Big Data Model Simulation on a Graph Database for Surveillance in Wireless Multimedia Sensor Networks

Sensors are present in various forms all around the world such as mobile phones, surveillance cameras, smart televisions, intelligent refrigerators and blood pressure monitors. Usually, most of the sensors are a part of some other system with similar sensors that compose a network. One of such networks is composed of millions of sensors connect to the Internet which is called Internet of things (IoT). With the advances in wireless communication technologies, multimedia sensors and their networks are expected to be major components in IoT. Many studies have already been done on wireless multimedia sensor networks in diverse domains like fire detection, city surveillance, early warning systems, etc. All those applications position sensor nodes and collect their data for a long time period with real-time data flow, which is considered as big data. Big data may be structured or unstructured and needs to be stored for further processing and analyzing. Analyzing multimedia big data is a challenging task requiring a high-level modeling to efficiently extract valuable information/knowledge from data. In this study, we propose a big database model based on graph database model for handling data generated by wireless multimedia sensor networks. We introduce a simulator to generate synthetic data and store and query big data using graph model as a big database. For this purpose, we evaluate the well-known graph-based NoSQL databases, Neo4j and OrientDB, and a relational database, MySQL.We have run a number of query experiments on our implemented simulator to show that which database system(s) for surveillance in wireless multimedia sensor networks is efficient and scalable

Chisio : a visual framework for compound graph editing and layout

Ankara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2007.Thesis (Master's) -- Bilkent University, 2007.Includes bibliographical references leaves 96-97Graphs are data models, widely used in many areas from networking to biology to computer science. Visualization, interactive editing ability and layout of graphs are critical issues when analyzing the underlying relational information. There are many commercial and non-commercial graph visualization tools. However, overall support for compound or hierarchically organized graph representations is very limited. We introduce a new open-source editing and layout framework named Chisio for compound graphs. Chisio is developed as a free, easy-to-use and powerful academic graph visualization tool, supporting various automatic layout algorithms. It is written in Java and based on Eclipse’s Graphical Editing Framework (GEF). Chisio can be used as a finished generic compound graph editor with standard graph editing facilities such as zoom, scroll, add or remove graph objects, move, and resize. Object property and layout options dialogs are provided to modify existing graph object properties and layout options, respectively. In addition, printing or saving the current drawing as a static image and persistent storage facilities are supported. Saved graphs or GraphML formatted files created by other tools can be loaded into Chisio. Furthermore, a highlight mechanism is provided to emphasize subgraphs of users interest. The framework has an architecture suitable for easy customization of the tool for end-users’ specific needs as well. Also Chisio offers several layout styles from the basic spring embedder to hierarchical layout to compound spring embedder to circular layout. Furthermore, new algorithms are straightforward to add, making Chisio an ideal test environment for layout algorithm developers.Küçükkeçeci, CihanM.S

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.Thesis (Ph.D.) -- Graduate School of Natural and Applied Sciences. Computer Engineering

A Graph Based Big Data Model for Wireless Multimedia Sensor Networks

Wireless multimedia sensor networks are of interest to researchers from different disciplines and many studies have been proposed in a wide variety of application domains, such as military surveillance systems, environmental monitoring, fault monitoring and distributed smart cameras in the last decade. In a wireless sensor network, a large number of sensors can be deployed to monitor target areas and autonomously collect sensor data. This produces a large amount of raw data that needs to be stored, processed, and analyzed. In this paper, we propose a graph-based big data model for simulating multimedia wireless sensor networks. The big sensor data is stored in a graph database for the purpose of advanced analytics like statistics, data mining, and prediction. A prototype implementation of the proposed model has been developed and a number of experiments have been done for measuring the accuracy and efficiency of our solution. In addition, we present a case study using the military surveillance domain with a number of complex experimental queries by using our prototype. The experimental results show that our proposed multimedia wireless sensor network model is efficient and applicable in large-scale real life applications