Evolutionary and principled search strategies for sensornet protocol optimization

Jonathan TATE, Benjamin WOOLFORD-LIM, Iain BATE, Xin YAO

Research output: Journal PublicationsJournal Article (refereed)peer-review

16 Citations (Scopus)

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. © 2011 IEEE.
Original languageEnglish
Article number5991982
Pages (from-to)163-180
Number of pages18
JournalIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Volume42
Issue number1
Early online date25 Aug 2011
DOIs
Publication statusPublished - Feb 2012
Externally publishedYes

Bibliographical note

A preliminary and much shorter version of this paper was published at CEC’09 [1]. This work was supported in part by EPSRC Grant EP/D052785/1 on “SEBASE: Software Engineering By Automated SEarch,” and BAE Systems plc by the Grant “Hierarchical System Management for Integrated Modular Systems.” This paper was recommended by Associate Editor E. Santos, Jr.

Keywords

  • Evolutionary Algorithms (EAs)
  • experiment design
  • multi-objective optimization
  • protocols
  • sensornets

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