2,212 research outputs found
Range-Free Localization with the Radical Line
Due to hardware and computational constraints, wireless sensor networks
(WSNs) normally do not take measurements of time-of-arrival or
time-difference-of-arrival for rangebased localization. Instead, WSNs in some
applications use rangefree localization for simple but less accurate
determination of sensor positions. A well-known algorithm for this purpose is
the centroid algorithm. This paper presents a range-free localization technique
based on the radical line of intersecting circles. This technique provides
greater accuracy than the centroid algorithm, at the expense of a slight
increase in computational load. Simulation results show that for the scenarios
studied, the radical line method can give an approximately 2 to 30% increase in
accuracy over the centroid algorithm, depending on whether or not the anchors
have identical ranges, and on the value of DOI.Comment: Proc. IEEE ICC'10, Cape Town, South Africa, May, 201
Accurate range free localization in multi-hop wireless sensor networks
To localize wireless sensor networks (WSN)s nodes, only the hop-based data have been so far utilized by range free techniques, with poor-accuracy, though. In this thesis, we show that localization accuracy may importantly advantage from mutual utilization, at no cost, of the information already offered by the advancing nodes (i.e., relays) between all anchors (i.e., position-aware) and sensor nodes join up. In addition, energy-based informant localization approaches are generally established corresponding to the channel path-loss models in which the noise is mostly expected to shadow Gaussian distributions. In this thesis, we signify the applied additive noise by the Gaussian mixture model and improve a localization algorithm depend on the received signal intensity to attain the greatest likelihood location, estimator. By employing Jensenâs inequality and semidefinite relaxation, the originally offered nonlinear and nonconvex estimator is relaxed into a convex optimization difficulty, which is able to be professionally resolved to acquire the totally best solution. Moreover, the resultant CramerâRao lower bound is originated for occurrence comparison. Simulation and experimental results show a substantial performance gain achieved by our proposed localization algorithm in wireless sensor networks. The performance is evaluated in terms of RMSE in terms of three algorithms WLS, CRLR, and GMSDP based on using the Monte Carlo simulation with account the number of anchors that varying from anchor=4 to anchor =20. Finally, the GMSDP- algorithm achieves and provides a better value of RMSEs and the greatest localization estimation errors comparing with the CRLR algorithm and WLS algorithm
Lower bounds for Arrangement-based Range-Free Localization in Sensor Networks
Colander are location aware entities that collaborate to determine
approximate location of mobile or static objects when beacons from an object
are received by all colanders that are within its distance . This model,
referred to as arrangement-based localization, does not require distance
estimation between entities, which has been shown to be highly erroneous in
practice. Colander are applicable in localization in sensor networks and
tracking of mobile objects.
A set is an -colander if by placing
receivers at the points of , a wireless device with transmission radius
can be localized to within a circle of radius . We present tight
upper and lower bounds on the size of -colanders. We measure the
expected size of colanders that will form -colanders if they
distributed uniformly over the plane
Accurate range-free localization for anisotropic wireless sensor networks
Journal ArticlePosition information plays a pivotal role in wireless sensor network (WSN) applications and protocol/ algorithm design. In recent years, range-free localization algorithms have drawn much research attention due to their low cost and applicability to large-scale WSNs. However, the application of range-free localization algorithms is restricted because of their dramatic accuracy degradation in practical anisotropic WSNs, which is mainly caused by large error of distance estimation. Distance estimation in the existing range-free algorithms usually relies on a unified per hop length (PHL) metric between nodes. But the PHL between different nodes might be greatly different in anisotropic WSNs, resulting in large error in distance estimation. We find that, although the PHL between different nodes might be greatly different, it exhibits significant locality; that is, nearby nodes share a similar PHL to anchors that know their positions in advance. Based on the locality of the PHL, a novel distance estimation approach is proposed in this article. Theoretical analyses show that the error of distance estimation in the proposed approach is only one-fourth of that in the state-of-the-art pattern-driven scheme (PDS). An anchor selection algorithm is also devised to further improve localization accuracy by mitigating the negative effects from the anchors that are poorly distributed in geometry. By combining the locality-based distance estimation and the anchor selection, a range-free localization algorithm named Selective Multilateration (SM) is proposed. Simulation results demonstrate that SM achieves localization accuracy higher than 0.3r, where r is the communication radius of nodes. Compared to the state-of-the-art solution, SM improves the distance estimation accuracy by up to 57% and improves localization accuracy by up to 52% consequently.This work is partially supported by the National Science Foundation of China (61103203, 61173169,
61332004, and 61420106009), the Hong Kong RGC General Research Fund (PolyU 5106/11E), the International Science & Technology Cooperation Program of China (2013DFB10070), and the EU FP7 QUICK project (PIRSES-GA-2013-612652)
Distance Measurement-Based Cooperative Source Localization: A Convex Range-Free Approach
One of the most essential objectives in WSNs is to determine the spatial coordinates
of a source or a sensor node having information. In this study, the problem of range
measurement-based localization of a signal source or a sensor is revisited. The main challenge of the problem results from the non-convexity associated with range measurements
calculated using the distances from the set of nodes with known positions to a xed sen-
sor node. Such measurements corresponding to certain distances are non-convex in two
and three dimensions. Attempts recently proposed in the literature to eliminate the non-
convexity approach the problem as a non-convex geometric minimization problem, using
techniques to handle the non-convexity.
