2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)最新文献_第10页

Context Aware Telco Churn Prediction Powered By Temporal Feature Engineering 基于时间特征工程的情境感知电信客户流失预测

2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) Pub Date : 2018-03-01 DOI: 10.1109/PERCOMW.2018.8480416

Ruirui Bai, Weixiong Rao, Mingxuan Yuan, Jia Zeng, Jianfeng Yan

{"title":"Context Aware Telco Churn Prediction Powered By Temporal Feature Engineering","authors":"Ruirui Bai, Weixiong Rao, Mingxuan Yuan, Jia Zeng, Jianfeng Yan","doi":"10.1109/PERCOMW.2018.8480416","DOIUrl":"https://doi.org/10.1109/PERCOMW.2018.8480416","url":null,"abstract":"It is still challenging in telecommunication (Telco) industry to precisely predict those who will churn from the current Telco operator to another one in next month. State of art approaches suffer from the issue that the predicted churners might be those silent customers with trivial profits to Telco operators. Thus, it is quite necessary to focus on the churn prediction problem with respect to active customers, instead of silent ones. Thus, we propose 3 kinds of new features in order to capture the context-aware customer behaviour in terms of temporal features. Such features include long-range Feature, trend feature and regression-based feature for Telco churn prediction. After that, we employ an ensemble process on the base learners: Random Forst (RF), XGB and GBDT + SVM. Experimental results confirm that the prediction performance has been improved by using these new features. From millions of active customers, this system can provide a list of prepaid customers who are most likely to churn in the next month, having 0.69 precision for the top 25000 predicted churners in the list. At the same time, achieve 44.69% improvement when given $5 times 10 ^{4}$.","PeriodicalId":190096,"journal":{"name":"2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123351694","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}

引用次数: 1

On Mining IoT Data for Evaluating the Operation of Public Educational Buildings 基于物联网数据挖掘的公共教育建筑运行评估

2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) Pub Date : 2018-03-01 DOI: 10.1109/PERCOMW.2018.8480226

Na Zhu, A. Anagnostopoulos, I. Chatzigiannakis

引用次数: 4

Implementing Personalized Web News Delivery Service Using Tales Of Familiar Framework 利用熟悉的故事框架实现个性化的网络新闻传递服务

2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) Pub Date : 2018-03-01 DOI: 10.1109/PERCOMW.2018.8480203

Kentaro Noda, Yoshihiro Wada, S. Saiki, Masahide Nakamura, K. Yasuda

引用次数: 1

A Study of the Detection of Pedestrian Flow Using Bluetooth Low Energy 基于低功耗蓝牙的行人流量检测研究

2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) Pub Date : 2018-03-01 DOI: 10.1109/PERCOMW.2018.8480336

Tomoya Kitazato, Masaki Ito, K. Sezaki

{"title":"A Study of the Detection of Pedestrian Flow Using Bluetooth Low Energy","authors":"Tomoya Kitazato, Masaki Ito, K. Sezaki","doi":"10.1109/PERCOMW.2018.8480336","DOIUrl":"https://doi.org/10.1109/PERCOMW.2018.8480336","url":null,"abstract":"Analysis of human mobility data provide us many important insights. For example, a detailed analysis of human mobility in urban space can provide important insights into gathering events. This insight is useful when addressing urban planning and public safety issues, and serves as a powerful tool for solving traffic congestion, early detection of social unrest, and so on. The analysis of human mobility in an indoor space such as in a museum exhibition, can assist us in anticipating the behavior of visitors; and in the early recognition of potential problems, such as the buildup of foot traffic at specific points. However, there is no universally accepted method for easily sensing human mobility. To address this problem, we developed a novel method to detect pedestrian flow using Bluetooth Low Energy (BLE). Our approach is based on the assumption that a BLE beacon is always affixed to the pedestrian. Thus, the individual’s velocity can be readily determined by analyzing the Received Signal Strength Indicator (RSSI) of their BLE beacon. Apart from velocity, the direction of the pedestrian can also be determined by detecting their BLE beacon with multiple sensors. In this investigation, we evaluated the proposed method using both experimental real- world data and simulations. Participants with BLE beacons walked straight in a hall while the RSSI of their beacons was monitored from a moving sensor. This information was used to estimate the velocities of the beacons. We also simulated the RSSIs of the beacons and estimated their velocities under various conditions. Our results indicate that the proposed method can precisely detect the velocities of pedestrians.","PeriodicalId":190096,"journal":{"name":"2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133372454","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}

