An improved multihop-based localization algorithm for wireless sensor network using learning approach. (November 2015)

• Record Type: Journal Article
• Authors: Yan, Xiaoyong; Song, Aiguo; Yang, Zhong; Yang, Wankou
• Is Part Of: Computers & electrical engineering. Volume 48(2015)
• Page Start: 247
• Page End: 257
• Publication Date: 2015-11
• Subjects: Wireless sensor network -- Multihop localization -- Kernel ridge regression; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2015.03.029
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Multihop range-free localization methods could obtain relatively reasonable location estimation in the isotropic network; however, during the practical application, it is often affected by various anisotropic factors such as the radio irregularity and barriers, which can significantly reduce its performance. In this paper, we propose a new approach for multihop localization in wireless sensor network based on nonlinear mapping and learning algorithm. The proposed method is simple, efficient, higher accuracy and no need to set complex parameter in that only hop-counts information and position information of the beacons are used for the localization. The proposed approach is composed of two steps: firstly, this algorithm uses kernel function to define the connectivity information (hop-counts) between nodes, then, learning method is used to guide and build the inter-node localization model; secondly, the hop-counts between the unknown nodes and beacons are used to estimate the coordinate of unknown nodes. We evaluate our algorithm under various anisotropic network and real environment, and analyze its performance. We also compare our approach with several available advanced approaches, and demonstrate the superior performance of our proposed algorithm in terms of location estimation adaptability.