AIoT Applications: Key Technologies & Development Steps

AI and IoT together have encouraged potential business opportunities across various industries. The fusion of data-driven intelligence and interconnected devices is reshaping our daily lifestyle.

From improving agriculture practices to optimizing supply chains,  AIoT applications leave impactful footprints to enhance overall business operations.

The combined usage of both AI and IoT offers enormous opportunities for businesses to create innovative solutions and tap into the profitable market of AIoT.

As per a report from Marketsandmarkets.com, the global market size of the AIoT market is currently valued at $5B in 2023 and is expected to reach $24.9B by 2028 at a CAGR of 37.7% from 2023 to 2028.

In this article, we will learn about AIoT, its development steps, potential benefits, and applications across industries.

What is AIoT?

AIoT stands for Artificial Internet of Things, which is an augmentation of AI and IoT technology. The seamless integration between AI and IoT enhances the growth of various industries, enabling businesses to predict changes and enhance the IoT devices’ abilities. 

When AI is integrated with IoT, devices, and sensors are not only just limited to data collection but can process and analyze them most efficiently. The combination of IoT and AI enables the creation of an ecosystem where intelligent devices can interact with each other and make data-driven decisions and learn from their interactions leading to more autonomous, efficient, and adaptive systems.

The mutual working of AI in IoT together creates a symbiotic relationship where AI enhances the capabilities of IoT devices, making them more responsive and intelligent towards data and mutual interaction between different sensors.

How AIoT works?

AIoT involves the combination of decision-making capabilities and data analysis of AI with IoT’s data collection. Here is how AIoT works.

1. Data Collection

Devices equipped with motion detectors, temperature sensors, and actuators collect data from the physical world. The collected data can fall under the range of human interactions (gestures, voice commands) to environmental conditions (temperature, humidity).

2. Data Transmission

Wi-Fi, cellular, or other communication protocols transmit the collected data over networks that belong to a central location or cloud server. The collected data can be both historical data or real-time data.

3. Edge Computing

There is no requirement to send all the data to centralize cloud server for processing. In edge computing, the data get processed closer to its source or on IoT devices itself which results in latency reduction and bandwidth conservation.

4. Data Processing and Analysis

Numerous algorithms get applied to collected data for detecting anomalies, analyzing patterns, and deriving meaningful insights. Deep learning, in addition to machine learning, can help data owners identify correlations, trends, and hidden relationships within the data.

5. Decision-Making

Artificial intelligence generates actionable insights and decisions based on the analyzed data. The decisions can range from complex predictions (e.g., forecasting equipment maintenance needs) to immediate responses (e.g., adjusting the temperature of a thermostat). 

6. Feedback Loop and Learning

Based on the outcomes of their decisions and interactions, the AI model can train itself. There is the involvement of a feedback loop that enables the AI system to improve and refine its actions and predictions over time continuously. Feedback loops and learning enable AI systems to adapt to new changes and conditions in accordance with user behaviors. 

7. Real-time Responses

Many IoT applications are programmed to perform real-time responses. AI models can trigger immediate actions by IoT devices, for example, an AIoT-enabled security camera, where the system can identify the person at the door and unlock it based on facial recognition.

8. Optimization and Automation

AIoT systems can optimize processes and automate tasks. For instance, in a smart manufacturing setting, AIoT can adjust production lines based on real-time demand data, reducing waste and increasing efficiency.

9. Interconnectivity

There is involvement of multiple devices and systems that work together, communicate and exchange data to create a holistic view of the environment. The interconnections of different IoT devices enable simplifying complex scenarios such as smart cities, smart homes, industrial automation, etc.

Key Technologies Of AIoT

The configuration of an AIoT involves various key technologies, as mentioned below.

1. Artificial Intelligence

AI process data more logically by efficiently utilizing various intelligent algorithms on different IoT sensors and devices. Additionally, the use of AI enables real-time processing of complex applications, further enhancing the capability of devices most accurately and efficiently.

