The present TV whitespace networks rely on spectrum occupancy databases to determine their operating channels. In this paper, we show that such databases cause non-negligible wastage of whitespace spectrum. We also report that whitespace channels can have very different quality due to interference from secondary devices and the leakage from TV broadcasts. Such disparity in channel quality is not captured by existing databases. We propose the use of spectrum measurements to overcome the above limitations of databases. In particular, we describe a system called V-Scope that leverages spectrum sensors on public vehicles to collect and report measurements from the road. These measurements are used as "anchor points" to construct various models to better determine whitespace spectrum, estimate its channel quality, and validate locations of primary and secondary devices. We have deployed our system on a single metro bus traveling across a mid-sized US city. Based on measurements collected at above 1 million locations over 120 square-km area, we find that a commercial database causes under-utilization of certain whitespace channels over a large area (up to 71% measured locations). Our system can reclaim this spectrum wastage at up to 59% locations, correctly selecting all the suitable whitespace channels at 72 -- 83% locations, and achieving a localization accuracy between 16 -- 27m.
{"title":"A vehicle-based measurement framework for enhancing whitespace spectrum databases","authors":"Tan Zhang, Ning Leng, Suman Banerjee","doi":"10.1145/2639108.2639114","DOIUrl":"https://doi.org/10.1145/2639108.2639114","url":null,"abstract":"The present TV whitespace networks rely on spectrum occupancy databases to determine their operating channels. In this paper, we show that such databases cause non-negligible wastage of whitespace spectrum. We also report that whitespace channels can have very different quality due to interference from secondary devices and the leakage from TV broadcasts. Such disparity in channel quality is not captured by existing databases. We propose the use of spectrum measurements to overcome the above limitations of databases. In particular, we describe a system called V-Scope that leverages spectrum sensors on public vehicles to collect and report measurements from the road. These measurements are used as \"anchor points\" to construct various models to better determine whitespace spectrum, estimate its channel quality, and validate locations of primary and secondary devices. We have deployed our system on a single metro bus traveling across a mid-sized US city. Based on measurements collected at above 1 million locations over 120 square-km area, we find that a commercial database causes under-utilization of certain whitespace channels over a large area (up to 71% measured locations). Our system can reclaim this spectrum wastage at up to 59% locations, correctly selecting all the suitable whitespace channels at 72 -- 83% locations, and achieving a localization accuracy between 16 -- 27m.","PeriodicalId":331897,"journal":{"name":"Proceedings of the 20th annual international conference on Mobile computing and networking","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130323927","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}
Anran Wang, Shuai Ma, Chunming Hu, J. Huai, Chunyi Peng, G. Shen
With the rapid proliferation of camera-equipped smart devices (e.g., smartphones, pads, tablets), visible light communication (VLC) over screen-camera links emerges as a novel form of near-field communication. Such communication via smart devices is highly competitive for its user-friendliness, security, and infrastructure-less (i.e., no dependency on WiFi or cellular infrastructure). However, existing approaches mostly focus on improving the transmission speed and ignore the transmission reliability. Considering the interplay between the transmission speed and reliability towards effective end-to-end communication, in this paper, we aim to boost the throughput over screen-camera links by enhancing the transmission reliability. To this end, we propose RDCode, a robust dynamic barcode which enables a novel packet-frame-block structure. Based on the layered structure, we design different error correction schemes at three levels: intra-blocks, inter-blocks and inter-frames, in order to verify and recover the lost blocks and frames. Finally, we implement RDCode and experimentally show that RDCode reaches a high level of transmission reliability (e.g., reducing the error rate to 10%) and yields a at least doubled transmission rate, compared with the existing state-of-the-art approach COBRA.
