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.

We examine the problem of minimizing feedbacks 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's 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.

The advent of practical rateless codes enables implementation of highly efficient packet-level forward error correction (FEC) strategies for reliable data broadcasting in loss-prone wireless networks, such as sensor networks. Yet, the critical question of accurately quantifying the proper amount of redundancy has remained largely unsolved. In this paper, we exploit advances in extreme value theory to rigorously address this problem. Under the asymptotic regime of a large number of receivers, we derive a closed-form expression for the cumulative distribution function (CDF) of the completion time of file distribution. We show the existence of a phase transition associated with this CDF and accurately locate the transition point. We derive tight convergence bounds demonstrating the accuracy of the asymptotic estimate for the practical case of a finite number of receivers. Further, we asymptotically characterize the CDF of the completion time under heterogeneous packet loss, by establishing a close relationship between the data broadcasting and multi-set coupon collector problems. We demonstrate the benefits of our approach through simulation and through real experiments on a Tmote Sky sensor testbed. Specifically, we augment the existing Rateless Deluge software dissemination protocol with an extreme value FEC strategy. The experimental results reveal reduction by a factor of five in retransmission request messages and by a factor of two in total dissemination time, at the cost of a marginally higher number of data packet transmissions in the order of 5%.

We analyze the performance limits of data dissemination with multichannel, single radio sensors under random packet loss. We formulate the problem of minimizing the average delay of data dissemination as a stochastic shortest path problem and show that, for an arbitrary topology network, an optimal control policy can be found in a finite number of steps, using value iteration or Dijkstra's algorithm. However, the computational complexity of this solution is generally prohibitive. We thus focus on two special classes of network topologies of practical interest, namely single-hop clusters and multihop cluster chains. For these topologies, we derive the structure of policies that achieve an asymptotically optimal average delay, in networks with large number of nodes. Our analysis reveals that a single radio in each node suffices to achieve performance gain directly proportional to the total number of channels available. Through simulation, we show that the derived policies perform close to optimal even for networks with small and moderate numbers of nodes and can be implemented with limited overhead.

The advent of practical rateless codes enables implementation of highly efficient packet-level forward error correction (FEC) strategies for reliable data broadcasting in loss-prone wireless networks. Yet, the critical question of accurately quantifying the proper amount of redundancy has remained largely unsolved. In this paper, we exploit advances in extreme value theory to rigorously address this problem. Under the asymptotic regime of a large number of receivers, we derive a closed-form expression for the cumulative distribution function (CDF) of the completion time of file distribution. We show the existence of a phase transition associated with this CDF and accurately locate the transition point. We derive tight convergence bounds demonstrating the accuracy of the asymptotic estimate for the practical case of a finite number of receivers. We also provide an asymptotic closed-form expression on the expected completion time under heterogeneous packet loss. We demonstrate the benefits of our approach through simulation and through real experiments on a testbed of 20 Tmote Sky sensors. Specifically, we augment the existing Rateless Deluge software dissemination protocol with an extreme value FEC strategy. The experimental results reveal reduction by a factor of five in retransmission request messages and by a factor of two in total dissemination time, at the cost of a marginally higher number of data packet transmissions in the order of 5%.

We analyze the performance limits of data dissemination with multi-channel, single radio sensors. We formulate the problem of minimizing the average delay of data dissemination as a stochastic shortest path problem and show that, for an arbitrary topology network, an optimal control policy can be found in a finite number of steps, using value iteration or Dijsktra's algorithm. However, the computational complexity of this solution is generally prohibitive. We thus focus on two special classes of network topologies of practical interest, namely single-hop clusters and multi-hop cluster trees. For these topologies, we derive the structure of policies that achieve an average delay within a factor 1 + e of the optimal average delay, in networks with large number of nodes. Through simulation, we show that these policies perform close to optimal even for networks with small and moderate numbers of nodes. Our analysis and simulations reveal that multichannel data dissemination policies lead to a drastic reduction in the average delay, up to a factor as large as the total number of channels available, even though each node can communicate over only one channel at any point of time. Finally, we present the foundations of a methodology, based on extreme value theory, allowing the implementation of our near-optimal dissemination policies with minimal overhead.

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