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:
- Comparative usage—how your consumption of a resource (for example, calories) compares with others in similar situations
- Understanding patterns and reasons for variation—developing statistical models that explain variation
- Predictive asset maintenance—using sensor data to detect potential problems in machinery (or your body) before they actually occur
- Optimization—using sensor data and analysis to optimize a process, as when a lumber mill optimizes the automated cutting of a log, or a poultry processor automates the preparation of a chicken, or when is the healthiest time to go to sleep or when in your sleep cycle to wake up
- Prescription—employing sensor and other types of data to tell the user what to do, as when an activity tracker nudges you to get off the couch or sit up straight
- Situational awareness—piecing together seemingly disconnected events and relating them to a larger repository of data to put together an explanation, as when a series of readings from activity trackers, glucose monitors, connected scales, and other devices tells you that you are in danger of contracting diabetes
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.
“Industry professionals know that the Industrial Internet of Things security is a problem today. More than half of the respondents said they don’t feel prepared to detect and stop cyber attacks against IIoT,” said David Meltzer, chief technology officer at Tripwire. “There are only two ways this scenario plays out: Either we change our level of preparation or we experience the realization of these risks. The reality is that cyber attacks in the industrial space can have significant consequences in terms of safety and the availability of critical operations.”
Market analyst firm Technavio has a new report that provides a comprehensive analysis of the global IIoT sensors market in oil and gas industry by product such as temperature sensors, flow sensors, flow sensors, pressure sensors, and other sensors. The report also provides a comprehensive analysis of the growth opportunities for companies in this market in regions such as the Americas, APAC, and EMEA.
The growing focus of the oil and gas industry on reducing the cost is encouraging them to adopt IIoT sensors as the installation of these sensors takes less time. In addition, the sensor manufacturers are increasingly offering sensors with easy assembling options and technical advancements. Moreover, the rising competition among major manufacturers of sensors and service providers of IoT products is increasing, which in turn, will boost the adoption of these sensors in the oil and gas industry. Research analysis on the global IIoT sensors market in oil and gas industry identifies that the growing commercial acceptance of IIoT sensors will be one of the major factors that will have a positive impact on the growth of the market. Technavio’s market research analysts predict that the market will grow at a CAGR of more than 5% by 2022.
The preference for industrial internet of things enabled smart asset monitoring solutions add intelligence to automated workflows, real-time alerts, dynamic edge control of assets, cross-domain analytics, insights from data, real-time visibility, and predictive maintenance. This is driving the adoption of smart asset monitoring, which in turn, is identified as one of the key trends that will stimulate growth in the IIoT sensors market in oil and gas industry throughout the projected period.