Business and technology seem to be in sync. They do. The advancement of these technologies can also profoundly affect how businesses operate. The most noticeable change observed is digital transformation. AI and IoT product development have proven to be essential in this regard. They have the potential to revolutionize companies in many different ways. For instance, you may require insights into your company’s operations. To achieve this, IoT devices can collect huge amounts of data. At the same time, AI algorithms can analyze and interpret the data. What you learn from an analysis is then utilized to improve processes and customer service.
This eclectic collection of IoT and AI use cases could be applied to various fields. Whether you run an industrial company or manage supply chains and supply chain management, these two tools will prove useful. However, before you search for an IoT software development firm for your project, let’s look at the other advantages IoT and AI can bring to business.
What is IoT?
The term AIoT, also known as Artificial Intelligence of Things, describes the combination of AI technology and IoT infrastructure. Incorporating AI into the integration increases IoT’s capabilities. IoT networks allow for enhanced analytics, autonomous decision-making, and faster data processing. They allow Internet of Things devices to work independently, make smart decisions, and analyze data in real-time.
The latest innovations in smart homes, industrial IoT, healthcare, and urban infrastructure stem from the merging of these systems, which makes them more intelligent as time passes. With AIoT, IoT solutions become more efficient and effective as IoT devices can now think, learn, and operate independently.
Working Mechanism of Artificial Intelligence of Things
The most important part of any guide on the artificial intelligence of objects is the outline of the working mechanisms. Most AI of Things examples demonstrate that AIoT devices function by integrating AI into infrastructure components. Infrastructure components, such as chipsets and software, are linked to one another through IoT networks.
Additionally, AIoT systems utilize APIs to guarantee seamless functioning and communication between platform components, hardware, and software. When they become functional, IoT gadgets can collect information, and AI will analyze the data to gain important insights. The operations of AIoT are also a reason to consider the two most common methods for creating AIoT systems. Here’s a brief overview of two popular applications for IoT systems.
Edge-Based AIoT
Another well-known method of implementing AIoT technology that has attracted the interest of technologists is edge-based AIoT. The search for responses for “What is the artificial intelligence of things?” could also direct you towards edge-based AIoT because of its unique capabilities. These types of AIoT implementations allow the processing of IoT data at the edges or near IoT devices. This is why edge-based AIoT systems use less bandwidth for data transfer and can avoid delays in data analysis.
Edge-based AIoT systems have three distinct layers: the collection terminal layer, the edge layer, the connectivity layer. Each layer of edge-based AIoT applications has its distinct function. For instance, the layer for collection consists of the entire hardware device, including sensors, car tags, embedded devices, mobile devices, and tags. The hardware devices within this section are linked to gateways via current power lines.
In addition, the connectivity layer is comprised of field gateways, which are connected to the collecting terminal layer over the power lines that are in place. The edge layer of edge-based AIoT systems provides the capabilities for efficient data storage and processing and the creation of new insights.
Cloud-Based AIoT
The primary AIoT instance you should be aware of to comprehend the functions of artificial intelligence includes cloud-based AIoT. Cloud-based IoT involves the processing and collecting of information from IoT devices via cloud-based computing systems. IoT devices need to be connected IoT devices to cloud computing because it is the perfect location for storing and processing information for various apps.
The four major layers of cloud-based AIoT systems are the devices layer, the connectivity layer, the user communications layer, and the cloud layer. There are various kinds of hardware through the layer of devices, including beacons and cars, sensors embedded devices, and tags. AIoT’s infrastructure to support artificial intelligence is cloud-connected. AIoT includes a connectivity layer that includes fields and cloud gateways. It is crucial in connecting cloud storage controllers, intelligent devices, and sensors.
The cloud layer is a key element in enabling cloud-based AIoT since it provides data processing via components like AI engine and data visualization API access to data storage and analytics. However, the layer of communication for users typically includes mobile apps and web portals.
Potential Use Cases of AIoT
The Artificial Intelligence of Things (IoT) transforms how devices work and interact by using AI intelligence and IoT connectivity. This combination of power and intelligence has practical applications in a variety of sectors, enhancing safety, efficiency, and convenience.
