2022 is a challenging year for many industries, but IoT technologies have already played an active role in shaping business and consumer trends. Every industry is getting smarter thanks to technologies like IoT, from healthcare and retail to automotive and manufacturing. Failure to remain competitive in this space can result in significant losses.

The global pandemic was a significant obstacle to IoT growth in 2020. Although a November 2019 forecast predicted that IoT spending would grow by 14.9% in 2020, it could only grow by 8.2%. Based on International Data Corporation forecasts, IoT will make a rapid comeback this year, reaching a growth rate of 11.3% between 2020 and 2024.

The recent shortage of semiconductors and other IoT components raises questions about IoT growth in 2021. Manufacturers will need to adapt quickly to maintain momentum to remain competitive. Although this shortage will not last too long, it will affect projects in the short term.

Let's go through the various trends that are most important to IoT in 2022 and beyond.

Trend 1: AIoT

In 2023, an incredible 46 billion devices will be connected to the Internet of Things. Most of these devices have only one processor and a minimal amount of memory. IoT permeates our society as it will not be limited to smart home devices and home automation; it will also expand to other sectors.

AI Analytics from IoT devices

Data collection by IoT devices has reached an unprecedented scale. Data science and machine learning are coming together to create a range of opportunities for advanced IoT data analytics solutions. Big Data, AI and IoT will come together to collect already pre-structured data, set up data pipelines and build AI components. The importance of this approach will remain relevant in the years to come.

A report by Research and Markets predicts that AI and IoT will surpass $26 billion in value by 2025. They also show that AI improves IoT data efficiency by 25% and improves industry analytics by 42%. Artificial intelligence plays a role in IoT center and edge networks. At the system's center, artificial intelligence can perform predictive analytics and alert users to anomalies.

Gaining insights from data from IoT solutions is only the first step. The role of artificial intelligence in IoT systems has much more potential to be unlocked.

IoT device management and decision making

Imagine a factory that uses IoT-connected assembly lines to reduce manufacturing defect rates in the manufacturing process using AI visual inspection for quality control. As an example with a much higher cost of error, consider a self-driving car. Not only does it get passengers safely to their destination, but it uses this transit data to predict traffic patterns accurately. This data could then be used to build more efficient roads and infrastructure in the future.

Face and voice recognition are other essential elements that are used for biometric authentication. AI-driven facial recognition is useful in various fields, such as detecting whether guests are wearing face masks or not.

Artificial intelligence is increasingly empowered in decision-making as smart homes, smart cities, self-driving vehicles and manufacturing tasks use the technology. However, human supervisors and data scientists are needed to maintain the system and solve non-trivial tasks.

Trend 2: Edge Computing with IoT devices

The cloud and local servers aren't the only places where calculations can be done. The use of remote servers may result in transmission delays. For this reason, cloud computing is not an option for implementations that require real-time computation, such as self-driving cars.

Edge IoT is used in traffic cameras for pedestrian detection, adaptive traffic lights, vehicle prioritization, parking detection and electronic tolls. Microsoft, IBM and Amazon have also invested heavily in cutting-edge computing. And the demand for smart IoT devices, fast data processing and data security continues to grow.

The second-generation Amazon AWS IoT Greengrass service went live, enabling developers to use Lambda functions with edge devices. It allows developers to perform machine learning and computational tasks within IoT devices.

More IoT solutions will include integrated artificial intelligence and move some of the computing from the cloud towards end devices. The three main reasons are response time, cloud processing costs, and data privacy and security.

Trend 3: IoT brings personalized experiences

Google statistics about our search trends spoils us. Netflix and Spotify also understand our viewing and listening habits exceptionally well. However, even these predictors can make mistakes, placing irrelevant content on our screens. This technology is constantly being improved.

Smart home technology is an industry where personalization is essential. The technology that drives daily home activities requires a highly personalized experience to achieve the best customer satisfaction. At CES 2021, Samsung introduced the Samsung Bot Handy and the JetBot 90 AI+. Home assistant robots are made possible by AI and data analysis.

The growth of AI and edge computing are poised to help this market area grow tremendously. To advance smart home technologies to the next level, the accuracy and decision-making of artificial intelligence needs to be improved. The AI ​​must make decisions based on the owner's habits. Due to the desired personalization, generalized data is not enough to train a neural network. Personal information is required. However, this data can often be very private and users do not want to share it.

The key to this problem may be edge computing, where data is stored and processed locally on users' devices. It can be crucial to improve customer perception of smart home technologies. A 2019 Statista report states that 46% of smart home users describe their experience as intrusive, while 36% describe their experience as terrible. Edge computing can help customers feel more secure when using smart home IoT technology.