引用次数: 3

Development of Energy-efficient Sensor Networks by Minimizing Sensors Numbers with a Machine Learning Model 基于机器学习模型最小化传感器数量的节能传感器网络开发

2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) Pub Date : 2018-03-01 DOI: 10.1109/PERCOMW.2018.8480343

Zhishu Shen, K. Yokota, A. Tagami, T. Higashino

{"title":"Development of Energy-efficient Sensor Networks by Minimizing Sensors Numbers with a Machine Learning Model","authors":"Zhishu Shen, K. Yokota, A. Tagami, T. Higashino","doi":"10.1109/PERCOMW.2018.8480343","DOIUrl":"https://doi.org/10.1109/PERCOMW.2018.8480343","url":null,"abstract":"With the increasing demand to construct sensor networks for a smart IoT (Internet of Things) world, numerous sensors with sensing and communication capabilities are expected to be deployed in the future. Thanks to the development of hardware manufacture technology, relatively small IoT smart sensors are now commercially available and cost-effective. However, the total power required by operating these sensors is expected to be enormous, due to their large number and frequent activity. Removing “unneeded sensors” is the most direct way to reduce the power consumption of sensor networks. Here, “unneeded sensors” refers to those that can be placed in sleep mode, or even be removed from the network topology entirely, without serious impact on the overall networks data processing performance. In this paper, we report the development of an energy-efficient sensor network by using a machine learning model to determine the actual necessity of all the sensors in a sensor network. Machine learning model is introduced to identify unneeded sensors by comparing the data from neighboring sensors to that from the potentially unneeded ones. For identifying unneeded sensors, different strategies with different computational complexity are also proposed. Numerical experiments conducted in two real indoor environments verify that our proposed scheme can reduce the total number of active sensors by around 1/3, while maintaining more than 90% of the original high monitoring performance of the sensor network.","PeriodicalId":190096,"journal":{"name":"2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124523865","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}

引用次数: 3

Scalable and Adaptive Polling Protocol for ConcurrentWireless Sensor Data Flows 并发无线传感器数据流的可扩展和自适应轮询协议

2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) Pub Date : 2018-03-01 DOI: 10.1109/PERCOMW.2018.8480129

Manos Koutsoubelias, A. Argyriou, S. Lalis

{"title":"Scalable and Adaptive Polling Protocol for ConcurrentWireless Sensor Data Flows","authors":"Manos Koutsoubelias, A. Argyriou, S. Lalis","doi":"10.1109/PERCOMW.2018.8480129","DOIUrl":"https://doi.org/10.1109/PERCOMW.2018.8480129","url":null,"abstract":"The uncoordinated transmission of sensor measure- ments to a collector over simple wireless technologies can lead to severely degraded performance when operating close to channel capacity. In this case, it can be far more effective to let the collector poll the sensor nodes to retrieve their data. We propose a coordinated protocol that exploits the broadcast ca- pability of the wireless channel to eliminate contention between nodes and to minimize the number of packet transmissions performed at each poll. Furthermore, to let the protocol follow the dynamic changes in the sensor data generation rate, we propose an application-agnostic method for estimating the data generation rate of each node locally, and extend the protocol so that the collector dynamically adjusts the polling rate accordingly. Our method is based on a Kalman filter tuned for stable data generation rates, which is controlled via simple signals generated at runtime by the underlying polling protocol. We experimentally evaluate an implementation of our protocol for different data traffic scenarios using real nodes that communicate with the collector over IEEE 802.15.4 radio, showing that the collector can successfully track the actual data rate for both periodic and stochastic data generation.","PeriodicalId":190096,"journal":{"name":"2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130945185","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}

引用次数: 1

Managing nonuniformities and uncertainties in vehicle-oriented sensor data over next generation networks 管理下一代网络中面向车辆的传感器数据的不均匀性和不确定性

2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) Pub Date : 2018-03-01 DOI: 10.1109/PERCOMW.2018.8480342

