T. Lahti, P. Pietarila, Liu Yingfei, Timo Pylvänäinen, Olli Vuorinen
Users expect efficient ways of handling their personal audio and image data using only the mobile device. Data sharing with friends and interest groups is also becoming increasingly important. Browsing and manually searching through your own and a shared data library can be difficult. For example, the simplest traditional attributes like time and date are normally unknown for other people's data. In this paper a search application running on mobile device is described. The application relies on automatic content analysis and metadata generation for audio and image data. Two search paradigms - class based search and example based search - are supported. In mobile devices the small display size makes it difficult to handle large amounts of information quickly. For this reason efficient metadata visualization is important. In our system the content metadata is converted into visual form that is fast to browse through at one glance.
{"title":"Audio and Image Browser for Mobile Devices","authors":"T. Lahti, P. Pietarila, Liu Yingfei, Timo Pylvänäinen, Olli Vuorinen","doi":"10.1109/MDM.2007.37","DOIUrl":"https://doi.org/10.1109/MDM.2007.37","url":null,"abstract":"Users expect efficient ways of handling their personal audio and image data using only the mobile device. Data sharing with friends and interest groups is also becoming increasingly important. Browsing and manually searching through your own and a shared data library can be difficult. For example, the simplest traditional attributes like time and date are normally unknown for other people's data. In this paper a search application running on mobile device is described. The application relies on automatic content analysis and metadata generation for audio and image data. Two search paradigms - class based search and example based search - are supported. In mobile devices the small display size makes it difficult to handle large amounts of information quickly. For this reason efficient metadata visualization is important. In our system the content metadata is converted into visual form that is fast to browse through at one glance.","PeriodicalId":393767,"journal":{"name":"2007 International Conference on Mobile Data Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2007-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130672426","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}
Personalization systems exploit preferences for providing users with only relevant data from the huge volume of information that is currently available. We consider preferences that dependent on context, such as the location of the user. We model context as a set of attributes, each taking values from hierarchical domains. Often, the context of the query may be too specific to match any of the given preferences. In this paper, we consider possible expansions of the query context produced by relaxing one or more of its context attributes. A hierarchical attribute may be relaxed upwards by replacing its value by a more general one, downwards by replacing its value by a set of more specific values or sideways by replacing its value by sibling values in the hierarchy. We present an algorithm based on a prefix-based representation of context for identifying the preferences whose context matches the relaxed context of the query and some initial performance results.
{"title":"On Relaxing Contextual Preference Queries","authors":"K. Stefanidis, E. Pitoura, Panos Vassiliadis","doi":"10.1109/MDM.2007.61","DOIUrl":"https://doi.org/10.1109/MDM.2007.61","url":null,"abstract":"Personalization systems exploit preferences for providing users with only relevant data from the huge volume of information that is currently available. We consider preferences that dependent on context, such as the location of the user. We model context as a set of attributes, each taking values from hierarchical domains. Often, the context of the query may be too specific to match any of the given preferences. In this paper, we consider possible expansions of the query context produced by relaxing one or more of its context attributes. A hierarchical attribute may be relaxed upwards by replacing its value by a more general one, downwards by replacing its value by a set of more specific values or sideways by replacing its value by sibling values in the hierarchy. We present an algorithm based on a prefix-based representation of context for identifying the preferences whose context matches the relaxed context of the query and some initial performance results.","PeriodicalId":393767,"journal":{"name":"2007 International Conference on Mobile Data Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2007-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130766290","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 ALS-MAC, a medium access control protocol for wireless sensor networks. ALS-MAC is a single channel MAC protocol suitable for location-aware networks. The protocol employs beacons to synchronize internode communications. Scalability and collision avoidance are achieved via contention-based advertising slots mapped to scheduled-based transmission slots. ALS-MAC has two dedicated modes of operation for data intensive traffic which assign temporary priority to nodes with many or long buffered packets. Beacons and signaling frames are combined to reduce the collision and control packet overhead. We study multihop optimization schemes for non-regular topologies under ALS-MAC.
