Pub Date : 2017-03-13DOI: 10.1109/PERCOMW.2017.7917576
Sebastian Troia, Gao Sheng, R. Alvizu, G. Maier, A. Pattavina
Due to the highly predictable daily movements of citizens in urban areas, mobile traffic shows repetitive patterns with spatio-temporal variations. This phenomenon is known as Tidal Effect analogy to the rise and fall of the sea levels. Recognizing and defining traffic load patterns at the base station thus plays a vital role in traffic engineering, network design and load balancing since it represents an important solution for the Internet Service Providers (ISPs) that face network congestion problems or over-provisioning of the link capacity. Previous works have dealt with the classification and identification of patterns through the use of techniques, which inspect the flow of data of a particular application. But they assume prior knowledge on the stream of data packets, making the trend identification much inefficient. Recent methods based on machine learning techniques build their classification models based on sample data collected at certain points of the network with high accuracy. Therefore, in this paper, we address the problem by applying matrix factorization based models on real-world datasets, identifying typical patterns from data streams, which frequently occur in the network, without investigating the type of flows. For that, we propose a Collective Non-negative Matrix Factorization based model combining multi-source data, such as point of interests attributes, traffic data and base station information, identifying the basic patterns of those areas of the city that present the same type of attributes. The experimental results show the effectiveness of our proposed approach compared with the baselines.
{"title":"Identification of tidal-traffic patterns in metro-area mobile networks via Matrix Factorization based model","authors":"Sebastian Troia, Gao Sheng, R. Alvizu, G. Maier, A. Pattavina","doi":"10.1109/PERCOMW.2017.7917576","DOIUrl":"https://doi.org/10.1109/PERCOMW.2017.7917576","url":null,"abstract":"Due to the highly predictable daily movements of citizens in urban areas, mobile traffic shows repetitive patterns with spatio-temporal variations. This phenomenon is known as Tidal Effect analogy to the rise and fall of the sea levels. Recognizing and defining traffic load patterns at the base station thus plays a vital role in traffic engineering, network design and load balancing since it represents an important solution for the Internet Service Providers (ISPs) that face network congestion problems or over-provisioning of the link capacity. Previous works have dealt with the classification and identification of patterns through the use of techniques, which inspect the flow of data of a particular application. But they assume prior knowledge on the stream of data packets, making the trend identification much inefficient. Recent methods based on machine learning techniques build their classification models based on sample data collected at certain points of the network with high accuracy. Therefore, in this paper, we address the problem by applying matrix factorization based models on real-world datasets, identifying typical patterns from data streams, which frequently occur in the network, without investigating the type of flows. For that, we propose a Collective Non-negative Matrix Factorization based model combining multi-source data, such as point of interests attributes, traffic data and base station information, identifying the basic patterns of those areas of the city that present the same type of attributes. The experimental results show the effectiveness of our proposed approach compared with the baselines.","PeriodicalId":319638,"journal":{"name":"2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116951748","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 : 2017-03-13DOI: 10.1109/PERCOMW.2017.7917603
K. Moriya, Eri Nakagawa, Manato Fujimoto, H. Suwa, Yutaka Arakawa, Aki Kimura, Satoko Miki, K. Yasumoto
Recently, IoT (Internet of Things) technologies have been attracting increasing attention. Among many applications of IoT, homes can be the most promising target. One of the purposes to deploy IoT in homes is automatic recognition of activities of daily living (ADLs). It is expected that ADL recognition in homes enables many new services such as elderly people monitoring and low energy appliance control. In existing studies on ADL recognition, however, it is hard to build a system to acquire data for ADL recognition in terms of installation cost. In this paper, we propose a method that reduces costs of the ADL recognition system by using ECHONET Lite-ready appliances which are expected to be widely spread in the future. ECHONET Lite is a communication protocol for control and sensor networks in smart-homes and standardized as ISO/IEC-4-3. The proposed method utilizes information (e.g., on/off state) from appliances and motion sensors attached to them as features and recognizes ADLs through machine learning. To evaluate the proposed method, we collected data in our smart-home testbed while several participants are living there. As a result, the proposed method achieved about 68% classification accuracy for 9 different activities.
