Measurement Based Interference Models for Wireless Scheduling Algorithms

Modeling physical layer behavior of packet reception in the presence of interference is central to achieving efficient spectrum use in wireless sensor networks via spatial reuse. On one hand, analytic and simulations research has largely relied on assumptions of geometric path loss and isotropic transmission which have not been borne out in experiments. Experimental research, on the other hand, has not adopted theoretical models and instead focused on measuring the reality on the ground. We propose a new framework for wireless algorithms. First, distance-based path loss is replaced by an arbitrary gain matrix, typically obtained by measurements of received signal strength (RSS). This allows for the modeling of complex environments, e.g., with obstacles and walls. Second, a new parameter ζ indicates how close the gain matrix is to a distance metric, effectively measuring the complexity of the environment. We experimentally validate our framework on two indoors testbeds with 20 and 60 motes. The results validate the basic properties of the model, the predictive ability of packet reception, dominance over distance-based models, and the sensitivity of ζ to the nature of the environment. Theoretically, we show that all known SINR scheduling algorithms that work in general metric spaces carry over and achieve equivalent performance guarantees in the new model. The conclusions suggest that wireless theory can finally be grounded in experimental practice.