things around are getting smarter and operating on their own, individuals are experiencing increasing convenience and benefit in their lives. Services, on the other hand, gain significant intelligence from the analysis of IoT information, which then assists them to make essential item modifications. Outstanding service to individuals can, unquestionably, boost client fulfillment levels, which will eventually produce higher profits. Businesses are setting up numerous sensors, video cameras, and actuators at multiple places to collect all the details that make good sense to their business. The rate at which the information is created and gathered makes it rather challenging for industry to evaluate and process it in real-time. Services who have launched their digital transformation do not merely have the requirement to collect data. They also want to perform data processing as quickly as possible to accomplish success. 

As the name indicates, edge analytics is moving the analysis and processing work towards the edge of the network instead of permitting all information to take a trip back to the cloud. With edge computing, companies can not only create more sales; but also generate cost-savings, increase effectiveness, and boost performance. Let’s have a look at a few markets that can take advantage of edge analytics and drive business growth.

Industries

Industries are producing and gathering high volumes of information every day. However, to make constructive usage of the collected data, there is a requirement to do something brand-new in addition to the standard computation approaches. Tracking devices health, creating specific employee security, and increasing quality and sales are time-sensitive activities that are performed by industries. All of these activities need constant tracking. Detailed manual inspections in the factories are impossible. Hence factories have started adding sensors to collect information from the production line. This information is then analyzed for additional insights and immediate actions. However, any hold-up in taking needed restorative procedures can lead to unwanted effects. The business has to gather, evaluate, procedure information, and acquire genuine intelligence practically immediately.

Downtime in manufacturing systems can have a direct effect on production levels. Due to such, companies are required to manage loss in earnings. With sensor innovation, these circumstances can be avoided if they get prompt insights on the very same. Also, edge analytics and computing can assist in industries to make this possible. With edge analytics, industries can obtain significant real-time predictions on whether any tool or machinery is most likely to breakdown. Sensors implanted in numerous parts of the production plant can gather metrics such as product quality and yield. Examining the live events as they occur can help industries to understand unanticipated quality loss or reduced sales better if any. Wearables can gather details on employee security status, which can indirectly assist in developing an outstanding culture and accomplishing efficiency objectives.

Transport

As the transport industry assists in the movement of products and guests from one place to another, it needs to pay individual attention to security and prompt shipments. From fleet management to mishap avoidance to freight and guest security, the transport market has to deal with numerous activities that should be run properly. The industry must gather real-time data from transportation systems to accomplish this level of elegance. The transportation business should, for that reason, develop company designs that perform real-time information analysis and processing that is acquired from their fleet of vehicles. 

Sensors can assist a business to get information on whether an automobile is most likely to breakdown. Motorists utilizing wearables will help enterprise get info on their driving routines. Details like this are essential to comprehend whether the modes of transport are safe and effective. Simply gathering this information will not be helpful for the transportation business. Real-time analysis and calculation are necessary to get actionable insights and act is exigent scenarios. Edge analytics in transport will assist the business gather, evaluate, and process the information in real-time, enabling them to take necessary actions right away. This method will guarantee enhanced utilization, low maintenance expenses, and improved products and personnel security.

Retail

Today, consumers have are being more tech-savvy, wise, and also, impatient. They do not just anticipate fast service shipments; however, too, want a smooth purchasing experience. Merchants, for that reason, need to remain upgraded on their client’s choices and provide the product and services that leave a favorable effect on their client base. Sellers are anticipated to be continually innovating their services to grow in this competitive market.

We understand that consumers today choose a multichannel purchasing method. One such principle that has made a significant buzz in the retail landscape is showrooming. Consumers want to go to shops to imagine and feel the item and after that purchase online. The principle is to look into the thing in a physical retailer before purchasing it. Sellers have to determine specific consumer purchasing routines and after that send them to markdown vouchers while guaranteeing that they do not bombard their consumers with unimportant advertisements. Also, to supply individualized service, merchants need to gather a variety of consumer information mainly. This information includes purchase history, social networks chatter, cookies, social networks activity, and market info. This information is essential to understand specific interests in a much better method. Later on, by utilizing beacon innovation, retail brand names will realize who has gotten in a physical retailer. Brands can then connect to consumers with pertinent deals and offers. However, a problematic situation will develop for brands with a more significant consumer base. Gathering and processing such high-volumes of consumer information can be frustrating. Here’s where edge analytics can assist retail brand names. As edge analytics has the power to examine the report at the business center, retail business will decrease the dependency on the central cloud and other analytics platforms. With edge analytics, retail brand names can be fast with their situational responsiveness, which will eventually assist them to create more leads and profits.

Health care

Quality health care help is necessary for rural locations only as much as for urban area. The health care market frequently stops working to supply the best care at the best time to rural areas even today. With the mix of proper IoT-enabled health care devices and edge analytics, the health care market can fix this concern. IoT-powered health care devices can continuously gather clients’ health information. Edge analytics will then examine the collected data without the requirement of a constant network connection. As an outcome of this, individuals living in a rural area can instantly understand if their health weakens. Trigger and prompt medical treatment can then be supplied to the rural population, which can save plenty of lives. Health care support like this will significantly enhance the efficiency of health care.

Summary

IoT Edge Analytics is a probable solution for businesses aiming to gain round-the-clock insights, build predictive models, and offer superior customer experiences. Moving data analytics to the edge, closer to users, is not an overnight task. It needs a powerful edge strategy that consumers both cost and time. However, businesses who wish to succeed in their digital transformation journey should leverage edge analytics as quickly as possible in multiple phases.

Footnotes:

Mobodexter, Inc based in Redmond, WA builds IoT Edge solutions for enterprises applications on Kubernetes & Dockers.

Register now at developers.paasmer.co and try a free Edge trial license of Edge Intelligence software that runs on Raspberry Pi.

Follow our all our weekly IoT Blogs: https://blogs.mobodexter.com

Our IoT Newsletter reaches 3000+ subscribers– Subscribe Now.

Become our affiliate partner today.

https://blogs.mobodexter.com/iot-edge-analytics-top-4-industry-usecases/

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IoT Edge Network - Is Today's Network Ready for Edge Computing?

Mobodexter | Mobodexter

IoT Edge Network – For a long time, IOT was revolving around the concept of centralized data storage with the cloud as its foundation. Then there was this swing to decentralized edge computing where the data is generated at the Edge. The idea of Edge was promising where the latency and cost of cloud computing can be addressed with the data processing at the Edge.

