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A Novel Topology Control Approach to Maintain the Node Degree in Dynamic Wireless Sensor Networks

机译:动态无线传感器网络中维持节点度的一种新型拓扑控制方法

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摘要

Topology control is an important technique to improve the connectivity and the reliability of Wireless Sensor Networks (WSNs) by means of adjusting the communication range of wireless sensor nodes. In this paper, a novel Fuzzy-logic Topology Control (FTC) is proposed to achieve any desired average node degree by adaptively changing communication range, thus improving the network connectivity, which is the main target of FTC. FTC is a fully localized control algorithm, and does not rely on location information of neighbors. Instead of designing membership functions and if-then rules for fuzzy-logic controller, FTC is constructed from the training data set to facilitate the design process. FTC is proved to be accurate, stable and has short settling time. In order to compare it with other representative localized algorithms (NONE, FLSS, k-Neighbor and LTRT), FTC is evaluated through extensive simulations. The simulation results show that: firstly, similar to k-Neighbor algorithm, FTC is the best to achieve the desired average node degree as node density varies; secondly, FTC is comparable to FLSS and k-Neighbor in terms of energy-efficiency, but is better than LTRT and NONE; thirdly, FTC has the lowest average maximum communication range than other algorithms, which indicates that the most energy-consuming node in the network consumes the lowest power.
机译:拓扑控制是一种重要的技术,它通过调整无线传感器节点的通信范围来提高无线传感器网络(WSN)的连接性和可靠性。本文提出了一种新颖的模糊逻辑拓扑控制(FTC),通过自适应地改变通信范围来达到任何期望的平均节点度,从而改善网络连接性,这是FTC的主要目标。 FTC是完全本地化的控制算法,不依赖邻居的位置信息。代替为模糊逻辑控制器设计隶属函数和if-then规则,而是从训练数据集中构造FTC,以简化设计过程。事实证明,FTC准确,稳定且建立时间短。为了将其与其他代表性的局部算法(NONE,FLSS,k邻域和LTRT)进行比较,通过广泛的仿真对FTC进行了评估。仿真结果表明:首先,与k-Neighbor算法相似,FTC在节点密度变化时最好达到期望的平均节点度。其次,FTC在能效方面可与FLSS和k-Neighbor相媲美,但优于LTRT和NONE。第三,FTC具有比其他算法最低的平均最大通信范围,这表明网络中能耗最高的节点消耗的功率最低。

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