Abstract
A crucial aspect of mobile 3D wireless sensor network application is cost effective localization of individual nodes. GPS based localization is costly and not feasible in many applications. Among GPS free localization methods, Multi-Dimensional Scaling (MDS) based localization methods has been well accepted solution for localization problem, due to low localization error. MDS based localization uses the connectivity information among the nodes to find the positions of nodes in the network. MDS needs a full distance matrix as input. Various methods, e.g., Dijkstra’s algorithm, have been used to generate full distance matrix from incomplete distance matrix obtained from the connectivity information of nodes at the price of O(n2) added computational complexity. This paper presents a novel idea of trying to generate the position information at lowered computational cost and correspondingly reduced power requirement without need for full distance matrix. We will present a modified cost function and demonstrate Sammon’s Mapping with standard gradient descent techniques as well as Genetic algorithms and Particle Swarm Optimization. Our experimental results show, that the proposed localization approach can lead to savings in numerous practical cases. With regard to mapping error reduction, standard GA and PSO so far did not offer a improvement. In future work variations of GA and PSO will be investigated.
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Rao, L., Iswandy, K., König, A. (2010). Cost Effective 3D Localization for Low-Power WSN Based on Modified Sammon’s Stress Function with Incomplete Distance Information. In: Gao, XZ., Gaspar-Cunha, A., Köppen, M., Schaefer, G., Wang, J. (eds) Soft Computing in Industrial Applications. Advances in Intelligent and Soft Computing, vol 75. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11282-9_16
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DOI: https://doi.org/10.1007/978-3-642-11282-9_16
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