{"title":"优化基于工业物联网的智能农业系统的RTPS平台","authors":"Basem Almadani, Saud Mohammad Mostafa","doi":"10.1109/CSDE48274.2019.9162424","DOIUrl":null,"url":null,"abstract":"With the ever-growing population, food and water demands have risen exponentially. Almost 80-85% of food is produced by agriculture but the rise in demand needs to be met by an increase in food production. The agriculture sector tends to tackle this issue by incorporating the Industrial Internet of Things (IIoT) and cloud technologies to improve productivity and reduce wastage of resources needed for it. The prime target of incorporation of these technologies would be to facilitate farmers to grow crops and raise livestock with ease and lower cost with increasing profits. Smart farming systems can comprise of one or more sub-systems that are automated using IIoT technology and processing is done either in local server or cloud. Most researchers work on individual sub-system and improve the performance providing local optimization. These sub-systems can have different vendors and integration is quite difficult at times. Therefore, we propose to use Data Distribution Service (DDS) middleware to integrate these heterogeneous sub-systems and provide high performance with low latency system. In this paper, we introduce a novel approach to tackle the issue of heterogeneity and provide global optimization. A suitable mathematical model for our proposed system is given and experiments are conducted to show the efficiency of the system in terms of throughput and delay.","PeriodicalId":238744,"journal":{"name":"2019 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Optimized RTPS platform for IIoT based smart farming systems\",\"authors\":\"Basem Almadani, Saud Mohammad Mostafa\",\"doi\":\"10.1109/CSDE48274.2019.9162424\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With the ever-growing population, food and water demands have risen exponentially. Almost 80-85% of food is produced by agriculture but the rise in demand needs to be met by an increase in food production. The agriculture sector tends to tackle this issue by incorporating the Industrial Internet of Things (IIoT) and cloud technologies to improve productivity and reduce wastage of resources needed for it. The prime target of incorporation of these technologies would be to facilitate farmers to grow crops and raise livestock with ease and lower cost with increasing profits. Smart farming systems can comprise of one or more sub-systems that are automated using IIoT technology and processing is done either in local server or cloud. Most researchers work on individual sub-system and improve the performance providing local optimization. These sub-systems can have different vendors and integration is quite difficult at times. Therefore, we propose to use Data Distribution Service (DDS) middleware to integrate these heterogeneous sub-systems and provide high performance with low latency system. In this paper, we introduce a novel approach to tackle the issue of heterogeneity and provide global optimization. A suitable mathematical model for our proposed system is given and experiments are conducted to show the efficiency of the system in terms of throughput and delay.\",\"PeriodicalId\":238744,\"journal\":{\"name\":\"2019 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)\",\"volume\":\"31 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CSDE48274.2019.9162424\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CSDE48274.2019.9162424","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Optimized RTPS platform for IIoT based smart farming systems
With the ever-growing population, food and water demands have risen exponentially. Almost 80-85% of food is produced by agriculture but the rise in demand needs to be met by an increase in food production. The agriculture sector tends to tackle this issue by incorporating the Industrial Internet of Things (IIoT) and cloud technologies to improve productivity and reduce wastage of resources needed for it. The prime target of incorporation of these technologies would be to facilitate farmers to grow crops and raise livestock with ease and lower cost with increasing profits. Smart farming systems can comprise of one or more sub-systems that are automated using IIoT technology and processing is done either in local server or cloud. Most researchers work on individual sub-system and improve the performance providing local optimization. These sub-systems can have different vendors and integration is quite difficult at times. Therefore, we propose to use Data Distribution Service (DDS) middleware to integrate these heterogeneous sub-systems and provide high performance with low latency system. In this paper, we introduce a novel approach to tackle the issue of heterogeneity and provide global optimization. A suitable mathematical model for our proposed system is given and experiments are conducted to show the efficiency of the system in terms of throughput and delay.