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IoT: BIG BENEFITS for Small and Medium Businesses
| October 4, 2018
remforce is a leading provider and manufacturer of remote monitoring solutions. Our flagship product: REM1 is redefining boiler maintenance.
Article | February 18, 2020
The Internet of Things is gradually penetrating every aspect of our lives. With the growth in numbers of internet-connected sensors built into cars, planes, trains, and buildings, we can say it is everywhere. Be it smart thermostats or smart coffee makers, IoT devices are marching ahead into mainstream adoption. But, these devices are far from perfect. Currently, there is a lot of manual input required to achieve optimal functionality — there is not a lot of intelligence built-in. You must set your alarm, tell your coffee maker when to start brewing, and manually set schedules for your thermostat, all independently and precisely.
Big data as a term and a field, has been around for some time. It relates to the ways in which we study, analyze and process data sets that are too large to be handled by traditional data-processing software. Data can be described as ‘big’ when it demonstrates the four ‘V’ qualities: veracity (accuracy), velocity (speed), volume (size) and variety (both structured and unstructured). IoT, on the other hand, came much later and relates to devices, data and marrying them together. This area looks at making devices ‘smart’ (anything from watches to kettles) and collecting data about their performance or usage to influence consumer behavior.
Edge computing refers to information being processed at the edge of the network, rather than being sent to a central cloud server. The benefits of edge computing include reduced latency, reduced costs, increased security and increased business efficiency. Transferring data from the edge of a network takes time, particularly if the data is being collected in a remote location. While the transfer may usually take less than a second, glitches in the network or an unreliable connection may increase the time required. For some IoT applications, for example, self-driving cars, even a second may be too long. Imagine a security camera that’s monitoring an empty hallway. There’s no need to send hours of large video files of an empty hallway to a cloud server (where you will need to pay to store them). With edge computing, the video could be sent to the cloud only if there is movement detected in the hallway.
Artificial intelligence (AI) has existed in the public consciousness for decades. The (mostly) sentient machines playing the villains in Hollywood movies have never been realistic depictions of the technology, but they have left an impression, nonetheless. AI has proved as exciting to the layman as it is to the expert. Usually based in remote data centers, AI is capable of collecting and examining immense volumes of data, generating insights based on analytical algorithms. With varying degrees of autonomy, these capabilities have been put to use streamlining decision-making processes. While AI is often thought of as a product in its own right, it is increasingly intersecting with other parallel trends. Chief among these is the Internet of things (IoT), which enables previously isolated machines to “talk” to one another and, at the same time, generate data that makes new modes of operation a possibility.
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