Edge Computing

How Edge Computing is Revolutionizing the Energy Industry

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August 31, 2023

How Edge Computing is Revolutionizing the Energy Industry

The energy industry is experiencing consequential changes as it encounters numerous challenges with an increasing population, like increasing demand for electricity, integration of renewable energy sources, and emerging electric vehicles. The best way to address these issues is by embracing edge computing and using it potentially. It is a distributed computing paradigm that allows data processing and analysis nearer to the source.

In this blog, we will understand how edge computing can change the outlook of the energy industry, making it more reliable, efficient, and sustainable.

Challenges faced by the current Energy Industry

The existing traditional power grid is one of the most necessary infrastructures in our day-to-day lives. It powers our homes, hospitals, schools, industries, and other essential things for our daily routines. However, power grids face numerous challenges because of the increasing electricity demand, the integration of renewable energy sources, and the growing market for electric vehicles. These challenges can be solved using innovative solutions to optimize the grid’s operation, improve its resilience, and diminish energy waste.

What is Edge Computing?

Edge computing can be explained as a distributed computing paradigm that allows data processing and analysis closer to the source. It can be installed in various locations in the power grid, like substations, to process and analyze the data generated by the sensors in real time. Edge can also support optimizing the power grid’s operation, improving its resilience, and cutting off energy waste.

3 essential Pillars of Edge Computing

Improved scalability: Edge computing allocates storage and processes it over many locations, reducing the investment cost for infrastructure and capacity for a higher traffic volume or better algorithm.
Better data security and sovereignty: As data remains at its original location, the risks for illegal access or theft are decreased automatically.
High amount of data processed with less latency: Frequency analysis allows it to work with thousands of data almost instantly, with just milliseconds required for analysis and response. This solved the near real-time use cases- something impossible in Cloud Environments that are more inclined to offline analysis of batch information.

The IDC’s report on ”Edge Computing Solution Driving the Fourth Industrial Revolution”- emphasizes the need for the pillars mentioned above. A survey was conducted in which around 802 industry leaders who adopted Edge Computing – almost 30% stated that their primary motivation was bandwidth costs, 27% data protection, 19% latency constraints, and 12% surveyed were from the energy sector.

Introducing Smart Power Grid with Edge Computing

In a smart grid system, multiple sensors are installed to gather data related to the health condition and performance of the power grid. These sensors create immense amounts of data that need to be processed and analyzed in real-time to make reasonable decisions. Rather than sending all the generated data to a centralized data center, edge computing is data processing at the network’s edge, closer to the source.

Edge computing supports optimizing the power grid’s operation, improving its resilience, and lowering energy waste.

For example, a power-consuming company can install edge servers at different locations in the power grid, like substations, to process and analyze the data created by the sensors in real time. The edge servers use machine learning algorithms to anticipate the power demand and supply, detect faults, and effectively manage electricity distribution.

Edge computing also enhances the power grid’s resiliency by allowing autonomous decision-making at the edge in case of network disruptions.

For example, assume any substation loses connectivity with the central control system. In that case, the edge servers can return to a backup mode and continue to function autonomously using locally stored data.

The Advantages of Edge Computing

Edge computing can bring change to the energy industry and make it more reliable, sustainable, and efficient. By opting for edge computing, power utilities can optimize their processes, lower energy waste, and offer higher-quality services to their customers. Edge computing can resolve the power grid’s challenges, such as growing demand for electricity, incorporating renewable energy sources, and emerging electric vehicles.

Besides this, edge computing can allow energy utilities and municipalities to develop and deploy data processed quickly and efficiently, allowing them to churn profit from edge computing solutions more effectively.

Users with no or low programming experience can design and deploy data processes promptly and efficiently using a convenient, low-code tool. This can assist in streamlining the deployment of edge computing solutions and allow energy utilities and municipalities to resolve the power grid challenges.

Main issues of IoT Edge Computing for the Energy Sector?

IoT Edge is powered by solid investment by technology manufacturers in cutting-edge solutions with smaller, lower-powered, and lower-priced microcomputers that can operate as IoT Edge Computing nodes at scale.

