A Cost-Effective Embedded Platform for Greenhouse Environment Control and Remote Monitoring

Sumalan, Radu; Stroia, Nicoleta; Moga, Daniel; Mureșan, Vlad; Lodin, Alexandru; Vintilă, Teodor; Popescu, Cosmin

doi:10.3390/agronomy10070936

Cited by 21 publications

(9 citation statements)

References 48 publications

(49 reference statements)

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“…Considering previously developed sensing modules (Figure 10) ( [45,[50][51][52][53]), and the versatile modular architecture that was demonstrated in a broad range of applications (greenhouse climate control, smart-home on-demand ventilation, air-quality monitoring, etc. ), the first approach considered was to reuse the hardware functionality of the host module, based on a 32-bit microcontroller, as much as possible, and to interface it to the energy metering board.…”

Section: Development Of the Smart-meter Node With Wireless Communicationmentioning

confidence: 99%

Integrated Smart-Home Architecture for Supporting Monitoring and Scheduling Strategies in Residential Clusters

Stroia

Moga²,

Petreuş³

et al. 2022

Buildings

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The monitoring of power consumption and the forecasting of load profiles for residential appliances are essential aspects of the control of energy savings/exchanges at multiple hierarchical levels: house, house cluster, neighborhood, and city. External environmental factors (weather conditions) and inhabitants’ behavior influence power consumption, and their usage as part of forecasting activity may lead to added value in the estimation of daily-load profiles. This paper proposes a distributed sensing infrastructure for supporting the following tasks: the monitoring of appliances’ power consumption, the monitoring of environmental parameters, the generation of records for a database that can be used for both identifying load models and testing load-scheduling algorithms, and the real-time acquisition of consumption data. The hardware/software codesign of an integrated architecture that can combine the typical distributed sensing and control networks present in modern buildings (targeting user comfort) with energy-monitoring and management systems is presented. Methods for generating simplified piecewise linear (PWL) representations of the load profiles based on these records are introduced and their benefits compared with classic averaged representations are demonstrated for the case of peak-shaving strategies. The proposed approach is validated through implementing and testing a smart-meter node with wireless communication and other wired/wireless embedded modules, enabling the tight integration of the energy-monitoring system into smart-home/building-automation systems. The ability of this node to process power measurements with a programable granularity level (seconds/minutes/hours) at the edge level and stream the processed measurement results at the selected granularity to the cloud is identified as a valuable feature for a large range of applications (model identification, power saving, prediction).

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Section: Development Of the Smart-meter Node With Wireless Communicationmentioning

confidence: 99%

Integrated Smart-Home Architecture for Supporting Monitoring and Scheduling Strategies in Residential Clusters

Stroia

Moga²,

Petreuş³

et al. 2022

Buildings

Self Cite

View full text Add to dashboard Cite

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“…Therefore, for each transmission of GA content (individual or fitness value), there is an overhead of 16 clock cycles because of the transmission of 2 bytes for command and acknowledge messages. Thus, in Equations (10) and (11), c trans CLK is 16 then. The communication protocol is used in two moments: during the selection and crossover and end to the collection of the best individual from all nodes.…”

Section: Communication Between Microcontrollersmentioning

confidence: 99%

Distributed Genetic Algorithms for Low-Power, Low-Cost and Small-Sized Memory Devices

Medeiros

Fernandes

2020

Electronics

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This work presents a strategy to implement a distributed form of genetic algorithm (GA) on low power, low cost, and small-sized memory aiming for increased performance and reduction of energy consumption when compared to standalone GAs. This strategy focuses on making a distributed version of GA feasible to run as a low cost and a low power consumption embedded system utilizing devices such as 8-bit microcontrollers (µCs) and Serial Peripheral Interface (SPI) for data transmission between those devices. Details about how the distributed GA was designed from a previous standalone implementation made by the authors and how the project is structured are presented. Furthermore, this work investigates the implementation limitations and shows results about its proper operation, most of them collected with the Hardware-In-Loop (HIL) technique, and resource consumption such as memory and processing time. Finally, some scenarios are analyzed to identify where this distributed version can be utilized and how it is compared to the single-node standalone implementation in terms of performance and energy consumption.

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“…Nonetheless, some conversions and the resulting approximations add to the uncertainty of the measurements. The use of low-cost sensors as substitutes to more accurate instruments, which has been proposed inter alia for control systems in greenhouses (Pisanu et al, 2020;Sumalan et al, 2020), can also be a source of uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Can Accuracy Issues of Low-Cost Sensor Measurements Be Overcome With Data Assimilation?

et al. 2023

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The use of mechanistic plant growth models relies on the availability of high-quality inputs to reduce uncertainty in estimates. Measurements of photosynthetically active radiation inside a protected environment are either more expensive to obtain or dependent on assumptions regarding external measurements. This study aimed to reduce the influence of uncertainty in the measurements of low-cost lux meters by using a data assimilation strategy. We first determined, by simulation, the impact of different sensors on the estimates. We then used the Ensemble Kalman Filter to assimilate artificial observations of tomato growth in the Reduced-State Tomgro model, in simulations for which the solar radiation inputs were obtained from a low-cost lux meter. We compared the assimilated estimates to the simulations that used solar radiation obtained with a scientific-grade quantum sensor. For periods of larger radiation intensity, in which the differences in measurements from both instruments are larger, assimilation of observations with low errors lead to estimates that are closer to the ones obtained by scientific grade sensors. These results suggest that low-cost sensors could be used to obtain inputs for growth models in protected environments, provided there are also imperfect observations of the state.

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A Cost-Effective Embedded Platform for Greenhouse Environment Control and Remote Monitoring

Cited by 21 publications

References 48 publications

Integrated Smart-Home Architecture for Supporting Monitoring and Scheduling Strategies in Residential Clusters

Integrated Smart-Home Architecture for Supporting Monitoring and Scheduling Strategies in Residential Clusters

Distributed Genetic Algorithms for Low-Power, Low-Cost and Small-Sized Memory Devices

Can Accuracy Issues of Low-Cost Sensor Measurements Be Overcome With Data Assimilation?

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