Search Results - (( web application bat algorithm ) OR ( iot application swarm algorithm ))
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Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection
Published 2022“…Therefore, in this paper, we proposed Dynamic tasks scheduling algorithm based on attribute reduction with an enhanced hybrid Genetic Algorithm and Particle Swarm Optimization for optimal device selection. …”
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Task scheduling in cloud computing environment using hybrid genetic algorithm and bat algorithm
Published 2022“…This research will conduct comparison of hybrid Genetic Algorithm and Bat Algorithm (GA-BA) with Genetic Algorithm (GA) and Bat Algorithm (BA). …”
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Dynamic Task Offloading Algorithm for optimising IoT network quality of service in the Mobile-Fog-Cloud System
Published 2023“…The application of the Internet of Things (IoT) is increasing to almost all aspects of human endevour. …”
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Resource-Efficient Coverage Path Planning for UAV-Based Aerial IoT Gateway
Published 2023“…As a result, the Energy Efficient Coverage Path Planning (EECPP) algorithm has been proposed. The EECPP is composed of two algorithms: the Stop Point Prediction Algorithm using K-Means, and Path Planning Algorithm using Particle Swarm Optimization. …”
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Lightweight ontology architecture for QoS aware service discovery and semantic CoAP data management in heterogeneous IoT environment
Published 2026“…User requests, transmitted via the Constrained Application Protocol (CoAP), are semantically enriched and matched to QoS parameters using Dynamic Shannon Entropy optimized with the Salp Swarm Algorithm. …”
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Resource-efficient coverage path planning for unmanned aerial vehicle based aerial gateway in LoRaWAN
Published 2022“…Due to finite resource, multiple issues need to be considered in designing such system, including AG flight time, coverage radius and the achievable data rate of the ground-to-air system, thus an Energy Efficient Coverage Path Planning (EECPP) algorithm has been proposed. EECPP consist of two algorithms which is Stop Point Prediction Algorithm using K-Means, which finding the stop point for the AG after grouping the IDs into clusters, and Path Planning Algorithm using Particle Swarm Optimization which connect all of the stop point in shortest route. …”
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Intelligent Optimization Techniques for Optimal Power Flow using Interline Power Flow Controller
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