2018 Eleventh International Conference on Mobile Computing and Ubiquitous Network (ICMU)最新文献_第4页

An Identity Privacy Preserving Incentivization Scheme for Participatory Sensing 参与式感知的身份隐私保护激励方案

2018 Eleventh International Conference on Mobile Computing and Ubiquitous Network (ICMU) Pub Date : 2018-10-01 DOI: 10.23919/ICMU.2018.8653614

M. Connolly, Ivana Dusparic, Mélanie Bouroche

{"title":"An Identity Privacy Preserving Incentivization Scheme for Participatory Sensing","authors":"M. Connolly, Ivana Dusparic, Mélanie Bouroche","doi":"10.23919/ICMU.2018.8653614","DOIUrl":"https://doi.org/10.23919/ICMU.2018.8653614","url":null,"abstract":"Participatory sensing is a paradigm through which mobile device users (or participants) collect and share data about their environments. The data captured by participants is typically submitted to an intermediary (the service provider) who will build a service based upon this data. For a participatory sensing system to attract the data submissions it requires, its users often need to be incentivized. Such an incentivization mechanism typically requires users to at least partially disclose their identity to be able to reward them. This, however, might deter privacy conscious users from participating. Therefore, an incentivization mechanism needs to support anonymous data submission and rewarding. In addition, inference attacks can illegitimately gain further information about participants through linking data submissions or tracing rewards. This paper presents Identity Privacy Preserving Incentivization (IPPI), a decentralized peer-to-peer exchange that preserves identity privacy by enabling anonymous and unlinkable data submission and anonymous and untraceable reward allocation. This is achieved through the modification and extension of the concept of decentralized trading for cryptocurrencies to make payments (i.e., rewards) sent to a recipient (i.e., the participant) untraceable. Furthermore, the use of the Diffie-Hellman Exchange Protocol is modified to enable participants to create their own untraceable reward currency in the form of tokens to which the service provider can then assign value. The preservation of identity privacy is demonstrated by way of proof. The performance of the approach is also evaluated.","PeriodicalId":398108,"journal":{"name":"2018 Eleventh International Conference on Mobile Computing and Ubiquitous Network (ICMU)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126697054","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: 4

Exercise Guidance Generation Based on Joint Vector Model Using Kinect Sensor 基于关节矢量模型的Kinect传感器运动引导生成

2018 Eleventh International Conference on Mobile Computing and Ubiquitous Network (ICMU) Pub Date : 2018-10-01 DOI: 10.23919/ICMU.2018.8653259

J. Tsuda, Yudai Iimura, Makoto Imamura

引用次数: 0

OmimamoriNet: An Outdoor Positioning System Based on Wi-SUN FAN Network 基于Wi-SUN FAN网络的户外定位系统OmimamoriNet

2018 Eleventh International Conference on Mobile Computing and Ubiquitous Network (ICMU) Pub Date : 2018-10-01 DOI: 10.23919/ICMU.2018.8653618

Yin Chen, Mina Sakamura, J. Nakazawa, Takuro Yonezawa, Akira Tsuge, Yuichi Hamada

{"title":"OmimamoriNet: An Outdoor Positioning System Based on Wi-SUN FAN Network","authors":"Yin Chen, Mina Sakamura, J. Nakazawa, Takuro Yonezawa, Akira Tsuge, Yuichi Hamada","doi":"10.23919/ICMU.2018.8653618","DOIUrl":"https://doi.org/10.23919/ICMU.2018.8653618","url":null,"abstract":"We propose in this paper an outdoor positioning system based on Wi-SUN FAN network, the goal of which is to protect the elderly, young children and even pets via estimating their locations in a city. In order to achieve long-term portability and network-side positioning, the system does not directly rely on GPS receiver mounted on terminals but use machine learning for location estimation via the received signal strength indication (RSSI) measurements. In particular, the system consists of Wi-SUN beacons, Wi-SUN base-stations and vehicular devices. A beacon, attached to the one to be positioned, broadcasts wireless signal periodically so that its location can be estimated using machine learning algorithms from the RSSIs measured at multiple base-stations that are densely deployed over a city to construct an ad hoc network. Using the mobility of vehicles that roam over a city routinely, such like garbage collection trucks, buses and taxies. Vehicular devices containing both a Wi-SUN beacon and a GPS are used to collect RSSIs and the corresponding GPS coordinates to train the estimation models. We develop a prototype system consisting of 9 base-stations and deploy it to our university campus to conduct a field experiment to validate the proposed approach. Offline analysis on the data collected from the experiment showed that a RandomForest learner performs best among four selected learning algorithms using the default parameters of Weka 3.8, which achieves a mean absolute error of 35.43m and a root mean squared error of 44.21m, respectively. Evaluation on network performance is also conducted.","PeriodicalId":398108,"journal":{"name":"2018 Eleventh International Conference on Mobile Computing and Ubiquitous Network (ICMU)","volume":"153 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116727759","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: 6

