Multilateration: Methods For Clustering Intersection Points For Wireless Sensor Networks Localization With Distance Estimation Error (original) (raw)
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Sensors, 2012
This research presents a distributed and formula-based bilateration algorithm that can be used to provide initial set of locations. In this scheme each node uses distance estimates to anchors to solve a set of circle-circle intersection (CCI) problems, solved through a purely geometric formulation. The resulting CCIs are processed to pick those that cluster together and then take the average to produce an initial node location. The algorithm is compared in terms of accuracy and computational complexity with a Least-Squares localization algorithm, based on the Levenberg–Marquardt methodology. Results in accuracy vs. computational performance show that the bilateration algorithm is competitive compared with well known optimized localization algorithms.
Geometric and decentralized approach for localization in wireless sensor network
Journal of Ambient Intelligence and Humanized Computing, 2020
The main function of a sensor node is to collect data from its environment and forward it to base station. In the absence of further information concerning their locations, those data will be unnecessary. Hence, developing algorithms for localizing all nodes of wireless sensor network is extremely important. We present in this paper, a new approach to determine geographical coordinates of unknown nodes, by using mobile anchor. The mobile anchor adopts a spiral trajectory, and diffuses its position periodically during its travel. The proposed approach uses Received Signal Strength Indicator to estimate distance with all broadcast messages received from mobile anchor. To calculate position, our approach determines a cloud of points that surround the solution; these points are selected from the set of intersection points of all beacons received by unknown node, by considering some constraints. The estimated position of unknown node represents the geometric center of this cloud. The behavior of our algorithm was studied by varying some metrics; the average error was minimized compared to those proposed in literature.
Multilateration Localization for Wireless Sensor Networks
Indian Journal of Science and Technology
Objectives/methodology: Localization in wireless sensor networks (WSNs) has long been one of the most interesting areas that researchers continue to study. This study presents the methodology of mathematical model for multilateration localization in WSNs verified by a simulation model using the NS-2 simulator. Findings: The new modules added to NS-2 can be extended to various rangebased localization techniques, which help many researchers in this field. Applications: This work makes a comparative study of atomic and iterative multilateration localization according to different performance metrics.
Localization Based on Probabilistic Multilateration Approach for Mobile Wireless Sensor Networks
IEEE Access, 2020
Localization is one of the main problems in Mobile Wireless Sensor Networks, since it provides the location of an event occurrence. This paper presents a performance evaluation of the localization algorithms: Multilateration Algorithm, Weighted Multilateration Algorithm and Probabilistic Multilateration Algorithm (PMA). In addition, we propose an Improved Probabilistic Multilateration Algorithm that decreases the localization error of the interest node by using an approach that computes iteratively the position of a node of interest until it reaches the solution that minimizes the localization error. The proposed approach regards the noisy environment by its impact on a correlation matrix that involves the variance of the separation distance between the node of interest and the respective reference nodes (RNs). Furthermore, we also introduce a constant parameter called damping factor; which enhances the convergence of the localization algorithm providing the solution that minimizes the localization error. In this study, we evaluate localization algorithms in a single-hop and multi-hop scenarios considering a distribution with solid geometry of the RNs and randomly distributed RNs in both scenarios. The results we obtained show that our proposed algorithm Improved PMA presents a better performance according to the Normalized Root Mean Squared Error varying the number of reference nodes and noise proportion.
Anchor node placement for localization in wireless sensor networks
Applications of wireless sensor network (WSN) often expect knowledge of the precise location of the nodes. One class of localization protocols patches together relativecoordinate, local maps into a global-coordinate map. These protocols require some nodes that know their absolute coordinates, called anchor nodes. While many factors influence the node position errors, in this class of protocols, the placement of the anchor nodes can significantly impacts the error. Through simulation, using the Curvilinear Component Analysis (CCA-MAP) protocol, we show the impact of anchor node placement and a set of rules to ensure the best possible outcome, while using the smallest number of anchor nodes possible. Scientists and researchers are thus enabled to focus on the sensed data with confidence in the node localization results.
Localization in Wireless Sensor Networks and Anchor Placement
Journal of Sensor and Actuator Networks, 2012
Applications of wireless sensor network (WSN) often expect knowledge of the precise location of the nodes. Many different localization protocols have been proposed that allow nodes to derive their location rather than equipping them with dedicated localization hardware such as GPS receivers, which increases node costs. We provide a brief survey of the major approaches to software-based node localization in WSN. One class of localization protocols with good localization performance patches together relative-coordinate, local maps into a global-coordinate map. These protocols require some nodes that know their absolute coordinates, called anchor nodes. While many factors influence the node position errors, in this class of protocols, using Procrustes Analysis, the placement of the anchor nodes can significantly impact the error. Through simulation, using the Curvilinear Component Analysis (CCA-MAP) protocol as a representative protocol in this category, we show the impact of anchor node placement and propose a set of guidelines to ensure the best possible outcome, while using the smallest number of anchor nodes possible.
Concentric Anchor-Beacons (CAB) Localization for Wireless Sensor Networks
2006 IEEE International Conference on Communications, 2006
Many applications in wireless sensor networks require sensor nodes to obtain their absolute or relative geographical positions. Although various localization algorithms have been proposed recently, most of them require nodes be equipped with range-determining hardware to obtain distance information. In this paper, we propose a concentric anchor-beacons (CAB) localization algorithm for wireless sensor networks. CAB is a range-free approach and uses a small number of anchor nodes. Each anchor emits beacons at different power levels. From the information received by each beacon heard, nodes determine which annular ring they are located within each anchor. Each node uses the approximated center of intersection of the rings as its position estimate. Simulation results show that the estimation error reduces by half when anchors transmit beacons at two different power levels instead of at a single level. CAB also gives a lower estimation error than other range-free localization schemes (e.g., Centroid, APIT) when the anchor-to-node range ratio is less than four.
Accurate anchor-free node localization in wireless sensor networks
PCCC 2005. 24th IEEE International Performance, Computing, and Communications Conference, 2005.
There has been a growing interest in the applications of wireless sensor networks in unattended environments. In such applications, sensor nodes are usually deployed randomly in an area of interest. Knowledge of accurate node location is essential in such network setups in order to correlate the gathered data to the origin of the sensed phenomena and assure the relevance of the reported information. In addition, awareness of the nodes' positions can enable employing efficient management strategies such as geographic routing and conducting important analyses such as node coverage properties. In this paper, we present an efficient anchor-free protocol for localization in wireless sensor networks. Each node discovers its neighbors that are within its transmission range and estimates their ranges. Our algorithm fuses local range measurements in order to form a network wide unified coordinate systems while minimizing the overhead incurred at the deployed sensors. Scalability is achieved through grouping sensors into clusters. Simulation results show that the proposed protocol achieves precise localization of sensors and maintains consistent error margins. In addition, we capture the effect of error accumulation of the node's range estimates and network's size and connectivity on the overall accuracy of the unified coordinate system.