Evolutionary and Principled Search Strategies for Sensornet Protocol Optimization

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Evolutionary and Principled Search Strategies for Sensornet Protocol Optimization. / Tate, J; Woolford-Lim, Benjamin; Bate, I; Yao, Xin.

In: IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), Vol. 42, No. 1, 01.02.2012, p. 163-180.

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@article{6f67528e257f4598ac6e1d1293301602,
title = "Evolutionary and Principled Search Strategies for Sensornet Protocol Optimization",
abstract = "Interactions between multiple tunable protocol parameters and multiple performance metrics are generally complex and unknown; finding optimal solutions is generally difficult. However, protocol tuning can yield significant gains in energy efficiency and resource requirements, which is of particular importance for sensornet systems in which resource availability is severely restricted. We address this multi-objective optimization problem for two dissimilar routing protocols and by two distinct approaches. First, we apply factorial design and statistical model fitting methods to reject insignificant factors and locate regions of the problem space containing near-optimal solutions by principled search. Second, we apply the Strength Pareto Evolutionary Algorithm 2 and Two-Archive evolutionary algorithms to explore the problem space, with each iteration potentially yielding solutions of higher quality and diversity than the preceding iteration. Whereas a principled search methodology yields a generally applicable survey of the problem space and enables performance prediction, the evolutionary approach yields viable solutions of higher quality and at lower experimental cost. This is the first study in which sensornet protocol optimization has been explicitly formulated as a multi-objective problem and solved with state-of-the-art multi-objective evolutionary algorithms.",
author = "J Tate and Benjamin Woolford-Lim and I Bate and Xin Yao",
year = "2012",
month = feb,
day = "1",
doi = "10.1109/TSMCB.2011.2161466",
language = "English",
volume = "42",
pages = "163--180",
journal = "IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)",
issn = "1083-4419",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
number = "1",

}

RIS

TY - JOUR

T1 - Evolutionary and Principled Search Strategies for Sensornet Protocol Optimization

AU - Tate, J

AU - Woolford-Lim, Benjamin

AU - Bate, I

AU - Yao, Xin

PY - 2012/2/1

Y1 - 2012/2/1

N2 - Interactions between multiple tunable protocol parameters and multiple performance metrics are generally complex and unknown; finding optimal solutions is generally difficult. However, protocol tuning can yield significant gains in energy efficiency and resource requirements, which is of particular importance for sensornet systems in which resource availability is severely restricted. We address this multi-objective optimization problem for two dissimilar routing protocols and by two distinct approaches. First, we apply factorial design and statistical model fitting methods to reject insignificant factors and locate regions of the problem space containing near-optimal solutions by principled search. Second, we apply the Strength Pareto Evolutionary Algorithm 2 and Two-Archive evolutionary algorithms to explore the problem space, with each iteration potentially yielding solutions of higher quality and diversity than the preceding iteration. Whereas a principled search methodology yields a generally applicable survey of the problem space and enables performance prediction, the evolutionary approach yields viable solutions of higher quality and at lower experimental cost. This is the first study in which sensornet protocol optimization has been explicitly formulated as a multi-objective problem and solved with state-of-the-art multi-objective evolutionary algorithms.

AB - Interactions between multiple tunable protocol parameters and multiple performance metrics are generally complex and unknown; finding optimal solutions is generally difficult. However, protocol tuning can yield significant gains in energy efficiency and resource requirements, which is of particular importance for sensornet systems in which resource availability is severely restricted. We address this multi-objective optimization problem for two dissimilar routing protocols and by two distinct approaches. First, we apply factorial design and statistical model fitting methods to reject insignificant factors and locate regions of the problem space containing near-optimal solutions by principled search. Second, we apply the Strength Pareto Evolutionary Algorithm 2 and Two-Archive evolutionary algorithms to explore the problem space, with each iteration potentially yielding solutions of higher quality and diversity than the preceding iteration. Whereas a principled search methodology yields a generally applicable survey of the problem space and enables performance prediction, the evolutionary approach yields viable solutions of higher quality and at lower experimental cost. This is the first study in which sensornet protocol optimization has been explicitly formulated as a multi-objective problem and solved with state-of-the-art multi-objective evolutionary algorithms.

U2 - 10.1109/TSMCB.2011.2161466

DO - 10.1109/TSMCB.2011.2161466

M3 - Article

C2 - 21859628

VL - 42

SP - 163

EP - 180

JO - IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)

JF - IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)

SN - 1083-4419

IS - 1

ER -