Americans were anxious over infectious disease such as Ebola. According to Voice of America's report, more than four in 10 were worried, even though there had only been a few confirmed. People are usually thinking they may have already had an indirect/direct contact with a suspected/confirmed patient because of visiting same places. The scare, therefore, spreads among general public as (i) they suspect the administrative agencies' infection controls are not sufficiently proper, and (ii) there is still no customized model to convince them that their infection probabilities are very low. To address these issues, we propose to utilize location and social networking information to jointly control the spread of infectious disease and the scare among people. This work-in-progress paper specifically introduces our model and research directions.
{"title":"Controlling the Spreads of Infectious Disease and Scare via Utilizing Location and Social Networking Information","authors":"Wei Cheng, F. Chen, Xiuzhen Cheng","doi":"10.1145/2757384.2757386","DOIUrl":"https://doi.org/10.1145/2757384.2757386","url":null,"abstract":"Americans were anxious over infectious disease such as Ebola. According to Voice of America's report, more than four in 10 were worried, even though there had only been a few confirmed. People are usually thinking they may have already had an indirect/direct contact with a suspected/confirmed patient because of visiting same places. The scare, therefore, spreads among general public as (i) they suspect the administrative agencies' infection controls are not sufficiently proper, and (ii) there is still no customized model to convince them that their infection probabilities are very low. To address these issues, we propose to utilize location and social networking information to jointly control the spread of infectious disease and the scare among people. This work-in-progress paper specifically introduces our model and research directions.","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":"126112689","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}
Recent years have witnessed an explosive growth of mobile applications. Thanks to improved network connectivity, it becomes a promising enabling solution to offload computation-intensive applications to the resource abundant public cloud to further augment the capacity of resource-constrained devices. As mobile applications usually have QoS requirements, it is critical to provide low latency services to the mobile users while maintain low leasing cost of cloud resources. However, the resources offered by cloud vendors are usually charged based on a time quanta while the offloading demand for heavy-lifting computation may occur infrequently on mobile devices. This mismatch would demotivate users to resort to public cloud for computation offloading. In this paper, we design a computation offloading middleware which bridges the aforementioned gap between cloud vendors and mobile clients, providing offloading service to multiple users with low cost and delay. The proposed middleware has two key components: Task Scheduler and Instance Manager. The Task Scheduler dispatches the received offloading tasks to execute in the instances reserved by the Instance Manager. Based on the arrival pattern of offloading tasks, the Instance Manager dynamically changes the number of instances to ensure certain service grade of mobile users. Our proposed mechanisms are validated through numerical results. It is shown that a lower average delay can be achieved through proposed scheduling heuristic, and the number of reserved instances well adapts to the offloading demands.
{"title":"A Mobile Cloud Computing Middleware for Low Latency Offloading of Big Data","authors":"Bo Yin, Wenlong Shen, L. Cai, Y. Cheng","doi":"10.1145/2757384.2757390","DOIUrl":"https://doi.org/10.1145/2757384.2757390","url":null,"abstract":"Recent years have witnessed an explosive growth of mobile applications. Thanks to improved network connectivity, it becomes a promising enabling solution to offload computation-intensive applications to the resource abundant public cloud to further augment the capacity of resource-constrained devices. As mobile applications usually have QoS requirements, it is critical to provide low latency services to the mobile users while maintain low leasing cost of cloud resources. However, the resources offered by cloud vendors are usually charged based on a time quanta while the offloading demand for heavy-lifting computation may occur infrequently on mobile devices. This mismatch would demotivate users to resort to public cloud for computation offloading. In this paper, we design a computation offloading middleware which bridges the aforementioned gap between cloud vendors and mobile clients, providing offloading service to multiple users with low cost and delay. The proposed middleware has two key components: Task Scheduler and Instance Manager. The Task Scheduler dispatches the received offloading tasks to execute in the instances reserved by the Instance Manager. Based on the arrival pattern of offloading tasks, the Instance Manager dynamically changes the number of instances to ensure certain service grade of mobile users. Our proposed mechanisms are validated through numerical results. It is shown that a lower average delay can be achieved through proposed scheduling heuristic, and the number of reserved instances well adapts to the offloading demands.","PeriodicalId":330286,"journal":{"name":"Proceedings of the 2015 Workshop on Mobile Big Data","volume":"44 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":"124104971","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}
Despite the increasing usage of cloud computing, there are still issues unsolved due to inherent problems of cloud computing such as unreliable latency, lack of mobility support and location-awareness. Fog computing can address those problems by providing elastic resources and services to end users at the edge of network, while cloud computing are more about providing resources distributed in the core network. This survey discusses the definition of fog computing and similar concepts, introduces representative application scenarios, and identifies various aspects of issues we may encounter when designing and implementing fog computing systems. It also highlights some opportunities and challenges, as direction of potential future work, in related techniques that need to be considered in the context of fog computing.
