Sequential Pluralization through Chromatic Operatives in Connected IoT Sensor's Topology

With the advent of the ubiquitous network, proliferation of sensors, wearables and other Internet of Things (IoT) devices are growing rapidly. All these devices generate a continual stream of data in different formats which is transmitted further over the network for processing. This ever-growing amount of data generated by different sensors need to be processed at a faster pace to maximize the benefits and take any corrective action if required. Although it is important to process data locally at the edge for rapid action, However, it is equally important to process data centrally to identify patterns across locations. Also, the gigantic time-series data captured by these IoT devices can't be stored and fully managed with the central system since it needs to be aggregated and analyzed within a short duration of time as produced by sensors. This paper provides a framework and mechanism that uses a chromatic value to represent sensor data for faster processing. Few bytes of data are passed by the framework with actual sensor data considering the standard Signal-to-Noise-interference for the receiver. These bytes are employed to represent pre-configured chromatic (`coloring' being used as a symbolic dimension) number that represents the different states/ranges of data associated which enables the integrator (logic/circuit) to parse this data quickly. This framework helps reduce the overhead cost and gain efficiency by mapping chromatic values with a range of values, which is a key contribution of this paper. This newly defined framework thus ensures optimal management of data at the edge of the device/sensor/network so that analytics on the central database can be performed in less duration.