User-Centric Internet of Things and Controlled Service Scheduling Scheme for a Software-Defined Network

被引:0
|
作者
Anjum, Mohd [1 ]
Min, Hong [2 ]
Ahmed, Zubair [3 ]
机构
[1] Aligarh Muslim Univ, Dept Comp Engn, Aligarh 202002, India
[2] Gachon Univ, Sch Comp, Seongnam 13120, South Korea
[3] King Saud Univ, Coll Sci, Dept Zool, Riyadh 11451, Saudi Arabia
来源
APPLIED SCIENCES-BASEL | 2024年 / 14卷 / 11期
基金
新加坡国家研究基金会;
关键词
Internet of Things; software-defined networks; real-time services; concurrency; power allocation; deep learning; service scheduling; POWER ALLOCATION; IOT NETWORKS; ENERGY; EFFICIENT;
D O I
10.3390/app14114951
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Mobile users can access vital real-time services through wireless paradigms like software-defined network (SDN) topologies and the Internet of Things. Point-of-contact-based infrastructures and dynamic user densities increase resource access and service-sharing concurrency. Thus, controlling power consumption and network and device congestion becomes a major issue for SDN-based IoT applications. This paper uses the Controlled Service Scheduling Scheme (CS3) to address the challenge of simultaneous scheduling and power allocation. The suggested approach uses deep recurrent learning and probabilistic balancing for power allocation and service distribution during user-centric concurrent sharing intervals. The SDN control plane decides how much power to use for service delivery while forecasting user service demands directs the scheduling interval allocation. Power management is under the control plane of the SDN, whereas service distribution is under the data plane. Power-to-service requirements are evaluated probabilistically, and updates for both aircraft are obtained via the deep learning model. This allocation serves as the basis for training the learning model to alleviate power deficits across succeeding intervals. The simulation experiments are modeled using the Contiki Cooja simulator, where 200 mobile users are placed. The proposed plan delivers a 14.9% high-service distribution for various users, 18.29% less delay, 13.34% less failure, 5.54% less downtime, and 18.68% less power consumption.
引用
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页数:23
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