Demet Aksoy

R/spl times/W: a scheduling approach for large-scale on-demand data broadcast

Broadcast is becoming an increasingly attractive data-dissemination method for large client populations. In order to effectively utilize a broadcast medium for such a service, it is necessary to have efficient on-line scheduling algorithms that can balance individual and overall performance and can scale in terms of data set sizes, client populations, and broadcast bandwidth. We propose an algorithm, called R/spl times/W, that provides good performance across all of these criteria and can be tuned to trade off average and worst-case waiting time. Unlike previous work on low overhead scheduling, the algorithm does not use estimates of the access probabilities of items, but rather, it makes scheduling decisions based on the current queue state, allowing it to easily adapt to changes in the intensity and distribution of the workload. We demonstrate the performance advantages of the algorithm under a range of scenarios using a simulation model and present analytical results that describe the intrinsic behavior of the algorithm.

Scheduling for large-scale on-demand data broadcasting

Advances in telecommunications have enabled the deployment of broadcast-based wide-area information services that provide on-demand data access to very large client populations. In order to effectively utilize a broadcast medium for such a service, it is necessary to have efficient, on-line scheduling algorithms that can balance individual and overall performance and can scale in terms of data set sizes, client populations, and broadcast bandwidth. In this study we introduce a parameterized algorithm that provides good performance across all of these criteria and can be tuned to emphasize either average or worst case waiting time. Unlike previous work on low overhead scheduling, the algorithm is not based on estimates of the access probabilities of items, but rather, it makes scheduling decisions based on the current queue state, allowing it to easily adapt to changes in the intensity and distribution of the workload. We examine the performance of the algorithm using a simulation model.

Design and implementation issues in the fuzzy object-oriented data model

Abstract Uncertainty modeling and data manipulation are desirable in object-oriented database systems to handle complex objects with imprecise properties. In this paper, we present an enhancement to the fuzzy object-oriented data model (FOOD) (George et al., Fuzzy Sets and Systems 60 (3) (1993) 259–272), that permits a truer representation of different types of uncertainty. Uncertainty is extended to the class. efinition level and is the basis for the determination of the membership of an object in a class. We describe a software architecture for the implementation of the model and discuss the significant details of a prototype implemented using the EXODUS storage manager (ESM).

Pull vs push: a quantitative comparison for data broadcast

Advances in wireless technology have resulted in an emerging broadcast-capable infrastructure. A major debate for such infrastructures is whether we should use push or pull to support large client populations. To date, push was suggested to provide a scalability that is not possible with pull. In this paper we conclude otherwise, based on simulation and experimental prototype results. A major contribution of this paper is to show that pull-based algorithms outperform push-based ones. Contrary to the conventional wisdom, this is true even for environments where request processing and scheduling overheads are a significant factor.

Information source selection for resource constrained environments

Distributed information retrieval has pressing scalability concerns due to the growing number of independent sources of on-line data and the emerging applications. A promising solution to distributed retrieval is metasearching, which dispatches a users query to multiple sources and gathers the results into a single result set. An important component of metasearching is selecting the set of information sources most likely to provide relevant documents. Recent research has focused on how to obtain statistics for the selection task. In this paper we discuss different information source selection approaches and their applicability for resource-constrained sensor network applications.

Multimedia Processing in Wireless Sensor Networks

In order to extract detailed information about the environment, multimedia sensor networks are becoming popular nowadays. However, due to unique properties of multimedia data delivery, we face novel challenges for resource-constrained sensor networks. Because of the high bandwidth demands of multimedia frames, the transmission of raw data collected at sensor nodes is costly. On the other hand, processing limitations prohibit the use of sophisticated multimedia processing at individual nodes to reduce the amount of data that needs to be communicated. In this paper, we present a novel framework for multimedia processing in wireless sensor networks considering needs of surveillance video applications. In our framework, automatically extracted moving objects are treated as intruders events and their positions are exploited for efficient communication. We then apply joint processing of collected data at the sink to identify events using fuzzy memberships and decide the actual multimedia data to be sent from sensors to sink.

Processed Data Collection using Opportunistic Routing in Location Aware Wireless Sensor Networks

A major concern in sensor networks is to reduce the data traffic and the energy consumption to extend the lifetime of the network. In this regard, radio transmission is the dominant factor in energy consumption. Therefore, the routing policy has a significant impact on the overall lifespan of the sensor network. For instance, pure multi-hop routing, e.g., tree-based routing, has the drawback that nodes closest to the data collection point (sink node) consume their energy and die first. In this paper, we propose an opportunistic routing mechanism that aims to extend network lifespan by balancing energy consumption. Our approach distributes the power consumption in the network by altering the communication range when applicable. Our experiment results suggest significant improvements when compared to tree-based routing pure multihoping and LEACH based routing.

Spatio-temporal aggregates over raster image data

Spatial, temporal and spatio-temporal aggregates over continuous streams of remotely sensed image data build a fundamental operation in many applications in the environmental sciences. Several approaches to efficiently compute multi-dimensional aggregates have been proposed in the literature. However, none of these approaches is suitable to compute aggregate values over streaming raster image data where the spatial extents and positions of individual images vary over time. In particular, the computation of a single aggregate value becomes less meaningful when the image data contribute only partially to a query region. In this paper, we present an indexing scheme -- based on the Box-Aggregation Tree -- to efficiently compute spatio-temporal aggregates over streams of raster image data that vary in position and size. Using information about the spatial extent of incoming image data, we show how multiple aggregate values are computed for a single spatio-temporal query, thus providing more meaningful query results over spatially varying image data. Using National Oceanic and Atmospheric Administrations (NOAA) Geostationary Operational Environmental Satellite (GOES) data, we show the feasibility and efficiency of the proposed approach.

Research in Data Broadcast and Dissemination

The proliferation of the Internet and intranets, the development of wireless and satellite networks, and the availability of asymmetric, high-bandwidth links to the home, have fueled the development of a wide range of new “dissemination-based” applications. These applications involve the timely distribution of data to a large set of consumers, and include stock and sports tickers, traffic information systems, electronic personalized newspapers, and entertainment delivery. Dissemination-oriented applications have special characteristics that render traditional client-server data management approaches ineffective. These include: tremendous scale. a high-degree of overlap in user data needs. asymmetric data flow from sources to consumers.

Adaptive energy-efficient registration and online scheduling for asymmetric wireless sensor networks

Increasing onboard processing capabilities of sensors enable self-organization in wireless sensor networks to dynamically adapt to ad hoc topologies and to react to task or network changes. Such self-organization, however. comes at a cost of additional energy consumption for the sensor nodes with already limited power resources. As energy limitations in unattended environments raise a major concern, such organizations need to trade-off between power consumption and topology maintenance. In this paper we present our adaptive energy-efficient registration and online scheduling (AEROS) protocol that exploits application based data flow characteristics to reduce power consumption during self-organization. Asymmetric data flow characteristics is used to govern route selection, and to formulate an organized transmission schedule with risk-free sleeping time. Our simulation results suggest that AEROSs transmission schedule allows the minimum number of data message exchanges and guarantees a collision-free communication. AEROS provides significant energy savings in steady state using a low number of control messages.