{"title":"A Survey of Fog Computing: Concepts, Applications and Issues","authors":"Shanhe Yi, Cheng Li, Qun A. Li","doi":"10.1145/2757384.2757397","DOIUrl":"https://doi.org/10.1145/2757384.2757397","url":null,"abstract":"Despite the increasing usage of cloud computing, there are still issues unsolved due to inherent problems of cloud computing such as unreliable latency, lack of mobility support and location-awareness. Fog computing can address those problems by providing elastic resources and services to end users at the edge of network, while cloud computing are more about providing resources distributed in the core network. This survey discusses the definition of fog computing and similar concepts, introduces representative application scenarios, and identifies various aspects of issues we may encounter when designing and implementing fog computing systems. It also highlights some opportunities and challenges, as direction of potential future work, in related techniques that need to be considered in the context of fog computing.","PeriodicalId":330286,"journal":{"name":"Proceedings of the 2015 Workshop on Mobile Big Data","volume":"76 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":"129358807","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: Social Networks, Sensor Networks, and Smartphone Systems","authors":"John Wang","doi":"10.1145/3260492","DOIUrl":"https://doi.org/10.1145/3260492","url":null,"abstract":"","PeriodicalId":330286,"journal":{"name":"Proceedings of the 2015 Workshop on Mobile Big Data","volume":"83 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":"129212971","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}
Feng Wang, Cong Wang, Z. Wang, Xueying Zhang, Chao Shang
Most of the existing algorithms for Wireless Sensor Networks (WSN) localization in underground coal mine are exposed such problems as unreasonable node model, low accuracy and unsteady. This paper presents a new method of nodes layout in coal mine roadway, and builds positioning model underground. Compared with traditional model, this model can reduce the number of sensor nodes when meets positioning requirement. Then the non-ranging positioning algorithm of TDOA/AOA hybrid algorithm, currently used as three-dimensional positioning algorithm, is introduced to the model. Simulation results show that: this algorithm has better positioning performance than the traditional algorithms, fitting into underground coal mine.
{"title":"Research on 3D Localization Algorithm of Wireless Sensor Networks in Underground Coal Mine","authors":"Feng Wang, Cong Wang, Z. Wang, Xueying Zhang, Chao Shang","doi":"10.1145/2757384.2757393","DOIUrl":"https://doi.org/10.1145/2757384.2757393","url":null,"abstract":"Most of the existing algorithms for Wireless Sensor Networks (WSN) localization in underground coal mine are exposed such problems as unreasonable node model, low accuracy and unsteady. This paper presents a new method of nodes layout in coal mine roadway, and builds positioning model underground. Compared with traditional model, this model can reduce the number of sensor nodes when meets positioning requirement. Then the non-ranging positioning algorithm of TDOA/AOA hybrid algorithm, currently used as three-dimensional positioning algorithm, is introduced to the model. Simulation results show that: this algorithm has better positioning performance than the traditional algorithms, fitting into underground coal mine.","PeriodicalId":330286,"journal":{"name":"Proceedings of the 2015 Workshop on Mobile Big Data","volume":"138 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":"127443345","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 application stores (appstores) are emerging digital distribution platforms with explosive growth. Although there have been some observations on the mobile application (app) popularity in Android appstores, there is no report on the app popularity in iOS appstores. What's more, the details about user downloads and app popularity, such as the composition of downloads traffic and the migration of user interests, are untouched yet. In this paper, we unreel these issues based on five-month measurements of four third-party appstores (two for Android and two for iOS respectively). Our main results include: 1) The app popularity distributions of third-party Android appstores are different from those of iOS third-party appstores. There is an exponential cut-off observed besides the Zipf-like distribution in the app popularity distribution of Android appstores. 2) In both Android and iOS families of appstores, the major part of downloads traffic is contributed by the large-size apps, counting 80% or more in the volume of total downloads traffic. 3) There is less rank variance of the most popular apps in the iOS appstores than those in the Android appstores. About 52% of the top 100 iOS apps observed in one month are still in the rank of top 100 in the following four months.
