K. Das, Satyabrata Das, R. K. Darji, Ananya Mishra
{"title":"Energy efficient model for the sensor cloud systems","authors":"K. Das, Satyabrata Das, R. K. Darji, Ananya Mishra","doi":"10.1109/RTEICT.2017.8256619","DOIUrl":null,"url":null,"abstract":"At the present time sensor is needed for many applications. A number of sensors combine to form Wireless Sensor Networks (WSN). Cloud computing is an emerging technique provides shared processing resources and data to the end user. The sensor cloud infrastructure constitutes WSN and cloud for managing physical sensors on IT infrastructure. Sensor cloud should be energy efficient as the battery in the sensor has a limited lifetime and there is requirement of more energy to run the servers. User requests are very frequent and if any user requests to access sensor network through cloud system, the request redirects to the sensor network every time which consumes more energy of the sensor. We have proposed a model in which cloud systems can predict the future sensor data and due to this, there is no need to redirect every user request to sensor network. The sensor network also uses load-balancing routing scheme, which uses different paths to route the data from sensor node to the gateway as a result all nodes are used uniformly and the network lifetime is more. There is less transmission overhead in our proposed approach due to the use prediction scheme in cloud system and the network lifetime of the sensor network is more as the variance of consumption of power of all nodes in the sensor network is less due to load balancing routing. Use of prediction scheme in cloud system and load-balancing routing in the sensor network will be the future direction of research to minimize energy consumption in the sensor cloud environment.","PeriodicalId":342831,"journal":{"name":"2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RTEICT.2017.8256619","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
At the present time sensor is needed for many applications. A number of sensors combine to form Wireless Sensor Networks (WSN). Cloud computing is an emerging technique provides shared processing resources and data to the end user. The sensor cloud infrastructure constitutes WSN and cloud for managing physical sensors on IT infrastructure. Sensor cloud should be energy efficient as the battery in the sensor has a limited lifetime and there is requirement of more energy to run the servers. User requests are very frequent and if any user requests to access sensor network through cloud system, the request redirects to the sensor network every time which consumes more energy of the sensor. We have proposed a model in which cloud systems can predict the future sensor data and due to this, there is no need to redirect every user request to sensor network. The sensor network also uses load-balancing routing scheme, which uses different paths to route the data from sensor node to the gateway as a result all nodes are used uniformly and the network lifetime is more. There is less transmission overhead in our proposed approach due to the use prediction scheme in cloud system and the network lifetime of the sensor network is more as the variance of consumption of power of all nodes in the sensor network is less due to load balancing routing. Use of prediction scheme in cloud system and load-balancing routing in the sensor network will be the future direction of research to minimize energy consumption in the sensor cloud environment.