1. Introduction

2. Related Work

3. The Implement of Algorithms

4. FR Algorithm

5. Conclusions

Acknowledgements

References

In the process of engineering applications, we often use a large number of vertices and edges to represent the complex system model, but sometimes the model is so complex that it is impossible to draw the graph neatly and beautifully by hand. So how to draw the graph automatically using the computer and how to make the graph visual become an important research topic. Based on the study on the genetic algorithm, SHLA algorithm and FR algorithm, we implement these algorithms by analyzing the pseudo-codes. Especially we focus on the study of the source programming about FR-boost and then implement an improved FR algorithm that selects XML document as the input and output graph files. It can effectively reduce the complexity of algorithm and increase the point-line graph’s visualization.

Introduction

With the development of Internet, graphical data is in everywhere. In the face of these massive graphic data, we often use the statistical analysis and data mining methods to extract the useful information. However most extracted result is unreadable, we have a strong demand of graph visualization. The study of algorithms for graph drawing and graph visualization has a long history, until now all kinds of new algorithms are emerging in succession. They are widely used in network management, program visualization, software engineering and the computer aided teaching. So it is very important to study the graph visualization and the design algorithm. In this paper, the layout algorithm of two-dimensional point-line graph is studied. Based on the study and analysis of pseudo-codes, we program and implement these algorithms, at the same time the visualization test graphs are provided. Further, an improved FR optimization algorithm is designed and programmed to increase the visualization degree of point-line graph.

1- Introduction

2- Background

3- Related work

4- Real WSN system

5- The graph-based big data model

6- Prototype implementation for proposed model

7- Simulation

8- A case study: surveillance application

9- Experimental work

10- Conclusions

References

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.

Introduction

A wireless multimedia sensor network (WMSN) is a distributed wireless network that consists of a set of multimedia sensor nodes, which are connected to each other or connected to leading gateways. Nowadays, smart devices such as mobile phones, smart televisions, and smart watches are equipped with sensors and network connections. Hence, with the advances in wireless communication technologies, multimedia sensor networks are expected to be one of the major components in the Internet of things (IoT). A typical application for a WMSN is a surveillance system or a monitoring system. Smart city surveillance cameras with 7/24 recording, or one million sensor nodes reporting meteorological data produce data in various formats as video, audio, and text [1]. All that huge structured or unstructured data is considered as big data, which is defined by a number of Vs; Volume, Velocity, Variety, Veracity, and Value. Min et al. [2] present a comprehensive survey of big data and they identify that “defining the structural model of big data” is a fundamental problem.

Smart cities produce large amount of data having a large variability, variety, velocity, and size; and thus complexity. The variety and variability of data can be due to the presence of several different formats, [3,17,23,27] and to the interoperability among semantics of the single fields and of the several data sets [6]. Static data are rarely updated, for instance once per month/year, which is quite the opposite with dynamic data: they can be updated from once a day up to every minute so as to get real time data. The data velocity is related to the frequency of data update for dynamic data such as position of buses, flow of people status, position of waste collectors, etc. or as data streams. Thus the size of the store grows over time accumulating new data every day and week. At architectural level, smart city solutions typically adopt n-tier architectures [2]. The Resource Description Framework specified by W3C allows the representation of facts using “triples” of the form (subject, predicate, object) where URIs are used to identify the entities and the predicates connecting them. Thus a triple represents the arc of the graph connecting two entities and the predicate describes the kind of relation between the two entities. Moreover, the object part of the triple can also be a low level data type as string, dates, integers, etc., to describe the relations among entities and specific information about them (e.g., name, email, birth date). RDF stores allow storing triples, while the SPARQL query language allows querying them. Some RDF stores can also manage set of triples as a single graph identified by a URI, in this way information about this graph can be provided using other triples (where the subject is the graph itself). It has to be highlighted that SPARQL performs search using triple matching, for example “?s rdf: type km4c: Hotel” searches for all the “?s” that are of type Hotel, and does not allow performing, in a general way, the typical graph operations like path search1 or connectivity check. The usage of RDF stores in the application domain of Smart City is quite recent, since in most cases services are vertically provided. For example, the Intelligent Transport System, ITS, in the city provides information regarding the location of buses and their delay, without addressing the location of city services, flow of people, real time events in the city. The integrated services are typically provided by data aggregators and service providers that perform data integration and allow exploiting integrated data models. Some city data integrators are well-known services such as bike and car sharing, navigator system, tourism information, hotel booking, etc. All these solutions have the need to integrate geo-located information with real time data and events continuously arriving from updated information such as: events, votes, traffic flows, comments, etc. [11,27,18]. As to these applications, RDF stores may be a solution to allow addressing the variability of data, so as to make reasoning on space, time, and concepts [28]. On this regard, comprehensive smart city data models and ontologies can be really effective (see for example http://smartcity.linkeddata.es/, produced by Read4SmartCity2 project of the European Commission). One of the ranked models in the Read4SmartCity research project is Km4City ontology that is also used as a reference for the definition of the data of the proposed benchmark [6], and adopted in European Commission international projects named RESOLUTE H2020, REPLICATE H2020, and in Sii-Mobility National Smart City project in Italy, as well as in GHOST MIUR and other projects.