An optimized DV-hop localization algorithm using average hop weighted mean in WSNs

Hadir, Abdelali; Zine-Dine, Khalid; Bakhouya, Mohamed; Kafi, Jamal El

doi:10.1109/wcccs.2014.7107903

Cited by 13 publications

(8 citation statements)

References 22 publications

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“…Şöyle ki temel Dv-Hop algoritmasında her düğüm için ortalama hop mesafesi bilgisi hesaplanıyorken bu yöntemde sistemin geneli için ortalama hop mesafesi bilgisi hesaplanıyor. Ağ içerisindeki her çapa düğümün ağ üzerinde bir ağırlığının olduğu ve bu ağırlığa göre hesaplamanın daha doğru sonuç verdiği belirtilmiştir [22]. Yöntemin işleyişi ile ilgili sözde kod Şekil 5'de verilmiş ve aşağıda açıklanmıştır [16]:…”

Section: Ağırlıklı Ortalamalı Hop Mesafeli Dv-hop Yöntemi (Weighted Mean Hop Distance Dv-hop Method)unclassified

Kablosuz Sensör Ağlarda Konumlandırma Yöntemleri ve K-means++ Kümeleme Yöntemi ile Yeni Yaklaşım

Kanmaz

Aydın

2018

Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi

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 New approaches in indoor positioning system  Anchor node-based positioning  k-means ++ use of clustering method in positioning With the great need for indoor positioning technology, new techniques have been searched for, and studies are being carried out on methods that can achieve the success of GPS (Global Positioning System) outdoor positioning. However, due to the objects and architectural structure of the interior, it is difficult to break down the signals and give the correct results to this method. Many methods have been tried and appropriate solutions are being sought to overcome this problem. A wireless sensor network is one of the technologies used for this. When positioning in sensor networks, anchor nodes that have their own position information are used, and their features such as power, arrival time, arrival angle and how many different nodes are used while the signal is transmitted can be used for positioning. One of these methods, Dv-hop method, has been studied in this study and it has been compared with different methods in terms of performance. It has been observed that when this comparison is made it is necessary to use an appropriate clustering method to create an appropriate topology when node points distributed around the center facilitate transmission. At this point k means ++ clustering method was proposed and it was seen that the accuracy rate could be increased up to 50% when probing.

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Section: Ağırlıklı Ortalamalı Hop Mesafeli Dv-hop Yöntemi (Weighted Mean Hop Distance Dv-hop Method)unclassified

Kablosuz Sensör Ağlarda Konumlandırma Yöntemleri ve K-means++ Kümeleme Yöntemi ile Yeni Yaklaşım

Kanmaz

Aydın

2018

Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi

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“…In this section, we provide a comprehensive explanation of the localization process in the DV-Hop approach. Additionally, we introduce two localization algorithms that we previously proposed in [39][40][41], denoted W-DV-Hop and HW-DV-Hop. These localization algorithms are designed with the intent of significantly improving the accuracy when estimating the sensor positions.…”

Section: Weight-based Enhancements To Dv-hopmentioning

confidence: 99%

Range-Free Localization Approaches Based on Intelligent Swarm Optimization for Internet of Things

Hadir,

Kaabouch,

El Houssaini

et al. 2023

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Recently, the precise location of sensor nodes has emerged as a significant challenge in the realm of Internet of Things (IoT) applications, including Wireless Sensor Networks (WSNs). The accurate determination of geographical coordinates for detected events holds pivotal importance in these applications. Despite DV-Hop gaining popularity due to its cost-effectiveness, feasibility, and lack of additional hardware requirements, it remains hindered by a relatively notable localization error. To overcome this limitation, our study introduces three new localization approaches that combine DV-Hop with Chicken Swarm Optimization (CSO). The primary objective is to improve the precision of DV-Hop-based approaches. In this paper, we compare the efficiency of the proposed localization algorithms with other existing approaches, including several algorithms based on Particle Swarm Optimization (PSO), while considering random network topologies. The simulation results validate the efficiency of our proposed algorithms. The proposed HW-DV-HopCSO algorithm achieves a considerable improvement in positioning accuracy compared to those of existing models.

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“…However, utilizing nature-inspired optimization algorithms such as PSO is one of the most efficient approaches for increasing the DV-Hop algorithm's accuracy. However, we firstly describe the process of the original DV-Hop algorithm and our proposed localization algorithms WDV-Hop and HWDV-Hop previously presented in [46] [47], and afterwards we present the new localization algorithms based on particle swarm optimization, named as WDV-HopPSO and HWDV-HopPSO respectively.…”

Section: Proposed Localization Algorithmsmentioning

confidence: 99%

“…In our previous paper [46], [47], we proposed an enhanced DV-Hop technique, namely WDV-Hop. In fact, alike DV-Hop, the WDV-Hop is divided into the following three enhanced steps: i) anchor nodes flood their locations, ii) new formula of the average hop size is adopted, and iii) trilateration method is applied to estimate the nodes' position.…”

Section: B Wdv-hop Localization Algorithmmentioning

confidence: 99%

Accurate Range-Free Localization Algorithms Based on PSO for Wireless Sensor Networks

Hadir¹,

Regragui

García

2021

IEEE Access

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Recently, localization accuracy of unknown nodes has become a critical and challenging issue for many Wireless Sensor Networks (WSNs) and Internet of Things (IoT) applications. Without associating the detected event with its precise geographic location will be surely considered meaningless for these applications. Among all localization algorithms, we observe that the DV-Hop localization algorithm is highly recommended to use in many fields of application due to its simplicity, feasibility, low cost, and no extra hardware requirements, but the localization error caused by the DV-Hop algorithm is relatively large. In this current work, based on both the DV-Hop algorithm and the Particle Swarm Optimization algorithm, we proposed four new localization algorithms to overcome the shortcomings of low accuracy that the basic DV-Hop based algorithms produce. The simulation results showed that the proposed localization algorithms can achieve a better localization performance in terms of accuracy in comparison with other existing algorithms such as basic DV-Hop, MDV-Hop and DV-HopPSO under different random network topologies. We also observed that a significant localization accuracy is achieved by the proposed algorithm HWDV-HopPSO.

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An optimized DV-hop localization algorithm using average hop weighted mean in WSNs

Cited by 13 publications

References 22 publications

Kablosuz Sensör Ağlarda Konumlandırma Yöntemleri ve K-means++ Kümeleme Yöntemi ile Yeni Yaklaşım

Kablosuz Sensör Ağlarda Konumlandırma Yöntemleri ve K-means++ Kümeleme Yöntemi ile Yeni Yaklaşım

Range-Free Localization Approaches Based on Intelligent Swarm Optimization for Internet of Things

Accurate Range-Free Localization Algorithms Based on PSO for Wireless Sensor Networks

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