System architecture directions for networked sensors
ASPLOS IX Proceedings of the ninth international conference on Architectural support for programming languages and operating systems
Telos: enabling ultra-low power wireless research
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Bursty traffic over bursty links
Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems
An empirical study of low-power wireless
ACM Transactions on Sensor Networks (TOSN)
Propagation measurements and models for wireless communications channels
IEEE Communications Magazine
TALENT: temporal adaptive link estimator with no training
Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems
Hi-index | 0.00 |
Wireless low-power embedded devices are populating indoor environments, where everyday activities drastically impact communication. We explore a statistical approach to identify changes to the communication links state during system operation. The long-term behavior of the link RSSI is modeled with a normal distribution and compared against the model of the most recent measurements. A Welch's t-Test is then employed to identify whether the short-term and long-term link evolutions stem from the same distribution. Upon significant divergence, the long-term model is updated and a significant change in the underlying communication state is inferred. We investigate this technique to efficiently store a compressed fingerprint of the evolution of communication. Considering the memory constraints of low-power embedded systems, this approach allows to gather extensive information on the behavior of communication directly from the deployed network. This fingerprint could then be used to replay the network dynamics in simulation. We implemented the introduced techniques to prove their feasibility. In controlled experiments, we evaluate the reactivity and sensitivity of the approach to changes in the environment, as well as the accuracy of the resulting channel fingerprint.