A Performance Evaluation of Solar Energy Prediction Approaches for Energy-Harvesting Wireless Sensor Networks

Year 2016, Special Issue (2016), 424 - 427, 01.12.2016

Selahattin Koşunalp

Abstract

Energy harvesting from the surrounding environment has been a superior
way of eliminating the burden of having to replace depleted batteries in
wireless sensor networks (WSNs), thereby achieving a perpetual lifetime.
However, the ambient energy is highly time-variable and depends on the
environmental conditions, which raises the need to design new approaches for
predicting future energy availability. This paper presents a performance evaluation and
comparison of three recently-proposed solar energy prediction algorithms for
WSNs. In order to provide an accurate performance of the algorithms, real-world
measurements obtained from a solar panel were considered. Also, the performance
characteristics of the algorithms in four seasons –winter, spring, summer and
autumn – were demonstrated. To do this, a month in each season was selected for
performance comparison, discussing the performance of the algorithms in each
season.

Keywords

Wireless sensor networks, Energy harvesting

References

• S. Kosunalp, “MAC protocols for energy harvesting wireless sensor networks: survey”, ETRI Journal, vol. 37, pp. 804-812, 2015.
• S. Kosunalp, “EH-TDMA: A TDMA-based MAC Protocol for Energy Harvesting Wireless Sensor Networks”, International Journal of Computer Science and Information Security, 14(8), pp. 325-328, 2016..
• S. Kosunalp, “A New Energy Prediction Algorithm for Energy-Harvesting Wireless Sensor Networks with Q-Learning,” IEEE Access, 4, pp. 5755-5763, 2016.