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Two years back when my employer asked me to take over the role of an IoT project manager, my first reaction to that was “Why me”? It was quite an obvious response you get when ask a mechanical engineer to jump into the IT world and to start dealing with terminologies like data protocols, cloud, database, microservices and so on. There are then two ways to handle this kind situation: Either you quit or to take the challenge. I (luckily) went for the second option. 

The major issue which the companies, pursuing digital transformation around the globe, facing is the lack of expertise. You cannot fire 50% of your existing staff just because they cannot program or cannot describe a cloud. On the other hand, the market (still) lack some comprehensive training or courses which can help the individuals with no IT background to undergo the transition from non-IT to basic-IoT and finally to advance IoT. To sum up, it comes down to two issues: Companies want to pursue digital transformation, but they lack expertise, and the existing staff is not capable of filling this gap. 

Let’s not consider the worst case scenario (though they exist) in which firms fire thousands of their once loyal employees and outsource the development projects to IT service provides. There is another way out in which employees take the initiative in their hand and start teaching themselves IoT in an easy and at the same time productive manner. Here are my three (proven) tips in this regards to fellow non-IT colleagues. 

Tip 1: Learn something new and narrate it to your spouse: 

Try explaining to your wife what the terms cloud, gateway, data protocol, digital twin etc. means. Do this in a way that you can map it onto his or her daily routine. For instance explain your spouse the concept behind the smart home or an intelligence dishwasher which calculates the number of cycles executed, amount of water, load and so on. This dishwasher speaks a unique language which is called MQTT which allows him to talk to the internet which in turn using some analytics try to make this dishwasher intelligent. 

If you are lucky enough then your spouse has almost nothing to do with the topic of IoT. That makes the task more challenging but will have a better outcome. This since you have to break down every buzzword into a simplified form to make the explanation quite easy. The more and more in-depth you explain, better you will get with the topics of IoT. 

Tip 2: Write a blog on IoT or related topics: 

That’s one of the reasons I am here. I wrote my first blog in 2017 on RAMI 4.0 topic. The idea here was not to get people’s attention but to gain an insight on the subject. You cannot write an article on a topic before doing intense research on it. I was finding it difficult to understand the concept behind RAMI 4.0, so I decided to write on it. The best thing about these kinds of blogs is that they result in some discussion which in turn enriches your knowledge about the topic. 

Here again, I would like to the point that you are not writing to impress someone but to make yourself and other non-IT individuals understand the concept behind a particular IoT topic. Last but not the least, keep the article and the content as simple as possible as Steve House said: “The simpler you can make the things the richer the experience becomes”. 

Tip 3: Buy yourself a single board computer and start experimenting

I am not marketing raspberry pi or any other single board computer here, but these devices are small wonder box which can show you the way to a “self-developed” IoT use case. What you need is a small programmable computer or an IoT device which you can customize depending on the type of use case you want to try. I decided for pi 3 since they are lots of literature and videos available on the net explaining IoT projects with Pi. The next step is to get a demo version of a cloud service provider of your choice and visit the tutorial page. You do not have to be an IT expert to try some of the use cases mentioned there. The examples cited there are described a simplified way and is like putting LEGO blocks together. I used the Microsoft Azure platform and tutorial to program a use case which sends an alarm /e-mail notification in case of temperature higher than 25 degrees C. 

The step by step description of the use case can be found at Azure tutorial (docs.microsoft). If you follow these carefully then your solution would look something like this:

                                                              Dashboard Azure IoT

 

                                                Code running on Raspberry Pi 3

 

Here for instance, if the temperature is above 25 degrees C, the signal is set to “true” and is transferred to your IoT hub within Azure using service bus. There the logic –App takes this information, process it and trigger the notification (G-mail-send email 2 function) to my Gmail.

                                                                                     Logic App

 

                                                                         Trigger view in Logic App

 

The screenshot below shows the number of incoming requests (from Pi to IoT Hub) as well as the outgoing messages at one particular run.

 

                                                                        Incoming requests vs outgoing messages

 

                                                                                Email-notification

I did not program even a single line here. So what’s holding you back? Start writing a blog or grab yourself an IoT device and start experimenting.

 

 

 

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Cloud computing allows companies to store and manage data over cloud platforms, providing scalability in the delivery of applications and software as a service. Cloud computing also allows data transfer and storage through the internet or with a direct link that enables uninterrupted data transfer between devices, applications, and cloud.

Role of Cloud Computing in IoT:

We know that the Internet of Things (sensors, machines, and devices) generate a huge amount of data per second. Cloud computing helps in the storage and analysis of this data so that enterprise can get the maximum benefit of an IoT infrastructure. IoT solution should connect and allow communication between things, people, and process, and cloud computing plays a very important role in this collaboration to create a high visibility. 

IoT is just not restricted to functions of systems connectivity, data gathering, storage, and analytics alone. It helps in modernizing the operations by connecting the legacy and smart devices, machines to the internet, and reducing the barriers between IT and OT teams with a unified view of the systems and data. With cloud computing, organizations do not have to deploy extensive hardware, configure and manage networks & infrastructure in IoT deployments. Cloud computing also enables enterprises to scale up the infrastructure, depending on their needs, without setting up an additional hardware and infrastructure. This not only helps speed up the development process, but can also cut down on development costs. Enterprises won’t have to spend money to purchase and provision servers and other infrastructure since they only pay for the consumed resources. 