近年来，物联网技术越来越受到人们的关注。在物联网的众多应用中，家庭可能是最有希望的目标。在家庭中部署物联网的目的之一是自动识别日常生活活动(adl)。预计家庭ADL识别可以实现许多新服务，如老年人监测和低能耗电器控制。然而，在现有的ADL识别研究中，由于安装成本的原因，很难建立一个用于ADL识别的数据采集系统。在本文中，我们提出了一种通过使用ECHONET life -ready设备来降低ADL识别系统成本的方法，这种设备有望在未来得到广泛应用。ECHONET Lite是一种用于智能家居控制和传感器网络的通信协议，已按照ISO/IEC-4-3进行了标准化。所提出的方法利用来自设备和附着在它们上的运动传感器的信息(例如，开/关状态)作为特征，并通过机器学习识别adl。为了评估所提出的方法，我们在我们的智能家居测试台上收集了数据，而几个参与者住在那里。结果表明，该方法对9种不同的活动达到了68%左右的分类准确率。
{"title":"Daily living activity recognition with ECHONET Lite appliances and motion sensors","authors":"K. Moriya, Eri Nakagawa, Manato Fujimoto, H. Suwa, Yutaka Arakawa, Aki Kimura, Satoko Miki, K. Yasumoto","doi":"10.1109/PERCOMW.2017.7917603","DOIUrl":"https://doi.org/10.1109/PERCOMW.2017.7917603","url":null,"abstract":"Recently, IoT (Internet of Things) technologies have been attracting increasing attention. Among many applications of IoT, homes can be the most promising target. One of the purposes to deploy IoT in homes is automatic recognition of activities of daily living (ADLs). It is expected that ADL recognition in homes enables many new services such as elderly people monitoring and low energy appliance control. In existing studies on ADL recognition, however, it is hard to build a system to acquire data for ADL recognition in terms of installation cost. In this paper, we propose a method that reduces costs of the ADL recognition system by using ECHONET Lite-ready appliances which are expected to be widely spread in the future. ECHONET Lite is a communication protocol for control and sensor networks in smart-homes and standardized as ISO/IEC-4-3. The proposed method utilizes information (e.g., on/off state) from appliances and motion sensors attached to them as features and recognizes ADLs through machine learning. To evaluate the proposed method, we collected data in our smart-home testbed while several participants are living there. As a result, the proposed method achieved about 68% classification accuracy for 9 different activities.","PeriodicalId":319638,"journal":{"name":"2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126198466","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 : 2017-03-13DOI: 10.1109/PERCOMW.2017.7917589
Kazuki Mizuyama, Yuzo Taenaka, K. Tsukamoto
Applying Software Defined Network (SDN) technology to wireless network attracts much attention. Our previous study proposed several channel utilization methods based on SDN/OpenFlow-enabled multi-channel wireless mesh network (WMN). However, since control messages are transmitted with data traffic on a same channel in WMN, it inevitably affects the network capacity. Especially, the amount of control messages for collecting statistical information of each flow (FlowStats) linearly increases in accordance with the number of flows, thereby being the dominant overhead. In this paper, we propose a method that prevents the increase of control traffic while maintaining network performance. Specifically, our proposed method uses statistical information of each interface (PortStats) instead of FlowStats, and handles multiple flows on the interface together. To handle a part of flows, we propose a way to estimate statistical information of individual flow without extra control messages. Finally, we show that the proposed method can maintain good network capacity with less packet losses and less control messages.
