基于马尔可夫链的触发驱动传感器网络能耗预测

W. Zha, W. Ng
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引用次数: 2

摘要

利用马尔可夫模型预测传感器节点能量状态的可行性得到了验证。因此,用户可以实时监控传感器节点的能量状态,而无需频繁查询。然而,应用马尔可夫模型需要一个稳态转移概率,这意味着该预测只适用于调度驱动的传感器网络,而不适用于触发驱动的传感器网络。在本文中,我们将介绍如何使用马尔可夫模型在触发驱动传感器网络中进行预测。该方法通过考虑事件分布和查询模式,实现了对触发驱动传感器网络中传感器节点能级信息的预测。实验结果表明,该模型能够准确地预测触发驱动传感器网络中传感器节点的能量状态。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predict Energy Consumption of Trigger-Driven Sensor Network by Markov Chains
Markov Model has been proved its feasibility of predicting the energy state of sensor nodes. Thus, user can monitor sensor nodes energy state in real-time without querying them frequently. However, a stationary state transition probability is required to apply Markov Model, which means the prediction is only applicable to schedule-driven sensor networks rather than trigger-driven sensor networks. In this paper, we will introduce how to use Markov Model to make prediction in trigger-driven sensor networks. By considering events distribution and query patterns, our proposed method managed to predict sensor node energy level information of trigger-driven sensor networks. Experimental results show that our proposed model is able to predict sensor node energy state accurately for trigger-driven sensor networks.
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