LocDyn: Robust Distributed Localization for Mobile Underwater Networks

Soares, Cláudia; Gomes, João; Ferreira, Beatriz Quintino; Costeira, João Paulo

doi:10.1109/joe.2017.2736951

Cited by 16 publications

(7 citation statements)

References 40 publications

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“…However, the authors did not consider the mobility of nodes as well as energy-saving, which is a big requirement for UWSN. Finally, Soares et al [16] addressed the benefit of encoding the dynamic conduct of moving vehicles. However, the authors did not address the range-based metrics to calculate the localization distance between ANs and TNs under energy efficiency requirements.…”

Section: Introductionmentioning

confidence: 99%

“…However, this algorithm works well with a smaller number of ANs only. Soares et al [16] have addressed dynamic localization (DL) algorithm as LocDyn, where the maximum posteriori probability (MAP) estimation problem was also removed by using coded DL kinematics information. This method was distributed, fast, and had low computation at each node.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Anchor-Based Localization in Underwater Wireless Sensor Networks for Industrial Oil Pipeline Monitoring

Goyal

Nain

Singh

et al. 2022

IEEE Can. J. Electr. Comput. Eng.

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Industries need solutions that can automatically monitor oil leakage from deployed underwater pipelines and to rapidly report any damage. The location prediction of mineral reservoirs like oil, gas, or metals in deep water is a challenge during the extraction of these resources. Moreover, the problem of ores and mineral deposits on the seafloor comes into play. The abovementioned challenges necessitate for the deployment of underwater wireless sensor networks (UWSNs). Anchor-based localization techniques are segregated into range-free and range-based processes. Range-based schemes depend on various techniques like angle of arrival (AoA), time of arrival (ToA), time difference of arrival (TDoA), and received signal strength indicator (RSSI).In this article, the localization of these leakages is performed by using range-based metrics for calculating the distance among anchor nodes (ANs) and target nodes (TNs). This estimated distance is further optimized to minimize the estimation error. A multilateralism procedure is used to estimate the optimal position of each TN. The results exhibit that the proposed algorithm shows a high performance when compared to previous works, in terms of minimum energy consumption, lower packet loss, rapid location estimation, and lowest localization error. The benefit of using the proposed methodology greatly impacts on identifying the leakage area in mobility-assisted UWSN, where rapid reporting helps to lower the loss of resources. Résumé-Les industries ont besoin de solutions permettant de surveiller automatiquement les fuites de pétrole des pipelines sous-marins déployés et de signaler rapidement tout dommage. La prédiction de l'emplacement des réservoirs de minéraux comme le pétrole, le gaz ou les métaux en eaux profondes est un défi lors de l'extraction de ces ressources. En outre, le problème des minerais et des dépôts minéraux sur le fond marin entre en jeu. Les défis mentionnés ci-dessus nécessitent le déploiement de réseaux de capteurs sans fil sous-marins (UWSN). Les techniques de localisation basées sur l'ancrage sont classées en deux catégories : les procédés sans portée et les procédés basés sur la portée. Les procédés basés sur la portée dépendent de diverses techniques comme l'angle d'arrivée (AoA), le temps d'arrivée (ToA), la différence de temps d'arrivée (TDoA) et l'indicateur de la force du signal reçu (RSSI). Dans cet article, la localisation de ces fuites est effectuée en utilisant des métriques basées sur la distance pour calculer la distance entre les noeuds d'ancrage (AN) et les noeuds cibles (TN). Cette distance estimée est ensuite optimisée pour minimiser l'erreur d'estimation. Une procédure multilatéralisme est utilisée pour estimer la position optimale de chaque TN. Les résultats montrent que l'algorithme proposé est très performant par rapport aux travaux précédents, en termes de consommation d'énergie minimale, de perte de paquets réduits, d'estimation rapide de la position et d'erreur de localisation minimale. L'avantage de l'utilisation de la méthodolog...

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Anchor-Based Localization in Underwater Wireless Sensor Networks for Industrial Oil Pipeline Monitoring

Goyal

Nain

Singh

et al. 2022

IEEE Can. J. Electr. Comput. Eng.