{"title":"Energy-Efficient Medium Access for Data Intensive Wireless Sensor Networks","authors":"Zdravko Karakehayov, N. Andersen","doi":"10.1109/MDM.2007.76","DOIUrl":"https://doi.org/10.1109/MDM.2007.76","url":null,"abstract":"This paper presents ALS-MAC, a medium access control protocol for wireless sensor networks. ALS-MAC is a single channel MAC protocol suitable for location-aware networks. The protocol employs beacons to synchronize internode communications. Scalability and collision avoidance are achieved via contention-based advertising slots mapped to scheduled-based transmission slots. ALS-MAC has two dedicated modes of operation for data intensive traffic which assign temporary priority to nodes with many or long buffered packets. Beacons and signaling frames are combined to reduce the collision and control packet overhead. We study multihop optimization schemes for non-regular topologies under ALS-MAC.","PeriodicalId":393767,"journal":{"name":"2007 International Conference on Mobile Data Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2007-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128934409","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}
In case the data which is stored and processed in a sensor network has some value, it needs to be protected from unauthorized access through a security mechanism. The idea of evasive data storage is that data moves around the sensor network instead of remaining at a fixed location. In this way, an adversary, who has once (through node capture) had access to the data stored at some particular node, must compromise more sensors in order to maintain his illegitimate access to the sensor data. We refine the previously published simple evasive data storage techniques in two ways: (1) we improve the efficiency of data retrieval by bounding the area in which data may move, (2) we introduce data splitting as a technique to protect against sleeper attacks in which the adversary simply takes over a subset of nodes and waits for valuable data to pass by. We demonstrate the effectiveness of our approach using extensive simulations.
{"title":"Advanced Evasive Data Storage in Sensor Networks","authors":"Z. Benenson, F. Freiling, Peter M. Cholewinski","doi":"10.1109/MDM.2007.29","DOIUrl":"https://doi.org/10.1109/MDM.2007.29","url":null,"abstract":"In case the data which is stored and processed in a sensor network has some value, it needs to be protected from unauthorized access through a security mechanism. The idea of evasive data storage is that data moves around the sensor network instead of remaining at a fixed location. In this way, an adversary, who has once (through node capture) had access to the data stored at some particular node, must compromise more sensors in order to maintain his illegitimate access to the sensor data. We refine the previously published simple evasive data storage techniques in two ways: (1) we improve the efficiency of data retrieval by bounding the area in which data may move, (2) we introduce data splitting as a technique to protect against sleeper attacks in which the adversary simply takes over a subset of nodes and waits for valuable data to pass by. We demonstrate the effectiveness of our approach using extensive simulations.","PeriodicalId":393767,"journal":{"name":"2007 International Conference on Mobile Data Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2007-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129204157","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}
C. Bolchini, C. Curino, G. Orsi, E. Quintarelli, F. Schreiber, L. Tanca
Nowadays user mobility requires that both content and services be appropriately personalized, in order for the (mobile) user to be always - and anywhere - equipped with the adequate share of data. Thus, the knowledge about the user, the adopted device and the environment, altogether called context, has to be taken into account in order to minimize the amount of information imported on mobile devices. The Context-ADDICT (Context-Aware Data Design, Integration, Customization and Tailoring) project aims at the definition of a complete framework which, starting from a methodology for the early design phases, supports mobile users through the dynamic hooking and integration of new, available information sources, so that an appropriate context-based portion of data, called data chunk, is delivered to their mobile devices. Data tailoring is needed because of two main reasons: the first is to keep the amount of information manageable, in order for the user not to be confused by too much, possibly noisy, information; the second is the frequent case when the mobile device is a small one, like a palm computer or a cellular phone, and thus only the most significant information must be kept on board. Context is, thus, key metainformation whose role becomes essential within the process of view design. Two main design-time activities are supported by our system in order to provide context-aware data filtering: 1) context design, based on a context model called context dimension tree and 2) definition of the relationship between each context and relevant portions of the application domain data.
{"title":"CADD: A Tool for Context Modeling and Data Tailoring","authors":"C. Bolchini, C. Curino, G. Orsi, E. Quintarelli, F. Schreiber, L. Tanca","doi":"10.1109/MDM.2007.42","DOIUrl":"https://doi.org/10.1109/MDM.2007.42","url":null,"abstract":"Nowadays user mobility requires that both content and services be appropriately personalized, in order for the (mobile) user to be always - and anywhere - equipped with the adequate share of data. Thus, the knowledge about the user, the adopted device and the environment, altogether called context, has to be taken into account in order to minimize the amount of information imported on mobile devices. The Context-ADDICT (Context-Aware Data Design, Integration, Customization and Tailoring) project aims at the definition of a complete framework which, starting from a methodology for the early design phases, supports mobile users through the dynamic hooking and integration of new, available information sources, so that an appropriate context-based portion of data, called data chunk, is delivered to their mobile devices. Data tailoring is needed because of two main reasons: the first is to keep the amount of information manageable, in order for the user not to be confused by too much, possibly noisy, information; the second is the frequent case when the mobile device is a small one, like a palm computer or a cellular phone, and thus only the most significant information must be kept on board. Context is, thus, key metainformation whose role becomes essential within the process of view design. Two main design-time activities are supported by our system in order to provide context-aware data filtering: 1) context design, based on a context model called context dimension tree and 2) definition of the relationship between each context and relevant portions of the application domain data.","PeriodicalId":393767,"journal":{"name":"2007 International Conference on Mobile Data Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2007-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131363690","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}
Spatial generalization has been recently proposed as a technique for the anonymization of requests in location based services. This paper presents the results of an extensive experimental study, considering known generalization algorithms as well as new ones proposed by the authors.
