P^2IT: Predicting Packet Interarrival Time in Asynchronous Duty-Cycling Sensor Networks

In this paper, we investigate the predictability of packet arrivals in asynchronous duty-cycling wireless sensor networks (WSNs). We conduct statistical analysis on data traces collected in both outdoor large-scale and indoor testbed WSN to show that traditional well-known traffic models, e.g., Poisson process and Self-similarity process, do not fit well for modeling packet arrivals in both networks. According to our observations, some key characteristics such as sleeping interval and sampling rate have significant impact on the traffic patterns under Low power listening (LPL) models. Hence, we raise a question: could we achieve accurate prediction on the packet arrivals in asynchronous duty-cycling WSNs? To answer this question, we design a novel data-driven predictor P²IT focusing on two prediction goals: (i) the arrival time of the next packet (ii) the number of arrival packets within a short time interval. We conduct extensive trace-driven experiments to demonstrate that our predictor achieves high acuracies on both prediction goals under various experimental settings.