(Case Study: DevOps for AWS, Continuous Testing and Monitoring for an IoT Smart City Solution)

How Cloud Services Benefit an IoT Ecosystem:

There are several cloud services and platforms that play different roles in the IoT ecosystem. Some of the platforms also come with inbuilt capabilities like machine learning, business intelligence tools, and SQL query engines to perform complex analytics. Let us understand how these cloud services and platforms benefit an IoT ecosystem.

Cloud Platform for Device Lifecycle Management:

Enterprises create applications and software through cloud services (SaaS), which can connect devices and enable device registration, on-boarding, remote device updates, and remote device diagnosis in minimal time with a reduction in the operational and support costs. Cloud introduces DevOps within the IoT ecosystem, which helps organizations automate many processes remotely. As more and more devices get connected, the challenges with data security, control, and management become critical. Cloud services enable IoT remote device lifecycle management that plays a key role in enabling a 360-degree data view of the device infrastructure. Certain cloud providers offer multiple IoT device lifecycle tools that can ease the update and setup of firmware and software over the air (FOTA).

Application Enablement Cloud Platform:

Cloud enables application development with portability and interoperability, across the network of different cloud setups. In other words, these are the intercloud benefits that businesses can take advantage of. Intercloud solutions possess SDKs (Software development Kits) on which enterprises can create their application and software without worrying about the backend processes.

Enterprises can run and update applications remotely, for example, Cisco is providing the application enablement platform for application hosting, update, and deployment through the cloud. Enterprises can move their applications between cloud and fog nodes to host the applications and analyze & monitor the data near the critical systems.

Many cloud service providers are focusing on building the cloud environment on the basis of OCF standards so that it can interoperate smoothly with the majority of applications, appliances, and platforms, that will allow D-to-D (device-to-device) M-to-M (machine-to-machine) communicationOpen Connectivity Foundation (OCF) standardization makes sure that the devices can securely connect and communicate in any cloud environment, which brings in the interoperability to the connected world.

Digital Twins:

Device shadowing or digital twins is another benefit that an enterprise can avail through cloud services. Developers can create a backup of the running applications and devices in the cloud to make the whole IoT system highly available for faults and failure events. Moreover, they can access these applications and device statistics when the system is offline. Organizations can also easily set up the virtual servers, launch a database, and create applications and software to help run their IoT solution.

Types of Cloud Computing Models for IoT Solutions

There are three types of cloud computing models for different types of connected environment that are being commonly offered by cloud service providers. Let’s have a look:

Cloud Computing Models

 

Infrastructure as a Service
  • It offers virtual servers and storage to the enterprises. Basically, it enables the access to the networking components like computers, data storage, network connections, load balancers, and bandwidth.
  • Increasing critical data within the organization lead to the security vulnerabilities and IaaS can help in distributing the critical data at different locations virtually (or can be physical) for improving the security.
Platform as a Service
  • It allows companies to create software and applications from the tools and libraries provided by the cloud service providers.
  • It removes the basic needs of managing hardware and operating systems and allows enterprises to focus more on the deployment and management of the software or applications.
  • It reduces the worry of maintaining the operating system, capacity planning, and any other heavy loads required for running an application.
Software as a Service
  • It provides a complete software or application that is run and maintained only by the cloud service provider.
  • Users just have to worry about the use of the product, they don’t have to bother about the underlying process of development and maintenance. Best examples of SaaS applications are social media platforms and email services.

 

Apart from these, cloud service providers are now offering IoT as a Service (IoTaaS) that has been reducing the hardware and software development efforts in IoT deployment.

Example of implementing cloud computing set-up in a connected-factory:

There are different sensors installed at various locations of an industrial plant, which are continuously gathering the data from machines and devices. This data is important to be analyzed in real time with proper analytics tools so that the faults and failures can be resolved in minimal time, which is the core purpose of an industrial IoT ecosystem. Cloud computing helps by storing all the data from thousands of sensors (IoT) and applying the needed rule engines and analytics algorithms to provide the expected outcomes of those data points.

Now, the query is which cloud computing model is good for industrial plants? The answer cannot be specific, as every cloud computing model has its own applications according to the computing requirement.

Leading Cloud Services for IoT Deployments

Many enterprises prefer to have their own cloud platform, within the premises, for security and faster data access, but this might not be a cost-effective way as there are many cloud service providers who are providing the cloud services on demands, and enterprises just have to pay for the services which they use.

At present, Amazon Web Services (AWS) and Microsoft Azure are the leading cloud service providers. Let’s see the type of cloud platforms and services AWS and Microsoft Azure provide for IoT implementations

AWS IoT Services

AWS has come up with specific IoT services such as AWS Greengrass, AWS lambda, AWS Kinesis, AWS IoT Core, and a few other cloud computing services, which can help in IoT developments.

