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

Location-based technologies for learning

Emerging technologies for learning report - Article exploring location based technologies and their potential for educatio

Spatial ability, urban wayfinding and location-based services:a review and first results

Location-Based Services (LBS) are a new industry at the core of which are GISand spatial databases. With increasing mobility of individuals, the anticipatedavailability of broadband communications for mobile devices and growingvolumes of location specific information available in databases there willinevitably be an increase in demand for services providing location relatedinformation to people on the move. New Information and CommunicationTechnologies (NICTs) are providing enhanced possibilities for navigating ?smartcities?. Urban environments, meanwhile, have increasing spatial complexity.Navigating urban environments is becoming an important issue. The time is ripefor a re-appraisal of urban wayfinding. This paper critically reviews the currentLBS applications and raises a series of questions with regard to LBS for urbanwayfinding. Research is being carried out to measure individuals? spatialability/awareness and their degree of preference for using LBS in wayfinding. Themethodology includes both the use of questionnaires and a virtual reality CAVE.Presented here are the results of the questionnaire survey which indicate therelationships between individuals? spatial ability, use of NICTs and modepreference for receiving wayfinding cues. Also discussed are our future researchdirections on LBS, particular on issues of urban wayfinding using NICTs

Assessing database and network threats in traditional and cloud computing

Cloud Computing is currently one of the most widely-spoken terms in IT. While it offers a range of technological and financial benefits, its wide acceptance by organizations is not yet wide spread. Security concerns are a main reason for this and this paper studies the data and network threats posed in both traditional and cloud paradigms in an effort to assert in which areas cloud computing addresses security issues and where it does introduce new ones. This evaluation is based on Microsoft’s STRIDE threat model and discusses the stakeholders, the impact and recommendations for tackling each threat

Locational wireless and social media-based surveillance

The number of smartphones and tablets as well as the volume of traffic generated by these devices has been growing constantly over the past decade and this growth is predicted to continue at an increasing rate over the next five years. Numerous native features built into contemporary smart devices enable highly accurate digital fingerprinting techniques. Furthermore, software developers have been taking advantage of locational capabilities of these devices by building applications and social media services that enable convenient sharing of information tied to geographical locations. Mass online sharing resulted in a large volume of locational and personal data being publicly available for extraction. A number of researchers have used this opportunity to design and build tools for a variety of uses – both respectable and nefarious. Furthermore, due to the peculiarities of the IEEE 802.11 specification, wireless-enabled smart devices disclose a number of attributes, which can be observed via passive monitoring. These attributes coupled with the information that can be extracted using social media APIs present an opportunity for research into locational surveillance, device fingerprinting and device user identification techniques. This paper presents an in-progress research study and details the findings to date