Designing a Wireless Sensor Network for Precision Agriculture Using Zigbee

Sahitya, G.; Balaji, N.; Naidu, C. D.; Abinaya, S.

doi:10.1109/iacc.2017.0069

Cited by 38 publications

(17 citation statements)

References 2 publications

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“…AT+CGATT? (2) Device creation, Resource subscription and Login AT+MIPLCONFIG=72,1003……501,1,1 AT+MIPLADDOBJ=0,3303,0 AT+MIPLNOTIFY=0,3303,0,5700,4,"20",1 AT+MIPLOPEN=0,30 (3) Data transceiver AT+MIPLNOTIFY=0,3303,0,5700,4,"21.05",1 (4) Device logout AT+MIPLCLOSE=0 AT+MIPLDELOBJ=0,3303,0 AT+MIPLDEL=0 Fig.6 The AT command flow for docking the OneNET platform…”

Section: Report Of Landing Resultsmentioning

confidence: 99%

“…deployed in agricultural production sites and wireless communication networks to realize intelligent perception, intelligent early warning, intelligent decision-making, intelligent analysis, expert online guidance, etc.in agricultural production environment, and provide accurate planting, visual management, intelligent decision making for agricultural production. Wisdom agriculture solutions based on WSN technology (Zigbee) have been widely used because of low cost, simple control and convenient to network [1][2][3]. At the same time, there are some shortcomings.…”

Section: Introductionmentioning

confidence: 99%

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Research on Data Sparse-Collection and Transmission System for WSN Based on NB-IoT and FPGA

Sun¹,

Xu²,

Zheng³

2019

International Journal of Advanced Research in Computer and Comm

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This paper designs and implements a data sparse-acquisition and transmission system for Wireless Sensor Network (WSN) based on Narrow Band-Internet of Things (NB-IoT) and Field-Programmable Gate Array (FPGA) for smart agriculture. The use of distributed wireless network nodes to collect agricultural environment information in a sparse sampling manner can overcome the disadvantages of limited bandwidth and significantly reduce the amount of data transmission, thereby helping to reduce the energy consumption of wireless network nodes and extend the use time. We use FPGA as the base station to receive the data collected by each node, and make full use of the advantages of parallel computing in FPGA for data recovery. And the method we used are matrix filling algorithm, which was named Latent Factor Model (LFM) in the recommendation system, and gradient descent algorithm. Finally, the recovered complete data will be transmitted to the cloud platform for display in the way of NB-IoT. NB-IoT is an emerging Wide Area Network (WAN) technology that has the advantages of supporting massive connections and being geographically unrestricted. The results show that the data can be perfectly recovered within the specified error range, and the algorithm runs nearly three times faster than the serial operation after adding parallel operations.

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Section: Report Of Landing Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on Data Sparse-Collection and Transmission System for WSN Based on NB-IoT and FPGA

Sun¹,

Xu²,

Zheng³

2019

International Journal of Advanced Research in Computer and Comm

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“…This is a low-cost system where the recorded information is transmitted over an SMS via a GSM network to remote location. The limitation of this setup is its reliance on the GSM network [11]. Authors applied LEACH protocol of wireless sensor network in agriculture to improve the irrigation system.…”

Section: Related Workmentioning

confidence: 99%

Smart Energy Control Internet of Things based Agriculture Clustered Scheme for Smart Farming

Awan¹,

Ahmed²,

Najam³

et al. 2020

IJACSA

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The era of smart farming has already begun, and its consequences for society and environment are expected to be massive. In this situation, Internet of Things (IoT) technologies have become a key route towards new agricultural practices. IoT nodes detect and track physical or environmental conditions and transmit data through multihop routing to their base station. However, these IoT nodes have come up with energy constraints and complex routing processes due to limited capacities. Hence, lead to data transmission failure and delay in the fields of IoTbased farming. Because of these limitations, the IoT nodes distant from the base station are dependent on their cluster heads (CHs), causing additional load on CHs leading to high energy consumption and shortening their lifetime. To address these issues, this research proposes a smart energy control IoT based agriculture clustered scheme to reduce load on CHs by introducing a novel clustering scheme. Simulations are conducted for validation and comparison is made with LEACH protocol in Agriculture and results show that proposed scheme has much lower energy consumption and longer network life as compared to its counterparts.

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“…Regarding the communication between these devices, in most of the cases wireless technologies to deploy WSNs and IoT-based systems for Precision Agriculture and Smart Agriculture are used [ 25 ], such as Bluetooth [ 26 ] or GSM and Infrared communications [ 27 ]. But due to its ideal characteristics to deploy WSNs (low power consumption, low cost and high number of devices allowed per network), ZigBee is the most employed wireless technology [ 28 , 29 , 30 , 31 , 32 , 33 ], where transceivers are mounted and controlled by Arduino and Raspberry Pi boards in most cases. Even for precision agriculture based on mobile UAVs (Unmanned Aerial Vehicles) communicating with ground sensors, ZigBee has been used [ 34 ].…”

Section: Introductionmentioning

confidence: 99%

Integration of Autonomous Wireless Sensor Networks in Academic School Gardens

López-Iturri

Celaya-Echarri

Azpilicueta

et al. 2018

Sensors

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In this work, the combination of capabilities provided by Wireless Sensor Networks (WSN) with parameter observation in a school garden is employed in order to provide an environment for school garden integration as a complementary educational activity in primary schools. Wireless transceivers with energy harvesting capabilities are employed in order to provide autonomous system operation, combined with an ad-hoc implemented application called MySchoolGardenApp, based on a modular software architecture. The system enables direct parameter observation, data analysis and processing capabilities, which can be employed by students in a cloud based platform. Providing remote data access allows the adaptation of content to specific classroom/homework needs. The proposed monitoring WSN has been deployed in an orchard located in the schoolyard of a primary school, which has been built with EnOcean’s energy harvesting modules, providing an optimized node device as well network layout. For the assessment of the wireless link quality and the deployment of the modules, especially the central module which needs to receive directly the signals of all the sensor modules, simulation results obtained by an in-house developed 3D Ray Launching deterministic method have been used, providing coverage/capacity estimations applicable to the specific school environment case. Preliminary trials with MySchoolGardenApp have been performed, showing the feasibility of the proposed platform as an educational resource in schools, with application in specific natural science course content, development of technological skills and the extension of monitoring capabilities to new context-aware applications.

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Designing a Wireless Sensor Network for Precision Agriculture Using Zigbee

Cited by 38 publications

References 2 publications

Research on Data Sparse-Collection and Transmission System for WSN Based on NB-IoT and FPGA

Research on Data Sparse-Collection and Transmission System for WSN Based on NB-IoT and FPGA

Smart Energy Control Internet of Things based Agriculture Clustered Scheme for Smart Farming

Integration of Autonomous Wireless Sensor Networks in Academic School Gardens

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