{"title":"Enhancing reliability to boost the throughput over screen-camera links","authors":"Anran Wang, Shuai Ma, Chunming Hu, J. Huai, Chunyi Peng, G. Shen","doi":"10.1145/2639108.2639135","DOIUrl":"https://doi.org/10.1145/2639108.2639135","url":null,"abstract":"With the rapid proliferation of camera-equipped smart devices (e.g., smartphones, pads, tablets), visible light communication (VLC) over screen-camera links emerges as a novel form of near-field communication. Such communication via smart devices is highly competitive for its user-friendliness, security, and infrastructure-less (i.e., no dependency on WiFi or cellular infrastructure). However, existing approaches mostly focus on improving the transmission speed and ignore the transmission reliability. Considering the interplay between the transmission speed and reliability towards effective end-to-end communication, in this paper, we aim to boost the throughput over screen-camera links by enhancing the transmission reliability. To this end, we propose RDCode, a robust dynamic barcode which enables a novel packet-frame-block structure. Based on the layered structure, we design different error correction schemes at three levels: intra-blocks, inter-blocks and inter-frames, in order to verify and recover the lost blocks and frames. Finally, we implement RDCode and experimentally show that RDCode reaches a high level of transmission reliability (e.g., reducing the error rate to 10%) and yields a at least doubled transmission rate, compared with the existing state-of-the-art approach COBRA.","PeriodicalId":331897,"journal":{"name":"Proceedings of the 20th annual international conference on Mobile computing and networking","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127716006","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}
Two-dimensional barcodes are widely used in mobile advertisement business, while their decoding performance is not always satisfactory under uncontrolled environments. The corner detection accuracy has been identified as a critical factor affecting the overall system performance. The standard barcode detector performs a candidate search in the binarized barcode image based on the rectangular shape of the barcode. Its performance is not very accurate due to the limited accuracy of the binarized image. In this work, we proposed a coarse-fine corner detection approach for locating the barcode region. It performs far more accurately than the standard barcode detection scheme while keeps the computational complexity affordable. Experimental results for high capacity barcodes show that the proposed detection scheme can extend the range of operation parameters, such as wider angles, and much lower the detection bit error rate, relative to the standard barcode decoder.
{"title":"Poster: a coarse-fine corner detection approach for two-dimensional barcode decoding","authors":"Changsheng Chen, W. Mow","doi":"10.1145/2639108.2642915","DOIUrl":"https://doi.org/10.1145/2639108.2642915","url":null,"abstract":"Two-dimensional barcodes are widely used in mobile advertisement business, while their decoding performance is not always satisfactory under uncontrolled environments. The corner detection accuracy has been identified as a critical factor affecting the overall system performance. The standard barcode detector performs a candidate search in the binarized barcode image based on the rectangular shape of the barcode. Its performance is not very accurate due to the limited accuracy of the binarized image. In this work, we proposed a coarse-fine corner detection approach for locating the barcode region. It performs far more accurately than the standard barcode detection scheme while keeps the computational complexity affordable. Experimental results for high capacity barcodes show that the proposed detection scheme can extend the range of operation parameters, such as wider angles, and much lower the detection bit error rate, relative to the standard barcode decoder.","PeriodicalId":331897,"journal":{"name":"Proceedings of the 20th annual international conference on Mobile computing and networking","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115825762","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}
Huan Huang, Hailong Sun, Guoqing Ma, Xu Wang, Xudong Liu
Mobile users often suffer from a slow page loading time due to intermittently connected wireless networks and increasing size of mobile Web pages. Although existing approaches of prefetching and caching are widely used to reduce the browsing latency, they may fail to work effectively because most Web page visits are singletons and the cache hit ratio is unsatisfying. In this work, we design and implement a framework to reduce Web browsing latency for mobile users with a smart prefetching strategy and caching mechanism. The prefetching strategy leverages the skSLRU model, which predicts and prefetches Web pages based on their contents with consideration of user contexts and the devices' status such as power consuming and cellular data usage. And our caching mechanism mainly consider the resources like CSS and JavaScript files shared among Web pages in a website. Moreover, instead of using RAM, we use ROM, the internal flash memory of devices to store cached resources with proper lifecycle so as to avoid the useful resources to be untimely evicted or expired and thus improve the hit ratio. Our evaluations show that 90% of the homepages and 60% of other pages are fetched before users' visiting, and the page loading time is no more than one second.
