Özgür Ulusoy

A data mining approach for location prediction in mobile environments

Mobility prediction is one of the most essential issues that need to be explored for mobility management in mobile computing systems. In this paper, we propose a new algorithm for predicting the next intercell movement of a mobile user in a Personal Communication Systems network. In the first phase of our three-phase algorithm, user mobility patterns are mined from the history of mobile user trajectories. In the second phase, mobility rules are extracted from these patterns, and in the last phase, mobility predictions are accomplished by using these rules. The performance of the proposed algorithm is evaluated through simulation as compared to two other prediction methods. The performance results obtained in terms of Precision and Recall indicate that our method can make more accurate predictions than the other methods.

A quadtree-based dynamic attribute indexing method

Dynamic attributes are attributes that change continuously over time making it impractical to issue explicit updates for every change. In this paper, we adapt a variant of the quadtree structure to solve the problem of indexing dynamic attributes. The approach is based on the key idea of using a linear function of time for each dynamic attribute that allows us to predict its value in the future. We contribute an algorithm for regenerating the quadtree-based index periodically that minimizes CPU and disk access cost. We also provide an experimental study of performance focusing on query processing and index update overheads.

A framework for use of wireless sensor networks in forest fire detection and monitoring

Forest fires are one of the main causes of environmental degradation nowadays. Current surveillance systems for forest fires lack in supporting real-time monitoring of every point of a region at all times and early detection of fire threats. Solutions using wireless sensor networks, on the other hand, can gather sensory data values, such as temperature and humidity, from all points of a field continuously, day and night, and, provide fresh and accurate data to the fire-fighting center quickly. However, sensor networks face serious obstacles like limited energy resources and high vulnerability to harsh environmental conditions, that have to be considered carefully. In this paper, we propose a comprehensive framework for the use of wireless sensor networks for forest fire detection and monitoring. Our framework includes proposals for the wireless sensor network architecture, sensor deployment scheme, and clustering and communication protocols. The aim of the framework is to detect a fire threat as early as possible and yet consider the energy consumption of the sensor nodes and the environmental conditions that may affect the required activity level of the network. We implemented a simulator to validate and evaluate our proposed framework. Through extensive simulation experiments, we show that our framework can provide fast reaction to forest fires while also consuming energy efficiently.

Free Riding in Peer-to-Peer Networks

Free riding in peer-to-peer (P2P) networks poses a serious threat to their proper operation. Here, the authors present a variety of approaches developed to overcome this problem. They introduce several unique aspects of P2P networks and discuss free ridings effects on P2P services. They categorize proposed solutions and describe each categorys important features and implementation issues together with some sample solutions. They also discuss open issues, including common attacks and security considerations.

Real-time transaction scheduling in database systems

A database system supporting a real-time application, which can be called “a real-time database system (RTDBS)”, has to provide real-time information to the executing transactions. Each RTDB transaction is associated with a timing constraint, usually in the form of a deadline. Efficient resource scheduling algorithms and concurrency control protocols are required to schedule the transactions so as to satisfy both timing constraints and data consistency requirements. In this paper,† we concentrate on the concurrency control problem in RTDBSs. Our work has two basic goals: real-time performance evaluation of existing concurrency control approaches in RTDBSs, and proposing new concurrency control protocols with improved performance. One of the new protocols is locking-based, and it prevents the priority inversion problem‡ by scheduling the data lock requests based on prioritizing data items. The second new protocol extends the basic timestamp-ordering method by involving real-time priorities of transactions in the timestamp assignment procedure. Performance of the protocols is evaluated through simulations by using a detailed model of a single-site RTDBS. The relative performance of the protocols is examined as a function of transaction load, data contention (which is determined by a number of system parameters) and resource contention. The protocols are also tested under various real-time transaction processing environments. The performance of the proposed protocols appears to be good, especially under conditions of high transaction load and high data contention.

A comparison of epidemic algorithms in wireless sensor networks

We consider the problem of reliable data dissemination in the context of wireless sensor networks. For some application scenarios, reliable data dissemination to all nodes is necessary for propagating code updates, queries, and other sensitive information in wireless sensor networks. Epidemic algorithms are a natural approach for reliable distribution of information in such ad hoc, decentralized, and dynamic environments. In this paper we show the applicability of epidemic algorithms in the context of wireless sensor environments, and provide a comparative performance analysis of the three variants of epidemic algorithms in terms of message delivery rate, average message latency, and messaging overhead on the network.

A histogram-based approach for object-based query-by-shape-and-color in image and video databases

Considering the fact that querying by low-level object features is essential in image and video data, an efficient approach for querying and retrieval by shape and color is proposed. The approach employs three specialized histograms, (i.e. distance, angle, and color histograms) to store feature-based information that is extracted from objects. The objects can be extracted from images or video frames. The proposed histogram-based approach is used as a component in the query-by-feature subsystem of a video database management system. The color and shape information is handled together to enrich the querying capabilities for content-based retrieval. The evaluation of the retrieval effectiveness and the robustness of the proposed approach is presented via performance experiments.

Fuzzy color histogram-based video segmentation

We present a fuzzy color histogram-based shot-boundary detection algorithm specialized for content-based copy detection applications. The proposed method aims to detect both cuts and gradual transitions (fade, dissolve) effectively in videos where heavy transformations (such as cam-cording, insertions of patterns, strong re-encoding) occur. Along with the color histogram generated with the fuzzy linking method on L*a*b* color space, the system extracts a mask for still regions and the window of picture-in-picture transformation for each detected shot, which will be useful in a content-based copy detection system. Experimental results show that our method effectively detects shot boundaries and reduces false alarms as compared to the state-of-the-art shot-boundary detection algorithms.

Content-based retrieval of historical Ottoman documents stored as textual images

There is an accelerating demand to access the visual content of documents stored in historical and cultural archives. Availability of electronic imaging tools and effective image processing techniques makes it feasible to process the multimedia data in large databases. A framework for content-based retrieval of historical documents in the Ottoman Empire archives is presented. The documents are stored as textual images, which are compressed by constructing a library of symbols occurring in a document, and the symbols in the original image are then replaced with pointers into the codebook to obtain a compressed representation of the image. The features in wavelet and spatial domains, based on angular and distance span of shapes, are used to extract the symbols. In order to make content-based retrieval in the historical archives, a query is specified as a rectangular region in an input image and the same symbol-extraction process is applied to the query region. The queries are processed on the codebook of documents and the query images are identified in the resulting documents using the pointers in the textual images. The query process does not require decompression of images. The new content-based retrieval framework is also applicable to many other document archives using different scripts.

Counteracting free riding in Peer-to-Peer networks

The existence of a high degree of free riding is a serious threat to Peer-to-Peer (P2P) networks. In this paper, we propose a distributed framework to reduce the adverse effects of free riding on P2P networks. Our solution primarily focuses on locating free riders and taking actions against them. We propose a framework in which each peer monitors its neighbors, decides if they are free riders, and takes appropriate actions. Unlike other proposals against free riding, our framework does not require any permanent identification of peers or security infrastructures for maintaining a global reputation system. Our simulation results show that the framework can reduce the effects of free riding and can therefore increase the performance of a P2P network.