View full text Add to dashboard Cite

show abstract

“…The authors in [29] employed half quadratic minimization to solve the localization problem in the presence of outliers. Soares et al converted the localization problem into a convex optimization problem using simple fast convex relaxation, and then they tested the method with 10% outlier measurements engaged [30].…”

Section: Introductionmentioning

confidence: 99%

“…It may be noted that the method in [28] is costly and has high complexity. The approaches proposed in [29] and [30] only considered outlier measurements that yielded Gaussian distribution, which was the ideal situation. In this paper, motivated by the above issues, we propose a practical, robust non-cooperative localization algorithm (RNLA) that regards the buoys as sensor nodes and uses RSSI in the presence of outlier measurements in OSNs.…”

Section: Introductionmentioning

confidence: 99%

A Robust, Non-Cooperative Localization Algorithm in the Presence of Outlier Measurements in Ocean Sensor Networks

Mei

Xian

et al. 2019

Sensors

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As an important means of multidimensional observation on the sea, ocean sensor networks (OSNs) could meet the needs of comprehensive information observations in large-scale and multifactor marine environments. In what concerns OSNs, accurate location information is the basis of the data sets. However, because of the multipath effect—signal shadowing by waves and unintentional or malicious attacks—outlier measurements occur frequently and inevitably, which directly degrades the localization accuracy. Therefore, increasing localization accuracy in the presence of outlier measurements is a critical issue that needs to be urgently tackled in OSNs. In this case, this paper proposed a robust, non-cooperative localization algorithm (RNLA) using received signal strength indication (RSSI) in the presence of outlier measurements in OSNs. We firstly formulated the localization problem using a log-normal shadowing model integrated with a first order Taylor series. Nevertheless, the problem was infeasible to solve, especially in the presence of outlier measurements. Hence, we then converted the localization problem into the optimization problem using squared range and weighted least square (WLS), albeit in a nonconvex form. For the sake of an accurate solution, the problem was then transformed into a generalized trust region subproblem (GTRS) combined with robust functions. Although GTRS was still a nonconvex framework, the solution could be acquired by a bisection approach. To ensure global convergence, a block prox-linear (BPL) method was incorporated with the bisection approach. In addition, we conducted the Cramer–Rao low bound (CRLB) to evaluate RNLA. Simulations were carried out over variable parameters. Numerical results showed that RNLA outperformed the other algorithms under outlier measurements, notwithstanding that the time for RNLA computation was a little bit more than others in some conditions.

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“…In practice, the number of anchors might be larger to attain the desired positioning accuracy. Lately, signal processing researchers produced solutions for dynamic network localization; Schlupkothen et al [9] and Soares et al [10] incorporated velocity information from past position estimates to bias the solution of a static relaxation of the localization problem via a regularization term.…”

Section: Introductionmentioning

confidence: 99%

DIeSEL: DIstributed SElf-Localization of a network of underwater vehicles

Soares,

Ji,

Gomes

et al. 2017

Preprint

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How can teams of artificial agents localize and position themselves in GPS-denied environments? How can each agent determine its position from pairwise ranges, own velocity, and limited interaction with neighbors? This paper addresses this problem from an optimization point of view: we directly optimize the nonconvex maximum-likelihood estimator in the presence of range measurements contaminated with Gaussian noise, and we obtain a provably convergent, accurate and distributed positioning algorithm that outperforms the extended Kalman filter, a standard centralized solution for this problem.

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LocDyn: Robust Distributed Localization for Mobile Underwater Networks

Cited by 16 publications

References 40 publications

An Anchor-Based Localization in Underwater Wireless Sensor Networks for Industrial Oil Pipeline Monitoring

An Anchor-Based Localization in Underwater Wireless Sensor Networks for Industrial Oil Pipeline Monitoring

A Robust, Non-Cooperative Localization Algorithm in the Presence of Outlier Measurements in Ocean Sensor Networks

DIeSEL: DIstributed SElf-Localization of a network of underwater vehicles

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