Internet of things is all about data; the data generated by the millions of sensors from every walk of life is humungous. As the connected device growth is exploding, so is the data generated by these devices. These data were pumped to the cloud, and the infrastructure built around the cloud was designed to handle the large volume of data in real-time, process, and synthesis them in real-time. 

As the concept of edge computing started catching fire, there were the questions of the value of sending all these data gathered every second by the edge devices to cloud arose. Hence the cost and time saved by synthesizing them at the Edge became promising.

However, the question remains how reliable and efficient are the edge network to support the large volume of data from the IOT sensors and real-time processing demand from the internet of things.

Networks requirements for Edge computing

IOT devices are deployed in many different locations, factories, streets, industry, cars, home. These sensors are collecting data at the remote network, and they are usually low power devices. Also, the sensors collecting the data in an IOT are traditionally distributed and in a remote location. The network used in these remote locations are slow, and high energy consumption device. The edge computing drives the data processing closer to the sensors for real-time actions, and some of this data needs to be sent to the cloud. The network connecting devices need to have the ability to process the large volumes of data and also be able to join the numbers of sensors in the locality for Realtime processing and action. These networks need more throughput and processing power — these existing network, like 4G fall short of the demand of the IOT.

What is required for edge computing is a cost-effective, highly available, low latency network that can lead the adoption of IOT in a more comprehensive and quicker format.

IOT and 5G

Though 5G was developed to meet the demand of the internet and mobile explosion, one technology that’s poised to take the great benefit of the 5G will be edge computing on the internet of thing. 

The 5G network provides ten times faster speed and has more processing power. 5G for IOT does not only address the connectivity issues for billions of IOT devices; it also can handle the massive volumes of data generated by the IOT device. The number of devices supported by 5 G network is multiple times more than the 4 G network. 

These capabilities of 5G are what is needed to meet the growing demands of IOT devices. 5G will become the linchpin of the IOT growth and expansion

Are the networks ready?

IoT Edge Network – While there have been several rollouts of 5G network by different players and continued interest and investment in the technology, these are still in their early deployment phase. There are still gaps in hardware/ software readiness, test completion, and broader adoption of the 5G networks in the mainstream. So it remains for observance whether IOT will wait out the adoption of 5G in mainstream or will charge forward in parallel with 5G.

Footnotes:

Mobodexter, Inc based in Redmond, WA builds IoT Edge solutions for enterprises applications on Kubernetes & Dockers.

Register now at developers.paasmer.co and try a free Edge trial license of Edge Intelligence software that runs on Raspberry Pi.

Follow our all our weekly IoT Blogs: https://blogs.mobodexter.com

Our IoT Newsletter reaches 3000+ subscribers– Subscribe Now.

https://blogs.mobodexter.com/iot-edge-network-is-todays-network-ready-for-edge-computing/

[mobodexter]

Edge Computing for Industry 4.0 - Too beneficial to ignore

Mobodexter | Mobodexter

Edge Computing for Industry 4.0: Organizations are starting to understand that they require unwavering control over every element of their organization to drive digital change. How they do this? At the edge.

Up until edge computing is as simple to establish and handle as a consumer gadget like a video games console. However, it will not be available to a lot of the commercial applications that it is most advantageous.

What do we mean by this?

As Industry 4.0 emerges, control and management of interlinked innovations (IIoT) at the application layer (edge) are critical. It allows the commercial business to take actions towards real-time and proactive management of applications, instead of the ad-hoc reactive control environment of market 3.0. With edge computing comes the ability for commercial applications to handle information where it is typically most helpful when it is produced. It enables analytics at the point of requirement, at a time and in a location where it can be actioned. It opens brand-new levels of control, effectiveness, and performance for market and genuine ROI for the interlinked business.

Evaluating the increasing quantity information, produced from an entire range of sources, can yield insights that even the most skilled professionals would not have even imagined. That’s what makes it so exciting.

When a company has an alignment on their objectives, they then gather the information and utilize analytics to take a look at it. Getting information from the application edge is the sharpest of mining tools, so having edge computing opens a world of chance.

Computing at this level, nevertheless, suggests that a brand-new, vitally interlinked application edge environment is running on the plant floor. Edge environment is at the Operational Technology (OT) level, along with in the server room and cloud environments at the IT level.

This blurring of the lines in between OT and IT innovations is part and parcel of a merging in changes that are opening the possibilities of market 4.0, however it is not without useful ramifications. Today, it’s much more difficult to release and manage software application at the edge. Because it remains in an information center or a cloud where the IT systems for the security, servers, networking exist. To release at the edge needs workarounds that include developing a series of mini-data-centers with associated abilities at the edge.

This layer of IT facilities requirement required at the edge will keep back its implementation unless it can be made much more comfortable with producing. If the OT engineers are releasing these mini-data centers all around the factory, then how can the IT department maintain and keep the levels of cybersecurity and networking abilities to support them? The response is that they can’t.

This observation of real-world applications of edge computing drives the design of many companies edge products and platforms.

Control of data

By providing a regular schedule of their server platforms, consumers can count on the stability and efficiency of the information produced. This approach marks an essential shift for market. It is one that unlocks to digital transformation and permits them to take the next actions towards more self-governing choice making. With reliable data returning from the edge environment, you get to the next level of analytics, then the insights and after that cycle back to manage. Eventually, what companies desire is machines and systems that drive themselves, that’s the AI part of it.

Conclusion:

Edge Computing for Industry 4.0: The market is beginning to take control of the data that is being produced in the period of IIoT. Moreover, in looking for to manage the information, to gain from it, commercial leaders understand that keeping a significant part of it in the context and environment of the application itself is better. Analyzing data in the edge environment naturally suggests that calculating requirements to occur there, and Edge computing is providing this ability. Notably, the business is also making all of this not merely possible, however also useful. Edge computing is making it simple, helping the factories to strive towards the autonomy they seek. However, it is also building in the security and networking functionality that reduces the IT implications of computing in the OT environment.

Footnotes:

Mobodexter, Inc based in Redmond, WA builds IoT Edge solutions for enterprises applications on Kubernetes & Dockers.

Register now at developers.paasmer.co and try a free Edge trial license of Edge Intelligence software that runs on Raspberry Pi.

Follow our all our weekly IoT Blogs: https://blogs.mobodexter.com

Our IoT Newsletter reaches 3000+ subscribers– Subscribe Now.

