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

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2018-03-01
Kucukkececi, Cihan
Yazıcı, Adnan
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 connected 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. (c) 2017 Elsevier Inc. All rights reserved.
BIG DATA RESEARCH

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Citation Formats
C. Kucukkececi and A. Yazıcı, “Big Data Model Simulation on a Graph Database for Surveillance in Wireless Multimedia Sensor Networks,” BIG DATA RESEARCH, pp. 33–43, 2018, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/37747.