In a Radio Frequency Identification (RFID) System, collision issues occurred by multiple tags communicating with the reader simultaneously influence the system efficiency. Therefore, researching on the anti-collision algorithm to reduce the collisions and increase the system efficiency becomes a hotspot. After analyzing the pros and cons of some existing DFSA-based (dynamic frame-slotted ALOHA) anti-collision algorithms, we propose our algorithm in adjustment strategy and tag estimation method. To decrease the computation complexity, we define a parameter S to dynamically segment the frame into some reading units (each reading unit includes at least 1 time slot). Finally, the simulation shows that our algorithm outperforms the other algorithms in the throughput of tags/s (reading speed) and the system efficiency.
{"title":"An Optimal Dynamic Frame Slot-Segment Algorithm","authors":"Litian Duan, Z. Wang, Fu Duan","doi":"10.1145/2757384.2757395","DOIUrl":"https://doi.org/10.1145/2757384.2757395","url":null,"abstract":"In a Radio Frequency Identification (RFID) System, collision issues occurred by multiple tags communicating with the reader simultaneously influence the system efficiency. Therefore, researching on the anti-collision algorithm to reduce the collisions and increase the system efficiency becomes a hotspot. After analyzing the pros and cons of some existing DFSA-based (dynamic frame-slotted ALOHA) anti-collision algorithms, we propose our algorithm in adjustment strategy and tag estimation method. To decrease the computation complexity, we define a parameter S to dynamically segment the frame into some reading units (each reading unit includes at least 1 time slot). Finally, the simulation shows that our algorithm outperforms the other algorithms in the throughput of tags/s (reading speed) and the system efficiency.","PeriodicalId":330286,"journal":{"name":"Proceedings of the 2015 Workshop on Mobile Big Data","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116325710","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}
Our everyday lives involve navigating in the public space. If the public space is not safe it will reduce our freedom of movement and ability to participate in school, work and public life. Awareness on the level of security and danger in our public space can help us prepare and secure the area around us. But the lack of boundaries of public space makes it difficult to assess the level of safety and designing of security. The theory suggest (Creating Defensible Space by Oscar Newman, 1976) that the more personal and individualize the space we have, the more control and influence we have of defending it. Based on this theory, we are proposing a new sociological term called "Individual's Local Community (ILC)", which addresses on the unique environment of each individual navigating the public space. ILC is designed as the smallest measurable unit crossover local communities. ILC will personalize each individual's navigation to the public space. To measure the level of danger and safety in ILC we developed an android-based mobile application that estimate the risk of being victimize of crime in specified space and time-period. To extend the concept, one resident's risk to become victim or perpetrators of crime incidents is evaluated by estimated values based on "stable" ILC and historical crime records. The evaluation system is developed as a mobile app, named as "Are You Safe". The application contains local crime maps and a GPS tracking component. Mathematical formulas are developed to evaluate the potential risk. Furthermore, a re-routing function is provided for "safer" ILC. The prototype of "Are You Safe" app is implemented and tested in the city of northwestern Pennsylvania, United States. We believe the ILC concept will enrich the study of Personal Big Data and enable us to study social science in a different level. Also the app will help residents to have a better understanding on the safety and security of their environment.
