Smart devices have changed how we live and have elevated the role of technology to a whole new level. With these devices, we can now control everything from temperature to lights, all with the click of a button or a quick voice command. At the heart of this transformation are two technologies: edge computing and artificial intelligence. Edge computing is a distributed connecting model that analyses data closer to the device, allowing real-time processing without needing that data to be sent to a cloud.
AI, on the other hand, allows computers to perform complex tasks like problem-solving, decision-making, and data analysis. When these two technologies are combined, they offer many advantages for time-sensitive applications, such as healthcare monitoring, where real-time data processing is of utmost importance. In this article, I’ll take a closer look at how edge computing and AI work together in smart devices. I’ll explore their roles, benefits, and real-world applications, among other key topics.
Understanding Edge Computing
Let’s look deeper into what edge computing is, why it’s important, and what its key components are.
What is Edge Computing?
As mentioned, edge computing allows data to be analyzed closer to the source, at the site where it’s being generated. This way, since data doesn’t need to be transported to another server environment, data can be processed and acted upon almost instantaneously, significantly reducing latency and improving response times. Unlike traditional cloud computing, where data has to be sent to a centralized server to be processed, edge computing performs analyses locally on the device or nearby infrastructure.
In cloud computing, the data must travel to a centralized on-premise infrastructure, which can result in latency issues since the data must travel longer to reach the source. With this in mind, what advances does processing data closer to the source have? Plenty of pros come with artificial intelligence and edge computing, but the main advantages are reduced latency and improved response times. This makes it great for situations requiring real-time decisions, such as in the case of autonomous vehicles, which I’ll look at closely later.
Importance of Edge Computing in the IoT Ecosystem
The Internet of Things is growing exponentially every single year. When the IoT started, these devices would send all of the data they’ve been collecting to the cloud to be processed. Since so much data was being sent to these clouds, latency times ensued. Edge computing solves this issue by processing most of that data locally instead. Thanks to this, edge computing allows IoT devices to be processed in real time, improving the performance and efficiency of IoT devices and allowing them to make decisions faster and more accurately.
Smartphones, for example, rely a lot on IoT to collect and analyze data from around the house. By using edge computing, sensitive information can be processed at the source, which, as mentioned, reduces latency times. This way, if you use Alexa to ask for a command, its response time is much faster. Another example is with lights. A smart light can use proximity sensors to light up a bulb before someone actually enters the room, and the data here would be analyzed in the switch itself for lightning-fast responses.
Key Components of Edge Computing
Below, I’ve outlined the main components of edge computing:
Edge Devices
This piece of hardware handles data transportation at the border of two networks. Indeed, for data collected by IoT to be transported between the cloud and local devices, an edge device is required, as it translates and securely sends information between these two sources. Edge devices include routers, IoT sensors, gateways, and integrated access devices.
Edge Gateways
Edge gateways act as the intermediary between edge devices and the cloud. Since they are network entry points, they can help improve the flow between local devices and remote cloud services. With edge gateways, data can be processed from edge devices, and only the needed data is sent to the cloud, while the other can be stored locally, reducing bandwidth usage and latency. Edge gateways can be used in security, especially regarding anti-theft alarm systems. Here, gateways can process data from sources such as motion detectors and security cameras. It can detect if there is usual activity at any point, such as a person walking past the house at an odd time.
Edge Analytics
This allows companies to process data locally, either on the edge or through the edge gateway, rather than in the cloud. As mentioned, this gives them real-time insight, reduces latency, and increases the efficiency of their bandwidth. This is very similar to edge computing, but edge analytics takes things to the next level as it can analyze complex analytics before taking any action. A great example to mention here can be found in the manufacturing business. Here, edge analytics can monitor production levels in real time, comparing them to previously measured levels. If the output is lower than normal, amendments can improve efficiency.
The Role of AI in Edge Computing
What exactly does AI bring to the table when it comes to edge computing?
AI Algorithms and Machine Learning at the Edge
Edge machine learning, or Edge ML, plays a vital role in improving the capabilities of edge devices. These items can collect data and make informed decisions using edge-device machine learning models. For example, Edge AI can detect transactions made through different payment platforms while detecting odd spending habits or activities. Edge AI sends users notifications to confirm the transactions, ensuring financial protection.
Enhancing Data Processing Efficiency
AI’s ability to process data close to the source unlocks various great benefits, including enhanced data processing efficiency and low latency. When AI algorithms can process data close to the edge, there is no need to transmit irrelevant information over the network to reach the cloud. This way, only helpful information is sent to the cloud. Thanks to this, bandwidth is used much more proficiently, which leads to impressive high-performance outcomes and even reduced costs when it comes to operating it. For example, Edge AI can instantly detect when stock is low in retail stores and trigger an alert to the staff. This saves the need to send information to the cloud, thus reducing bandwidth while ensuring that the store always has the needed items on the shelf and is aware of them in real-time.
