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.
authors.affiliation: Department of Computer Science, University of York, YO10 5DD York, U.K. jt@cs.york.ac.uk
authors.forename: Jonathan
authors.initials: J
authors.lastname: Tate
authors.forename: Benjamin
authors.initials: B
authors.lastname: Woolford-Lim
authors.forename: Iain
authors.initials: I
authors.lastname: Bate
authors.forename: Xin
authors.initials: X
authors.lastname: Yao
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003661:Decision Support Techniques; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated / Q000379:methods*; D014159:Transducers