An unmanned aerial vehicle-aided node localization using an efficient multilayer perceptron neural network in wireless sensor networks

Annepu, Visalakshi; Anbazhagan, Rajesh

doi:10.1007/s00521-019-04653-4

Cited by 19 publications

(11 citation statements)

References 30 publications

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“…e wireless sensor network serves the material world [12,13], and the difference of the material world directly determines the very different network structure and characteristics. In order to enable the sensor network to complete the access to the target domain information, it is necessary to classify the network path monitoring capabilities, thereby enhancing the deployment monitoring coverage and significance of the monitoring of the sensor network [14].…”

Section: Intelligent Sensor Network and Informatization Teaching Methodsmentioning

confidence: 99%

Building Campus Sports Information Learning Platform Based on Intelligent Sensor Network

Chen

et al. 2022

Wireless Communications and Mobile Computing

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In the present era of rapid network growth and in the face of growing national power rivalry, education, as the cornerstone of a nation and a national prosperity symbol, has been considered by countries around the world as an important indicator of comprehensive national power. From this perspective, education should keep pace with the times and integrate into the Internet era of “Internet+” and big data. Education computerization is a great product of education’s absorption into the Internet era and is an important content and an important symbol of education modernization. It can realize the modernization of education, and its construction can better reduce the teaching differences between regions, improve teaching quality, and balance resources in education. Moreover, it is conducive to the implementation of quality education and the cultivation of innovative talents, and it is an effective way to realize a learning society and build a lifelong education system. The experiment draws the results of the experiment by comparing the results of students receiving two different education methods. From the specific performance point of view, the average long jump score of boys in the experimental group was 4.40 m, while the average score of boys in the control group was 4.28 m, and the score of the experimental group was 0.12 m higher than that of the control group. The average long jump performance of the girls in the experimental group was 3.52 m, and the average performance of the girls in the control group was 3.30 m. In the long jump scores assessed in the second grade, the full score of boys is 4.35 m, and the score of girls is 3.49 m; the average scores of the experimental group students reached full marks. Compared with traditional teaching methods, information-based teaching facilitates the analysis of key and difficult points for teachers, greatly improves the quality and efficiency of teaching, and improves the teaching level of the classroom.

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Section: Intelligent Sensor Network and Informatization Teaching Methodsmentioning

confidence: 99%

Building Campus Sports Information Learning Platform Based on Intelligent Sensor Network

Chen

et al. 2022

Wireless Communications and Mobile Computing

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“…Another localization scheme based on multilayer perceptron (MLP) is proposed in [108]. The key focus of the work is to provide unknown nodes localization in unmanned aerial vehicles-aided WSNs.…”

Section: Deployment and Localization Solutions Based Ai Techniques In...mentioning

confidence: 99%

Coverage, Deployment and Localization Challenges in Wireless Sensor Networks Based on Artificial Intelligence Techniques: A Review

et al. 2022

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The growing importance and widespread adoption of Wireless Sensor Network (WSN) technologies have helped the enhancement of smart environments in various fields such as manufacturing, smart city, transport, health and the Internet of Things, by providing pervasive real-time applications. In this paper, we analyze the existing research trends of Coverage, Deployment and Localization challenges in WSN with respect to Artificial Intelligence (AI) methods for WSN enhancement. We present a comprehensive discussion on the recent studies that utilized various AI methods to meet specific objectives of WSN, during the span of 2010 to 2021. This would guide the reader towards an understanding on upto-date applications of AI methods with respect to different WSN challenges. Then, we provide a general evaluation and comparison of different AI methods used in WSNs, which will be a guide for research community in identifying the mostly adapted methods and the benefits of using various AI methods for solving the Coverage, Deployment and Localization challenges related to WSNs. Finally, we conclude the paper stating the open research issues and new directions for future research.

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“…thousands of nodes which create the GPS fixing on every SN expensive and furthermore GPS cannot give proper localization outcomes in an indoor platform. In case of dense networks, manual configuring place reference on every SN is not practical [13,14]. It causes a problem where the SNs have to define its existing location without manual configuration and without using any hardware like GPS.…”

Section: *Author For Correspondencementioning

confidence: 99%

Enhanced seagull optimization based node localization scheme for wireless sensor networks

2022

IJATEE

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Wireless sensor network (WSN) has become an emergent paradigm of networking and computing. It uses several application domains like military, healthcare, surveillance, target tracking, etc. Despite the benefits of WSN, node localization (NL) remains a crucial problem. It intends to define the exact place of unknown nodes from the network depends upon the anchor nodes. NL issues may be processed as non-deterministic polynomial-time (NP) hard problems and can be resolved using meta-heuristic optimization techniques. An enhanced seagull optimization-based node localization (ESGOBNL) scheme for WSN was developed in this aspect. The ESGOBNL approach aims to determine the proper location coordinates of the sensor nodes (SNs) in WSN. The proposed ESGBONL technique has been derived by the inclusion of the levy movement (LM) idea into the classification seagull optimization (SGO) algorithm, which is based on the migration as well as the attacking nature of seagulls. A wide range of experiments was implemented in order to report the promising localization efficacy of the ESGOBNL approach. The extensive comparative outcomes highlighted the betterment of the ESGOBNL system on the recent approaches with minimal mean localized error (MLE) of 0.120214. In contrast, the elephant herding optimization (EHO), hybrid elephant herding optimization (HEHO), and tree growth algorithm (TGA) techniques have obtained maximumMLE of 0.793293, 0.333199, and 0.280031, respectively.

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An unmanned aerial vehicle-aided node localization using an efficient multilayer perceptron neural network in wireless sensor networks

Cited by 19 publications

References 30 publications

Building Campus Sports Information Learning Platform Based on Intelligent Sensor Network

Building Campus Sports Information Learning Platform Based on Intelligent Sensor Network

Coverage, Deployment and Localization Challenges in Wireless Sensor Networks Based on Artificial Intelligence Techniques: A Review

Enhanced seagull optimization based node localization scheme for wireless sensor networks

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