Become our affiliate partner today.

https://blogs.mobodexter.com/edge-computing-for-industry-4-0-too-beneficial-to-ignore/

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IOT and System Integration Goes Hand-in-Hand

Mobodexter | Mobodexter

IOT and System Integration: In the Internet of Things economy, the fractured business can not even want to complete. System integration merely makes the fractured business whole.

Who would have believed we would be discussing “system integration” in 2019? Wasn’t that all looked after years ago?

Well, yes: Remember “middleware” and 90s-style combination? Then came 2000s-style system integration and Service-oriented Architecture. Today we are discussing the mom of all system integrations in IoT: Device integration, cloud integration, hybrid integration, edge integration, and API integration/ management. One must not exclude other vital challenges, like microservices, APIs, and container clusters.

How to Be Successful in IoT Service by Actually, Truly Attempting

And given that whatever else AI, analytics, service choices, speed, agility, earnings, loss, disturbance rests on leading the completely incorporated business, System Integration with a capital “SI” is most likely the most exciting and definitely the most essential area to IoT success in IT and operational technology (OT) today.

It is essential and unusual enough for Salesforce to have acquired MuleSoft for $6.5 in 2015. That was a $6.5 billion verification of the knowledge of Software AG’s near 30-year  focus on system integration technology.

Why?

System Integration makes the fractured business whole.

Simple words, however, what a valid declaration, and what remarkable ramifications being “whole” has for the business in today’s digital economy.

If system integration makes the business whole, what is the different state? We frequently discuss silos in the business. However, I believe fractured is a more accurate description of the effect of not being whole.

In the Internet of Everything economy, the fractured business can not even wish to compete. Half responses from disconnected business assets lead to ill-founded choices or service response times that are too sluggish to be efficient.

Overall, system integration is what makes a fractured business whole. Total integration on assets, in the cloud, and at the edge is the only method to construct effective IoT architectures, landscapes, applications. Cloud integration is at the center of digital improvement, with 80% percent of the work in IoT jobs associated with system integration and, today, the majority of that remains in the cloud.

Believe how basic to success this remains in IoT. We have moved the business limits from on-premises to the cloud to the edge. Essentially wherever a business has an asset or digital sensor is now the border of that business. Extend that to include partners, customers, and customers’ clients. Extend that more to consist of information from any 3rd party source: weather condition, natural events, or popular opinion.

Believe how efficient and precise company choices, production choices, or maintenance choices would be if whatever were connected, analyzed, and reviewed by those closest to the problem. How could this be achieved in a fractured business?

Difficult– or not possible– is it?

System Integration is essential to useful IoT. System Integration is not merely any old integration. System integration needs to be neutral and independent. System integration needs to connect applications from any supplier hosted anywhere, sensing units from any producer on any store flooring, or up any mountain, down any valley. System integration needs to provide maximum consumer choice.

A business can have the flexibility and speed required to perform service on the Internet of Things if they are masters of their own IT fate. Business requires independent system integration that keeps their options open, securing IT investment and a real agility layer that lays the basis for useful IoT.

Summary

To recover a fractured business, a broken client relationship, or a broken supply chain to get ready for the Internet of Things or Industry 4.0, businesses should completely integrate everything. It makes the whole higher than the amount of the parts.

Footnotes:

Mobodexter, Inc based in Redmond, WA builds IoT Edge solutions for enterprises applications on Kubernetes & Dockers.

Register now at developers.paasmer.co and enjoy free Edge trial license of Edge Intelligence software that runs on Raspberry Pi.

Follow our all our weekly IoT Blogs: https://blogs.mobodexter.com

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https://blogs.mobodexter.com/iot-and-system-integration-goes-hand-in-hand/

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Edge Computing for Healthcare Drives The Future of Healthcare

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Edge Computing for Healthcare: With an industry as important as the healthcare sector, the adoption of edge computing will drive this sector into a better and brighter future. Edge computing provides the healthcare sector an efficient technology that has the potential to offer higher efficiencies hospitals and clinics around the world. 

Edge Computing Basics

Edge computing is used to refer to a physical computing infrastructure that is intentionally located outside of a company’s central data center or cloud infrastructure. This concept allows applications, computing, and storage resources, to be placed where they are most required and where the data is being collected. Precisely, in the healthcare sector, information is being collected in a variety of facilities, such as individual hospitals, clinics, or practices.

Individual healthcare units can gather and process information from on-site medical applications, hospital administrative systems, and the increasing number of healthcare-related IoT devices without having to send it over a connectivity network. However, they can still connect to the network and transfer data if and when necessary. These localized components form the core of an edge computing environment usually has a hyper-converged infrastructure, that integrates storage, compute, and virtualization layers of infrastructure into a single solution architecture.

Edge Computing Leads to Increased Responsiveness and Efficiency

An edge computing facility enhances network effectiveness while decreasing the volume of data movement. Edge computing reduces reliance on remote central servers or dispersed local servers.

Regardless of the growing information volumes experienced throughout the health care sector, edge computing still provides enhancements in effectiveness all around. Edge computing implementation results in faster visit scheduling quicker authorized access to medical records, rapid test outcome processing and, eventually, a more prompt medical diagnosis. With an edge computing implementation, physicians, medical personnel, and administration groups do not need to wait for their patient information to be sent out to the central repository for the processing.

In cases where more critical data analysis is needed, data kept on the localized facilities can be sent out to either a remote data center or a cloud repository. Hence network bandwidth is utilized better, and latency is decreased considerably.

Edge computing establishes a smart data management practice with selective data transfer policies. Due to this, edge computing has the prospective to conserve expenses throughout a whole health care trust, which is a group of healthcare facilities with a centralized off-site administration. Instead, financial investment and resources can be concentrated on other mission-critical locations such as medical personnel, devices, and materials.

Coming Across Healthcare-Sector Challenges.

The health care sector is no different from other industries in need of innovation. The health care sector has numerous IT-related challenges like restricted spending plans, reasonably small IT departments, and legacy computing systems that are tough to maintain. Experts state that information is the lifeline of their organization and that network downtime is a life or death circumstance; it’s really in the health care sector. Lives depend upon quick, effective, and highly-available IT facilities.

With minimal IT personnel on-site, it’s not always simple to keep on top of every system administration job and firefight an IT emergency. There are many examples where there are 4 or 5 IT personnel charged with handling 500+ types of hardware within a healthcare facility. Hence this situation is a real-life concern. It’s good to understand that the localized edge computing can be set up and handled from another location like a trust’s head office or central information repository.

Edge computing has the advantage of releasing IT personnel at each site from everyday jobs and assist them to cope much better with IT emergency situations. Some edge computing systems also use built-in, automatic self-healing abilities to reduce the problems of over-worked IT groups.

