{"title":"A substrate for in-network sensor data integration","authors":"Svilen R. Mihaylov, Marie Jacob, Z. Ives, S. Guha","doi":"10.1145/1402050.1402059","DOIUrl":"https://doi.org/10.1145/1402050.1402059","url":null,"abstract":"With the ultimate goal of extending the data integration paradigm and query processing capabilities to ad hoc wireless networks, sensors, and stream systems, we consider how to support communication between sets of nodes performing distributed joins in sensor networks. We develop a communication model that enables in-network join at a variety of locations, and which facilitates coordination among nodes in order to make optimization decisions. While we defer a discussion of the optimizer to future work, we experimentally compare a variety of strategies, including at-base and in-network joins. Results show significant performance gains versus prior work, as well as opportunities for optimization.","PeriodicalId":281465,"journal":{"name":"Data Management for Sensor Networks","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127158222","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":"Probabilistic processing of interval-valued sensor data","authors":"S. Evers, M. Fokkinga, P. Apers","doi":"10.1145/1402050.1402060","DOIUrl":"https://doi.org/10.1145/1402050.1402060","url":null,"abstract":"When dealing with sensors with different time resolutions, it is desirable to model a sensor reading as pertaining to a time interval rather than a unit of time. We introduce two variants on the Hidden Markov Model in which this is possible: a reading extends over an arbitrary number of hidden states. We derive inference algorithms for the models, and analyse their efficiency. For this, we introduce a new method: we start with an inefficient algorithm directly derived from the model, and visually optimize it using a sum-factor diagram.","PeriodicalId":281465,"journal":{"name":"Data Management for Sensor Networks","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134636604","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":"An energy-efficient data-driven power management for wireless sensor networks","authors":"Mingjian Tang, Jinli Cao","doi":"10.1145/1402050.1402056","DOIUrl":"https://doi.org/10.1145/1402050.1402056","url":null,"abstract":"Providing energy-efficient continuous data collection services is of paramount importance to Wireless Sensor Network (WSN) applications. This paper proposes a new power management framework called Data-Driven Power Management (DDPM) as the infrastructure for integrating various energy efficient techniques, such as approximate querying and sleep scheduling. By utilizing the beneficial properties of these techniques, we can achieve not only better energy efficiency but also meet specific criteria, such as data accuracy and communication latency. The distinguished feature of DDPM is that it generates a precision-guaranteed estimation for each sensor node as its maximum sleep time to make deterministic schedules. Furthermore, two decentralized algorithms are proposed to avoid undesirable communication delays caused by staggered local sleep schedules. The experimental results show that nodes' sleep times can be significantly increased while incurring only a minor rise in latency.","PeriodicalId":281465,"journal":{"name":"Data Management for Sensor Networks","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134372529","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}
Maria Kontaki, Dimitrios Katsaros, Y. Manolopoulos
{"title":"The d-hop k-data coverage query problem in wireless sensor networks","authors":"Maria Kontaki, Dimitrios Katsaros, Y. Manolopoulos","doi":"10.1145/1402050.1402054","DOIUrl":"https://doi.org/10.1145/1402050.1402054","url":null,"abstract":"The task of querying wireless sensor network (WSN) to retrieve data of interest is very significant and a wealth of query types have been proposed in the context of WSNs. This article describes the d-hop k-data coverage query problem, which is a novel query type, aiming at extracting \"feature(s) distribution maps\" from WSNs. This problem generalizes earlier research problems, like top-k, skyband, and d-hop dominating sets. For this problem, we provide a fully distributed solution, the DaCoN protocol, that avoids constructing a \"network spanner\", since such a structure requires an expensive initialization procedure, misses the notion of neighborhoods, and most importantly, it creates hot-spots of communication, that shorten the network lifetime (i.e., some nodes deplete their energy very fast). We have developed a simulator to study the performance and behavior of the DaCoN protocol for various sensornet topologies and data distributions, and the obtained results attested the energy-efficiency and effectiveness of the protocol.","PeriodicalId":281465,"journal":{"name":"Data Management for Sensor Networks","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131383788","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":"Stream-based macro-programming of wireless sensor, actuator network applications with SOSNA","authors":"M. Karpinski, V. Cahill","doi":"10.1145/1402050.1402061","DOIUrl":"https://doi.org/10.1145/1402050.1402061","url":null,"abstract":"Wireless sensor, actuator networks distinguish themselves from wireless sensor networks by the need to coordinate actuators' actions, real-time constraints on communication and the frequently feedback-based nature of computation performed in the network. In this paper we propose a functional macro-programming language, SOSNA, that employs the stream programming paradigm to concisely specify data transformations in the network so that wireless sensor actuator network (WSAN) application developers can focus on higher-level control-oriented problems rather than on designing the way in which communication is organised in the network. SOSNA accommodates a broad class of WSAN coordination models, supports mobility and provides a means of employing feedback for distributed state maintenance. Program execution proceeds in rounds providing real-time guarantees on actuator decision making and synchronisation. In addition, static program semantics permit nodes to switch their radios off to conserve energy.","PeriodicalId":281465,"journal":{"name":"Data Management for Sensor Networks","volume":"355 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133283950","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":"Dynamic balanced storage in wireless sensor networks","authors":"Yongxuan Lai, Hong Chen, Yufeng Wang","doi":"10.1145/1286380.1286383","DOIUrl":"https://doi.org/10.1145/1286380.1286383","url":null,"abstract":"Data-centric storage is an effective and important technique in the wireless sensor networks. It stores the sensing data according to their values by mapping them to some point in the network in order to avoid routing all the values outside the network and flooding the queries. However, in most data-centric storage schemes, there is a \"hotspot\" problem due to the skewness of data and randomness of the mapping functions. Large number of sensor readings (events) may be routed to the same point by the predefined hashed function. In this paper, we propose a new Dynamic BAlanced data-centric Storage (DBAS) scheme, a cooperative strategy