One of the new technology trends is the combination or overlapping of multiple technologies to get the best benefits out of them. Edge computing, which has surpassed the need, and cloud computing, will grab $34 billion market value by 2023 as per market statistics. To explain more, it is equal to the expected growth of 35% annually. Inclining with that, Artificial Intelligence has emerged as the new force in the technology field, which has brought a new level in Edge computing. And it is an undeniable fact that the potential of AI in Edge computing is vast.
Edge computing has received significant attention these days for some reasons, which we will discuss later in this blog. These force companies to design products that will in line with Edge computing technology. Some of the most driving factors are like it reduces the overall latency of moving data and increases bandwidth utilization. As a result, it reduces the overall cost. Additionally, it enhances data security. So, even if you don’t opt for moving data to the cloud, you can leverage Edge computing benefits.
Thus, we can say it is a paradigm shift in cloud space, as well. Before Edge computing came into the picture, Machine learning and Artificial intelligence depended on the cloud. Now, with Edge computing edge AI has gained potential benefits. Now it is all about processing real-time data and the ability to respond fast in IoT related activities. Besides, it serves better security features.
[We had an overview of what is Edge computing in the previous blog]
Not to mention, Edge computing has overcome the issue of agility, which is though good in cloud computing but not enough. When IoT and cloud computing came into the market at the initial level, they dealt with dumb machines. Now, as intelligence is amalgamating with it, AI-enabled edge devices will create a new landscape.Â
As per one statistic, the generations of AI edge devices will increase from 161.4 million to 2.6 billion units within 2018 - 2015. Furthermore, the categories will range in all sectors from daily use smart devices to health sensors or building sensors, and many more.
Edge computing at present scenario delivers three critical capabilities from the data point of view, whether it is AI-related or IoT –
-Faster decision making
-Transferring filtered data
-Local data processinghttps://youtu.be/FgcKP1vBqnw
Reasons why AI in Edge computing is a rage
Will you be surprised to know that the need for AI in Edge computing is more than cloud computing? Maybe the principal reason behind this is the need for real-time insights and analysis, which is the apparent reason to use AI in Edge computing. There are several other aspects of using AI in Edge computing, which we will discuss in the below section.
#An intelligent Edge architecture and infrastructure can leverage better results
It is inevitably a beneficial side of Edge computing that data processing is performed closer to the edge network's data generation point. This helps to avoid pushing all data to the cloud. This is an infrastructural benefit which brings significant improvement in data streaming and reduces latency issue. However, in most cases, the implementation goes without any intelligence. Hence, to make infrastructure smarter, AI is the need of the hour in Edge computing. With intelligence moving to the Edge network, it enables quick data processing for pattern matching and inference.
So, if we consider this for IoT devices, then the sensors will eventually support internal algorithms of unsupervised machine learning. Moreover, Edge intelligence is more often needed in the medical field, where on-time response matters a lot in operations and time-based actions for physicians.
#AI edge computing means economic savings in IoT
Researches show that there is a vast scope of economic savings in Edge-based deployments in industrial IoT spaces. Edge computing is an emerging area where enterprises are investing a lot. It is an undeniable fact that almost all business verticals have witnessed a boom in the Internet of Things (IoT). This not only brings the process efficiency but also process improvements. Businesses are harnessing this power of IoT to reduce downtime, improving customer service, and increasing operational efficiency. They can also utilize it to enable new services and products, enhance risk management, and define a better prediction model. Though cloud computing can serve this huge data stream reliably and cost-effectively to increase the network strength and speed up the process, Edge computing's role is unparalleled. Hence, putting AI, along with Edge, can give enhanced productivity, which is a potential benefit because of economic savings.
#Autonomous Vehicle Industry
Autonomous vehicle – the new craze in the automobile industry significantly depends on the mobile network, specifically 5G. As per Gartner reports, by 2025, around 1 TB of sensor and vehicle data will be uploaded to the cloud, which is undoubtedly enormous indeed! Now, 5G is not the sole player that can make this journey successful. It also needs multiple technologies like Edge computing, Artificial Intelligence, etc., for its core functionalities. Together Edge computing and AI handle data on a real-time basis. They collect data, analyze it, and enable making intelligent decisions. In addition to that, 5G technology ensures high-speed data transmission that helps track and communicate the vehicles. While AI enables vehicles to make intelligent decisions in real-time, Edge computing empowers them to process and share data between vehicles and mobile towers with almost zero latency.
#AI-based Edge computing ensures better security
Though data processing happens at the Edge network case of Edge computing, it is not free of cyber threats. Due to a distributed edge architecture, the edge network often gets more prone to malware attacks. AI-enabled edge environment works better in this respect because AI can recognize the risk patterns and anomalies. This helps in taking preventive actions against possible threats. In addition to that, machine learning works to predict the outcomes based on past cyber events.
#AI-based Edge computing is highly flexible
In AI-based edge devices, there is no need for intervention from a data scientist or AI developer as insights are generated automatically. The insights can be traced through the dashboard or graphical interfaces.
#AI and Edge computing 5G – a combination of intelligent Edge in 5G
Edge computing is one of the prime technologies going to be used in 5G cellular. And what about if AI joins the hand? No doubt increased revenue and low cost in 5G! The operational point of view will result in more powerful network operations that will serve more endpoints. Hence, we can expect that the combination of AI and mobile edge computing will improve the process.
Some of the benefits that AI introduction in mobile edge computing can deliver for 5G are:
- Low latency for real-time services
- Enhanced security
- Backhaul cost savings, which means not sending all the data up to AI in the cloud.
Additionally, operators can provide a platform to partners for innovation with open edge services. This, in turn, helps developers create applications for consumer support, which ultimately add significant value to their business.
Related post - How Mobile Edge computing is contributing to the 5G?
#Edge-based AI is highly responsive
Since the data processing happens near the data source, the processing happens almost closer to real-time than the typical conventional and centralized IoT architecture deployed to date. We can get insights immediately delivered and processed, most likely within the same hardware or devices.
#Edge-based AI doesn't require a Ph.D. to operate
Being self-contained, AI-based edge devices don’t require data scientists or AI developers to maintain. Furthermore, they can either automatically deliver where they are needed or visible on the spot through highly graphical interfaces or dashboards.
#Edge-based AI provides for superior customer experiences
By enabling responsiveness through location-awareness services or rerouting travel plans in the event of delays, AI helps companies build trust and rapport with their customers.
#More robust edge computing for smart home operation
With AI and Edge computing, people can manage daily operations in smart home through connected devices. The involvement of AI can learn possible anomalies in the function and required action for it.
Do we have any challenges associated with this progress?
Yes, of course! While AI with Edge computing brings us a lot of advantages, at the same time, it associates multiple challenges related to hardware and software components. The first challenge is related to power consumption and processing load. As we know, interpreting data in AI needs software inference and training data, and it is a learning-based prediction. Secondly, Edge computing demands high processing capabilities of edge hardware. This poses a higher power requirement.
It is a challenge to develop hardware that must have high processing power but low energy consumption. Also, we will need software with efficient learning and inferencing capability.
Final words
To conclude, as we are living in a highly connected digital economy, no doubt, intelligence will encapsulate every sphere of our life. Thus, while we think about AI and edge computing combinations, there is no doubt about the intelligent digital era. This powerful combination will help to make an optimal decision and open up a new horizon of opportunities around them.
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