An Adaptive Collection Scheme-Based Matrix Completion for Data Gathering in Energy-Harvesting Wireless Sensor Networks

oleh: Jiawei Tan, Wei Liu, Tian Wang, Neal N. Xiong, Houbing Song, Anfeng Liu, Zhiwen Zeng

Format: Article
Diterbitkan: IEEE 2019-01-01

Deskripsi

Advanced communications and networks greatly enhance the user experience and have a major impact on all aspects of people’s lifestyles. Widely deployed sensor nodes provide support for these services. However, although energy harvesting and transfer technology provides a solution to allow the long-term survival of wireless sensor nodes for wireless sensor networks, the single collection scheme causes a lot of energy waste. Thus, efficient energy utilization and fast data collection are still serious challenges for energy harvesting wireless sensor networks. To overcome these challenges, an adaptive collection scheme based on matrix completion (ACMC) is proposed to reduce delay and to improve the energy utilization of the network. In the ACMC scheme, compared with traditional data collection schemes, the data collection schemes vary with the available energy, collecting large amounts of data when the available energy is sufficient to obtain high-quality data-based applications. Otherwise, adaptive selecting the collected data based on previously collected data, the amount of data collected can be effectively reduced based on the application requirements, thereby improving the energy utilization of the network. The ACMC scheme also proposes a method for reducing the delay by increasing the duty cycle of the nodes that are far from the CC. At the same time, the transmission reliability of these nodes increases due to the increase in the transmission frequency. Thus, the ACMC scheme can also further reduce the delay of the network. The experimental results of the ACMC scheme in planar networks show better performance than the traditional data collection schemes and can improve the energy utilization of the network by 4.26%–6.68% while reducing the maximum delay by 9.4%.