Pub Date : 2020-11-24DOI: 10.1109/CloudTech49835.2020.9365905
Driss El Majdoubi, H. Bakkali, Souad Sadki
Nowadays, the digitalization of urban environments is redefining the public and private sectors. Moreover, Internet of Things (IoT) platforms, cloud computing infrastructure and smart devices are exchanging tremendous amount of data. This harmonious integration of the cyber capabilities of the corresponding devices with the physical world generates new opportunities in many areas; however it raises a lot of security and privacy challenges due to the diversity of sources and stakeholders, the centralized data management and the resulting lack of trust and governance. Hence, we introduce "SmartPrivChain" a Smart Blockchain Based System for preserving privacy and security in a smart city environment. The proposed scheme is different from the existing approaches on many points. The data privacy is preserved by combining data access control and data usage auditing measures based on smart contracts. In addition, the proposed solution is compliant with the main privacy laws and regulations especially the obligations of the European Union General Data Protection Regulation (GDPR). Lastly, we propose an enhanced Proof of Reputation (PoR) consensus scheme using a multidimensional Trust model.
{"title":"Towards Smart Blockchain-Based System for Privacy and Security in a Smart City environment","authors":"Driss El Majdoubi, H. Bakkali, Souad Sadki","doi":"10.1109/CloudTech49835.2020.9365905","DOIUrl":"https://doi.org/10.1109/CloudTech49835.2020.9365905","url":null,"abstract":"Nowadays, the digitalization of urban environments is redefining the public and private sectors. Moreover, Internet of Things (IoT) platforms, cloud computing infrastructure and smart devices are exchanging tremendous amount of data. This harmonious integration of the cyber capabilities of the corresponding devices with the physical world generates new opportunities in many areas; however it raises a lot of security and privacy challenges due to the diversity of sources and stakeholders, the centralized data management and the resulting lack of trust and governance. Hence, we introduce \"SmartPrivChain\" a Smart Blockchain Based System for preserving privacy and security in a smart city environment. The proposed scheme is different from the existing approaches on many points. The data privacy is preserved by combining data access control and data usage auditing measures based on smart contracts. In addition, the proposed solution is compliant with the main privacy laws and regulations especially the obligations of the European Union General Data Protection Regulation (GDPR). Lastly, we propose an enhanced Proof of Reputation (PoR) consensus scheme using a multidimensional Trust model.","PeriodicalId":272860,"journal":{"name":"2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122742939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-24DOI: 10.1109/CloudTech49835.2020.9365873
K. E. Moutaouakil, A. Touhafi
Fuzzy mean square clustering is one of the simplest and most performant versions of the k-means non-hierarchical clustering methods. In this work, we extend and improve this method by a recurrent neural network, leading to a new clustering method called Recurrent Neural Network Fuzzy Mean Square. In this approach the fuzzy mean square error is modeled by a constrained non-linear optimization program. The latter is solved by a recurrent neural network in which an original energy function is defined. The energy function makes a compromise between the objective function and the constraints by using appropriate Lagrange relaxation scales. The Euler-Cauchy method is then used to calculate the centers and the membership functions. Simulation results on academic datasets show the effectiveness of the proposed method.
{"title":"A New Recurrent Neural Network Fuzzy Mean Square Clustering Method","authors":"K. E. Moutaouakil, A. Touhafi","doi":"10.1109/CloudTech49835.2020.9365873","DOIUrl":"https://doi.org/10.1109/CloudTech49835.2020.9365873","url":null,"abstract":"Fuzzy mean square clustering is one of the simplest and most performant versions of the k-means non-hierarchical clustering methods. In this work, we extend and improve this method by a recurrent neural network, leading to a new clustering method called Recurrent Neural Network Fuzzy Mean Square. In this approach the fuzzy mean square error is modeled by a constrained non-linear optimization program. The latter is solved by a recurrent neural network in which an original energy function is defined. The energy function makes a compromise between the objective function and the constraints by using appropriate Lagrange relaxation scales. The Euler-Cauchy method is then used to calculate the centers and the membership functions. Simulation results on academic datasets show the effectiveness of the proposed method.","PeriodicalId":272860,"journal":{"name":"2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132740920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-24DOI: 10.1109/CloudTech49835.2020.9365901
Khalid Chennoufi, M. Ferfra
The modelling of solar modules is essential for the diagnostics, optimization and for Maximum Power Tracking algorithms. This article proposes a method to determine the five unknown parameters for single diode, in order to carry out a modelling of photovoltaic modules at different Operating Conditions. The present method integrates analytical and numerical approaches, the analytical modelling is developed based on the equations of the open circuit voltage (VOC) , short circuit current (ISC) and maximum power, and a fast iteration has been employed in order to find series resistance value by adjusting the computed and datasheet powers. The reliability of the obtained results has been compared with the literature, and the precision was evaluated using the curves fitting in various temperatures and irradiations values. The results show good corresponding, furthermore the absolute error and the Root Mean Square Error have been computed and affirmed the validity of the proposed approach.
