无线传感器网络容错无碰撞数据聚合调度

A. Jhumka
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引用次数: 14

摘要

数据聚合调度是无线传感器网络(WSNs)中的一种基本通信模式,其中传感器节点将数据聚合并中继到汇聚节点。对于需要快速响应时间的WSN应用程序,必须尽可能快地将数据到达接收器。对于这种时效性保证,可以使用基于tdma的调度来为节点分配可以传输消息的时隙。然而,任何插槽分配方法都需要认识到崩溃失败可能发生的事实(例如,由于电池耗尽,硬件缺陷)。本文研究了这种数据聚合调度协议的设计。我们的贡献如下:(我)我们识别要解决的一个必要条件的收敛问题,(2)我们介绍两个版本的收敛问题,即(一)一个强大的版本,和(b)弱版本,(3)我们表明,强大的收敛问题不能解决,我们(iv)表明,确定性弱收敛不能在事故的解决故障,(v)我们表明,没有1美元——算法,解决了稳定弱收敛在崩溃的失败,(vi)我们提供了一个模块化的$d$-local算法,该算法解决了在存在崩溃故障时的稳定弱收敛投射,其中$d$是网络半径;(vii)我们展示了参数的具体实例如何导致实现更有效稳定的$d$-local算法。我们的贡献是新颖的:(i)第一个贡献(必要条件)提供了解释现有收敛投射算法结构的理论基础,(ii)在崩溃失败的情况下收敛投射的研究以前没有研究过。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Crash-Tolerant Collision-Free Data Aggregation Scheduling for Wireless Sensor Networks
Data aggregation scheduling, or converge cast, is a fundamental pattern of communication in wireless sensor networks (WSNs), where sensor nodes aggregate and relay data to a sink node. For WSN applications that require fast response time, it is imperative that the data reaches the sink as fast as possible. For such timeliness guarantees, TDMA-based scheduling can be used to assign time slots to nodes in which they can transmit messages. However, any slot assignment approach needs to be cognisant of the fact that crash failures can occur (e.g., due to battery exhaustion, defective hardware). In this paper, we study the design of such data aggregation scheduling (converge cast) protocols. We make the following contributions: (i) we identify a necessary condition to solve the converge cast problem, (ii) we introduce two versions of the converge cast problem, namely (a) a strong version, and (b) a weak version , (iii) we show that the strong converge cast problem cannot be solved, (iv) we show that deterministic weak converge cast cannot be solved in presence of crash failures, (v) we show that there is no $1$-local algorithm that solves stabilising weak converge cast in presence of crash failures, (vi) we provide a modular $d$-local algorithm that solves stabilising weak converge cast in presence of crash failures where $d$ is the network radius, and (vii) we show how specific instantiations of parameters can lead to an $d$-local algorithm that achieves more efficient stabilization. Our contributions are novel: (i) the first contribution (necessary condition) provides the theoretical basis which explains the structure of existing converge cast algorithms, and (ii) the study of converge cast in presence of crash failures has not previously been studied.
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