Introduction of smart devices in our day-to-day lives has driven the need for futuristic innovations! Artificial Intelligence (AI) and the Internet of Things (IoT) have been pillars of this decade’s disruptive devices.
The data-collection capabilities enabled by IoT’s distributed sensor nodes, combined with the learning and analyzing capabilities integrated by AI algorithms, have given rise to an intelligent and connected technology in the form of Artificial Intelligence of Things (AIoT). AIoT devices are known for their ability to make intelligent, data-driven decisions almost Instantaneously. In an AIoT device, AI is integrated into the infrastructure of IoT components such as processors and chipsets.
They combine IoT's capability of collecting massive amounts of data from multiple points with the computational prowess of AI techniques, such as deep learning and machine learning.
In this article, we explore the fundamentals of this emerging technology, how it is being used across the globe, and how this technology can be improved upon!
- Edge Computing – An Enabler of AIoT
- Artificial Intelligence – The Brain behind AIoT
- Internet of Things – The Nervous System of AIoT Devices
- The Convergence of AI and IoT
- Advantages of AIoT
- Applications of AIoT Devices
- Challenges of Implementing AIoT
- What’s Next for AIoT
Edge Computing – A Key Enabler of AIoT
AIoT is a thriving example of Edge Computing. Due to the cloud-based framework of IoT, connected devices are prone to latency issues. These issues arise due to two reasons:
- The IoT Gateway preprocesses the data
- The IoT Cloud analyses the data
The IoT components, cloud and gateway, are remotely located. Hence, data transmission over the communication network, remote data analysis and triggering action, result in loss of time.
However, AIoT devices are computational devices (IoT sensors, smart cameras, uCPE equipment, servers and processors) that are used for data preprocessing and analytics on-site or near the device, i.e., at the edge.
This leads to instantaneous results, solving the problem of low latency.
Edge Computing transforms system design, enabling efficient on-device data processing for scalability and low latency. Previously, Machine Learning and Deep Learning were cloud-bound due to resource limitations. Recent advancements allow lightweight ML models to run on low-power devices, enhancing flexibility and scalability.
This shift empowers the development of real-world applications across critical sectors like health and logistics, previously unattainable.
Our blog on Edge AI explains how the technology is fueling high performance machine learning solutions.
Now, let’s breakdown the complex convergence powering AIoT devices.
Artificial Intelligence – The Brain behind AIoT
The Birth of Artificial Intelligence (AI)
In 1956, John McCarthy termed the birth of machine intelligence as Artificial Intelligence (AI). He defined it as the science and engineering that gives machines the ability to understand human language, solve problems, and reach goals just like human beings.
Here are the key principles governing AI:
| Principles to follow during the development of AIoT Devices | |
| Human-AI Collaboration | AI should support humans in accomplishing tasks. For instance, collaborative robots work hand-in-hand with humans for hazardous tasks like mining, while prioritizing the safety of the human workers. |
| Transparent AI | The workings of AI and its learning models should be transparent and strictly adhere to rules, safeguarding privacy and ethics. Humans should be informed about the functioning of machines to ensure ethical collaboration between technology and users. |
| Human-Centric AI Development | AI's goal is to maximize effectiveness while respecting human dignity, preserving cultural values, and fostering diversity. Technology should not dictate future values. |
| Privacy-First AI Design | Intelligent privacy is a priority in AI design, necessitating sophisticated protective measures to ensure the trust and security of personal information. |
| Ethical Algorithm Development | Algorithmic responsibility is essential in AI development to allow humans to rectify unintended consequences. Designing AI to anticipate both expected and unexpected outcomes is crucial. |
| Optimum Sample Selection | AI algorithms should steer clear of biased results as they can result in inaccurate reporting. This can be achieved by conducting representative research, wherein the sample taken into consideration accurately portrays the qualities of the larger group. |
| Unbiased Decision-Making | The decisions taken by AI should not be biased. The samples fed to them during their training phase need to be representative in terms of the characteristics of the larger population. This helps in avoiding error-filled discoveries. |
Complying with the above principles will help maximize the effectiveness of AIoT devices. Using Embitel’s Artificial Intelligence services, meet the required compliance expectations, at ease.