{"title":"Parameters extraction of photovoltaic modules using a combined analytical - numerical method","authors":"Khalid Chennoufi, M. Ferfra","doi":"10.1109/CloudTech49835.2020.9365901","DOIUrl":"https://doi.org/10.1109/CloudTech49835.2020.9365901","url":null,"abstract":"The modelling of solar modules is essential for the diagnostics, optimization and for Maximum Power Tracking algorithms. This article proposes a method to determine the five unknown parameters for single diode, in order to carry out a modelling of photovoltaic modules at different Operating Conditions. The present method integrates analytical and numerical approaches, the analytical modelling is developed based on the equations of the open circuit voltage (VOC) , short circuit current (ISC) and maximum power, and a fast iteration has been employed in order to find series resistance value by adjusting the computed and datasheet powers. The reliability of the obtained results has been compared with the literature, and the precision was evaluated using the curves fitting in various temperatures and irradiations values. The results show good corresponding, furthermore the absolute error and the Root Mean Square Error have been computed and affirmed the validity of the proposed approach.","PeriodicalId":272860,"journal":{"name":"2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115436124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-24DOI: 10.1109/CloudTech49835.2020.9365908
Li Dong, Yongzheng Lin, Yishen Pang
Registration, which is exploited to establish the corresponding relationship between a group of images, is of importance for medical applications. Within the image processing process, a similarity measure is an essential stage. To note that the effectiveness of similarity measure is to evaluate the discrepancy between a set of image slices, which greatly affects the performance of registration. Most of the previous algorithms can be categorized in model-based methods, which rely on their suitability to the images. Meanwhile, these similarity measures can not satisfy the requirements of efficiency and accuracy in medical image registration. To address the above-mentioned problems, one novel similarity measure is presented with a convolutional neural network. Experiments were conducted to evaluate the proposed similarity measure with two public DIARETDB1 and RIRE. The numerical and visual outcome both support our work.
{"title":"Medical Image Registration via Similarity Measure based on Convolutional Neural Network","authors":"Li Dong, Yongzheng Lin, Yishen Pang","doi":"10.1109/CloudTech49835.2020.9365908","DOIUrl":"https://doi.org/10.1109/CloudTech49835.2020.9365908","url":null,"abstract":"Registration, which is exploited to establish the corresponding relationship between a group of images, is of importance for medical applications. Within the image processing process, a similarity measure is an essential stage. To note that the effectiveness of similarity measure is to evaluate the discrepancy between a set of image slices, which greatly affects the performance of registration. Most of the previous algorithms can be categorized in model-based methods, which rely on their suitability to the images. Meanwhile, these similarity measures can not satisfy the requirements of efficiency and accuracy in medical image registration. To address the above-mentioned problems, one novel similarity measure is presented with a convolutional neural network. Experiments were conducted to evaluate the proposed similarity measure with two public DIARETDB1 and RIRE. The numerical and visual outcome both support our work.","PeriodicalId":272860,"journal":{"name":"2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114947403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-24DOI: 10.1109/CloudTech49835.2020.9365867
Amine Chraibi, Said Ben Alla, A. Touhafi, Abdellah Ezzati
The majority of optimization algorithms and methods generally necessitate a considerable run time to reach their goal. Most of them are used mainly in real-world applications. This article concentrates on an efficient and well-known algorithm to solve optimization problems: the Particle Swarm Optimisation algorithm (PSO). This algorithm needs a considerable run time to solve an optimization problem with a high dimension space and data. The article also concentrates on OpenCL, which defines a common parallel programming language for various devices such as GPU, CPU, FPGA, etc. In order to minimize the run time of PSO, this paper introduces a new implementation of PSO in OpenCL. By decomposing the PSO code into two fragments, each one can run simultaneously. The experimental results covered both the sequential and parallel implementations. Furthermore, show that the PSO’ OpenCL implementation is faster than the Sequential-PSO implementation. The OpenCL profiling results show the timing of each part of the executing of PSO in OpenCL.
