Range-free 3D node localization in anisotropic wireless sensor networks

TLDR: Both the proposed applications of the two algorithms are compared with the earlier proposed range-free algorithms in literature and are better as compared to centroid and weighted centroid methods in terms of error and scalability.

Abstract: Graphical abstractDisplay Omitted HighlightsPropose two computationally effective range-free (RF) 3D node localization schemes using applications of biogeography based optimization (BBO) and hybrid particle swarm optimization (HPSO) for anisotropic wireless sensor networks.Nodes are randomly deployed with constraints over three layer boundaries. The anchor nodes are randomly distributed over top layer only and target nodes are distributed over the middle and bottom layers.Non-linearity between received signal strength (RSS) and distance is modeled using fuzzy logic system (FLS) to reduce the computational complexity and further optimized by HPSO and BBO to minimize the error.Knowledge based edge weight of the anchor node to determine the accurate coordinates of the target node.A novel proximity based performance index, to evaluate the proposed schemes. In this paper, we propose two computationally efficient 'range-free' 3D node localization schemes using the application of hybrid-particle swarm optimization (HPSO) and biogeography based optimization (BBO). It is considered that nodes are deployed with constraints over three layer boundaries, in an anisotropic environment. The anchor nodes are randomly distributed over the top layer only and target nodes distributed over the middle and bottom layers. Radio irregularity factor, i.e., an anisotropic property of propagation media and heterogenous properties of the devices are considered. To overcome the non-linearity between received signal strength (RSS) and distance, edge weights between each target node and neighboring anchor nodes have been considered to compute the location of the target node. These edge weights are modeled using fuzzy logic system (FLS) to reduce the computational complexity. The edge weights are further optimized by HPSO and BBO separately to minimize the location error. Both the proposed applications of the two algorithms are compared with the earlier proposed range-free algorithms in literature, i.e., the simple centroid method and weighted centroid method. The results of our proposed applications of the two algorithms are better as compared to centroid and weighted centroid methods in terms of error and scalability.