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.
Over the years, various methods have been used for wireless sensor networks data representation and management ( @cite_3 @cite_22 ). Our approach is different basically by the aspects of big data, graph database storage, and our unique graph data model.
@cite_22 @cite_3
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Wireless sensor networks have attracted attention from a diverse set of researchers, due to the unique combination of distributed, resource and data processing constraints. However, until now, the lack of real sensor network deployments have resulted in ad-hoc assumptions on a wide range of issues including topology characteristics and data distribution. As deployments of sensor networks become more widespread [1, 2], many of these assumptions need to be revisited.This paper deals with the fundamental issue of spatio-temporal irregularity in sensor networks We make the case for the existence of such irregular spatio-temporal sampling, and show that it impacts many performance issues in sensor networks. For instance, data aggregation schemes provide inaccurate results, compression efficiency is dramatically reduced, data storage skews storage load among nodes and incurs significantly greater routing overhead. To mitigate the impact of irregularity, we outline a spectrum of solutions. For data aggregation and compression, we propose the use of spatial interpolation of data (first suggested by in [3] and temporal signal segmentation followed by alignment. To reduce the cost of data-centric storage and routing, we propose the use of virtualization, and boundary detection. 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. Wireless sensor networks offer the potential to span and monitor large geographical areas inexpensively. Sensors, however, have significant power constraint (battery life), making communication very expensive. Another important issue in the context of sensor-based information systems is that individual sensor readings are inherently unreliable. In order to address these two aspects, sensor database systems like TinyDB and Cougar enable in-network data aggregation to reduce the communication cost and improve reliability. The existing data aggregation techniques, however, are limited to relatively simple types of queries such as SUM, COUNT, AVG, and MIN MAX. In this paper we propose a data aggregation scheme that significantly extends the class of queries that can be answered using sensor networks. These queries include (approximate) quantiles, such as the median, the most frequent data values, such as the consensus value, a histogram of the data distribution, as well as range queries. In our scheme, each sensor aggregates the data it has received from other sensors into a fixed (user specified) size message. We provide strict theoretical guarantees on the approximation quality of the queries in terms of the message size. We evaluate the performance of our aggregation scheme by simulation and demonstrate its accuracy, scalability and low resource utilization for highly variable input data sets. 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.