G. Fortino, F. Messina, D. Rosaci, G. Sarné, Claudio Savaglio
{"title":"通过可信的物联网设备团队进行协作环境监测","authors":"G. Fortino, F. Messina, D. Rosaci, G. Sarné, Claudio Savaglio","doi":"10.1109/ICHMS49158.2020.9209433","DOIUrl":null,"url":null,"abstract":"In the Internet of Things (IoT) age, the majority of real environments will become smart through the massive spread of novel devices empowered with cyber-physical abilities. Provided with increasing computation capabilities and pervasively deployed, smart IoT devices (SDs) will exhibit pro-active behaviors and will perform more and more complex tasks, thus enabling the provision of advanced cyber-physical services which make the environment smarter and smarter. Such technology advancements together with an increased environmental awareness suggested the exploitation of these SDs for natural environments monitoring purposes. However, in presence of many and heterogeneous SDs, the formation of good teams requires high levels of trustworthiness among the members and, therefore, it is necessary to adequately represent their mutual trustworthiness. To this aim, the contribution provided by this paper consists in (i) defining a trust measure combining the reputation of SDs and the precision of their sensory data; (ii) designing a framework which adopts such measures as main criteria for the formation of temporary teams of humans and SDs; (iii) testing the proposed trust-based framework on a case study simulating the collaborative monitoring of a natural environment. The obtained results confirmed the potential improvements in the teams composition in terms of both performance and appreciation.","PeriodicalId":132917,"journal":{"name":"2020 IEEE International Conference on Human-Machine Systems (ICHMS)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Collaborative Environmental Monitoring through Teams of Trusted IoT devices\",\"authors\":\"G. Fortino, F. Messina, D. Rosaci, G. Sarné, Claudio Savaglio\",\"doi\":\"10.1109/ICHMS49158.2020.9209433\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the Internet of Things (IoT) age, the majority of real environments will become smart through the massive spread of novel devices empowered with cyber-physical abilities. Provided with increasing computation capabilities and pervasively deployed, smart IoT devices (SDs) will exhibit pro-active behaviors and will perform more and more complex tasks, thus enabling the provision of advanced cyber-physical services which make the environment smarter and smarter. Such technology advancements together with an increased environmental awareness suggested the exploitation of these SDs for natural environments monitoring purposes. However, in presence of many and heterogeneous SDs, the formation of good teams requires high levels of trustworthiness among the members and, therefore, it is necessary to adequately represent their mutual trustworthiness. To this aim, the contribution provided by this paper consists in (i) defining a trust measure combining the reputation of SDs and the precision of their sensory data; (ii) designing a framework which adopts such measures as main criteria for the formation of temporary teams of humans and SDs; (iii) testing the proposed trust-based framework on a case study simulating the collaborative monitoring of a natural environment. The obtained results confirmed the potential improvements in the teams composition in terms of both performance and appreciation.\",\"PeriodicalId\":132917,\"journal\":{\"name\":\"2020 IEEE International Conference on Human-Machine Systems (ICHMS)\",\"volume\":\"42 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE International Conference on Human-Machine Systems (ICHMS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICHMS49158.2020.9209433\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE International Conference on Human-Machine Systems (ICHMS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICHMS49158.2020.9209433","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Collaborative Environmental Monitoring through Teams of Trusted IoT devices
In the Internet of Things (IoT) age, the majority of real environments will become smart through the massive spread of novel devices empowered with cyber-physical abilities. Provided with increasing computation capabilities and pervasively deployed, smart IoT devices (SDs) will exhibit pro-active behaviors and will perform more and more complex tasks, thus enabling the provision of advanced cyber-physical services which make the environment smarter and smarter. Such technology advancements together with an increased environmental awareness suggested the exploitation of these SDs for natural environments monitoring purposes. However, in presence of many and heterogeneous SDs, the formation of good teams requires high levels of trustworthiness among the members and, therefore, it is necessary to adequately represent their mutual trustworthiness. To this aim, the contribution provided by this paper consists in (i) defining a trust measure combining the reputation of SDs and the precision of their sensory data; (ii) designing a framework which adopts such measures as main criteria for the formation of temporary teams of humans and SDs; (iii) testing the proposed trust-based framework on a case study simulating the collaborative monitoring of a natural environment. The obtained results confirmed the potential improvements in the teams composition in terms of both performance and appreciation.