In a connected world, one would think that the Pandemic would have seized power from the development of IoT as we were adamant about social distancing. However, IoT actually saw a boom in different fields relating to the pandemic, such as enabling remote work and safe methods in healthcare. Therefore, we thought that it would be interesting to take a look at where IoT is headed next after serving us well for the last 2 years.
Our technical landscape is dominated by AI, data and connectivity – and IoT is the building block for all these different, yet, interconnected technologies. The landscape is rapidly evolving, so much so, that it’s important for businesses to keep track of these changes and think creatively around them in order to garner success. Because coming up with ideas of how to reap the benefits of these different technologies alone or together, isn’t possible without keeping a close eye on upcoming trends. That said, in this post we are taking a look at a couple of trends to keep track of in 2022.
1. Alternative connectivity technologies
One of the many hurdles IoT has encountered is that of connectivity. Iot requires extensive bandwidth to be able to support various devices, sensors, and smart homes. Connectivity technologies to support and improve IoT are steadily evolving, and we can expect to see more alternative connectivity methods take place in the future.
- 5G – Before setting up a network of different IoT devices, it’s paramount to set up proper connectivity infrastructure. 5G network is a great option here as it supports much faster data processing than its predecessor 4G.
- Wifi 6 – Operating on 6 GHz bandwidth, this can help unlock potential for indoor IoT technology such as smart homes that need a reliable and fast connection for devices to communicate seamlessly.
- LPWAN – A cost as well as energy efficient alternative that is great for connecting devices with low bandwidth and low bit rate users over larger areas.
- Satellite – This is a great alternative to cover very large areas and geographically separated networks.
2. Edge Computing
Edge computing is a technology that aims to bring computation and data storage closer to the user by sending data to an edge device instead of the traditional route to a central server. IoT depends on reduced lantency and edge computing has huge potential here in reducing latency as well as increasing the security of data processing.
3. Iot wearables
Iot wearable devices are becoming all the more mainstream and I suspect a reader or two is sitting with their apple earbuds or apple watch on as we speak. However, the prediction that IOT wearables would soon replace phones and computers is nowhere to be seen in the foreseeable future. This is due to the limited usability in tasks that would usually require for example a computer. They do however offer usability in healthcare as they may be used for automatic alerts in cases of emergency. Also, there is much potential to be unlocked in AR and VR headsets in healthcare. With improvement, we may soon see surgeons solely performing surgeries using VR headsets. Combining this with for example an apple watch, surgeons could also potentially be provided with real time data.
4. Smart homes & cities
- Smart homes are also becoming more mainstream, and I booked a hotel for the first time this summer that was labeled smart. There is still much more room for automation improvement in the coming years. This could for example mean automating tasks like energy usage, lights, temperature and security. These could either be configured manually or turned on automatically by AI. Given the current situation with rising electricity costs – this could be a great solution to control energy usage at home.
- Smart cities is also set to expand in the coming years as there are many applications to todays society ranging from traffic monitoring, smart street lights to more advanced technology like self-driving public transportation.
5. AIoT
IoT technology and AI are heavily intertwined and there are many interesting use cases in the future. With data driven AI, IoT can be a great asset to AI learning by providing real time data. This data can be used to calculate new machine learning techniques and thus improve performance in many cases. While AI is built to monitor patterns, it can’t do so without high quality data. Given that IoT technology is able to provide this type of data, we an expect many interesting use cases in the future.
