Organizing sensor nodes into a clustered architecture is an effective method for load balancing and prolonging the network lifetime. However, a serious drawback of the clustering approach is the imposed energy overhead caused by the “global” clustering operations in every round of the global round-based policy (GRBP). To mitigate this problem, this paper proposes a hierarchical clustering-task scheduling policy (HCSP), which triggers node-driven clustering as opposed to GRBP's time-driven clustering. Based on HCSP, each cluster is reconfigured only once at each local super round. Therefore, the cluster reconfiguration frequency varies on-demand and may differ from one cluster to another throughout the network lifetime. However, in order to refresh the entire network structure, global clustering is performed at the end of every global hyper round. Accordingly, HCSP aims to achieve a more flexible, energy-efficient, and scalable clustering-task scheduling than that of GRBP. This policy mitigates the clustering overhead, which is the worst disadvantage of clustering approaches. Energy consumption calculations and extensive simulations show the effectiveness of HCSP in saving energy and in prolonging the network lifetime.

中文翻译：

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基于集群的无线传感器网络中的分层集群任务调度策略

将传感器节点组织到群集体系结构中是有效的负载平衡和延长网络寿命的方法。但是，群集方法的一个严重缺点是，在基于全局回合的策略（GRBP）的每一轮中，由于“全局”群集操作而导致的能源开销过大。为了缓解这个问题，本文提出了一种层次化的群集任务调度策略（HCSP），该策略触发了节点驱动的群集，而不是GRBP的时间驱动的群集。基于HCSP，每个集群在每个本地超级轮次仅重新配置一次。因此，群集重新配置频率按需变化，并且在整个网络生命周期中一个群集之间可能会有所不同。但是，为了刷新整个网络结构，全局聚类在每个全局超级回合结束时执行。因此，HCSP旨在实现比GRBP更灵活，节能和可扩展的群集任务调度。此策略减轻了群集开销，这是群集方法的最大缺点。能耗计算和大量模拟结果表明，HCSP在节省能源和延长网络寿命方面是有效的。