Similarly, operating systems and software are also created to give these nodes the capacity to conduct algorithms in a cyber secure way, generally packaged in virtual software “containers” like Docker.

Conclusion

Today, if we look into the energy industry, we’ll find out that the energy industry encounters many challenges, like increasing demand for electricity, incorporating renewable energy sources, and overflowing of electric vehicles in the market. With the increasing population and development of industries, it has become necessary to adopt and embrace technology that cannot just solve the problem but also does not negotiate with the quality of operations.

Edge computing has the prospect of revolutionizing the energy industry, making it more dependable, sustainable, and efficient. By embracing edge computing, power utilities can enhance their operations and reduce the risk of energy wastage.

Which solution is best for your Connected Device- Edge or Cloud Computing_

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September 5, 2022

Which Solution is Best for Your Connected Device – Edge or Cloud Computing?

If you have adopted IoT and are developing an IoT-connected device, you may wish to do some valuable computation to resolve the important issues that have been hindering growth. You might be desiring to install sensors in remote locations, create a device that can do data analytics to watch a renewable energy source, or develop health-related devices that can detect the early signs of diseases.

While creating the IoT-enabled device or IoT solution, at some point, you might get into a dilemma where you have to choose between edge or cloud computation. But what would be best for your device? Where should your device do the valuable computations in the cloud or at the edge?

Selecting between computing on edge or cloud can be an impacting decision, like it can influence a device’s efficiency or cost. Therefore, everyone does great research and thinks twice to avoid the cost of making the wrong decision and then the money spent correcting it.

What is Cloud Computing?

Cloud- It is a collection of servers accessed over the internet. Some renowned cloud providers are Microsoft Azure, Amazon Web Services, and Google Cloud.

These servers offer on-demand computing resources for data processing and storage purposes. You can easily say that cloud is a centralized platform for storing your files and programs, and you can easily connect any device to the cloud to access the data. Some of the cloud-based services are Dropbox or Google Drive etc.

Cloud computing is the process of doing computation in the cloud. These computations include data analysis and visualization, machine learning, and computer vision.

What is Edge Computing?

Edge is described as the “edge” of the network that includes devices at entry or exit points of the cloud, but it is not a part of the cloud. For instance, a server in a data center is part of the cloud; however, smartphones and routers that connect to that server are part of the edge.

Edge computing can be defined as the process of performing computations on edge. In this, the processing is completed closer or at the location where data is collected or acted.

One example of an edge computing process is object detection attached to an autonomous vehicle. The vehicle processes the data from its sensors and utilizes the result to avoid obstacles. In this process, the data is processed locally rather than sent to the cloud.

What are the points to be considered?

Before opting between edge and cloud computing, a few key questions must be considered.

Quality of Your Device’s Network

Conducting computation on the cloud can be beneficial if you have high bandwidth, low latency, and a sturdy connection to the internet, as you’ll have to send your data back and forth between cloud servers and your devices. If you have to use your device, for example, in an office or home with a steady internet connection, this back and forth can be done seamlessly. In most cases, if computation is conducted on edge, it won’t be affected by the bad or lost internet connection in a distant place. The processing can continue as it is not performed in the cloud. You would never want your vehicle’s objection detection to be failed while driving on the road. It is one of the reasons why autonomous vehicles perform computations like object detection on edge.

How Swift and How Often Does Your Data Need to be Processed?

Edge computing can be best suited in cases where customers demand response times from devices prompt than waiting for it in a decent network connection, such as monitoring components of the device.

The latency of the travel time between the cloud and the device can be minimized or eliminated. It means data can be processed immediately. It implies that if data processing is quick, one can achieve real-time responses from the devices. Cloud computation is also useful when device use is unsteady. For example, smart home devices running computation in the cloud allows sharing of the same computing resources between multiple customers. This decrease costs by restraining the need to provide the device with upgraded hardware to run the data processing.

What Part of Your Data is Crucial to You?

