Congestion Control in Cognitive IoT-Based WSN Network for Smart Agriculture

Wireless sensor networking is being used extensively in agricultural activities to increase productivity and reduce losses in various ways. The greenhouse simplifies the concept of planting, which has several benefits in agriculture. In agricultural models, soil pH sensors and gas sensors are common...

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Autores principales:	Daniyal Alghazzawi, Omaima Bamasaq, Surbhi Bhatia, Ankit Kumar, Pankaj Dadheech, Aiiad Albeshri
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Irrigation methods agriculture wireless sensor network congestion control Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/5e866dc036b24cf796031964b49c657b
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spelling	oai:doaj.org-article:5e866dc036b24cf796031964b49c657b2021-11-17T00:00:42ZCongestion Control in Cognitive IoT-Based WSN Network for Smart Agriculture2169-353610.1109/ACCESS.2021.3124791https://doaj.org/article/5e866dc036b24cf796031964b49c657b2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9598939/https://doaj.org/toc/2169-3536Wireless sensor networking is being used extensively in agricultural activities to increase productivity and reduce losses in various ways. The greenhouse simplifies the concept of planting, which has several benefits in agriculture. In agricultural models, soil pH sensors and gas sensors are commonly used. These sensors are applicable in various Internet of Things (IoT) integrated agricultural activities. The paper discusses the hardware design and working of the proposed model. In addition, various agricultural models used for evapotranspiration are also explained. The key factors such as congestion control are evaluated using the Penman-Monteith equation. This paper focuses on implementing more than two references parameters like evapotranspiration and humidity under different conditions, which aids in splitting the relationship evenly by the number of sources. Furthermore, the paper shows the implementation done with MATLAB and values are adjusted using the code. The paper claims to achieve similar variations with the same source value, validating the proposed model’s efficiency and fairness. In an optimal region, these schemes also demonstrate higher throughput and lower delay rates. The improved packet propagation through the IoT network is demonstrated using visualization tools, and the feedback is computed to determine the overall access amount (A1 + A2) obtained. The experimental results show that the propagation rate is 1.24, more significant than the link capacity value. The claims are verified by showing the improved congestion control as it outperforms different parameters, considering an additive increase condition by 0.3% and multiplicative decrease condition by 1.2 %.Daniyal AlghazzawiOmaima BamasaqSurbhi BhatiaAnkit KumarPankaj DadheechAiiad AlbeshriIEEEarticleIrrigation methodsagriculturewireless sensor networkcongestion controlElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 151401-151420 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Irrigation methods agriculture wireless sensor network congestion control Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Irrigation methods agriculture wireless sensor network congestion control Electrical engineering. Electronics. Nuclear engineering TK1-9971 Daniyal Alghazzawi Omaima Bamasaq Surbhi Bhatia Ankit Kumar Pankaj Dadheech Aiiad Albeshri Congestion Control in Cognitive IoT-Based WSN Network for Smart Agriculture
description	Wireless sensor networking is being used extensively in agricultural activities to increase productivity and reduce losses in various ways. The greenhouse simplifies the concept of planting, which has several benefits in agriculture. In agricultural models, soil pH sensors and gas sensors are commonly used. These sensors are applicable in various Internet of Things (IoT) integrated agricultural activities. The paper discusses the hardware design and working of the proposed model. In addition, various agricultural models used for evapotranspiration are also explained. The key factors such as congestion control are evaluated using the Penman-Monteith equation. This paper focuses on implementing more than two references parameters like evapotranspiration and humidity under different conditions, which aids in splitting the relationship evenly by the number of sources. Furthermore, the paper shows the implementation done with MATLAB and values are adjusted using the code. The paper claims to achieve similar variations with the same source value, validating the proposed model’s efficiency and fairness. In an optimal region, these schemes also demonstrate higher throughput and lower delay rates. The improved packet propagation through the IoT network is demonstrated using visualization tools, and the feedback is computed to determine the overall access amount (A1 + A2) obtained. The experimental results show that the propagation rate is 1.24, more significant than the link capacity value. The claims are verified by showing the improved congestion control as it outperforms different parameters, considering an additive increase condition by 0.3% and multiplicative decrease condition by 1.2 %.
format	article
author	Daniyal Alghazzawi Omaima Bamasaq Surbhi Bhatia Ankit Kumar Pankaj Dadheech Aiiad Albeshri
author_facet	Daniyal Alghazzawi Omaima Bamasaq Surbhi Bhatia Ankit Kumar Pankaj Dadheech Aiiad Albeshri
author_sort	Daniyal Alghazzawi
title	Congestion Control in Cognitive IoT-Based WSN Network for Smart Agriculture
title_short	Congestion Control in Cognitive IoT-Based WSN Network for Smart Agriculture
title_full	Congestion Control in Cognitive IoT-Based WSN Network for Smart Agriculture
title_fullStr	Congestion Control in Cognitive IoT-Based WSN Network for Smart Agriculture
title_full_unstemmed	Congestion Control in Cognitive IoT-Based WSN Network for Smart Agriculture
title_sort	congestion control in cognitive iot-based wsn network for smart agriculture
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/5e866dc036b24cf796031964b49c657b
work_keys_str_mv	AT daniyalalghazzawi congestioncontrolincognitiveiotbasedwsnnetworkforsmartagriculture AT omaimabamasaq congestioncontrolincognitiveiotbasedwsnnetworkforsmartagriculture AT surbhibhatia congestioncontrolincognitiveiotbasedwsnnetworkforsmartagriculture AT ankitkumar congestioncontrolincognitiveiotbasedwsnnetworkforsmartagriculture AT pankajdadheech congestioncontrolincognitiveiotbasedwsnnetworkforsmartagriculture AT aiiadalbeshri congestioncontrolincognitiveiotbasedwsnnetworkforsmartagriculture
_version_	1718426049757315072

Congestion Control in Cognitive IoT-Based WSN Network for Smart Agriculture

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