A Low-Complexity Geometric Bilateration Method for Localization in Wireless Sensor Networks and Its Comparison with Least-Squares Methods

Cota-Ruiz, Juan; Rosiles, Jose Gerardo; Sifuentes, Ernesto; Rivas, Pablo

doi:10.3390/s120100839

Cited by 36 publications

(33 citation statements)

References 38 publications

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“…This algorithm outperforms the simple gradient descent methodology, and also it avoids dangerous operations with singular matrices as the pure Newton method does. Also other more complex algorithms were tested, such as trust region methods, but it weren't found significant differences in localization result (as confinned also in [3]). It was also decided to use the LLS to initialize the optimization, because this doesn't impair its final result but helps saving computational time (that could be useful for a possible future tracking application).…”

Section: Multilateration: Linear Least Squares (Lls) [3]; Optimizatiomentioning

confidence: 92%

“…The error model generally adopted in literature is here used (noisefactor error model): r = rT + err = rT(1 + NF randn(n, 1) (3) where r is the set of n simulated measured distances for each true distance rT. In this model the range error err is gaussian, with zero mean and standard deviation given in percentage of the true distance, through the so called Noise Factor (NF).…”

Section: Multilateration: Linear Least Squares (Lls) [3]; Optimizatiomentioning

confidence: 99%

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An ubiquitous positioning system based on IEEE 802.15.4 radio signals

Rolando

Amoruso

2013

International Conference on Indoor Positioning and Indoor Navigation

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This work presents a theoretical and experimental study of methods and strategies to implement an ubiquitous positioning system based on IEEE 802.15.4 radio signals. Ranging phase is first analyzed. An empirical method, for a combined use of both Received Signal Strength Indicator (RSSI) and Time Of Flight (TOF) measures, is proposed. Distance measurements performed in different scenarios highlight that RSSI measures provide lower errors on short distance while TOF measures are more suitable for longer distances. The RSSI threshold in use can be different for every scenario but our measurements show that it can be well identified by observing the relevant RSSI calibration curve. Once this threshold is identified, a complementary use of RSSI and TOF measures isthen possible. Subsequently the positioning phase is analyzed. The main range-based and centralized localization algorithms are at first compared using simulated localizations, both in their cooperative and non cooperative version, in order to have a complete overview of their performances. However, the considered driving case study (the position monitoring of vehicles/assets in a typical airport area) puts in evidence the requirement for a non cooperative localization strategy. In this condition simulations show that the optimization approach outperforms the other localization algorithms. The deterministic optimization has been also preferred to the stochastic one: position accuracy being equal, the stochastic optimization methods also need the preliminary set up of different parameters through several simulations and trials. Real-world positioning tests, considering indoor and outdoor scenarios of different sizes (from ten to some hundreds of meters) confirm the validity of this choice, the effectiveness of the proposed ranging method and also the importance of exploiting anchors' redundancy. However, position accuracy is still poor for the most noisy scenarios. Future works should focus on the development of an efficient weighting algorithm to further decrease position error.

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Section: Multilateration: Linear Least Squares (Lls) [3]; Optimizatiomentioning

confidence: 92%

Section: Multilateration: Linear Least Squares (Lls) [3]; Optimizatiomentioning

confidence: 99%

An ubiquitous positioning system based on IEEE 802.15.4 radio signals

Rolando

Amoruso

2013

International Conference on Indoor Positioning and Indoor Navigation

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“…19 For the algorithm evaluation, three algorithms found in the recent state of the art were used as reference. The first algorithm against was compared with the DSCL, 21 the second algorithm was the Levenberg Marquardt, 22,23 and the third one was the SOCP 15,16 algorithm. The main characteristics evaluated were the estimation accuracy and the iterations to converge.…”

Section: Simulation Resultsmentioning

confidence: 99%

A distributed adaptive local searching algorithm for wireless sensor network localization

Hernandez

Cota-Ruiz

González-Landaeta

et al. 2016

International Journal of Distributed Sensor Networks

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The location of sensors represents a relevant attribute in wireless sensor networks. In this article, we propose a distributed range-based localization algorithm for wireless sensor network. The algorithm is mainly based on an adaptively local circular searching area where each unknown sensor must find inside a position that minimizes the error distance with neighboring sensors using a local function. The new position of a sensor is broadcast to neighboring sensors allowing collaboratively to other sensors re-estimate their new positions. The iterative process is repeated until a stopping criterion is reached. The performance of the proposed algorithm is evaluated and compared with other state-of-the-art algorithms, and results indicate that the approach overcomes the other methods in both accuracy and time of convergence.

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“…The algorithm is looked at as far as exactness and computational complexity with a Least-Squares localization algorithm, in light of the Levenberg-Marquardt approach. The outputs in precision versus computational execution demonstrate that the bilateration algorithm is aggressive contrasted with the optimization localization algorithms [12]. New advances in the innovation of wireless electronic devices have influenced conceivable to construct ad-hoc Wireless Sensor Networks (WSNs) utilizing cheap nodes comprising of low-power processors, an unassuming amount of money, and basic wireless transceivers.…”

Section: Introductionmentioning

confidence: 99%

Levenberg-marquardt algorithm to identify the fault analysis for industrial applications

Bhuvaneswari¹,

G.P²

2017

IJET

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The data are collected and forwarding it to the goal is a significant function of a sensor network. For some applications, it is additionally imperative to admit the fault signal to the collected data. To monitor the industrial environment through a wireless sensor network (WSNs), present a neural network based Levenberg-Marquardt (LM) Algorithm for detecting the fault using the gradient value and mean square error of the signal. The data are collected and presented by the magnetic flux sensor and MEMS acoustic sensor. The simulation model is developed in MATLAB/Simulink.

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A Low-Complexity Geometric Bilateration Method for Localization in Wireless Sensor Networks and Its Comparison with Least-Squares Methods

Cited by 36 publications

References 38 publications

An ubiquitous positioning system based on IEEE 802.15.4 radio signals

An ubiquitous positioning system based on IEEE 802.15.4 radio signals

A distributed adaptive local searching algorithm for wireless sensor network localization

Levenberg-marquardt algorithm to identify the fault analysis for industrial applications

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