{"title":"Poster: a framework for instant mobile web browsing with smart prefetching and caching","authors":"Huan Huang, Hailong Sun, Guoqing Ma, Xu Wang, Xudong Liu","doi":"10.1145/2639108.2642899","DOIUrl":"https://doi.org/10.1145/2639108.2642899","url":null,"abstract":"Mobile users often suffer from a slow page loading time due to intermittently connected wireless networks and increasing size of mobile Web pages. Although existing approaches of prefetching and caching are widely used to reduce the browsing latency, they may fail to work effectively because most Web page visits are singletons and the cache hit ratio is unsatisfying. In this work, we design and implement a framework to reduce Web browsing latency for mobile users with a smart prefetching strategy and caching mechanism. The prefetching strategy leverages the skSLRU model, which predicts and prefetches Web pages based on their contents with consideration of user contexts and the devices' status such as power consuming and cellular data usage. And our caching mechanism mainly consider the resources like CSS and JavaScript files shared among Web pages in a website. Moreover, instead of using RAM, we use ROM, the internal flash memory of devices to store cached resources with proper lifecycle so as to avoid the useful resources to be untimely evicted or expired and thus improve the hit ratio. Our evaluations show that 90% of the homepages and 60% of other pages are fetched before users' visiting, and the page loading time is no more than one second.","PeriodicalId":331897,"journal":{"name":"Proceedings of the 20th annual international conference on Mobile computing and networking","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128112554","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 : 2014-09-07DOI: 10.1007/978-3-031-16829-1
Yi-Chao Chen, L. Qiu, Yin Zhang, Guangtao Xue, Zhenxian Hu
{"title":"Robust network compressive sensing","authors":"Yi-Chao Chen, L. Qiu, Yin Zhang, Guangtao Xue, Zhenxian Hu","doi":"10.1007/978-3-031-16829-1","DOIUrl":"https://doi.org/10.1007/978-3-031-16829-1","url":null,"abstract":"","PeriodicalId":331897,"journal":{"name":"Proceedings of the 20th annual international conference on Mobile computing and networking","volume":"226 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116855127","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}
Jeff Gehlhaar, VP of Technology for Qualcomm Technologies, Inc. will discuss the future of mobile computing which will entail a much more personalized user experience. As the world continues to shift towards a more connected world, people are using their mobile devices in new ways and having higher expectations of what they can do with those devices. This is leading to the next frontier of mobile being much more personalized with mobile devices becoming extensions of the user, understanding where you are, where you've been and what you want to do or act on next. This is also resulting in services and applications that have traditionally been delivered via the cloud, migrating to mobile which present unique challenges and opportunities in mobile. In this talk we will explore the new and exciting technologies that Qualcomm Research is working on to advance the frontier in personalized devices and help solve some of these challenges. Starting with always on sensor processing that make basic predictions about where you are and what actions you are performing, to research in brain inspired computing and embedded cognition, Qualcomm is working to make devices which are always with you, smarter and truly personal. To further this vision of transforming the future of mobile computing, Qualcomm Research has also been working in the area of brain inspired computation and neural networks for a number of years. Early work has focused on biologically inspired approaches, involving spiking neural networks and understanding the complex behaviors of the brain. Recent advances in the industry around deep neural networks have resulted in state-of-the-art performance in machine learning pattern matching tasks. As mobile SOCs continue to gain significant computational power, bringing applications powered by deep learning to mobile devices is becoming possible. We will explore the near term opportunities like object recognition, scene classification, applications for smarter mobile cameras, automotive, and robotics. We will also look at other aspects of brain inspired computing including the potential of a new kind of low power hardware. Join Jeff as he shares his insight on how Qualcomm Research has been spearheading research to make future mobile devices smarter with the ability to "sense" and "see" their environment as humans do in a power efficient manner.
{"title":"The future of mobile computing","authors":"Jeff Gehlhaar","doi":"10.1145/2639108.2639417","DOIUrl":"https://doi.org/10.1145/2639108.2639417","url":null,"abstract":"Jeff Gehlhaar, VP of Technology for Qualcomm Technologies, Inc. will discuss the future of mobile computing which will entail a much more personalized user experience. As the world continues to shift towards a more connected world, people are using their mobile devices in new ways and having higher expectations of what they can do with those devices. This is leading to the next frontier of mobile being much more personalized with mobile devices becoming extensions of the user, understanding where you are, where you've been and what you want to do or act on next. This is also resulting in services and applications that have traditionally been delivered via the