By Richard Pain
Oct. 25, 2017
This article is sponsored by
The emergence and adoption of new Internet of Things (IoT) technologies is increasing at an exponential rate. According to Gartner, 8.4 billion connected "things" will be in use in 2017, up 31 per cent from 2016, reaching 20.4 billion by 2020.
Whilst IoT has already been talked about for years, promising seemingly limitless opportunities to monitor, control and automate all manner of processes, most organisations are still far from harnessing its full potential.
This is now about to change according to Satyajit Dwivedi, Principal Industry Lead at SAS, who sees us entering a new phase of technology maturity and adoption.
Image: Satyajit Dwivedi, Principal Industry Lead, SAS
"There is now a greater focus on delivering real return on investment by generating intelligence out of data, images and videos using various technologies such as machine learning (ML) and artificial intelligence (AI). These technologies combined will allow organisations to achieve new capabilities and efficiencies they were unable to before, which I call IoT 2.0."
In line with this, SAS now defines its purpose as providing "Intelligence of Things", deriving insights from IoT data, to solve the world's greatest challenges. To learn more about what this means for business and government, I asked Dwivedi to share some of the most exciting use cases:
Telecommunications: When the first wave of IoT came along, telcos were the first ones to adopt it, but they primarily kept their focus on their core business of providing network connectivity and data. Now there is a realisation that in order to differentiate themselves from the competition, telcos need to create value added services by leveraging IoT, analytics and partnering. Consider for example, a connected vehicle requires a telco operator as its backbone. By collaborating with automotive players, telcos can offer multiple new services. We are also starting to see collaborations between telcos and insurance companies, creating new opportunities for insurance premiums based on use or activity.
Government: Public safety was a main focus of Smart City 1.0, deploying CCTV based surveillance and deploying lots of monitoring sensors, but now there is a second stage which involves integrating other data sources. Examples of this include the vehicle registration system, driver's license system and criminal records database. Combing these data sources with CCTV, intelligent lighting systems and geo-location data will make, the ability to detect, predict and ensuring public safety, very powerful. Government also runs the transportation sector both Rail and Road. For big cities having a highly efficient transportation is a lifeline. Under the Smart City 2.0 programs driven by IoT and Big Data analytics, connected transport is a thrust area of all Central State and Local Government (city administration). Advanced Analytics such as ML technologies are being applied to sensor data from fleets (locomotives, coaches) to provide real time decision support for predictive maintenance and operations excellence. This can be applied to all transportation network of Police, Medical Services, Fire Brigade, Waste Management. A common architecture for the entire city transportation network would be the end state and leading cities are already moving towards it.