Computing on edge is helpful if you are only concerned about the result of your data after it has been processed. One can only send only important things for long-term storage in the cloud, which may cut down the expense of data storage and processing in the cloud. Suppose you are developing a traffic surveillance device that needs to inform about the congestion situation on the road. You could pre-process the videos on edge- instead of running hours of raw video in the cloud-one can send images or clips of the traffic only when it is present.

Do you know Your Devices’ Power and Size Limitations?

If you think your device will be limited in size and power, provided it has a strong network connection, sending the computing work to be done on the cloud will permit your device to remain small and low-power. For example, Amazon Alexa and Google Home capture the audio and send it to the cloud for processing, letting complex computations run on the audio as it can not run on the small computers inside the device themselves.

Data Processing Model Your Intellectual Property?

If you are creating a device for costumer and the methods you are adopting to process data are part of Intellectual Property, you must rethink the plan to protect it. Placing your IP on your device without a proper security plan can make your device vulnerable to hacks. If you are unaware of resources to secure your IP on edge, it is best to opt for the cloud, which already has security measures.

Final Reasons for Choosing Between Edge and Cloud Computing

Hence, we can conclude that one must consider a few things when choosing between computing on edge or the cloud. In complex issues, you might find the combination of both very beneficial by leaving some parts of processing on the cloud and rest on the edge.

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May 3, 2021

How Emerging Technologies Support IoT?

Today, IoT technology is flourishing with full potential globally and is getting embraced by a wide range of industries and organizations. These industries are striving hard to achieve IoT’s complete potential-more insights, efficiency and excellent productivity.

The emerging technologies are bringing digital and physical worlds closer and have now become essential because IoT solutions are becoming part of new applications and environment. The utilization of these emerging technologies to enhance the abilities of IoT solution is just not a trend but the market demand. It also generates real-world results.

Businesses incorporating advanced technologies are enjoying more benefits from IoT deployment, resulting in more investment in IoT solutions in their organizations. Microsoft IoT Signal report shares that IoT adoption has been continued to grow from 2019, rising from 85 per cent of companies using the technology to 91 per cent. Furthermore, 95 per cent of institutions expect to use IoT potentially in the coming years.

What are the increasing Capabilities of Emerging Technologies?

Today most organizations are already familiar with emerging technologies. Artificial intelligence, edge computing, and digital twins are already integral parts of their solutions as they facilitate solutions to work both online and offline while adding more analytic and predicting power and producing benefits by linking digital and physical domains together.

For example, Digital twins technology grants users to test the effects of changes to a system, process, or physical structure before implementing them in the real-world system or format. Not just this, this technology also enables users to manage the systems of a structure/model or equipment remotely by adopting a 3D digital model, whether you’re overseeing a single building or an entire structure.

On the other hand, Edge computing provides less latency time, offers real-time processing and analysis of massive data workloads. It also provides security, convenience and cuts down the storage costs of IoT data.

By migrating cloud databases, analytics and custom business logic to edge devices, organizations can concentrate more on business insights instead of spending time on data management and security in the cloud.

Meanwhile, Artificial intelligence adds more skills like analyzing vast amounts of information and images, machine learning, understanding speech, making predictions, and providing automated decisions. These features combine to optimize the productivity of IoT solutions.

Innovative edge modules and intelligent cloud systems develop systems that can understand their environment, learn, and adjust to maximize operations.

Strengthening Existing Solutions

Emerging technologies contribute to accelerating their RoI (Return on Investment) while automating or providing remote control and monitoring of assets. Since the COVID-19 has hit the world, the adoption of IoT technology for the purpose of remote monitoring and other related tasks has increased. The pandemic has limited on-site work and restricted staffing at offices or production plants, and other facilities.

Many building owners ( property owners in New York) use IoT solutions to handle the Covid-19 situation and ensure the health of its tenant. For examples, The company uses the solution to improve the health and safety of its tenants. The company integrated new safety measures for the tenants when buildings were reopened for business during the COVID-19 pandemic. It created and deployed an innovative, secure, scalable remote monitoring solution. The developed solution uses physical and digital assets, including Azure Digital Twins, to keep tenants safe and informed via apps, the cloud and on-site devices such as no-touch thermal sensors to detect fevers and social distancing detectors.

