What is AIoT?
Currently, Artificial Intelligence (AI) and the Internet of Things (IoT) are the most advanced and transformative technologies that are elevating the growth of various industries. The seamless integration between AI software and IoT hardware will be the technology that revolutionizes the smart industries era due to the rapid need for machine intelligence and hyper-automation. This concept is becoming known as the Artificial Intelligence of Things or AIoT.
The Evolution of IoT Network Architectures
To better understand the potential of AIoT let's first break down the evolution of IoT network architectures. The evolution of IoT network architectures mainly addresses how data is generated, processed, collected, and used for smarter algorithms.
The Beginning – IoT and Cloud Computing
The initial development of IoT (Internet of Things) was created from a deployment of sensors and IoT devices that were used to gather data for valuable insights. The collection of data for analysis continues to be beneficial in the age of digital transformation due to its value for insights. With large data sets, new-age applications can leverage machine intelligence, real-time insights, and ultimately avoid major risks ahead of time. The main benefit for IoT is the ability to collect and access data for better insights but in real-time. Therefore, with more and more sensors and IoT devices coming online, big data and its importance will continue to drive critical storage frameworks for better data collection.
After data is generated and collected it is often pushed to larger data centers for additional processing which, has become known as the “cloud.” Cloud computing from a centralized data center is beneficial for processing data because of its ability to provide endless amounts of computing resources and high-capacity storage options that feed models for deep learning. Although highly beneficial, cloud computing is often reserved for training new AI models for machine intelligence. Real-time insights and the ability for decision-making require compute resources even closer to where data is generated, eliminating the latency issues with accessing resources in the cloud. Enter Edge Computing, a new framework for how data is processed with speed and accuracy in closer proximity to IoT sensors and devices.
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A Transformational Shift – From Cloud to the Edge
Companies are shifting their computing workloads from the cloud to the edge to reduce the latency requirement to access the cloud. Edge architectures utilize edge computers that are deployed directly where sensors and IoT devices gather data. Being deployed right where the data is generated reduces the latency backhaul to the cloud and allows local applications to run in real-time with incredible performance. Moreover, edge computing significantly reduces the amount of data sent to the cloud by filtering raw data before sending it away for additional processing; this process significantly alleviates bandwidth usage and saves workloads that require the resources of the cloud.
The Rise of AIoT – Artificial Intelligence in IoT Devices
In the early stages, IoT devices were pretty simple. For example, a remote on/off switch or a temperature sensor that would send an alert when parameters were outside their normal operational state. As technology advanced, these IoT sensors evolved into smarter devices that were capable to be aware of their environment, understand data patterns, and perform decision-making actions to optimize new workloads and applications. In order to achieve this level of optimization, a new architecture known as the Artificial Intelligence of Things (AIoT) is growing in popularity in various applications and their data infrastructure for edge computing. AIoT embeds AI algorithms into IoT devices in order to facilitate automation and machine intelligence without any human intervention. With AI models being integrated into IoT devices and edge computing platforms, these new intelligent devices are providing new levels of optimization and efficiency in many edge computing applications. To learn more about AIoT here are some of the key technologies shaping AIoT and how they are beneficial for the next wave of intelligent computing at the edge.
AIoT Key Technologies:
1. Artificial Intelligence
Deep learning models are getting more accurate and efficient where they need less power to operate. With AI, IoT devices are enhanced in two different aspects. First, the telemetry data process is much more efficient by having intelligent sensors and IoT devices. Second, AI enables stream processing (real-time) and batch processing (Big Data) at the edge for mission-critical and complex applications.
For example, a security camera that is not augmented with intelligence will transmit every frame to the IoT framework to analyze the feed for suspicious actions. By implementing AI onto the security camera device, it only sends the frame where there is a suspicious action. This considerably improves the efficiency of the IoT application from a software and hardware perspective.
2. Hardware Accelerators
More powerful computer processors such as CPUs and GPUs from Intel, AMD, NVIDIA, and Qualcomm are pushing forward the capabilities of IoT devices to easily execute AI, Deep Learning, and Machine Learning models. Moreover, technology companies are creating more AI-focused processors such as Intel’s Movidius VPUs and Google’s TPUs that can run AI models extremely fast and efficiently. With continuous technological innovation for machine learning, manufacturers can create more compact and powerful AIoT devices.
3. 5G Networks
The 5G network is the next generation of wireless connectivity that delivers ultra-fast speed (100x faster than 4G/LTE) and a 100-fold increase in the number of connected devices. 5G networks will supercharge AIoT applications, making them much more powerful, mobile, reliable, and efficient.
4. Big Data
Currently, there are an estimated 7 billion IoT devices with more than 1 million new devices connecting to the internet daily. Experts are expecting the number to increase to 30 billion IoT devices by 2025. The proliferation of connected IoT caused an explosion in the collection of Big Data that is moving between devices and networks. With so much data generated from IoT devices, AI developers are training more intelligent Deep Learning models to implement into AIoT devices. Additionally, AIoT devices alleviate the workloads at the cloud by having the AIoT devices collect, filter, process, and analyze data at the edge before sending the most essential information to the cloud.
The Future of AIoT Applications
AIoT applications are being developed with the latest technological innovations. Companies are constantly testing newer levels of machine intelligence and how future advancements will provide new opportunities for AIoT devices. Here are some examples of how some applications are using AIoT for new benefits.
1. AI Edge Computing
AI edge computing is processing the data with an AI algorithm using only the computer itself. This AI edge computer is performing AI-enabled applications right at the edge without relying on remote data centers.
2. Vision AI
Machine inferencing is capable of detecting a massive number of objects in real-time. With powerful dedicated AI processors, machine vision will not only be able to detect different objects but also quickly predict the behavior of the objects for a thorough comprehension of the situation.
3. Voice AI
Speech recognition and the voice assistant is just the early stage for voice AI. The future will enable much more complex Voice AI applications such as natural language processing and real-time language translation.
AIoT and The Industry 4.0
Smart cities are going through a digital transformation to bring innovative solutions to urban challenges. Being data-centric and deploying intelligent AIoT devices will create a safer and convenient environment. Tech-based urban planning is utilizing a myriad of AIoT devices to optimize all levels of the city from energy consumption to traffic flow.
Smart Industrial Automation
The AIoT is also pushing forward the industry 4.0 transformation. IIoT or Industrial Internet of Things requires rugged AI edge computers systems to run real-time data analytics and manage M2M (machine-to-machine) communications to optimize operations, logistics, and manufacturing processes. Data-driven devices help manufacturers to foresee challenges and prevent costly downtime incidents on the factory floor.
Global implementation of the Artificial Internet of Things (AIoT) is much closer than ever before; And one day it will be everywhere around us, seamlessly working behind the scenes improving people’s lives and businesses without us even realizing it.