This offers a segment of literature on how IoT devices aided by AI, ML and Analytics are revolutionizing several industry-specific processes. Their applications are wide and mostly beyond the scope of one article.
The pandemic spanning 2020-21 has deterred the growth of all industries. Inspite of that AI and IoT features among technologies having been put to test during these trying times. Physical access having been restricted, people had to resort to technology including automation, AI, and IoT.
However, IoT growth is going through a dip since 2020. As per a forecast in 2019 the spending on IoT was to touch 14.9% in 2020 but it could only achieve 8.2%. International Data Corporation foresees a hike in the growth rate of 11.2% during 2021-24
Lately, a shortage in semiconductors and other IoT components has led to a lapse in growth in 2021 causing closure in many plants across the world. Although it is expected to not last for long, the shortage has affected the projects meanwhile.
Innovations on IoT
Looking at the IoT based projects that have kept the wheels of commerce turning:
AI and IoT
As 2021 unfolds, the number of IoT-connected devices rises; as of now reaching a neat 46 billion. Most of these devices come with a single processor and small memory space.
Analytics-Based on IoT Data
IoT devices generate data at an unprecedented rate. Data science and Machine Learning combine to produce numerous opportunities for advanced IoT data analytics solutions.
Big Data, AI, and IoT are combined to capture the pre-structured data, set data pipelines, and build AI components based on it. The result of this effort will remain relevant for years to come.
A report from surveys reveals that AI and IoT will surpass a value of $26 billion by 2025. The reports emphasize that AI improves IoT data efficiency by 25%, and analytics by 42% in an application. Placed at the center of the system, AI can perform predictive analytics to alert users of anomalies.
AI Managing IoT Devices in Decision Making
A factory may utilize IoT-connected assembly lines to reduce manufacturing defects in the fabrication process by enforcing AI-driven visual inspection for quality control.
Face and voice recognition make for other essential components used for biometric verification. AI-driven facial recognition is useful for varied purposes.
Edge Computing With IoT Devices
Edge IoT is used in traffic cameras to detect the presence of pedestrians, adaptive traffic lights, vehicle prioritization, parking detection, and electronic tolling. Industry giants like Microsoft, IBM, and Amazon have invested heavily in edge computing technologies.
The latest entrant to the scene is Amazon’s second-generation AWS IoT Greengrass service. This empowers developers to use Lambda functions with edge devices, allowing developers to perform machine learning and compute tasks on IoT devices.
Other IoT solutions would include onboard AI to move some computing from the cloud toward end-point devices. The three main reasons for this move are reaction time, cost per cloud processing, and data privacy and security.
Yet another sector that is going through huge progress related to IoT applications is the automotive industry. Firmware over the air (FOTA) allows for wireless firmware updates on embedded systems, thus, providing a platform to allow easy bug fixes and for replacement of older versions of firmware. Road condition analysis is another sphere where IoT can make a difference in the automotive industry, especially dealing in autonomous vehicles.
Telematics is an important domain in automotive IoT. Telematics transforms a vehicle into an IoT device. Emergency calls, GPS, and Bluetooth constitute some of the functions made possible through telematics. This comprises the initial steps in the road to achieving V2X (vehicle-to-everything) technology. This can make over-the-air updates possible.
Vehicle-to-vehicle communication is yet another innovation in view of futuristic autonomous vehicles as well. If it can be made possible for driverless cars to communicate with one another, and share other important data greater safety can be maintained.
In order to remain competitive manufacturers are looking to explore industrial internet of things (IIoT) applications. Embedded edge networks are being sought after due to their ability to maintain greater efficiency when integrated with artificial intelligence. This is one of many industrial IoT use cases.
Predictive maintenance forms a major plus made possible with machine learning and IoT technology. By analyzing existing data, AI algorithms come to know when to implement preventative measures before a machine requires repairs.
Visual inspection is a critical technology inducing cost reduction and greater efficiency. ML algorithms work efficiently when integrated with a visual inspection, given the right training data and hardware. Organizations like BMW are already experimenting with this technology to ensure quality control for their automotive parts.
This offers a segment of literature on how IoT devices aided by AI, ML and Analytics are revolutionizing several industry-specific processes. Their applications are wide and mostly beyond the scope of one article. Here’s a slice-window to what’s going on industry-wide.