Browsing by Author "Mosavi, Amir"
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Item The Implementation of Border Gateway Protocol Using Software-Defined Networks: A Systematic Literature Review(Institute of Electrical and Electronics Engineers Inc., 2021) Zhao, Xi; Band, Shahab S.; Elnaffar, Said; Sookhak, Mehdi; Mosavi, Amir; Salwana, ElyAs a global community, the Internet is comprised of thousands of administrative entities that operate and interact with each other. Transferring data among these entities is possible due to the process of routing, which is challenging due to the lack of centrality. Consequently, the Border Gateway Protocol (BGP) can play a vital role in the routing process as a central hub for disseminating routing information to the various autonomous systems. Yet, the BGP poses security vulnerability due to the difficulty of validation and authentication. Recent studies argue that it would be beneficial to apply the Software-Defined Networking (SDN) approach to address some of the BGP problems. The SDN can help handle BGP-based networks at a low cost and with minimal complexity. However, there are still many scientific and operational problems in this field of study. The main objective of this paper is to identify the challenges that the BGP facing with respect to the adoption of the SDN. The findings revealed that most researchers focused on improving convergence time, while other essential features such as scalability and privacy were overlooked. © 2013 IEEE.Item When Smart Cities Get Smarter via Machine Learning: An In-Depth Literature Review(Institute of Electrical and Electronics Engineers Inc., 2022) Band, Shahab S.; Ardabili, Sina; Sookhak, Mehdi; Chronopoulos, Anthony Theodore; Elnaffar, Said; Moslehpour, Massoud; Csaba, Mako; Torok, Bernat; Pai, Hao-Ting; Mosavi, AmirThe manuscript represents a comeprehensive and systematic literature review on the machine learning methods in the emerging applications of the smart cities. Application domains include the essential aspects of the smart cities including the energy, healthcare, transportation, security, and pollution. The research methodology presents a state-of-the-art taxonomy, evaluation and model performance where the ML algorithms are classified into one of the following four categories: decision trees, support vector machines, artificial neural networks, and advanced machine learning methods, i.e., hybrid methods, ensembles, and Deep Learning. The study found that the hybrid models and ensembles have better performance since they exhibit both a high accuracy and low overall cost. On the other hand, the deep learning (DL) techniques had a higher accuracy than the hybrid models and ensembles, but they demanded relatively higher computation power. Moreover, all these advanced ML methods had a slower processing speed than the single methods. Likewise, the support vector machine (SVM) and decision tree (DT) generally outperformed the artificial neural network (ANN) for accuracy and other metrics. However, since the difference was negligible, it can be concluded that using either of them is appropriate. © 2013 IEEE.