{"title":"A Comparison of Spatial Generalization Algorithms for LBS Privacy Preservation","authors":"S. Mascetti, C. Bettini","doi":"10.1109/MDM.2007.54","DOIUrl":"https://doi.org/10.1109/MDM.2007.54","url":null,"abstract":"Spatial generalization has been recently proposed as a technique for the anonymization of requests in location based services. This paper presents the results of an extensive experimental study, considering known generalization algorithms as well as new ones proposed by the authors.","PeriodicalId":393767,"journal":{"name":"2007 International Conference on Mobile Data Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2007-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130593068","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}
Clustering sensors into groups, so that sensors communicate information only to cluster-heads and then the cluster-heads communicate the aggregated information to the base station, saves energy and thus prolongs network lifetime. Adapting this approach, we propose a Distributive Energy Efficient Adaptive Clustering (DEEAC) protocol. This protocol is adaptive in terms of data reporting rates and residual energy of each node within the network. Motivated by the LEACH protocol [I], we extend its stochastic cluster selection algorithm for networks having spatio-temporal variations in data reporting rates across different regions. Simulation results demonstrate that DEEAC is able to distribute energy consumption more effectively among the sensors, thereby prolonging the network lifetime by as much as 50% compared to LEACH.
{"title":"Distributive Energy Efficient Adaptive Clustering Protocol for Wireless Sensor Networks","authors":"Udit Sajjanhar, Pabitra Mitra","doi":"10.1109/MDM.2007.69","DOIUrl":"https://doi.org/10.1109/MDM.2007.69","url":null,"abstract":"Clustering sensors into groups, so that sensors communicate information only to cluster-heads and then the cluster-heads communicate the aggregated information to the base station, saves energy and thus prolongs network lifetime. Adapting this approach, we propose a Distributive Energy Efficient Adaptive Clustering (DEEAC) protocol. This protocol is adaptive in terms of data reporting rates and residual energy of each node within the network. Motivated by the LEACH protocol [I], we extend its stochastic cluster selection algorithm for networks having spatio-temporal variations in data reporting rates across different regions. Simulation results demonstrate that DEEAC is able to distribute energy consumption more effectively among the sensors, thereby prolonging the network lifetime by as much as 50% compared to LEACH.","PeriodicalId":393767,"journal":{"name":"2007 International Conference on Mobile Data Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2007-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129535128","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}
The explosive growth of location-detection devices (e.g., GPS-like devices and handheld devices) along with wireless communications and mobile databases results in realizing location-based applications that deliver specific information to their users based on their current locations. Examples of such applications include location-based store finder, location-based traffic reports, and location-based advertisements. Although location-based services promise safety and convenience, they threaten the privacy and security of users as such services explicitly require users to share private location information with the service. If a user wants to keep her location information private, she has to turn off her location-aware device and temporarily unsubscribe from the service. Recent studies show that such privacy concerns - ranging from worries over employers snooping on their workers' whereabouts to fears of tracking by potential stalkers - are a serious obstacle to wider adoption of location-based services. This article aims to provide practitioners, researchers, and graduate students with the state of the art and major research issues in the important and practical research area of location privacy. In general, the tutorial is divided into the following five parts: (1) legislative issues and privacy concerns, (2) location privacy in mobile environments, (3) privacy attack models, (4) privacy-aware location query processing; (5) concluding remarks.