Utilizing edge computing facilities also can provide central, automatic security policies to each place and make sure combined HIPAA compliance throughout several sites within a company. Keeping information regional similarly suggests that on-site security can be enhanced in general.

Dealing with the Future.

Edge computing might quickly allow the health care sector to more remote locations of the nation thanks to its capability to run numerous essential functions without a connection to remote information. Its localized processing power will continue to get a considerable increase as more healthcare-related IoT gadgets that can collect and process info are provided to the marketplace.

In the United States, edge computing release might move the reach and schedule of cancer screening centers and ‘pop-up’ centers. It also will provide physicians to more instant and actionable insights by utilizing IoT connection within heart pacemakers, defibrillators, and sensors in insulin pumps.

Besides, edge computing with smart IoT gadgets will have the ability to collect on-patient information, send out the findings to their regional center or surgical treatment, and offer nearly real-time details to medical personnel. Client information could, possibly, be evaluated even if the client is not present and has not made a visit.

In the future, edge computing might assist medical facilities, and health care trusts run a network of telemedicine cubes. This opportunity offers online, onscreen, real-time access to medical professionals, and health care experts. Clients may be able to ‘see’ a medical professional at their regional shopping mall.

Summary:

Edge Computing for Healthcare: This future looks beneficial and, in lots of aspects, edge computing indicates this future is much nearer than you may believe. In the face of growing information volumes experienced throughout the health care sector, edge computing will still provide enhancements ineffectiveness, conference particular health care difficulties and scaling for the future.

Footnotes:

Mobodexter, Inc based in Redmond, WA builds IoT Edge solutions for enterprises applications on Kubernetes & Dockers.

Edge Computing for Healthcare is a sector Mobodexter helps clients.

Register now at developers.paasmer.co and enjoy free Edge trial license of Edge Intelligence software that runs on Raspberry Pi.

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https://blogs.mobodexter.com/edge-computing-for-healthcare-drives-the-future-of-healthcare/

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AI on Edge Computing - Can it solve IoT Data Privacy Issues?

Mobodexter | Mobodexter

AI on Edge Computing: The rapid development in Artificial Intelligence, smart gadgets, and smart cities guarantees to change the way we function, live, as well as interact. Nevertheless, current scandals around the handling of customer information have motivated a wave of personal privacy issues. The smarter a city obtains, the much more it can maintain tabs on every move of its citizens. Furthermore, with connected home devices and digital assistant grabbing our everyday tasks as well as inquiries, the capacity for personal privacy violations are limitless.

Europe’s introducing General Information Personal privacy Policy (GDPR) is among several efforts by federal governments to alleviate extensive deficiencies in client information security, for both firms and also federal governments. Various other nations, as well as even US states like California, have adopted similar regulations. However, stringent data regulations do not come without their disadvantages. More policy implementations are on the horizon across the globe. Hence many worry if future data-driven technology innovations and advances are at risk.

One possible remedy is Edge computing solutions. Edge computing provides a method to integrate the expanding numbers of IoT tools in our individual, public, and professional lives with user data privacy.

AI Voice Assistants like Alexa, Siri for Smart Home

Voice aides like Alexa as well as Siri, remain to expand in appeal. In 2018, United States wise voice aide adoption expanded 40 percent, getting to 66.4 million or 26 percent of the United States adult population.

The Alexa, as well as Siri systems, might be “wise,” however the AI abilities that are required to support all the variety of inquiries do not reside on these gadgets. Instead, a regular smart device, for instance, documents what customers state, transfers it to the webserver for handling, after that beams back an outcome. In voice assistants, Natural Language Processing is used to convert speech to text for data processing and then text to speech for playback the processed results to the user.

“Alexa, can you play the most recent struck by Justin Bieber?”

This command converts the audio to text and transfers the data countless miles to data centers for processing. Then the processed data is converted from text to speech to playback on your device. Because of this process, Alexa, as well as Siri, usually, do not function offline and is also why Google Home’s offline performance is restricted.

Much of the personal privacy worry in Smart home gadgets are, in fact, not a problem with the devices themselves, but with the procedure of sending and also saving information. That is because data is most vulnerable in transit, and centralized databases of customer data serve as hotspots for cyber hackers.

One solution? A genuinely smart gadget would undoubtedly have the ability to process all required information by itself. That is at the Edge. This process would not only make the device much more functional, receptive, much faster but also a lot more protected. It would considerably help in GDPR conformity.

To understand, one can check out Amazon.com’s newest Alexa-related personal privacy error. This July, Amazon.com inadvertently sent out a guy 1,700 Alexa documents that belong to another Alexa owner. The twist: these documents were sent in response to the guy’s GDPR data ownership request.

Data processing for the devices of tomorrow in Smart Cities:

These innovations could also apply to smart technologies well beyond the home, including smart cities. Many intelligent cities and cutting-edge public safety programs urgently need to educate themselves towards new regulations. GDPR, for example, restricts how potentially identifiable information can be stored, processed, and collected not just by corporations and municipalities.

Under GDPR, user data can be used without explicit consent only if the user identity information is removed and directly contributes to approved purposes. At the same time, the performance of critical functions like scanning video for missing persons, criminal activity, or traffic violations requires substantial processing power. If the data is processed outside the device, new privacy concerns come up. However, current device processors are limited in their ability to process information, and together, these limitations curtail basic functionality.

For smart cities, most of the computing power must be driven to the edge of the network. This change means the devices and sensors will be responsible for processing decisions locally, on their own, without a remote server. There will always be at least some data sent to a server given the massive amount of information that needs to be processed. However, Edge computing guarantees that only the most relevant, digested data will be sent there. 

Summary:

AI on Edge Computing: A key solution to GDPR’s current limitations on innovation is edge computing. However, edge computing alone is not sufficient. Intuitive edge processing with AI is key to making these devices truly smart.

Footnotes:

AI on Edge Computing.

Mobodexter, Inc based in Redmond, WA builds IoT Edge solutions for enterprises applications on Kubernetes & Dockers.

Register now at developers.paasmer.co and enjoy free Edge trial license of Edge Intelligence software that runs on Raspberry Pi.

Follow our all our weekly IoT Blogs: https://blogs.mobodexter.com

Our IoT Newsletter reaches 3000+ subscribers– Subscribe Now.

