Parity-based loss recovery for reliable multicast transmission
SIGCOMM '97 Proceedings of the ACM SIGCOMM '97 conference on Applications, technologies, architectures, and protocols for computer communication
Scalable feedback for large groups
IEEE/ACM Transactions on Networking (TON)
RMDP: an FEC-based reliable multicast protocol for wireless environments
ACM SIGMOBILE Mobile Computing and Communications Review
FOCS '02 Proceedings of the 43rd Symposium on Foundations of Computer Science
PfHSN '96 Proceedings of the TC6 WG6.1/6.4 Fifth International Workshop on Protocols for High-Speed Networks V
Feedback-Free Multicast Prefix Protocols
ISCC '98 Proceedings of the Third IEEE Symposium on Computers & Communications
An analysis of packet loss correlation in FEC-enhanced multicast trees
ICNP '00 Proceedings of the 2000 International Conference on Network Protocols
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Proceedings of the 1st international conference on Embedded networked sensor systems
The dynamic behavior of a data dissemination protocol for network programming at scale
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
Simulating the power consumption of large-scale sensor network applications
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
Exploiting multi-Channel diversity to speed up over-the-air programming of wireless sensor networks
Proceedings of the 3rd international conference on Embedded networked sensor systems
Rateless Deluge: Over-the-Air Programming of Wireless Sensor Networks Using Random Linear Codes
IPSN '08 Proceedings of the 7th international conference on Information processing in sensor networks
RT oblivious erasure correcting
IEEE/ACM Transactions on Networking (TON)
Extreme value FEC for reliable broadcasting in wireless networks
IEEE Journal on Selected Areas in Communications - Special issue on simple wireless sensor networking solutions
IEEE/ACM Transactions on Networking (TON)
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We examine the problem of minimizing feedback in reliable wireless broadcasting by pairing rateless coding with extreme value theory. Our key observation is that, in a broadcast environment, this problem resolves into estimating the maximum number of packets dropped among many receivers rather than for each individual receiver. With rateless codes, this estimation relates to the number of redundant transmissions needed at the source in order for all receivers to correctly decode a message with high probability. We develop and analyze two new data dissemination protocols, called Random Sampling (RS) and Full Sampling with Limited Feedback (FSLF), based on the moment and maximum likelihood estimators in extreme value theory. Both protocols rely on a single-round learning phase, requiring the transmission of a few feedback packets from a small subset of receivers. With fixed overhead, we show that FSLF has the desirable property of becoming more accurate as the receivers' population gets larger. Our protocols are channel-agnostic, in that they do not require a priori knowledge of (i.i.d.) packet loss probabilities, which may vary among receivers. We provide simulations and an improved full-scale implementation of the Rateless Deluge over-the-air programming protocol on sensor motes as a demonstration of the practical benefits of our protocols, which translate into about 30% latency and energy consumption savings. Furthermore, we apply our protocols to real-time (RT) oblivious rateless codes in broadcast settings. Through simulations, we demonstrate a 100-fold reduction in the amount of feedback packets while incurring an increase of only 10%-20% in the number of encoded packets transmissions.