AI Technologies that Power AIoT
In this section, let’s look at the AI technologies that help us in replicating and simulating human intelligence on AIoT devices.
Machine Learning (ML):
Machine Learning gives gadgets the ability to learn without being explicitly programmed. How do machine learning algorithms work?
First, data with output labels (training data) is fed to the machine learning model, and it is trained to understand the patterns. Validation data is then fed to the algorithm without any output labels. The difference between the expected output and the predicted one is given back to the training loop as errors.
This improves the accuracy of the algorithm over various iterations. Eventually, a trained algorithm can accurately predict the output when fresh set of data is provided as input.
The cognitive technologies that elevate the pattern recognition capabilities of Machine Learning are mentioned below. Integrating them into devices powered by Artificial Intelligence of Things (AIoT) will lead to disruptive innovations!
- Computer Vision: This field of Artificial Intelligence gives devices equipped with cameras and or sensors the power to identify objects through feature extraction and pattern recognition. Computer vision is considered the most complex when compared to other branches of AI due to the sheer variations and visual angles involved in the objects present in the world.
- Natural Language Processing (NLP): Also known as Computational Linguistics, NLP is a fast-progressing branch of AI that allows humans to communicate with machines just like how they communicate with other humans. While communicating, the machines have to quickly process large amounts of textual data to extract key features of human conversations such as emotion, sentiment and intent.
- Detecting and notifying customers of irregular activities to prevent fraud and enable advanced security measures.
- Understanding the customer better and suggesting suitable services to them when they need it.
- Addressing customer pain points.
NLP has given rise to AI powered assistants such as Amazon – Alexa, Microsoft- Cortana, Apple – Siri, and Google Assistant. These AI assistants, when integrated on an IoT device, serve as an AIoT application.
The above branches of Artificial Intelligence enable the analysis of data through text/numerical, action, or speech-based commands.
Today, AI is effectively used by organisations to forecast customer behaviour. Developing AI & ML algorithms will help collect data on the user's emotions and preferences. Forecasting customer behaviour has several benefits, such as:
IoT - The Nervous System of AIoT Devices
Think of an AIoT device as a human body, where AI gives the power to comprehend information, the IoT Devices represent the sense organs and nervous system.
The sense organs i.e., the (sensor nodes), collect the data from the surroundings, while the nervous system (acting as the cloud and gateway) transmit the data to the brain (IoT Cloud). AI Techniques and Machine Learning Algorithms control the analytical and learning functions in the IoT Cloud.
Teaming up with IoT Hardware & Software teams that also excel in AI/ML services can help you develop intelligent and interactive AIoT devices.
The Convergence of AI and IoT:
The AIoT framework represents the convergence of Artificial Intelligence (AI) and the Internet of Things (IoT), creating a powerful synergy that enhances the capabilities of traditional IoT systems. The framework at its essence is an IoT framework fueled with quick and automated data analysis capabilities.
Here are the IoT components that make up the nervous system of an AIoT device:
| Components | Description |
|---|---|
| Devices and Sensors | Physical devices are equipped with sensors to gather real-time data. Examples include temperature sensors, motion detectors, cameras, etc. The |
| Connectivity | Devices need the means to connect to the internet through Wi-Fi, cellular networks, Bluetooth, or specialized IoT networks. The low latency and high bandwidth of 5G networks facilitate real-time communication, a critical factor for time-sensitive applications in Artificial Intelligence of Things (AIoT). |
| Data Communication | Devices communicate with each other or with a central system by transmitting data over the chosen network. |
| Data Processing | The collected data often undergoes some level of processing either on the device itself or in the cloud. Processing may involve filtering and aggregating the data on the IoT Gateway. This process is a pre-requisite for AI data analysis based on machine learning algorithms. In AIoT Devices, data is processed in the form of labelled and unlabelled data, used to identify patterns. |
| Cloud Storage | Processed data is sent to cloud-based platforms for storage. Cloud storage allows for scalability and accessibility from anywhere with an internet connection. |
| Data Analytics | Data can be analyzed in the cloud to derive meaningful insights. Analytics may involve identifying patterns, trends, or anomalies in the data. Data is now processed by Artificial Intelligence in AIoT Devices. |
| End-User Applications | The final stage involves utilizing the derived insights to make intelligent decisions or trigger specific actions. The results obtained from AI-driven data analysis go beyond mere data interpretation. |
They yield actionable insights and patterns that may not be immediately apparent through traditional analytical methods.
