能量收集传感器网络中改进的能耗管理方案研究

能量收集传感器平台为网络协议的设计开辟了新的领域。为了支持网络的运行,能耗率不得高于能量收集率,否则,传感器节点会最终耗尽它们的能量。与资源处于静态的传统网络资源分配问题相比,充电速率的时间可变性带来了新的挑战。在本文中,我们首先研究了一种基于高效对偶分解和次梯度策略的算法QuickFix,计算出数据采样率和路由。然而,再次充电发生波动时的时标可能会快于传统方法的收敛时间,进而导致电池断电和溢出,这会造成采样丢失和能量收集机会丢失。为了解决这一动态问题,我们提出一种本地算法SnapIt,通过对采集率进行调节以维持电池电量在目标水平上。基于TOSSIM模拟器的性能评估表明,QuickFix和SnapIt联合起来可以跟踪网络瞬时最优效用,同时维持电池电量处于目标水平。与基于余压的IFRC相比,本文方法使总体数据速率平均提升42%,同时显著提升了网络效用。

Research on Improved Power Management Scheme in Energy Harvesting Sensor Networks

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, time variations in recharging rate presents a new challenge. In this paper, we first explore the performance of an efficient dual decomposition and sub-gradient 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.