CropWaterNeed: A Machine Learning Approach for Smart Agriculture

In this paper, we propose an approach CropWaterNeed in order to estimate and predict the future water needs and maximize the productivity in the irrigated areas. Unfortunately, we have not identified data available to be employed in such machine learning process in order to predict plants water needs. The proposed approach consists of extending the classic machine learning process. Particularly, we define a process to build dataset that contains plant water requirements. To collect data, we extract meteorological data from Climwat database and plants water requirements using Cropwat Tool. Then, we aggregate the extracted data into a dataset. Subsequently, we use the dataset to perform the learning process using XGBRegressor, Decision Tree, Random Forest and Gradiant Boost Regressor. Afterward, we evaluate the model generated by each algorithm by measuring the performance measures such as MSE, RMSE and MAE. Our work shows that the model generated by XGBRegressor is the most efficient in our case while Random Forest is the least efficient. As future work, we aim to apply the proposed process to test the performance of other regression algorithms and to test the impact of using deep learning techniques with the extracted data

A Multi-Criteria Decision Making Approach for Cloud-Fog Coordination

© 2020, Springer Nature Switzerland AG. This paper presents a multi-criteria cloud-fog coordination model to recommend where data that things generate should be sent (either cloud, fog, or cloud & fog concurrently) and in what order (either cloud then fog, fog then cloud, or fog & cloud concurrently). The model considers end-users’ concerns such as data latency, sensitivity, and freshness. The coordination model uses fuzzy logic when addressing these concerns in preparation for producing the recommendations. For validation purposes, a healthcare-driven IoT application along with an in-house testbed, that features real sensors and fog and cloud platforms, have permitted to carry out different experiments that demonstrate the technical feasibility of both the multi-criteria cloud-fog coordination model and the fuzzy-logic-based approach

A framework to coordinate web services in composition scenarios

This paper looks into the coordination of web services following their acceptance to participate in a composition scenario. We identify two types of behaviours associated with component web services: Operational and control behaviours. These behaviours are used to specify composite web services that are built upon component web services. In term of orchestration a composite web service could be either centralised or peer-to-peer. To support component/composite web services coordination per type of orchestration schema, various types of messages are exchanged between these web services. Experiments showing the use of these messages are reported in this paper as well. Copyright © 2010 Inderscience Enterprises Ltd

Towards a Modular Ontology for Cloud Consumer Review Mining

Nowadays, online consumer reviews are used to enhance the effectiveness of finding useful product information that impacts the consumers’ decision-making process. Many studies have been proposed to analyze these reviews for many purposes, such as opinion-based recommendation, spam review detection, opinion leader analysis, etc. A standard model that presents the different aspects of online review (review, product/service, user) is needed to facilitate the review analysis task. This research suggests SOPA, a modular ontology for cloud Service OPinion Analysis. SOPA represents the content of a product/service and its related opinions extracted from the online reviews written in a specific context. The SOPA is evaluated and validated using cloud consumer reviews from social media and using quality metrics. The experiments revealed that the SOPA-related modules exhibit a high cohesion and a low coupling, besides their usefulness and applicability in real use case studies

Towards an explainable irrigation scheduling approach by predicting soil moisture and evapotranspiration via multi-target regression

Significant population growth and ongoing socioeconomic development have increased reliance on irrigated agriculture and agricultural intensification. However, accurately predicting crop water demand is problematic since it is affected by several factors such as weather, soil, and water properties. Many studies have shown that a hybrid irrigation system based on two irrigation strategies (i.e., evapotranspiration and soil-based irrigation) can provide a credible and reliable irrigation system. The latter can also alert farmers and other experts to phenomena such as noise, erroneous sensor signals, numerous correlated input and target variables, and incomplete or missing data, especially when the two irrigation strategies produce inconsistent results. Hence, we propose Multi-Target soil moisture and evapotranspiration prediction (MTR-SMET) for estimating soil moisture and evapotranspiration. These predictions are then used to compute water needs based on Food and Agriculture Organization (FAO) and soil-based methods. Besides, we propose an explainable MTR-SMET (xMTR-SMET) that explains the ML-based irrigation to the farmers/users using several explainable AI to provide simple visual explanations for the given predictions. It is the first attempt that explains and offers meaningful insights into the output of a machine learning-based irrigation approach. The conducted experiments showed that the proposed MTR-SMET model achieves low error rates (i.e., MSE = 0.00015, RMSE = 0.0039, MAE = 0.002) and high R 2 score (i.e., 0.9676)