In the same way, the logistics business is also leveraging the benefits provided by IoT solutions. They are using IoT Solutions to inform their customers about their freight in real-time at any place. The logistic companies use integrated Intel-developed sensors and Intel Connected logistics platform technology with Azure IoT Central to run a system that can provide real-time shipment insights to employees and customers to ensure that their goods reach customers on time in healthy condition. The information shared includes the location and other vital necessary related aspects like temperature, humidity and shock during transit while ensuring security.

Building Strong Deployments

Successful integration and adoption of IoT in an organization can be measured on the basis of cost as well as production efficiency and reliability, improved quality and security. Integrating emerging technologies with IoT solution can enhance intelligent cloud or offline with intelligent edge computing. Besides this, innovations in hardware such as a growing option of lightning fast-processor or plug-and-play IoT enabled devices, field-programmable gate arrays and video processing units to handle specialized workloads in new or retrofitted IoT solutions. IoT is providing a plethora of options for different industries to enhance their productivity and efficiency. The combination of IoT with other advanced technologies is opening ‘n’ number of opportunities to make better tomorrow.

Prompt Softech, an IoT based company, is providing highly advanced IoT solutions to improve the efficiency and productivity of the organization. They have IoT enabled solution for the logistic industry to track the real-time location of the vehicle and provide other essential data like weather etc. Prompt has also developed an IoT-based security system for homes and organizations. There are many other smart solutions that are developed by the Softech Company to improve the working process of companies.

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November 25, 2020

How can Edge Computing Change the Outlook of Manufacturing Industry?

IoT, cloud, AI, ML and Edge have been quite familiar terms for technology lovers. There has been a wrong idea or approach that Edge and Cloud are mutually independent. Though they may operate in different ways; leveraging one does not prevent the utilization of the other. In fact, they powerfully complement each other.

Edge Computing in Manufacturing

With the growth and penetration of the Internet of Things in different sectors, the edge computing framework is also findings its way in several sectors. Today, the most promising edge computing use cases are present in the manufacturing industry as it welcomes new technologies, and these advanced technologies effectively improve performance as well as productivity.

IoT is already providing its best for the optimal result in the manufacturing industry; manufacturers are looking for some platform to boost the responsiveness of their production systems. To accomplish this, companies are adopting smart manufacturing with edge computing as its leading enabler.

Smart manufacturing indicates a futuristic factory where equipment can make autonomous decisions based on operations going on the factory floor.

The new technology allows businesses to integrate all steps of the manufacturing process like design, manufacturing, supply chain, and operations. This provides better flexibility and reactivity at competitive markets. But no doubt, this whole vision requires a combination of related technologies like IoT, AI, ML and Edge computing.

One of the critical reason for gathering analytics at the edge of the network is that it enables us to analyze and execute on real-time data without bandwidth costs that come with sending data offsite for analysis.
We all are well-aware that manufacturing is time-sensitive in terms of avoiding the production of out-of-spec components, equipment downtime, worker injury, or death.

In fact, for more complex, longer-term tasks, data can be transferred to the cloud and coupled with other structured and unstructured forms of data. Thus, this supports that the application of these two different computing frameworks is not mutually exclusive but its a symbiotic relationship leveraging the benefits provided by each.

Why businesses need Edge for Manufacturing?

In the manufacturing sector, the purpose of edge computing is to process and analyze data near a machine that require prompt action in a time-sensitive manner. It demands a quick decision right away without any delay. In traditional IoT platform set up, data produced by a device is collected through an IoT device is sent back to the central network server (cloud).

In the cloud, all the collected data is processed in a centralized location, usually in a data centre. This implies that all the devices which need access to this data or use applications associated with it should be connected to the cloud. Thus everything is centralized, and the cloud is easy to secure and control even if it allows for reliable remote access to data. Well, data processing is completed in the cloud; it can be accessed through IoT platforms in several ways, i.e. via real-time visualization, diagnostic analytics, reporting to support better decision making based on real data.

