Practical Data Prediction for Real-World Wireless Sensor Networks
Practical Data Prediction for Real-World Wireless Sensor Networks
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DOI：
10.1109/tkde.2015.2411594
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发表时间：
2015-08
影响因子：
8.9
通讯作者：
Usman Raza;A. Camerra;A. Murphy;Themis Palpanas;G. Picco
中科院分区：
文献类型：
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作者：
Usman Raza;A. Camerra;A. Murphy;Themis Palpanas;G. Picco
数据预测被提出在无线传感器网络（WSNs），以延长系统的生命周期，使sink确定采样的数据，在一定的精度范围内，只有最小的通信从源节点。一些理论研究清楚地证明了这种方法的巨大潜力，能够抑制源节点上的绝大多数数据报告。然而，所采用的技术是相对复杂的，它们的可行性资源稀缺的无线传感器网络设备往往是不确定的。更一般地说，文献缺乏来自真实世界部署的报告，量化了由数据预测与底层网络的相互作用确定的整体系统范围的生命周期改进。这两个方面，可行性和系统范围内的收益，是决定数据预测在现实世界中的WSN应用程序的实用性的关键。在本文中，我们描述了基于导数的预测（DBP），一种新的数据预测技术比文献中发现的更简单。使用来自不同WSN部署的真实的数据集进行的评估表明，DBP通常比竞争对手表现更好，数据抑制率高达99%，预测准确性良好。然而，一个真实的无线传感器网络在公路隧道中的实验表明，当网络堆栈被考虑在内，DBP只有三倍的寿命本身是一个显着的结果，但从上述数据抑制率相差甚远。为了充分实现数据预测带来的节能效果，必须对数据层和网络层进行联合优化。在我们的测试床实验中，考虑到DBP的操作，对MAC和路由栈进行简单的调整，可以使w.r.t.主流定期报告。
Data prediction is proposed in wireless sensor networks (WSNs) to extend the system lifetime by enabling the sink to determine the data sampled, within some accuracy bounds, with only minimal communication from source nodes. Several theoretical studies clearly demonstrate the tremendous potential of this approach, able to suppress the vast majority of data reports at the source nodes. Nevertheless, the techniques employed are relatively complex, and their feasibility on resource-scarce WSN devices is often not ascertained. More generally, the literature lacks reports from real-world deployments, quantifying the overall system-wide lifetime improvements determined by the interplay of data prediction with the underlying network. These two aspects, feasibility and system-wide gains, are key in determining the practical usefulness of data prediction in real-world WSN applications. In this paper, we describe derivative-based prediction (DBP), a novel data prediction technique much simpler than those found in the literature. Evaluation with real data sets from diverse WSN deployments shows that DBP often performs better than the competition, with data suppression rates up to 99 percent and good prediction accuracy. However, experiments with a real WSN in a road tunnel show that, when the network stack is taken into consideration, DBP only triples lifetime-a remarkable result per se, but a far cry from the data suppression rates above. To fully achieve the energy savings enabled by data prediction, the data and network layers must be jointly optimized. In our testbed experiments, a simple tuning of the MAC and routing stack, taking into account the operation of DBP, yields a remarkable seven-fold lifetime improvement w.r.t. the mainstream periodic reporting.