Here are some of the major uses of AIoT explained in plain language:
Smart Homes
Our homes are equipped with various devices that collect data and allow us to use AIoT technologies to improve user experience. Through AIoT,
- Altering the room’s temperature and electricity use can reduce energy costs and consumption.
- Voice assistants can leverage live-time Natural Language Processing (NLP) capabilities to enhance their language understanding and offer personalized responses.
Autonomous Things (AuT)
Autonomous Things (AuT) are an aspect of AIoT that performs certain tasks without the intervention of humans. The most popular AuT devices are robotic drones, vehicles, and various other autonomous software.
Automated Delivery Robots are used primarily in assembly, manufacturing, and storage. They are already becoming popular in some cities and universities for package delivery. Since the beginning of the COVID-19 epidemic, the demand for them has risen dramatically because of the lack of human interaction.
Autonomous vehicles are among the most viewed AuT devices, and 8 million AuT devices are believed to be sold by 2025. For autonomous vehicles to be successful, the AI-based computing platform, computer vision/sensor Fusion, and high-definition maps must be present.
Manufacturing and Industrial Sector
Also known as Industrial IoT (IIoT), AIoT in manufacturing optimizes manufacturing processes, enhances control of the supply chain, enables the maintenance of predictive accuracy, and improves worker safety. It is most prevalent in smart factories, where machine learning and automation create an effective production space. AIoT can improve the efficiency of industrial machines by giving them live data in real-time.
Smart Cities
According to the United Nations (UN),
- At present, one in eight people live in 33 megacities across the globe
- It is anticipated that there will be 43 megacities that will have over 10 million people living there by 2050.
- About 70 percent of the population of the globe is predicted to be living in urban areas by 2050.
These developments present several challenges in terms of governing cities efficiently and in a sustainable manner. Making the transition to modern AIoT solutions could provide help in many areas:
- Management of resources (e.g., energy distribution)
- Public service management
- Control of traffic
- Waste management
Industrial Internet of Things (IIoT)
The Industrial Internet of Things (IIoT) is a system that connects individuals, products, and processes to create digital transformation for industrial enterprises. Through IIoT, advanced business processes like manufacturing can be transformed by using data. For instance, AIOT systems, including cameras and computer vision models, could be utilized to look for quality problems.
Supply Chain and Logistics
AIoT improves the visibility and efficiency of the supply chain, allowing the ability to track goods in real-time and use predictive analytics to manage inventory. Automated warehouse systems and predictive analytics predict disruptions to supply chains and shifts in demand in a breeze. Aiding in ensuring storage efficiency and improved logistic processes. In the logistics industry, industrial robots powered by AIoT are commonly utilized to manage inventory, help anticipate demand, and match supply to demand.
Wearable Devices
Wearable devices are devices that sit close to users’ skin. They can detect, analyze, and transmit data about the body’s signals and provide immediate feedback to the user wearing them. Wearables such as smartwatches and activity trackers are two examples of IoT, which are becoming more popular due to their ease of communication and health monitoring capabilities.
Wearable cardiac devices for detection: The COVID-19 epidemic made access to one-on-one health counseling more difficult. Smart wearable devices could help alleviate this issue by allowing remote screening and diagnosis. However, their use faces some challenges regarding accuracy, clinical reliability, the absence of standard regulations, and privacy concerns for patients.
AIoT used in sports science may help improve the performance of teams and athletes, as well as the participation of fans and sports. Data can be gathered by analyzing athletes’ performances or through sensors. While a portion of this data is immediately acted on by machine learning programs, it is also possible that data can be used for data visualization.
Healthcare
AIoT devices, such as wearables, can monitor vital indicators and provide instantaneous data to healthcare providers. Wearables and other smart devices can monitor health indicators and inform healthcare professionals and patients. Predictive analytics to improve patient health is revolutionizing the healthcare industry through the Internet of Things. From monitoring patient flow to monitoring the inventory of medical equipment and medications, AIoT optimizes hospital operations.
