2021 is a challenging year for many industries, but the Internet of Things technology has already played an active role in shaping business and consumer trends. From healthcare and retail to automotive and manufacturing, every industry is getting smarter with technologies like IoT. Failing to stay competitive in this space can result in significant losses.
Data collection with IoT devices has reached an unprecedented scale. Data science and Machine Learning unite to produce an array of opportunities for advanced IoT data analytics solutions. Big Data, AI, and IoT come together to collect already pre-structured data, set data pipelines, and build AI components on top of it all. The importance of this approach will remain relevant for years to come.A report from Research and Markets forecasts that AI and IoT will surpass a value of $26 billion by 2025. They also demonstrate that AI improves IoT data’s efficiency by 25%, improving analytics by 42% for the industry. AI plays a role in both IoT center and edge networks. At the center of the system, AI can perform predictive analytics and alert users of anomalies. Getting insights out of the data from IoT solutions is only the first step. AI’s role in IoT systems has much more potential that can be unlocked.
The cloud and local servers are not the only places where computation can be performed. Using remote servers can result in transfer delays. Because of this, cloud computing is simply not an option for implementations that require real-time computations like self-driving cars.
Edge IoT is utilized in traffic cameras for pedestrian detection, adaptive traffic lights, vehicle prioritization, parking detection, and electronic tolling. Microsoft, IBM, and Amazon have also invested heavily in edge computing technologies. And there has been an increasing demand for smart IoT devices, fast data processing, and data security.Amazon’s second-generation “AWS IoT Greengrass” service has entered use, empowering developers to use Lambda functions with edge devices. It allows developers to perform machine learning and compute tasks within IoT devices. More IoT solutions will include onboard AI and push some computing from the cloud toward end-point devices. The three main reasons for this are reaction time, cost per cloud processing, and data privacy and security. Find out more about IoT development.
Netflix and Spotify also understand our viewing and listening habits exceptionally well. However, even these predictors can make mistakes, resulting in irrelevant content being placed on our screens. This technology is ever-improving. Smart home technologies are a sector where personalization is essential. Technology that manages daily home activities requires a highly personal experience to achieve the best customer satisfaction.
AI growth and edge computing are poised to help this area of the market grow tremendously. To bring Smart Home technologies to the next level, AI’s precision and decision-making need to improve. AI must make choices based on owner habits. Because of the personalization required, generalized data is not enough to train the neural network. Personal data is needed. However, this data can often be very private, and users are unwilling to share it. The key to this problem may be edge computing, where the data is kept and processed locally on the users’ devices. It may be critical to improving customer perceptions of smart home technologies. A 2019 Statista report indicates that 46% of smart home users describe their experience as intrusive, while 36% describe their experience as fearful. Edge computing can help make customers feel safer while using smart home IoT technology.
Smart cities are second in line for 5G implementation after industrial IoT. This will allow for a stable network with enough bandwidth capacity. The connectivity diversity for smart city solutions is among the top issues for technology. Data is the most intriguing element. Smart city data is mostly public and can be collected much more quickly than data required for smart home systems. Therefore, an opportunity is there for onboard AI in combination with IoT to prove successful. In the early stages, AI will create suggestions and insights out of the data. As the technology improves, smarter city decision-making will be delegated to AI. This has beneficial implications for traffic management, water, flood monitoring, and video surveillance.
One sector that is seeing a great deal of progress is IoT applications in the automotive industries. Firmware over the air (FOTA) allows for wireless firmware updates on embedded systems. This provides a platform to allow for bug fixes easily and replace older versions of firmware. Road condition analysis is another application where IoT can shine in the automotive industry, especially in autonomous vehicles.
Telematics is also a serious topic in automotive IoT. Telematics transforms your vehicle into an IoT device. Emergency calls, GPS, and Bluetooth, are just some of the connections made possible through telematics. This is the first step in the process of achieving V2X (vehicle-to-everything) technology. This can enable features like over-the-air updates. Vehicle-to-vehicle communication is important for the future of autonomous vehicles as well. If driverless cars can communicate with one another, they can better maintain a safe distance and share other important data.
Manufacturers are looking to remain competitive and explore industrial internet of things (IIoT) applications. Embedded edge networks are becoming utilized due to their ability to maintain greater efficiency while being powered by artificial intelligence. Predictive maintenance is also another major benefit made possible with machine learning and IoT technology. With existing data, AI algorithms can identify when to implement preventative measures before a machine requires repairs. Computer vision for visual inspection is also a critical technology that can reduce costs and improve efficiency. ML algorithms are more efficient at visual inspection when given the right training data and hardware than humans. Companies like BMW are already using this technology to ensure quality control for their automotive parts.
Advancements in the Internet of Things technology are propelling us farther than ever thought possible. It is important to invest in these growing technologies in order to remain competitive in the long term. Without artificial intelligence, machine learning, embedded systems, and comprehensive IoT frameworks, businesses won’t keep up with an increasingly interconnected world. By taking advantage of these powerful technologies, companies can reap the benefits of smart features, functionality, and productivity from connected IoT ecosystems.
