Energy management algorithm for solar‐powered energy harvesting wireless sensor node for Internet of Things. Issue 12 (1st August 2016)

• Record Type: Journal Article
• Authors: Shin, Minchul; Joe, Inwhee
• Is Part Of: IET communications. Volume 10:Issue 12(2016)
• Page Start: 1508
• Page End: 1521
• Publication Date: 2016-08-01
• Subjects: Internet of Things -- energy harvesting -- wireless sensor networks -- telecommunication power management -- solar cell arrays; energy management algorithm -- solar‐powered energy harvesting wireless sensor node -- Internet of Things -- IoT -- energy storage -- transmission interval management -- energy consumption level -- energy prediction algorithm -- fluorescent lamp light intensity -- solar panel -- optimal transmission interval -- residual energy -- energy prediction error; Telecommunication systems -- Periodicals; Speech processing systems -- Periodicals; 621.38205
• DOI: 10.1049/iet-com.2015.0223
• Languages: English
• ISSNs: 1751-8628

Abstract

Abstract : The solar powered energy harvesting sensor node is a key technology for Internet of Things (IoT), but currently it offers only a small amount of energy storage and is capable of harvesting only a trivial amount of energy. Therefore, new technology for managing the energy associated with this sensor node is required. In particular, it is important to manage the transmission interval because the level of energy consumption during data transmission is the highest in the sensor node. If the proper transmission interval is calculated, the sensor node can be used semi‐permanently. In this study, the authors propose an energy prediction algorithm that uses the light intensity of fluorescent lamps in an indoor environment. The proposed algorithm can be used to accurately estimate the amount of energy that will be harvested by a solar panel using a weighted average for light intensity. Then, the optimal transmission interval is calculated using the amount of predicted harvested energy and residual energy. The results from the authors' experimental testbeds show that their algorithm's performance is better than the existing approaches. The energy prediction error of their algorithm is approximately 0.5%.