A New Distributed Localization Method for Sensor Networks

This paper studies the problem of determining the sensor locations in a large sensor network using relative distance (range) measurements only. Our work follows from a seminal paper by Khan et al. [1] where a distributed algorithm, known as DILOC, for sensor localization is given using the barycentric coordinate. A main limitation of the DILOC algorithm is that all sensor nodes must be inside the convex hull of the anchor nodes. In this paper, we consider a general sensor network without the convex hull assumption, which incurs challenges in determining the sign pattern of the barycentric coordinate. A criterion is developed to address this issue based on available distance measurements. Also, a new distributed algorithm is proposed to guarantee the asymptotic localization of all localizable sensor nodes

Opportunistic Localization Scheme Based on Linear Matrix Inequality

Enabling self-localization of mobile nodes is an important problem that has been widely studied in the literature. The general conclusions is that an accurate localization requires either sophisticated hardware (GPS, UWB, ultrasounds transceiver) or a dedicated infrastructure (GSM, WLAN). In this paper we tackle the problem from a different and rather new perspective: we investigate how localization performance can be improved by means of a cooperative and opportunistic data exchange among the nodes. We consider a target node, completely unaware of its own position, and a number of mobile nodes with some self-localization capabilities. When the opportunity occurs, the target node can exchange data with in-range mobile nodes. This opportunistic data exchange is then used by the target node to refine its position estimate by using a technique based on Linear Matrix Inequalities and barycentric algorithm. To investigate the performance of such an opportunistic localization algorithm, we define a simple mathematical model that describes the opportunistic interactions and, then, we run several computer simulations for analyzing the effect of the nodes duty-cycle and of the native self-localization error modeling considered. The results show that the opportunistic interactions can actually improve the self-localization accuracy of a strayed node in many different scenarios

Distributed Finite-Time Cooperative Localization for Three-Dimensional Sensor Networks

This paper addresses the distributed localization problem for a network of sensors placed in a three-dimensional space, in which sensors are able to perform range measurements, i.e., measure the relative distance between them, and exchange information on a network structure. First, we derive a necessary and sufficient condition for node localizability using barycentric coordinates. Then, building on this theoretical result, we design a distributed localizability verification algorithm, in which we propose and employ a novel distributed finite-time algorithm for sum consensus. Finally, we develop a distributed localization algorithm based on conjugate gradient method, and we derive a theoretical guarantee on its performance, which ensures finite-time convergence to the exact position for all localizable nodes. The efficiency of our algorithm compared to the existing ones from the state-of-the-art literature is further demonstrated through numerical simulations.Comment: 39 pages, 7 figures, under revie

Range-Only Node Localization: The Arbitrary Anchor Case In D-Dimensions

This work is situated at the intersection of two large fields of research the Localization problem and applications in Wireless Networks. We are interested in providing good estimations for network node locations in a defined space based on sensor measurements. Many methods have being created for the localization problem, in special we have the classical Triangulation and Trilateration procedures and MultiDimensional Scaling. A more recent method, DILOC, utilizes barycentric coordinates in order to simplify part of the non-linearities inherent to this problem. Except for Triangulation in which we require angle measurements between nodes, the other cited methodologies require, typically only, range measurements. Off course, there exists variants which allow the use of range and angle measurements. We specialize our interest in range only methods utilizing barycentric coordinates by first providing a novel way to compute barycentric coordinates for any possible node-neighbor spatial configuration in any given dimension. Which, we use as basis for our experiments with averaging processes and the development of our centralized and distributed gradient descent algorithms. Our distributed algorithm is able to receive range measurements with noise of uncharacterized distributions as it inputs. Using simulations in Matlab, we provide comparisons of our algorithms with Matlab\u27s MDS function. Lastly, we show our efforts on providing a physical network implementation utilizing existing small form factor computers, wireless communication modules and range sensors

Enhancing Received Signal Strength-Based Localization through Coverage Hole Detection and Recovery

In wireless sensor networks (WSNs), Radio Signal Strength Indicator (RSSI)-based localization techniques have been widely used in various applications, such as intrusion detection, battlefield surveillance, and animal monitoring. One fundamental performance measure in those applications is the sensing coverage of WSNs. Insufficient coverage will significantly reduce the effectiveness of the applications. However, most existing studies on coverage assume that the sensing range of a sensor node is a disk, and the disk coverage model is too simplistic for many localization techniques. Moreover, there are some localization techniques of WSNs whose coverage model is non-disk, such as RSSI-based localization techniques. In this paper, we focus on detecting and recovering coverage holes of WSNs to enhance RSSI-based localization techniques whose coverage model is an ellipse. We propose an algorithm inspired by Voronoi tessellation and Delaunay triangulation to detect and recover coverage holes. Simulation results show that our algorithm can recover all holes and can reach any set coverage rate, up to 100% coverage

Finding Perimeter of Query Regions in Heterogenous Wireless Sensor Networks

Some applications in wireless sensor networks (WSNs) only need to record the information of a target entering or leaving some specific regions of WSNs perimeter. One important issue in this context is to detect the perimeter of the deployed network to ensure that the sensor nodes cover the target area. In this paper we propose two distributed algorithms to elect the perimeter nodes of query regions in a WSN. We consider the most general case, where every sensor has a different sensing radius. We provide performance metrics to analyze the performance of our approach and show by simulation that the proposed algorithms give good performance