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We introduce State-Informed Link-Layer Queuing (SILQ), a system that models, predicts, and avoids packet delivery failures due to temporary wireless outages in everyday scenarios. By stabilizing connections in adverse link conditions, SILQ boosts throughput and reduces performance variation for network applications, for example by preventing unnecessary TCP timeouts caused by dead zones, elevators, and subway tunnels. SILQ makes predictions in real-time by actively probing links, matching measurements to an overcomplete dictionary of patterns learned offline, and classifying the resulting sparse feature vectors to identify those that precede outages. We use a clustering method called sparse coding to build our data-driven link model, and show that it produces more variation-tolerant predictions than traditional loss-rate, location-based, or Markov chain techniques. We present extensive data collection and field-validation of SILQ in airborne, indoor, and urban scenarios of practical interest. We show how offline unsupervised learning discovers link-state patterns that are stable across diverse networks and signal-propagation environments. Using these canonical primitives, we train outage predictors for 802.11 (Wi-Fi) and 3G cellular networks to demonstrate TCP throughput gains of 4x with off-the-shelf mobile devices. SILQ addresses delivery failures solely at the link layer, requires no new hardware, and upholds the end-to-end design principle, enabling easy integration across applications, devices, and networks., Engineering and Applied Sciences

Gait signals detectable by sensors on ubiquitous personal devices such as smartphones can reveal characteristics unique to each individual, and thereby offer a new approach to recognizing users. Conventional pattern matching approaches use inner-product based distance measures which are not robust to common variations in time-series analysis (e.g., shifts and stretching). This is unfortunate given that it is well understood that capturing such variations is paramount for model performance. This work shows how machine learning methods which encode gait signals into a feature space based on a dictionary can use convolution and Dynamic TimeWarping (DTW) similarity measures to improve classification accuracy in a variety of situations common to gait recognition. We also show that data augmentation is crucial in gait recognition, as diverse training data in practical applications is very limited. We validate the effectiveness of these methods empirically, and demonstrate the identification of user gait patterns where shift and stretch variations in measurements are substantial. We present a new gait dataset that contains a complete representation of the variations that can be expected in real-world recognition scenarios. We compare our techniques against the current state of the art gait period detection and normalization schemes on our dataset and show improved classification accuracy under all experimental scenarios., Engineering and Applied Sciences