{"title":"Run Time Optimization using a novel implementation of Parallel-PSO for real-world applications","authors":"Amine Chraibi, Said Ben Alla, A. Touhafi, Abdellah Ezzati","doi":"10.1109/CloudTech49835.2020.9365867","DOIUrl":"https://doi.org/10.1109/CloudTech49835.2020.9365867","url":null,"abstract":"The majority of optimization algorithms and methods generally necessitate a considerable run time to reach their goal. Most of them are used mainly in real-world applications. This article concentrates on an efficient and well-known algorithm to solve optimization problems: the Particle Swarm Optimisation algorithm (PSO). This algorithm needs a considerable run time to solve an optimization problem with a high dimension space and data. The article also concentrates on OpenCL, which defines a common parallel programming language for various devices such as GPU, CPU, FPGA, etc. In order to minimize the run time of PSO, this paper introduces a new implementation of PSO in OpenCL. By decomposing the PSO code into two fragments, each one can run simultaneously. The experimental results covered both the sequential and parallel implementations. Furthermore, show that the PSO’ OpenCL implementation is faster than the Sequential-PSO implementation. The OpenCL profiling results show the timing of each part of the executing of PSO in OpenCL.","PeriodicalId":272860,"journal":{"name":"2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130262825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-24DOI: 10.1109/CloudTech49835.2020.9365915
S. Lahmiri, R. Saadé, Danielle Morin, F. Nebebe
Learning analytics is receiving a growing attention from both machine learning and education communities, where support vector machines (SVM) are gaining popularity over existing data mining techniques. In the scope of this work, we employ SVM to predict student success in mathematics course in Portugal under two common nonlinear kernel functions: polynomial and radial basis function kernel. In addition, we employ the k-nearest-neighbor (kNN) algorithm as a reference model since it is known to be fast and effective in various classification problems. Furthermore, we adopt the Bayesian optimization (BO) technique in a cross-validation framework to optimize SVM key parameters; namely, the slack parameter and penalty coefficient. The obtained experimental results show that the SVM outperform k-nearest-neighbor algorithm under both nonlinear kernel functions. Additionally, processing time associated with SVM optimization process increases with polynomial order. Furthermore, the SVM trained with third-order polynomial kernel performs the best. Finally, k-nearest-neighbor algorithm is found to be faster compared to all SVM classifiers.
{"title":"Learning Analytics based on Bayesian Optimization of Support Vector Machines with Application to Student Success Prediction in Mathematics Course","authors":"S. Lahmiri, R. Saadé, Danielle Morin, F. Nebebe","doi":"10.1109/CloudTech49835.2020.9365915","DOIUrl":"https://doi.org/10.1109/CloudTech49835.2020.9365915","url":null,"abstract":"Learning analytics is receiving a growing attention from both machine learning and education communities, where support vector machines (SVM) are gaining popularity over existing data mining techniques. In the scope of this work, we employ SVM to predict student success in mathematics course in Portugal under two common nonlinear kernel functions: polynomial and radial basis function kernel. In addition, we employ the k-nearest-neighbor (kNN) algorithm as a reference model since it is known to be fast and effective in various classification problems. Furthermore, we adopt the Bayesian optimization (BO) technique in a cross-validation framework to optimize SVM key parameters; namely, the slack parameter and penalty coefficient. The obtained experimental results show that the SVM outperform k-nearest-neighbor algorithm under both nonlinear kernel functions. Additionally, processing time associated with SVM optimization process increases with polynomial order. Furthermore, the SVM trained with third-order polynomial kernel performs the best. Finally, k-nearest-neighbor algorithm is found to be faster compared to all SVM classifiers.","PeriodicalId":272860,"journal":{"name":"2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121849451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-24DOI: 10.1109/CloudTech49835.2020.9365904
Sraidi Soukaina, Smaili El Miloud, Salma Azzouzi, M. E. H. Charaf
Massive Open Online Courses (MOOC) have become popular around the world as a free way of online learning. However, one of the crucial problems associated with MOOC is their low completion rate. The analysis of data obtained from the forums and the social media groups associated with MOOCS provides a helpful mean to understand the behavior of the learners. The idea is to examine the correlation between the sentiment level reported on the basis of the forum messages and the rate of students dropping out of the courses. Moreover, a good number of quality tools are used on the domain of Education. Therefore, we propose in this paper to combine the Sentiment Analysis (Machine learning approach) of the forum posts and the ISHIKAWA method (Quality approach) to handle these issues. The aim is to predict the main causes of MOOCs’ failures