2. Big Data

With the integration of intelligent deep learning models, IoT devices will be able to collect, filter, process, and analyze data for accurate insights across different industries and businesses. The usage of IoT with AI increases the collection of data through various devices and networks, which helps data developers to train IoT devices with more intelligent deep learning models.

3. 5G Networks

The fifth generation of cellular technology enables high data speeds, greater connectivity density, and lower latency which is beneficial for supporting the high demands of AIoT applications.

4. Data Analytics Platform 

The platform provides tools to analyze processes and visualize massive amounts of big data generated by IoT devices and sensors, enabling them to extract actionable insights from the data.

Top Benefits of AIoT

Enhancing the scalability of IoT devices with AI integration involves carefully implementing strategies. Here are the top benefits of combining AI and IoT across businesses and industries.

1. IoT Scalability Enhancements

The integration of AI help IoT devices to expand the horizon of their potential by enabling them to enhance their data collection and interaction process.

2. Real-Time Actions Monitoring 

By utilizing AIoT technology, businesses can monitor their real-time task and process to supervise the devices, software, and equipment continuously. With access to real-time monitoring, businesses can autonomously resolve any issues or disappearances within their operations. 

3. Improving Business Operational Efficiency

Businesses can often utilize AI to deliver experience optimum results by improving various operational tasks and enhancing the utilization of IoT devices and resources. Moreover, AIoT offers insights to recognize patterns and help businesses prevent IoT malfunction.

4. More Business Opportunities

Integrating AI with IoT paves the way for new opportunities with technological advancements. Thus, it provides great opportunities for businesses to develop new products for various requirements and improve the infrastructure overall.

5. Supply Chain Optimization

AIoT can streamline the supply chain operation by route optimization, real-time good tracking, and inventory management, reducing delays and thus improving business efficiency.

6. Real-time Decision-Making

The utilization of AIoT enables businesses to make real-time decisions with the help of a reduction in latency and minimize delays without relying on cloud-based servers.

7. Better Risk Management

Businesses can often face the threat of risks when building new ventures. By leveraging AIoT, businesses can implement more secure protocols by identifying loopholes for preventing and tackling any potential threats to their infrastructure. 

Real-Life Applications of AIoT

The Internet of Things with AI integration has made a fascinating progression for delivering benefits across major industries and businesses. Here are the real-life examples that are implementing the core concepts of AIoT.

1. Tesla’s Autonomous Cars

The manufacturing of autonomous cars has become possible with the help of IoT. For example, the Tesla autonomous cars provide a variety of autopilot features for facilitating an autonomous driving facility to the drivers.

The Tesla car has become a hub of emerging devices, sensors, gadgets, cloud architecture, and different data processing models. Moreover, autonomous cars gather data from various sources to determine the weather and road conditions and encourage their drivers to access the benefits of autonomous driving facilities.

2. ET City Brain

A smart city utilizing various AIoT technology is no longer a dream. Thanks to Alibaba Group, they have taken an innovative step towards building a city that uses AIoT technologies and solutions to monitor road usage, traffic, accidents, etc. 

On top of that, ET City Brain uses neural networks and big data computing to process large sets of data, which helps China to reduce traffic congestion by 15%.

3. Boston Dynamics’ Spot

An innovative robotic solution where the Boston Dynamic company are now adapting to modern requirements and providing smarter robotics solution. The company has introduced various robotic solutions, and one such solution is “Spot.” 

The Spot is an agile, mobile robot that navigates through various terrains to capture different kinds of data, which turned out to be an innovative solution for more efficient and predictable execution of tasks of studying different kinds of terrain.

4. Amazon Go

A revolutionizing concept in the retail industry. The platform enables users to walk into stores, pick an item and leave. Moreover, there are no cashiers present in these stores where IoT devices manage the overall operations, and the users make payments from their e-wallets.

