iHRNL: Iterative Hessian-based manifold regularization mechanism for localization in WSN

Abhishek, Abhishek; Yadav, Rakesh Kumar; Verma, Shekhar; Venkatesan, S.

doi:10.1007/s11227-021-03761-0

Cited by 6 publications

(4 citation statements)

References 26 publications

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“…Moreover, the number of chromosome lengths to be taken as 2 for the experimentation. The test results of the developed ME‐SOA‐based NL in WSN have been validated with the existing NL approaches like 3D‐Manifold machine learning, 40 FCM, 41 ensemble, 42 and CKF 43 . The heuristic algorithms to be considered for analyzing the effectiveness of the developed model were Electric Fish Optimization (EFO), 44 Deer Hunting Optimization Algorithm (DHOA), 45 and Sail Fish Optimization (SFO) 46 .…”

Section: Computation Of Resultsmentioning

confidence: 99%

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Development of Fused Recurrent Neural Network‐Gated Recurrent Unit with Improved Sandpiper Optimization‐based Intelligent Node Localization Framework in WSN

Raghuvanshi,

Kumar,

Chandra

2024

Int J Communication

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SummaryEmploying mobile nodes or Sensor Nodes (SN) in the Wireless Sensor Networks (WSNs) is a critical task within the coverage area. However, these techniques produce huge errors in the evaluated distance between source nodes and unknown nodes, which increases the average NL error of the unknown node. The usage of a Global Positioning System (GPS) for all the SNs needs high cost for implementation in large‐scale WSNs. The designing of NL in WSN with less energy requirements and less cost is very significant. Enhanced NL is performed with deep learning techniques together with the heuristic strategy for performance enhancement. The developed localization model is mainly utilized for providing fast, scalable, and easily implementable results over heterogeneous and homogeneous networks. Moreover, it is applicable for sparse and dense deployment of nodes. Here, a hybridized deep structured approach is employed by integrating the Recurrent Neural Network (RNN) with a Gated Recurrent Unit (GRU) for calculating the distance among the source nodes to facilitate determining the optimal location for the unidentified node by analyzing the node information present in the received packets. Here, the optimization of constraints executed in the deep learning techniques with the Modified Exploration‐based Sandpiper Optimization Algorithm (ME‐SOA), and also it is utilized to find the optimal location of unknown nodes based on computed distance from the received packets that minimize the average localization error. The experimental results are validated and show the effectiveness of the proposed algorithm with a reduced error rate than other algorithms.

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Section: Computation Of Resultsmentioning

confidence: 99%

“…Moreover, the number of chromosome lengths to be taken as 2 for the experimentation. The test results of the developed ME-SOA-based NL in WSN have been validated with the existing NL approaches like 3D-Manifold machine learning,40 FCM,41 ensemble,42 and CKF 43. The heuristic algorithms to be considered forF I G U R E 4 Steps involved in ME-SOA-based NL in WSN.…”

mentioning

confidence: 99%

Development of Fused Recurrent Neural Network‐Gated Recurrent Unit with Improved Sandpiper Optimization‐based Intelligent Node Localization Framework in WSN

Raghuvanshi,

Kumar,

Chandra

2024

Int J Communication

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“…The localization algorithms use few location-aware sensor nodes that are called reference nodes and apply different methods to estimate the proximity between location unknown nodes and location-aware reference nodes [14]. The obtained proximity measures are further processed using different algorithms such as convex algorithm [15], optimized distance [16], swarm intelligence algorithms [17], Hessian regularization regression [18], self localization protocol [19], deep neural network [20], sine cosine algorithm [21]. A convex algorithm based localization method is developed in [15] to estimate the locations of unknown nodes in WSN used in real time monitoring of uranium tailings reservoir.…”

Section: Introductionmentioning

confidence: 99%

“…The proposed cost function is optimized using particle swarm optimization, whale optimization, and grey wolf optimization algorithms to obtain accurate localization. Another localization algorithm is reported in [18] using Received Signal Strength (RSS) measurements. The locations of nodes are then estimated using Hessian regularization regression.…”

Section: Introductionmentioning

confidence: 99%

Localization of isotropic and anisotropic wireless sensor networks in 2D and 3D fields

Bhat

Santhosh

2021

Telecommun Syst

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Internet of Things (IoT) has changed the way people live by transforming everything into smart systems. Wireless Sensor Network (WSN) forms an important part of IoT. This is a network of sensor nodes that is used in a vast range of applications. WSN is formed by the random deployment of sensor nodes in various fields of interest. The practical fields of deployment can be 2D or 3D, isotropic or anisotropic depending on the application. The localization algorithms must provide accurate localization irrespective of the type of field. In this paper, we have reported a localization algorithm called Range Reduction Based Localization (RRBL). This algorithm utilizes the properties of hop-based and centroid methods to improve the localization accuracy in various types of fields. In this algorithm, the location unknown nodes identify the close-by neighboring nodes within a predefined threshold and localize themselves by identifying and reducing the probable range of existence from these neighboring nodes. The nodes which do not have enough neighbors are localized using the least squares method. The algorithm is tested in various irregular and heterogeneous conditions. The results are compared with a few state-of-the-art hop-based and centroid-based localization techniques. RRBL has shown an improvement in localization accuracy of 28% at 10% reference node ratio and 26% at 20% reference node ratio when compared with other localization algorithms.

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Localization of sensor nodes in wireless sensor networks using bat optimization algorithm with enhanced exploration and exploitation characteristics

2022

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Wireless sensor networks (WSNs) contain sensor nodes in enormous amount to accumulate the information about the nearby surroundings, and this information is insignificant until the exact position from where data have been collected is revealed. Localization of sensor nodes in WSNs plays a significant role in several applications such as detecting the enemy movement in military applications. The major aim of localization problem is to find the coordinates of all target nodes with the help of anchor nodes. In this paper, two variants of bat optimization algorithm (BOA) are proposed to localize the sensor nodes in a more efficient way and to overcome the drawbacks of original BOA, i.e. being trapped in local optimum solution. The exploration and exploitation features of original BOA are modified in the proposed BOA variants 1 and 2 using improved global and local search strategies. To validate the efficiency of the proposed BOA variants 1 and 2, several simulations have been performed for various numbers of target nodes and anchor nodes, and the results are compared with original BOA and other existing optimization algorithms applied to node localization problem. The proposed BOA variants 1 and 2 outperform the other algorithms in terms of mean localization error, number of localized nodes and computing time. Further, the proposed BOA variants 1 and 2 and original BOA are also compared in terms of various errors and localization efficiency for several values of target and anchor nodes. The simulations results signify that the proposed BOA variant 2 is superior to the proposed BOA variant 1 and existing BOA in terms of several errors. The node localization based on the proposed BOA variant 2 is more effective as it takes less time to perform computations and has less mean localization error than the proposed BOA variant 1, BOA and other existing optimization algorithms.

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iHRNL: Iterative Hessian-based manifold regularization mechanism for localization in WSN

Cited by 6 publications

References 26 publications

Development of Fused Recurrent Neural Network‐Gated Recurrent Unit with Improved Sandpiper Optimization‐based Intelligent Node Localization Framework in WSN

Development of Fused Recurrent Neural Network‐Gated Recurrent Unit with Improved Sandpiper Optimization‐based Intelligent Node Localization Framework in WSN

Localization of isotropic and anisotropic wireless sensor networks in 2D and 3D fields

Localization of sensor nodes in wireless sensor networks using bat optimization algorithm with enhanced exploration and exploitation characteristics

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