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Improved DHOA-Fuzzy Based load scheduling in IoT Cloud Environment

Computers, Materials & Continua 2022年第5期71卷 4101-4114页

作者： R.Joshua Samuel Raj V.Ilango Prince Thomas V.R.Uma Fahd N.Al-Wesabi Radwa Marzouk Anwer Mustafa Hilal Department of Information Science&Engineering CMR Institute of TechnologyBengaluru560037India Department of Computer Application CMR Institute of TechnologyBangalore560037India School of Computing Woldia Institute of TechnologyWoldia UniversityEthiopia Department of Commerce School of CommerceFinance and AccountancyChrist UniversityBangalore560029India Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Faculty of Computer and IT Sana’a UniversityYemen Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia

Internet of things (IoT) has been significantly raised owing to thedevelopment of broadband access network, machine learning (ML), big dataanalytics (BDA), cloud computing (CC), and so on. The development of IoTtechnologies has resulted in a massive quantity of data due to the existenceof several people linking through distinct physical components, indicatingthe status of the CC environment. In the IoT, load scheduling is realistictechnique in distinct data center to guarantee the network suitability by fallingthe computer hardware and software catastrophe and with right utilize ofresource. The ideal load balancer improves many factors of Quality of Service(QoS) like resource performance, scalability, response time, error tolerance,and efficiency. The scholar is assumed as load scheduling a vital problem inIoT environment. There are many techniques accessible to load scheduling inIoT environments. With this motivation, this paper presents an improved deerhunting optimization algorithm with Type II fuzzy logic (IDHOA-T2F) modelfor load scheduling in IoT environment. The goal of the IDHOA-T2F is todiminish the energy utilization of integrated circuit of IoT node and enhancethe load scheduling in IoT environments. The IDHOA technique is derivedby integrating the concepts of Nelder Mead (NM) with the DHOA. Theproposed model also synthesized the T2L based on fuzzy logic (FL) systemsto counterbalance the load distribution. The proposed model finds usefulto improve the efficiency of IoT system. For validating the enhanced loadscheduling performance of the IDHOA-T2F technique, a series of simulationstake place to highlight the improved performance. The experimental outcomesdemonstrate the capable outcome of the IDHOA-T2F technique over therecent techniques.

关键词： load scheduling internet of things cloud computing metaheuristics fuzzy logic

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Improved Chameleon Swarm Optimization-Based load scheduling for IoT-Enabled Cloud Environment

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Computer Systems Science & Engineering 2023年第8期46卷 1371-1383页

作者： Manar Ahmed Hamza Shaha Al-Otaibi Sami Althahabi Jaber S.Alzahrani Abdullah Mohamed Abdelwahed Motwakel Abu Sarwar Zamani Mohamed I.Eldesouki Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science College of Science&Art atMahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityMecaa24382Saudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Information System College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia

Internet of things(IoT)and cloud computing(CC)becomes widespread in different application domains such as business,e-commerce,healthcare,*** recent developments of IoT technology have led to an increase in large amounts of data from various *** IoT enabled cloud environment,load scheduling remains a challenging process which is applied for ensuring network stability with maximum resource *** load scheduling problem was regarded as an optimization problem that is solved by *** this view,this study develops a new Circle Chaotic Chameleon Swarm Optimization based load scheduling(C3SOA-LS)technique for IoT enabled cloud *** proposed C3SOA-LS technique intends to effectually schedule the tasks and balance the load uniformly in such a way that maximum resource utilization can be ***,the presented C3SOA-LS model involves the design of circle chaotic mapping(CCM)with the traditional chameleon swarm optimization(CSO)algorithm for improving the exploration process,shows the novelty of the *** proposed C3SOA-LS model computes an objective with the minimization of energy consumption and *** experimental outcome implied that the C3SOA-LS model has showcased improved performance and uniformly balances the load over other approaches.

关键词： Cloud environment load scheduling energy consumption internet of things metaheuristics

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Machine learning and price-based load scheduling for an optimal IoT control in the smart and frugal home

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能源与人工智能(英文) 2021年第1期3卷 49-63页

作者： Rachneet Kaur Clara Schaye Kevin Thompson Daniel C.Yee Rachel Zilz R.S.Sreenivas Richard B.Sowers Department of Industrial and Enterprise Systems Engineering University of IllinoisUrbanaIL 61801USA Department of Electrical and Computer Engineering University of IllinoisUrbanaIL 61801USA Department of Mathematics and Department of Industrial and Enterprise Systems Engineering University of IllinoisUrbanaIL 61801USA

We pose and study a scheduling problem for an electric load to develop an Internet of Things(IoT)control system for power appliances,which takes advantage of real-time dynamic energy *** historical pricing data from a large *** supplier,we study and compare several dynamic scheduling policies,which can be implemented in a smart home to activate a major appliance(dishwasher,washing machine,clothes dryer)at an optimal time of the day,to minimize electricity *** formulate our scheduling task as a supervised machine learning classification problem which activates the load during one of two preferred time *** features used in the machine learning problem are hourly market,spot and day-ahead prices along with delayed label of the prior *** find that boosting tree-based algorithms outperform any other classification approach with measurable reduction of energy costs over certain types of naive and static *** observe that the delayed label has most predictive power across features,followed,on average,by spot,hourly market,and day-ahead energy *** further discuss implementation issues using a micro controller system coupled with cloud-based serverless computing and dynamic data *** test system includes an interactive voice interface via an intelligent personal assistant.