Additionally, users can pick up an item from the stores. It automatically gets added to their digital cart for billing purposes and gets deleted automatically whenever users keep it back to the store.

Steps To Make An AIoT Application 

The development procedure of AIoT Applications can vary based on the technologies involved and specific use cases. Here are the steps to get started with AIoT application development.

1. Understand AI and IoT Concepts

An understanding of both artificial intelligence and the Internet of Things concept is crucial. AI involves algorithms that can learn from data and make smart decisions. Whereas IoT refers to the network of interconnected devices and sensors that exchange and collect data. 

2. Set Up Development Environment

A well-prepared development environment is essential to facilitate AIoT development. Depending on your business requirements, you can consider using programming languages like Python for AI development while Rasberry Pi or Arduino for IoT development.

3. Choose a Suitable AIoT Platform

Various platforms are available that support AI and IoT integration, where you can develop AIOT applications with pre-built AI models, IoT device management, cloud connectivity, and data analytics. Selecting an appropriate AIOT development platform can help you simplify the development process and accelerate your AIOT project.

4. Decide on the Use Case

Determine the specific IoT use case you want to develop for your project. You can decide on the appropriate AIoT use case, such as industrial automation, smart home automation, environmental sensing, healthcare monitoring, etc. 

5. Select the Right Hardware

The selection of appropriate IoT sensors and hardware depends on your IoT use case with your business operation. The hardware selection should align with the data you need to collect in the actions you want to perform using artificial intelligence. IoT sensors include motion sensors, temperature sensors, actuators, cameras, etc.

6. Implement AI Algorithms

Integrate AI algorithms into your IoT devices or cloud platforms to enhance the efficiency and capability of your AIoT system. The AI algorithms include deep learning, machine learning, natural language processing, computer vision, etc. The AI implementation will help IoT devices to extract valuable insight from the data collected by various IoT devices and sensors.

7. Data Collection and Preprocessing

IoT devices require an efficient system that enables extensive data collection and pre-processing. Therefore, it is crucial to ensure that data is accurately collected, cleaned, and prepared for AI model training.

8. Train and Deploy AI Models

The data collected and pre-processed from IoT devices and sensors will help you train your AI models. Consider implementing techniques such as supervised and unsupervised learning depending on your business use case.

9. Monitor and Optimize

It is crucial to monitor IoT applications to ensure their optimal performance continuously. The data collected from IoT devices will help you train your AI models. Based on your business use case, you can consider implementing techniques such as supervised and unsupervised learning and fine-tune them to improve the efficiency and accuracy of your AI models.

10. Ensure Security and Privacy

Security is another important concern for IoT development. The developers can implement robust security measures for data protection and preventing unauthorized access, and safeguarding against cyber threats. Moreover, developers can consider privacy regulations for responsibly handling their user’s data. 

11. Test and Iterate

There are different scenarios to test your IoT application and gather feedback from users and stakeholders to identify areas for improvement. Based on users’ feedback and testing output, you can modify your IoT application to enhance its performance and user experience.

One must ensure that IoT application development is an ongoing process that requires careful implementation of AI and IoT technologies for developing innovative and impactful AIoT applications.

AIoT Development Tech Stack

Building AIoT (Artificial Intelligence of Things) applications involves combining the power of AI and IoT technologies. However, the selection of a specific tech stack may vary depending on your project’s requirements, but here’s a general overview of the technologies you might consider:

1. IoT Hardware and Sensors

• IoT Development Kits (e.g., Raspberry Pi, Arduino, ESP32)
• Sensors (temperature, humidity, motion, light, etc.)
• Actuators (motors, servos, relays)

2. Edge Computing

Implementing AI algorithms on IoT devices for local processing and decision-making.