These insights contribute to a deeper understanding of the environment, enabling informed decision-making.
Advantages Offered by Artificial Intelligence of Things (AIoT)
Integrating machines and gadgets with thinking capabilities through AIoT is set to facilitate the next phase of industrial evolution. The versatile nature of AIoT devices makes it a smart solution.
This smart solution can be used to develop a transparent supply chain, enable sustainability through predictive maintenance, actively manage energy consumption at homes and industries, and so on!
Industries that choose to integrate this intelligent network will experience the following benefits:
- Enhanced Efficiency: AIoT empowers devices to optimize recommendations, adapt to user preferences, and perform tasks with increased efficiency, contributing to a more streamlined user experience.
AIoT Devices also increase critical factors such as operational efficiency in resource crunched industries such as Oil, automotive, construction industries by offering real-time tracking of raw materials and practices. - Real-time Decision-Making: The seamless integration of AI and IoT enables real-time data analysis, allowing for prompt decision-making and proactive interventions in response to dynamic scenarios. For Example – AIoT cameras powered by computer vision in Industrial infrastructures can be used for quality control. Automated suggestions on the errors and possible fixes can help take better decisions in real-time.
- Adaptive Learning: Through the principles of Machine Learning, AIoT devices can adapt and learn from data, continuously improving their performance and functionality over time.
- Scalability and Accessibility: Cloud-based platforms in the AIoT framework facilitate scalable storage and accessibility of processed data from anywhere with an internet connection, enhancing the overall flexibility and reach of the system.
AIoT Applications: Where is AIoT Extensively Used?
Superior data interpretation and analytical decision-making capabilities have led to the wide-spread application of AIoT Devices. Major consumer-centric industrial sectors that have successfully integrated AIoT in their race to modernization.
The application of AIoT has led the digital transformation in the following industries:
- Intelligent Automobiles
Modern vehicles are designed to provide feature-rich experiences to users. With intelligent connectivity solutions on high demand, AIoT devices are used at the base of all smart vehicles.
Vehicle performance and user preferences are recorded and analysed by the AIoT framework, to provide the vehicle timely performance tune-ups and offer users a sophisticated experience.
The use of IoT sensors and cameras in the car are of common occurrence today. These devices, when integrated with powerful Machine learning algorithms and Computer Vision, can help in the development of intelligent automotive services such as Face Recognition and Autonomous Driving, to varied degrees of maturity.
- Manufacturing Industry or Industrial Production
The manufacturing industry is a well-known consumer of resources such as metals, plastic, wood, energy, etc. With excessive consumption, comes responsible and sustainable use of these raw materials. The integration of AIoT in the manufacturing industry has enabled sustainable growth in the following ways:
I. Monitoring machine performance indicators like vibrations and temperature in real-time ensures notifications for predictive maintenance.
II. AIoT devices help in analysing current manufacturing data and understanding the optimized production plan to create realistic simulations of manufacturing operations.
- Supply Chain and Fleet Management
AI and IoT in the supply chain offers a transformative potential, making operations more efficient and adaptive to the dynamic challenges of the global market.
I. AIoT devices placed on fleets, enables continuous monitoring of goods providing real-time visibility into the supply chain. This allows for better tracking, reducing the risk of delays and improving overall efficiency.
II. IoT device powered by trained AI models can analyze various risk factors, including geopolitical events, weather conditions, and market trends that could effect the product supply chain.
