{"title":"物联网技术扩散预测","authors":"Y. Marinakis, S. Walsh, R. Harms","doi":"10.23919/PICMET.2017.8125435","DOIUrl":null,"url":null,"abstract":"Prognosticators and pundits are forecasting an explosion over the next decade in the number of sensors connected to wired and wireless networks, also referred to as the Internet of Things. The challenge is that these sensor forecasts are being made without taking into account the infrastructure required to manufacture and operate the sensors. Financial forecasts of individual infrastructure components have been made, but they give point forecasts rather than diffusion curves. It is also often not clear what models these forecasters are using, as they are often in proprietary reports. The present study provides sensor and sensor infrastructure technology component diffusion forecasts using a sigmoidal model of product diffusion. A plurality of technology diffusion curves was computed, one for each sensor infrastructure component technology. To identify the potential lack of availability of a component or a set of components, the forecast curves were then examined for temporal commonalities and differences. Thus this study provides a method for forecasting an emerging technology.","PeriodicalId":438177,"journal":{"name":"2017 Portland International Conference on Management of Engineering and Technology (PICMET)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Internet of Things Technology Diffusion Forecasts\",\"authors\":\"Y. Marinakis, S. Walsh, R. Harms\",\"doi\":\"10.23919/PICMET.2017.8125435\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Prognosticators and pundits are forecasting an explosion over the next decade in the number of sensors connected to wired and wireless networks, also referred to as the Internet of Things. The challenge is that these sensor forecasts are being made without taking into account the infrastructure required to manufacture and operate the sensors. Financial forecasts of individual infrastructure components have been made, but they give point forecasts rather than diffusion curves. It is also often not clear what models these forecasters are using, as they are often in proprietary reports. The present study provides sensor and sensor infrastructure technology component diffusion forecasts using a sigmoidal model of product diffusion. A plurality of technology diffusion curves was computed, one for each sensor infrastructure component technology. To identify the potential lack of availability of a component or a set of components, the forecast curves were then examined for temporal commonalities and differences. Thus this study provides a method for forecasting an emerging technology.\",\"PeriodicalId\":438177,\"journal\":{\"name\":\"2017 Portland International Conference on Management of Engineering and Technology (PICMET)\",\"volume\":\"9 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-11-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 Portland International Conference on Management of Engineering and Technology (PICMET)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.23919/PICMET.2017.8125435\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 Portland International Conference on Management of Engineering and Technology (PICMET)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/PICMET.2017.8125435","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Prognosticators and pundits are forecasting an explosion over the next decade in the number of sensors connected to wired and wireless networks, also referred to as the Internet of Things. The challenge is that these sensor forecasts are being made without taking into account the infrastructure required to manufacture and operate the sensors. Financial forecasts of individual infrastructure components have been made, but they give point forecasts rather than diffusion curves. It is also often not clear what models these forecasters are using, as they are often in proprietary reports. The present study provides sensor and sensor infrastructure technology component diffusion forecasts using a sigmoidal model of product diffusion. A plurality of technology diffusion curves was computed, one for each sensor infrastructure component technology. To identify the potential lack of availability of a component or a set of components, the forecast curves were then examined for temporal commonalities and differences. Thus this study provides a method for forecasting an emerging technology.