Pub Date : 2016-03-14DOI: 10.1109/PERCOM.2016.7456516
Suyeon Kim, Yohan Chon, Seokjun Lee, H. Cha
Mobile data offloading through WiFi is an essential requirement to reduce cellular network traffic. While extensive attempts have been made at mobile data offloading, previous studies have rarely addressed practical issues, such as dealing with diverse user contexts. In this paper, we propose a personalized data offloading scheme to provide maximum throughput within the cellular budget in daily life. We propose an adaptive policy that considers a user's mobility patterns, cellular budget, and network usage for applications. The proposed system employs an adaptive model to predict the throughput of WiFi APs and the network usage of smartphones. Among the three types of predictor model (i.e., spatial, temporal, and spatio-temporal), the system automatically chooses the optimal model for each mobile user without user intervention. The experimental results from 10 mobile users show that the proposed system provides 29% higher throughput than previous systems and minimizes extra data charges.
{"title":"Prediction-based personalized offloading of cellular traffic through WiFi networks","authors":"Suyeon Kim, Yohan Chon, Seokjun Lee, H. Cha","doi":"10.1109/PERCOM.2016.7456516","DOIUrl":"https://doi.org/10.1109/PERCOM.2016.7456516","url":null,"abstract":"Mobile data offloading through WiFi is an essential requirement to reduce cellular network traffic. While extensive attempts have been made at mobile data offloading, previous studies have rarely addressed practical issues, such as dealing with diverse user contexts. In this paper, we propose a personalized data offloading scheme to provide maximum throughput within the cellular budget in daily life. We propose an adaptive policy that considers a user's mobility patterns, cellular budget, and network usage for applications. The proposed system employs an adaptive model to predict the throughput of WiFi APs and the network usage of smartphones. Among the three types of predictor model (i.e., spatial, temporal, and spatio-temporal), the system automatically chooses the optimal model for each mobile user without user intervention. The experimental results from 10 mobile users show that the proposed system provides 29% higher throughput than previous systems and minimizes extra data charges.","PeriodicalId":275797,"journal":{"name":"2016 IEEE International Conference on Pervasive Computing and Communications (PerCom)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132499745","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 : 2016-03-14DOI: 10.1109/PERCOM.2016.7456519
A. Forkan, I. Khalil
Chronic diseases are major causes of deaths in Australia and throughout the world. This necessitates the need for a self-care, preventive, predictive and protective assisted living system where a patient can be monitored continuously using wearable and wireless sensors. In real-time home monitoring system, various biological signals of a patient are obtained continuously using a mobile device (smart phone or tablet) and sent to the cloud to discover patient-specific abnormalities. The objective of this work is to develop a probabilistic model that identifies the future clinical abnormalities of a patient using recent and past values of multiple vital signs (e.g. heart rate, blood pressure, respiratory rate). Chronic patients living alone in home die of various diseases for the lack of an efficient automated system having prior prediction ability in the irregularities of vital signs. In this paper, Hidden Markov Model (HMM) is adopted to predict different clinical onsets using the temporal behaviours of six biosignals. The HMM models are trained and evaluated using continuous monitoring data of more than 1000 patients collected from the MIMIC-II database of MIT physiobank archive. The best models are selected using expectation maximisation (EM) algorithm and used in personalized remote monitoring system to forecast the most probable forthcoming clinical states of a continuously monitored patient. The scalable power of cloud computing is utilized for fast learning of various clinical events from large samples. The results obtained from the innovative home-based monitoring application show a new approach of detecting clinical anomalies using multi-parameter trends.
{"title":"A probabilistic model for early prediction of abnormal clinical events using vital sign correlations in home-based monitoring","authors":"A. Forkan, I. Khalil","doi":"10.1109/PERCOM.2016.7456519","DOIUrl":"https://doi.org/10.1109/PERCOM.2016.7456519","url":null,"abstract":"Chronic diseases are major causes of deaths in Australia and throughout the world. This necessitates the need for a self-care, preventive, predictive and protective assisted living system where a patient can be monitored continuously using wearable and wireless sensors. In real-time home monitoring system, various biological signals of a patient are obtained continuously using a mobile device (smart phone or tablet) and sent to the cloud to discover patient-specific abnormalities. The objective of this work is to develop a probabilistic model that identifies the future clinical abnormalities of a patient using recent and past values of multiple vital signs (e.g. heart rate, blood pressure, respiratory rate). Chronic patients living alone in home die of various diseases for the lack of an efficient automated system having prior prediction ability in the irregularities of vital signs. In this paper, Hidden Markov Model (HMM) is adopted to predict different clinical onsets using the temporal behaviours of six biosignals. The HMM models are trained and evaluated using continuous monitoring data of more than 1000 patients collected from the MIMIC-II database of MIT physiobank archive. The best models are selected using expectation maximisation (EM) algorithm and used in personalized remote monitoring system to forecast the most probable forthcoming clinical states of a continuously monitored patient. The scalable power of cloud computing is utilized for fast learning of various clinical events from large samples. The results obtained from the innovative home-based monitoring application show a new approach of detecting clinical anomalies using multi-parameter trends.","PeriodicalId":275797,"journal":{"name":"2016 IEEE International Conference on Pervasive Computing and Communications (PerCom)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125659028","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 : 2016-03-14DOI: 10.1109/PERCOM.2016.7456506
Junghyo Lee, Ayan Banerjee, S. Gupta
In this paper, we propose MT-Diet, a smartphone-based automated diet monitoring system that interfaces a thermal camera with a smartphone and identifies types of food consumed at the click of a button. The system uses thermal maps of a food plate to increase accuracy of segmentation and extraction of food parts, and combines thermal and visual images to improve accuracy in the detection of cooked food. Test results on 80 different types of cooked food show that MT-Diet can isolate food parts with an accuracy of 97.5% and determine the type of food with an accuracy of 88.93%, which is a significant improvement (nearly 25%) over the state-of-the-art.
