Extending wireless algorithm design to arbitrary environments via metricity

Abstract

Efficient spectrum use in wireless sensor networks through spatial reuse requires effective models of packet reception at the physical layer in the presence of interference. Despite recent progress in analytic and simulations research into worst-case behavior from interference effects, these efforts generally assume geometric path loss and isotropic transmission, assumptions which have not been borne out in experiments.Our paper aims to provide a methodology for grounding theoretical results into wireless interference in experimental reality. We develop a new framework for wireless algorithms in which distancebased path loss is replaced by an arbitrary gain matrix, typically obtained by measurements of received signal strength (RSS). We experimentally evaluate the framework in two indoors testbeds with 20 and 60 motes, and confirm superior predictive performance in packet reception rate for a gain matrix model over a geometric distance-based model.At the heart of our approach is a new parameter ζ called metricity which indicates how close the gain matrix is to a distance metric, effectively measuring the complexity of the environment. A powerful theoretical feature of this parameter is that all known SINR scheduling algorithms that work in general metric spaces carry over to arbitrary gain matrices and achieve equivalent performance guarantees in terms of ζ as previously obtained in terms of the path loss constant. Our experiments confirm the sensitivity of ζ to the nature of the environment. Finally, we show analytically and empirically how multiple channels can be leveraged to improve metricity and thereby performance. We believe our contributions will facilitate experimental validation for recent advances in algorithms for physical wireless interference models.

Deterministic Digital Clustering of Wireless Ad Hoc Networks

“…) Recently Halldorsson et al [18] proposed an algorithm which can be faster assuming that nodes are equipped with some extra capabilities (e.g., detection whether a received message is sent from a close neighbor) and the interference function on the local (one-hop) level is defined as in the classic radio network model. Given additional model features as channel/carrier sensing, total interference estimation by signal measurements and others, efficient algorithms for various communication problems have been designed in recent years, e.g., [17,33,31].…”

Effective Wireless Scheduling via Hypergraph Sketches

“…Indeed, the additivity of interference and the near-threshold nature of signal reception has been well established in experiments. The model is though far from perfect: the assumption of signal strength decreasing inversely polynomially with distance can be far off [52,48,50,21]. We discuss here the various proposed alternatives and explain why analysis in the pure SINR model is of fundamental importance.…”

“…For every-case analysis, a natural generalization of the SINR model would be to shed the geometry and allow for an arbitrary signal-quality matrix. One could in practice obtain this in the form of facts-on-the-ground signal strength measurements [21,7]. This generalization is, however, too expensive as it runs into the computational intractability monster: with such a formulation one can encode the coloring problem in general graphs [18], which is known to be famously hard to approximate [15].…”