Kelly Rael, G. Fragkos, J. Plusquellic, Eirini-Eleni Tsiropoulou
{"title":"无人机支持的人类物联网","authors":"Kelly Rael, G. Fragkos, J. Plusquellic, Eirini-Eleni Tsiropoulou","doi":"10.1109/DCOSS49796.2020.00056","DOIUrl":null,"url":null,"abstract":"In this paper, an Unmanned Aerial Vehicles (UAVs) - enabled human Internet of Things (IoT) architecture is introduced to enable the rescue operations in public safety systems (PSSs). Initially, the first responders select in an autonomous manner the disaster area that they will support by considering the dynamic socio-physical changes of the surrounding environment and following a set of gradient ascent reinforcement learning algorithms. Then, the victims create coalitions among each other and the first responders at each disaster area based on the expected- maximization approach. Finally, the first responders select the UAVs that communicate with the Emergency Control Center (ECC), to which they will report the collected data from the disaster areas by adopting a set of log-linear reinforcement learning algorithms. The overall distributed UAV-enabled human Internet of Things architecture is evaluated via detailed numerical results that highlight its key operational features and the performance benefits of the proposed framework.","PeriodicalId":198837,"journal":{"name":"2020 16th International Conference on Distributed Computing in Sensor Systems (DCOSS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"UAV-enabled Human Internet of Things\",\"authors\":\"Kelly Rael, G. Fragkos, J. Plusquellic, Eirini-Eleni Tsiropoulou\",\"doi\":\"10.1109/DCOSS49796.2020.00056\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, an Unmanned Aerial Vehicles (UAVs) - enabled human Internet of Things (IoT) architecture is introduced to enable the rescue operations in public safety systems (PSSs). Initially, the first responders select in an autonomous manner the disaster area that they will support by considering the dynamic socio-physical changes of the surrounding environment and following a set of gradient ascent reinforcement learning algorithms. Then, the victims create coalitions among each other and the first responders at each disaster area based on the expected- maximization approach. Finally, the first responders select the UAVs that communicate with the Emergency Control Center (ECC), to which they will report the collected data from the disaster areas by adopting a set of log-linear reinforcement learning algorithms. The overall distributed UAV-enabled human Internet of Things architecture is evaluated via detailed numerical results that highlight its key operational features and the performance benefits of the proposed framework.\",\"PeriodicalId\":198837,\"journal\":{\"name\":\"2020 16th International Conference on Distributed Computing in Sensor Systems (DCOSS)\",\"volume\":\"6 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 16th International Conference on Distributed Computing in Sensor Systems (DCOSS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DCOSS49796.2020.00056\",\"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 16th International Conference on Distributed Computing in Sensor Systems (DCOSS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DCOSS49796.2020.00056","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this paper, an Unmanned Aerial Vehicles (UAVs) - enabled human Internet of Things (IoT) architecture is introduced to enable the rescue operations in public safety systems (PSSs). Initially, the first responders select in an autonomous manner the disaster area that they will support by considering the dynamic socio-physical changes of the surrounding environment and following a set of gradient ascent reinforcement learning algorithms. Then, the victims create coalitions among each other and the first responders at each disaster area based on the expected- maximization approach. Finally, the first responders select the UAVs that communicate with the Emergency Control Center (ECC), to which they will report the collected data from the disaster areas by adopting a set of log-linear reinforcement learning algorithms. The overall distributed UAV-enabled human Internet of Things architecture is evaluated via detailed numerical results that highlight its key operational features and the performance benefits of the proposed framework.