The Internet of Things (IoT) is like a network of ever-replicating entities, generating an unprecedented and compounding amount of data. It is estimated that by 2025, there will be 75.44 billion connected devices in the world.

While challenging to rationalize these numbers, one thing that’s certain is our world is becoming increasingly connected, contextual, and responsive. The data we will get from these devices will be used to power a new generation of intelligent applications, but it also presents a challenge: How best do we process this to generate value for custodians of this data?

This is where edge computing comes in. Edge computing is a distributed computing paradigm that brings computing resources closer to the source of the data, in other words, the assets, processes, and actors that generate the events that result in data.

While much excitement has been created around graphical processing (NVIDIA’s share price is but a single proxy), the edge is a crucial frontier for differentiation and gaining competitive advantage in situations where the time and complexity required to make a decision or trigger an event, is table-stakes.

Edge computing enables real-time data processing and low latency feedback, which are essential for AIoT applications. AIoT, or Artificial Intelligence of Things, is the application of machine learning models, powered by edge computing devices to generate meaningful insights, in near-real-time.

These devices come in the way of sensors, that process and assimilate data such as energy meters, temperature sensors, and asset trackers, to – more critically – gateway devices that consume and process this data collectively.

Statista predicts that the global edge computing market is expected to reach $257.3 billion by 2025, and according to an article by the National Science Foundation, the average latency for edge computing is ten milliseconds, compared to one hundred milliseconds for cloud computing.

Edge computing can reduce the cost of data processing by up to 70 percent, according to GlobalData, by having low-latency and over-burdened mainframe, cloud databases, and processing environments, providing further benefits to AI.

Traditionally, BI and advanced analytics have been used to analyze historical data to identify trends and patterns. However, with edge computing, it is now possible to compute and generate meaningful and game-changing outcomes from data in real time. This allows businesses to make decisions in real time, which can lead to significant improvements in efficiency and productivity.

For example, in a smart cell site, sensors are used to collect data on everything from the temperature of the environment, and equipment, to the power consumption and capacity placed on the site. This data can be used to improve efficiency, prevent downtime, and optimize production – in this sense, high-quality, consistent signal relay.  

However, if data is transported and processed centrally, there could be costly delays, where a split second of poor service delivery impacts customer satisfaction, and staff availability to serve and operate.

This could lead to problems such as machinery running hot, being damaged outside of controllable circumstances, or delivering sub-par operations by way of quantity or quality. The same framework can be applied to mining machinery, smart buildings, factories, medical facilities, and more.

With edge computing, the data is processed locally, which eliminates these delays. This allows for faster decision-making and improved performance. In addition, edge computing can help to improve security by keeping data local, where it is less vulnerable to cyberattacks.

Ten elements must be factored into and considered to deliver AIoT at the edge. This shows how multifaceted AIoT is, and the levels required to power the various functions and capabilities.

Building a strong edge computing infrastructure is crucial. This includes deploying edge devices and gateways that can process and analyze data locally.

These devices should have sufficient computational power, storage capacity, and connectivity to manage the data generated by IoT devices with clear translation from edge to cloud or where required, hybrid architectures.

Edge devices need to be equipped with AI capabilities, such as machine learning algorithms and neural networks. These AI models can process data in real time, enabling intelligent decision-making at the edge without the need to send data to centralized servers.

As data is generated by IoT devices, it may be too voluminous or noisy to process entirely at the edge. Effective data preprocessing and filtering techniques are essential to extract relevant information and reduce data transmission to optimize processing.

AIoT applications often require low latency and high bandwidth to provide real-time responses. Ensuring a robust network infrastructure that can process the data flow between edge devices and central systems is critical.

Security is paramount in AIoT implementations. Edge devices should have strong security measures in place to protect against cyber threats and unauthorized access to AI. Data privacy is equally important, especially when dealing with sensitive information that might be locally processed.

AIoT relies on distributed intelligence, where decision-making is not solely centralized but shared between edge devices and cloud platforms. Developing intelligent algorithms that can collaborate and adapt to changing conditions is essential.

While AI processing occurs at the edge, cloud platforms remain crucial for tasks like model training, updating, and global insights. A constructive interaction between edge and cloud is vital for optimal AIoT performance.

Edge devices are battery-powered, making energy efficiency a critical consideration. Optimizing algorithms and resource usage can extend the lifespan of edge devices and reduce energy consumption.

As the number of connected devices and data volume grow, the AIoT system must be scalable to accommodate increasing demands. It should also be flexible enough to adapt to evolving requirements and technological advancements, whereby a strong object model depicting the physical instance to align to the virtual rendition, is crucial.

AIoT implementations must adhere to data governance regulations and industry standards to ensure ethical and legal use of data.

The future of AI is at the edge. As the amount of data that is being generated continues to grow, edge computing will become even more important. This will allow us to build intelligent applications that can make real-time decisions and improve our lives in countless ways.