Çoklu ortam duyarga ağlarında aralık tip-2 bulanık sistemler kullanarak büyük çizge verilerde çokkatmanlı nesne takibi

Wireless multimedia sensor networks (WMSN) are the key elements of automation systems applied in different domains from home security to immigrant surveillance at a border station. In most of the applications, sensor data needs to be processed for data analytics. However, the interpretation of raw sensor data and unveiling the information inside remains a challenging issue from many aspects. As the interval of the sensor data is frequent, data needs to be treated as big data because of the volume and velocity. Unfortunately, traditional approaches do not perform well in big data analytics, especially in extracting the complex relationships between data. In this dissertation, a novel fuzzy object tracking approach which is developed using a big graph data model is proposed by utilization of a multilevel fusion. This approach consists of three main steps: intra-node fusion, inter-node fusion, and object trajectory construction. Intra-node fusion exploits object detection and tracking in each sensor while inter-node fusion uses spatiotemporal data along with neighbor sensors. Then, all trajectories from all sensor nodes are integrated using fuzziness to construct trajectories in the common ground-plane across the wireless multimedia sensor network. Since uncertainty naturally exists in trajectory data, fuzzy logic systems have been studied on the extracted trajectories as well as for further analytics like trajectory prediction and anomaly detection. A prototype system was implemented and several experiments were conducted to evaluate the performance of the proposed approach with both synthetic and real world datasets. The results show that usage of third-level fusion, in addition to inter-node and intra-node fusions provides significantly better performance for object tracking in WMSN applications. GeoLife Trajectories and Maritime Cadastre datasets were used as input of two different real world use cases to perform experiments, and results validate that interval type-2 fuzzy logic utilization improves performance in both trajectory extraction and analytics.