Pub Date : 2019-03-01DOI: 10.1109/PERCOMW.2019.8730758
Saket Kunwar
This paper describes the winning method and results of the Human Behaviour Challenge 201811https://competitions.codalab.org/competitions/17401. GPS trajectories from smartphones and associated metadata of people involved in a game of finding hidden objects were collected at Nagoya Institute of Technology, Japan. The competition, hosted at codalab.org, challenged the participants to find a solution that could predict the future destination and past starting point from the given data. Knowing the future and past track data of users could enable the organizers to provide personalized and localized services. The source code22https://github.com/saketkunwar/hbc2018 under open source license has been released at github.
{"title":"Human Behavior Challenge Winning Solution","authors":"Saket Kunwar","doi":"10.1109/PERCOMW.2019.8730758","DOIUrl":"https://doi.org/10.1109/PERCOMW.2019.8730758","url":null,"abstract":"This paper describes the winning method and results of the Human Behaviour Challenge 201811https://competitions.codalab.org/competitions/17401. GPS trajectories from smartphones and associated metadata of people involved in a game of finding hidden objects were collected at Nagoya Institute of Technology, Japan. The competition, hosted at codalab.org, challenged the participants to find a solution that could predict the future destination and past starting point from the given data. Knowing the future and past track data of users could enable the organizers to provide personalized and localized services. The source code22https://github.com/saketkunwar/hbc2018 under open source license has been released at github.","PeriodicalId":437017,"journal":{"name":"2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132943934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-01DOI: 10.1109/PERCOMW.2019.8730573
L. Rahman, T. Ozcelebi, J. Lukkien
The first step in a system design process is to perform domain analysis. This entails acquiring stakeholder concerns throughout the life cycle of the system. The second step is to design solutions addressing those stakeholder concerns. This entails applying patterns for solving known, recurring problems. For these there are architecture patterns and design patterns for architecture design and detailed design respectively. For Internet of Things (IoT) systems such patterns are hardly defined yet since experience is just evolving. In this paper, we propose our definition of an IoT pattern along with its formal specification, explained by a running example. IoT systems are characterized by the variety of stakeholders involved throughout their life cycle, therefore our pattern specification includes means for identifying possible conflicts between these stakeholders.
{"title":"Designing IoT Systems: Patterns and Managerial Conflicts","authors":"L. Rahman, T. Ozcelebi, J. Lukkien","doi":"10.1109/PERCOMW.2019.8730573","DOIUrl":"https://doi.org/10.1109/PERCOMW.2019.8730573","url":null,"abstract":"The first step in a system design process is to perform domain analysis. This entails acquiring stakeholder concerns throughout the life cycle of the system. The second step is to design solutions addressing those stakeholder concerns. This entails applying patterns for solving known, recurring problems. For these there are architecture patterns and design patterns for architecture design and detailed design respectively. For Internet of Things (IoT) systems such patterns are hardly defined yet since experience is just evolving. In this paper, we propose our definition of an IoT pattern along with its formal specification, explained by a running example. IoT systems are characterized by the variety of stakeholders involved throughout their life cycle, therefore our pattern specification includes means for identifying possible conflicts between these stakeholders.","PeriodicalId":437017,"journal":{"name":"2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127843532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-01DOI: 10.1109/percomw.2019.8730774
{"title":"BiRD'19 - International Workshop on Behavior analysis and Recognition for knowledge Discovery - Welcome and Committees","authors":"","doi":"10.1109/percomw.2019.8730774","DOIUrl":"https://doi.org/10.1109/percomw.2019.8730774","url":null,"abstract":"","PeriodicalId":437017,"journal":{"name":"2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122252862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-01DOI: 10.1109/PERCOMW.2019.8730651
S. Pal
The Internet of Things (IoT), smart sensors and mobile wearable devices are helping to provide services that are more ubiquitous, smarter, faster and easily accessible to users. However, security is a significant concern for the IoT, with access control and identity management are being two major issues. With the growing size and presence of these systems and the resource constrained nature of the IoT devices, an important question is how to manage policies in a manner that is both scalable and flexible. In this research, we aim at proposing a fine-grained and flexible access control architecture, and to examine an identity model for constrained IoT resources. To achieve this, first, we outline some key limitations in the state of the art access control and identity management for IoT. Then we devise our approach to address those limitations in a systematic way.
