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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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As if the Internet of Things (IoT) was not complicated enough, the Marketing team at Cisco introduced its Fog Computing vision in January 2014, also known as Edge Computing  for other more purist vendors.

Given Cisco´s frantic activity in their Internet of Everything (IoE) marketing campaigns, it is not surprising that many bloggers have abused of shocking headlines around this subject taking advantage of the Hype of the IoT.

I hope this post help you better understand what is  the role of Fog Computing  in the IoT Reference Model and how companies are using IoT Intelligent gateways in the Fog to connect the "Things" to the Cloud through some applications areas and examples of Fog Computing.

The problem with the cloud

As the Internet of Things proliferates, businesses face a growing need to analyze data from sources at the edge of a network, whether mobile phones, gateways, or IoT sensors. Cloud computing has a disadvantage: It can’t process data quickly enough for modern business applications.

The IoT owes its explosive growth to the connection of physical things and operation technologies (OT) to analytics and machine learning applications, which can help glean insights from device-generated data and enable devices to make “smart” decisions without human intervention. Currently, such resources are mostly being provided by cloud service providers, where the computation and storage capacity exists.

However, despite its power, the cloud model is not applicable to environments where operations are time-critical or internet connectivity is poor. This is especially true in scenarios such as telemedicine and patient care, where milliseconds can have fatal consequences. The same can be said about vehicle to vehicle communications, where the prevention of collisions and accidents can’t afford the latency caused by the roundtrip to the cloud server.

“The cloud paradigm is like having your brain command your limbs from miles away — it won’t help you where you need quick reflexes.”

Moreover, having every device connected to the cloud and sending raw data over the internet can have privacy, security and legal implications, especially when dealing with sensitive data that is subject to separate regulations in different countries.

IoT nodes are closer to the action, but for the moment, they do not have the computing and storage resources to perform analytics and machine learning tasks. Cloud servers, on the other hand, have the horsepower, but are too far away to process data and respond in time.

The fog layer is the perfect junction where there are enough compute, storage and networking resources to mimic cloud capabilities at the edge and support the local ingestion of data and the quick turnaround of results.

The variety of IoT systems and the need for flexible solutions that respond to real-time events quickly make Fog Computing a compelling option.

The Fog Computing, Oh my good another layer in IoT!

A study by IDC estimates that by 2020, 10 percent of the world’s data will be produced by edge devices. This will further drive the need for more efficient fog computing solutions that provide low latency and holistic intelligence simultaneously.

“Computing at the edge of the network is, of course, not new -- we've been doing it for years to solve the same issue with other kinds of computing.”

The Fog Computing or Edge Computing  is a paradigm championed by some of the biggest IoT technology players, including Cisco, IBM, and Dell and represents a shift in architecture in which intelligence is pushed from the cloud to the edge, localizing certain kinds of analysis and decision-making.

Fog Computing enables quicker response times, unencumbered by network latency, as well as reduced traffic, selectively relaying the appropriate data to the cloud.

The concept of Fog Computing attempts to transcend some of these physical limitations. With Fog Computing processing happens on nodes physically closer to where the data is originally collected instead of sending vast amounts of IoT data to the cloud.

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The OpenFog Consortium

The OpenFog Consortium, was founded on the premise based on open architectures and standards that are essential for the success of a ubiquitous Fog Computing ecosystem.

The collaboration among tech giants such as ARM, Cisco, Dell, GE, Intel, Microsoft and Schneider Electric defining an Open, Interoperable Fog Computing Architecture is without any doubt good news for a vibrant supplier ecosystem.

The OpenFog Reference Architecture is an architectural evolution from traditional closed systems and the burgeoning cloud-only models to an approach that emphasizes computation nearest the edge of the network when dictated by business concerns or critical application the functional requirements of the system.

The OpenFog Reference Architecture consists of putting micro data centers or even small, purpose-built high-performance data analytics machines in remote offices and locations in order to gain real-time insights from the data collected, or to promote data thinning at the edge, by dramatically reducing the amount of data that needs to be transmitted to a central data center. Without having to move unnecessary data to a central data center, analytics at the edge can simplify and drastically speed analysis while also cutting costs.

