1 research outputs found
Wireless Scheduling Algorithms in Complex Environments
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 distance-based path loss is replaced by an
arbitrary gain matrix, typically obtained by measurements of received signal
strength (RSS). Gain matrices allow for the modeling of complex environments,
e.g., with obstacles and walls. 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 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 as previously obtained in
terms of the path loss constant. Our experiments confirm the sensitivity of
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