Machine Learning
A high-throughput path metric for multi-hop wireless routing
Proceedings of the 9th annual international conference on Mobile computing and networking
Taming the underlying challenges of reliable multihop routing in sensor networks
Proceedings of the 1st international conference on Embedded networked sensor systems
CODA: congestion detection and avoidance in sensor networks
Proceedings of the 1st international conference on Embedded networked sensor systems
Mitigating congestion in wireless sensor networks
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Incentive Compatible Cost- and Stability-Based Routing in Ad Hoc Networks
ICPADS '06 Proceedings of the 12th International Conference on Parallel and Distributed Systems - Volume 1
On multipath routing in multihop wireless networks: security, performance, and their tradeoff
EURASIP Journal on Wireless Communications and Networking - Special issue on wireless network security
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Efficient routing in wireless sensor networks entails the establishment of high quality links. Recent research has shown that metric-based routing, such as ETX [6]can significantly improve routing performance by tracking various link-quality metrics. Such metrics, however, may fail to capture link quality at relatively high traffic rates. This poster describes how machine learning techniques can be leveraged to help estimate link quality in those adverse scenarios. We also present MetricMap, a metric-based protocol using our learning-enabled link quality assessment method. Experimental results on MoteLab show that MetricMap can achieve up to 400% improvement on data delivery rate in a high traffic rate application, with no negative impact on other performance metrics, such as data latency.