Yuste-Delgado Antonio-Jesus, Cuevas-Martinez Juan-Carlos, Triviño-Cabrera Alicia
Department of Telecommunication Engineering, Universidad de Jaén, 23700 Linares, Spain.
Department of Electrical Engineering, Universidad de Málaga, 29071 Málaga, Spain.
Sensors (Basel). 2020 Apr 18;20(8):2312. doi: 10.3390/s20082312.
Clustering algorithms are necessary in Wireless Sensor Networks to reduce the energy consumption of the overall nodes. The decision of which nodes are the cluster heads (CHs) greatly affects the network performance. The centralized clustering algorithms rely on a sink or Base Station (BS) to select the CHs. To do so, the BS requires extensive data from the nodes, which sometimes need complex hardware inside each node or a significant number of control messages. Alternatively, the nodes in distributed clustering algorithms decide about which the CHs are by exchanging information among themselves. Both centralized and distributed clustering algorithms usually alternate the nodes playing the role of the CHs to dynamically balance the energy consumption among all the nodes in the network. This paper presents a distributed approach to form the clusters dynamically, but it is occasionally supported by the Base Station. In particular, the Base Station sends three messages during the network lifetime to reconfigure the s k i p value of the network. The s k i p , which stands out as the number of rounds in which the same CHs are kept, is adapted to the network status in this way. At the beginning of each group of rounds, the nodes decide about their convenience to become a CH according to a fuzzy-logic system. As a novelty, the fuzzy controller is as a Tagaki-Sugeno-Kang model and not a Mandami-one as other previous proposals. The clustering algorithm has been tested in a wide set of scenarios, and it has been compared with other representative centralized and distributed fuzzy-logic based algorithms. The simulation results demonstrate that the proposed clustering method is able to extend the network operability.
聚类算法在无线传感器网络中是必要的，以降低整个节点的能量消耗。哪些节点作为簇头（CH）的决策对网络性能有很大影响。集中式聚类算法依赖于汇聚节点或基站（BS）来选择簇头。为此，基站需要来自节点的大量数据，这有时需要每个节点内部有复杂的硬件或大量的控制消息。或者，分布式聚类算法中的节点通过相互交换信息来决定哪些是簇头。集中式和分布式聚类算法通常会交替让不同节点扮演簇头的角色，以动态平衡网络中所有节点的能量消耗。本文提出了一种动态形成簇的分布式方法，但偶尔会得到基站的支持。具体来说，基站在网络生命周期内发送三条消息来重新配置网络的跳数（skip）值。跳数，即保持相同簇头的轮数，通过这种方式适应网络状态。在每组轮次开始时，节点根据模糊逻辑系统决定自己是否适合成为簇头。新颖的是，模糊控制器采用的是田口-菅野-康（Tagaki-Sugeno-Kang）模型，而不是像其他先前提议那样采用曼达尼（Mandami）模型。该聚类算法已在多种场景下进行了测试，并与其他具有代表性的基于集中式和分布式模糊逻辑的算法进行了比较。仿真结果表明，所提出的聚类方法能够扩展网络的可操作性。
