TY - GEN
T1 - Screening interacting factors in a wireless network testbed using locating arrays
AU - Compton, Randy
AU - Mehari, Michael T.
AU - Colbourn, Charles
AU - De Poorter, Eli
AU - Syrotiuk, Violet
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/9/6
Y1 - 2016/9/6
N2 - Wireless systems exhibit a wide range of configurable parameters (factors), each with a number of values (levels), that may influence performance. Exhaustively analyzing all factor interactions is typically not feasible in experimental systems due to the large design space. We propose a method for determining which factors play a significant role in wireless network performance with multiple performance metrics (response variables). Such screening can be used to reduce the set of factors in subsequent experimental testing, whether for modelling or optimization. Our method accounts for pairwise interactions between the factors when deciding significance, because interactions play a significant role in real-world systems. We utilize locating arrays to design the experiment because they guarantee that each pairwise interaction impacts a distinct set of tests. We formulate the analysis as a problem in compressive sensing that we solve using a variation of orthogonal matching pursuit, together with statistical methods to determine which factors are significant. We evaluate the method using data collected from the w-iLab.t Zwijnaarde wireless network testbed and construct a new experiment based on the first analysis to validate the results. We find that the analysis exhibits robustness to noise and to missing data.
AB - Wireless systems exhibit a wide range of configurable parameters (factors), each with a number of values (levels), that may influence performance. Exhaustively analyzing all factor interactions is typically not feasible in experimental systems due to the large design space. We propose a method for determining which factors play a significant role in wireless network performance with multiple performance metrics (response variables). Such screening can be used to reduce the set of factors in subsequent experimental testing, whether for modelling or optimization. Our method accounts for pairwise interactions between the factors when deciding significance, because interactions play a significant role in real-world systems. We utilize locating arrays to design the experiment because they guarantee that each pairwise interaction impacts a distinct set of tests. We formulate the analysis as a problem in compressive sensing that we solve using a variation of orthogonal matching pursuit, together with statistical methods to determine which factors are significant. We evaluate the method using data collected from the w-iLab.t Zwijnaarde wireless network testbed and construct a new experiment based on the first analysis to validate the results. We find that the analysis exhibits robustness to noise and to missing data.
UR - http://www.scopus.com/inward/record.url?scp=84988864314&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84988864314&partnerID=8YFLogxK
U2 - 10.1109/INFCOMW.2016.7562157
DO - 10.1109/INFCOMW.2016.7562157
M3 - Conference contribution
AN - SCOPUS:84988864314
T3 - Proceedings - IEEE INFOCOM
SP - 650
EP - 655
BT - 2016 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 35th IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2016
Y2 - 10 April 2016 through 14 April 2016
ER -