As an integral part of reliable communication in wireless networks, effective link estimation is essential for routing protocols. However, due to the dynamic nature of wireless channels, accurate link quality estimation remains a challenging task. In this article, we propose 4C, a novel link estimator that applies link quality prediction along with link estimation. Our approach is data driven and consists of three steps: data collection, offline modeling, and online prediction. The data collection step involves gathering link quality data, and based on our analysis of the data, we propose a set of guidelines for the amount of data to be collected in our experimental scenarios. The modeling step includes offline prediction model training and selection. We present three prediction models that utilize different machine learning methods, namely, naive Bayes classifier, logistic regression, and artificial neural networks. Our models take a combination of PRR and the physical-layer information, that is, Received Signal Strength Indicator (RSSI), Signal-to-Noise Ratio (SNR), and Link Quality Indicator (LQI) as input, and output the success probability of delivering the next packet. From our analysis and experiments, we find that logistic regression works well among the three models with small computational cost. Finally, the third step involves the implementation of 4C, a receiver-initiated online link quality prediction module that computes the short temporal link quality. We conducted extensive experiments in the Motelab and our local indoor testbeds, as well as an outdoor deployment. Our results with single- and multiple-senders experiments show that with 4C, CTP improves the average cost of delivering a packet by 20% to 30%. In some cases, the improvement is larger than 45%.

Link quality estimation is a fundamental component of the low-power wireless network protocols and is essential for routing protocols in Wireless Sensor Networks (WSNs). However, accurate link quality estimation remains a challenging task due to the notoriously dynamic and unpredictable wireless environment. In this article we argue that, in addition to the estimation of current link quality, prediction of the future link quality is more important for the routing protocol to establish low-cost delivery paths. We propose to apply machine learning methods to predict the link quality in the near future to facilitate the utilization of intermediate links with frequent quality changes. Moreover, we show that, by using online learning methods, our adaptive link estimator (TALENT) adapts to network dynamics better than statically trained models without the need of a priori data collection for training the model before deployment. We implemented TALENT in TinyOS with Low-Power Listening (LPL) and conducted extensive experiments in three testbeds. Our experimental results show that the addition of TALENT increases the delivery efficiency 1.95 times on average compared with a 4B, state-of-the-art link quality estimator, as well as improves the end-to-end delivery rate when tested on three different wireless testbeds.

Link quality estimation is a fundamental component of the low power wireless network protocols and is essential for routing protocols in Wireless Sensor Networks (WSNs). However, accurate link quality estimation remains a challenging task due to the notoriously dynamic and unpredictable wireless environment. In this paper, we argue that in addition to the estimation of current link quality, prediction of the future link quality is more important for the routing protocol to establish low cost delivery paths. We propose to apply machine learning methods to predict the link quality in the near future to facilitate the utilization of intermediate links with frequent quality changes. Moreover, we show that by using online learning methods, our adaptive link estimator (TALENT) adapts to network dynamics better than statically trained models without the need of a priori data collection for training the model before deployment. We implemented TALENT in TinyOS with Low-Power Listening (LPL) and conducted extensive experiments in three testbeds. Our experimental results show that the addition of TALENT increases the delivery efficiency 1.95 times on average compared with 4B, the state of the art link quality estimator, as well as improve the end-to-end delivery rate when tested on three different wireless testbeds.

The lossy nature of wireless communication leads to many challenges while designing multihop networks. As an integral part of reliable communication in wireless networks, effective link estimation is essential for routing protocols. Recent studies have shown that link reliability-based metrics like ETX have better performance than traditional metrics such as hop count or latency. Usually, such metrics employ techniques like blacklisting, involving thresholds during the link estimation process. In this paper, we conduct a detailed performance analysis of three commonly used link-quality metrics in wireless sensor networks: ETX, 4Bit, and RNP. We study the interplay between these metrics and CTP, a tree-based routing protocol provided by TinyOS. The objectives of our experiment are two fold. First, by applying different link-quality metrics to the same routing protocol, we provide extensive evaluation on ETX, 4Bit and RNP with insights on their performance under different criteria. Second, we study the impact of the presence or absence of a blacklisting policy when using these link quality estimation metrics. As to our knowledge, this paper is the first to compare the performance between these link quality based metrics with networks of different qualities under realistic conditions.