Benefits of Fog Computing

  • ·         Frees up network capacity - Fog computing uses much less bandwidth, which means it doesn't cause bottlenecks and other similar occupancies. Less data movement on the network frees up network capacity, which then can be used for other things.
  • ·         It is truly real-time - Fog computing has much higher expedience than any other cloud computing architecture we know today. Since all data analysis are being done at the spot it represents a true real time concept, which means it is a perfect match for the needs of Internet of Things concept.
  • ·         Boosts data security - Collected data is more secure when it doesn't travel. Also makes data storing much simpler, because it stays in its country of origin. Sending data abroad might violate certain laws.
  • ·         Analytics is done locally- Fog computing concept enables developers to access most important IoT data from other locations, but it still keeps piles of less important information in local storages;
  • ·         Some companies don't like their data being out of their premises- with Fog Computing lots of data is stored on the devices themselves (which are often located outside of company offices), this is perceived as a risk by part of developers' community.
  • ·         Whole system sounds a little bit confusing- Concept that includes huge number of devices that store, analyze and send their own data, located all around the world sounds utterly confusing.

Disadvantages of Fog Computing

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Examples of Fog Computing

The applications of fog computing are many, and it is powering crucial parts of IoT ecosystems, especially in industrial environments. See below some use cases and examples.

  • Thanks to the power of fog computing, New York-based renewable energy company Envision has been able to obtain a 15 percent productivity improvement from the vast network of wind turbines it operates. The company is processing as much as 20 terabytes of data at a time, generated by 3 million sensors installed on the 20,000 turbines it manages. Moving computation to the edge has enabled Envision to cut down data analysis time from 10 minutes to mere seconds, providing them with actionable insights and significant business benefits.
  • Plat One is another firm using fog computing to improve data processing for the more than 1 million sensors it manages. The company uses the Cisco-ParStream platform to publish real-time sensor measurements for hundreds of thousands of devices, including smart lighting and parking, port and transportation management and a network of 50,000 coffee machines.
  • In Palo Alto, California, a $3 million project will enable traffic lights to integrate with connected vehicles, hopefully creating a future in which people won’t be waiting in their cars at empty intersections for no reason.
  • In transportation, it’s helping semi-autonomous cars assist drivers in avoiding distraction and veering off the road by providing real-time analytics and decisions on driving patterns.
  • It also can help reduce the transfer of gigantic volumes of audio and video recordings generated by police dashboard and video cameras. Cameras equipped with edge computing capabilities could analyze video feeds in real time and only send relevant data to the cloud when necessary.

See more at: Why Edge Computing Is Here to Stay: Five Use Cases By Patrick McGarry  

What is the future of fog computing?

The current trend shows that fog computing will continue to grow in usage and importance as the Internet of Things expands and conquers new grounds. With inexpensive, low-power processing and storage becoming more available, we can expect computation to move even closer to the edge and become ingrained in the same devices that are generating the data, creating even greater possibilities for inter-device intelligence and interactions. Sensors that only log data might one day become a thing of the past.

Janakiram MSV  wondered if Fog Computing  will be the Next Big Thing In Internet of Things? . It seems obvious that while cloud is a perfect match for the Internet of Things, we have other scenarios and IoT solutions that demand low-latency ingestion and immediate processing of data where Fog Computing is the answer.

Does the fog eliminate the cloud?

Fog computing improves efficiency and reduces the amount of data that needs to be sent to the cloud for processing. But it’s here to complement the cloud, not replace it.

The cloud will continue to have a pertinent role in the IoT cycle. In fact, with fog computing shouldering the burden of short-term analytics at the edge, cloud resources will be freed to take on the heavier tasks, especially where the analysis of historical data and large datasets is concerned. Insights obtained by the cloud can help update and tweak policies and functionality at the fog layer.

And there are still many cases where the centralized, highly efficient computing infrastructure of the cloud will outperform decentralized systems in performance, scalability and costs. This includes environments where data needs to be analyzed from largely dispersed sources.

“It is the combination of fog and cloud computing that will accelerate the adoption of IoT, especially for the enterprise.”

In essence, Fog Computing allows for big data to be processed locally, or at least in closer proximity to the systems that rely on it. Newer machines could incorporate more powerful microprocessors, and interact more fluidly with other machines on the edge of the network. While fog isn’t a replacement for cloud architecture, it is a necessary step forward that will facilitate the advancement of IoT, as more industries and businesses adopt emerging technologies.

'The Cloud' is not Over

Fog computing is far from a panacea. One of the immediate costs associated with this method pertains to equipping end devices with the necessary hardware to perform calculations remotely and independent of centralized data centers. Some vendors, however, are in the process of perfecting technologies for that purpose. The tradeoff is that by investing in such solutions immediately, organizations will avoid frequently updating their infrastructure and networks to deal with ever increasing data amounts as the IoT expands.

There are certain data types and use cases that actually benefit from centralized models. Data that carries the utmost security concerns, for example, will require the secure advantages of a centralized approach or one that continues to rely solely on physical infrastructure.

Though the benefits of Fog Computing are undeniable, the Cloud has a secure future in IoT for most companies with less time-sensitive computing needs and for analysing all the data gathered by IoT sensors.


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