Influence of Mobility Models in Precision Spray Aided by Wireless Sensor Networks

Goncalves, Leandro; Costa, Fausto Guzzo da; Neves, Leandro Alves; Ueyama, Jó; Zafalon, Geraldo Francisco Donegá; Montez, Carlos; Pinto, Alex Sandro Roschildt

doi:10.1088/1742-6596/574/1/012153

Cited by 6 publications

(4 citation statements)

References 6 publications

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“…After considering the implementation of WSN in agriculture [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ] as a distributed system, we researched attacks on the WSN [ 23 , 24 , 25 , 34 , 35 , 36 , 37 ]. Attacks on WSN exploit vulnerabilities in all layers of network communication architecture, while disrupting security requirements, such as authentication, authorization, data integrity, data trustworthiness, availability, non-repudiation, trust, and privacy [ 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 ].…”

Section: Methodsmentioning

confidence: 99%

“…Simultaneously, the message goes through a hash transformation as with the sender, SNA node. Then, obtained hash values are compared (11), and if the comparison is all right, then the message integrity, authenticity, and non-repudiation are preserved. If contrary, the message was modified in the process of transmission or was sent by somebody else.…”

Section: Case Study Example For the Application Of The Model In The Imentioning

confidence: 99%

“…WSNs have become increasingly used in agriculture. They may be applied in the following areas [ 8 ]: environmental status monitoring [ 9 , 10 , 11 ], precise agriculture [ 12 , 13 , 14 ], machinery and processes control [ 15 , 16 ], automatization of monitored areas, and monitoring systems [ 17 , 18 , 19 , 20 , 21 ].…”

Section: Related Workmentioning

confidence: 99%

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Wireless Sensor Network in Agriculture: Model of Cyber Security

Prodanovic¹,

Rančić

Vulić

et al. 2020

Sensors

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Nowadays, wireless sensor networks (WSN) are widely used in agriculture monitoring to improve the quality and productivity of farming. In this application, sensors gather different types of data (i.e., humidity, carbon dioxide level, and temperature) in real-time scenarios. Thus, data gathering, transmission, and rapid response to new circumstances require a secured data mechanism to avoid malicious adversaries. Therefore, this paper focuses on data security from the data origin source to the end-user, and proposes a general data security model that is independent of the network topology and structure, and can be widely used in the agriculture monitoring application. The developed model considers practical aspects, the architecture of the sensor node, as well as the necessity to save energy while ensuring data security, and optimize the model through the application of organizational and technical measures. The model evaluation is conducted through simulation in terms of energy consumption. The result shows that the proposed model ensures good data security at the cost of a slight increase in energy consumption at receiver and sender nodes, and energy consumption per bit, up to 2%, 7%, and 1.3%, respectively, due to overhead added for authentication in the network.

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Section: Methodsmentioning

confidence: 99%

Section: Case Study Example For the Application Of The Model In The Imentioning

confidence: 99%

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Wireless Sensor Network in Agriculture: Model of Cyber Security

Prodanovic¹,

Rančić

Vulić

et al. 2020

Sensors

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“…The feasibility of deploying WSNs at low cost has made these systems highly desirable for military, agriculture, sports, medicine, and industry. The potential applications of WSNs as cost-effective processes to improve agricultural resource management have been reported: irrigation management systems [3], farming systems monitoring [4][5][6], pest and disease control [7], controlled use of fertilizers [8], cattle movement monitoring [9], groundwater quality monitoring [10], greenhouse gases monitoring [11], asset tracking [12], and remote control and diagnosis [13,14]. The most promising applications for WSN in precision agriculture are irrigation management, farming monitoring, disease control, and fertilizer accuracy [15].…”

Section: Introductionmentioning

confidence: 99%

Unmanned Aerial System-Based Data Ferrying over a Sensor Node Station Network in Maize

Singh

Heeren

et al. 2022

Sensors

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Agriculture is considered a hotspot for wireless sensor network (WSN) facilities as they could potentially contribute towards improving on-farm management and food crop yields. This study proposes six designs of unmanned aerial system (UAS)-enabled data ferries with the intent of communicating with stationary sensor node stations in maize. Based on selection criteria and constraints, a proposed UAS data ferrying design was shortlisted from which a field experiment was conducted for two growing seasons to investigate the adoptability of the selected design along with an established WSN system. A data ferry platform comprised of a transceiver radio, a mini-laptop, and a battery was constructed and mounted on the UAS. Real-time monitoring of soil and temperature parameters was enabled through the node stations with data retrieved by the UAS data ferrying. The design was validated by establishing communication at different heights (31 m, 61 m, and 122 m) and lateral distances (0 m, 38 m, and 76 m) from the node stations. The communication success rate (CSR) was higher at a height of 31 m and within a lateral distance of 38 m from the node station. Lower communication was accredited to potential interference from the maize canopy and water losses from the maize canopy.

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Wireless sensor networks in agriculture through machine learning: A survey

Rahaman

Azharuddin

2022

Computers and Electronics in Agriculture

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Influence of Mobility Models in Precision Spray Aided by Wireless Sensor Networks

Cited by 6 publications

References 6 publications

Wireless Sensor Network in Agriculture: Model of Cyber Security

Wireless Sensor Network in Agriculture: Model of Cyber Security

Unmanned Aerial System-Based Data Ferrying over a Sensor Node Station Network in Maize

Wireless sensor networks in agriculture through machine learning: A survey

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