Agriculture
AIoT for agriculture (called Smart Agriculture) includes precision farming methods in which sensors offer data to optimize irrigation, planting, and harvesting. This leads to higher yields for crops and less waste. Check out this article for more information about the significance that smart farming plays in the smart village.
Energy Sector
AIoT is utilized for smart grid management, optimizing energy distribution and production, and improving renewable energy management using predictive analytics. It helps to balance energy demand and supply, particularly during peak usage times.
Benefits of AIoT
A combination of Artificial Intelligence (AI) and the Internet of Things (IoT), referred to as AIoT, has many advantages that are changing industries and daily life. Combining intelligent technology, AIoT creates efficient systems that can solve real-world issues. Here are the main advantages:
Better Decision-Making
IoT and AI allow devices to process huge amounts of data and provide important insight. AI analyses data in real-time, aiding both individuals and businesses in making better choices. For example, in the field of agriculture, AI-enhanced IoT solutions rely on soil and weather information to advise farmers on the best time to plant or water crops, increasing yield and managing resources.
Enhanced Security and Safety
Smart systems that incorporate AI and IoT increase security by identifying and reacting to threats more efficiently. AI-enhanced IoT surveillance applications use AI to analyze video streams from IoT cameras and detect unusual activities in real-time. In cybersecurity, AI-enhanced IoT solutions safeguard networks by stopping malicious activity before causing harm.
Environmental Benefits
IoT and AI together aid in the sustainability effort. Intelligent technology in urban areas helps reduce pollution by optimizing traffic flow through AI-powered IoT sensors. In agriculture, AIoT minimizes water usage by monitoring and directing irrigation systems efficiently.
Improved User Safety
AIoT improves safety in sectors such as mining and construction. Smart systems that incorporate AI and IoT can monitor dangerous surroundings, alerting workers of potential dangers. For example, wearable IoT sensors can detect dangerous gases, while AI analyzes data to anticipate accidents.
Improved Efficiency and Automation
AIoT applications and devices can automate tasks that typically require human intervention. With IoT sensors collecting data and AI processing it, processes get faster and more precise. For instance, in manufacturing, smart systems utilizing AI and IoT can predict when machines will require maintenance, reducing downtime and improving productivity. This AI IoT synergy ensures tasks can be completed quickly and efficiently without delays.
Personalized Experiences
AIoT devices can learn users’ preferences as time passes, providing personalized experiences. Smart homes are smart. Thermostats powered by AI alter temperatures based on personal preferences and voice assistants such as Alexa and Google Assistant learn user commands to give better answers. This integration of smart technology enhances comfort and efficiency.
Cost Savings
AIoT can reduce operational costs by enhancing resource utilization. For instance, AIoT gadgets optimize electricity usage in managing energy by automatically shutting off appliances and lights when they are not being used. Businesses can use AI IoT synergy to streamline supply chains while reducing waste and transport costs.
Better Healthcare Solutions
AIoT revolutionizes healthcare, enabling remote monitoring of patients and quicker diagnosis. AI IoT synergy allows wearable devices to monitor vital indicators, communicate information to physicians in real-time, and improve the quality of patient care. For instance, AIoT devices like smartwatches can detect irregular heartbeats and notify people to get medical attention.
Easier Risk Management
Companies across all industries have to deal with risk management issues. Intelligent distributed systems based on AIoT can anticipate future risks and suggest proactive measures to mitigate them in the near future. The most effective AIoT illustration of risk management is people’s analysis of crowds in public spaces.
AIoT systems also provide an extensive evaluation of water levels and worker safety analysis based on the desired objectives. Businesses can utilize AIoT to stay one step ahead of planning and managing risks that could arise in the future. For instance, insurance firms are utilizing AIoT applications to collect information from machines and other factors to ensure efficient risk management in insurance.
How to Implement Artificial Intelligence of Things?
Prior to investing in any IoT development solutions initiative, it is important to plan your business to manage this new innovation with the highest efficiency and minimal disruptions. In this article, we will provide an easy-to-follow process for implementing IoT coupled with AI.