cloud, migrating to mobile which present unique challenges and opportunities in mobile. In this talk we will explore the new and exciting technologies that Qualcomm Research is working on to advance the frontier in personalized devices and help solve some of these challenges. Starting with always on sensor processing that make basic predictions about where you are and what actions you are performing, to research in brain inspired computing and embedded cognition, Qualcomm is working to make devices which are always with you, smarter and truly personal. To further this vision of transforming the future of mobile computing, Qualcomm Research has also been working in the area of brain inspired computation and neural networks for a number of years. Early work has focused on biologically inspired approaches, involving spiking neural networks and understanding the complex behaviors of the brain. Recent advances in the industry around deep neural networks have resulted in state-of-the-art performance in machine learning pattern matching tasks. As mobile SOCs continue to gain significant computational power, bringing applications powered by deep learning to mobile devices is becoming possible. We will explore the near term opportunities like object recognition, scene classification, applications for smarter mobile cameras, automotive, and robotics. We will also look at other aspects of brain inspired computing including the potential of a new kind of low power hardware. Join Jeff as he shares his insight on how Qualcomm Research has been spearheading research to make future mobile devices smarter with the ability to \"sense\" and \"see\" their environment as humans do in a power efficient manner.","PeriodicalId":331897,"journal":{"name":"Proceedings of the 20th annual international conference on Mobile computing and networking","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122002737","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}
Andrew T. Irish, Jason T. Isaacs, Daniel Iland, J. Hespanha, E. Belding-Royer, Upamanyu Madhow
Due to frequent non-line-of-sight (NLOS) signal reception, geopositioning using Global Navigation Satellite Systems (GNSS), such as GPS, is unreliable in urban environments, with errors on the order of tens of meters. This poses a major problem for mobile services that benefit from accurate urban localization, such as navigation, geofencing, and hyperlocal advertising applications. Mobile network operators also seek improvements in localization, as government regulators increase handset location accuracy requirements of enhanced 991 service (e911). In our demonstration, we will present the most recent prototype of our urban location improvement technology, called ShadowMaps, which will be shown to accurately track a mobile device in an urban environment, with up to an order of magnitude reduction in GNSS positioning error.
{"title":"Demo: ShadowMaps, the urban phone tracking system","authors":"Andrew T. Irish, Jason T. Isaacs, Daniel Iland, J. Hespanha, E. Belding-Royer, Upamanyu Madhow","doi":"10.1145/2639108.2641755","DOIUrl":"https://doi.org/10.1145/2639108.2641755","url":null,"abstract":"Due to frequent non-line-of-sight (NLOS) signal reception, geopositioning using Global Navigation Satellite Systems (GNSS), such as GPS, is unreliable in urban environments, with errors on the order of tens of meters. This poses a major problem for mobile services that benefit from accurate urban localization, such as navigation, geofencing, and hyperlocal advertising applications. Mobile network operators also seek improvements in localization, as government regulators increase handset location accuracy requirements of enhanced 991 service (e911). In our demonstration, we will present the most recent prototype of our urban location improvement technology, called ShadowMaps, which will be shown to accurately track a mobile device in an urban environment, with up to an order of magnitude reduction in GNSS positioning error.","PeriodicalId":331897,"journal":{"name":"Proceedings of the 20th annual international conference on Mobile computing and networking","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129823321","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}
Dynamic voltage and frequency scaling (DVFS) has long been used as a technique to save power in a variety of computing domains but typically not in communications devices. A fundamental limit that prevents decreasing the clock frequency is the Nyquist(-Shannon) sampling theorem, which states that the sampling rate must be twice the signal bandwidth. Recently, researchers have leveraged compressive sensing to demonstrate the possibility of decoding a sparse signal below Nyquist rate. In this work, we dramatically extend the state of the art by showing how to decode non-sparse signals, in particular, OFDM systems at sub-Nyquist rates. We exploit the aliasing that results from under-sampling and observe that there exists well-defined structure in terms of how OFDM signals are "folded up" under aliasing. Based on our observations, we present Enfold, which allows existing WiFi chipsets to decode standards-compliant WiFi frames while operating at 50% and 25% of their rated clock rate. Our design is able to attain greater than 96% and 83% raw packet reception rates for moderate SNR while reducing the clock rate by 2x and 4x, respectively. Moreover, our approach can be easily applied to other communication systems based on OFDM modulation. When evaluated on popular smartphone app traces, Enfold reduces energy consumption by up to 34%.
