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A novel approach for trade-off between computation and communication cost in wireless sensor networks

机译:无线传感器网络计算与通信成本的权衡新方法

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Wireless sensor networks consist of nodes with severe energy constraints and thus, they must use energy saving algorithms in different layers. Despite the prior work, the computational cost in a wireless sensor network is usually neglected compared to the communication cost; it may not admit to in which large volumes of data packets are being transmitted, such as those cases in which This packets are video frames captured by video sensor nodes. In this paper, we consider the problem of minimizing the communication needed to send readings from a set of sensors to a single destination with obtaining a trade-off between computation and communication energy. We propose a centralized as well as a distributed algorithm. In the former, the sink node and in the latter, each node endeavors to reduce network energy consumption adaptively, making a trade-off between data compression at the appropriate level and sending row data. The data compression technique used is dependent on the application type and considers spatial correlation. Comparing the simulation results of our approach with similar work, without consideration of trade-off between computation and comparison, shows significant improvement in energy saving of the network.
机译:无线传感器网络由具有严重能量约束的节点组成,因此,它们必须使用不同层中的节能算法。尽管事先工作,与通信成本相比,无线传感器网络中的计算成本通常被忽略;它可能不承认正在发送大量的数据分组,例如该分组是由视频传感器节点捕获的视频帧的情况。在本文中,我们考虑最小化从一组传感器向单个目的地发送读数所需的通信的问题,其中获得计算和通信能量之间的权衡。我们提出了集中式和分布式算法。在前者中,水槽节点和后者中,每个节点致力于自适应地降低网络能量消耗,在适当的级别和发送行数据的数据压缩之间进行权衡。使用的数据压缩技术取决于应用类型并考虑空间相关性。比较我们方法的模拟结果与类似工作的方法,不考虑计算与比较之间的权衡,显示了网络节能的显着提高。

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