A Large-Scale Multi-Hop Localization Algorithm Based on Regularized Extreme Learning for Wireless Networks

Abstract

A novel large-scale multi-hop localization algorithm based on regularized extreme learning is proposed in this paper. The large-scale multi-hop localization problem is formulated as a learning problem. Unlike other similar localization algorithms, the proposed algorithm overcomes the shortcoming of the traditional algorithms which are only applicable to an isotropic network, therefore has a strong adaptability to the complex deployment environment. The proposed algorithm is composed of three stages: data acquisition, modeling and location estimation. In data acquisition stage, the training information between nodes of the given network is collected. In modeling stage, the model among the hop-counts and the physical distances between nodes is constructed using regularized extreme learning. In location estimation stage, each node finds its specific location in a distributed manner. Theoretical analysis and several experiments show that the proposed algorithm can adapt to the different topological environments with low computational cost. Furthermore, high accuracy can be achieved by this method without setting complex parameters.

A Weighted and Distributed Algorithm for Range-Based Multi-Hop Localization Using a Newton Method

“…First, it is assumed that each sensor node has a partial set of neighboring sensors according to Equation (7). Furthermore, anchors and unknown sensors are assumed with the same circular radio range R. Each pairwise distance is affected with a noise factor of n f e = 0.1 using Equation (35). This benchmark network, with n f e = 0.1 and Θ = 0, is used as a basis to create other five networks, affected with different levels of outliers Θ = 10%, Θ = 20%, Θ = 30%, Θ = 40%, and Θ = 50%.…”

“…Finally, each one of the six networks is run in every iterative algorithm to evaluate its accuracy and rate of convergence (or iterations) at different levels of outliers. It must be remarked that DV-hop [13] and RELM [35] algorithms modify distances between anchors and unknown sensors, where such distances are unaffected by noise or by outliers as they depend on hop counts between them. Initial estimates average of the 10 networks is around 86.33 m.…”

“…Clearly, the accuracy performance of the combination algorithms RELM [ 35 ] +LM [ 36 ] and DV-Hop+LM are linearly affected with the increase of outliers on the range measurements among sensor nodes. However, without presence of outliers, the best accuracy on position estimates is obtained by the DV-Hop+LM algorithm with a = 21.83 m. The worst - performance is carried out by the combination RELM [ 35 ] +SOCP [ 34 , 37 ] algorithms. This is because the SOCP algorithm is highly dependent on the number and location of the anchors.…”

A weighted and distributed algorithm for multi-hop localization

“…25 A variety of methods to estimate distances between two non-neighboring sensors are achieved by routing algorithms. 12,16,18,19,26 The combination network, sensornodes, links f g can be represented in a mathematical form as fG, V, Lg, respectively, where G = (V, L) is the graph. V = fv 1 , v 2 , .…”

“…In view of this problem, some research works have proposed robust schemes that help to reduce errors on distance estimates for multi-hop networks considering irregular topologies. [9][10][11][12][13][14][15][16] For instance, Zheng et al 16 propose a regularized extreme learning machine method (RELM) for multi-hop localization. This scheme is composed of three stages: training, modeling, and locating the nodes.…”