This study proposes a new fuzzy range-free sensor localization method. The method
suggests using some notions of Euclidean geometry to convert the problem into a convex
geometric problem. The convex equivalent problem is built using convex fuzzy sets, thus
avoiding multiple stable local minima issues, then a gradient based localization algorithm
is chosen to solve the problem.
Next, the proposed algorithm is simulated considering various scenarios, including the
number of available source nodes, fuzzi cation level, and area coverage. The results are
compared with an algorithm having similar fuzzy logic settings. Also, the behaviour of
both algorithms with noisy measurements are discussed. Finally, future extensions of the
algorithm are suggested, along with some guidelines
Efficient Range-Free Monte-Carlo-Localization for Mobile Wireless Sensor Networks
Das Hauptproblem von Lokalisierungsalgorithmen fĂŒr WSNs basierend auf Ankerknoten ist die AbhĂ€ngigkeit von diesen. MobilitĂ€t im Netzwerk kann zu Topologien fĂŒhren, in denen einzelne Knoten oder ganze Teile des Netzwerks temporĂ€r von allen Ankerknoten isoliert werden. In diesen FĂ€llen ist keine weitere Lokalisierung möglich. Dies wirkt sich primĂ€r auf den Lokalisierungsfehler aus, der in diesen FĂ€llen stark ansteigt. Des weiteren haben Betreiber von Sensornetzwerken Interesse daran, die Anzahl der kosten- und wartungsintensiveren Ankerknoten auf ein Minimum zu reduzieren. Dies verstĂ€rkt zusĂ€tzlich das Problem von nicht verfĂŒgbaren Ankerknoten wĂ€hrend des Netzwerkbetriebs. In dieser Arbeit werden zunĂ€chst die Vor- und Nachteile der beiden groĂen Hauptkategorien von Lokalisierungsalgorithmen (range-based und range-free Verfahren) diskutiert und eine Studie eines oft fĂŒr range-based Lokalisierung genutzten Distanzbestimmungsverfahren mit Hilfe des RSSI vorgestellt. Danach werden zwei neue Varianten fĂŒr ein bekanntes range-free Lokalisierungsverfahren mit Namen MCL eingefĂŒhrt. Beide haben zum Ziel das Problem der temporĂ€r nicht verfĂŒgbaren Ankerknoten zu lösen, bedienen sich dabei aber unterschiedlicher Mittel. SA-MCL nutzt ein dead reckoning Verfahren, um die PositionsschĂ€tzung vom letzten bekannten Standort weiter zu fĂŒhren. Dies geschieht mit Hilfe von zusĂ€tzlichen Sensorinformationen, die von einem elektronischen Kompass und einem Beschleunigungsmesser zur VerfĂŒgung gestellt werden. PO-MCL hingegen nutzt das MobilitĂ€tsverhalten von einigen Anwendungen in Sensornetzwerken aus, bei denen sich alle Knoten primĂ€r auf einer festen Anzahl von Pfaden bewegen, um den Lokalisierungsprozess zu verbessern. Beide Methoden werden durch detaillierte Netzwerksimulationen evaluiert. Im Fall von SA-MCL wird auĂerdem eine Implementierung auf echter Hardware vorgestellt und eine Feldstudie in einem mobilen Sensornetzwerk durchgefĂŒhrt. Aus den Ergebnissen ist zu sehen, dass der Lokalisierungsfehler in Situationen mit niedriger Ankerknotendichte im Fall von SA-MCL um bis zu 60% reduziert werden kann, beziehungsweise um bis zu 50% im Fall von PO-MCL.
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