Optimal placement of sensors in protected cultivation systems to maximize monitoring and control capabilities can guide effective decision-making toward achieving the highest levels of productivity and other desirable outcomes. Reinforcement learning, unlike conventional machine learning methods such as supervised learning does not require large, labeled datasets thereby providing opportunities for more efficient and unbiased design optimization. With the objective of determining the optimal locations of sensors in a greenhouse, a multi-arm bandit problem was formulated using the Beta distribution and solved by the Thompson sampling algorithm. A total of 56 two-in-one sensors designed to measure both internal air temperature and relative humidity were installed at a vertical distance of 1 m and a horizontal distance of 3m apart in a greenhouse used to cultivate strawberries. Data was collected over a period of seven months covering four major seasons, February (winter), March, April, and May (spring), June and July (summer), and October (autumn) and analyzed separately. Results showed unique patterns for sensor selection for temperature and relative humidity during the different months. Furthermore, temperature and relative humidity each had different optimal location selections suggesting that two-in-one sensors might not be ideal in these cases. The use of reinforcement learning to design optimal sensor placement in this study aided in identifying 10 optimal sensor locations for monitoring and controlling temperature and relative humidity.
In conventional cloud systems, large volumes of data streams are sent to the data centres for monitoring, storage, and analytics. However, migrating all the data to the cloud is often not feasible due to cost, privacy, and performance concerns. Deep neural network (DNN) based applications typically require a tremendous amount of computation, hence cannot be directly deployed on resource-constrained edge devices for learning and analytics. Edge-enhanced compressive offloading becomes a sustainable solution that allows data to be compressed at the edge and offloaded to the cloud for further analytics, therefore reducing bandwidth consumption and communication latency. However, it poses a unique challenge to decode the data and perform inference at the server-side within an acceptable quality of service (QoS) limit. This paper makes the first contribution to address the gaps by designing and implementing a principled compression learning method for discovering the compression models that offer the right QoS for an application. It works by a novel modularisation approach that maps features to models and classifies them for a range of quality of service models. An automated QoS-aware orchestrator has been designed to select the best autoencoder model in real-time for compressive offloading in edge-enhanced clouds based on changing QoS requirements. The orchestrator has been designed to have diagnostic capabilities to search appropriate parameters that give the best compression. To our knowledge, this is one of the first attempts at harnessing the capabilities of autoencoders for edge-enhanced compressive offloading based on portable encodings, latent space splitting, and fine-tuning network weights. Due to the discoverable pool of features offering variety of QoS models, the system is capable of processing a large number of QoS requests in a given time. The search strategy, based on a narrowed search over the entire neural architectural space, reduces the computational cost of searching through the entire space by up to 89%. When deployed on an edge-enhanced cloud in Azure IoT testbed, the approach saves up to 70% data transfer costs and takes 32% less time for job completion. It eliminates the additional computational cost of decompression, thereby reducing the processing cost by up to 30%.
Irregular changes in the internal climates of protected cultivation systems can prevent attainment of optimal yield when the environmental conditions are not adequately monitored and controlled. Key to indoor environment monitoring and control and potentially reducing operational costs are the strategic placement of an optimal number of sensors using a robust method. A multi-objective approach based on supervised machine learning was used to determine the optimal number of sensors and installation positions in a protected cultivation system. Specifically, a gradient boosting algorithm, a form of a tree-based model, was fitted to measured (temperature and humidity) and derived conditions (dew point temperature, humidity ratio, enthalpy, and specific volume). Feature variables were forecasted in a time-series manner. Training and validation data were categorized without randomizing the observations to ensure the features remained time-dependent. Evaluations of the variations in the number and location of sensors by day, week, and month were done to observe the impact of environmental fluctuations on the optimal number and location of placement of sensors. Results showed that less than 32% of the 56 sensors considered in this study were needed to optimally monitor the protected cultivation system’s internal environment with the highest occurring in May. In May, an average change of −0.041% in consecutive RMSE values ranged from the 1st sensor location (0.027°C) to the 17th sensor location (0.013°C). The derived properties better described the ambient condition of the indoor air than the directly measured, leading to a better performing machine learning model. A machine learning model was developed and proposed to determine the optimal sensors number and positions in a protected cultivation system.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.