{"title":"Quality Approach to Analyze the Causes of Failures in MOOC","authors":"Sraidi Soukaina, Smaili El Miloud, Salma Azzouzi, M. E. H. Charaf","doi":"10.1109/CloudTech49835.2020.9365904","DOIUrl":"https://doi.org/10.1109/CloudTech49835.2020.9365904","url":null,"abstract":"Massive Open Online Courses (MOOC) have become popular around the world as a free way of online learning. However, one of the crucial problems associated with MOOC is their low completion rate. The analysis of data obtained from the forums and the social media groups associated with MOOCS provides a helpful mean to understand the behavior of the learners. The idea is to examine the correlation between the sentiment level reported on the basis of the forum messages and the rate of students dropping out of the courses. Moreover, a good number of quality tools are used on the domain of Education. Therefore, we propose in this paper to combine the Sentiment Analysis (Machine learning approach) of the forum posts and the ISHIKAWA method (Quality approach) to handle these issues. The aim is to predict the main causes of MOOCs’ failures","PeriodicalId":272860,"journal":{"name":"2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115104518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-24DOI: 10.1109/CloudTech49835.2020.9365896
An Braeken, A. Touhafi
Security questions and answers for authentication are a common approach to enable the user to reset forgotten passwords. Moreover, they are also sometimes used as alternative for the classical username-password system, which fails in offering a good balance between user friendliness and security as long and complex passwords are required. However, in order to guarantee the privacy of the user as imposed by the new General Data Protection Regulation (GDPR), it should be impossible to derive the answer of the user by any other entity, including the server provider or the server managing the authentication.In this paper, we present an efficient mobile based security mechanism to realise this goal. The proposed scheme can be applied on top of any type of question-answer based authentication system. In addition, our solution also offers anonymity and untraceability of the user, such that no activity patterns can be drawn by simply eavesdropping on the communication channel to the service provider or the authentication server. We show that our proposed mechanism not only offers more security features compared to related work, but it is also significantly faster, in particular at the side of the user.
{"title":"Efficient Mobile User Authentication Service with Privacy Preservation and User Untraceability","authors":"An Braeken, A. Touhafi","doi":"10.1109/CloudTech49835.2020.9365896","DOIUrl":"https://doi.org/10.1109/CloudTech49835.2020.9365896","url":null,"abstract":"Security questions and answers for authentication are a common approach to enable the user to reset forgotten passwords. Moreover, they are also sometimes used as alternative for the classical username-password system, which fails in offering a good balance between user friendliness and security as long and complex passwords are required. However, in order to guarantee the privacy of the user as imposed by the new General Data Protection Regulation (GDPR), it should be impossible to derive the answer of the user by any other entity, including the server provider or the server managing the authentication.In this paper, we present an efficient mobile based security mechanism to realise this goal. The proposed scheme can be applied on top of any type of question-answer based authentication system. In addition, our solution also offers anonymity and untraceability of the user, such that no activity patterns can be drawn by simply eavesdropping on the communication channel to the service provider or the authentication server. We show that our proposed mechanism not only offers more security features compared to related work, but it is also significantly faster, in particular at the side of the user.","PeriodicalId":272860,"journal":{"name":"2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127948246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-24DOI: 10.1109/CloudTech49835.2020.9365877
Rabie Madani, Abderrahmane Ezzahout, A. Idrissi
The concept of smart city appeared following technological, societal and organizational changes. A smart city uses huge number of equipments connected to internet, to gather data and use it to effectively manage resources and enhance urban services quality.The use of Recommender Systems(RS) in smart cities plays take a leading part to guid citizens in the process of finding services that match with their preferences. Recommendations provided allow users to satisfy their needs in an efficient and easy way and make their daily lifes less complicated. This paper introduces an overview of Recommender Systems in Smart Cities, also presents real-world application of RS in IoT and in Smart Cities.