Become our affiliate partner today.

https://blogs.mobodexter.com/ai-on-edge-computing-can-it-solve-iot-data-privacy-issues/

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AI+IoT will redefine the future of industrial automation

Mobodexter | Mobodexter

AI+IoT: Two significant trends that are dominating the technology industry are the Internet of Things (IoT) and Artificial Intelligence (AI). However, for industrial automation, these two technologies are much more than buzzwords or trending topics. The convergence of AI+IoT is going to redefine the future of industrial automation for the Industry 4.0 revolution. 

IoT and AI are two discrete technologies that have a significant impact on multiple industry verticals. While IoT is the digital nervous system, AI becomes the brain that makes decisions which control the overall system. The lethal combination of AI and IoT delivers intelligent and connected systems that are capable of self-correcting and self-healing themselves.

To value the pledge of IoT with Artificial Intelligence, we require to take a look at the advancement of the connected IoT and machine systems.

Cloud computer supplied three vital elements to connected systems are connection, storage space, as well as compute. Apart from sending out machine-to-machine (M2M) messages to each various other entities, these gadgets sent out telemetry information to the cloud that was consumed as well as saved centrally.

Connection, storage space, and compute ended up being the foundation of the IoT. IoT and also Big Data aided stakeholders to comprehend the patterns as well as the relationship between numerous devices and sensors.

In recap, the very first generation of cloud-based IoT gave five vital abilities:

Accumulate – Telemetry information from a lot of tools as well as sensing units is gathered at the central place.

Save – The telemetry information is saved in scalable storage space systems called data lakes.

Refine – Big Data systems are made use of to procedure and also examine the telemetry datasets.

Analyze – The understandings from the Big Data systems were used to provide the evaluation via abundant visualizations

Control – Device drivers and also area designers regulate the gadgets based upon the referrals from Big Data systems.

By integrating AI with commercial IoT, we include a crucial capability to connected systems to Act on the signals and data.

AI goes past the visualizations by acting on the patterns as well as connections from the telemetry information. It ends up being the mind of the connected systems.

AI is going to turbocharge commercial IoT in two aspects. It connects itself right into the begin (tools) and also the end (evaluation) of the IoT paradigm.

For example, a camera device that is treated as an imaging device will typically send all the image frames to the IoT system to analyze the feed for particular objects. By applying data intelligence to the camera device, it sends the frame only when a specific object is detected. This process hugely speeds up the process while saving the processor from processing every frame. The same concept may be applied to speech synthesis and other forms of data. AI-enabled sensors are going to be the future of IoT systems. Smart cameras powered by AI-supported concept from Intel, NVIDIA, and Qualcomm will soon become the standard image sensors. 

By using deep knowing versions based on neural networks to inbound sensing unit telemetry information, innovative IoT systems will undoubtedly be able to locate abnormalities in real-time. Existing IoT systems are created to respond to an occasion while AI+IoT systems can proactively find failures.

The future of commercial automation depends on the merging of AI and also IoT. AI+IoT systems will undoubtedly affect every sector and verticle consisting of auto, air travel, money, medical care, manufacturing, and supply chain.

Footnotes:

Mobodexter, Inc based in Redmond, WA builds IoT Edge solutions for enterprises applications on Kubernetes & Dockers.

Register now at developers.paasmer.co and enjoy free Edge trial license of Edge Intelligence software that runs on Raspberry Pi.

Follow our all our weekly IoT Blogs: https://blogs.mobodexter.com

Our IoT Newsletter reaches 3000+ subscribers– Subscribe Now.

Become our affiliate partner today.

https://blogs.mobodexter.com/aiiot-will-redefine-the-future-of-industrial-automation/

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Edge Computing Data Center - An Overview

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Edge Computing Data Center: Edge computing has many layers and flavors and is defined in varying size, scope, and market-based to fit the unique requirements of the customers. Edge data centers are designed based on the customer’s needs. Hence, the definition of Edge can range from 10kW to over 10MW.

The network, content, and cloud providers will continue to drive the of Edge’s expansion. Edge proof of concepts is seen in autonomous vehicles, smart cities, and other IoT use cases. These use cases will also help add new flavors to the Edge in 2019 and beyond. The critical business element is to be flexible, fast, and scalable to support the dynamic requirements for these edge use cases.

One can see a heightened focus on enterprise edge computing now than before. Most clients are moving their compute environments closer to their end-users with smaller, more distributed deployments. This evolution creates an excellent opportunity for data center providers. However, it also highlights the importance of flexibility, global scale, and interconnection.

For most medium-to-large enterprises, it’s not enough for a data center provider to be in just one or two markets. They should look for Edge data center providers that give them the flexibility to move capacity between markets without penalty.

Time to provide interconnection and dedicated infrastructure impede edge computing adoption. Enterprises are looking to data center providers that not only offer various, low-latency Edge to cloud providers and cloud edge POPs. They need to easily link their data center to the ecosystem of service providers in a metro region.

The evolution of edge computing that is taking shape now requires early pioneers to be self-sufficient. Many on-premise test facilities are already working with the support of co-alliances, and it appears to be a race just getting started. Artificial intelligence (AI) and Internet of Things (IOT) use cases are becoming standard. Edge computing will continue to see increased adoption to support these use cases.

In 2019 and 2020, 5G buildout will be one extensive, universal phenomenon. Telecom Carriers will need to deploy more compute and storage closer to users to address latency challenges. Also, the Content Delivery Network (CDN) providers will likely expand their regional presence to support streaming and eventually AR/VR applications.

The other primary driver for Edge will be Industry 4.0. There is enormous value in collecting, trending, and analyzing data from manufacturing operations in real-time. However, doing that in the cloud is cost-prohibitive for most data other than top-level reporting. This use case will require to compute on-premise.

In 2019 the edge use case is in High Bandwidth, Large Volume Applications like

Mobile HD video

Autonomous driving

Content delivery

Healthcare (imaging and diagnostics)

Caching and surveillance

Retail

The next round of use cases after that are going to be moved up with 5G enabled-networks for machines-2-machine communication, and machines to talk to humans. We need the new systems to allow the number of simultaneous connections all the devices require.

Today’s Edge computing use cases are still focused on localizing specific business applications/content within colocation facilities in less populated cities. This model is used to maximize performance for particular end-users or disaster recovery. SDN (software-defined networking) encourage to add edge data centers in non-urban cities a viable option. Additionally, with the commoditized cost of fiber, end-users have various choices and negotiating capability to make the most out of their edge deployments.