Now, the question which triggers is that, if everything is quite favourable, then why do we need edge computing. The main problem is that the whole process takes time, and the situation turns complicated when there is a need to take prompt decision based on data.

In the traditional process, the data travels the distance from the edge device back to the cloud, and a slight delay can be critical for taking a specific decision like stopping a machine tool from avoiding breaking. In fact, these IoT connected machines produce a massive amount of data and all the data travelling back and forth between edge and cloud disrupts the communication bandwidth.

The only way to achieve real-time decision making is to adopt edge computing. Edge enabled machines to collect and process data in real-time at the edge of the machine that allows them to respond promptly and effectively.

Edge Use Cases in Manufacturing:

Let’s now check the practical reasons to add edge computing as a necessary thing in manufacturing. There are many business benefits to ensure that all networks are correctly connected to the cloud while providing on-time delivery of powerful computing resources at the edge.

1) Updated equipment uptime:

The adoption of edge computing in manufacturing predicts failure in a subsystem, component or impact of running in a degraded state in real-time. It regularly refines as more data is analyzed and is used to boost operational purposes and maintenance schedule.

2) Decreased sustenance costs:

Better analysis of data for required maintenance means that maintenance can be completed on first visits by providing mechanics detailed guidance about the cause of the problem, required action, what part requires extra attention which ultimately deduces repair cost.

3) Lower spare parts inventory:

Edge analytics models are business-friendly; they can be tailored as per the need of an individual device or system. This implies reading sensors directly associated with specific components/subsystems.

Thus, the edge model describes how the system should be optimally configured to accomplish the business goal, making spare parts inventory more efficient at a minimum cost.

4) Critical failure prevention:

By collecting, analyzing and monitoring data related to components, edge analytics detect a cause for future failure before it affects actualize. This enables early problem detection and prevention.

5) Condition-based monitoring:

The convergence of I.T. and O.T. has allowed manufacturers to access machine data to know the condition of their equipment on the factory floor; either it is new or legacy equipment.

6) New business models:

This is an essential point because edge analytics helps in shaping new business models to catch opportunities. Let’s check an example; edge analytics can enhance just-in-time parts management systems using self-monitoring analysis to predict machine component failure and provides parts replacement notification throughout the value chain. This affirms for a needed maintenance schedule to reduce downtime and parts inventory and ensures an efficient model.

In the CNC machine tool, in-cycle stoppages to the tool are edge decision, whereas end-of-cycles can be a cloud decision. The reason behind this is that in-cycle stoppages require a very low, near-zero, lag time whereas end-of-cycle stoppages have a more lenient lag time. Thus in the former scenario, the machine would have to leverage edge analytics when in-cycle to adapt and shut down the machine automatically to avoid potential costly downtime and maintenance.

Edge and cloud computing

As we already know that IIoT aims to apply the latest analytics to large quantities of machine data to reduce unplanned downtime, reduction in the overall cost of machine maintenance and potentially utilizing the machine learning capabilities. The cloud has been responsible for making this kind of massive data acquisition, transfer, and analysis.

So, if data speed is high and connectivity should be stable and then adopting edge solution is the best option. Therefore it is clear that edge computing will not replace cloud computing but it will complement each other for the optimal result. Thus, integration of edge computing with cloud computing capabilities can enhance efficiency and maximize the productivity of the business.

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August 24, 2020

Why is IoT Impossible Without an Open Ecosystem?

IoT, i.e. Internet of Things has submerged itself in almost every sector, either its agriculture or smart home. IoT is the new need of the market and is looking for maximum growth. IoT has all the power to turn an idea into a reality, but it is dependent on the ecosystem.

Have you ever thought why IoT needs an open system? Assume, the internet had been a closed ecosystem controlled by a small set of organizations. It would have been a very different experience from the present. Today, we are entirely dependent on the internet, and if would have a closed ecosystem, then it would have a pay-per-use model along with a lack of tools and services. The open internet provides a space for growth, change and an opportunity for the innovation of new technologies.