Real-Time Decision Making
Instead of sending data to the cloud server, AI at the edge allows data to be processed instantly at the source where it is generated. This is crucial in industries where quick decisions need to be made. For example, in self-driving cars, sensors allow the vehicle to make real-time decisions about the world around them, such as which road to take to avoid traffic and detect traffic light changes. Here, data is processed on the spot, which is why such quick decisions can be made. The health industry also benefits from Edge AI’s real-time decision-making. Hospitals use many machines to track their patient’s health, such as heart rate and blood pressure. If there are any changes to the baseline levels, Edge AI can send instant alerts to their staff to act upon the problem.
Real-World Applications of AI in Edge Computing
Now, let’s look at popular applications of Edge AI, taking a closer look at the numerous ways it benefits our day-to-day lives.
Smart Home Devices
AI in edge computing is used in many smart home devices. Some examples here include doorbells, which ring automatically as soon as someone approaches the porch, and lights, which can switch on and off as soon as a person enters or leaves the room. Smart speakers such as Alexa or Echo utilize Edge AI to recognize voice commands. After a voice command is made, data is processed at the edge, and action is taken instantaneously. AI thermostats are another great example. These don’t just show and allow you to adjust temperatures; they are also designed to learn their user preferences and habits, creating personalized heating and cooling timetables designed for maximum comfort.
AI thermostats are also much more efficient than standard thermostats, as they can switch off when the house is empty. Another excellent task at which Edge AI excels is home security. Through cameras and motion detection, Edge AI can identify security threats as soon as they occur. For example, suppose a human figure is detected on the porch at the usual time. In that case, Edge AI security devices can instantly monitor the homeowners to take immediate action without sending the footage to the cloud.
Healthcare Innovations
Edge AI benefits the healthcare industry, especially regarding remote patient monitoring, medical imaging analysis, and real-time data processing for critical care. Remote Patient Monitoring, or RPM, uses technology to monitor patients’ health without them even having to be at the hospital. For example, glucose meters can be used to detect high blood sugar levels, which would require immediate intervention.
Medical imaging analysis also benefits from Edge AI, analyzing CT scans and MRIs locally and, in turn, becoming better at detecting any anomalies that, in the past, might have been missed by doctors as they might be too small to notice. In critical care environments, Edge AI can help healthcare providers make quicker decisions that could improve patient outcomes. For example, Intensive Care Units are equipped with numerous monitoring devices that track signs such as heart rate and blood pressure. If there are any rapid changes, it will alert the staff to act accordingly, potentially saving lives.
Industrial IoT (IIoT)
Industrial AI has changed the manufacturing industry with data-driven decision-making, predictive maintenance, and automation. Thanks to sensors placed on machinery monitors, which are the ‘eyes of the machine,’ AI algorithms can predict when a machine might fail. In turn, it notifies the maintenance team to act upon it, preventing downtime, which could lead to loss of money. In quality control, Edge AI uses machine vision technology to inspect products in real time, ensuring they meet standards. Edge AI technology can even be used to automate production line operations, such as product packaging and transporting materials, to mention a few examples.
Autonomous Vehicles
The final real-world application of AI in edge computing is Autonomous vehicles or AVs. Through sensors, such as cameras, radar, and LiDAR, AVs collect vast amounts of data about the vehicles surrounding, including everything from measuring distances from other objects to detecting traffic signals. Edge AI is essential here because it processes this data locally, allowing the AV to make quick decisions on the spot. For example, if a car approaches the vehicle, Edge AI will analyze the world around it to determine what it has to do to get out of the way of the approaching car safely. Indeed, in the case of AVs, Edge AI is of utmost importance here as decisions must be made almost immediately to ensure the car and its passengers’ safety.
Benefits of AI in Edge Computing for Smart Devices
Let’s wrap up some of the main advantages of AI in edge computing for smart devices:
Reduced Latency and Improved Performance
As mentioned, one of the main benefits of processing data closer to the source is that it reduces latency, which, as we saw above, is crucial for applications that require immediate responses. Whether that’s a smartphone device reacting to a voice command, an auto vehicle responding to the world around it, or even detecting complications early in imaging or assisting robotic systems when to ensure surgical precision, Edge AI allows for instantaneous processing of analysis and immediate action.
Enhanced Privacy and Security
Data privacy has become a huge concern in our day and age. Since Edge AI doesn’t require all of your sensitive data to be sent to the cloud but rather analyzes it at the source, this enhances privacy and security. In cases where data is sensitive, such as medical data, for example, keeping data at the edge makes it less vulnerable to unauthorized access. Adding onto this, if certain data is seen as irrelevant by the Edge AI, it’s immediately discarded, which means it would be completely unavailable for a hacker to get hold of it.