As we move forward, innovations like Amazon Web Services’ Outpost, Snowball, and Snowmobile will bring real Edge computing closer to mass adoption. Futuristic solutions featuring data center containers deployed in remote, global locations such as telecom towers are already being tested. With comprehensive 5G technology around the corner, smart cities, and game-changing network-based offerings nearing completion like self-driving cars, edge computing is the next step in the evolution. We are in the early stages and a couple of years out still for actual Edge computing adoption, but the momentum is starting to build.

Edge Computing Data Center: Two significant data center trends in 2018 were the dominance of massive, hyper-scale cloud deployments and the emergence of new edge computing architectures. Datacenter products will integrate hyper-scale and edge computing data centers. Edge will also support advanced technologies such as artificial intelligence, Internet of Things, blockchain, virtual reality, big data. Keep an eye on the 100,000+ cell towers in the U.S. as a platform for edge roll-outs.

Footnotes:

Mobodexter, Inc based in Redmond, WA builds IoT Edge solutions for enterprises applications on Kubernetes & Dockers.

Register now at developers.paasmer.co and enjoy free Edge trial license of Edge Intelligence software that runs on Raspberry Pi.

Follow our all our weekly IoT Blogs: https://blogs.mobodexter.com

Our IoT Newsletter reaches 3000+ subscribers– Subscribe Now.

Become our affiliate partner today.

https://blogs.mobodexter.com/edge-computing-data-center-an-overview/

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Edge Computing Layers of implementation in real-world applications

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Edge Computing Layers: Edge computing will create a transformational impact on American society and business, thereby enabling new technologies and services across instant wireless connectivity.  For that to happen, edge computing must navigate a confusing maze and conflicting definitions, that are caused by the diverse nature of edge infrastructure and business models.

Significant stakeholders in edge computing are taking steps to clarify the various infrastructure layers and use cases. Multiple associations are being created by large corporations to define edge computing standards. Thes steps are essential to lay the groundwork for success. Edge computing is not a single entity like a computer, but several technologies that will work together to create a solution to solve a particular problem.

However, there are many challenges. Ege computing implementation timing remains unclear with many enterprises on when the business demand for the different flavors of edge infrastructure will materialize. This uncertainty is complicating decisions on how and when to fund these projects. 

Edge needs new technology:

Edge computing is not a single technology. It is instead a phrase that describes several layers of infrastructure, some of which are updates of several current models like a high-speed cellular connection – 5G. Edge computing solution is a response to new technologies such as autonomous vehicles and distributed AI applications which need low latency and proximity to users. These technologies are transforming the future of the Internet.

The transformation to edge computing will be an enormous effort for enterprises. It will take many years, cost billions and billions of dollars, and ultimately become massive in scale.

Multiple Layers and definitions:

For edge computing to be deployed at a vast scale, the edge will need to be compact, cheaper, and take the network to other new places. The critical components of edge computing include wireless networks, mobile devices, telecom towers, small cells, and distributed antenna systems as well as data centers and cloud platforms. Edge is a universe that transforms traditional business models built on specialization.

The data center industry defines an edge is a module. The small cell industry defines an edge is a small cell. Device makers envision analytics will run on sensors.

For cloud computing, it will take time for the market to grasp a holistic understanding of the edge and its many layers. Industry veterans believe things have progressed further due in part to the scope of the business opportunity.

Five layers of infrastructure:

Data Sources: Wearables, Things & Sensors

Processing Endpoints: Cars, Robots or Personal Computers

Local Data Hubs: Data processing at a small IT room or a building

Urban Data Hubs: Data processing at a cell tower or metro data center.

Regional Data Hubs: Cloud availability zones and colocation center.

The Linux Foundation offers a chart of edge layers and provides four principles that frame the participants’ lens on edge:

The edge is a location and not a single thing.

There are many edges, but the edge of the last mile network is essential.

The edge has two sides: an infrastructure edge and a device edge.

Data Computing will happen on both sides, with edge working in coordination with the centralized cloud.

Disruption demands new solutions:

This revolution is going to cost to implement. Many executives predict that the buildout of edge computing will pressure the economics of data infrastructure. Users will not pay a premium to go faster. Demand for services has a way of doing economics work, even in cases like this where the disruption is along the way.

There will be data centers in these new places, but they may be built and powered very differently than the server farms of Silicon Valley and Northern Virginia. The core compute doesn’t exist in one place like a datacenter anymore. It is instead distributed across multiple layers and locations. Edge Computing Layers are essential for real-world implementation, and they need careful strategic planning to create a transformational business impact.

Footnotes:

Mobodexter, Inc based in Redmond, WA builds IOT Edge solutions for enterprises applications on Kubernetes & Dockers.

Register now at developers.paasmer.co and enjoy free Edge trial license of Edge Intelligence software that runs on Raspberry Pi.

Follow our all our weekly IoT Blogs: https://blogs.mobodexter.com

Our IoT Newsletter reaches 3000+ subscribers– Subscribe Now.

Become our affiliate partner today.

https://blogs.mobodexter.com/edge-computing-layers-of-implementation-in-real-world-applications/

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Edge Computing for IIoT requires a relook at Enterprise Security

Mobodexter | Mobodexter

Edge Computing for IIoT: Companies are racing to adopt the Industrial Internet of Things (IIoT) technologies. Many are evaluating edge computing for efficiencies, where data from IoT devices is processed on-premise, rather than in the cloud. The increased processing speed of edge computing allows businesses to analyze data and make critical decisions in almost real-time, thereby drastically drive to improve operational efficiencies.

However, introducing these new devices and endpoints also introduces new security risks and considerations. Adding IoT devices previously located within the network to remote locations means businesses must protect new devices and endpoints from a variety of external security threats.

Every company has to explore unique technical infrastructure and security needs of the Edge computing infrastructure.

Here are the set of best practices that are emerging to improve IIoT network security with edge computing.

Identity definite Responsibility matrix for Edge Security

Enterprise digital organizations are split into two independent teams – Informational Technology (IT) and Operational Technology (OT). One of the challenging aspects of modern cybersecurity is the increasingly vague distinction between Informational Technology (IT) and Operational Technology (OT). Traditionally, all production equipment was the responsibility of OT, while network equipment was under the IT arena. Both organizations can manage the IIoT and edge computing devices now, as they reside at the edge but connect to centralized networks. IT-OT convergence is the best practice for an IIoT strategy. It is also critical for the security of the organization.

Companies must ensure alignment and collaboration between IT and OT teams on their divided responsibilities for securing the network. When possible, OT and IT teams should also understand each other’s roles and responsibilities so that they are capable of addressing any potential catastrophic events.

