Samira Achki ¹, Fatima Gharnati ², Abdellah Ait Ouahman ²

Affiliations
1 Department of Computer Science, Faculty of Science Semlalia, Cadi Ayyad University, MA
2 Department of Physics, Faculty of Science Semlalia, Cadi Ayyad University, MA

Abstract

Collaborative communication technologies have known a great development that allows achieving various communications. However, the uncontrolled selection of the communication technology spent more energy. The main goal of this paper is minimizing the energy consumed in accessing to data by users. To do so, we propose to integrate an efficient weighted sum selection approach in order to choice the suitable communication system that can be used by user. This smart selection considered a number of essential criteria. Implementation results confirmed that the proposed approach is more efficient than the traditional process of communication.

Keywords

Green Communication, 4G, Multi-Criteria Selection, Wireless, Weighted Sum.

Full Text

   

Addi Ait-Mlouk ¹, Fatima Gharnati ¹, Tarik Agouti ¹

Affiliations
1 Laboratory of Intelligent Energy Management and Information Systems, Faculty of Sciences Semlalia, Cadi Ayyad University, Marrakech, MA

Abstract

Objectives: In transportation field, a huge amount of data collected by IoT systems, remote sensing and other data collection tools brings new challenges, the size of this data becomes extremely big and more complex for traditional techniques of data mining. To deal with this challenge, Apache Spark stand as a powerful large scale distributed computing platform that can be used successfully for machine learning against very large databases. This work employed large-scale machine learning techniques especially Decision Tree with Apache Spark framework for big data analysis to build a model that can predict the factors lead to road accidents based on several input variables related to traffic accidents. Based on this, the predicting model first preprocesses the big accident data and analyze it to create data for a learning system. Empirical results show that the proposed model could provide new information that can assist the decision makers to analyze and improve road safety

Keywords

Data mining, Big Data, Road accident, Decision Tree, Apache Spark, Mllib

Full Text

   

Raja Mouachi ¹, Addi Ait-Mlouk ¹, Fatima Gharnati ¹, Mustapha Raoufi ¹

Affiliations
1 Laboratory of Intelligent Energy Management and Information Systems, Faculty of Sciences Semlalia, Cadi Ayyad University, Marrakech, MA

Abstract

In order to optimize production, consumption and distribution of energy, the different devices of a Smart Grid (SG) exchange daily increasing flows of information. Moreover, SG produces much more data stream than the traditional network. In addition to the large volume, the data of the SG are characterized by their diversity. However, securing these data flows is essential. Indeed, a single failure or attack could compromise the safety of the whole electrical network, the malfunction of which could have serious repercussions. Therefore, cryptography as a solution is necessary for SG to become realizable and secure. Being able to classify and to make a good choice of symmetric cryptographic algorithms for security of SG, we proposed to use an approach based on multi-criteria analysis.

Keywords

Confidentiality, Cryptography, Security, Smart Grid, Multi-criteria Analysis, PROMETHEE GAIA

Full Text