DV-Hop-based localization algorithm using optimum anchor nodes subsets for wireless sensor network

Cao, Yuxiao; XU, Jinbao

doi:10.1016/j.adhoc.2022.103035

Cited by 21 publications

(4 citation statements)

References 26 publications

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“…The proposed GNLMA localization method estimates the location of TNs which is very close to their true locations as shown in figure 3b to figure 3g. The simulated results (each value is averaged for thirty simulations) are also compared with the existing state-of-the-art localization algorithms [33][34][35][36]. The average or mean localization error is computed using equation 10 as,…”

Section: Resultsmentioning

confidence: 99%

Gaussian-newton Localization Through Multilateration Algorithm for Wireless Sensor Networks

Reddy

Chandra²

2023

Preprint

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With the continuous prevalence of wireless sensor network (WSN) applications in the recent days, localization of sensor nodes became an important aspect in research in terms of its accuracy, communication overhead and computational complexity. Localization plays an important role in location sensitive applications like object tracking, nuclear attacks, biological attacks, fire detection, traffic monitoring systems, intruder detections, and finding survivors in post-disasters, etc. The objective of localization is to identify the coordinates of target nodes using information provided by anchor nodes. Precision improvement of the sensor node positions is a key issue for an effective data transmission between sensor nodes and save the node’s energy as well as enhance the network lifetime. In this article, a cost-effective localization algorithm with minimal number of anchor nodes is proposed that uses nature inspired optimization techniques to enhance the localization accuracy compared to the state-of-the-art localization algorithms. The performance metrics considered for simulations and comparison with the existing algorithms include average localization accuracy, communication range, and the number of anchor nodes. The simulation results prove that the proposed gaussian-newton localization through multilateration algorithm (GNLMA) enhances the mean localization accuracy to 92.8% and the range measurement error is limited to 1.22meters. Depending on the communication range of sensor nodes, the average localization accuracy is achieved up to 94.4% using the proposed GNLMA.

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Section: Resultsmentioning

confidence: 99%

Gaussian-newton Localization Through Multilateration Algorithm for Wireless Sensor Networks

Reddy

Chandra²

2023

Preprint

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“…At present, for the minimum hop count problem, many scholars have proposed various solutions, mainly using multiple communication radii [20], that is, the communication radius R is divided, and then gradually broadcast in the order from small to large, the method can effectively reduce the error, but if the division of too much will increase the communication overhead, complexity and delay, reduce energy efficiency and positioning accuracy, leading to network congestion, uneven load on nodes, and premature network death, signal interference and increased management complexity. Therefore, in this paper, we choose to use a dual communication radius [21] and the model is shown in Fig.…”

Section: Dv-hop Algorithm Optimizationmentioning

confidence: 99%

Localization algorithm based on deep neural network and improved COA optimization

yu,

ye,

liu

et al. 2024

Preprint

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To improve the positioning accuracy of range-independent positioning algorithms in wireless sensor networks, a DV-Hop localization algorithm (DDCO) based on deep learning and improved crayfish optimization was proposed. Firstly, the dual communication radius subdivision of the minimum number of hops is introduced to reduce the error due to the number of hops; then the trained deep neural network model is used to correct the estimated distance to reduce the distance estimation error; finally, the random center of gravity inverse learning and the improved crayfish algorithm with nonlinear function are introduced to calculate the coordinates of the unknown nodes, and the global optimization capability of the intelligent algorithm is used to reduce the error generated by the DNN. The simulation results show that the positioning error of the DDCO algorithm is reduced by 54.4%, 23.4%, and 10.5%, respectively, compared with DV-Hop and other comparison algorithms under different communication radii. Under different beacon node densities, the error decreases by 46.2%, 24.2%, and 10.6%, respectively. Under different node densities, the error decreases by 49.6%, 30.3%, and 17.3%, respectively.

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“…Distributedlocalization algorithms areclassified into two wide classes exactly, beacon-based and signature-based techniques. The beacon-based systems require the existence of particular nodes that recognizetheir locations, named beacon nodes (Jadliwala et al, 2007;Zhong et al, 2007;Qian et al, 2011;Cabero et al, 2014;Cao and Xu, 2023).…”

Section: Introductionmentioning

confidence: 99%

Comparative evaluation of localization range-free algorithms in wireless sensor networks

Edam,

Abubakr,

Rahma

et al. 2024

Int. J. Eng. Sci. Tech

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Localization in wireless sensor networks is essential not only for determining the location but also for routing, managing density, tracking, and a wide range of other communication network functions. There are two main categories of localization algorithms in wireless sensor networks: range-based algorithms and range-free techniques. Localization based on range-free algorithms has benefits in terms of requiring less hardware and energy, making it cost-efficient. This paper examines the impact of beacon nodes on range-free localization algorithms. The findings indicate that ADLA has intermediate localization errors and the best detecting nodes. It also addresses the effect of the number of locators on the algorithm’s efficiency. The findings demonstrate that as the number of locators increased, the number of detected nodes in Centroid also increased. Compared to Centroid, ADLA has the second-best detecting nodes but with better average error. Moreover, it considers the impact of the number of static nodes on range-free localization algorithms, and ADLA achieved the best detection nodes. According to the evaluated results, this paper proposes a hybrid algorithm that combines the Centroid algorithm and Active Distributed Localization Algorithm (ADLA) algorithm. However, combining these two algorithms results in less localization error.

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DV-Hop-based localization algorithm using optimum anchor nodes subsets for wireless sensor network

Cited by 21 publications

References 26 publications

Gaussian-newton Localization Through Multilateration Algorithm for Wireless Sensor Networks

Gaussian-newton Localization Through Multilateration Algorithm for Wireless Sensor Networks

Localization algorithm based on deep neural network and improved COA optimization

Comparative evaluation of localization range-free algorithms in wireless sensor networks

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