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Adaptive Distributed Hierarchical Sensing algorithm for reduction of wireless sensor network cluster-heads energy consumption

机译：减少无线传感器网络簇头能耗的自适应分布式层次感知算法

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Energy efficiency is a crucial performance metric in sensor networks, directly determining the network lifetime. Consequently, a key factor in WSN is to improve overall energy efficiency to extend the network lifetime. Although many algorithms have been presented to optimize the energy factor, energy efficiency is still one of the major problems of WSNs, especially when there is a need to sample an area with different types of loads. Unlike other energy-efficient schemes for hierarchical sampling, our hypothesis is that it is achievable, in terms of prolonging the network lifetime, to adaptively re-modify CHs sensing rates (the processing and transmitting stages in particular) in some specific regions that are triggered significantly less than other regions. In order to do so we introduce the Adaptive Distributed Hierarchical Sensing (ADHS) algorithm. This algorithm employs a homogenous sensor network in a distributed fashion and changes the sampling rates of the CHs based on the variance of the sampled data without damaging significantly the accuracy of the sensed area.
机译：能源效率是传感器网络中至关重要的性能指标，直接决定网络的寿命。因此，WSN中的关键因素是提高整体能效以延长网络寿命。尽管已经提出了许多算法来优化能量因数，但是能量效率仍然是WSN的主要问题之一，尤其是当需要对具有不同类型负载的区域进行采样时。与其他用于分层采样的节能方案不同，我们的假设是，在延长网络寿命方面，可以在触发的某些特定区域中自适应地重新修改CHs感应速率（尤其是处理和传输阶段）。明显少于其他地区。为了做到这一点，我们引入了自适应分布式分层感知（ADHS）算法。该算法以分布式方式使用同质传感器网络，并基于采样数据的方差来更改CH的采样率，而不会显着损害感测区域的准确性。

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