{"title":"Limitations and Approaches in Access Control and Identity Management for Constrained IoT Resources","authors":"S. Pal","doi":"10.1109/PERCOMW.2019.8730651","DOIUrl":"https://doi.org/10.1109/PERCOMW.2019.8730651","url":null,"abstract":"The Internet of Things (IoT), smart sensors and mobile wearable devices are helping to provide services that are more ubiquitous, smarter, faster and easily accessible to users. However, security is a significant concern for the IoT, with access control and identity management are being two major issues. With the growing size and presence of these systems and the resource constrained nature of the IoT devices, an important question is how to manage policies in a manner that is both scalable and flexible. In this research, we aim at proposing a fine-grained and flexible access control architecture, and to examine an identity model for constrained IoT resources. To achieve this, first, we outline some key limitations in the state of the art access control and identity management for IoT. Then we devise our approach to address those limitations in a systematic way.","PeriodicalId":437017,"journal":{"name":"2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115755822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-01DOI: 10.1109/percomw.2019.8730592
{"title":"SmartEdge'19 - The Third International Workshop on Smart Edge Computing and Networking - Welcome and Committeees","authors":"","doi":"10.1109/percomw.2019.8730592","DOIUrl":"https://doi.org/10.1109/percomw.2019.8730592","url":null,"abstract":"","PeriodicalId":437017,"journal":{"name":"2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115946203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-01DOI: 10.1109/percomw.2019.8730704
{"title":"PerVehicle'19: PerVehicle'19 – 1st International Workshop on Pervasive Computing for Vehicular Systems - Program","authors":"","doi":"10.1109/percomw.2019.8730704","DOIUrl":"https://doi.org/10.1109/percomw.2019.8730704","url":null,"abstract":"","PeriodicalId":437017,"journal":{"name":"2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122666297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-01DOI: 10.1109/PERCOMW.2019.8730653
Kanon Takemura, Tsubasa Hirakawa, Y. Mizutani, Hirokazu Suzuki, Michi Tsuruya, K. Yoda
Revealing the route selection of wild animals is of fundamental importance in understanding their movements and foraging strategy. In this study, we attached GPS loggers to black-tailed gulls Larus crassirostris and recorded their movement trajectories during their foraging trips. Using inverse reinforcement learning (IRL), we analyzed the factors that affected their route selection. During the training phase, using pre-defined feature maps, we estimated a reward map that may affect the decision making of black-tailed gulls. The reward map can be used for predicting the trajectories of the gulls during the test phase. In addition, the resultant weight vector enabled us to analyze to which degree the black-tailed gulls favor each area.
{"title":"Trajectories Prediction of the Black-Tailed Gull Using the Inverse Reinforcement Learning","authors":"Kanon Takemura, Tsubasa Hirakawa, Y. Mizutani, Hirokazu Suzuki, Michi Tsuruya, K. Yoda","doi":"10.1109/PERCOMW.2019.8730653","DOIUrl":"https://doi.org/10.1109/PERCOMW.2019.8730653","url":null,"abstract":"Revealing the route selection of wild animals is of fundamental importance in understanding their movements and foraging strategy. In this study, we attached GPS loggers to black-tailed gulls Larus crassirostris and recorded their movement trajectories during their foraging trips. Using inverse reinforcement learning (IRL), we analyzed the factors that affected their route selection. During the training phase, using pre-defined feature maps, we estimated a reward map that may affect the decision making of black-tailed gulls. The reward map can be used for predicting the trajectories of the gulls during the test phase. In addition, the resultant weight vector enabled us to analyze to which degree the black-tailed gulls favor each area.","PeriodicalId":437017,"journal":{"name":"2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131226226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-01DOI: 10.1109/percomw.2019.8730726
{"title":"PerCom PhD Forum 2019: PhD Forum on Pervasive Computing and Communications 2019 - Welcome and Committees","authors":"","doi":"10.1109/percomw.2019.8730726","DOIUrl":"https://doi.org/10.1109/percomw.2019.8730726","url":null,"abstract":"","PeriodicalId":437017,"journal":{"name":"2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131447252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-01DOI: 10.1109/PERCOMW.2019.8730589
Karl Casserfelt, R. Mihailescu
Pervasive technologies permeating our immediate surroundings provide a wide variety of means for sensing and actuating in our environment, having a great potential to impact the way we live, but also how we work. In this paper, we address the problem of activity recognition in office environments, as a means for inferring contextual information in order to automatically and proactively assists people in their daily activities. To this end we employ state-of-the-art image processing techniques and evaluate their capabilities in a real-world setup. Traditional machine learning is characterized by instances where both the training and test data share the same distribution. When this is not the case, the performance of the learned model is deteriorated. However, often times, the data is expensive or difficult to collect and label. It is therefore important to develop techniques that are able to make the best possible use of existing data sets from related domains, relative to the target domain. To this end, we further investigate in this work transfer learning techniques in deep learning architectures for the task of activity recognition in office settings. We provide herein a solution model that attains a 94% accuracy under the right conditions.
