A Publish-Subscribe Scheme Based Open Architecture for Crowd-Sourcing

Participatory sensing, when a crowd of users collaborate to collect useful information, based applications are getting popular these days thanks to the proliferation of powerful mobile devices. The built-in sensors of smartphones offer an easy and handy way to monitor the environment and collect data which can serve as the basis of smart applications. However, the quick and flexible development and deployment of these applications call for a unifying open architecture. In this paper we propose a publish-subscribe based open participatory sensing architecture

Live Public Transport Information Service Using Crowdsourced Data (Demo Paper)

Abstract—Infrastructural problems of modern cities cannot be solved through sheer power of will alone. The public transportation system is one of the most effected parts and as the situation is degrading, more and more people become reluctant to take public transport. The fine tuning of the system, or even its restructuring, requires an immense amount of data, which traditionally can only be collected via costly and time consuming ways, like deploying sensors, conveying surveys, just to name a few. Not to mention that during this process, the citizens do not experience too much improvement, and become easily skeptical concerning the outcomes. Is there really no other way? In this demo, we present and demonstrate our approach to solve this problem in the form of a smart phone application providing real-time feedback on public transport, transits, and user-reviews based on crowdsensing

Participatory Sensing Based Real-time Public Transport Information Service

Abstract—Modern cities continuously struggle with infrastructural problems especially when the population is massively growing. One affected area is public transportation. In default of offering convenient and reliable service the passengers tend to consider other transport alternatives. However, even a relatively simple functional enhancement, such as providing real-time timetable information, requires considerable investment and effort following traditional means, e.g. deploying sensors and building a background communication and processing infrastructure. Using the power of crowd to gather the required data, share information and send feedback is a viable and cost effective alternative. In this demonstration, we present TrafficInfo, our prototype smart phone application to implement a participatory sensing based live public transport information service. TrafficInfo visualizes the actual position of public transport vehicles with live updates on a map, and gives support to crowd sourced data collection and passenger feedback

Measurements of a Real-time Transit Feed Service Architecture for Mobile Participatory Sensing

Abstract—We spend a substantial part of our time with traveling, in crowded cities usually taking public transportation. It is important, making travel planning easier, to have accurate information about vehicle arrival times at the stops. Most of the public transport operators make their timetables freely available either on the web or in some special format, like GTFS (General Transit Feed Specification). However, they contain static information only, not reflecting the actual traffic conditions. Mobile participatory sensing can help extend the basic service with real-time updates letting the crowd collect the required data. With this respect we believe that such participatory sensing based application must offer a day zero service following incremental service extension. In this paper, we discuss how to realize real-time refinements to static GTFS data based on mobile participatory sensing. We show how this service can be implemented by an XMPP (Extensible Messaging and Presence Protocol) based mobile participatory sensing architecture and we evaluate its performance

Smart Timetable Service Based on Crowdsensed Data

The rapid technological development and the introduction of smart services make it possible for modern cities to offer an enhanced perception of city life for their inhabitants. For instance, a smart timetable service of the city’s public transportation lines updated in real-time can decrease unnecessary waiting times at stops and increase the efficiency of travel planning. However, the implementation of such a service in a traditional way requires the deployment and maintenance of some costly sensing and tracking infrastructure. Fortunately, mobile crowdsensing, when the crowd of passengers and their mobile devices are used to gather data, can be a viable and almost free of charge alternative for implementing sensing based smart city services. In this chapter, we put the emphasis on the introduction of a crowdsensing based smart timetable service, which has been developed as a prototype smart city application. The front-end interface of this service is called TrafficInfo. It is a simple and easy-to-use Android application which visualizes public transport information of the given city on Google Maps in real-time. The live updates of transport schedule information rely on the automatic stop event detection of public transport vehicles. TrafficInfo is built upon an Extensible Messaging and Presence Protocol (XMPP) based communication framework which was designed to facilitate the development of crowd assisted smart city applications. The chapter introduces this generic framework shortly, then describes the prototype smart timetable service

