Research Article
Energy Management Strategy Based on Battery Capacity Degradation in EH-CRSN (Workshop)
@INPROCEEDINGS{10.1007/978-3-030-41117-6_26, author={Errong Pei and Shan Liu and Maohai Ran}, title={Energy Management Strategy Based on Battery Capacity Degradation in EH-CRSN (Workshop)}, proceedings={Communications and Networking. 14th EAI International Conference, ChinaCom 2019, Shanghai, China, November 29 -- December 1, 2019, Proceedings, Part II}, proceedings_a={CHINACOM PART 2}, year={2020}, month={2}, keywords={Energy management Cognitive radio Battery degradation Sampling rate control}, doi={10.1007/978-3-030-41117-6_26} }- Errong Pei
Shan Liu
Maohai Ran
Year: 2020
Energy Management Strategy Based on Battery Capacity Degradation in EH-CRSN (Workshop)
CHINACOM PART 2
Springer
DOI: 10.1007/978-3-030-41117-6_26
Abstract
Energy Harvesting Cognitive Wireless Sensor Network (EH-CRSN) is a novel network which introduces cognitive radio (CR) technology and energy harvesting (EH) technology into traditional WSN. Most of the existing works do not consider that battery capacity of the sensor is limited and will decay over time. Battery capacity degradation will reduce the lifetime of the sensor and affect the performance of the network. In this paper, in order to maximize the network utility of the energy harvesting sensor node in its life cycle, we are concerned with how to determine the optimal sampling rate of sensor node under the condition of battery capacity degradation. Therefore, we propose an optimal adaptive sampling rate control algorithm (ASRC), which can adaptively adjust the sampling rate according to the battery level and effectively manage energy use. In addition, the impact of link capacity on network utility is further investigated. The simulation results verify the effectiveness of the algorithm, which shows that the algorithm is more realistic than the existing algorithm. It can maximize the network utility and improve the overall performance of the network.
