Neural network-based soil parameters predictive coordination algorithm for energy efficient wireless sensor network

The utilization of Wireless Sensor Networks (WSN) in the agricultural field represents a significant stride in the application of Information Technology. Recent advancements in technology have made it possible for sensor networks not only to provide real-time information about soil nutrient levels b...

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Veröffentlicht in:	Journal of ambient intelligence and humanized computing Jg. 15; H. 11; S. 3733 - 3743
Hauptverfasser:	Sharma, Dinesh, Tomar, Geetam Singh
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2024 Springer Nature B.V
Schlagworte:	Agricultural production Agriculture Algorithms Artificial Intelligence Computational Intelligence Coordination Data analysis Energy consumption Energy efficiency Engineering Neural networks Original Research Pattern analysis Real time Robotics and Automation Soil analysis User Interfaces and Human Computer Interaction Wireless sensor networks Recurrent neural networks (RNN) Wireless sensor networks (WSN) Agricultural forecasting Coordination Energy conservation Artificial neural networks
ISSN:	1868-5137, 1868-5145
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Abstract	The utilization of Wireless Sensor Networks (WSN) in the agricultural field represents a significant stride in the application of Information Technology. Recent advancements in technology have made it possible for sensor networks not only to provide real-time information about soil nutrient levels but also to assist in the automation of various agricultural processes. However, it’s crucial to acknowledge a substantial limitation associated with WSN, namely, energy consumption. Through the analysis of experimental data gathered from diverse soil types and employing sophisticated data analytics, it has been observed that the Nutrient Index exhibits a relatively stable pattern over time. Consequently, predictive neural network techniques can be employed to extract detailed insights from the primary inputs received from WSN. This approach eliminates the need for continuous operation of the WSN throughout the day, contributing to enhanced energy efficiency. To achieve this energy-efficient operation, the NR-MDEC protocol is implemented in conjunction with a coordination algorithm, resulting in a substantial improvement in overall efficiency.
AbstractList	The utilization of Wireless Sensor Networks (WSN) in the agricultural field represents a significant stride in the application of Information Technology. Recent advancements in technology have made it possible for sensor networks not only to provide real-time information about soil nutrient levels but also to assist in the automation of various agricultural processes. However, it’s crucial to acknowledge a substantial limitation associated with WSN, namely, energy consumption. Through the analysis of experimental data gathered from diverse soil types and employing sophisticated data analytics, it has been observed that the Nutrient Index exhibits a relatively stable pattern over time. Consequently, predictive neural network techniques can be employed to extract detailed insights from the primary inputs received from WSN. This approach eliminates the need for continuous operation of the WSN throughout the day, contributing to enhanced energy efficiency. To achieve this energy-efficient operation, the NR-MDEC protocol is implemented in conjunction with a coordination algorithm, resulting in a substantial improvement in overall efficiency.
Author	Tomar, Geetam Singh Sharma, Dinesh
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Cites_doi	10.1007/s11227-020-03288-w 10.3389/fpls.2019.01750 10.1007/s12652-020-02530-w 10.1016/j.adhoc.2008.06.003 10.3390/s17081781 10.1016/j.camwa.2008.10.050 10.1016/j.compag.2018.08.001 10.1155/2022/3434646 10.1016/j.compag.2022.107105 10.1145/332833.332838 10.1109/TPAMI.2005.159 10.1109/72.846731 10.3390/sym12050837 10.1007/s12652-019-01177-6 10.1109/JIOT.2019.2911295 10.1007/s12652-020-02687-4
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SubjectTerms	Agricultural production Agriculture Algorithms Artificial Intelligence Computational Intelligence Coordination Data analysis Energy consumption Energy efficiency Engineering Neural networks Original Research Pattern analysis Real time Robotics and Automation Soil analysis User Interfaces and Human Computer Interaction Wireless sensor networks
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Title	Neural network-based soil parameters predictive coordination algorithm for energy efficient wireless sensor network
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