Agricultural Monitoring and Controlling System Using Wireless Sensor Network

Karthik, Chunduri; Menaka, R.

doi:10.1007/978-981-13-3393-4_6

Cited by 8 publications

(4 citation statements)

References 18 publications

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“…Layer IV-Output layer: For overall output computation as all input signals summing, the single node o (4) in this layer is designated with.…”

Section: Uniform Distribution Based Fuzzy Neural Network (Udfnn)mentioning

confidence: 99%

“…Irrigation management helps determine water levels for the budding crop for attaining definite management purposes and is considered a multifaceted decision-making method. The existing conditions will be informed when the farmer is remote from the agricultural land [4]. The major precision irrigation objectives are increasing water efficiency and crop productivity through technology like wireless sensor networks, mobile devices, remote sensing, real-time control, information systems, etc., [5].…”

Section: Introductionmentioning

confidence: 99%

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Automated Irrigation System Using Improved Fuzzy Neural Network in Wireless Sensor Networks

Sakthivel¹,

Vivekanandhan²,

Manikandan³

2023

Intelligent Automation &Amp; Soft Computing

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Irrigation plays a significant role in various agricultural cropping methods deployed in semiarid and arid regions where valuable water applications and managing are considered crucial concerns. Multiple factors such as weather, soil, water, and crop data need to be considered for irrigation maintenance in an efficient besides uniform manner from multifaceted and different information-based systems. A Multi-Agent System (MAS) has been proposed recently based on diverse agent subsystems with definite objectives for attaining global MAS objective and is deployed on Cloud Computing paradigm capable of gathering information from Wireless Sensor Networks (WSNs) positioned in rice, cotton, cassava crops for knowledge discovery and decision making. The radial basis function network has been used for irrigation prediction. However, in recent work, the security of data has not focused on where intruder involvement might corrupt the data at the time of data transferring to the cloud, which would affect the accuracy of decision making. To handle the above mentioned issues, an efficient method for irrigation prediction is used in this work. The factors considered for decision making are soil moisture, temperature, plant height, root depth. The above-mentioned data will be gathered from the sensors that are attached to the crop field. Sensed data will be forwarded to the local server, where data encryption will be performed using Adaptive Elliptic Curve Cryptography (AECC). After the encryption process, the data will be forwarded to the cloud. Then the data stored in the cloud will be decrypted key before being given to the decision-making module. Finally, the uniform distribution-based fuzzy neural network is formulated based on the received data information in the decision-making module. The final decision regarding the level of water required for crop fields would be taken. Based on this outcome, the water volve opening duration and the level of fertilizers required will be considered. Experimental results demonstrate the effectiveness of the proposed model for the United States Geological Survey (USGS) database in terms of precision, accuracy, recall, and packet delivery ratio.

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“…Layer IV-Output layer: For overall output computation as all input signals summing, the single node o (4) in this layer is designated with.…”

Section: Uniform Distribution Based Fuzzy Neural Network (Udfnn)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automated Irrigation System Using Improved Fuzzy Neural Network in Wireless Sensor Networks

Sakthivel¹,

Vivekanandhan²,

Manikandan³

2023

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

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“…These include (but are not limited to) management of underground planting [72], modern drone spraying and control techniques [29,73], robotic enhancements [74,75], security and privacy matters regarding the sensed data [39,76], and of course, food safety and quality issues [2,38,50]. A characteristic example of such approaches was followed in [77], which reported on a control system that continuously analyzed the input from camera and sensor nodes deployed in crops and actuated the control devices according to given threshold values regarding various environmental factors. Another, found in [78], presented an IoT system, called "MOCCA" (Cacao Monitoring System), that monitored climatic and soil factors, which influenced the growth of cacao trees to provide plans for protection against pests and diseases.…”