我们的日常生活涉及在公共空间中导航。如果公共空间不安全，我们的行动自由和参与学校、工作和公共生活的能力就会减少。对公共空间的安全和危险程度的认识可以帮助我们做好准备，保护我们周围的区域。但是，公共空间边界的缺乏使得安全水平的评估和安全设计变得困难。该理论认为(创造可防御的空间，Oscar Newman, 1976)，我们拥有的空间越个性化，我们就越能控制和影响它。基于这一理论，我们提出了一个新的社会学术语“个人的本地社区”(Individual’s Local Community, ILC)，它涉及到每个人在公共空间中导航的独特环境。ILC被设计为跨区域社区的最小可测量单位。ILC将个性化每个人对公共空间的导航。为了衡量ILC的危险和安全程度，我们开发了一个基于android的移动应用程序，可以估计在特定空间和时间段内成为犯罪受害者的风险。为了扩展这一概念，一个居民成为犯罪事件受害者或肇事者的风险是通过基于“稳定”的ILC和历史犯罪记录的估计值来评估的。该评估系统是作为一款名为“你安全吗”的移动应用程序开发的。该应用程序包含本地犯罪地图和GPS跟踪组件。开发了数学公式来评估潜在的风险。此外，为“更安全”的ILC提供了重路由功能。“你安全吗”应用程序的原型在美国宾夕法尼亚州西北部的城市实施和测试。我们相信ILC的概念将丰富个人大数据的研究，使我们能够在不同的层面上研究社会科学。此外，该应用程序将帮助居民更好地了解他们环境的安全和保障。
{"title":"Crime Risk Evaluation in Individual's Local Community","authors":"Yunkai Liu, Anirudh Marthur, Christopher Magno","doi":"10.1145/2757384.2757387","DOIUrl":"https://doi.org/10.1145/2757384.2757387","url":null,"abstract":"Our everyday lives involve navigating in the public space. If the public space is not safe it will reduce our freedom of movement and ability to participate in school, work and public life. Awareness on the level of security and danger in our public space can help us prepare and secure the area around us. But the lack of boundaries of public space makes it difficult to assess the level of safety and designing of security. The theory suggest (Creating Defensible Space by Oscar Newman, 1976) that the more personal and individualize the space we have, the more control and influence we have of defending it. Based on this theory, we are proposing a new sociological term called \"Individual's Local Community (ILC)\", which addresses on the unique environment of each individual navigating the public space. ILC is designed as the smallest measurable unit crossover local communities. ILC will personalize each individual's navigation to the public space. To measure the level of danger and safety in ILC we developed an android-based mobile application that estimate the risk of being victimize of crime in specified space and time-period. To extend the concept, one resident's risk to become victim or perpetrators of crime incidents is evaluated by estimated values based on \"stable\" ILC and historical crime records. The evaluation system is developed as a mobile app, named as \"Are You Safe\". The application contains local crime maps and a GPS tracking component. Mathematical formulas are developed to evaluate the potential risk. Furthermore, a re-routing function is provided for \"safer\" ILC. The prototype of \"Are You Safe\" app is implemented and tested in the city of northwestern Pennsylvania, United States. We believe the ILC concept will enrich the study of Personal Big Data and enable us to study social science in a different level. Also the app will help residents to have a better understanding on the safety and security of their environment.","PeriodicalId":330286,"journal":{"name":"Proceedings of the 2015 Workshop on Mobile Big Data","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123604588","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}
Many data-intensive sensor network applications are potential big-data enabler: they are deployed in challenging environments to collect large volume of data for a long period of time. However, in the challenging environments, it is not possible to deploy base stations in or near the sensor field to collect sensory data. Therefore, the overflow data of the source nodes is first offloaded to other nodes inside the network, and is then collected when uploading opportunities become available. We call this process data preservation in sensor networks. In this paper, we take into account spatial correlation that exist in sensory data, and study how to minimize the total energy consumption in data preservation. We call this problem data preservation problem with data correlation. We show that with proper transformation, this problem is equivalent to minimum cost flow problem, which can be solved optimally and efficiently. Via simulations, we show that it outperforms an efficient greedy algorithm.