{"title":"Estimation based adaptable Flow Aggregation Method for reducing control traffic on Software Defined wireless Networks","authors":"Kazuki Mizuyama, Yuzo Taenaka, K. Tsukamoto","doi":"10.1109/PERCOMW.2017.7917589","DOIUrl":"https://doi.org/10.1109/PERCOMW.2017.7917589","url":null,"abstract":"Applying Software Defined Network (SDN) technology to wireless network attracts much attention. Our previous study proposed several channel utilization methods based on SDN/OpenFlow-enabled multi-channel wireless mesh network (WMN). However, since control messages are transmitted with data traffic on a same channel in WMN, it inevitably affects the network capacity. Especially, the amount of control messages for collecting statistical information of each flow (FlowStats) linearly increases in accordance with the number of flows, thereby being the dominant overhead. In this paper, we propose a method that prevents the increase of control traffic while maintaining network performance. Specifically, our proposed method uses statistical information of each interface (PortStats) instead of FlowStats, and handles multiple flows on the interface together. To handle a part of flows, we propose a way to estimate statistical information of individual flow without extra control messages. Finally, we show that the proposed method can maintain good network capacity with less packet losses and less control messages.","PeriodicalId":319638,"journal":{"name":"2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133209072","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 : 2017-03-01DOI: 10.1109/PERCOMW.2017.7917598
Benjamin Nicholls, C. Ang, Christos Efstratiou, Yongkuk Lee, W. Yeo
In this paper, we explore the feasibility of developing a sensor-driven rehabilitation game for people suffering from dysphagia. This study utilizes the skin-like electronics for unobtrusive, comfortable, continuous recording of surface electromyograms (EMG) during swallowing and use them for driving game-based, user-controlled feedback. The experimental study includes the development and evaluation of a real-time swallow detection algorithm using skin-like sensors and a game-based human-computer interaction. The user evaluations support the ease of use of the skin-like electronics as a motivational tool for people with dysphagia.
{"title":"Swallowing detection for game control: Using skin-like electronics to support people with dysphagia","authors":"Benjamin Nicholls, C. Ang, Christos Efstratiou, Yongkuk Lee, W. Yeo","doi":"10.1109/PERCOMW.2017.7917598","DOIUrl":"https://doi.org/10.1109/PERCOMW.2017.7917598","url":null,"abstract":"In this paper, we explore the feasibility of developing a sensor-driven rehabilitation game for people suffering from dysphagia. This study utilizes the skin-like electronics for unobtrusive, comfortable, continuous recording of surface electromyograms (EMG) during swallowing and use them for driving game-based, user-controlled feedback. The experimental study includes the development and evaluation of a real-time swallow detection algorithm using skin-like sensors and a game-based human-computer interaction. The user evaluations support the ease of use of the skin-like electronics as a motivational tool for people with dysphagia.","PeriodicalId":319638,"journal":{"name":"2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128383698","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 : 2017-03-01DOI: 10.1109/PERCOMW.2017.7917557
Mohammad-Mahdi Moazzami, Jasvinder Singh, Vijay Srinivasan, G. Xing
Semantic labels are crucial parts of many location-based applications. Previous efforts in location-based systems have mostly paid attention to achieve high accuracy in localization or navigation, with the assumption that the mapping between the locations and the semantic labels are given or will be done manually. In this paper, we propose a system called Deep-Crowd-Label that automatically assigns semantic labels to locations. We propose a novel transfer learning method that leverages deep learning models deployed on many crowd-workers to assign semantic labels to locations by classifying associated visual data. Deep-Crowd-Label uses the power of the crowd to aggregate the individual predictions done by the model across the crowd-workers visiting the same location. Our preliminary experiments with 26 different types of locations show that, our method and our prototype system is able to find the right label for the locations i.e., coffee shop to the Starbucks.
