Ordinal MDS-Based Localization for Wireless Sensor Networks

2014

— We investigate on the localization of nodes in a sensor network based on multidimensional scaling (MDS). The distances of node pairs are given and MDS attempts to locate the position of nodes given the distance matrix. However, conventional MDS addresses the mapping problem using eigenvector decomposition which is complicated in computation, mitigating the efficiency of this approach for real-time applications. In this paper, we introduce a sequential MDS for the fast implementations of MDS for a changing sensor network. The fast-fixed point algorithm is used to initialize the locations of nodes. When the distance matrix is varied due to the movements of nodes, sequential MDS aims at reallocating the nodes with a fast and effective update process. We validated sequential MDS with a number of experiments and shown the proposed approach is superior to conventional MDS when it deals with the location problems of moving nodes of the sensor network. Keywords-Multidimensional Scaling; L...

ACM Transactions on Sensor …, 2006

Computer Communications, 2013

Sensors can be deployed along a road or a bridge to form a wireless sensor network with a linear topology where nodes have a spatial ordering. This spatial characteristic indicates relative locations for nodes and facilitates such functions as object tracking and monitoring in the network. We attempt to derive this ordering with RSSI (Received Signal Strength Indicator) as the only input, which does not require attaching extra hardware to the sensor nodes. This requires a method for translating RSSI into spatial constraints. There are two candidate methods in the literature. The first method uses connectivity information among the nodes to calculate their relative locations. But analysis of real-world trace data indicates that it does not work well in reality. The second method assumes that closer nodes receive higher RSSI. However, we have proved that the relative localization problem is actually NP-hard under such an assumption. Fortunately, the problem turns out to be efficiently solvable if we adopt a new observation slightly different from the above-mentioned closer-higher RSSI assumption. This observation that the closest node always receives the highest RSSI is verified by the analytical results of the same realworld trace data. We then propose a spatial ordering method based on the observation, and evaluate it through various field experiments. Results show that the proposed method achieves an accuracy of over 99%.

Journal of Computers, 2009

In wireless sensor networks, three-dimensional localization is important for applications. It becomes a challenge with the scale of network getting large. This paper proposes a three-dimensional localization algorithm for large scale WSN on the basis of cluster. Focusing on the MDS-based localization, it adopts the cluster structure and the global coordinate system to represent the whole network logically, and reduces the influence of range measurement errors through decreasing the probability of multi-hop. With the combination of variable power of nodes and the triangle principle, the range measurement errors can be corrected. Through the comparison of three different computations in the algorithm of simulations, correction effects are presented. To address the proposed algorithm, CBLALS, more convincible, the comparison of CBLALS and DV-Distance (3D) is taken. The result shows that the positioning accuracy of CBLALS is much better than the one of DV-Distance (3D). With the increasing of the range measurement error, the positioning error of CBLALS varies gently, and could be controlled within 55% while the range measurement error is 30%.

IEEE Transactions on Parallel and Distributed Systems, 2004

We propose an approach that uses connectivity information-who is within communications range of whom-to derive the locations of nodes in a network. The approach can take advantage of additional information, such as estimated distances between neighbors or known positions for certain anchor nodes, if it is available. It is based on multidimensional scaling (MDS), an efficient data analysis technique that takes Oðn 3 Þ time for a network of n nodes. Unlike previous approaches, MDS takes full advantage of connectivity or distance information between nodes that have yet to be localized. Two methods are presented: a simple method that builds a global map using MDS and a more complicated one that builds small local maps and then patches them together to form a global map. Furthermore, least-squares optimization can be incorporated into the methods to further improve the solutions at the expense of additional computation. Through simulation studies on uniform as well as irregular networks, we show that the methods achieve more accurate solutions than previous methods, especially when there are few anchor nodes. They can even yield good relative maps when no anchor nodes are available.

