In this paper, we investigate the application of network coding to all-optical networks from both the algorithmic and infrastructural perspectives. We study the effectiveness of using network coding for optical-layer dedicated protection of multicast traffic which provides robustness against link failures in the network. We present a heuristic for solving this problem and compare it with both inefficient optimal methods and non-network coding approaches. Our experiments show that our heuristic provides near optimal performance while significantly outperforming existing approaches for dedicated multicast protection. We also propose architectures for specialized alloptical circuits capable of performing the processing required for network coding and show how these devices can be effectively deployed in an all-optical multicast network.

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Abstract

Abstract-In this paper we investigate the problem of localizing a mobile device based on readings from its embedded sensors utilizing machine learning methodologies. We consider a realworld environment, collect a large dataset of 3110 datapoints, and examine the performance of a substantial number of machine learning algorithms in localizing a mobile device. We have found algorithms that give a mean error as accurate as 0.76 meters, outperforming other indoor localization systems reported in the literature. We also propose a hybrid instance-based approach that results in a speed increase by a factor of ten with no loss of accuracy in a live deployment over standard instance-based methods, allowing for fast and accurate localization. Further, we determine how smaller datasets collected with less density affect accuracy of localization, important for use in real-world environments. Finally, we demonstrate that these approaches are appropriate for real-world deployment by evaluating their performance in an online, in-motion experiment.

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