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An Adaptive Clustering Approach Based on Minimum Travel Route Planning for Wireless Sensor Networks with a Mobile Sink

机译：基于最小行程路径规划的带移动接收器的无线传感器网络自适应聚类方法

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In recent years, Wireless Sensor Networks with a Mobile Sink (WSN-MS) have been an active research topic due to the widespread use of mobile devices. However, how to get the balance between data delivery latency and energy consumption becomes a key issue of WSN-MS. In this paper, we study the clustering approach by jointly considering the Route planning for mobile sink and Clustering Problem (RCP) for static sensor nodes. We solve the RCP problem by using the minimum travel route clustering approach, which applies the minimum travel route of the mobile sink to guide the clustering process. We formulate the RCP problem as an Integer Non-Linear Programming (INLP) problem to shorten the travel route of the mobile sink under three constraints: the communication hops constraint, the travel route constraint and the loop avoidance constraint. We then propose an Imprecise Induction Algorithm (IIA) based on the property that the solution with a small hop count is more feasible than that with a large hop count. The IIA algorithm includes three processes: initializing travel route planning with a Traveling Salesman Problem (TSP) algorithm, transforming the cluster head to a cluster member and transforming the cluster member to a cluster head. Extensive experimental results show that the IIA algorithm could automatically adjust cluster heads according to the maximum hops parameter and plan a shorter travel route for the mobile sink. Compared with the Shortest Path Tree-based Data-Gathering Algorithm (SPT-DGA), the IIA algorithm has the characteristics of shorter route length, smaller cluster head count and faster convergence rate.
机译：近年来，由于移动设备的广泛使用，带有移动接收器的无线传感器网络（WSN-MS）一直是活跃的研究主题。但是，如何在数据传递延迟和能耗之间取得平衡成为WSN-MS的关键问题。在本文中，我们通过结合考虑移动宿的路由规划和静态传感器节点的聚类问题（RCP）来研究聚类方法。我们通过使用最小旅行路线聚类方法来解决RCP问题，该方法应用移动接收器的最小旅行路线来指导聚类过程。我们将RCP问题公式化为整数非线性规划（INLP）问题，以在以下三个约束条件下缩短移动接收器的旅行路线：通信跳数约束，旅行路线约束和避免环路约束。然后，我们基于以下特性提出了一种不精确归纳算法（IIA）：跳数较小的解决方案比跳数较大的解决方案更可行。 IIA算法包括三个过程：使用旅行商问题（TSP）算法初始化旅行路线计划，将群集头转换为群集成员以及将群集成员转换为群集头。大量的实验结果表明，IIA算法可以根据最大跳数参数自动调整簇头，并为移动汇点规划一条较短的行驶路线。与基于最短路径树的数据收集算法（SPT-DGA）相比，IIA算法具有路由长度更短，簇头数更小，收敛速度更快的特点。

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