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    Article
    Citation Count: 2
    Clustered Routing Using Chaotic Genetic Algorithm With Grey Wolf Optimization To Enhance Energy Efficiency in Sensor Networks
    (Mdpi, 2024) Khujamatov, Halimjon; Pitchai, Mohaideen; Shamsiev, Alibek; Mukhamadiyev, Abdinabi; Cho, Jinsoo; Department of Data Communication Networks and Systems
    As an alternative to flat architectures, clustering architectures are designed to minimize the total energy consumption of sensor networks. Nonetheless, sensor nodes experience increased energy consumption during data transmission, leading to a rapid depletion of energy levels as data are routed towards the base station. Although numerous strategies have been developed to address these challenges and enhance the energy efficiency of networks, the formulation of a clustering-based routing algorithm that achieves both high energy efficiency and increased packet transmission rate for large-scale sensor networks remains an NP-hard problem. Accordingly, the proposed work formulated an energy-efficient clustering mechanism using a chaotic genetic algorithm, and subsequently developed an energy-saving routing system using a bio-inspired grey wolf optimizer algorithm. The proposed chaotic genetic algorithm-grey wolf optimization (CGA-GWO) method is designed to minimize overall energy consumption by selecting energy-aware cluster heads and creating an optimal routing path to reach the base station. The simulation results demonstrate the enhanced functionality of the proposed system when associated with three more relevant systems, considering metrics such as the number of live nodes, average remaining energy level, packet delivery ratio, and overhead associated with cluster formation and routing.
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    Article
    Citation Count: 0
    Clustering Algorithm Based on Object Similarity
    (al-Farabi Kazakh State National University, 2024) Nishanov, A.Kh.; Akbarova, M.Kh.; Tursunov, A.T.; Ollamberganov, F.F.; Rashidova, D.E.; Department of System and Applied Programming
    The article examines the issue of drug clustering. Initially, k classes are arbitrarily formed and the resulting training sample is pre-processed, then the similarities between the objects of each class are evaluated based on the proximity function and the criterion for evaluating the contribution of objects to the formation of their own class. Usually, it is in percentage and is the degree of mutual similarity of objects of each class. In the next steps of the algorithm, first, one object is taken from the first class, and by adding it to all k classes, the contribution of this object to this class is measured. The object will be left in the class which has the most contribution. This process is repeated several times in a row for all objects of the class. The process is stopped when the location of objects does not change and the degree of similarity exceeds the required percentage. As a result, the required clusters are formed. © 2024 Al-Farabi Kazakh National University.
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    Citation Count: 0
    Possibilities and Importance of Using Artificial Intelligence Technologies in Smart Grid Systems
    (EDP Sciences, 2024) Khasanov, D.; Khujamatov, H.; Jumanov, K.; Rakhimov, A.; Department of Data Communication Networks and Systems
    Smart Grid systems are generally aimed at solving many problems in energy supply, such as: balancing supply and demand, ensuring grid stability, ensuring reliability of electricity supply, and ensuring a wider integration of different generators and consumers. In order to achieve such goals in Smart Grid systems, the possibility of using modern artificial intelligence techniques, like other technologies, is very wide, and its use plays an important role in solving many problems in Smart Grid systems. This paper provides information on various applications, technologies and methods of artificial intelligence that serve to successfully implement the Smart Grid system. Also, the paper focuses on the characteristics of various artificial intelligence techniques and their importance in the developing energy ecosystem. © The Authors, published by EDP Sciences.