C4.5: programs for machine learning
C4.5: programs for machine learning
Evidence-based static branch prediction using machine learning
ACM Transactions on Programming Languages and Systems (TOPLAS)
Machine Learning
Bug isolation via remote program sampling
PLDI '03 Proceedings of the ACM SIGPLAN 2003 conference on Programming language design and implementation
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
Distributed regression: an efficient framework for modeling sensor network data
Proceedings of the 3rd international symposium on Information processing in sensor networks
Link-level measurements from an 802.11b mesh network
Proceedings of the 2004 conference on Applications, technologies, architectures, and protocols for computer communications
Mitigating congestion in wireless sensor networks
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
Temporal properties of low power wireless links: modeling and implications on multi-hop routing
Proceedings of the 6th ACM international symposium on Mobile ad hoc networking and computing
Failure Diagnosis Using Decision Trees
ICAC '04 Proceedings of the First International Conference on Autonomic Computing
Proceedings of the 3rd international conference on Embedded networked sensor systems
Siphon: overload traffic management using multi-radio virtual sinks in sensor networks
Proceedings of the 3rd international conference on Embedded networked sensor systems
Near-optimal sensor placements: maximizing information while minimizing communication cost
Proceedings of the 5th international conference on Information processing in sensor networks
Statistical model of lossy links in wireless sensor networks
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Telos: enabling ultra-low power wireless research
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
A wireless sensor network for structural health monitoring: performance and experience
EmNets '05 Proceedings of the 2nd IEEE workshop on Embedded Networked Sensors
Quality-Aware Routing Metrics for Time-Varying Wireless Mesh Networks
IEEE Journal on Selected Areas in Communications
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Routing in sensor networks maintains information on neighbor states and potentially many other factors in order to make informed decisions. Challenges arise both in (a) performing accurate and adaptive information discovery and (b) processing/analyzing the gathered data to extract useful features and correlations. In this paper, we explore using supervised learning techniques to address such challenges in wireless sensor networks. Machine learning has been very effective in discovering relations between attributes and extracting knowledge and patterns using a large corpus of samples.As a case study, we use link quality prediction to demonstrate the effectiveness of our approach. For this purpose, we present MetricMap,a link-quality aware collection protocol atop MintRoute that derives link quality information using knowledge acquired from a training phase. Our approach allows MetricMap to maintain efficient routing in situations where traditional approaches fail. Evaluation on a 30-node sensor network testbed shows that MetricMap can achieve up to 300% improvement on data delivery rate in a high data-rate application, with no negative impact on other performance metrics, such as data latency. Our approach is based on real-world measurement and provides a new perspective to routing optimizations in wireless sensor networks.