{"title":"Privacy in Location-Based Services: State-of-the-Art and Research Directions","authors":"M. Mokbel","doi":"10.1109/MDM.2007.45","DOIUrl":"https://doi.org/10.1109/MDM.2007.45","url":null,"abstract":"The explosive growth of location-detection devices (e.g., GPS-like devices and handheld devices) along with wireless communications and mobile databases results in realizing location-based applications that deliver specific information to their users based on their current locations. Examples of such applications include location-based store finder, location-based traffic reports, and location-based advertisements. Although location-based services promise safety and convenience, they threaten the privacy and security of users as such services explicitly require users to share private location information with the service. If a user wants to keep her location information private, she has to turn off her location-aware device and temporarily unsubscribe from the service. Recent studies show that such privacy concerns - ranging from worries over employers snooping on their workers' whereabouts to fears of tracking by potential stalkers - are a serious obstacle to wider adoption of location-based services. This article aims to provide practitioners, researchers, and graduate students with the state of the art and major research issues in the important and practical research area of location privacy. In general, the tutorial is divided into the following five parts: (1) legislative issues and privacy concerns, (2) location privacy in mobile environments, (3) privacy attack models, (4) privacy-aware location query processing; (5) concluding remarks.","PeriodicalId":393767,"journal":{"name":"2007 International Conference on Mobile Data Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2007-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132637831","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}
Ananda Swarup, Das Kannan, Srinathan Ritesh, Kumar Tiwari
Shortest path computation has always been a subject of study and research in the history of computer science. In this paper we introduce and initiate the study of the problem of finding the shortest path in a privacy preserving manner, in presence of single convex polygonal obstacle. We also propose an efficient, elegant and simple solution for the problem.
{"title":"Privacy Preserving Computation of Shortest Path in Presence of a Single Convex Polygonal Obstacle","authors":"Ananda Swarup, Das Kannan, Srinathan Ritesh, Kumar Tiwari","doi":"10.1109/MDM.2007.52","DOIUrl":"https://doi.org/10.1109/MDM.2007.52","url":null,"abstract":"Shortest path computation has always been a subject of study and research in the history of computer science. In this paper we introduce and initiate the study of the problem of finding the shortest path in a privacy preserving manner, in presence of single convex polygonal obstacle. We also propose an efficient, elegant and simple solution for the problem.","PeriodicalId":393767,"journal":{"name":"2007 International Conference on Mobile Data Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2007-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133140700","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}
Spatio-temporal data streams that are generated from mobile stream sources (e.g., mobile sensors) experience similar environmental conditions that result in distinct phenomena. Several research efforts are dedicated to detect and track various phenomena inside a data stream management system (DSMS). In this paper, we use the detected phenomena to reduce the demand on the DSMS resources. The main idea is to let the query processor observe the input data streams at the phenomena level. Then, each incoming continuous query is directed only to those phenomena that participate in the query answer. Two levels of indexing are employed, a phenomenon index and a query index. The phenomenon index provides a fine resolution view of the input streams that participate in a particular phenomenon. The query index utilizes the phenomenon index to maintain a query deployment map in which each input stream is aware of the set of continuous queries that the stream contributes to their answers. Both indices are updated dynamically in response to the evolving nature of phenomena and to the mobility of the stream sources. Experimental results show the efficiency of this approach with respect to the accuracy of the query result and the resource utilization of the DSMS.
{"title":"Phenomenon-Aware Stream Query Processing","authors":"Mohamed H. Ali, M. Mokbel, Walid G. Aref","doi":"10.1109/MDM.2007.11","DOIUrl":"https://doi.org/10.1109/MDM.2007.11","url":null,"abstract":"Spatio-temporal data streams that are generated from mobile stream sources (e.g., mobile sensors) experience similar environmental conditions that result in distinct phenomena. Several research efforts are dedicated to detect and track various phenomena inside a data stream management system (DSMS). In this paper, we use the detected phenomena to reduce the demand on the DSMS resources. The main idea is to let the query processor observe the input data streams at the phenomena level. Then, each incoming continuous query is directed only to those phenomena that participate in the query answer. Two levels of indexing are employed, a phenomenon index and a query index. The phenomenon index provides a fine resolution view of the input streams that participate in a particular phenomenon. The query index utilizes the phenomenon index to maintain a query deployment map in which each input stream is aware of the set of continuous queries that the stream contributes to their answers. Both indices are updated dynamically in response to the evolving nature of phenomena and to the mobility of the stream sources. Experimental results show the efficiency of this approach with respect to the accuracy of the query result and the resource utilization of the DSMS.","PeriodicalId":393767,"journal":{"name":"2007 International Conference on Mobile Data Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2007-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133798233","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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