Telsiz sensör ağlarında gerçek zamanlı ve güvenilir video iletimi.

Many wireless sensor network (WSN) applications require efficient multimedia communication capabilities. However, the existing communication protocols in the literature mainly aim to achieve energy efficiency and reliability objectives and do not address the multimedia communication challenges in WSN. In this thesis, comprehensive performance evaluation of the existing transport protocols is performed and it has been shown that the existing proposals achieve very poor performance in terms of large set of metrics such as packet delivery rate, end-to-end packet delay, bandwidth and energy efficiency, frame peak signal-to-noise ratio (PSNR), delay-bounded frame PSNR, frame delivery probability, frame end-to-end delay and jitter. Based on these results, an energy-efficient real-time and reliable video sensor communication protocol (VSCP) is introduced for WSN. VSCP estimates video quality perceived by sink using lost segments of video frames and aims to maintain the overall reliability at a given level with minimum energy expenditure. Source data rates are adjusted in a quality adaptable manner according to the network conditions and the overall reliability computed by sink. QSC (quality scalable coding) encoding technique is used to produce a nearly constant quality video at a given maximum data rate during adjustment of source data rates. Performance evaluations show that VSCP protocol significantly outperforms the existing proposals in terms of multimedia communication performance metrics in WSN.M.S. - Master of Scienc

Trajectory pattern extraction and anomaly detection for maritime vessels

Trajectory analysis and extraction of trajectory patterns are crucial to enhance marine safety and marine status awareness. The major data source for such analysis is Automatic Identification System (AIS), which publishes data related to movement of the ship while cruising. AIS broadcasts information including type of ship, identity number, state, destination, estimated time of arrival (ETA), location, speed, direction, and cargo. In this paper, we focus on extracting a variety of trajectory patterns for maritime vessels. The first group of analysis we focus on is arrival port, arrival time, and next position prediction on AIS messages, which are useful to aid maritime operators. We propose three different approaches for the prediction of marine vessel movement. As the second type of analysis, anomaly detection on marine vessel trajectory is studied. For vessel movement prediction, the experiments show that the proposed solutions brought improvement against conventional supervised learning approaches. The proposed anomaly detection technique is demonstrated on a case study