Perform a detailed Security Audit

Companies must first understand their vulnerabilities, to protect their operations from ransomware, endpoint manipulation, and other malicious security threats. The first critical step in improving network security is thoroughly mapping the IT infrastructure across the organization to identify all potential risks.

Corporate security teams should create an inventory of all assets, identify points of weakness, and build recovery plans for securing the network in the event of a security breach. Contracting an external security auditor will help organizations view their assets with an external perspective. Security audits should always involve both OT and IT teams. Similarly, effective recovery plans must establish the different roles of IT and OT in protecting company assets.

Select Security-Based Solutions

By design, IIoT and edge computing hardware face unique security challenges than traditional technologies. Security has not been a priority for purchasing or operational teams, as operational environments seldom pose a potential threat to company-wide networks.

Usually, production teams are focused on minimizing downtime and maximizing operational value. Hence they have a reliable set of priorities for hardware: simplicity, affordability, and autonomy, as required. However, the growth of IIoT devices and other increasingly smart operational equipment that is communicating with the network, make edge computing a new frontier in the cybersecurity war.

Many hardware manufacturers are finding unique ways to prioritize security without sacrificing operational simplicity. ‘Self-protecting’ edge computing and IIoT devices can autonomously identify data anomalies that might signal a cyber-attack or component malfunction. These devices reflect the increasing importance of edge security without introducing too much complexity for production teams. With self-protecting hardware, operational teams can ensure safety at the edge without sacrificing their ability to keep critical equipment up and running. Hence security is an important use case of Edge Machine Learning and Edge Artificial Intelligence.

Summary:

Edge Computing for IIoT: As companies explore the value of edge computing and increase the intelligence and efficiency of their industrial operations, they must remain aware of the potential security vulnerabilities. Developing a comprehensive cybersecurity plan requires full consideration of a company’s computing infrastructure, a clear awareness of security responsibilities, and invest in self-protecting hardware. Companies must first ensure they are adequately protected to maximize the value of IIoT and edge computing projects.

Footnotes:

Mobodexter, Inc based in Redmond, WA builds IOT Edge solutions for enterprises applications on Kubernetes & Dockers.

Register now at developers.paasmer.co and enjoy free Edge trial license that can run on Raspberry Pi.

Follow our weekly IoT Blogs: https://blogs.mobodexter.com

Join our IoT Hub of 3000+ subscribers to get these blogs in Email –  Subscribe.

Become our affiliate partner today.

https://blogs.mobodexter.com/edge-computing-for-iiot-requires-a-relook-at-enterprise-security/

[mobodexter]

Edge computing is a key growth driver for the automotive industry

Mobodexter | Mobodexter

Edge computing, the wide-ranging types of calculations and analyses at the extreme ends of an IT topology, has become a hot trend. According to TrendForce, a market research firm, the global market for edge computing is predicted to grow at CAGR of above 30% between now and 2022.

By 2025, the market size is expected to reach $3.24 billion. Also, edge computing is increasingly catching on in the auto industry. There are two distinct areas of application.

First one relates to all the in-vehicle computing operations, which are expanding rapidly with the rise of new generations of networked vehicles. Second, edge computing is making inroads into manufacturing processes, where growing concentrations of computing power moves as close to sensors as possible.

There are already a few critical projects in the connected-car space. BMW’s new car-as-a-sensor-service (Carasso) was developed in six months. It uses various services of AWS to achieve a comprehensive functionality. Using AWS, Carasso can adjust to very rapidly changing load requirements. In just 24 hours, they can go up or down two orders of magnitude.

Volkswagen has entered into a strategic partnership with Microsoft in the connected-car area. The new Volkswagen automotive cloud is expected to cover all the future digital services and mobility offerings from Volkswagen group. Their cloud is one sizeable industrial cloud, thereby creating the most significant digital ecosystem in the auto sector. In the future, all the services in VW vehicles and the group-wide, cloud-based carrier architecture One Digital Platform (ODP) are based on the Microsoft cloud platform Azure.

Automotive Edge Consortium

During 2018 summer, Ericsson established the Automotive Edge Computing Consortium (AECC). The cross-industry alliance, which includes AT&T, Denso, Intel, KDDI, NTT, Sumitomo Electric, and Toyota, wants to ensure that the new technologies and standards meet the future requirements of the value creation chain for networked cars.

The alliance assumes that networked cars will be sharing 10,000 times the current quantities of data by 2025. This data growth is a critical issue, as developments in the production machinery and robotics fields have shown. Edge computing has been expanding there with systems from a variety of manufacturers and with proprietary protocols. This variety creates a substantially increases complexity. Hence intermediate steps are needed to standardize data and processes.

Amazon is a very active player here. AWS recently announced four new services designed to simplify data capture in the IoT and edge computing fields. They are IoT SiteWise, IoT Events, IoT Things Graph, and IoT Greengrass Connector.

AWS services for Automated Cars

IoT SiteWise is a managed service for data capture and data cataloging. A preview version of SiteWise is now available and is currently being beta tested.

IoT Events supports responses to incidents generated by IOT sensors and applications. For example, if the temperature rises in a cold room, the change can be an indication that a door has been properly closed. A text is immediately sent to the responsible technician who can then look into the problem. The service is also only available as a preview version now.

IoT Things Graph can be used to create IoT apps on a visual basis with a drag-and-drop feature. Besides, there is a library with predefined cloud and device models. Amazon believes they can be used to develop applications with minimal coding.

The new IOT Greengrass Connector is available immediately. It helps app IoT developers connect external application securely to Greengrass devices.

Summary:

Autonomous cars equipped with Edge computing devices are seen as one large Edge computing entity that interacts with cloud computing for achieving various functionality. Hence cloud player like AWS is launching a few services to address the cloud side of this requirement.

Footnotes:

Mobodexter, Inc based in Redmond, WA builds IOT Edge solutions for enterprises applications on Kubernetes & Dockers.

Register now at developers.paasmer.co and enjoy free Edge trial license that can run on Raspberry Pi.

Follow our weekly IoT Blogs: https://blogs.mobodexter.com

Join our IoT Hub of 3000+ subscribers to get these blogs in Email –  Subscribe.

