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  • Open Access


    Fuzzy Fruit Fly Optimized Node Quality-Based Clustering Algorithm for Network Load Balancing

    P. Rahul1,*, N. Kanthimathi1, B. Kaarthick2, M. Leeban Moses1

    Computer Systems Science and Engineering, Vol.44, No.2, pp. 1583-1600, 2023, DOI:10.32604/csse.2023.027424

    Abstract Recently, the fundamental problem with Hybrid Mobile Ad-hoc Networks (H-MANETs) is to find a suitable and secure way of balancing the load through Internet gateways. Moreover, the selection of the gateway and overload of the network results in packet loss and Delay (DL). For optimal performance, it is important to load balance between different gateways. As a result, a stable load balancing procedure is implemented, which selects gateways based on Fuzzy Logic (FL) and increases the efficiency of the network. In this case, since gateways are selected based on the number of nodes, the Energy More >

  • Open Access


    Improving Network Longevity in Wireless Sensor Networks Using an Evolutionary Optimization Approach

    V. Nivedhitha1,*, A. Gopi Saminathan2, P. Thirumurugan3

    Intelligent Automation & Soft Computing, Vol.28, No.3, pp. 603-616, 2021, DOI:10.32604/iasc.2021.016780

    Abstract Several protocols strive to improve network longevity but fail to ameliorate the uneven overhead imparted upon the sensor nodes that lead to temporal deaths. The proposed work uses a metaheuristic approach that promotes load balancing and energy-efficient data transmission using the fruit fly optimization algorithm (FFOA). The approach combines the LEACH protocol with differential evolution (DE) to select an optimum cluster head in every cluster. The algorithm is designed to provide energy-efficient data transmissions based on the smell and vision foraging behavior of fruit flies. The approach considers the compactness of nodes, energy capacity, and More >

  • Open Access


    Fire Detection Method Based on Improved Fruit Fly Optimization-Based SVM

    Fangming Bi1, 2, Xuanyi Fu1, 2, Wei Chen1, 2, 3, *, Weidong Fang4, Xuzhi Miao1, 2, Biruk Assefa1, 5

    CMC-Computers, Materials & Continua, Vol.62, No.1, pp. 199-216, 2020, DOI:10.32604/cmc.2020.06258

    Abstract Aiming at the defects of the traditional fire detection methods, which are caused by false positives and false negatives in large space buildings, a fire identification detection method based on video images is proposed. The algorithm first uses the hybrid Gaussian background modeling method and the RGB color model to perform fire prejudgment on the video image, which can eliminate most non-fire interferences. Secondly, the traditional regional growth algorithm is improved and the fire image segmentation effect is effectively improved. Then, based on the segmented image, the dynamic and static features of the fire flame More >

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