Sustainable Operations in IoT by Combining Spatiotemporal Data Correlation With Silent Symbol Based Communication Strategy

We exploit spatiotemporal correlation in sensor data and redundant binary number system based representation for encoding data, where the number of 0 s in the encoded data is significantly larger than the other symbols. Next, using a hybrid FSK-ASK modulation/demodulation technique with a non-coherent receiver, we have designed an energy-efficient communication scheme where the transmitter is kept silent during the transmission of the dominant symbol in the encoded messages. In contrast to the conventional techniques of data reduction and aggregation which exploit temporal and spatial correlation, our technique does not require dropping any data value while generating significantly more transmission energy savings. Our approach is particularly useful for sustainable energy-efficient communications in IoT applications like environment monitoring, agriculture, etc., where dropping any data value may not be desirable. Simulation results on different real-life sensor data sets using several popular low-cost, low data-rate transceivers like the RFM TR 1,000 show about
$90.06\% - 90.86\%$
savings in transmitter energy expenditure over conventional BFSK, and
$15\% - 20\%$
savings at receiver. Simulation results also demonstrate a proportionately reduced
$CO_{2}$
footprint generation of
$0.12 - 0.14$
mg/day only, when using the RFM TR 1,000 radio. These savings are considerably higher than those generated with the existing techniques.