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

An Architecture for Big Data Analytics

Big Data is the new experience curve in the new economy driven by data with high volume, velocity, variety, and veracity. They come from various sources that include the Internet, mobile devices, social media, geospatial devices, sensors, and other machine-generated data. Unlocking the value of Big Data allows business to better sense and respond to the environment, and is becoming a key to creating competitive advantages in a complex and rapidly changing market. Government is also taking notice of the Big Data phenomenon and has created initiatives to exploit Big Data in many areas such as science and engineering, healthcare and national security. Traditional data processing and analysis of structured data using RDBMS and data warehousing no longer satisfy the challenges of Big Data. Technology trends for Big Data embrace open source software, commodity servers, and massively parallel-distributed processing platforms. Analytics is at the core of exploiting values from Big Data to create consumable insights for business and government. This paper presents architecture for Big Data Analytics and explores Big Data technologies that include NoSQL databases, Hadoop Distributed File System and MapReduce

Example of IoT platform usage for wireless video surveillance with support of NoSQL and cloud systems

Today there is an increasing need for implementation of area security systems, especially in sense of monitoring areas of interest. Many of the solutions related to wireless security cameras that are available on the market are mainly limited with their predefined functionalities. Furthermore, these sets of functionalities largely affect price levels. Thanks to an increasing development and availability of open-source hardware and DIY (Do It Youself) electronics applicable in the field of Internet of Things (IoT), as well as new methods in data storage, such as NoSQL, new opportunities for creation of custom systems for video monitoring and storing video data are opened. Through this work it is presented the solution for wireless security cameras, based on the IoT enabled open-source hardware and MongoDB database as the storage system. Also, in order to achieve replication of created content, possibilities of storing this content on the cloud storage system are explored. Established solution can be used on a\ud daily basis, both in the private and business environments. Also, in this paper are presented technologies used for system development. The solution can be used as a starting point for further development of the systems for areal monitoring and content of interest creation using the emerging technologies

A HADOOP-ENABLED SENSOR-ORIENTED INFORMATION SYSTEM FOR KNOWLEDGE DISCOVERY ABOUT TARGET-OF-INTEREST

To obtain a real-time situational awareness about the specific behavior of targets-of-interest using large-scale sensory data-set, this paper presents a generic sensor-oriented information system based on Hadoop Ecosystem, which is denoted as SOIS-Hadoop for simplicity.  Robotic heterogeneous sensor nodes bound by wireless sensor network are used to track things-of-interest. Hadoop Ecosystem enables highly scalable and fault-tolerant acquisition, fusion and storage, retrieval, and processing of sensory data. In addition, SOIS-Hadoop employs temporally and spatially dependent mathematical model to formulate the expected behavior of targets-of-interest, based on which the observed behavior of targets can be analyzed and evaluated.  Using two real-world sensor-oriented information processing and analysis problems as examples, the mechanism of SOIS-Hadoop is also presented and validated in detail