Wireless sensor network for agricultural environment using raspberry pi based sensor nodes

Cabaccan, Carlo N.; Cruz, Febus Reidj G.; Agulto, Ireneo

doi:10.1109/hnicem.2017.8269427

Cited by 29 publications

(17 citation statements)

References 6 publications

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“…Due to their high customization capabilities, a popular WSN configuration, uses Arduino sensors [56] to generate environmental measurements and utilizes the ZigBee module for communication purposes, as described by Satyanarayana and Mazaruddin [57] and Papamichail et al [58], whilst extensively documented by Kooijman [59]. However, with the coming of IoT, more microcontrollers have come to light in order to support or even remotely control the Arduino processes, such as Raspberry Pis [60,61], which can be setup to act as sensors [62], gateways [63], databases [64], servers [65] or fog devices [66].…”

Section: Wsns In the Agricultural Domainmentioning

confidence: 99%

Wireless Sensor Network Synchronization for Precision Agriculture Applications

et al. 2020

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The advent of Internet of Things has propelled the agricultural domain through the integration of sensory devices, capable of monitoring and wirelessly propagating information to producers; thus, they employ Wireless Sensor Networks (WSNs). These WSNs allow real time monitoring, enabling intelligent decision-making to maximize yields and minimize cost. Designing and deploying a WSN is a challenging and multivariate task, dependent on the considered environment. For example, a need for network synchronization arises in such networks to correlate acquired measurements. This work focuses on the design and installation of a WSN that is capable of facilitating the sensing aspects of smart and precision agriculture applications. A system is designed and implemented to address specific design requirements that are brought about by the considered environment. A simple synchronization scheme is described to provide time-correlated measurements using the sink node’s clock as reference. The proposed system was installed on an olive grove to assess its effectiveness in providing a low-cost system, capable of acquiring synchronized measurements. The obtained results indicate the system’s overall effectiveness, revealing a small but expected difference in the acquired measurements’ time correlation, caused mostly by serial transmission delays, while yielding a plethora of relevant environmental conditions.

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Section: Wsns In the Agricultural Domainmentioning

confidence: 99%

Wireless Sensor Network Synchronization for Precision Agriculture Applications

et al. 2020

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“…On a different path, the authors in [87] dealt with the deployment of a WSN consisting entirely of Raspberry Pi nodes for implementing a smart agricultural model. In their system, the monitoring of the WSN was accomplished by a Graphical User Interface (GUI) application, programmed using "MATLAB" software, installed on the base station's board.…”

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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“…Another recent application using Raspberry Pi’s is the monitoring of growing conditions for lettuce ( Lactuca sativa ). Here researchers used a network of Raspberry Pi computers equipped with sensors for light, temperature, and humidity as well as a real-time clock and external battery to monitor the plants (Cabaccan et al 2017). These two applications provide excellent examples of the scalability of this technology as well as its ability to run on an independent power source for a period of time, giving it the flexibility needed to be used in places with limited access to electricity.…”

Section: Introductionmentioning

confidence: 99%

Affordable Remote Monitoring of Plant Growth and Facilities using Raspberry Pi Computers

Grindstaff

Mabry

Blischak

et al. 2019

Preprint

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Premise of the study: Environmentally controlled facilities, such as growth chambers, are essential tools for experimental research. Automated remote monitoring of such facilities with low-cost hardware can greatly improve both the reproducibility and the accurate maintenance of their conditions. • Methods and Results: Using a Raspberry Pi computer, open-source software, environmental sensors, and a camera, we developed a cost-effective system for monitoring growth chamber conditions, which we have called 'GMpi.' Coupled with our software, GMpi_Pack, our setup automates sensor readings, photography, alerts when conditions fall out of range, and data transfer to cloud storage services. • Conclusions: The GMpi offers low-cost access to environmental data logging, improving reproducibility of experiments, as well as reinforcing the stability of controlled environmental facilities. The device is also flexible and scalable, allowing customization and expansion to include other features such as machine vision.

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Wireless sensor network for agricultural environment using raspberry pi based sensor nodes

Cited by 29 publications

References 6 publications

Wireless Sensor Network Synchronization for Precision Agriculture Applications

Wireless Sensor Network Synchronization for Precision Agriculture Applications

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

Affordable Remote Monitoring of Plant Growth and Facilities using Raspberry Pi Computers

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