Parameter adaptation in stochastic optimization
On-line learning in neural networks
Wireless integrated network sensors
Communications of the ACM
Machine Learning
A high-throughput path metric for multi-hop wireless routing
Proceedings of the 9th annual international conference on Mobile computing and networking
Understanding packet delivery performance in dense wireless sensor networks
Proceedings of the 1st international conference on Embedded networked sensor systems
Taming the underlying challenges of reliable multihop routing in sensor networks
Proceedings of the 1st international conference on Embedded networked sensor systems
Analysis of wireless sensor networks for habitat monitoring
Wireless 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
A wireless sensor network For structural monitoring
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
Versatile low power media access for 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
Numerical Methods for Unconstrained Optimization and Nonlinear Equations (Classics in Applied Mathematics, 16)
Statistical model of lossy links in wireless sensor networks
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
MoteLab: a wireless sensor network testbed
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Experimental study of concurrent transmission in wireless sensor networks
Proceedings of the 4th international conference on Embedded networked sensor systems
X-MAC: a short preamble MAC protocol for duty-cycled wireless sensor networks
Proceedings of the 4th international conference on Embedded networked sensor systems
Fidelity and yield in a volcano monitoring sensor network
OSDI '06 Proceedings of the 7th symposium on Operating systems design and implementation
Link quality prediction in mesh networks
Computer Communications
The β-factor: measuring wireless link burstiness
Proceedings of the 6th ACM conference on Embedded network sensor systems
Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems
Bursty traffic over bursty links
Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems
Fingerprinting channel dynamics in indoor low-power wireless networks
Proceedings of the 8th ACM international workshop on Wireless network testbeds, experimental evaluation & characterization
Data-driven link quality prediction using link features
ACM Transactions on Sensor Networks (TOSN)
| Hi-index | 0.00 |
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.