AWS IoT Core is a managed cloud platform that allows devices to connect easily and securely with cloud and other devices. It can connect to billions of devices, store their data, and transmit messages to edge devices, securely.

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

AWS Kinesis enables data streaming that can continuously capture the data in terabytes per hour.

AWS Lambda is a compute service that lets you run code without provisioning or managing servers. It executes code only when required and scales automatically from a few requests per day to thousands per second.

AWS DynamoDB is a fast, reliable, and flexible NoSQL database service that allows enterprises to have millisecond latency in data processing, enabling quick response from applications. It can scale up automatically due to its throughput capacity, which makes it perfect for gaming, mobile, ad tech, IoT, and many other applications.

AWS Shield is a managed Distributed Denial of Service (DDoS) protection service that safeguards applications running on AWS. It provides automatic inline mitigation and always-on detection that minimize the application downtime and latency. This is why there is no need to engage AWS Support to benefit from DDoS protection. There are two tiers of AWS Shield — Standard and Advanced.

Microsoft Azure IoT Services:

Microsoft has come up with many initiatives in the field of IoT, providing industrial automation solutions, predictive maintenance, and remote device monitoring, etc. It is also providing services like Azure service bus, IoT hub, blob storage, stream analytics, and many more.

Azure Stream Analytics provides real-time analytics on the data generated from the IoT devices with the help of the Azure IoT Hub and Azure IoT Suite. Azure stream analytics is a part of the Azure IoT Edge that allows developers to analyze the data in real-time and closer to devices, to unleash the full value of the device generated data.

Azure IoT Hub establishes bidirectional communication between billions of IoT devices and cloud. It analyzes the device-to-cloud data to understand the state of the device and takes actions accordingly. In cloud-to-device messages, it reliably sends commands and notifications to connected devices and tracks message delivery with acknowledgment receipts. It authenticates devices with individual identities and credentials that help in maintaining the integrity of the system.

Azure Service Bus is a great example of cloud messaging as a service (MaaS). It enables on-premises communication between devices and cloud in the offline conditions also. It establishes a reliable and secure connection to the cloud, and ability to see and monitor activities. Apart from this, it protects applications from temporary spikes of traffic and distributes messages to multiple independent back-end-systems.

Azure Security Centre is a unified security management and threat protection service. It monitors security across on-premises and cloud workload, blocks malicious activities, advanced analytics system to detect threats and attacks, and also can fix vulnerabilities before any damages.

AWS and Microsoft Azure are providing a robust IoT solution to enterprises. An IoT Gateway can collaborate with multiple cloud service providers to maximize the advantages of the cloud solutions for IoT systems.

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Guest post by Toby McClean

In 2016 Microsoft, IBM, and AWS each made concerted efforts to extend their IoT platforms to the edge. The main reasons for this are economics, physics and legal. Using the terminology defined in this paper; the edge is the hubs and devices in the system. In this article, we focus on the analytics capabilities that extend to the edge.

Descriptive Analytics

A descriptive analytics capability will identify what is happening. Descriptive analytics can be as simple as providing an alert if a value exceeds a certain value.

The IBM Watson IoT Platform provides an environment for defining rules that run in the cloud or on a hub at the edge. IBM announced the capability as part of the Cisco partnership and recently made it generally available.

The recent AWS Greengrass announcement allows for AWS Lambda behavior to run on a hub. A descriptive analytic is written in one of the languages supported by AWS Lambda.

The Azure platform mentions edge analytics here, but it does not provide any specific tools or extensions to existing analytics capabilities to run edge analytics.

Diagnostic Analytics

Why is it happening? Diagnostic analytics can help to determine why an alert is triggered and whether it is relevant or not. Often organizations use diagnostic analytics they develop the models for predictive analytics.

None of the three platforms offers the ability to run diagnostic analytics models at the edge. With AWS Greengrass, in theory, a diagnostic model could be developed as a Lambda and run at the edge.

Predictive Analytics

What will happen? The most common use case of predictive analytics is predictive maintenance. More and more use cases are attempting to predict positive outcomes. For example, analyzing parts that come off a production line to predict those parts that do not need further testing.

The three platforms provide cloud-based services to build and execute predictive models. However, none of them provides the ability to provision and run the predictive model at the edge.

ADLINK, IBM, and Intel collaborated on enabling predictive maintenance and quality models to run on a hub at the edge. For more information see,

Analytics provisioning, configuration, and management

Being able to build analytics models is fine. But, there is a need to be able to push those models to the parts of the system where it makes the most sense to execute them. For this article, we are concerned with the ability to provision the gateways or things in the system.

Provisioning of descriptive analytics to the edge can be configured and managed from the Watson IoT Platform. AWS IoT is fully capable of provisioning of Lambdas from the AWS IoT cloud to hubs or things running AWS Greengrass. For Microsoft Azure IoT, the public documentation does not reveal anything on this aspect.

Conclusion

The article has made no attempt to make any specific recommendations about which platform is better. Its goal is to provide the reader with information in order to help them make an informed decision for their specific use case.

Hopefully, you find it useful and please leave comments and suggestions.

This article originally appeared here. Cover Photo: Tomas Havel

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