The Internet of Things inherently calls for an extremely distributed architecture. By its nature, the real-time "thing-originating data" (i.e., sensory, ID, time, and location data that emanates from the intelligent things themselves) originates at the edges of the network-out where the "things" are. In many cases, a lot of processing is done on IoT data very near to the source. Hence the intelligence about the things within the IoT is very distributed.
Real-time IoT data comes from many sources, including sensors, locating devices, and identification devices. The raw data generated at the lowest level in these devices generally goes through many layers and levels of filtering, summarizing, pattern detection, interpreting and processing. This is required for many reasons. First is just the sheer volume of data. A sensor may emit data hundreds or thousands of times per second. Few if any central enterprise applications want to receive those volumes of raw data. Additionally, the data often is much easier to comprehend and use when it is interpreted into a meaningful business event by a local processor. Also, often there is a feedback loop controlling the “thing” (device or machine) based on this data as well. There is local intelligence, embedded in devices, readers and local appliances, that makes sense of the low-level data and provides intelligence to higher levels.
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