- Medicine and Healthcare
An industry that could change lives through continuous monitoring and early interventions is the medical industry. AIoT can be game-changer in easing monitoring of patient’s vitals in the following ways:
I. AIoT devices can monitor blood coagulation and glucose levels, critical to avoid fatal conditions such as stroke. These devices have initiated self-testing in patients, while allowing them to record test findings and communicate their results with health practitioners for quick intervention.
II. AIoT’s ability to derive and analyze data such as weight, pulse rate, and discomfort on the go has allowed patients to stay on top of their health conditions.
Apart from this, the continuous tracking of SPO2 levels and emotions through a connected devices such as smart watches can help manage health conditions such as asthma and depression. - Know your Customer Better – Retail Shopping
In an intelligent retail environment, an AIoT device in the form of a camera system utilizing Computer Vision can identify customers through facial recognition as they enter the store.
This technology gathers detailed information on customers, such as their gender, favourite products, and movement patterns within the store.
By analyzing this data, the system can predict consumer behaviour with high accuracy and use these insights to guide decisions on various aspects of store operations, including marketing efforts, product arrangement, and other strategic actions.
- AI-powered Traffic Control for Safer Roads
Real-time traffic surveillance using drones allows for dynamic adjustments to traffic flow, helping to reduce congestion. When drones are used to oversee extensive areas, they can relay traffic data to an AI system, which then analyzes the information and makes decisions to optimize traffic conditions.
These adjustments, like changing speed limits and traffic light timings, are made autonomously, without human intervention.
- Artificial Intelligence of Things for Smart Homes, Offices
Edge devices, such as IoT sensors, can enhance the intelligence of your home and office environments. These devices can identify occupants and automatically adjust temperature and lighting to match individual preferences. Additionally, smart buildings benefit from advanced access control systems.
A great example of AIoT devices in action is the use of connected cameras paired with AI to analyze real-time images against a database to decide who is allowed entry.
Similarly, in office settings, employees wouldn't need to manually clock in or mark attendance for mandatory meetings, as the AIoT system would handle these tasks seamlessly.
Challenges Involved in Implementing AIoT Solutions
The complexity of integrating IoT infrastructure with AI algorithms poses a significant hurdle. Scaling up such systems introduces technological intricacies, demanding careful management:
- With the software side of things developing quickly, AIoT integrators face the challenge of developing hardware modules in time that can maximize performance. In general terms, software for an AIoT product is developed much more quickly when compared to the hardware.
- Data confidentiality and protection are critical concerns, given the vast amount of data shared through IoT devices, edge devices, and cloud platforms. Safeguarding this diverse dataset against breaches and ensuring secure transmission becomes a paramount challenge.
- AI algorithms within IoT devices may perpetuate biases present in training data, leading to discriminatory outcomes in sectors like healthcare and finance. Addressing these biases demands careful examination of the algorithms and the data used to train them. Strategically placing strict protocols that safeguard the rules of creating an AI system can help prevent breach of ethics.
- Regulatory and legal concerns further complicate the integration, with ambiguous data ownership and consent issues. Resolving conflicts among stakeholders becomes imperative to navigate potential legal disputes.
- Budget and Resource availability is often a challenge in developing AIoT powered solutions. As the application of AIoT devices are in the early stages of maturity, there is a need for constant investments in this domain.
Hence, AIoT developers face a challenge of striking a balance between associated hardware costs, performance and appropriate AI algorithm and application.
What’s Next for AIoT Devices?
AIoT devices are here to stay. With intelligent data-driven technologies set to play a pivotal role in meeting the needs of the consumer-centric market, AIoT devices are here to stay.
However, the enhanced connectivity and intelligence also bring forth concerns, notably in terms of security and ethics.
The substantial data involved coupled with the intelligence of the algorithms poses a risk of data breaches and security vulnerabilities. This, in turn, may expose devices to unauthorized access. Additionally, ensuring fairness and transparency in AI algorithms is paramount.
As we embrace the advancements made by artificial intelligence of things, we at Embitel respect the judicious approach with robust safeguards and ethical considerations that are essential to navigating the evolving AIoT landscape responsibly.
Contact our experts here to develop ethical AIoT Devices!
