title: Optimisation in the Design of Environmental Sensor Networks with Robustness Consideration.
abstract: This work proposes the design of Environmental Sensor Networks (ESN) through balancing robustness and redundancy. An Evolutionary Algorithm (EA) is employed to find the optimal placement of sensor nodes in the Region of Interest (RoI). Data quality issues are introduced to simulate their impact on the performance of the ESN. Spatial Regression Test (SRT) is also utilised to promote robustness in data quality of the designed ESN. The proposed method provides high network representativeness (fit for purpose) with minimum sensor redundancy (cost), and ensures robustness by enabling the network to continue to achieve its objectives when some sensors fail.
authors.affiliation: School of Engineering and ICT, University of Tasmania, Private Bag 87, Hobart, TAS 7001, Australia. Setia.Budi@utas.edu.au.|Commonwealth Scientific and Industrial Research Organisation, 15 College Road, Sandy Bay, TAS 7005, Australia. Setia.Budi@utas.edu.au.
authors.forename: Setia
authors.initials: S
authors.lastname: Budi
authors.affiliation: Commonwealth Scientific and Industrial Research Organisation, 15 College Road, Sandy Bay, TAS 7005, Australia. Paulo.Desouza@csiro.au.
authors.forename: Paulo
authors.initials: P
authors.lastname: de Souza
authors.affiliation: Commonwealth Scientific and Industrial Research Organisation, 15 College Road, Sandy Bay, TAS 7005, Australia. Greg.Timms@csiro.au.
authors.forename: Greg
authors.initials: G
authors.lastname: Timms
authors.affiliation: School of Engineering and ICT, University of Tasmania, Private Bag 87, Hobart, TAS 7001, Australia. Vishv.Malhotra@utas.edu.au.
authors.forename: Vishv
authors.initials: V
authors.lastname: Malhotra
authors.affiliation: School of Engineering and ICT, University of Tasmania, Private Bag 87, Hobart, TAS 7001, Australia. Paul.Turner@utas.edu.au.
authors.forename: Paul
authors.initials: P
authors.lastname: Turner
keywords: data quality; environmental sensor networks; evolutionary algorithm; gap filling; noise detection; optimisation; sensor networks deployment; sensor networks design; spatial regression test