Mobile platform sampling for designing environmental sensor networks.

This paper proposes a method to design the deployment of sensor nodes in a new region where historical data is not available. A number of mobile platforms are simulated to build initial knowledge of the region. Further, an evolutionary algorithm is employed to find the optimum placement of a given number of sensor nodes that best represents the region of interest.

In search for a robust design of environmental sensor networks.

This paper presents an approach to the design of environmental sensor networks (ESN) which aims at providing a robust, fit-for-purpose network with minimum redundancy. A set of near optimum ESN designs is sought using an evolutionary algorithm, which incorporates redundancy and robustness as fitness functions. This work can assist the decision-making process when determining the number of sensor nodes and how the nodes are going to be deployed in the region of interest.

Spatiotemporal Interpolation for Environmental Modelling.

A variation of the reduction-based approach to spatiotemporal interpolation (STI), in which time is treated independently from the spatial dimensions, is proposed in this paper. We reviewed and compared three widely-used spatial interpolation techniques: ordinary kriging, inverse distance weighting and the triangular irregular network. We also proposed a new distribution-based distance weighting (DDW) spatial interpolation method.