Become our affiliate partner today.

https://blogs.mobodexter.com/edge-computing-is-a-key-growth-driver-for-the-automotive-industry/

[mobodexter]

Edge computing nodes - Key factors influencing deployments

Mobodexter | Mobodexter

Edge computing nodes: The potential “edge-native” applications are wide-ranging: it ranges from mobile and cloud gaming, augmented and virtual reality applications, safety, to surveillance in smart cities. It also extends to customer engagement in assisted and autonomous driving, autonomous drones, intelligent retail and defect detection services and quality control in intelligent manufacturing.

All such “edge-native” or “edge-assisted” applications derive motivations from ultra-low latencies, managing data volume, and data privacy issues. The type of edge deployments and related services that are taking shape fall under three main categories: (1) Telcom edge or Multiple access edge computing (MEC) driven by telecom operator offerings targeted to developers. (2) Hosted cloud edge services such as from Microsoft Azure, AWS, Cloudflare, and Packet. (3) Private edge deployments have seen today in enterprises such as in the industrial IoT.

As the shape and size of each implementation will differ based on usage case, so too will the matching edge node (or edge computing system). In edge nodes, one size will certainly NOT fit all.

Following are a few of the essential factors to consider that are driving edge node style:

( 1)Type of edge network or service: This factor to consider will drive connection options such as 4G or 5G or private LTE or WiFi for regional and cloud connection of the node. It will also drive the software application that the node will need. This type of edge node will find use in business security, iot, personal privacy functions, and multi-tenancy options.

( 2) Type of work: The moving information gravity mainly drives Edge-native applications to the edge, such as from sensing units, video cameras, Lidars, and others. The task for this class of applications is associated with pre-processing or processing this information. Thereby leading to a limited set of specialized work, e.g., maker vision, time-series information analytics, deep knowing reasoning, signal processing, and comparable.

The majority of these work benefit substantially from unique function accelerators such as ASICs, FPGA, and GPU to satisfy efficiency/ watt and efficiency/ expense objectives. Very important in this context is how the node’s compute abilities appear like to designers. The type of APIs would these edge accelerators be provided.

( 3) Type of Data : Data source user interfaces, security, and personal privacy, storage is driven by volume and life expectancy, information company. The information is processed throughout end-points, edge, cloud, along with arranged and saved is a considerable part of edge computing. We depend on partners who are professionals in this location to assist us to resolve this.

( 4) Decisions are driven by data processing: Edge will exist to set off low latency choices. Hence edge nodes will require the ideal user interfaces to carry out such decisions whether autonomously or with human-in-the-loop.

( 5) Scaling the deployment and operation of date: In our viewpoint, this is among the essential elements of edge releases, and edge nodes will require to supply the best assistance for this function.

Conclusion: The choice of Edge computing nodes depends on many factors like the type of network, type of workload, type of data, requirements for data processing, and scaling requirements. These are a complex set of requirements typically managed and solved with support from Edge computing experts.

Footnotes:

Mobodexter, Inc based in Redmond, WA builds IOT Edge solutions for enterprises applications on Kubernetes & Dockers.

Register now at developers.paasmer.co and enjoy free Edge trial license that can run on Raspberry Pi.

Follow our all our weekly IoT Blogs: https://blogs.mobodexter.com

Join our IoT Hub of 3000+ subscribers to get these blogs in Email –  Subscribe.

Become our affiliate partner today.

https://blogs.mobodexter.com/edge-computing-nodes-key-factors-influencing-deployments/

[mobodexter]

Edge computing nodes - Key factors influencing deployments

Mobodexter | Mobodexter

Edge computing nodes: The potential “edge-native” applications are wide-ranging: it ranges from mobile and cloud gaming, augmented and virtual reality applications, safety, to surveillance in smart cities. It also extends to customer engagement in assisted and autonomous driving, autonomous drones, intelligent retail and defect detection services and quality control in intelligent manufacturing.

All such “edge-native” or “edge-assisted” applications derive motivations from ultra-low latencies, managing data volume, and data privacy issues. The type of edge deployments and related services that are taking shape fall under three main categories: (1) Telcom edge or Multiple access edge computing (MEC) driven by telecom operator offerings targeted to developers. (2) Hosted cloud edge services such as from Microsoft Azure, AWS, Cloudflare, and Packet. (3) Private edge deployments have seen today in enterprises such as in the industrial IoT.

As the shape and size of each implementation will differ based on usage case, so too will the matching edge node (or edge computing system). In edge nodes, one size will certainly NOT fit all.

Following are a few of the essential factors to consider that are driving edge node style:

( 1)Type of edge network or service: This factor to consider will drive connection options such as 4G or 5G or private LTE or WiFi for regional and cloud connection of the node. It will also drive the software application that the node will need. This type of edge node will find use in business security, iot, personal privacy functions, and multi-tenancy options.

( 2) Type of work: The moving information gravity mainly drives Edge-native applications to the edge, such as from sensing units, video cameras, Lidars, and others. The task for this class of applications is associated with pre-processing or processing this information. Thereby leading to a limited set of specialized work, e.g., maker vision, time-series information analytics, deep knowing reasoning, signal processing, and comparable.

The majority of these work benefit substantially from unique function accelerators such as ASICs, FPGA, and GPU to satisfy efficiency/ watt and efficiency/ expense objectives. Very important in this context is how the node’s compute abilities appear like to designers. The type of APIs would these edge accelerators be provided.

( 3) Type of Data : Data source user interfaces, security, and personal privacy, storage is driven by volume and life expectancy, information company. The information is processed throughout end-points, edge, cloud, along with arranged and saved is a considerable part of edge computing. We depend on partners who are professionals in this location to assist us to resolve this.

( 4) Decisions are driven by data processing: Edge will exist to set off low latency choices. Hence edge nodes will require the ideal user interfaces to carry out such decisions whether autonomously or with human-in-the-loop.

( 5) Scaling the deployment and operation of date: In our viewpoint, this is among the essential elements of edge releases, and edge nodes will require to supply the best assistance for this function.

Conclusion: The choice of Edge computing nodes depends on many factors like the type of network, type of workload, type of data, requirements for data processing, and scaling requirements. These are a complex set of requirements typically managed and solved with support from Edge computing experts.

Footnotes:

Mobodexter, Inc based in Redmond, WA builds IOT Edge solutions for enterprises applications on Kubernetes & Dockers.

Register now at developers.paasmer.co and enjoy free Edge trial license that can run on Raspberry Pi.

Follow our all our weekly IoT Blogs: https://blogs.mobodexter.com

Join our IoT Hub of 3000+ subscribers to get these blogs in Email –  Subscribe.

Become our affiliate partner today.

https://blogs.mobodexter.com/edge-computing-nodes-key-factors-influencing-deployments/