Wireless Communications
Link-level measurements from an 802.11b mesh network
Proceedings of the 2004 conference on Applications, technologies, architectures, and protocols for computer communications
Telos: enabling ultra-low power wireless research
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
ATPC: adaptive transmission power control for wireless sensor networks
Proceedings of the 4th international conference on Embedded networked sensor systems
Improving wireless simulation through noise modeling
Proceedings of the 6th international conference on Information processing in sensor networks
Mirage: a microeconomic resource allocation system for sensornet testbeds
EmNets '05 Proceedings of the 2nd IEEE workshop on Embedded Networked Sensors
Investigating a physically-based signal power model for robust low power wireless link simulation
Proceedings of the 11th international symposium on Modeling, analysis and simulation of wireless and mobile systems
The β-factor: measuring wireless link burstiness
Proceedings of the 6th ACM conference on Embedded network sensor systems
A measurement study of interference modeling and scheduling in low-power wireless networks
Proceedings of the 6th ACM conference on Embedded network sensor systems
Physically-based models of low-power wireless links using signal power simulation
Computer Networks: The International Journal of Computer and Telecommunications Networking
Improving wireless link simulation using multilevel markov models
ACM Transactions on Sensor Networks (TOSN)
Discrete-time Markov Model for Wireless Link Burstiness Simulations
Wireless Personal Communications: An International Journal
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We observe that low-power wireless links have non-trivial time-scaling characteristics at both the physical- and link-layers. Packet reception rate (PRR) analysis shows that links are bursty rather than constant, i.e., their reception quality varies greatly from the overall packet reception rate at different times. Furthermore, this variation is seen at many timescales. We provide a possible explanation for burstiness using wavelet analysis of RSSI traces from a variety of wireless links. We show that these traces can be considered as consistent with statistical self-similarity but not with long range dependence. Using the variance in RSSI, we suggest a way to easily characterize when scaling occurs. Finally, while current simulators do not capture scaling, we propose and validate a possible modeling technique for network links that conforms to scaling phenomena.