Energy‐aware cluster‐based routing optimization for WSNs in the livestock industry

Awan, Khalid Mahmood; Sherazi, Hafiz Husnain Raza; Ali, Ahmad; Khan, Zuhaib Ashfaq; Mukherjee, Mithun

doi:10.1002/ett.3816

Cited by 12 publications

(16 citation statements)

References 53 publications

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“…By contrast with the line of previous work, Solinski et al focused on feature extraction from the airflow signal of raw spirometry curve data. The authors speculated that the huge NHANES database collected from patients during spirometry tests 19,39,40 could be useful for developing a generic model of cough detection. The authors proposed a two‐layered ANN trained using six features from airflow signals (Sensitivity ‐ 0.852, Specificity ‐ 0.961, Accuracy ‐ 0.904).…”

Section: Review Of Literaturementioning

confidence: 99%

Towards cough sound analysis using the Internet of things and deep learning for pulmonary disease prediction

Kumar

Abhishek

Ghalib

et al. 2020

Trans Emerging Tel Tech

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Cough is a symptom in over a hundred respiratory diseases. The audio features in cough signals contain erudition about the predicament of the respiratory system. Using deep learning or signal processing, these features can be used to build an effective disease prediction system. However, cough analysis remains an area that has received scant attention from machine learning researchers. This can be attributed to several factors such as inefficient ancillary systems, high expenses in obtaining datasets, or difficulty in building classifiers. This article categorized and reviewed the current progress on cough audio analysis for the classification of pulmonary diseases. It also explored potential future issues in research. In addition, it proposed a model for the classification of 10 serious pulmonary ailments commonly seen in Indian adolescents. The proposed model is evaluated against four existing state-of-the-art techniques in the literature.

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Section: Review Of Literaturementioning

confidence: 99%

Towards cough sound analysis using the Internet of things and deep learning for pulmonary disease prediction

Kumar

Abhishek

Ghalib

et al. 2020

Trans Emerging Tel Tech

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“…Therefore, energy harvesting routing strategies have gained the attention of many researchers for a decade. [2][3][4] The decision mechanism is implemented by a central device that does not have battery problems, that is, the base station (BS), to minimize the energy consumption of sensor nodes. 5 Through this methodology, by using a controller device that has enough information about the network, not only the extra cost arising from exchanging control messages between the nodes is eliminated and more stable, but also long-term transmission routes can be established.…”

Section: Introductionmentioning

confidence: 99%

“…6 Clustering architectures, 7 in which adjacent nodes form groups within the network and elect a leader node to transmit sensed data to the BS, are usually preferred, especially in large-scale WSNs as they ensure effective usage of energy in the network. 4,8 The member nodes in a cluster (ie, cluster member, CM) monitor the environment and send their sensed data to the leader node (ie, cluster head, CH). The leaders transmit the aggregated data collected from their CMs either through a relay node or directly to the BS.…”

Section: Introductionmentioning

confidence: 99%

“…Despite the advantages, since the sensor nodes have limited power, recharging or replacing their battery is usually not possible; ensuring energy efficiency in WSNs is one of the major problems. Therefore, energy harvesting routing strategies have gained the attention of many researchers for a decade 2‐4 . The decision mechanism is implemented by a central device that does not have battery problems, that is, the base station (BS), to minimize the energy consumption of sensor nodes 5 .…”

Section: Introductionmentioning

confidence: 99%

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A fully distributed energy‐aware multi‐level clustering and routing for WSN‐based IoT

Abasıkeleş‐Turgut

Altan

2021

Trans Emerging Tel Tech

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One of the major problems in wireless sensor networks (WSNs) is that resource-constrained sensor nodes consume their limited batteries quickly due to long-distance data communications. The communication distance of the nodes can be decreased using clustering architectures and multi-hop data transmissions; hence, the lifetime of the network can be increased. In this study, two-level intra-cluster and multi-level inter-cluster communication are proposed. The coverage area of the second-level clusters is dynamically determined according to the distance of cluster heads to the BS. Self-organized nodes in the network designate the clustering ranges, the clusters, and the cluster heads without a central control mechanism using a fully distributed approach. Moreover, with the help of static clustering, the control messages generated by the system are decreased. The proposed approach, FDEAM, is compared with recent approaches in terms of the network lifetime, network energy consumption, cluster head alteration frequency, dead parent statistics, and data collection.The results show that FDEAM outperforms state-of-the-art approaches for all performance metrics.

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“…12 The sensor nodes provide constant battery power supply. 4 The frequent replacement of battery is not feasible. Till battery drains, the node can facilitate.…”

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confidence: 99%

Self‐attention based generative adversarial network with Aquila optimization algorithm espoused energy aware cluster head selection in WSN

Soundararajan,

Tapas Bapu,

Sargunavathi

et al. 2023

Int J Communication

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SummaryWSNs have a wide range of applications, and the effective Wireless Sensor Network (WSN) design includes the best energy optimization techniques. The nodes in wireless sensor networks run on batteries. The existing cluster head selection methods do not take into account the latency and rate of wireless network traffic when optimizing the node's energy constraints. To overcome these issues, a self‐attention based generative adversarial network (SabGAN) with Aquila Optimization Algorithm (AqOA) is proposed for Multi‐Objective Cluster Head Selection and Energy Aware Routing (SabGAN‐AqOA‐EgAwR‐WSN) for secured data transmission in wireless sensor network. The proposed method implements the routing process through cluster head. SabGAN classifiers are utilized to select the CH based on firm fitness functions, including delay, detachment, energy, cluster density, and traffic rate. After the selection of the cluster head, the malicious node gains access to the cluster. Therefore, the ideal path selection is carried out by three parameters: trust, connectivity, and degree of amenity. These three parameters are optimized under proposed AqOA. The data are transferred to the base station with the support of optimum trust path. The proposed SabGAN‐AqOA‐EgAwR‐WSN method is activated in NS2 simulator. Finally, the proposed SabGAN‐AqOA‐EgAwR‐WSN method attains 12.5%, 32.5%, 59.5%, and 32.65% higher alive nodes; 85.71%, 81.25%, 82.63%, and 71.96% lower delay; and 52.25%, 61.65%, 37.83%, and 20.63% higher normalized network energy compared with the existing methods.

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Energy‐aware cluster‐based routing optimization for WSNs in the livestock industry

Cited by 12 publications

References 53 publications

Towards cough sound analysis using the Internet of things and deep learning for pulmonary disease prediction

Towards cough sound analysis using the Internet of things and deep learning for pulmonary disease prediction

A fully distributed energy‐aware multi‐level clustering and routing for WSN‐based IoT

Self‐attention based generative adversarial network with Aquila optimization algorithm espoused energy aware cluster head selection in WSN

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