Loss-Aware Efficient Energy Balancing in Mobile Opportunistic Networks

Loss-Aware Efficient Energy Balancing in Mobile Opportunistic Networks
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DOI:
10.1109/globecom38437.2019.9014073
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发表时间:
2019-12
期刊:
2019 IEEE Global Communications Conference (GLOBECOM)
影响因子:
--
通讯作者:
Aashish Dhungana;E. Bulut
Aashish Dhungana;E. Bulut
中科院分区:
其他
文献类型:
--
作者:
Aashish Dhungana;E. Bulut

文献摘要

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在由电池供电设备组成的网络中,能源管理是一个需要解决的具有挑战性的问题。随着最近新兴的无线电能传输技术的出现,许多研究已经利用无线充电来解决这个问题,并为这些设备提供无处不在的能量,使它们能够持续运行。除了移动充电器的优化调度等已被广泛研究的问题外,最近还考虑了一个有趣的问题,即移动节点群体之间的能量平衡问题,通过节点之间的机会能量交换来延长网络的生命周期。最先进的解决方案旨在尽可能快地在设备之间实现能量平衡,但由于对等能量传输过程中的损失,它们会浪费能量。本文研究的是能量平衡问题,其目标是最小化节点间的能量差和节点间的能量损失。为此,我们基于不同的启发式算法,提出了三种不同的节点间能量共享协议。通过仿真实验表明,所有提出的算法都表现出了比现有算法更好的性能。第三种算法通过在保持网络能量最大(即最小损耗)的同时实现节点之间的能量平衡来实现最佳性能。
Energy management is a challenging issue to be addressed in networks consisting of battery-powered devices. With the recently emerging wireless power transfer technology, many studies have utilized wireless charging to address this problem and provide energy ubiquitously to these devices for making them functional continuously. Besides the well- studied problems such as optimal scheduling of mobile chargers, recently an interesting problem of energy balancing among a population of mobile nodes has been considered to prolong the lifetime of the network through the opportunistic energy exchanges between the nodes. The state-of-the-art solutions target an energy balance among the devices as fast as possible but they waste energy due to the loss during peer-to-peer energy transfer. In this paper, we study the energy balancing problem that aims to minimize both the energy difference between nodes and the energy loss during this process. To this end, we propose three different energy sharing protocols between nodes based on different heuristics. Through simulations, we show that all the proposed algorithms show better performance than the state-of-the-art. The third proposed algorithm achieves the best performance by reaching an energy balance between nodes while keeping the maximum possible energy in the network (i.e., minimum loss).