Regularized least square multi-hops localization algorithm based on DV-Hop for wireless sensor networks

Liouane, Hend; Messous, Sana; Cheikhrouhou, Omar

doi:10.1007/s11235-022-00897-z

Cited by 14 publications

(8 citation statements)

References 46 publications

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“…As described in the introduction section, if nodes are deployed in irregular networks, the accuracy of DV-hop suffers greatly in overall positioning errors when the routing path was not close to straight lines. Moreover, the multilateration-based method may produce incorrect positioning results because of the potential of the anchor nodes' collinearity issue [17,18].…”

Section: Related Workmentioning

confidence: 99%

NRAP: Nearest Reliable Anchors-Based Wireless Positioning for Irregular Multi-hop Networks

Wang

YAN

et al. 2023

Wireless Pers Commun

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The position of nodes is the critical foundation for information transfer between "person to things" and "things to things" at any time and from any location in wireless multi-hop networks. The hop-based wireless positioning technology has received widespread attention because it does not requiring additional ranging devices. However, most existing hop-based positioning algorithms ignore the network topology irregularities issues, which are frequently observed in multi-hop networks and may lead to poor positioning performance. In this paper, we present a novel wireless positioning algorithm, named NRAP, for irregular networks based on the nearest reliable anchors to mitigate the impact of topology irregularities. Specifically, a more accurate per-hop distance Article Title estimation model is firstly adopted. Then, NRAP divides the entire network into multiple sub-networks with the node to be positioned and its nearest four neighbor anchor nodes. Moreover, a hybrid particle swarm optimization and natural selection algorithm are employed to search in each sub-network to find the optimal estimated position of the node to be positioned. We evaluate and analyze the performance of NRAP under various network topologies and parameters in comparison with the many state-of-the-art works, and the results further demonstrated the superior performance than these benchmarks.

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Section: Related Workmentioning

confidence: 99%

NRAP: Nearest Reliable Anchors-Based Wireless Positioning for Irregular Multi-hop Networks

Wang

YAN

et al. 2023

Wireless Pers Commun

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“…The authors in [52] proposed a Cosine Rule-Based Localization (CRL) algorithm with a mobile anchor node that moves in static trajectories. Instead of using the trilateration method [32] to localize sensor nodes, the authors in [52] use the cosine rule on received anchor positions and distances obtained from RSSI. The authors conducted simulations to prove the performance in terms of localization accuracy of their proposed approach in comparison with other works based on the trilateration technique.…”

Section: Han Et Al Have Proposed In [43] a Mobile Anchor-based Locali...mentioning

confidence: 99%

“…Multi-hop methods are almost range-free but can also use the estimation of distances via hop-count information [22,32]. They aim to determine a connectivity graph and then try to make it fit the known positions as accurately as possible.…”

Section: Introductionmentioning

confidence: 99%

Mobile Anchor and Kalman Filter Boosted Bounding Box for Localization in Wireless Sensor Networks

et al. 2022

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Event detection is usually the primary purpose of wireless sensor networks (WSNs). Therefore, it is crucial to determine where and when an event occurs in order to map the event to its spatio-temporal domain. In WSN localization, a few anchor nodes are those aware of their locations via the Global Positioning System (GPS), which is energy-consuming. Non-anchor nodes self-localize by gathering information from anchor nodes to estimate their positions using a localization technique. Traditional algorithms use at least three static anchors for the localization process. Recently, researchers opted to replace multiple static anchors by a single mobile anchor during the localization process. This paper proposes a Kalman filter based on bounding box localization algorithm (KF-BBLA) in WSNs with mobile anchor node. We present a new mobile anchor localization strategy to minimize energy, hardware costs, and computation complexity, while improving accuracy and cost-effectiveness. Network connectivity measurement and the bounding box localization method are used in order to identify the bounded possible localization zone. The Kalman filter is then used to minimize the uncertainty produced by the connectivity process. We aim also to minimize the localization inaccuracies generated by the bounding box algorithm. Simulation results show that our proposed approach significantly reduces the localization error compared to other localization algorithms chosen from the recent literature by up to 20%. We use the cumulative distribution function (CDF) as an indicator to assess the accuracy of our proposed algorithm.

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“…Zhao et al [ 15 ] proposed a DV-Hop algorithm based on locally weighted linear regression (LWLR-DV-Hop), using a kernel approach to improve localization accuracy by increasing the weights of neighboring anchor nodes. Liouane et al [ 16 ] proposed an improved method of the DV-Hop algorithm for wireless sensor networks based on the Tikhonov regularization method. Liu et al [ 17 ] proposed an improved DV-Hop algorithm based on neural dynamics (ND-DV-Hop) for improving the accuracy of the DV-Hop algorithm.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Flower Pollination Algorithm with Gaussian Perturbation for Node Location of a WSN

Zheng

Yuan

Xie

et al. 2023

Sensors

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Localization is one of the essential problems in internet of things (IoT) and wireless sensor network (WSN) applications. However, most traditional range-free localization algorithms cannot fulfill the practical demand for high localization accuracy. Therefore, a localization algorithm based on an enhanced flower pollination algorithm (FPA) with Gaussian perturbation (EFPA-G) and the DV-Hop method is proposed.FPA is widely applied, but premature convergence still cannot be avoided. How to balance its global exploration and local exploitation capabilities still remains an outstanding problem. Therefore, the following improvement schemes are introduced. A search strategy based on Gaussian perturbation is proposed to solve the imbalance between the global exploration and local exploitation search capabilities. Meanwhile, to fully exploit the variability of population information, an enhanced strategy is proposed based on optimal individual and Lévy flight. Finally, in the experiments with 26 benchmark functions and WSN simulations, the former verifies that the proposed algorithm outperforms other state-of-the-art algorithms in terms of convergence and search capability. In the simulation experiment, the best value for the normalized mean squared error obtained by the most advanced algorithm, RACS, is 20.2650%, and the best value for the mean distance error is 5.07E+00. However, EFPA-G reached 19.5182% and 4.88E+00, respectively. It is superior to existing algorithms in terms of positioning, accuracy, and robustness.

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Regularized least square multi-hops localization algorithm based on DV-Hop for wireless sensor networks

Cited by 14 publications

References 46 publications

NRAP: Nearest Reliable Anchors-Based Wireless Positioning for Irregular Multi-hop Networks

NRAP: Nearest Reliable Anchors-Based Wireless Positioning for Irregular Multi-hop Networks

Mobile Anchor and Kalman Filter Boosted Bounding Box for Localization in Wireless Sensor Networks

An Enhanced Flower Pollination Algorithm with Gaussian Perturbation for Node Location of a WSN

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