{"title":"基于云的网络物理系统中的能源管理","authors":"Efe F. Orumwense, Khaled M. Abo-Al-Ez","doi":"10.1049/cps2.12008","DOIUrl":null,"url":null,"abstract":"<p>Cyber-physical systems (CPSs) are embodied systems of highly unified computational, control and communicational elements tightly fused with the physical world. Normally, CPSs are seen to have limited storage and computational abilities due to the fact that they are implemented across several platforms and also embedded into larger systems. The fusion of Cloud computing and CPS gives rise to Cloud-based CPS; Cloud computing will no doubt provide numerous opportunities for CPSs to increase their capabilities by taking advantage of the resources (applications, servers, storage and network capabilities). With this new addition, there will definitely be an increase in energy consumption in the system and this becomes a huge and daunting task that needs to be overcome in actualising this goal. Here, the energy consumption in the network system is being evaluated to ensure an effective data transmission amongst sensor nodes. A simulation environment is considered where a particle swarm optimization algorithm is introduced to optimise and balance the consumption of energy in the system network. To ensure an energy-efficient Cloud data control center, an energy consumption model is developed based on resource utilisation. This, however, assists in managing the amount of resources to be utilised for a specific amount of workload.</p>","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"6 2","pages":"93-103"},"PeriodicalIF":1.7000,"publicationDate":"2021-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.12008","citationCount":"0","resultStr":"{\"title\":\"Energy management in a cloud-based cyber-physical system\",\"authors\":\"Efe F. Orumwense, Khaled M. Abo-Al-Ez\",\"doi\":\"10.1049/cps2.12008\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Cyber-physical systems (CPSs) are embodied systems of highly unified computational, control and communicational elements tightly fused with the physical world. Normally, CPSs are seen to have limited storage and computational abilities due to the fact that they are implemented across several platforms and also embedded into larger systems. The fusion of Cloud computing and CPS gives rise to Cloud-based CPS; Cloud computing will no doubt provide numerous opportunities for CPSs to increase their capabilities by taking advantage of the resources (applications, servers, storage and network capabilities). With this new addition, there will definitely be an increase in energy consumption in the system and this becomes a huge and daunting task that needs to be overcome in actualising this goal. Here, the energy consumption in the network system is being evaluated to ensure an effective data transmission amongst sensor nodes. A simulation environment is considered where a particle swarm optimization algorithm is introduced to optimise and balance the consumption of energy in the system network. To ensure an energy-efficient Cloud data control center, an energy consumption model is developed based on resource utilisation. This, however, assists in managing the amount of resources to be utilised for a specific amount of workload.</p>\",\"PeriodicalId\":36881,\"journal\":{\"name\":\"IET Cyber-Physical Systems: Theory and Applications\",\"volume\":\"6 2\",\"pages\":\"93-103\"},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2021-04-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.12008\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IET Cyber-Physical Systems: Theory and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1049/cps2.12008\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Cyber-Physical Systems: Theory and Applications","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/cps2.12008","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Energy management in a cloud-based cyber-physical system
Cyber-physical systems (CPSs) are embodied systems of highly unified computational, control and communicational elements tightly fused with the physical world. Normally, CPSs are seen to have limited storage and computational abilities due to the fact that they are implemented across several platforms and also embedded into larger systems. The fusion of Cloud computing and CPS gives rise to Cloud-based CPS; Cloud computing will no doubt provide numerous opportunities for CPSs to increase their capabilities by taking advantage of the resources (applications, servers, storage and network capabilities). With this new addition, there will definitely be an increase in energy consumption in the system and this becomes a huge and daunting task that needs to be overcome in actualising this goal. Here, the energy consumption in the network system is being evaluated to ensure an effective data transmission amongst sensor nodes. A simulation environment is considered where a particle swarm optimization algorithm is introduced to optimise and balance the consumption of energy in the system network. To ensure an energy-efficient Cloud data control center, an energy consumption model is developed based on resource utilisation. This, however, assists in managing the amount of resources to be utilised for a specific amount of workload.