A machine learning-based approach for smart agriculture via stacking-based ensemble learning and feature selection methods

Smart irrigation has many advantages in optimizing resource usage (e.g., saving water, reducing energy consumption) and improving crop productivity. In this paper, we contribute to this field by proposing a robust and accurate machine learning-based approach that combines the power of feature selection methods and stacking ensemble method to effectively determine the optimal quantity of water needed for a plant. Random Forest, Recursive Feature Elimination (RFE), and SelectKBest are used to assess the importance of the features. Then, based on the best subset of features, a stacking ensemble model is proposed that combines CART, Gradient Boost Regression (GBR), Random Forest (RF) and XGBoost regressors. The different models involved in this approach are trained and tested using a collected dataset about various crops such as tomatoes, grapes, and lemon and encompasses different features such as meteorological data, soil data, irrigation data, and crop data. The experiments demonstrated the performance of RF in analyzing the feature importance. The findings of feature selection highlight the importance level of the evapotranspiration, the depletion, and the deficit to maximize the model’s accuracy. The results also showed that the proposed stacking model (Stacking_GBR+CART+RF+XGB) with the 10 most essential features outperforms individual models and other stacking models by achieving low error rates (i.e., MSE=0.0026, MAE=0.0279, RMSE=0.0509) and high R2 score (i.e., 0.9927)

Privacy-aware web services in smart homes

Smart homes can assist their inhabitants with mental and physical tasks, and monitor their safety and health. However, the systems that empower such homes use sensitive information that could be misused by external systems resulting in violations of the inhabitants\u27 privacy. In this paper, we introduce a Web services-centric solution to handle privacy concerns. Web services provide the functionalities that allow users remotely interact with the systems empowering the smart homes. In addition the solution uses policies to control how Web services handle privacy concerns and penalize those Web services that do not bind to these policies. Moreover the solution uses trust and reputation to select the most trustworthy Web services. © 2009 Springer Berlin Heidelberg

Specification and verification of views over composite web services using high level Petri-Nets

This paper presents a high level Petri-Net approach for specifying and verifying views over composite Web service. High level Petri-Nets have the capacity of formally modelling and verifying complex systems. A view is mainly used for tracking purposes as it permits representing a contextual snapshot of a composite Web service specification. The use of the proposed high level Petri-Net approach is illustrated with a running example that shows how Web services composition satisfies users\u27 needs. A proof-of-concept of this approach is also presented in the paper

On the Use of Social Networks in Web Services: Application to the Discovery Stage

This chapter discusses the use of social networks in Web services with focus on the discovery stage that characterizes the life cycle of these Web services. Other stages in this life cycle include description, publication, invocation, and composition. Web services are software applications that end users or other peers can invoke and compose to satisfy different needs such as hotel booking and car rental. Discovering the relevant Web services is, and continues to be, a major challenge due to the dynamic nature of these Web services. Indeed, Web services appear/disappear or suspend/resume operations without prior notice. Traditional discovery techniques are based on registries such as Universal Description, Discovery and Integration (UDDI) and Electronic Business using eXtensible Markup Language (ebXML). Unfortunately, despite the different improvements that these techniques have been subject to, they still suffer from various limitations that could slow down the acceptance trend of Web services by the IT community. Social networks seem to offer solutions to some of these limitations but raise, at the same time, some issues that are discussed in this chapter. The contributions of this chapter are three: social network definition in the particular context of Web services; mechanisms that support Web services build, use, and maintain their respective social networks; and social networks adoption to discover Web services