DRDE: Dual Run Distribution Based Encoding Scheme for Sustainable IoT Applications

Abstract

Nowadays, data is ubiquitous and has a significant influence on our day-to-day activities due to the emergence of high speed internet and widespread use of sensor-enabled Internet of Things (IoT) devices. Owing to major improvement in sampling rate of sensors in recent years, a low variation of the sensed physical parameters is witnessed during a small time interval of observation, which in turn shows high correlation in time domain. Data-critical applications like personal healthcare monitoring, video surveillance, and other applications where data dropping creates significant barriers are attracted by high correlation data. However, due to their power-constrained nature, such applications do not benefit much from the transmission of redundant data. The ideal solution to this problem could possibly be achieved by adopting typical lossless source coding strategy with low-complex design. This paper presents a novel encoding scheme termed as Dual Run Distribution based Encoding (DRDE) scheme by exploiting high correlation of sensor data to suitably encode them using symbol run statistics, leading to a reduced length of data with a very large percentage of 0s. Employing silent symbol based communication, the transmitter can be kept in silent state during periods of the most dominant symbol ’0’ in the encoded messages and using a hybrid $FSK-ASK$ modulation/demodulation technique for communication with a non-coherent receiver results in a significant reduction in transmitter energy. We simulate the proposed sensor data encoding technique on real-life data with low-cost, low data-rate transceivers like CC2420. Simulation results show about 88% (theoretical) and 79-82% (practical) savings in transmitter and 12% (theoretical) 23.5% (practical) savings in receiver energy over conventional BFSK with real-life sensor dataset. Furthermore, our proposed method outperforms in terms of overall energy savings and reduction of $CO_{2}$ footprint, generating 1.48 – 0.041 mg/day, which is 78% lesser than conventional BFSK modulation scheme, making our proposed scheme suitable for sustainable IoT applications in WSN compared to existing schemes. Furthermore, we investigate the influence of various data compression algorithms on computation time, CPU power consumption, and transmission cost on an LPC2148 microcontroller built upon a 16-bit/32-bit ARM7TDMI chipset.

Keywords

• Energy-efficient communication
• wireless sensor networks
• Internet of Things
• sustainable computing
• sensor data correlation