The β-factor: measuring wireless link burstiness
Proceedings of the 6th ACM conference on Embedded network sensor systems
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Advances in low-cost, low-power micro-sensor and radio design have led to active research in large-scale networks of small, wireless, low-power sensors and actuators. Radio communication is one of the critical components in any wireless distributed systems, but wireless sensor networks make extensive use of communications in order to perform the coordinated sensing tasks. These systems will be deployed in many environments the present very harsh conditions for wireless communication using low-power radios, including multipath/fading effects, reflections from obstacles, and attenuation from foliage. Moreover, recently several landmark wireless sensor network deployment studies clearly demonstrated a large discrepancy between experimentally observed communication properties and properties produced by widely used simulation models. New approaches and models are required to deal with vagaries of the communication channel. We first motivate the need for precise wireless link characterization, and provide a case study of an algorithm that uses on-line neighbor density estimation. We then describe the methodology used to collect extensive experimental data traces, using different hardware, in different environment, under systematic varied conditions. We perform statistical analysis on the data and provide sound foundations for our conclusions by extracting relationships between location (e.g distance) and communication properties (e.g. reception rate) using non-parametric statistical techniques. The objective is to provide a probability density function that completely characterizes the relationship. Furthermore, we study individual link properties and their correlation with respect to common transmitters, receivers and geometrical location. Using the modeled communication properties, we develop a series of wireless network models that produce networks of arbitrary sizes with realistic properties. We use an iterative improvement-based optimization procedure to generate network instances that are statistically similar to empirically observed networks. We evaluate the accuracy of our conclusions using our models on a set of standard communication tasks, like connectivity maintenance and routing. We study more deeply the temporal properties of links in low power wireless communications. We investigate short term temporal issues, like lagged autocorrelation of individual links, lagged correlation of reverse links, and consecutive same path links. We also study long term temporal aspects, gaining insight on the length of time the channel needs to be measured and how often we should update our models. In addition, we explore how statistical temporal properties impact routing protocols. We analyze one-to-one routing schemes and develope new routing algorithms that consider autocorrelation, and reverse link and consecutive same path link lagged correlations. We have developed two new routing algorithms for the cost link model: (i) a generalized Dijkstra algorithm with centralized execution, and (ii) a localized distributed probabilistic algorithm. Finally, we describe all the insight we obtained by analyzing all these data, and propose new research direction for network protocol and system designers of sensor networks.