Here is why IoT is impossible without an open ecosystem?

Gartner expects a boom in Iot growth by 2021. It states that 25 billion connected things will be part of global use through smart homes, factories, smart cities, vehicles etc. As IoT devices are making their way online, edge computing becomes a vital part to consider. Edge computing allows data processing and analysis in real-time for business-centred uses such as safety, security, industrial automation and many other purposes.

The need for infrastructure for IoT and edge is the same as for the internet; both need an open and consistent foundation for potential growth and services. However, the challenges in the development of IoT are different and requires attention to get immediate solutions before it becomes a significant problem for industries.

Where we are today with IoT?

Before we know about IoT, let’s have a quick look at the journey of the internet. In the 1990s, America allowed access to the internet to the masses through the use of CD. With the time-lapse, users became smart and realized they could connect to the internet using ISPs and enjoy more powerful and advanced search capabilities like Google. Again with the introduction of advancement, people started using the internet through their medium of choice, and this led to the acceleration in innovation and gave birth to the internet and ecosystem we are enjoying today.

To describe the present stage, we are on “AOL stage” of IoT, which means a phase of getting the device connected at scale and working through the balance of open approaches vs. proprietary. Internet of Things is complex and diverse in nature, which means it is made up of different technologies and domain-specific use cases.

Till now, the market has created an exclusive IoT platform to connect people and operations, each having different methods for data collection, management and security. Well, we can say that it is similar to a situation of having multiples “AOLs” that are continually striving to connect devices to the internet, ultimately causing avoidable complications.

Read More: Six Inevitable Steps to Bring Digital Transformation in Your Business

Today, companies switching to IoT journey are dependent and bound to their vendors and will have to provide extra costs or integration issues whenever they wish to scale deployments and take on new cases. Hence, we can conclude that IoT’s diversity has become an obstacle to its progress.

However, the only way to end all these unnecessary chaos and hassle, we must develop an open ecosystem for IoT and edge computing to scale the commercialization of offerings and services while focusing on ROI.

Closed ecosystem Vs Open ecosystem:

Any idea about how an open ecosystem looks like? Well, while creating an ecosystem, there are many approaches, from closed to open philosophies.

Generally, a closed ecosystem is part of strictly governed relationships, proprietary designs and sometimes exclusive APIs. Close guarding and control over closed ecosystem are often referred to as “walled gardens”; it provides marvellous customer experience but with the high cost and fewer choices. Open approaches provide a plethora of APIs and tools option which can be openly programmed. Open standard, like Android’s operating system, provides developers with an opportunity for more innovation and bolters a network effect.

Let’s compare the differences between open and closed ecosystem by taking Apple’s iOS and Android.

Apple’s iOS provides the most organized experience, whereas Android device makers have less control over the overall experience through hardware/software integration and thus need to search for other differentiators.

However, openness provides choice and scale and thus, Android holds over 70 per cent of global mobile OS market share. Companies like Samsung create market share by developing innovation and investing in a broader ecosystem.

IoT will bloom in an open ecosystem:

The progress and growth of IoT are not possible through hundreds of siloed and closed ecosystems governed by vendors. The future of IoT is dependent on us, and it could be improved by the development of an open ecosystem.

Development of commercial offering which has an open foundation provides more scalability, choice, transparency and flexibility. Thus open-source collaboration is an excellent starting for an open foundation.

IoT is more like a demand of time, and if you are not aware of its potential, then you need to check yourself and business too. Add IoT in your business and give us a chance to show you the most amazing looks of an IoT through our services.

How Edge Computing is Revolutionizing the Energy Industry

Challenges faced by the current Energy Industry

What is Edge Computing?

3 essential Pillars of Edge Computing

Introducing Smart Power Grid with Edge Computing

The Advantages of Edge Computing

Main issues of IoT Edge Computing for the Energy Sector?

Conclusion

How Emerging Technologies Support IoT?

What are the increasing Capabilities of Emerging Technologies?

Strengthening Existing Solutions

Building Strong Deployments

Why is IoT Impossible Without an Open Ecosystem?

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