{"title":"An investigation of transfer learning for deep architectures in group activity recognition","authors":"Karl Casserfelt, R. Mihailescu","doi":"10.1109/PERCOMW.2019.8730589","DOIUrl":"https://doi.org/10.1109/PERCOMW.2019.8730589","url":null,"abstract":"Pervasive technologies permeating our immediate surroundings provide a wide variety of means for sensing and actuating in our environment, having a great potential to impact the way we live, but also how we work. In this paper, we address the problem of activity recognition in office environments, as a means for inferring contextual information in order to automatically and proactively assists people in their daily activities. To this end we employ state-of-the-art image processing techniques and evaluate their capabilities in a real-world setup. Traditional machine learning is characterized by instances where both the training and test data share the same distribution. When this is not the case, the performance of the learned model is deteriorated. However, often times, the data is expensive or difficult to collect and label. It is therefore important to develop techniques that are able to make the best possible use of existing data sets from related domains, relative to the target domain. To this end, we further investigate in this work transfer learning techniques in deep learning architectures for the task of activity recognition in office settings. We provide herein a solution model that attains a 94% accuracy under the right conditions.","PeriodicalId":437017,"journal":{"name":"2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132535416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-01DOI: 10.1109/PERCOMW.2019.8730746
Samy El-Tawab, Zachary Yorio, A. Salman, Raymond Oram, B. Park
There is a dramatic increase in the deployment of Internet of Things (IoT) devices in the last couple of years. In Intelligent Transportation Systems (ITS), we introduced a cyber-physical system that monitors the quality of service (QoS) for transit buses around a mid-size university city using Internet of Things (IoT). The sensing IoT devices detected the number of riders waiting for the bus system at each bus station. The experiments ran for six weeks continuously to monitor seven different bus stations around the university. The collected data reports the number of people waiting for the bus at each station, the wait time for a particular bus station at different time/day(s). In this paper, we analyze the collected data for the various bus stations and the origin/destination of some riders who used one of the seven stations as origin and destination stations. Also, several security measurements have been added to address privacy concerns that might occur with the collection, transmission, and storage of data in the Cloud (e.g., the privacy of the ridership Media Access Control (MAC) addresses and tracking of a particular bus rider in the system). We implement security measurements to emphasize the privacy protection of bus riders.
{"title":"Origin-Destination Tracking Analysis of an Intelligent Transit Bus System using Internet of Things","authors":"Samy El-Tawab, Zachary Yorio, A. Salman, Raymond Oram, B. Park","doi":"10.1109/PERCOMW.2019.8730746","DOIUrl":"https://doi.org/10.1109/PERCOMW.2019.8730746","url":null,"abstract":"There is a dramatic increase in the deployment of Internet of Things (IoT) devices in the last couple of years. In Intelligent Transportation Systems (ITS), we introduced a cyber-physical system that monitors the quality of service (QoS) for transit buses around a mid-size university city using Internet of Things (IoT). The sensing IoT devices detected the number of riders waiting for the bus system at each bus station. The experiments ran for six weeks continuously to monitor seven different bus stations around the university. The collected data reports the number of people waiting for the bus at each station, the wait time for a particular bus station at different time/day(s). In this paper, we analyze the collected data for the various bus stations and the origin/destination of some riders who used one of the seven stations as origin and destination stations. Also, several security measurements have been added to address privacy concerns that might occur with the collection, transmission, and storage of data in the Cloud (e.g., the privacy of the ridership Media Access Control (MAC) addresses and tracking of a particular bus rider in the system). We implement security measurements to emphasize the privacy protection of bus riders.","PeriodicalId":437017,"journal":{"name":"2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132922196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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