{"title":"Data Preservation in Data-Intensive Sensor Networks With Spatial Correlation","authors":"Nathaniel Crary, Bin Tang, Setu Taase","doi":"10.1145/2757384.2757389","DOIUrl":"https://doi.org/10.1145/2757384.2757389","url":null,"abstract":"Many data-intensive sensor network applications are potential big-data enabler: they are deployed in challenging environments to collect large volume of data for a long period of time. However, in the challenging environments, it is not possible to deploy base stations in or near the sensor field to collect sensory data. Therefore, the overflow data of the source nodes is first offloaded to other nodes inside the network, and is then collected when uploading opportunities become available. We call this process data preservation in sensor networks. In this paper, we take into account spatial correlation that exist in sensory data, and study how to minimize the total energy consumption in data preservation. We call this problem data preservation problem with data correlation. We show that with proper transformation, this problem is equivalent to minimum cost flow problem, which can be solved optimally and efficiently. Via simulations, we show that it outperforms an efficient greedy algorithm.","PeriodicalId":330286,"journal":{"name":"Proceedings of the 2015 Workshop on Mobile Big Data","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131065586","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}
Mobile phones equipped with powerful sensors have become ubiquitous in recent years. Mobile sensing applications present an unprecedented opportunity to collect and analyze information from mobile devices. Much of the work in mobile sensing has been done on designing monolithic applications but inadequate attention has been paid to general mobile data collection frameworks. In this paper, we provide a survey on how to build a general purpose mobile data collection framework. We identify the basic requirements and present an architecture for such a framework. We survey existing works to summarize existing approaches to address the basic requirements. Eight major mobile data collection frameworks are compared with respect to the requirements as well as additional issues on privacy, energy and incentives.
{"title":"Mobile Data Collection Frameworks: A Survey","authors":"Paul Y. Cao, Gang Li, Guoxing Chen, Biao Chen","doi":"10.1145/2757384.2757396","DOIUrl":"https://doi.org/10.1145/2757384.2757396","url":null,"abstract":"Mobile phones equipped with powerful sensors have become ubiquitous in recent years. Mobile sensing applications present an unprecedented opportunity to collect and analyze information from mobile devices. Much of the work in mobile sensing has been done on designing monolithic applications but inadequate attention has been paid to general mobile data collection frameworks. In this paper, we provide a survey on how to build a general purpose mobile data collection framework. We identify the basic requirements and present an architecture for such a framework. We survey existing works to summarize existing approaches to address the basic requirements. Eight major mobile data collection frameworks are compared with respect to the requirements as well as additional issues on privacy, energy and incentives.","PeriodicalId":330286,"journal":{"name":"Proceedings of the 2015 Workshop on Mobile Big Data","volume":"151 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120930394","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 presents an efficient mobile POS (point of sale) system-one that will not cripple an entire company that happens to the business. Another goal is to make an easy-to-understand user's interface (UI) and allow for improvements to be made easily based on the needs of the business owner. The former will be accomplished by creating a web-based POS, which means that the only thing limiting the system is the access of the internet. Outages and "down time" are virtually non-existent with proper management. The latter will be assessed in the actual formation, using a minimalistic format which can easily be tailored to the needs of the user. The benefits of using this sort of POS go far beyond simply improving the user experience and minimizing technical errors; businessmen that have to interact with clients on-the-go will be able to easily access the system all the same, allowing for some much needed flexibility in small businesses.
{"title":"A Mobile Retail POS: Design and Implementation","authors":"Wesley C. Davis, Z. Wang","doi":"10.1145/2757384.2757391","DOIUrl":"https://doi.org/10.1145/2757384.2757391","url":null,"abstract":"This paper presents an efficient mobile POS (point of sale) system-one that will not cripple an entire company that happens to the business. Another goal is to make an easy-to-understand user's interface (UI) and allow for improvements to be made easily based on the needs of the business owner. The former will be accomplished by creating a web-based POS, which means that the only thing limiting the system is the access of the internet. Outages and \"down time\" are virtually non-existent with proper management. The latter will be assessed in the actual formation, using a minimalistic format which can easily be tailored to the needs of the user. The benefits of using this sort of POS go far beyond simply improving the user experience and minimizing technical errors; businessmen that have to interact with clients on-the-go will be able to easily access the system all the same, allowing for some much needed flexibility in small businesses.","PeriodicalId":330286,"journal":{"name":"Proceedings of the 2015 Workshop on Mobile Big Data","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134237175","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}