between the base station and the in-network processing in wireless sensor network. Our scheme, which utilizes the rich resources in the base station and is aware of the data distributions of the network, dynamically adjusts the mappings from readings to the storage points to balance the storage and workload in the network, as well as to reduce the cost of storing these readings. Moreover, it takes advantage of perimeter routing algorithm of the GPSR routing protocol to store multiple copies of readings to improve the robustness of the network with little overhead. Simulation results show that DBAS is more balanced and energy efficient than the traditional data-centric storage mechanism in wireless sensor network.","PeriodicalId":281465,"journal":{"name":"Data Management for Sensor Networks","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122449131","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":"Declarative temporal data models for sensor-driven query processing","authors":"Yanif Ahmad, U. Çetintemel","doi":"10.1145/1286380.1286390","DOIUrl":"https://doi.org/10.1145/1286380.1286390","url":null,"abstract":"Many sensor network applications monitor continuous phenomena by sampling, and fit time-varying models that capture the phenomena's behaviors. We introduce Pulse, a framework for processing continuous queries over these continuous-time data models. Pulse allows users to declaratively specify both their queries and models, and transforms these queries into simultaneous equation systems, which in many cases are significantly cheaper to process than a stream of discrete tuples. Pulse is able to guarantee user-defined error bounds between query results from continuous-time data models and sampled data, including cases of null results. We present a high-level overview of the design and architecture of Pulse and propose several query optimization techniques that are novel to our context, such as the simplification of our equation systems. We also discuss our plans for extending Pulse to support several novel model types, including differential equations and time series, and outline an abstraction to support query processing on these classes of models.","PeriodicalId":281465,"journal":{"name":"Data Management for Sensor Networks","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115855058","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}
H. H. Shahri, Galileo Namata, Saket Navlakha, A. Deshpande, N. Roussopoulos
{"title":"A graph-based approach to vehicle tracking in traffic camera video streams","authors":"H. H. Shahri, Galileo Namata, Saket Navlakha, A. Deshpande, N. Roussopoulos","doi":"10.1145/1286380.1286386","DOIUrl":"https://doi.org/10.1145/1286380.1286386","url":null,"abstract":"Vehicle tracking has a wide variety of applications from law enforcement to traffic planning and public safety. However, the image resolution of the videos available from most traffic camera systems, make it difficult to track vehicles based on unique identifiers like license plates. In many cases, vehicles with similar attributes are indistinguishable from one another due to image quality issues. Often, network bandwidth and power constraints limit the frame rate, as well. In this paper, we discuss the challenges of performing vehicle tracking queries over video streams from ubiquitous traffic cameras. We identify the limitations of tracking vehicles individually in such conditions and provide a novel graph-based approach using the identity of neighboring vehicles to improve the performance. We evaluate our approach using streaming video feeds from live traffic cameras available on the Internet. The results show that vehicle tracking is feasible, even for low quality and low frame rate traffic cameras. Additionally, exploitation of the attributes of neighboring vehicles significantly improves the performance.","PeriodicalId":281465,"journal":{"name":"Data Management for Sensor Networks","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121587155","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":"Imagers as sensors: correlating plant CO2 uptake with digital visible-light imagery","authors":"Josh Hyman, E. Graham, Mark H. Hansen, D. Estrin","doi":"10.1145/1286380.1286387","DOIUrl":"https://doi.org/10.1145/1286380.1286387","url":null,"abstract":"There exist many natural phenomena where direct measurement is either impossible or extremely invasive. To obtain approximate measurements of these phenomena we can build prediction models based on other sensing modalities such as features extracted from data collected by an imager. These models are derived from controlled experiments performed under laboratory conditions, and can then be applied to the associated event in nature. In this paper we explore various different methods for generating such models and discuss their accuracy, robustness, and computational complexity. Given sufficiently computationally simple models, we can eventually push their computation down towards the sensor nodes themselves to reduce the amount of data required to both flow through the network and be stored in a database. The addition of these models turn in-situ imagers into powerful biological sensors, and image databases into useful records of biological activity.","PeriodicalId":281465,"journal":{"name":"Data Management for Sensor Networks","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125231981","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":"Similarity-aware query allocation in sensor networks with multiple base stations","authors":"Shili Xiang, H. Lim, K. Tan, Yongluan Zhou","doi":"10.1145/1286380.1286382","DOIUrl":"https://doi.org/10.1145/1286380.1286382","url":null,"abstract":"In this paper, we consider a large scale sensor network comprising multiple, say K, base stations and a large number of wireless sensors. Such an infrastructure is expected to be more energy efficient and scale well with the size of the sensor nodes. To support a large number of queries, we examine the problem of allocating queries across the base stations to minimize the total data communication cost among the sensors. In particular, we examine similarity-aware techniques that exploit the similarities among queries when allocating queries, so that queries that require data from a common set of sensor nodes are allocated to the same base stations. We first approximate the problem of allocating queries to K base stations as a max-K-cut problem, and adapts an existing solution to our context. However, the scheme only works in a static context, where all queries are known in advance. In order to operate in a dynamic environment with frequent query arrivals and termination, we further propose a novel similarity-aware strategy that allocates queries to base stations one at a time. We also propose several heuristics to order a batch of queries for incremental allocation. We conducted experiments to evaluate our proposed schemes, and our results show that our similarity-aware query allocation schemes can effectively exploit the sharing among queries to greatly reduce the communication cost.","PeriodicalId":281465,"journal":{"name":"Data Management for Sensor Networks","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114606152","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}