{"title":"A Measurement-based Study on Application Popularity in Android and iOS App Stores","authors":"Wei Liu, Ge Zhang, Jun Chen, Y. Zou, Wenchao Ding","doi":"10.1145/2757384.2757392","DOIUrl":"https://doi.org/10.1145/2757384.2757392","url":null,"abstract":"Mobile application stores (appstores) are emerging digital distribution platforms with explosive growth. Although there have been some observations on the mobile application (app) popularity in Android appstores, there is no report on the app popularity in iOS appstores. What's more, the details about user downloads and app popularity, such as the composition of downloads traffic and the migration of user interests, are untouched yet. In this paper, we unreel these issues based on five-month measurements of four third-party appstores (two for Android and two for iOS respectively). Our main results include: 1) The app popularity distributions of third-party Android appstores are different from those of iOS third-party appstores. There is an exponential cut-off observed besides the Zipf-like distribution in the app popularity distribution of Android appstores. 2) In both Android and iOS families of appstores, the major part of downloads traffic is contributed by the large-size apps, counting 80% or more in the volume of total downloads traffic. 3) There is less rank variance of the most popular apps in the iOS appstores than those in the Android appstores. About 52% of the top 100 iOS apps observed in one month are still in the rank of top 100 in the following four months.","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":"130325721","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}
For speaker recognition systems, short-time spectrum of speech signal is obtained by using windowed discrete Fourier transform (DFT) in feature extraction. Although windowed DFT can reduces spectral leakage, variance of the spectrum estimation remains high, which reduces the stability of spectrum estimation. Multiple orthogonal window spectrum estimation(referred Multitapering) method, which can not only reduces spectral leakage but also reduces the variance of the spectrum estimation, has more stable performance of spectrum estimate, is utilized in this paper. After how number of windows affects performance of spectrum estimation is studied, the performance of speaker recognition system is also tested in noisy environment. The results show that multiple orthogonal spectrum estimation method has more stable performance and better noise robustness than Hamming windowed DFT.
{"title":"Application of multiple orthogonal window spectrum estimation in speaker recognition","authors":"Bai Jing, Zhang Yiran, Yin Cong","doi":"10.1145/2757384.2757394","DOIUrl":"https://doi.org/10.1145/2757384.2757394","url":null,"abstract":"For speaker recognition systems, short-time spectrum of speech signal is obtained by using windowed discrete Fourier transform (DFT) in feature extraction. Although windowed DFT can reduces spectral leakage, variance of the spectrum estimation remains high, which reduces the stability of spectrum estimation. Multiple orthogonal window spectrum estimation(referred Multitapering) method, which can not only reduces spectral leakage but also reduces the variance of the spectrum estimation, has more stable performance of spectrum estimate, is utilized in this paper. After how number of windows affects performance of spectrum estimation is studied, the performance of speaker recognition system is also tested in noisy environment. The results show that multiple orthogonal spectrum estimation method has more stable performance and better noise robustness than Hamming windowed DFT.","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":"125959116","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}
Qiang Liu, E. Ngai, Xiping Hu, Zhengguo Sheng, Victor C. M. Leung, Jianping Yin
The heterogeneous cloud radio access network (H-CRAN) has been emerging as a cost-effective solution supporting huge volumes of mobile traffic in the big data era. This paper investigates potential security challenges on H-CRAN and analyzes their likelihoods and difficulty levels. Typically, the security threats in H-CRAN can be categorized into three groups, i.e., security threats towards remote radio heads (RRHs), those towards the radio cloud infrastructure and towards backhaul networks. To overcome challenges made by the security threats, we propose a hierarchical security framework called Secure H-CRAN (SH-CRAN) to protect the H-CRAN system against the potential threats. Specifically, the architecture of SH-CRAN contains three logically independent secure domains (SDs), which are the SDs of radio cloud infrastructure, RRHs and backhauls. The notable merits of SH-CRAN include two aspects: (i) the proposed framework is able to provide security assurance for the evolving H-CRAN system, and (ii) the impacts of any failure are limited in one specific component of H-CRAN. The proposed SH-CRAN can be regarded as the basis of the future security mechanisms of mobile bag data computing.
{"title":"SH-CRAN: Hierarchical Framework to Support Mobile Big Data Computing in a Secure Manner","authors":"Qiang Liu, E. Ngai, Xiping Hu, Zhengguo Sheng, Victor C. M. Leung, Jianping Yin","doi":"10.1145/2757384.2757388","DOIUrl":"https://doi.org/10.1145/2757384.2757388","url":null,"abstract":"The heterogeneous cloud radio access network (H-CRAN) has been emerging as a cost-effective solution supporting huge volumes of mobile traffic in the big data era. This paper investigates potential security challenges on H-CRAN and analyzes their likelihoods and difficulty levels. Typically, the security threats in H-CRAN can be categorized into three groups, i.e., security threats towards remote radio heads (RRHs), those towards the radio cloud infrastructure and towards backhaul networks. To overcome challenges made by the security threats, we propose a hierarchical security framework called Secure H-CRAN (SH-CRAN) to protect the H-CRAN system against the potential threats. Specifically, the architecture of SH-CRAN contains three logically independent secure domains (SDs), which are the SDs of radio cloud infrastructure, RRHs and backhauls. The notable merits of SH-CRAN include two aspects: (i) the proposed framework is able to provide security assurance for the evolving H-CRAN system, and (ii) the impacts of any failure are limited in one specific component of H-CRAN. The proposed SH-CRAN can be regarded as the basis of the future security mechanisms of mobile bag data computing.","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":"131006822","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}
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