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26 November 2021

Enabling Smarter Industrial Processes with Edge-to-Cloud Intelligence

edge-to-cloud intelligence

Smart devices surround us these days. From our smartphones to smartwatches, self-driving cars to smart devices in the agriculture industries, the world is booming with smart devices everywhere. In addition, the evolution of the Internet of Things, IoT, technology is leading to the sudden rise in the number of connected smart devices with edge-to-cloud intelligence.

In the early years of computing, during the 1960s, the emphasis was on improving computing power. Later during the 1980s, we saw the rise in personalized computers, the beginning of distributed computing. Finally, during the turn of the millennium, the focus has been on developing centralized data processing with the help of cloud computing. As a result, we saw the boom in cloud providers like Amazon, Microsoft, Google, and IBM.

Right now, we are in the cloud computing era. Of course, we still have personal computers like laptops and desktops at our home, tablets, smartphones, and wearables with us all the time. However, we use those devices to access centralized services like Gmail, Office 365, Dropbox, etc. Moreover, cloud-based devices like Amazon Echo, Google Home, etc., are also in exponential growth. Therefore, cloud computing is becoming a revelation in Information Technology not just for the mass consumer market but also for industrial applications.

Read more: The Value of IoT at the Edge

In traditional computing and cloud computing, data processing takes place far away from the data source. However, because of the sheer number of smart devices connected to the cloud, capturing, storing, and processing data alone is becoming increasingly inefficient. As per Gartner, there will be over 5.8 billion smart devices connected for IoT in 2020. Moreover, it will only rise in the coming years. This exponential growth of the Internet of Things is pushing computing back to the ‘edge’ of local networks close to where the data generation and collection happens.

So what does edge computing mean?

Edge computing means distributing computational operations at or near the data source instead of depending on the cloud at one of the data centers to process data. It doesn’t mean cloud computing is irrelevant, but what it means is that the cloud is coming to the edge, near you. 

Why is edge computing necessary? But, first, let us look at some of the problems that traditional cloud computing brings.


Latency is one of the fundamental and unavoidable issues that come with cloud computing. It is inevitable because latency occurs due to the limitation in the speed of light. If a computer needs to communicate with another computer that resides at the other corner of the globe, the former perceive latency. Any data that a computer transfers cannot travel faster than light apart from delays due to signal strength, traffic, and distance.

For example, a brief moment that it takes to load a web page from the moment you click on a link is basically due to the limit in the speed of light.

Voice assistant services like Google Assistant, Siri, and Amazon Alexa need to process your voice and send your voice’s digital representation to the cloud with data compression.

Then the cloud has to uncompress that digital representation and process it to find the proper response. Finally, the cloud sends an appropriate response to your assistant that you use to decide “if you need an umbrella while going out.” This way, the completion time for all these processes dramatically increases due to data transfer latency between these devices and systems.

Privacy and Security

Many accept the security and privacy features of an iPhone as an example of edge computing. Apple stores biometric information like touchID and faceID in the iPhone itself. It allows Apple to offload a lot of security concerns from the centralized cloud to the users’ devices themselves.

The management aspect of edge computing is crucial for security. Poorly managed Internet of Things devices can create many security problems, as proven by the malware Mirai in 2016.


Apart from privacy and security, Bandwidth savings is another way in which edge computing will help solve the problems created due to the extremely high volume of devices connected to IoT. For example, if you have only one security camera, you will not have any problem streaming all its footage to the cloud. But if you have a dozen security cameras, uploading all of the footage from all of those security cameras will create a bandwidth problem.

However, if the cameras are smart enough to know which are essential and which are not, only important footage can be streamed to the cloud while neglecting the rest. Therefore, it will significantly decrease the bandwidth. It is why running AI on a consumer’s device instead of doing all the work in the cloud is a massive focus of tech giants like Apple and Google at the moment. Google releasing Live Caption, transcribing in the recorder app, “now playing” feature in the recent versions of android is an excellent example of edge computing targeted towards reducing bandwidth.

Google is also working towards developing Progressive Web Apps that have offline-first functionality. It means you can work on a “website” on your phone or your pc without having to connect to the internet, do some work, save your work on your device itself and sync your work with the cloud only after you have your internet connection back.

Read more: How Will AI Transform IoT Architecture?

edge-to-cloud intelligence

What is edge intelligence?

There is a subtle difference between edge computing and edge intelligence. Edge computing can be defined as a process of collecting data and performing analysis on it, all taking place close to the edge device. This processed data is then sent to the cloud for further analysis.

Edge intelligence is a step ahead of edge computing because you perform actions after analysis at the edge itself using Artificial Intelligence in edge intelligence. It deviates from cloud computing and cloud intelligence, where we send all the data over the network to the centralized data store and perform analysis and decisions.

Why implement edge intelligence?

Apart from eliminating the problems in cloud computing, the implementation of edge computing and edge intelligence has the following benefits.

● It takes a long time and is very costly to transfer vast data generated by IoT devices across large geographic areas. The edge intelligence allows for the analysis, distribution, and computing of enormous volumes of data at the edge, rather than having it ship/transfer to a central processing location. Edge intelligence allows businesses to manage and analyze data anywhere, with fast response times to queries.

● The applications of edge intelligence in telecommunications include subscriber analytics to optimize customer lifetime values, increase network monetization, deliver a seamless customer experience, customize product bundles, prevent churn, and manage capital expenditures more wisely.

● By developing personalized, data-driven experiences, SaaS services can result in greater adoption of applications and higher levels of engagement and customer satisfaction.

● With the Internet of Things, manufacturers can automate, monitor real-time, and gain insights for better predictive maintenance and uptime – resulting in improved efficiency and profits.

● Government agencies can enhance their operations, use location-based data for criminal investigations, and allocate more intelligently.

● The edge intelligence and the cloud form an ecosystem that brings together all components of the infrastructure. Edge computing enables a consistent programming experience across multiple devices and systems. With the help of a database management system (DBMS), you can replicate data from the edge to the cloud. Raima Database Manager supports almost any operating system (OS) and can even run in a barebones configuration.

● In edge computing, devices and systems are integrated and synchronized faster and more effectively by replicating databases. In addition, by using edge computing, service interruptions during the transfer of app functionality are eliminated – data is replicated between edge network databases.

● By using edge computing, data transfers to cloud data centers are reduced. The cloud can be used for other tasks by delegating some of the work of processing data. In addition to improving system efficiency, this method reduces the cost of data transfer between devices and the cloud.

edge-to-cloud intelligence

Edge Intelligence—The Future of AI

Edge computing solves two problems by merging them into one solution. On the one hand, the constraint on cloud data centers to handle increasing amounts of data is about to reach a breaking point. On the other hand, Artificial Intelligence systems consume information at such a speed that there isn’t enough. Edge databases enable applications to bring Machine Learning (ML) models to the edge. With the power of real-time databases and Artificial Intelligence (AI), edge intelligence can provide real-time insights for the improvement of many industries. For example, last-mile delivery is made faster and more efficient with features such as smart tracking and real-time route navigation. Smart systems can provide customers with customized insights as they browse the store, while fraudulent activity can be detected by facial recognition software. In addition, healthcare professionals can make better health predictions and become more aware of their health risks by using edge intelligence. Traditional cloud computing can’t even begin to compare to the benefits that edge-to-cloud intelligence can provide.