Earticle

Although localization has been widely studied for Wireless Sensor Networks (WSNs), the complex environments and the large network scale pose severe challenges and make it necessary to develop new reliable localization algorithms. In this paper, we propose a novel Multi-Hop Localization Algorithm for large-scale WSNs in complex environments. This work is based on the consideration that the localization process would encounter several kinds of adverse factors with different nature at the same time (e.g. anisotropic network characteristics, ranging uncertainty, link quality of multihop paths, etc.), which lead to obvious degradation of localization performance. Unlike most of the existing schemes, we transform the localization problem in complex environments into a hybrid constraint satisfaction problem (CSP) which is composed of three different kinds of constraints, i.e. spatial constraint, network situation constraint, and confidence constraint. Set-membership approach and interval analysis method have been utilized to deal with the CSP and determine the positions of sensor nodes. Simulation results show that our scheme is an effective and efficient approach to localization in large-scale WSNs.

In this paper, a novel approach to multi-hop range-free localization algorithm in wireless sensor network is proposed using principal component regression. The localization problem in the wireless sensor network is formulated as a multiple regression problem, which is resolved by principal component regression. The proposed methods are simple and efficient that no additional hardware is required for the measurements, and only hop-counts information and location information of the beacons are used for the localization. The proposed method consists of two phases: the offline training phase and the online localization phase. In offline training phase, the real distances and the hop-counts among sensor nodes are collected to build localization model. In online localization phase, each unknown sensor node finds its own location using the localization model. The experimental results show that compared with previous localization methods, the proposed method exhibits excellent and robust performances not only in the isotropic sensor networks but also in the anisotropic sensor networks.