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Baojian Gao 1 and Xiaoning Zhao 2 and Jun Wang 1 and Xiaojiang Chen 1
Academic Editor:Jianping He
1, School of Information Science and Technology, Northwest University, Xi'an 710127, China
2, AVIC Xi'an Aircraft Industry (Group) Company, Xi'an 710089, China
Received 19 June 2015; Accepted 17 September 2015; 29 October 2015
This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
1. Introduction
Node localization is one of the most fundamental technologies in wireless sensor networks (WSNs). For most applications of WSNs [1-3], only when nodes know their locations can they tell the system "what is happening in what position." For instance, in a fire monitoring system, a sensor node detects that a fire breaks out. The system is able to notify people where the fire is only when it knows the node's location. This allows fireman to reach the correct location and take effective measures immediately in order to prevent the spread of the fire. In addition, accurate localization is the foundation of many other WSNs technologies, such as position-aware data processing [4-6] and geographic routing [7, 8].
Among existing localization algorithms, range-free schemes [9-12] have been widely used due to their low cost and low power consumption. However, these schemes assume that the hop count distance between two nodes correlates well with their Euclidean distance, which will be satisfied only in isotropic networks [9, 13] (the definition of isotropic and anisotropic networks can be found in [14]). In practical applications, such as large-scale heritage protection [15] and military reconnaissance [16], sensor nodes are often randomly deployed in complex environments, which makes their network topologies highly irregular, with the possibility of holes or obstacles. Such network is called anisotropic network.
For example, Figure 1 shows an application of WSNs. In this scenario, sensors are distributed in the region to be monitored, and Figure 2 shows the corresponding node deployment. It is obvious in Figure 2 that the hop count distances between some pairs of nodes heavily deviate from their Euclidean distances (Take [figure omitted; refer to PDF] and [figure omitted; refer to PDF] in Figure 2, e.g., the black solid line represents the hop...