Spatial and Temporal Sentiment Analysis of Twitter data

The public have used Twitter world wide for expressing opinions. This study focuses on spatio-temporal variation of georeferenced Tweets’ sentiment polarity, with a view to understanding how opinions evolve on Twitter over space and time and across communities of users. More specifically, the question this study tested is whether sentiment polarity on Twitter exhibits specific time-location patterns. The aim of the study is to investigate the spatial and temporal distribution of georeferenced Twitter sentiment polarity within the area of 1 km buffer around the Curtin Bentley campus boundary in Perth, Western Australia. Tweets posted in campus were assigned into six spatial zones and four time zones. A sentiment analysis was then conducted for each zone using the sentiment analyser tool in the Starlight Visual Information System software. The Feature Manipulation Engine was employed to convert non-spatial files into spatial and temporal feature class. The spatial and temporal distribution of Twitter sentiment polarity patterns over space and time was mapped using Geographic Information Systems (GIS). Some interesting results were identified. For example, the highest percentage of positive Tweets occurred in the social science area, while science and engineering and dormitory areas had the highest percentage of negative postings. The number of negative Tweets increases in the library and science and engineering areas as the end of the semester approaches, reaching a peak around an exam period, while the percentage of negative Tweets drops at the end of the semester in the entertainment and sport and dormitory area. This study will provide some insights into understanding students and staff ’s sentiment variation on Twitter, which could be useful for university teaching and learning management

European Handbook of Crowdsourced Geographic Information

This book focuses on the study of the remarkable new source of geographic information that has become available in the form of user-generated content accessible over the Internet through mobile and Web applications. The exploitation, integration and application of these sources, termed volunteered geographic information (VGI) or crowdsourced geographic information (CGI), offer scientists an unprecedented opportunity to conduct research on a variety of topics at multiple scales and for diversified objectives. The Handbook is organized in five parts, addressing the fundamental questions: What motivates citizens to provide such information in the public domain, and what factors govern/predict its validity?What methods might be used to validate such information? Can VGI be framed within the larger domain of sensor networks, in which inert and static sensors are replaced or combined by intelligent and mobile humans equipped with sensing devices? What limitations are imposed on VGI by differential access to broadband Internet, mobile phones, and other communication technologies, and by concerns over privacy? How do VGI and crowdsourcing enable innovation applications to benefit human society? Chapters examine how crowdsourcing techniques and methods, and the VGI phenomenon, have motivated a multidisciplinary research community to identify both fields of applications and quality criteria depending on the use of VGI. Besides harvesting tools and storage of these data, research has paid remarkable attention to these information resources, in an age when information and participation is one of the most important drivers of development. The collection opens questions and points to new research directions in addition to the findings that each of the authors demonstrates. Despite rapid progress in VGI research, this Handbook also shows that there are technical, social, political and methodological challenges that require further studies and research

European Handbook of Crowdsourced Geographic Information

"This book focuses on the study of the remarkable new source of geographic information that has become available in the form of user-generated content accessible over the Internet through mobile and Web applications. The exploitation, integration and application of these sources, termed volunteered geographic information (VGI) or crowdsourced geographic information (CGI), offer scientists an unprecedented opportunity to conduct research on a variety of topics at multiple scales and for diversified objectives. The Handbook is organized in five parts, addressing the fundamental questions: What motivates citizens to provide such information in the public domain, and what factors govern/predict its validity?What methods might be used to validate such information? Can VGI be framed within the larger domain of sensor networks, in which inert and static sensors are replaced or combined by intelligent and mobile humans equipped with sensing devices? What limitations are imposed on VGI by differential access to broadband Internet, mobile phones, and other communication technologies, and by concerns over privacy? How do VGI and crowdsourcing enable innovation applications to benefit human society? Chapters examine how crowdsourcing techniques and methods, and the VGI phenomenon, have motivated a multidisciplinary research community to identify both fields of applications and quality criteria depending on the use of VGI. Besides harvesting tools and storage of these data, research has paid remarkable attention to these information resources, in an age when information and participation is one of the most important drivers of development. The collection opens questions and points to new research directions in addition to the findings that each of the authors demonstrates. Despite rapid progress in VGI research, this Handbook also shows that there are technical, social, political and methodological challenges that require further studies and research.