The increasing density of WiFi Access Points (APs) in metropolitan areas is enabling an opportunistic model of wireless networking, whereby a "guest" user within range of one or more wireless APs can gain temporary Internet access through these APs. In this paper, we address the problem of TCP throughput prediction for opportunistic networks. Applications of opportunistic networking can benefit from such predictions by adapting to prevailing network conditions. Our approach is different from prior efforts to model wireless network throughput in that only the two communicating endpoints participate in the prediction, and no information about network topology or traffic loads generated by interfering sources is required. Our goal is to understand how accurate throughput predictions can be under the above assumptions. The physical environment considered in our study includes varying degrees of interference, indoor and outdoor networks, and nodes that are stationary or moving at walking or driving speeds. We use throughput predictors based on time series analysis and machine learning techniques, as they are well-suited to predicting phenomena with unknown variables. The prediction accuracy that our methods yield is cause for cautious optimism. We find that 80% to 100% of predictions are within a factor of two of actual throughput. This bound on accuracy means that predictions are useful for certain applications, because this bound (a) can be achieved by measurements lasting for as little as 0.3 seconds, and (b) holds even when nodes are driving at speeds of 15-25 mph.
['Mariyam Mirza', 'Kevin Springborn', 'Suman Banerjee', 'Paul Barford', 'Michael Blodgett', 'Xiaojin Zhu']
On The Accuracy of TCP Throughput Prediction for Opportunistic Wireless Networks