According to IoT Analytics’ latest “State of the IoT & Short-term outlook” update, the number of connected devices that are in use worldwide now exceeds 17 billion, with the number of IoT devices at 7 billion (that number does not include smartphones, tablets, laptops or fixed line phones).
IoT Analytics, a leading provider of market insights & competitive intelligence for the Internet of Things (IoT), M2M, and Industry 4.0, today published a comprehensive Market Report, focusing on sizing the quickly developing market for Connected Streetlights during the period 2018 to 2023. It is estimated that there will be 41 million IoT connected Streetlights installed globally by 2023. The overall streetlights market will surpass US$3.6B in 2023, growing at a compound annual growth rate of 21% from 2018. Deployment of connected streetlights is gaining traction globally as the technology is one of the key pillars for Smart City initiatives. The growth is fueled by government policies and increasing awareness on the benefits of connected Streetlights which go beyond energy savings.
The original conception of the Internet of Things (IoT) was of a network of physical objects or “things” embedded with electronics, software, sensors, and connectivity to enable objects to exchange data with a centralized operator and/or other connected devices.
Smart grids, smart homes and smart cities were all representations of what an IoT could be/do.
The IoT equivalent of the human brain is the cloud-based analysis of the data rising up from sensors to generate insights and decide on actions. Much of the benefit of the Internet of Things lies in our ability to leverage the (useful) data we collect with it. This is the “analytics of things,” and this area has, in many ways, received the least attention of all. This is unfortunate, because it is analytics that can add the most business, lifestyle, and health value to the IoT. It has been said that “data without meaning, without soul, will not move people to change their behaviors over the long term.”1 Value-added analytics are what many early adopters of activity trackers believe has been most missing and disappointing.
Sensor data have some unique attributes, so related analytics are unique as well. The data are typically continuous and fast-flowing, so there must be processes for continuous analysis of the data. Technologies such as “complex event processing” and “event stream processing” bring the data to the analysis capability, where they are processed in real time, and then results are sent back where they are needed. Because there is so much data, a major focus of the analytics of things is anomaly detection. Is something broken in our operational network? Does a bike ride appear to be in the middle of a corn field? Are you about to end the day without reaching 10,000 steps? Analytics can identify situations that require some form of human intervention.
Some other typical analytical applications for the IoT include the following:
The analytics of things is often a precursor to cognitive action—taking action based on the results of analyzed sensor data. Comparative usage statistics, for example, might motivate an energy consumer to cut back on usage, while smart thermostats can monitor and optimize the household environment. Predictive asset maintenance suggests the best time to service machinery, which is usually much more efficient than servicing at predetermined intervals. A municipal government could analyze traffic data sensors in roads and other sources to determine where to add lanes and how to optimize stoplight timing and other drivers of traffic flow.
Machine learning has experienced a boost in popularity among industrial companies thanks to the hype surrounding the Internet of Things (IoT). Many companies are already designating IoT as a strategically significant area, while others have kicked off pilot projects to map the potential of IoT in business operations.
As a result, nearly every IT vendor is suddenly announcing IoT platforms and consulting services.
But achieving financial benefits through IoT is not easy. The lack of concrete objectives is disconcerting. The advancement of digitization and IoT places new prerequisites on both buyers and sellers. Many businesses have failed to clearly determine what areas will change with the implementation of an IoT strategy.
In other words, clearly defined, concrete intermediary objectives are missing. For example, industrial companies produce a massive amount of data on a daily basis. However, by and large, companies fail to systematically collect, store, analyze and use such data to improve process efficiency or meet other goals.
Furthermore, not many vendors are able to establish, in concrete terms, to the client how to prudently create positive impact on business operations with IoT solutions. Simply the promise of a cloud-based IoT platform is not enough.