Cost Efficiency
By processing data locally on devices, Edge AI decreases the need for costly cloud infrastructure. Since data doesn’t have to travel to the cloud for processing, businesses can save a lot of money regarding storage fees and server maintenance. This is an important concern when it comes to large industries, such as hospitals or manufacturing, which require a large number of devices to operate seamlessly, all of which generate a lot of data. Edge computing also reduces bandwidth costs since a lower set of data needs to be sent to the cloud.
Challenges and Considerations
Now that you know about all the great things that Edge AI can offer, it is time to have a quick look at some of the challenges and considerations it can bring:
Infrastructure Limitations
Edge AI applications often require a specialized network infrastructure, such as high-speed networks with as little to no latency as possible, enabling them to perform real-time processing and analysis. It also requires sufficient storage capability to handle the volumes of data generated by the connected devices, allowing data to be processed locally without overwhelming the system.
Data Management and Integration
Data management comes in the form of data movement, storage, and governance. Since Edge AI captures real-time data that can be incomplete, it can sometimes lead to faulty predictions. Ensuring that the data of the edge is accurate is crucial, as gaps can significantly affect the performance of the AI algorithms. Additionally, sorting out how data is stored and transferred between edge devices and cloud systems is important to ensure data consistency and enable their integration across many platforms.
Scalability Issues
Scalability refers to the ability of a system to handle ever-increasing amounts of data without compromising its efficiency. Unfortunately, Edge AI devices can face significant challenges regarding their performance and reliability. This requires continuous advancements in hardware, software, and networking capabilities.
The Future of AI and Edge Computing in Smart Devices
What trends can we expect to see regarding AI and edge computing in the world of smart devices?
Emerging Trends
In the present and near future, we can expect even more integration of 5G into Edge AI, as it provides various advantages to its operations. 5G provides instant and high-bandwidth connectivity crucial for real-time, data-heavy applications. For example, 5G can enable vehicles to communicate with each other, ensuring safety by allowing for instant responses when it comes to traffic changes. In smart cities, 5G will facilitate the rapid data transfer amongst IoT devices such as traffic sensors and energy meters, allowing for optimization of resources such as traffic flow or energy usage. This integration will make devices smarter, enabling them to process and act on data locally while remaining interconnected across systems.
The Convergence of AI, Edge Computing, and IoT
The convergence of AI, edge computing, and IoT will be a game-changer, redefining industries and creating new possibilities for smart devices. As mentioned above, sectors such as healthcare and transportation will allow for predictive maintenance and real-time monitoring, which can help reduce downtime and costs, amongst other features.
Preparing for the Future
There are many ways that businesses and developers can prepare for the integration of AI and edge computing in their smart devices. First, I suggest running a test pilot of Edge AI by making a small pilot project, which will allow you to see the benefits of Edge AI and how it can work for your business. This can also help you spot any challenges that could arise. Next, select the appropriate hardware and connections and use effective data management by deciding which data could be processed locally and which could be sent to the cloud. By staying ahead of these technological advancements, companies can unlock the full potential of AI and edge computing, which could greatly help improve their operations.
Conclusion
Edge AI has brought numerous advantages to our day-to-day lives. By processing data locally, issues such as latency are improved, allowing these devices to make real-time decisions, which in turn, can help improve industries’ performances while also ensuring better privacy and security. AI further enhances these benefits by allowing these devices to learn from their environment and, in turn, make autonomous decisions based on said data.
I encourage readers to keep familiarizing themselves with artificial intelligence and edge computing, as it can significantly help improve businesses and keep them ahead of competitors. These technologies have already brought numerous benefits to our world, especially in the healthcare and industrial industries, and embracing their usage is sure to hold even more potential for our future as a society.
References and Further Reading
Are you curious to know more about Artificial Intelligence and Edge Computing? I recommend checking out the list of credible articles and books below, which are very informative and helpful to keep strengthening your knowledge about these fields. Research Articles and Sources
● https://xailient.com/blog/a-comprehensive-guide-to-edge-ai/
● https://www.sciencedirect.com/science/article/pii/S2667345223000196
● https://www.wevolver.com/article/2024-state-of-edge-ai-report/the-future-of-edge-ai
● https://www.cio.com/article/2096863/the-impact-of-ai-on-edge-computing.html Books
● AI Frontier: Navigating the Cutting Edge: Book Three of the NewBits AI Trilogy by Gil Orden
● Machine Learning for Edge Computing by Amitoj Singh (Editor), Vinay Kukreja (Editor), Taghi Javdani Gandomani (Editor)
● AI at the Edge: Solving Real-World Problems with Embedded Machine Learning by Daniel Situnayake (Author), Jenny Plunkett (Author)
● Introduction to Artificial Intelligence: Second, Enlarged Edition by Philip C. Jackson Jr.