{"title":"Deep-Crowd-Label: A deep-learning based crowd-assisted system for location labeling","authors":"Mohammad-Mahdi Moazzami, Jasvinder Singh, Vijay Srinivasan, G. Xing","doi":"10.1109/PERCOMW.2017.7917557","DOIUrl":"https://doi.org/10.1109/PERCOMW.2017.7917557","url":null,"abstract":"Semantic labels are crucial parts of many location-based applications. Previous efforts in location-based systems have mostly paid attention to achieve high accuracy in localization or navigation, with the assumption that the mapping between the locations and the semantic labels are given or will be done manually. In this paper, we propose a system called Deep-Crowd-Label that automatically assigns semantic labels to locations. We propose a novel transfer learning method that leverages deep learning models deployed on many crowd-workers to assign semantic labels to locations by classifying associated visual data. Deep-Crowd-Label uses the power of the crowd to aggregate the individual predictions done by the model across the crowd-workers visiting the same location. Our preliminary experiments with 26 different types of locations show that, our method and our prototype system is able to find the right label for the locations i.e., coffee shop to the Starbucks.","PeriodicalId":319638,"journal":{"name":"2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134007769","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 : 2017-03-01DOI: 10.1109/PERCOMW.2017.7917646
Sougata Sen, Karan Grover, Vigneshwaran Subbaraju, Archan Misra
Due to numerous benefits, sensor-rich smartwatches and wrist-worn wearable devices are quickly gaining popularity. The popularity of these devices also raises privacy concerns. In this paper we explore one such privacy concern: the possibility of extracting the location of a user's touch-event on a smartphone, using the inertial sensor data of a smartwatch worn by the user on the same arm. This is a major concern not only because it might be possible for an attacker to extract private and sensitive information from the inputs provided but also because the attack mode utilises a device (smartwatch) that is distinct from the device being attacked (smartphone). Through a user study we find that such attacks are possible. Specifically, we can infer the user's entry pattern on a qwerty keyboard, with an error bound of ±2 neighboring keys, with 73.85% accuracy. As a possible preventive mechanism, we also show that adding a little white noise to inertial sensor data can reduce the inference accuracy by almost 30%, without affecting the accuracy of macro-gesture recognition.
{"title":"Inferring smartphone keypress via smartwatch inertial sensing","authors":"Sougata Sen, Karan Grover, Vigneshwaran Subbaraju, Archan Misra","doi":"10.1109/PERCOMW.2017.7917646","DOIUrl":"https://doi.org/10.1109/PERCOMW.2017.7917646","url":null,"abstract":"Due to numerous benefits, sensor-rich smartwatches and wrist-worn wearable devices are quickly gaining popularity. The popularity of these devices also raises privacy concerns. In this paper we explore one such privacy concern: the possibility of extracting the location of a user's touch-event on a smartphone, using the inertial sensor data of a smartwatch worn by the user on the same arm. This is a major concern not only because it might be possible for an attacker to extract private and sensitive information from the inputs provided but also because the attack mode utilises a device (smartwatch) that is distinct from the device being attacked (smartphone). Through a user study we find that such attacks are possible. Specifically, we can infer the user's entry pattern on a qwerty keyboard, with an error bound of ±2 neighboring keys, with 73.85% accuracy. As a possible preventive mechanism, we also show that adding a little white noise to inertial sensor data can reduce the inference accuracy by almost 30%, without affecting the accuracy of macro-gesture recognition.","PeriodicalId":319638,"journal":{"name":"2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129304247","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 : 2017-03-01DOI: 10.1109/PERCOMW.2017.7917538
A. Montanari
Social interactions have been traditionally studied via questionnaires and participant observations, imposing high burden, low scalability and precision. The goal of my research is to explore novel techniques to detect and monitor social interactions in indoor settings. Through the development of a scalable research platform it would be possible to study social dynamics at a finer granularity. The collected data will inform models about people's behaviour and support architectural research.