{"title":"An Overview of Recommender Systems in the Context of Smart Cities","authors":"Rabie Madani, Abderrahmane Ezzahout, A. Idrissi","doi":"10.1109/CloudTech49835.2020.9365877","DOIUrl":"https://doi.org/10.1109/CloudTech49835.2020.9365877","url":null,"abstract":"The concept of smart city appeared following technological, societal and organizational changes. A smart city uses huge number of equipments connected to internet, to gather data and use it to effectively manage resources and enhance urban services quality.The use of Recommender Systems(RS) in smart cities plays take a leading part to guid citizens in the process of finding services that match with their preferences. Recommendations provided allow users to satisfy their needs in an efficient and easy way and make their daily lifes less complicated. This paper introduces an overview of Recommender Systems in Smart Cities, also presents real-world application of RS in IoT and in Smart Cities.","PeriodicalId":272860,"journal":{"name":"2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125825386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-24DOI: 10.1109/CloudTech49835.2020.9365912
Houda Daki, A. Hannani, H. Ouahmane
Now a day, educational institutions present one of the highest power consuming sector due to their new activities and occupancy pattern. This enormous amount of energy consumption at the university need a huge effort to reduce it. Smart grid is among the efficient solution to save energy and balance supply and demand. For the same purpose, the National School of Applied Sciences of El Jadida-Morocco wants take advantage from smart grid to maintain the balance between energy production and consumption. Despite of all added value of this smart grid solution for the school, it has the issue of managing energy production surplus, because it cannot inject it into Moroccan electrical infrastructure neither store it using storage devices. So, to overcome this challenge the system need to predict electrical consumption to be able to produce exactly the same value. Recently, Big Data contributed very well in analysing electrical consumption data using many tools and advanced techniques. It process, interprets and analyzes huge quantity of data to make it more profitable and valuable. For that reason, the school will take refuge in Big data technology to implement a custom system to predict electrical energy consumption by analyze all factors that influence electrical energy use. In this paper, we propose a benchmark of the main Big Data architectures in the field and that will cover all electrical energy data processing from data collection, data storage, data analytic and data visualization. The aim of this benchmark is to choose the optimal architecture in term of fault tolerance, resource management, data storage and data modelling to forecast electricity consumption in educational institutions.
{"title":"Big Data Architectures Benchmark for Forecasting Electricity Consumption","authors":"Houda Daki, A. Hannani, H. Ouahmane","doi":"10.1109/CloudTech49835.2020.9365912","DOIUrl":"https://doi.org/10.1109/CloudTech49835.2020.9365912","url":null,"abstract":"Now a day, educational institutions present one of the highest power consuming sector due to their new activities and occupancy pattern. This enormous amount of energy consumption at the university need a huge effort to reduce it. Smart grid is among the efficient solution to save energy and balance supply and demand. For the same purpose, the National School of Applied Sciences of El Jadida-Morocco wants take advantage from smart grid to maintain the balance between energy production and consumption. Despite of all added value of this smart grid solution for the school, it has the issue of managing energy production surplus, because it cannot inject it into Moroccan electrical infrastructure neither store it using storage devices. So, to overcome this challenge the system need to predict electrical consumption to be able to produce exactly the same value. Recently, Big Data contributed very well in analysing electrical consumption data using many tools and advanced techniques. It process, interprets and analyzes huge quantity of data to make it more profitable and valuable. For that reason, the school will take refuge in Big data technology to implement a custom system to predict electrical energy consumption by analyze all factors that influence electrical energy use. In this paper, we propose a benchmark of the main Big Data architectures in the field and that will cover all electrical energy data processing from data collection, data storage, data analytic and data visualization. The aim of this benchmark is to choose the optimal architecture in term of fault tolerance, resource management, data storage and data modelling to forecast electricity consumption in educational institutions.","PeriodicalId":272860,"journal":{"name":"2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128313442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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