Joint Energy Management and Resource Allocation in Rechargeable Sensor Networks
Joint Energy Management and Resource Allocation in Rechargeable Sensor Networks
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DOI：
10.1109/infcom.2010.5461958
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发表时间：
2010-03
期刊：
影响因子：
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通讯作者：
Ren-Shiou Liu;P. Sinha;C. E. Koksal
中科院分区：
文献类型：
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作者：
Ren-Shiou Liu;P. Sinha;C. E. Koksal
能量收集传感器平台为网络协议的设计开辟了新的维度。为了维持网络的运行，能量消耗率不能高于能量收集率，否则，传感器节点最终会耗尽它们的电池。相对于传统的网络资源分配问题，资源是静态的，时变的充值率提出了一个新的挑战。在本文中，我们首先探讨了一个有效的对偶分解和次梯度方法为基础的算法，称为QuickFix，用于计算数据采样率和路由的性能。然而，充电中的波动可以在比传统方法的收敛时间更快的时间尺度上发生。这导致电池断电和溢出场景，这两者都是不期望的，分别由于错过的样本和丢失的能量收集机会。为了解决这种动态，一个本地算法，称为SnapIt，旨在适应采样率的目标是保持电池在一个目标水平。我们使用TOSSIM模拟器进行的评估表明，QuickFix和SnapIt协同工作可以跟踪瞬时最佳网络效用，同时将电池保持在目标水平。与IFRC（一种基于反压的方法）相比，我们的解决方案将总数据速率平均提高了42%，同时显着提高了网络效用。
Energy harvesting sensor platforms have opened up a new dimension to the design of network protocols. In order to sustain the network operation, the energy consumption rate cannot be higher than the energy harvesting rate, otherwise, sensor nodes will eventually deplete their batteries. In contrast to traditional network resource allocation problems where the resources are static, the time-varying recharging rate presents a new challenge. In this paper, We first explore the performance of an efficient dual decomposition and subgradient method based algorithm, called QuickFix, for computing the data sampling rate and routes. However, fluctuations in recharging can happen at a faster time-scale than the convergence time of the traditional approach. This leads to battery outage and overflow scenarios, that are both undesirable due to missed samples and lost energy harvesting opportunities respectively. To address such dynamics, a local algorithm, called SnapIt, is designed to adapt the sampling rate with the objective of maintaining the battery at a target level. Our evaluations using the TOSSIM simulator show that QuickFix and SnapIt working in tandem can track the instantaneous optimum network utility while maintaining the battery at a target level. When compared with IFRC, a backpressure-based approach, our solution improves the total data rate by 42% on the average while significantly improving the network utility.