{"title":"Mobile sensing for social interaction monitoring and modelling","authors":"A. Montanari","doi":"10.1109/PERCOMW.2017.7917538","DOIUrl":"https://doi.org/10.1109/PERCOMW.2017.7917538","url":null,"abstract":"Social interactions have been traditionally studied via questionnaires and participant observations, imposing high burden, low scalability and precision. The goal of my research is to explore novel techniques to detect and monitor social interactions in indoor settings. Through the development of a scalable research platform it would be possible to study social dynamics at a finer granularity. The collected data will inform models about people's behaviour and support architectural research.","PeriodicalId":319638,"journal":{"name":"2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114298874","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 : 2017-03-01DOI: 10.1109/PERCOMW.2017.7917597
Rawan Alharbi, Nilofar Vafaie, K. Liu, Kevin Moran, Gwendolyn Ledford, A. Pfammatter, B. Spring, N. Alshurafa
Energy balance is one component of weight management, but passive objective measures of caloric intake are non-existent. Given the recent success of actigraphy as a passive objective measure of the physical activity construct that relieves participants of the burden of biased self-report, computer scientists and engineers are aiming to find a passive objective measure of caloric intake. Passive sensing food intake systems have failed to go beyond the lab and into behavioral research in part due to low adherence to wearing passive monitoring systems. While system accuracy and battery lifetime are sine qua non to a successfully deployed technology, they come second to adherence, since a system does nothing if it remains unused. This paper focuses on adherence as affected by: 1) perceived data privacy; 2) stigma of wearing devices; 3) comfort. These factors highlight new challenges surrounding participant informed consent and Institutional Review Board (IRB) risk assessment. The wearables examined include neck- and wrist-worn sensors, and video camera-based systems. Findings support the potential for adherence using wrist- and shoulder-based video cameras, and personalized style-conscious neck-worn sensors. The feasibility of detecting fine-grained eating gestures to validate the machine learning models is shown, improving the potential of translation of this technology.
{"title":"Investigating barriers and facilitators to wearable adherence in fine-grained eating detection","authors":"Rawan Alharbi, Nilofar Vafaie, K. Liu, Kevin Moran, Gwendolyn Ledford, A. Pfammatter, B. Spring, N. Alshurafa","doi":"10.1109/PERCOMW.2017.7917597","DOIUrl":"https://doi.org/10.1109/PERCOMW.2017.7917597","url":null,"abstract":"Energy balance is one component of weight management, but passive objective measures of caloric intake are non-existent. Given the recent success of actigraphy as a passive objective measure of the physical activity construct that relieves participants of the burden of biased self-report, computer scientists and engineers are aiming to find a passive objective measure of caloric intake. Passive sensing food intake systems have failed to go beyond the lab and into behavioral research in part due to low adherence to wearing passive monitoring systems. While system accuracy and battery lifetime are sine qua non to a successfully deployed technology, they come second to adherence, since a system does nothing if it remains unused. This paper focuses on adherence as affected by: 1) perceived data privacy; 2) stigma of wearing devices; 3) comfort. These factors highlight new challenges surrounding participant informed consent and Institutional Review Board (IRB) risk assessment. The wearables examined include neck- and wrist-worn sensors, and video camera-based systems. Findings support the potential for adherence using wrist- and shoulder-based video cameras, and personalized style-conscious neck-worn sensors. The feasibility of detecting fine-grained eating gestures to validate the machine learning models is shown, improving the potential of translation of this technology.","PeriodicalId":319638,"journal":{"name":"2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124670082","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 : 2017-03-01DOI: 10.1109/PERCOMW.2017.7917556
D. Reinhardt
The paradigm of crowd-assisted sensing primarily relies on volunteers, who use their personal devices to collect sensor information. To foster participants' contributions and hence ensure the viability of the underlying applications, different approaches, such as novel incentive schemes and privacy-preserving mechanisms, have been proposed. In most cases, these approaches have been evaluated by means of simulations and proof-of-concept implementations. While these evaluations are necessary to measure the efficacy and performance of the introduced solutions, they often neglect the human factors, despite their central role in crowd assisted applications. In my keynote, I will therefore emphasize on these aspects by presenting different studies, which my research team and I have conducted in the last years. Covered challenges range from the exploration of attitudes to participatory sensing tasks in location-based gaming communities to the participants' expectation in terms of rewards based on the invested resources. Our studies share common goals including analyzing the requirements from the perspective of potential users, which may contribute to their acceptance of novel solutions as well as motivate them to engage in crowd-assisted sensing applications in both short- and long-term.
