Taming Wireless Fluctuations by Predictive Queuing Using a Sparse-Coding Link-State Model

We introduce State-Informed Link-Layer Queuing (SILQ), a system that models, predicts, and avoids packet delivery failures caused by 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 due to 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 ...

• Type: Journal
• Year: 2015
• Where: MOBIHOC
• Authors: Stephen J. Tarsa, Marcus Z. Comiter, Michael B. Crouse, Bradley McDanel, H. T. Kung
• Keywords: Computer Networks, MOBIHOC 2015