A Smooth Transition to Edge Computing: The internet of things has been growing at a phenomenal phase in the last few years. As per IIDC, there will be around 41.6 billion things connected to the internet by 2025. The IOT market is anticipated to be worth $24.88 billion by 2022. As the internet of things is growing massively, it has also accelerated the growth of Edge computing. The global Edge Computing market is estimated at USD 2.8 billion in 2019 and will reach USD 9 billion by 2024. It is also anticipated that more than 50% of the data will be processed outside the cloud. By the next year 2020, it is expected that Edge computing will get the push it needs for a steep ramp. This data is showing that more eagerness by businesses to adopt edge computing in the Internet of Things. The main thrust behind this growth is the value that edge computing brings to the table. It makes instant compute and Realtime action feasible.
The critical value of the internet of things is its ability to get an immense amount of data. For example, the internet of things implementation in a factory can gather a wealth of information about the tools, process, environment, and heath of the machinery. The goal is to ensure the business runs successfully without any downtime. The idea behind gathering the large volume of data is to meet this goal and ensure maximum efficiency with zero downtime. IOT does this by analyzing the data to monitor the health of the system continuously. IOT also brings Artificial Intelligence and machine learning to predict any failures based on the data collected, and this ensuring no unplanned downtime.
Sending the data to the cloud to process introduces the delay. It can hinder the real-time action needed for the internet of things. Maintaining a large volume of data in the cloud also increases the cost. This inefficiency is where the edge computing pikes an interest in adoption. With edge computing, one gets the benefit of real-time action and on-demand computing. Edge computing also reduces the cost involved in sending all the data to the cloud. These critical factors drive interest in the adoption of Edge computing. However, there is a certain amount of preparedness needed to ensure that the edge computing is seamlessly integrated into the internet is things.
There are a few challenges for making a smooth transition to Edge Computing. Some of them are discussed below.
For the edge computing market to have steady growth, the ecosystem should be ready and thriving. The ecosystem comprises of hardware, software application, and all that contributes to the edge architecture. For example, in a gateway centric architecture, the gateway should have the ability to process the data from the edge devices. Then manage the data, analyze the data, and, if needed, run AI or ML to derive meaningful information. Similarly, in some instances, sensors can have the ability to process the data like in the case of video surveillance at home or drone tracking data in remote places. The processor in these sensors and gateway should have high-compute power to process and analyze the data. They should have enough power to run ML and AI. At the same time, it should be optimized for power, size, and cost. So custom processors designed specifically to fit the edge market are needed. Processors of this kind are the area of focus for manufacturers like Intel, Qualcomm, AMD & Nvidia. A similar concept applies to software designed for edge computing. The software should be capable of running in the low power, lower processing power environment compared to that of the cloud. Hence the software application needs to be built for edge computing.
The edge devices should offer a cost-effective solution to make the transition from cloud to edge attractive. Yes, real-time data processing and action are sure to make a strong case for Edge computing, but at the same time, the cost is also an essential factor. The availability of hardware and software cost-effectively customized for edge computing boosts the interest in the adoption of the edge computing solutions.
Another major bottleneck for edge computing comes from the network infrastructure that is currently available. Most of the edge device comes with legacy network connectivity protocols. These were built with the assumptions these devices didn’t have to connect to the internet. For real-time action to work, these devices should have a network that can send data at a faster speed and are connected to the internet. For example, in case of a drone trying to post the data from a remote field or an autonomous car sending the data, it should have better network connectivity.
In the internet of things, privacy and security of data have always been a cause of concern. With a massive amount of data being collected, the protection of this data and ensuring that it’s secured is one of the motivating factors for the growth of edge computing.
The cloud is not going to disappear. Even as edge computing is widely adopted, the cloud will still be a favorable solution for data hosting for a long time to come. Defining a system that can allow the co-existence of both edge and cloud is critical. And then, making the transfer of data seamless in this system will be another driving factor for the success of Edge computing.
With interest in edge computing growing, there has been a keen attempt to address these challenges and propel the growth of edge computing. For example, the growth of 5G is a positive step for edge computing. It can boost adoption. Similarly, hardware and software companies have released products that are focused on edge computing. With these developments, we can see more technology advancements in the coming years to facilitate a Smooth Transition to Edge Computing.
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This UrIoTNews article is syndicated fromMobodexter