关键词： Power markets load scheduling Machine learning Feature analysis Internet of Things Autonomous scheduling Serverless computing

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Intelligent Building load scheduling Based on Multi-Objective Multi-Verse Algorithm

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Energy and Power Engineering 2021年第4期13卷 19-29页

作者： Jiangyong Liu Jiankang Liu Lv Fan Lingzhi Yi Huina Song Qingna Zeng College of Information Engineering & Hunan Province Engineering Research Center for Multi-Energy Collaborative Control Technology Xiangtan University Xiangtan China Willfar Information Technologies Co. Ltd. Changsha China Lianhu Town Central Health Center Chongqing China

In the multi-objective of intelligent building load scheduling, aiming at the problem of how to select Pareto frontier scheme for multi-objective optimization algorithm, the current optimal scheme mechanism combined with multi-objective multi-verse algorithm is used to optimize the intelligent building load scheduling. The update mechanism is changed in updating the position of the universe, and the process of correction coding is omitted in the iterative process of the algorithm, which reduces the com-putational complexity. The feasibility and effectiveness of the proposed method are verified by the optimal scheduling experiments of residential loads.

关键词： Intelligent Building load scheduling Multi-Objective Optimization Multi-Objective Multi-Verse Algorithm

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Intelligent Building load scheduling Based on Multi-Objective Multi-Verse Algorithm

Intelligent Building Load Scheduling Based on Multi-Objectiv...

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In the multi-objective of intelligent building load scheduling, aiming at the problem of how to select Pareto frontier scheme for multi-objective optimization algorithm, the current optimal scheme mechanism combined with multi-objective multi-verse algorithm is used to optimize the intelligent building load scheduling. The update mechanism is changed in updating the position of the universe, and the process of correction coding is omitted in the iterative process of the algorithm, which reduces the computational *** feasibility and effectiveness of the proposed method are verified by the optimal scheduling experiments of residential loads.

关键词： Intelligent Building load scheduling Multi-Objective Optimization Multi-Objective Multi-Verse Algorithm

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Coordinated Workload scheduling in Hierarchical Sensor Networks for Data Fusion Applications

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Journal of Computer Science & Technology 2008年第3期23卷 355-364页

作者： Xiao-Lin Li 曹建农 Scalable Software Systems Lab Department of Computer Science Oklahoma State University Stillwater OK 74078 U.S.A. Internet and Mobile Computing Lab Department of Computing Hong Kong Polytechnic University Hung Horn Kowloon Hong Kong China

To minimize the execution time of a sensing task over a multi-hop hierarchical sensor network, we present a coordinated scheduling method following the divisible load scheduling paradigm. The proposed scheduling strategy builds on eliminating transmission collisions and idle gaps between two successive data transmissions. We consider a sensor network consisting of several clusters. In a cluster, after related raw data measured by source nodes are collected at the fusion node, in-network data aggregation is further considered. The scheduling strategies consist of two phases： intra-cluster scheduling and inter-cluster scheduling. Intra-cluster scheduling deals with assigning different fractions of a sensing workload among source nodes in each cluster; inter-cluster scheduling involves the distribution of fused data among all fusion nodes. Closed-form solutions to the problem of task scheduling are derived. Finally, numerical examples are presented to demonstrate the impacts of different system parameters such as the number of sensor nodes, measurement, communication, and processing speed, on the finish time and energy consumption.

关键词： wireless sensor networks load scheduling divisible load theory data fusion

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MPC-based interval number optimization for electric water heater scheduling in uncertain environments

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Frontiers in Energy 2021年第1期15卷 186-200页

作者： Jidong WANG Chenghao LI Peng LI Yanbo CHE Yue ZHOU Yinqi LI Key Laboratory of Smart Grid of Ministry of Education Tianjin UniversityTianjin 300072China School of Engineering Cardiff UniversityCardiff CF 243 AAUK State Grid Chengdu Power Supply Company Chengdu 610041China

In this paper,interval number optimization and model predictive control are proposed to handle the uncertain-but-bounded parameters in electric water heater load *** of all,interval numbers are used to describe uncertain parameters including hot water demand,ambient temperature,and real-time price of ***,the traditional thermal dynamic model of electric water heater is transformed into an interval number model,based on which,the day-ahead load scheduling problem with uncertain parameters is formulated,and solved by interval number *** tolerance degrees for constraint violation and temperature preferences are also discussed for giving consumers more ***,the model predictive control which incorporates both forecasts and newly updated information is utilized to make and execute electric water heater load schedules on a rolling basis throughout the *** results demonstrate that interval number optimization either in day-ahead optimization or model predictive control format is robust to the uncertain hot water demand,ambient temperature,and real-time price of electricity,enabling customers to flexibly adjust electric water heater control strategy.

关键词： electric water heater load scheduling interval number optimization model predictive control uncertainty

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Smart homes: potentials and challenges

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Clean Energy 2021年第2期5卷 302-315页

作者： Rasha El-Azab Electrical Power and Machines Department Faculty of Engineering at Helwan UniversityCairoEgypt

Decentralized distributed clean-energy sources have become an essential need for smart grids to reduce the harmful effects of conventional power *** homes with a suitable sizing process and proper energy-management schemes can share in reducing the whole grid demand and even sell clean energy to the *** homes have been introduced recently as an alternative solution to classical power-system problems,such as the emissions of thermal plants and blackout hazards due to bulk plants/transmission *** appliances,sources and energy storage of smart homes should be coordinated with the requirements of homeowners via a suitable energy-management ***-management systems are the main key to optimizing both home sources and the operation of loads to maximize home-economic benefits while keeping a comfortable *** intermittent uncertain nature of smart homes may badly affect the whole grid *** prospective high penetration of smart homes on a smart power grid will introduce new,unusual scenarios in both generation and *** this paper,the main features and requirements of smart homes are *** review aims also to address recent proposed smart-home energy-management ***,smart-grid challenges with a high penetration of smart-home power are discussed.

关键词： smart homes energy-management system electrical tariff smart-home infrastructure load scheduling power-quality control

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