Yu Cao, Peng Hou, Donald Brown, Jie Wang, Songqing Chen
Biomedical research and clinical practice are entering a data-driven era. One of the major applications of biomedical big data research is to utilize inexpensive and unobtrusive mobile biomedical sensors and cloud computing for pervasive health monitoring. However, real-world user experiences with mobile cloud-based health monitoring were poor, due to the factors such as excessive networking latency and longer response time. On the other hand, fog computing, a newly proposed computing paradigm, utilizes a collaborative multitude of end-user clients or near-user edge devices to conduct a substantial amount of computing, storage, communication, and etc. This new computing paradigm, if successfully applied for pervasive health monitoring, has great potential to accelerate the discovery of early predictors and novel biomarkers to support smart care decision making in a connected health scenarios. In this paper, we employ a real-world pervasive health monitoring application (pervasive fall detection for stroke mitigation) to demonstrate the effectiveness and efficacy of fog computing paradigm in health monitoring. Fall is a major source of morbidity and mortality among stroke patients. Hence, detecting falls automatically and in a timely manner becomes crucial for stroke mitigation in daily life. In this paper, we set to (1) investigate and develop new fall detection algorithms and (2) design and employ a real-time fall detection system employing fog computing paradigm (e.g., distributed analytics and edge intelligence), which split the detection task between the edge devices (e.g., smartphones attached to the user) and the server (e.g., servers in the cloud). Experimental results show that distributed analytics and edge intelligence, supported by fog computing paradigm, are very promising solutions for pervasive health monitoring.
{"title":"Distributed Analytics and Edge Intelligence: Pervasive Health Monitoring at the Era of Fog Computing","authors":"Yu Cao, Peng Hou, Donald Brown, Jie Wang, Songqing Chen","doi":"10.1145/2757384.2757398","DOIUrl":"https://doi.org/10.1145/2757384.2757398","url":null,"abstract":"Biomedical research and clinical practice are entering a data-driven era. One of the major applications of biomedical big data research is to utilize inexpensive and unobtrusive mobile biomedical sensors and cloud computing for pervasive health monitoring. However, real-world user experiences with mobile cloud-based health monitoring were poor, due to the factors such as excessive networking latency and longer response time. On the other hand, fog computing, a newly proposed computing paradigm, utilizes a collaborative multitude of end-user clients or near-user edge devices to conduct a substantial amount of computing, storage, communication, and etc. This new computing paradigm, if successfully applied for pervasive health monitoring, has great potential to accelerate the discovery of early predictors and novel biomarkers to support smart care decision making in a connected health scenarios. In this paper, we employ a real-world pervasive health monitoring application (pervasive fall detection for stroke mitigation) to demonstrate the effectiveness and efficacy of fog computing paradigm in health monitoring. Fall is a major source of morbidity and mortality among stroke patients. Hence, detecting falls automatically and in a timely manner becomes crucial for stroke mitigation in daily life. In this paper, we set to (1) investigate and develop new fall detection algorithms and (2) design and employ a real-time fall detection system employing fog computing paradigm (e.g., distributed analytics and edge intelligence), which split the detection task between the edge devices (e.g., smartphones attached to the user) and the server (e.g., servers in the cloud). Experimental results show that distributed analytics and edge intelligence, supported by fog computing paradigm, are very promising solutions for pervasive health monitoring.","PeriodicalId":330286,"journal":{"name":"Proceedings of the 2015 Workshop on Mobile Big Data","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113967934","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}
{"title":"Session details: Mobile Computing and Data Collection","authors":"Qun A. Li","doi":"10.1145/3260493","DOIUrl":"https://doi.org/10.1145/3260493","url":null,"abstract":"","PeriodicalId":330286,"journal":{"name":"Proceedings of the 2015 Workshop on Mobile Big Data","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116537406","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}
{"title":"Proceedings of the 2015 Workshop on Mobile Big Data","authors":"","doi":"10.1145/2757384","DOIUrl":"https://doi.org/10.1145/2757384","url":null,"abstract":"","PeriodicalId":330286,"journal":{"name":"Proceedings of the 2015 Workshop on Mobile Big Data","volume":"186 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123050150","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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