[Paper Review] Wireless Scheduling Algorithms in Complex Environments

Helga Gudmundsdottir, Eyjólfur Ingi Ásgeirsson+4 more|arXiv (Cornell University)|Jan 8, 2014

Mobile Ad Hoc Networks37 references5 citations

TL;DR

This paper proposes a measurement-based wireless scheduling framework using a received signal strength (RSS) gain matrix to replace geometric path loss models, enabling accurate prediction of packet reception rates in real-world environments. It introduces the metricity parameter ζ to quantify environmental complexity, showing that SINR scheduling algorithms maintain equivalent theoretical performance in realistic settings as in idealized metric spaces.

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 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 $ζ$ 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.

Motivation & Objective

• Address the gap between theoretical wireless interference models and real-world signal propagation by moving beyond geometric path loss assumptions.
• Develop a practical framework for wireless scheduling that integrates empirical signal strength measurements into theoretical algorithm design.
• Introduce a new metric, metricity (ζ), to quantify environmental complexity in terms of how closely signal strength matrices resemble distance metrics.
• Demonstrate that existing worst-case SINR scheduling algorithms can be directly applied to real-world gain matrices with performance guarantees preserved via ζ.
• Validate the model's predictive power and robustness through experimental evaluation in indoor testbeds with 20 and 60 motes.

Proposed method

• Replace geometric path loss with an arbitrary gain matrix derived from empirical RSS measurements between wireless nodes.
• Define metricity ζ as a measure of how closely the RSS matrix approximates a distance metric, capturing environmental complexity.
• Adapt known SINR scheduling algorithms designed for general metric spaces to work with arbitrary gain matrices, preserving performance guarantees in terms of ζ.
• Use experimental testbeds with 20 and 60 motes to collect RSS measurements and validate predictive accuracy of the model.
• Evaluate the impact of multi-channel operation on metricity and performance, showing that frequency diversity can improve ζ values.
• Apply linear algebra and regression techniques to infer interference patterns and improve measurement consistency across nodes.

Experimental results

Research questions

• RQ1 Can a measurement-based gain matrix model outperform geometric path loss models in predicting real-world packet reception rates?
• RQ2 How can the complexity of real-world wireless environments be quantitatively captured in a way that preserves theoretical algorithmic guarantees?
• RQ3 To what extent does the metricity parameter ζ reflect the actual physical complexity of indoor wireless environments?
• RQ4 Can existing worst-case SINR scheduling algorithms be adapted to work with empirical RSS matrices while maintaining their performance bounds?
• RQ5 Does multi-channel operation improve metricity and thus enhance scheduling performance in complex propagation environments?

Key findings

• The RSS-based gain matrix model achieved 95% prediction accuracy for packet reception rate even when using RSS matrices collected weeks in advance, demonstrating robustness to temporal variations.
• The metricity parameter ζ effectively captures environmental complexity, with higher ζ values corresponding to more complex propagation environments such as those with multi-path fading.
• Scheduling algorithms originally designed for geometric path loss models achieved equivalent performance guarantees in the new mb-sinr model, with ζ replacing the path loss constant α as the key parameter.
• Multi-channel operation improved metricity in environments with extensive multi-path propagation, indicating that frequency diversity can enhance model fidelity.
• The mb-sinr model significantly outperformed the traditional geo-sinr model in predictive accuracy for real-world testbeds, confirming its higher fidelity to actual signal behavior.
• Empirical evidence supports the validity of the SINR model in real environments, with strong alignment between predicted and observed PRR when using measured RSS matrices.