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首页> 外文期刊>The mediterranean journal of electronics and communications >Improving the Fault Tolerance of Wireless Sensor Networks by a Weighted Criteria Matrix
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Improving the Fault Tolerance of Wireless Sensor Networks by a Weighted Criteria Matrix

机译：通过加权准则矩阵提高无线传感器网络的容错能力

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摘要

Wireless sensor networks (WSNs) are used in various fields such as official buildings, industrial factories, and military applications to monitor environmental conditions (e.g., humidity and temperature). They can be affected by various faults because of some natural occasions (e.g., internal conflicts). Such faults cause the sensed data not to be transmitted to given stations and the network efficiency to be diminished, considerably. Hence, fault tolerance is one of the main challenges in WSNs that has been considered by researches in the last decades. The sensor nodes must discover faulty data and, thereby, transmit only faultless data to the stations (e.g., sink). This paper proposes a new fault tolerance approach for WSNs designed by a weighted criteria matrix, called FTW-WCM. Any cluster node uses this approach to select an appropriate successor node on transmission paths in order to enhance the fault tolerance of network. The FTW-WCM approach applies four parameters including remaining energy, hop count, selection count, and node rate via a weighted multi-criteria procedure. The neighbor node with the highest node rate is selected as an appropriate successor node from among a list of neighboring nodes. Simulation results show that the proposed approach surpasses some of the existing methods in terms of packet delivery ratio, network lifetime, and average remaining energy.
机译：无线传感器网络（WSN）被用于各种领域，例如官方建筑，工业工厂和军事应用，以监视环境条件（例如湿度和温度）。由于某些自然情况（例如内部冲突），它们可能会受到各种故障的影响。这样的故障导致所感测的数据不被发送到给定的站，并且网络效率大大降低。因此，容错是WSN的主要挑战之一，最近几十年来研究一直在考虑这一问题。传感器节点必须发现有故障的数据，从而仅将无故障的数据传输到站（例如接收器）。本文提出了一种新的基于加权标准矩阵FTW-WCM设计的WSN容错方法。任何群集节点都使用此方法在传输路径上选择适当的后继节点，以增强网络的容错能力。 FTW-WCM方法通过加权多准则程序应用四个参数，包括剩余能量，跳数，选择计数和节点速率。从相邻节点列表中选择具有最高节点速率的相邻节点作为适当的后继节点。仿真结果表明，该方法在分组传输率，网络寿命和平均剩余能量方面都超过了现有方法。

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