Stavros Nousias, C. Tselios, Dimitris Bitzas, O. Orfila, S. Jamson, P. Mejuto, Dimitrios Amaxilatis, O. Akrivopoulos, I. Chatzigiannakis, A. Lalos, K. Moustakas

引用次数: 10

Enhancing Community Detection for Big Sensor Data Clustering via Hyperbolic Network Embedding 利用双曲网络嵌入增强大传感器数据聚类中的社区检测

2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) Pub Date : 2018-03-01 DOI: 10.1109/PERCOMW.2018.8480134

V. Karyotis, Konstantinos Tsitseklis, Konstantinos Sotiropoulos, S. Papavassiliou

{"title":"Enhancing Community Detection for Big Sensor Data Clustering via Hyperbolic Network Embedding","authors":"V. Karyotis, Konstantinos Tsitseklis, Konstantinos Sotiropoulos, S. Papavassiliou","doi":"10.1109/PERCOMW.2018.8480134","DOIUrl":"https://doi.org/10.1109/PERCOMW.2018.8480134","url":null,"abstract":"In this paper we present a novel big data clustering approach for measurements obtained from pervasive sensor networks. To address the potential very large scale of such datasets, we map the problem of data clustering to a community detection one. Datasets are cast in the form of graphs, representing the relations among individual observations and data clustering is mapped to node clustering (community detection) in the data graph. We propose a novel computational approach for enhancing the traditional Girvan-Newman (GN) community detection algorithm via hyperbolic network embedding. The data dependency graph is embedded in the hyperbolic space via Rigel embedding, making it possible to compute more efficiently the hyperbolic edge-betweenness centrality (HEBC) needed in the modified GN algorithm. This allows for more efficient clustering of the nodes of the data graph without significantly sacrificing accuracy. We demonstrate the efficacy of our approach with artificial network and data topologies, and real benchmark datasets. The proposed methodology can be used for efficient clustering of datasets obtained from massive pervasive smart city/building sensor networks, such as the FIESTA-IoT platform, and exploited in various applications such as lower-cost sensing.","PeriodicalId":190096,"journal":{"name":"2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121205327","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}

引用次数: 0

A Propagation Based Model for Communication in Vehicular Cloud 基于传播的车载云通信模型

2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) Pub Date : 2018-03-01 DOI: 10.1109/PERCOMW.2018.8480399

Puya Ghazizadeh, Reza Fathi

引用次数: 4

Allergymap: A Hybrid mHealth Mobile Crowdsensing System for Allergic Diseases Epidemiology : a multidisciplinary case study 过敏症地图:一个混合移动健康移动人群传感系统过敏性疾病流行病学:多学科案例研究

2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) Pub Date : 2018-03-01 DOI: 10.1109/PERCOMW.2018.8480280

L. Kalogiros, K. Lagouvardos, S. Nikoletseas, Nikos Papadopoulos, Pantelis Tzamalis

{"title":"Allergymap: A Hybrid mHealth Mobile Crowdsensing System for Allergic Diseases Epidemiology : a multidisciplinary case study","authors":"L. Kalogiros, K. Lagouvardos, S. Nikoletseas, Nikos Papadopoulos, Pantelis Tzamalis","doi":"10.1109/PERCOMW.2018.8480280","DOIUrl":"https://doi.org/10.1109/PERCOMW.2018.8480280","url":null,"abstract":"Based on its large-scale, ubiquitous sensing ability of the physical world and its direct interaction with users, Mobile Crowd Sensing can be used for public health monitoring and improvement of the quality of life of patients. This paper presents Allergymap, a mHealth mobile crowdsensing system that aims to address several aspects of management of allergic diseases, like identification of allergens season onsets, patient stratification, control of allergy and monitoring treatment progress. Subjective data inputs from users, as well as objective environmental data from fixed stations, are inserted into the system (in a privacy-aware manner) for further processing and analysis, and finally exporting data visualization in distinct spatial maps The envisioned system attempts in a novel way to combine a hybrid architecture for analysis and visualization of allergens and irritants onsets, as well as new methods for increasing patient’s stratification to treatment. Also, some additional features are implemented for motivating users to become members of Allergymap community and enhance its potential. The system is currently deployed and validated in a multidisciplinary case study for Greece but is general enough to be used in any region.","PeriodicalId":190096,"journal":{"name":"2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117332637","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}

引用次数: 23