Improved many-objective optimization algorithms for the 3D indoor deployment problem

Abstract

Compared with the two-dimensional deployment, the three-dimensional deployment of sensor networks is more challenging. We studied the problem of 3D repositioning of sensor nodes in wireless sensor networks. We aim essentially to add a set of nodes to the initial architecture. The positions of the added nodes are determined by the proposed algorithms while optimizing a set of objectives. In this paper, we suggest two main contributions. The first one is an analysis contribution where the modelling of the problem is given and a set of modifications is incorporated on the tested multi-objective evolutionary algorithms to resolve the issues encountered when resolving manyobjective problems. These modifications concern essentially an adaptive mutation and recombination operators with neighbourhood mating restrictions, the use of a multiple scalarizing functions concept and the incorporation of the reduction of dimensionality. The second contribution is an application one, where an experimental study on real test-beds is detailed to test the behaviour of the enhanced algorithms on a real-world context. Experimental tests followed by numerical results prove the efficiency of the proposed modifications against original algorithms.