Defining Business Objectives and Use Cases
It’s sensible to start by determining the exact goals and challenges the AIoT solution should target within your company. It is important to see tangible results and determine the most significant applications that align with your business’s high-level goals. Creating a clearly defined roadmap will ensure the AIoT implementation provides tangible business benefits and the greatest ROI.
Building a Robust Data Infrastructure
Then, create a secure and scalable ecosystem that includes the following essential elements:
- IoT devices for real-time data collection
- Data warehouses that provide central storage of large amounts of data
- Advanced computing platforms that effectively process and analyze data.
Choosing the Right AI and IoT Technologies
With the infrastructure and goals set, it’s time to choose the best tools and platforms that meet both the business and technical needs. It is important to consider the latest methods and technologies to ensure the future of your solution. When choosing a technology stack, pay close attention to connectivity, compatibility with devices, and the ability to integrate with other software.
Addressing Security and Compliance
When dealing with data, there shouldn’t be too much focus on data security and ensuring compliance. The standard practice is to build a solid security system that is continuously updated for IoT devices and in line with the most current rules for data protection.
Fostering Cross-Functional Collaboration
Another important aspect of the successful implementation of an IoT initiative is the alignment of every department connected to this new technology. The departments could include a variety of IT operational, operational, and business units, such as top management. Common practices for enabling cross-departmental collaboration are:
- Unified communication channels
- Regular session of planning
- The creation of cross-functional teams
Beginning Small by Launching Pilot Projects
Then, experiment with new technologies, models, workflows, and strategies on a smaller scale. By using pilot projects to test new ideas, you’ll be able to gather crucial data, enhance strategies, and overcome the many issues of AIoT. Once you have made the necessary adjustments, slowly increase your effort to ensure seamless integration without major interruptions.
What Future Holds for AIoT?
The future for artificial intelligence and bespoke IoT product development looks bright. Given technological advancements and the growth of the power of computers, AI and IoT will become even more effective. Let’s examine the most promising emerging trends.
Edge AI
A promising future emerging trend, Edge AI, is all about implementing AI algorithms directly on IoT devices instead of using cloud servers centrally. The implementation of Edge Computing reduces latency by processing data locally and increasing the security of sensitive data. Furthermore, Edge AI improves the overall performance of IoT systems, ensuring that crucial functions can continue running smoothly even when internet connectivity is lost.
Federated Learning for IoT Devices
This is a new trend to watch. It is a decentralized Machine Learning approach in which multiple IoT devices collaborate to create a shared model while conserving the data stored on the devices instead of sending it to central servers.
This technique protects users’ privacy by storing data on local devices while complying with data protection regulations. It also reduces bandwidth consumption by sharing model updates instead of raw data.
Integration with 5G
According to GSMA Intelligence, the number of 5G connections had surpassed 1.5 billion by the end of 2023. IoT networks are gaining enormously from this phenomenal growth, with access to higher speeds for data transmission, lower latency, and more stable connections.
The areas which will benefit from improvements in 5G are:
- Thanks to improved bandwidth and decreased latency, more accurate and faster decisions are made, allowing IoT devices to analyze and process data at a rapid pace.
- There will be more comprehensive analysis and data collection because 5 G is capable of accommodating a wide range of connected gadgets.
- Significantly improved monitor and remote control of IoT devices.
Faster and more efficient data exchange unlocks the benefits of another advancing technology, boosting the efficiency of big data for businesses and organizations.
Artificial Intelligence can indeed improve 5G connectivity with the aid of AI-optimized network cutting. AI is a tool for managing and optimizing slices of networks in 5G to ensure reliable and efficient communication across a range of IoT applications.
Autonomous IoT Systems
Self-managing IoT systems that rely on AI to automatically analyze, monitor, and improve their operations with minimum human involvement are expected to lead the way in automation in industrial processes. As long as ScienceDirect can be trusted, autonomous IoT systems are currently split between different industries according to the following pattern:
- 30% Transportation
- 21% Military and Security
- 19% Aerospace
- 16% of Healthcare
- 14% of home robotics.
Implementing AI to create autonomous IoT devices allows them to make smart decisions based on real-time data, improving efficiency and efficacy. Most importantly, AI-powered IoT devices can scale effectively by adapting to changes in requirements and conditions without substantial human oversight.
