Abstract 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.
In this article we demonstrate that spatial big data can play a key role in many emerging wireless networking applications. We also argue that spatial and spatiotemporal problems have their own very distinct role in the big data context compared to the commonly considered relational problems. We describe three major application scenarios for spatial big data, each imposing specific design and research challenges. We then present our work on developing highly scalable parallel processing frameworks for spatial data in the Hadoop framework using the MapReduce computational model. Our results show that using Hadoop enables highly scalable implementations of algorithms for common spatial data processing problems. However, development of these implementations requires significant specialized knowledge, demonstrating the need for development of more user-friendly alternatives.