{"title":"Enfold: downclocking OFDM in WiFi","authors":"Feng Lu, P. Ling, G. Voelker, A. Snoeren","doi":"10.1145/2639108.2639123","DOIUrl":"https://doi.org/10.1145/2639108.2639123","url":null,"abstract":"Dynamic voltage and frequency scaling (DVFS) has long been used as a technique to save power in a variety of computing domains but typically not in communications devices. A fundamental limit that prevents decreasing the clock frequency is the Nyquist(-Shannon) sampling theorem, which states that the sampling rate must be twice the signal bandwidth. Recently, researchers have leveraged compressive sensing to demonstrate the possibility of decoding a sparse signal below Nyquist rate. In this work, we dramatically extend the state of the art by showing how to decode non-sparse signals, in particular, OFDM systems at sub-Nyquist rates. We exploit the aliasing that results from under-sampling and observe that there exists well-defined structure in terms of how OFDM signals are \"folded up\" under aliasing. Based on our observations, we present Enfold, which allows existing WiFi chipsets to decode standards-compliant WiFi frames while operating at 50% and 25% of their rated clock rate. Our design is able to attain greater than 96% and 83% raw packet reception rates for moderate SNR while reducing the clock rate by 2x and 4x, respectively. Moreover, our approach can be easily applied to other communication systems based on OFDM modulation. When evaluated on popular smartphone app traces, Enfold reduces energy consumption by up to 34%.","PeriodicalId":331897,"journal":{"name":"Proceedings of the 20th annual international conference on Mobile computing and networking","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129138750","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}
This paper introduces the first design that enables full-duplex communication on battery-free backscatter devices. Specifically, it gives receivers a way to provide low-rate feedback to the transmitter on the same frequency as that of the backscatter transmissions, using neither multiple antennas nor power-consuming cancellation hardware. Our design achieves this goal using only fully-passive analog components that consume near-zero power. We integrate our design with the backscatter network stack and demonstrate that it can minimize energy wastes that occur due to collisions and also correct for errors and changes in channel conditions at a granularity smaller than that of a packet. To show the feasibility of our design, we build a hardware prototype using off-the-shelf analog components. Our evaluation shows that our design cancels the self-interference down to the noise floor, while consuming only 0.25 μW and 0.54 μW of transmit and receive power, respectively.
{"title":"Enabling instantaneous feedback with full-duplex backscatter","authors":"Vincent Liu, V. Talla, Shyamnath Gollakota","doi":"10.1145/2639108.2639136","DOIUrl":"https://doi.org/10.1145/2639108.2639136","url":null,"abstract":"This paper introduces the first design that enables full-duplex communication on battery-free backscatter devices. Specifically, it gives receivers a way to provide low-rate feedback to the transmitter on the same frequency as that of the backscatter transmissions, using neither multiple antennas nor power-consuming cancellation hardware. Our design achieves this goal using only fully-passive analog components that consume near-zero power. We integrate our design with the backscatter network stack and demonstrate that it can minimize energy wastes that occur due to collisions and also correct for errors and changes in channel conditions at a granularity smaller than that of a packet. To show the feasibility of our design, we build a hardware prototype using off-the-shelf analog components. Our evaluation shows that our design cancels the self-interference down to the noise floor, while consuming only 0.25 μW and 0.54 μW of transmit and receive power, respectively.","PeriodicalId":331897,"journal":{"name":"Proceedings of the 20th annual international conference on Mobile computing and networking","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124445520","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}
Nowadays, distracted driving is becoming a very dangerous epidemic on the roadways. A lot of activities may lead to distracted driving, such as texting, making phone calls, using GPS or road maps, eating, using in-car entertainment systems, etc. As the number of smartphones is rapidly growing year by year, using smartphone is by far the most dangerous and alarming driver distraction. In this paper, we design an app on iPhone for reducing the smartphone-related distracted driving, which can run in the background and can lock the smartphone screen with no passwords required when it detects that the user is driving. After the user finishes driving, the lock will be removed immediately. We also describe a DTM algorithm to detect the transportation mode and give the performance analysis of our system. The results show that our system can save more energy than other related applications.
{"title":"Poster: detection of transportation mode based on smartphones for reducing distracted driving","authors":"Xin Gao, Jie Tian, Guiling Wang","doi":"10.1145/2639108.2642911","DOIUrl":"https://doi.org/10.1145/2639108.2642911","url":null,"abstract":"Nowadays, distracted driving is becoming a very dangerous epidemic on the roadways. A lot of activities may lead to distracted driving, such as texting, making phone calls, using GPS or road maps, eating, using in-car entertainment systems, etc. As the number of smartphones is rapidly growing year by year, using smartphone is by far the most dangerous and alarming driver distraction. In this paper, we design an app on iPhone for reducing the smartphone-related distracted driving, which can run in the background and can lock the smartphone screen with no passwords required when it detects that the user is driving. After the user finishes driving, the lock will be removed immediately. We also describe a DTM algorithm to detect the transportation mode and give the performance analysis of our system. The results show that our system can save more energy than other related applications.","PeriodicalId":331897,"journal":{"name":"Proceedings of the 20th annual international conference on Mobile computing and networking","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121986758","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：