3. Cloud Services

• Cloud Storage (Amazon S3, Google Cloud Storage)
• Cloud Compute (Amazon EC2, Google Compute Engine)
• AI/ML Services (Amazon SageMaker, Google AI Platform, Azure AI)

4. AI and Machine Learning

• Machine Learning Frameworks (TensorFlow, PyTorch, scikit-learn)
• Deep Learning Libraries (Keras, MXNet)
• Natural Language Processing (NLTK, spaCy)
• Computer Vision Libraries (OpenCV)

5. Security and Identity Management

• Secure communication protocols (TLS/SSL)
• Access control and authentication mechanisms
• Encryption and data privacy solutions

6. Data Processing and Analytics

• Apache Spark
• Apache Kafka for real-time data streaming
• Data warehousing solutions (Amazon Redshift, Google BigQuery)

7. Database and Storage

• Relational Databases (MySQL, PostgreSQL)
• NoSQL Databases (MongoDB, Cassandra)
• Time Series Databases (InfluxDB)

8. User Interfaces and Visualization

• Web Development (HTML, CSS, JavaScript)
• Data Visualization Libraries (D3.js, Plotly)
• Mobile App Development (iOS, Android)

9. Containerization and Orchestration

• Docker for containerization
• Kubernetes for orchestration

10. Version Control and Collaboration

• GitHub 
• GitLab

11. Deployment and DevOps

• CI/CD pipelines for automated deployment
• Infrastructure as Code (IaC) tools like Terraform

12. Monitoring and Logging

• Monitoring tools (Prometheus, Grafana)
• Logging tools (ELK Stack – Elasticsearch, Logstash, Kibana)

13. Networking and Connectivity

• Networking protocols and configurations
• Load balancers, reverse proxies

14. Communication Protocols

• MQTT (Message Queuing Telemetry Transport)
• CoAP (Constrained Application Protocol)
• HTTP/HTTPS
• LoRaWAN (Long Range Wide Area Network)
• Bluetooth, Zigbee, Wi-Fi

15. IoT Platforms

• AWS IoT
• Google Cloud IoT
• Microsoft Azure IoT
• IBM Watson IoT

The complexity, size, scope, budget, and technical proficiency of the project each play a major role in choosing a tech stack. 

Additionally, keeping up with the most recent developments in AI and IoT technology can help you create AIoT applications that are more reliable and creative. Therefore, it’s crucial to do your study and choose the tools that best suit the specifications and objectives of your project.

Conclusion

The AIoT applications can potentially transform and improve businesses and their services. The combined usage of AI-based algorithms and IoT-based devices and sensors is bringing innovative solutions to businesses across various industries.

However, the successful implementation of AIoT requires careful consideration of different practices such as scalability, security, privacy, and other ethical considerations. Therefore, working with a software development firm experienced in AI and IoT technology is the best way to start an AIoT application development. 

You can contact our team if you are looking for a company that can help you identify your business needs to deliver an optimal solution with an AIoT application development. Our team comprises experienced AI and IoT experts having years of work experience across different projects in various industries.

Here is more information about our IoT and AI development solutions.

Contact us today to learn more about how we can help you with AIoT application development.

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FAQ

Q. What are AIoT devices?

A. AIoT devices combine AI components, such as algorithms and programs, with IoT devices and sensors. AIoT system ensures all the hardware, software, and platform components can communicate and operate without effort from the end users.

Q. What are the uses of AIoT?

A. The AIoT can be used to automate IoT operations and analyze the data collected from IoT devices to enhance business operations across various industries such as smart cities, healthcare, energy management, retail and customer experience, environmental monitoring, agriculture, transportation and logistics, home automation, manufacturing, security, and surveillance, etc.

Q. How to Start AIOT Application Development?

A. AIoT application development can be done by following the given development steps: understand AI and IoT concepts, set up a development environment, choose a suitable AIoT platform, decide on the use case, select the  right hardware, implement AI algorithms, data collection and preprocessing, train and deploy AI models, monitor and optimize AIoT system, ensure data security and privacy, test and iterate.