Computer Communications, 2012

A wide range of applications used in wireless sensor networks requires location information of multimedia sensor nodes. In general, the topographical location information of data acquired by a sensor is applied for smart, interactive multimedia services. However, conventional techniques employ GPS or other location-tracking devices installed on sensor nodes and thus incur additional costs, making it impractical for wireless sensor networks. In contrast, some methods provide location information by node connectivity only. One of these methods, called multidimensional scaling -MAP (MDS-MAP), provides the most accurate positioning to date. However, MDS-MAP has a computational overhead of O(n 3 ) in a network of n nodes and, in particular, results in significant localization accuracy error in environments with holes. Thus, this paper proposes a cluster-based MDS (CMDS) for range-free localization that overcomes the shortcomings of MDS and yields smaller accuracy error in all environments. Simulations demonstrate the proposed CMDS approach provides up to 23% improvement in localization accuracy compared to the newest version of conventional MDS-MAP, hierarchical MDS (HMDS) in a sensor network environment with holes.

International Journal of Wireless and Mobile Computing, 2019

Localisation precision is one of the predominant challenges in static and mobile wireless sensor networks. It is a critical issue to be tackled when developing applications that rely on this type of networks. This paper presents three localisation techniques, named eDV-Hop1, eDV-Hop2, and eDV-Hop3, based on geometry principles. The performance of these techniques was evaluated using the network simulator (OMNeT++) and compared to the basic DV-Hop, iDV-Hop1, and iDV-Hop2. Results show that significant improvement is obtained in terms of energy consumption and localisation error according to several parameters, mainly the number of sensors, the number of anchor nodes, and the communication range of sensor nodes.

International Journal of Engineering and Management Research

Many localization schemes are designed for finding the geographical coordinates of the unlocalized node in the network. Still, it is a difficult problem to find accurate and efficient localization schemes in the Wireless Sensor Networks (WSNs). We proposed a new method, connectivity based WSN node localization using one of the geometrical method namely centroid of a triangle. By developing the centroid of a triangle from the WSN network model in terms of localization requirements. The simulation outcomes have shown that the modified centroid (centroid_T) performs marginally better than the existing centroid method with a marginally increase in the computation process. We also observe the variation of localization error with various anchor nodes, radio range, and network size.

2014

As Wireless Sensor Network (WSN) has become a key technology for different types of smart environment, nodes localization in WSN has arisen as a very challengin g problem in the research community. Most of the applications for WSNs necessitate a priory known nodes positions. In this paper, we propose an algorithm for three dimensional (3D) nod es localization in surface WSN based on multidimension al scaling (MDS) technique. Using extensive simulations, we in vestigated in details our approach regarding different network topologies, various network parameters and performance issues. The results from simulations show that our algorithm produces small localization error and outperforms MDS-MAP in terms of accuracy. Keywords-wireless sensor networks; multidimensional scaling; 3D surface localization; nodes positioning.

IEEE INFOCOM 2004, 2004

It is often useful to know the geographic positions of nodes in a communications network, but adding GPS receivers or other sophisticated sensors to every node can be expensive. MDS-MAP is a recent localization method based on multidimensional scaling (MDS). It uses connectivity information-who is within communications range of whom-to derive the locations of the nodes in the network, and can take advantage of additional data, such as estimated distances between neighbors or known positions for certain anchor nodes, if they are available. However, MDS-MAP is an inherently centralized algorithm and is therefore of limited utility in many applications. In this paper, we present a new variant of the MDS-MAP method, which we call MDS-MAP(P) standing for MDS-MAP using patches of relative maps, that can be executed in a distributed fashion. Using extensive simulations, we show that the new algorithm not only preserves the good performance of the original method on relatively uniform layouts, but also performs much better than the original on irregularly-shaped networks. The main idea is to build a local map at each node of the immediate vicinity and then merge these maps together to form a global map. This approach works much better for topologies in which the shortest path distance between two nodes does not correspond well to their Euclidean distance. We also discuss an optional refinement step that improves solution quality even further at the expense of additional computation.