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机构地区:[1]Department of Computer Science and Engineering,Kings College of Engineering,Punalkulam,613303,Tamilnadu,India [2]Department of Computer Science and Engineering,University College of Engineering,Ariyalur,621704,Tamilnadu,India
出 处:《Computer Systems Science & Engineering》2023年第3期2033-2047,共15页计算机系统科学与工程(英文)
摘 要:Fog computing is a promising technology that has been emerged to handle the growth of smart devices as well as the popularity of latency-sensitive and location-awareness Internet of Things(IoT)services.After the emergence of IoT-based services,the industry of internet-based devices has grown.The number of these devices has raised from millions to billions,and it is expected to increase further in the near future.Thus,additional challenges will be added to the traditional centralized cloud-based architecture as it will not be able to handle that growth and to support all connected devices in real-time without affecting the user experience.Conventional data aggregation models for Fog enabled IoT environ-ments possess high computational complexity and communication cost.There-fore,in order to resolve the issues and improve the lifetime of the network,this study develops an effective hierarchical data aggregation with chaotic barnacles mating optimizer(HDAG-CBMO)technique.The HDAG-CBMO technique derives afitness function from many relational matrices,like residual energy,average distance to neighbors,and centroid degree of target area.Besides,a chaotic theory based population initialization technique is derived for the optimal initial position of barnacles.Moreover,a learning based data offloading method has been developed for reducing the response time to IoT user requests.A wide range of simulation analyses demonstrated that the HDAG-CBMO technique has resulted in balanced energy utilization and prolonged lifetime of the Fog assisted IoT networks.
关 键 词:Internet of things fog computing barnacles mating optimizer data offloading data aggregation
分 类 号:TP311.13[自动化与计算机技术—计算机软件与理论]
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