2021
DOI: 10.1038/s41598-021-86462-1
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Genetic algorithm optimized node deployment in IEEE 802.15.4 potato and wheat crop monitoring infrastructure

Abstract: This proposal investigates the effect of vegetation height and density on received signal strength between two sensor nodes communicating under IEEE 802.15.4 wireless standard. With the aim of investigating the path loss coefficient of 2.4 GHz radio signal in an IEEE 802.15.4 precision agriculture monitoring infrastructure, measurement campaigns were carried out in different growing stages of potato and wheat crops. Experimental observations indicate that initial node deployment in the wheat crop experiences n… Show more

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Cited by 10 publications
(4 citation statements)
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References 36 publications
(35 reference statements)
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“…However, Figure 4 illustrates a comparison between the 16 works presented in Table 1 according to the number of objectives taken into account for the WSNs deployment. We found that 43.75% were SOGAs [21] [34]. The difference between these three types is in the number of constraints to be taken into consideration when deploying nodes.…”
Section: Related Workmentioning
confidence: 93%
“…However, Figure 4 illustrates a comparison between the 16 works presented in Table 1 according to the number of objectives taken into account for the WSNs deployment. We found that 43.75% were SOGAs [21] [34]. The difference between these three types is in the number of constraints to be taken into consideration when deploying nodes.…”
Section: Related Workmentioning
confidence: 93%
“…These recordings are analyzed to collect data from different crops, such as Gossypium arboreum (L.) [66], Triticum aestivum (L.) [67], Triticale rimpaui (Wittm.) Muntz [68], Zea mays (L.) [69], Hordeum vulgare (L.) [70], Saccharum officinarum (L.) [64], and Solanum tuberosum (L.) [71].…”
Section: Optical Sensorsmentioning
confidence: 99%
“…For the exergy efficiency of the integrated system, from a bottom-up perspective, Irr. predominantly affects the exergy efficiency because it is the total exergy input, which is the dominator in Equation (18). Therefore, higher Irr.…”
Section: Sensitivity Analysismentioning
confidence: 99%
“…Non-dominated sorting genetic algorithm-II (NSGA-II) is a mature, evolutionary, and effective multi-objective optimization (MOO) algorithm [15,16], which utilizes a selection operator that selects the best of a mating pool consisting of the parent and offspring population [17], thus achieving ideal convergence to the true Pareto optimum solutions [18,19]. It has been effectively and popularly used in solving large-scale engineering problems [17,20], such as achieving high CO 2 purity, enhanced recovery rate for pressure-vacuum swing (PVS) CO 2 adsorption process [21][22][23][24], thermo-economic optimization for a CO 2 capture integrated system [25] and the balance between emission reduction and profit for a carbon capture and utilization (CCU) system [13].…”
Section: Introductionmentioning
confidence: 99%