Section: Emerging Technologies For Smart Agriculturementioning

confidence: 99%

Latency-Adjustable Cloud/Fog Computing Architecture for Time-Sensitive Environmental Monitoring in Olive Groves

et al. 2020

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The emerging and vast adoption of the Internet of Things (IoT) has sprung a plethora of research works regarding the potential benefits in smart agriculture. A popular implementation involves the deployment of Wireless Sensor Networks (WSNs), which embed low energy consumption sensory nodes to capture the critical environmental parameters prevailing on the farms. However, to manage the ever-increasing volumes of raw data successfully, new approaches must be explored. Under this scope, current work reports on the design and development of an IoT system, having in mind the case of olive groves, which are considered the dominant sector for agricultural activity in the Mediterranean Basin. The system incorporates the cloud/fog computing paradigm to equip the olive growers with a low-cost solution for accurate, reliable, and almost real-time monitoring of their crops. Its core is based on a three-layered network architecture, capable of dynamically balancing the generated load, by pushing cloud-elastic resources to the underlying fog network. As such, the premise of the approach lies in the conforming character of the system that allows for targeted alterations to its operational functionality to meet stringent latency and traffic load environmental monitoring constraints. To evaluate the performance of the proposed architecture, a demo prototype is developed and deployed in the facilities of the Ionian University. Experimental results illustrate the efficiency, flexibility, and scalability of the approach in terms of latency, achieving response time reduction across all platforms, a subject of the utmost importance when it comes to precision agriculture of the future. Moreover, it is shown that the system is capable of dynamic functionality adaptation, to meet network traffic load constraints, achieving high throughput (on average 95%) and addressing potential environmental dangers to olive oil production.

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“…With that in mind, many systems, besides deceasing spread [52] or pest infestations [53], now tackle other aspects for climate protection, as identified in [54]. A recent appliance that meets these guidelines was described in [55]; the paper details a control system for monitoring field data originating from camera and sensor nodes deployed in crops. It then actuated the control devices adhering to threshold constraints relating to specific climate agents.…”

Section: Related Research In the Agricultural/environmental Monitorin...mentioning

confidence: 99%

An Alertness-Adjustable Cloud/Fog IoT Solution for Timely Environmental Monitoring Based on Wildfire Risk Forecasting

et al. 2020

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Internet of Things (IoT) appliances, especially those realized through wireless sensor networks (WSNs), have been a dominant subject for heavy research in the environmental and agricultural sectors. To address the ever-increasing demands for real-time monitoring and sufficiently handle the growing volumes of raw data, the cloud/fog computing paradigm is deemed a highly promising solution. This paper presents a WSN-based IoT system that seamlessly integrates all aforementioned technologies, having at its core the cloud/fog hybrid network architecture. The system was intensively validated using a demo prototype in the Ionian University facilities, focusing on response time, an important metric of future smart applications. Further, the developed prototype is able to autonomously adjust its sensing behavior based on the criticality of the prevailing environmental conditions, regarding one of the most notable climate hazards, wildfires. Extensive experimentation verified its efficiency and reported on its alertness and highly conforming characteristics considering the use-case scenario of Corfu Island’s 2019 fire risk severity. In all presented cases, it is shown that through fog leveraging it is feasible to contrive significant delay reduction, with high precision and throughput, whilst controlling the energy consumption levels. Finally, a user-driven web interface is highlighted to accompany the system; it is capable of augmenting the data curation and visualization, and offering real-time wildfire risk forecasting based on Chandler’s burning index scoring.

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Agricultural Monitoring and Controlling System Using Wireless Sensor Network

Cited by 8 publications

References 18 publications

Automated Irrigation System Using Improved Fuzzy Neural Network in Wireless Sensor Networks

Automated Irrigation System Using Improved Fuzzy Neural Network in Wireless Sensor Networks

Latency-Adjustable Cloud/Fog Computing Architecture for Time-Sensitive Environmental Monitoring in Olive Groves

An Alertness-Adjustable Cloud/Fog IoT Solution for Timely Environmental Monitoring Based on Wildfire Risk Forecasting

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