A Route Map for Wireless Sensor Networks over Large Coverage Area 一种大覆盖区域无线传感器网络路径图

2018 Eleventh International Conference on Mobile Computing and Ubiquitous Network (ICMU) Pub Date : 2018-10-01 DOI: 10.23919/ICMU.2018.8653627

Rishabh Jain, Partha Saha Adhikary, Guillermo Ramírez-Prado, B. Barmada, Iman Tabatabaei Ardekani

引用次数: 1

Traffic Shaping based Queue Management for Delay Sensitive Multimedia Applications 基于流量整形的延迟敏感多媒体应用队列管理

2018 Eleventh International Conference on Mobile Computing and Ubiquitous Network (ICMU) Pub Date : 2018-10-01 DOI: 10.23919/ICMU.2018.8653591

Yu-ning Dong, Zheng Wu, Yu-jue Peng

引用次数: 0

Deep Reinforcement Learning-Based Method of Mobile Data Offloading 基于深度强化学习的移动数据卸载方法

2018 Eleventh International Conference on Mobile Computing and Ubiquitous Network (ICMU) Pub Date : 2018-10-01 DOI: 10.23919/ICMU.2018.8653588

Daisuke Mochizuki, Yu Abiko, H. Mineno, Takato Saito, Daizo Ikeda, M. Katagiri

{"title":"Deep Reinforcement Learning-Based Method of Mobile Data Offloading","authors":"Daisuke Mochizuki, Yu Abiko, H. Mineno, Takato Saito, Daizo Ikeda, M. Katagiri","doi":"10.23919/ICMU.2018.8653588","DOIUrl":"https://doi.org/10.23919/ICMU.2018.8653588","url":null,"abstract":"The demand for mobile data communication is increasing due to diversification and the increase in the number of mobile devices accessing mobile networks. This demand is likely to increase further. In a mobile network, communication quality deteriorates due to the congestion of the cellular infrastructure because of the concentration of demand for mobile data communication. Therefore, improving the cellular infrastructure bandwidth utilization efficiency is crucial. To improve the cellular infrastructure bandwidth utilization efficiency, we previously proposed the mobile data offloading protocol. Although this method balances the load by focusing on the delay tolerance of contents in the uplink, accurately balancing the load is challenging. In this paper, we propose a mobile data offloading method using deep reinforcement learning for increasing offloading performance of the uplink. The proposed method can balance the load appropriately by learning what the bandwidth and transmission timing provide to the user equipment when the previous method does not work properly.","PeriodicalId":398108,"journal":{"name":"2018 Eleventh International Conference on Mobile Computing and Ubiquitous Network (ICMU)","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115742865","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: 2

Recovery of Synchronization for Wireless Sensor Networks 无线传感器网络的同步恢复

2018 Eleventh International Conference on Mobile Computing and Ubiquitous Network (ICMU) Pub Date : 2018-10-01 DOI: 10.23919/ICMU.2018.8653260

Hiromi Yagiri, T. Okadome

引用次数: 1

Proposal for Safety Confirmation System That Launches Bulletin Board Automatically 自动启动公告板的安全确认系统方案

2018 Eleventh International Conference on Mobile Computing and Ubiquitous Network (ICMU) Pub Date : 2018-10-01 DOI: 10.23919/ICMU.2018.8653581

Rikuto Goto, A. Watanabe

引用次数: 0

Mobile Terminals Cooperative Communication Framework for Energy Saving: Evaluation of Energy Efficiency Using Smartphone Test-Bed 面向节能的移动终端协同通信框架:基于智能手机试验台的能效评估

2018 Eleventh International Conference on Mobile Computing and Ubiquitous Network (ICMU) Pub Date : 2018-10-01 DOI: 10.23919/ICMU.2018.8653583

K. Temma, K. Hattori, Lin Shan, Ou Zhao, Y. Owada

引用次数: 0

VR Classroom: Enhancing Learning Experience with Virtual Class Rooms VR教室:用虚拟教室增强学习体验

2018 Eleventh International Conference on Mobile Computing and Ubiquitous Network (ICMU) Pub Date : 2018-10-01 DOI: 10.23919/ICMU.2018.8653607

Kodai Oiwake, Kosuke Komiya, Hina Akasaki, T. Nakajima

引用次数: 7