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.
Recent technological advances enable realization of wireless sensor networks suitable for many applications including environmental monitoring, biological and chemical contamination detection, earthquake management. Depending on the application, the properties of sensor network databases include a distributed environment, approximate and long-running queries, large volumes of data, uncertainty and fuzziness in data and in queries, activeness, etc., which bring out new database processing and management problems. In particular, reactive sensor network database applications must be able to detect the occurrences of specific events or changes in the network state, and to respond by automatically executing the appropriate application logic. For such applications, an ECA rule-based fuzzy active database approach fits well. In this study, we propose an active database approach employing a fuzzy Petri net model, which processes uncertain sensor data and handles flexible (i.e., fuzzy and approximate) and continuous queries. The proposed model provides a natural data processing and management approach for sensor and actuator network applications that require reactive behavior. In the recent past, search in sensor systems focused on node hardware constraints and very limited energy resources. But nowadays, that new applications need data processing with temporal constraints in their tasks; then one of the new challenges faced by wireless sensor networks (WSNs) is handling real-time storage and querying the data they process. Two main approaches to storage and querying data are generally considered warehousing and distributed. The warehousing approach stores data in a central database and then queries may be performed to it. In a distributed approach, sensor devices are considered as local databases and data are managed locally. The data collected by sensors must represent the current state of the environment; for this reason they are subject to logic and time constraints. Then, this paper identifies the main specifications of real-time data management and presents the available real-time data management solutions for WSNs, in order to discuss them and identify some open issues and provide guidelines for further contributions.