In distance vector (DV)-hop localisation, the estimated distances between unknown nodes and anchors are usually determined through multiplying the hop-count and the average per-hop distance, so the per-hop distance represents a physical distance. Unfortunately, in practice, wireless communication networks may be affected by the environment, which makes the per-hop distance deviating from the physical distance. A number of improved DV-hop localisation algorithms have been proposed previously, which give the weight to the per-hop distance. When applied as it is, sub-optimal results were achieved as the differences between per-hop distances and physical distances could not be determined. In this Letter, the authors analyse the error during the process of converting hop distances to physical distances and take advantage of the optimal weighted function, which achieves a more accurate distance conversion model. Then, they correct the estimated position of unknown nodes according to the relationship between the estimated distances calculated by hop-distance conversion and the distances from the estimated positions of unknown nodes to anchor nodes. The simulation results show that the localisation performance of the proposed algorithm is better than the existing weighted methods in irregular areas.

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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.