{"title":"MT-Diet: Automated smartphone based diet assessment with infrared images","authors":"Junghyo Lee, Ayan Banerjee, S. Gupta","doi":"10.1109/PERCOM.2016.7456506","DOIUrl":"https://doi.org/10.1109/PERCOM.2016.7456506","url":null,"abstract":"In this paper, we propose MT-Diet, a smartphone-based automated diet monitoring system that interfaces a thermal camera with a smartphone and identifies types of food consumed at the click of a button. The system uses thermal maps of a food plate to increase accuracy of segmentation and extraction of food parts, and combines thermal and visual images to improve accuracy in the detection of cooked food. Test results on 80 different types of cooked food show that MT-Diet can isolate food parts with an accuracy of 97.5% and determine the type of food with an accuracy of 88.93%, which is a significant improvement (nearly 25%) over the state-of-the-art.","PeriodicalId":275797,"journal":{"name":"2016 IEEE International Conference on Pervasive Computing and Communications (PerCom)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115878209","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 : 2016-03-14DOI: 10.1109/PERCOM.2016.7456517
S. Tan, Xiaoliang Wang, G. Maier, Wenzhong Li
Public transport plays an importation role in our daily life. The information related to passengers satisfaction is very beneficial for optimizing the transportation service. This paper investigates an application of mobile crowd sensing to detect and analyze the riding quality of public transport vehicles. The lightweight system leverages sensors equipped on participants' smartphones to collect surrounding information. By analyzing the uploaded data at a server, we are able to estimate both aggressive driving behaviors and environment contexts. Series of data processing methods are exploited to overcome the affection of body movement and road condition, and crowd sourcing is applied to improve the robustness of the results. We have tested this system in 3 different transportation in 3 cities. The results indicate that the system can provide sufficient accuracy (up to 91% with 7 phones) to identify dozens of riding-comfort metrics.
{"title":"Riding quality evaluation through mobile crowd sensing","authors":"S. Tan, Xiaoliang Wang, G. Maier, Wenzhong Li","doi":"10.1109/PERCOM.2016.7456517","DOIUrl":"https://doi.org/10.1109/PERCOM.2016.7456517","url":null,"abstract":"Public transport plays an importation role in our daily life. The information related to passengers satisfaction is very beneficial for optimizing the transportation service. This paper investigates an application of mobile crowd sensing to detect and analyze the riding quality of public transport vehicles. The lightweight system leverages sensors equipped on participants' smartphones to collect surrounding information. By analyzing the uploaded data at a server, we are able to estimate both aggressive driving behaviors and environment contexts. Series of data processing methods are exploited to overcome the affection of body movement and road condition, and crowd sourcing is applied to improve the robustness of the results. We have tested this system in 3 different transportation in 3 cities. The results indicate that the system can provide sufficient accuracy (up to 91% with 7 phones) to identify dozens of riding-comfort metrics.","PeriodicalId":275797,"journal":{"name":"2016 IEEE International Conference on Pervasive Computing and Communications (PerCom)","volume":"317 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122111995","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 : 2016-03-14DOI: 10.1109/PERCOM.2016.7456523
He Du, Zhiwen Yu, Fei Yi, Zhu Wang, Qi Han, Bin Guo
Monitoring group mobility and structure is crucial for public safety management and emergency evacuation. In this paper, we propose a fine-grained mobility classification and structure recognition approach for social groups based on hybrid sensing using mobile devices. First, we present a method which classifies group mobility into four levels, including stationary, strolling, walking and running. Second, by combining mobile sensing and Wi-Fi signals, a novel relative position relationship estimation algorithm is developed to understand moving group structures of different shapes. We have conducted real-life experiments in which eight volunteers form two to three small groups moving in a teaching building with different speed and structures. Experimental results show that our approach achieves an accuracy of 99.5% in mobility classification and about 80% in group structure recognition.
{"title":"Group mobility classification and structure recognition using mobile devices","authors":"He Du, Zhiwen Yu, Fei Yi, Zhu Wang, Qi Han, Bin Guo","doi":"10.1109/PERCOM.2016.7456523","DOIUrl":"https://doi.org/10.1109/PERCOM.2016.7456523","url":null,"abstract":"Monitoring group mobility and structure is crucial for public safety management and emergency evacuation. In this paper, we propose a fine-grained mobility classification and structure recognition approach for social groups based on hybrid sensing using mobile devices. First, we present a method which classifies group mobility into four levels, including stationary, strolling, walking and running. Second, by combining mobile sensing and Wi-Fi signals, a novel relative position relationship estimation algorithm is developed to understand moving group structures of different shapes. We have conducted real-life experiments in which eight volunteers form two to three small groups moving in a teaching building with different speed and structures. Experimental results show that our approach achieves an accuracy of 99.5% in mobility classification and about 80% in group structure recognition.","PeriodicalId":275797,"journal":{"name":"2016 IEEE International Conference on Pervasive Computing and Communications (PerCom)","volume":"145 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132093466","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：