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首页> 外文期刊>International Journal of Distributed Sensor Networks >A task allocation strategy for complex applications in heterogeneous cluster–based wireless sensor networks
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A task allocation strategy for complex applications in heterogeneous cluster–based wireless sensor networks

机译:基于异构集群的无线传感器网络中复杂应用的任务分配策略

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摘要

To a wireless sensor network, cooperation among multiple sensors is necessary when it executes applications that consist of several computationally intensive tasks. Most previous works in this field concentrated on energy savings as well as load balancing. However, these schemes merely considered the situations where only one type of resource is required which drastically constrains their practical applications. To alleviate this limitation, in this article, we investigate the issue of complex application allocation, where various distinctive types of resources are demanded. We propose a heuristic-based algorithm for distributing complex applications in clustered wireless sensor networks. The algorithm is partitioned into two phases, in the inter-cluster allocation stage, tasks of the application are allocated to various clusters with the purpose of minimizing energy consumption, and in the intra-cluster allocation stage, the task is distributed to appropriate sensor nodes with the consideration of both energy cost and workload balancing. In so doing, the energy dissipation can be reduced and balanced, and the lifetime of the system is extended. Simulations are conducted to evaluate the performance of the proposed algorithm, and the results demonstrate that the proposed algorithm is superior in terms of energy consumption, load balancing, and efficiency of task allocation.
机译:对于无线传感器网络,当它执行由多个计算密集型任务组成的应用程序时,多个传感器之间的协作是必要的。该领域以前的大多数工作都集中在节能和负载平衡上。但是,这些方案仅考虑仅需要一种类型的资源的情况,这极大地限制了它们的实际应用。为了减轻这种限制,在本文中,我们研究了复杂的应用程序分配问题,其中需要各种不同类型的资源。我们提出了一种基于启发式的算法,用于在集群无线传感器网络中分布复杂的应用程序。该算法分为两个阶段,在集群间分配阶段,将应用程序的任务分配到各个集群,以最大程度地减少能耗,在集群内部分配阶段,将任务分配给适当的传感器节点同时考虑能源成本和工作量平衡。这样,可以减少和平衡能量消耗,并延长系统的使用寿命。通过仿真评估了所提算法的性能,结果表明所提算法在能耗,负载均衡和任务分配效率方面均具有优势。

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