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A New Reliable and Self-Stabilizing Data Fusion Scheme in Unsafe Wireless Sensor Networks

机译:不安全的无线传感器网络中一种新的可靠且自稳定的数据融合方案

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In this paper, we deal with the problem of distributed data fusion in unsafe large-scale sensor networks. Data fusion application is the phase of processing the collected data by sensor nodes before sending it the end user. During this phase, resource failures are more likely to occur and can have an adverse effect on the application. Hence, we introduce first an efficient self-stabilizing algorithm to achieve/ensure the convergence of node states to the average of the initial measurements of the network. Next, we present a fault tolerant scheme to resist to frequent and unexpected not concomitant fail-silent/fail-stop node failures. The major contribution of this paper is the design of an analytical expression (an upper bound) of the actual number of moves/iterations required by the algorithm. We provide a comprehensive set of experimental results, that fully demonstrate the usefulness of the proposed schemes.
机译:在本文中,我们处理不安全的大规模传感器网络中的分布式数据融合问题。数据融合应用程序是在将传感器数据发送给最终用户之前由传感器节点处理收集到的数据的阶段。在此阶段,资源故障更有可能发生,并且可能对应用程序产生不利影响。因此,我们首先介绍一种有效的自稳定算法,以实现/确保节点状态收敛到网络初始测量值的平均值。接下来,我们提出一种容错方案,以抵抗频繁发生的和意外的不伴随的故障静默/故障停止节点故障。本文的主要贡献是该算法所需的实际移动/迭代次数的解析表达式(上限)的设计。我们提供了一套全面的实验结果,充分证明了所提出方案的实用性。

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