As the world experiences a quantum leap in the speed and scope of digital connections, industries are gaining new and enhanced tools to boost productivity and spur innovation.
Over the next decade, existing technologies like fiber, low- to mid-band 5G networks, low-power wide-area networks (LPWANs), and Wi-Fi 6—as well as short-range connections like radio-frequency identification (RFID)—will expand their reach as networks are built out and adoption grows. At the same time, new generations of these technologies will appear with upgraded standards. In addition, new types of more revolutionary and more capital-intensive frontier connectivity like high-band 5G and low-Earth orbit (LEO) satellites will begin to come online. Together, these technological developments will unlock powerful new capabilities across industries. Near global coverage will allow the expansion of use cases even to remote areas and enable constant connectivity universally. Massive IoT advances will be enabled as new technologies allow high device densities, and mission critical services will take advantage of ultra-low latency, highly reliable, and highly secure connections.
From healthcare and manufacturing to mobility and retail, there are hundreds of promising use cases for the emerging generation of enhanced connectivity. Together, advanced and frontier connectivity could help seven sectors add a total of $2 trillion to $3 trillion in additional value to global GDP.
According to B2B market research firm MarketsandMarkets, the remote asset management market is projected to grow from USD 16.5 billion in 2020 to USD 32.6 billion by 2025, at a Compound Annual Growth Rate (CAGR) of 14.6% during the forecast period. The major factors driving the remote asset management market include the adoption of IoT-enabled remote asset management solutions to manage assets efficiency, decreasing cost of IoT-based sensors optimize asset life cycle through remote asset management solutions, and use of predictive maintenance to boost the adoption of remote asset management, thereby reducing operational costs of remote assets.
Other key findings of the research include
- Solution segment to lead the remote asset management market in 2020
- Analytics and reporting solution sub segment to grow at a higher CAGR during the forecast period
- Mobile asset segment to grow at a higher CAGR during the forecast period
- On-premises segment to lead the remote asset management market in 2020
- Small and medium-sized enterprises segment to grow at a higher CAGR during the forecast period
- Manufacturing vertical to lead the remote asset management market in 2020
- North America is estimated to hold the highest market share of the remote asset management market in 2020.
- In Asia Pacific (APAC), the market is projected to grow at the highest CAGR of 17.3% during the forecast period due to the demand for remote asset management solutions and services in countries, such as China, Japan, India, and rest of APAC.
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.