Major Challenges of Adopting AIoT
Although combining AI and IoT offers seemingly endless potential applications in a variety of sectors, certain major issues must be tackled.
Scalability Issues
As the number of sensors and devices increases, scaling becomes a significant issue for IoT networks powered by AI algorithms. Traditional centralized cloud computing methods may not be able to handle this challenge, so it is logical to use Edge Analytics to process data nearer to the origin. This reduces the time to process and the burden on centrally located servers.
Our experts tackle this issue by using Edge Analytics to process data as close to the source as possible, which reduces latency and the burden on central servers.
Resource Constraints
Certain limitations exist with IoT devices, such as limited processing power, memory, and battery lifespan. These limitations make it difficult to run complicated AI techniques directly onto devices and fail to maximize the full capabilities of Artificial Intelligence of Things.
We can overcome resource limitations and ensure effective IoT operations using less powerful devices. We have experience creating light AI models and algorithms specifically for certain IoT devices. Our team employs a range of methods, including quantization and model compression, to decrease the computational demands and size of AI models.
Data Integration Complexity
Let’s first discuss the difficulty of data integration, which is a common occurrence within the IoT Artificial Intelligence realm. Because IoT devices create huge quantities of data from diverse sources and in various formats, figuring out how to make sense of it is a constant issue. Additionally, Artificial Intelligence and Machine Learning solutions need this data to be properly compiled in order to make it suitable for AIoT applications.
Security Concerns
Certain IoT devices might have weak security features, whereas AI algorithms could be susceptible to ever-changing attacks from cybercriminals. In the end, collecting data for AIoT applications could put highly sensitive data at risk. We are always concerned about data security by implementing strong protection measures and encryption techniques to secure the data in transit and at rest. Our security experts conduct periodic security audits to ensure current security updates to IoT gadgets and AI-based systems. They also ensure the security of stored and transmitted data in the greatest extent possible.
Interoperability
There is also the issue of the wide range of available IoT platforms, devices, and communication protocols. To improve interoperability, companies must establish standards and protocols to facilitate information processing and transmission. Middleware software could help connect different systems and is a feasible solution to this issue.
To improve interoperability, we use protocols and standards that allow data processing and transmission. We can bridge the gap between systems by implementing middleware software.
The Key Takeaway
The summary of artificial intelligence in the form of AIoT provides the rationale behind why AIoT should be considered one of the most prominent trends in the world of technology. Artificial Intelligence and the Internet of Things are the two technologies with the most power, and they have had different impacts on various industries. However, it is important to think about the possibility of mixing AI with IoT to develop AIoT.
With the help of AI, IoT consulting can process and analyze data collected from IoT devices. Incredibly, the success of various instances of AIoT in real-world settings shows an exciting outlook for AIoT. Learn more about AIoT by thoroughly understanding AI and IoT basics.
FAQs
Is there an example of AIoT?
AIoT technology is used in a wide range of industries, one instance being smart manufacturing. AIoT is employed in manufacturing to analyze production data and recommend ways to increase efficiency and productivity. By analyzing data from real-time sensors, prescriptive maintenance, and process optimization, AIoT can help manufacturers reduce time and cost.
What’s the difference between AIoT and IoT?
The major difference between AIoT and IoT is that IoT concentrates on connecting devices with the Internet to collect and share data, whereas AIoT uses artificial intelligence to analyze and make decisions based on information gathered. IoT basically collects data, and AIoT utilizes and processes the data in an intelligent way and collects it. Together, IoT and AI form AIoT.
How is IIoT different from AIoT?
The Industrial Internet of Things (IIoT) primarily focuses on industrial applications that use IoT to enhance industrial and manufacturing processes. Artificial Intelligence of Things (IoT) combines IoT and AI’s data analysis and decision-making power in IoT devices. AIoT could be utilized not just in industrial settings but also in other areas.
Is artificial intelligence a part of the Internet of Things?
IoT is not part of AI as they are distinct technologies; however, they can be combined to form AIoT. In the end, AI enhances IoT systems by providing data analysis and decision-making capabilities.