{"title":"Human factors in crowd-assisted sensing","authors":"D. Reinhardt","doi":"10.1109/PERCOMW.2017.7917556","DOIUrl":"https://doi.org/10.1109/PERCOMW.2017.7917556","url":null,"abstract":"The paradigm of crowd-assisted sensing primarily relies on volunteers, who use their personal devices to collect sensor information. To foster participants' contributions and hence ensure the viability of the underlying applications, different approaches, such as novel incentive schemes and privacy-preserving mechanisms, have been proposed. In most cases, these approaches have been evaluated by means of simulations and proof-of-concept implementations. While these evaluations are necessary to measure the efficacy and performance of the introduced solutions, they often neglect the human factors, despite their central role in crowd assisted applications. In my keynote, I will therefore emphasize on these aspects by presenting different studies, which my research team and I have conducted in the last years. Covered challenges range from the exploration of attitudes to participatory sensing tasks in location-based gaming communities to the participants' expectation in terms of rewards based on the invested resources. Our studies share common goals including analyzing the requirements from the perspective of potential users, which may contribute to their acceptance of novel solutions as well as motivate them to engage in crowd-assisted sensing applications in both short- and long-term.","PeriodicalId":319638,"journal":{"name":"2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131106241","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 : 2017-03-01DOI: 10.1109/PERCOMW.2017.7917633
Ruth M. Ogunnaike, Brent Lagesse
The use of Internet of Things (IoT) devices has grown significantly in the past decade. While IoT is expected to improve life for many by enabling smart living spaces, the number of security risks that consumers and businesses will face is also increasing. A high number of vulnerable IoT devices are prone to attacks and easy exploit. Existing research has focused on security that must be implemented by administrators and manufacturers to be effective. Our work focuses on a system that does not rely on best practices by IoT device companies, but rather allows inexperienced users to be confident about the security of the devices that they add to their network. We present an implementation of an IoT architectural framework based on Software Defined Networking (SDN). In this architecture, IoT devices attempting to join an IoT network are scanned for vulnerabilities using custom vulnerability scanners and penetration testing tools before being allowed to communicate with any other device. In the case that a vulnerability is detected, the system will try to fix the vulnerability. If the fix fails, then the user will be alerted to the vulnerability and provided with suggestions for fixing it before it will be allowed to join the network. Our implementation demonstrates that the approach works and causes minimal overhead to the network once the device is deemed trustworthy.
{"title":"Toward consumer-friendly security in smart environments","authors":"Ruth M. Ogunnaike, Brent Lagesse","doi":"10.1109/PERCOMW.2017.7917633","DOIUrl":"https://doi.org/10.1109/PERCOMW.2017.7917633","url":null,"abstract":"The use of Internet of Things (IoT) devices has grown significantly in the past decade. While IoT is expected to improve life for many by enabling smart living spaces, the number of security risks that consumers and businesses will face is also increasing. A high number of vulnerable IoT devices are prone to attacks and easy exploit. Existing research has focused on security that must be implemented by administrators and manufacturers to be effective. Our work focuses on a system that does not rely on best practices by IoT device companies, but rather allows inexperienced users to be confident about the security of the devices that they add to their network. We present an implementation of an IoT architectural framework based on Software Defined Networking (SDN). In this architecture, IoT devices attempting to join an IoT network are scanned for vulnerabilities using custom vulnerability scanners and penetration testing tools before being allowed to communicate with any other device. In the case that a vulnerability is detected, the system will try to fix the vulnerability. If the fix fails, then the user will be alerted to the vulnerability and provided with suggestions for fixing it before it will be allowed to join the network. Our implementation demonstrates that the approach works and causes minimal overhead to the network once the device is deemed trustworthy.","PeriodicalId":319638,"journal":{"name":"2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129906512","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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