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.
PipeNet @cite_17 is a multi-layered wireless sensor networks application focused on pipeline monitoring. System aims to detect the leaks and other anomalies in water pipelines. They have used various types of sensors like pressure, pH and ultrasonic sensors on top of Intel Moto platform. Our sensor nodes are built on Rasberry Pi platform and have seismic, acoustic, and PIR sensors but also a multimedia camera. A camera needs further analysis like image processing and feature extraction. Their multi-layer architecture is similar to our prototype but in another domain with different sensors and different analytical approaches. They try to analyze the collected multi-modal data for detection of the leaks. But we try to identify objects and track their movement. On the other hand, we approach our sensor data as big data.
@cite_17
2120117050
US water utilities are faced with mounting operational and maintenance costs as a result of aging pipeline infrastructures. Leaks and ruptures in water supply pipelines and blockages and overflow events in sewer collectors cost millions of dollars a year, and monitoring and repairing this underground infrastructure presents a severe challenge. In this paper, we discuss how wireless sensor networks (WSNs) can increase the spatial and temporal resolution of operational data from pipeline infrastructures and thus address the challenge of near real-time monitoring and eventually control. We focus on the use of WSNs for monitoring large diameter bulk-water transmission pipelines. We outline a system, PipeNet, we have been developing for collecting hydraulic and acoustic vibration data at high sampling rates as well as algorithms for analyzing this data to detect and locate leaks. Challenges include sampling at high data rates, maintaining aggressive duty cycles, and ensuring tightly time- synchronized data collection, all under a strict power budget. We have carried out an extensive field trial with Boston Water and Sewer Commission in order to evaluate some of the critical components of PipeNet. Along with the results of this preliminary trial, we describe the results of extensive laboratory experiments which are used to evaluate our analysis and data processing solutions. Our prototype deployment has led to the development of a reusable, field-reprogrammable software infrastructure for distributed high-rate signal processing in wireless sensor networks, which we also describe.