Performance Evaluation of Localization Algorithms for WSNs

Vargas-Rosales, César; Mass-Sanchez, Joaquin; Ruiz-Ibarra, Erica; Torres-Roman, D.; Espinoza-Ruiz, Adolfo

doi:10.1155/2015/493930

Cited by 17 publications

(53 citation statements)

References 31 publications

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“…Localization techniques are classified into two categories: range-free [31] and range-based [19]. The former use connectivity information from the network to estimate the distance between the Node of Interest (NOI) and the Reference Nodes (RNs).…”

Section: Classification Of the Localization Techniquesmentioning

confidence: 99%

“…This group includes techniques such as centroid; Distance Vector-Hop (DV-Hop) [32]; Approximate Point In Triangle (APIT); circular, rectangular, and hexagonal interaction, respectively; among others. Range-free localization techniques are more inaccurate in localizing the NOI than range-based localization techniques, but they are less complex in terms of computational cost [19]. Range-based techniques, for their part, need distance between the NOI and the RNs to estimate the position of the NOI.…”

Section: Classification Of the Localization Techniquesmentioning

confidence: 99%

“…Range-based techniques, for their part, need distance between the NOI and the RNs to estimate the position of the NOI. The distance between the NOI and the RNs can be calculated using the Received Signal Strength (RSS), Time of Arrival (ToA), Time Difference of Arrival (TDoA) or Angle of Arrival (AoA) [19]. Some range-based localization techniques are multilateration, Multidimensional-Scaling (MDS), hyperbolic and weighted hyperbolic positioning algorithm, circular and weighted circular positioning algorithm, and Weighted Least-Squares (WLS) multilateration [19].…”

Section: Classification Of the Localization Techniquesmentioning

confidence: 99%

“…In that sense, several proposals, including some novel solutions, have been developed; in most cases, the evaluation of proposals is done through simulations since their physical implementation involves a high cost and intensive work. The evaluation of all proposals is based on common metrics related to the estimation error and the cost, (i.e., accuracy, precision, and computational complexity [19,20]). The network's performance can be influenced by the choice of key factors when a localization scenario is simulated, for example, node density [21], and propagation model, among others, and the simulation of all possible combinations can prove to be excessive.…”

Section: Introductionmentioning

confidence: 99%

“…The remainder of the paper is organized as follows: Section 2 describes the classification of localization techniques and some scenarios for evaluating localization algorithm performance in real and simulated environments [19,[22][23][24][25][26]; mention is also made of some examples of experiment designs that use 2 k factorial analysis [27][28][29][30]. Section 3 describes the 2 k factorial methodology used.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Factorial Design Analysis for Localization Algorithms

Mass-Sanchez¹,

Ruiz-Ibarra²,

Gonzalez-Sanchez³

et al. 2018

Applied Sciences

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Localization is a fundamental problem in Wireless Sensor Networks, as it provides useful information regarding the detection of an event. There are different localization algorithms applied in single-hop or multi-hop networks; in both cases their performance depends on several factors involved in the evaluation scenario such as node density, the number of reference nodes and the log-normal shadowing propagation model, determined by the path-loss exponent (η) and the noise level (σdB) which impact on the accuracy and precision performance metrics of localization techniques. In this paper, we present a statistical analysis based on the 2k factorial methodology to determine the key factors affecting the performance metrics of localization techniques in a single-hop network to concentrate on such parameters, thus reducing the amount of simulation time required. For this proposal, MATLAB simulations are carried out in different scenarios, i.e., extreme values are used for each of the factors of interest and the impact of the interaction among them in the performance metrics is observed. The simulation results show that the path-loss exponent (η) and noise level (σdB) factors have the greatest impact on the accuracy and precision metrics evaluated in this study. Based on this statistical analysis, we recommend estimating the propagation model as close to reality as possible to consider it in the design of new localization techniques and thus improve their accuracy and precision metrics.

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Section: Classification Of the Localization Techniquesmentioning

confidence: 99%

Section: Classification Of the Localization Techniquesmentioning

confidence: 99%

Section: Classification Of the Localization Techniquesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Factorial Design Analysis for Localization Algorithms

Mass-Sanchez¹,

Ruiz-Ibarra²,

Gonzalez-Sanchez³

et al. 2018

Applied Sciences

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Processing‐in‐memory based multilateration localization in wireless sensor networks using memristor crossbar arrays

Mohammed Siyad,

Mohan

2024

Concurrency and Computation

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SummaryMost of the multilateration algorithms for object localization rely heavily on matrix multiplication for calculating the precise location of the target from all the reference points in a 3D domain. A significant disadvantage is that performing matrix multiplication can be computationally expensive, particularly for larger matrices, leading to slower performance and longer processing times. In this article, we propose a Processing‐in‐Memory (PIM) based localization approach for multilateration using a memristor crossbar array for performing faster matrix multiplication. The experiments were conducted on real‐time data collected through ultra‐wideband (UWB) chip. The simulation results demonstrate that the proposed model is around 50% faster than the fastest known localization algorithm considered for evaluation and exhibits an average of 55% improvement in localization accuracy.

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Weighted Hyperbolic DV-Hop Positioning Node Localization Algorithm in WSNs

Mass-Sanchez

Ruiz-Ibarra

Cortez

et al. 2016

Wireless Pers Commun

Self Cite

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The localization of nodes plays a fundamental role in Wireless Sensor and Actors Networks (WSAN) identifying geographically where an event occurred, which facilitates timely response to this action. This article presents a performance evaluation of multi-hop localization range-free algorithms used in WSAN, such as Distance Vector Hop (DV-Hop), Improved DV-Hop (IDV-Hop), and the Weighted DV-Hop (WDV-Hop). In addition, we propose a new localization algorithm, merging WDV-Hop, with the weighted hyperbolic localization algorithm (WH), which includes weights to the correlation matrix of the estimated distances between the node of interest (NOI) and the reference nodes (RN) in order to improve accuracy and precision. As performance metrics, the accuracy, precision, and computational complexity are evaluated. The algorithms are evaluated in three scenarios where all nodes are randomly distributed in a given area, varying the number of RNs, the density of nodes in the network, and radio coverage of the nodes. The results show that in networks with 100 nodes, WDV-Hop outperforms the DV-Hop and IDV-Hop even if the number of RNs is reduced to 10. Moreover, our proposal shows an improvement in terms of accuracy and precision at the cost of increased computational complexity, specifically in the algorithm execution time, but without affecting the hardware cost or power consumption.

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Performance Evaluation of Localization Algorithms for WSNs

Cited by 17 publications

References 31 publications

Factorial Design Analysis for Localization Algorithms

Factorial Design Analysis for Localization Algorithms

Processing‐in‐memory based multilateration localization in wireless sensor networks using memristor crossbar arrays

Weighted Hyperbolic DV-Hop Positioning Node Localization Algorithm in WSNs

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