A Smart Collaborative Charging Algorithm for Mobile Power Distribution in 5G Networks | Zendy

Zhengyang Ai | Zendy; Ying Liu | Zendy; Fei Song | Zendy; Hongke Zhang | Zendy

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A Smart Collaborative Charging Algorithm for Mobile Power Distribution in 5G Networks

Author(s) -

Zhengyang Ai,

Ying Liu,

Fei Song,

Hongke Zhang

Publication year - 2018

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2018.2818790

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

The issues of energy supply had been widely investigated to upgrade the network lifetime, power utilization, and system stability of communication networks. However, the deployable charging solution for massive mobile terminals in 5G is still lack. Although prior studies in wireless rechargeable sensor networks focused on the energy usage effectiveness, the charging time was frequently overlooked in most schemes. In this paper, we propose a novel distributed mobile charging (DMC) algorithm to optimize the charging time and charger quantity. The traditional policies are first analyzed based on the energy source. We noticed that the location of base station is extremely significant for charging performance. Then, the details of the DMC algorithm are illustrated through the main process of energy transfer, formula deductions, and performance optimization. To further promote the network capacity, an improved algorithm named adaptive dynamic energy transfer is proposed by introducing linear node sleeping mechanism. The simulation results demonstrate that the algorithms are able to improve the charging time and charger quantity in multiple scenarios.

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