OppNet: Enabling citizen-centric urban IoT data collection through opportunistic connectivity service

Urban IoT data collection is challenging due to the limitations of the fixed sensing infrastructures. Instead of transmitting data directly through expensive cellular networks, citizen-centric data collection scheme through opportunistic network takes advantage of human mobility as well as cheap WiFi and D2D communication. In this paper, we present OppNet, which implements a context aware data forwarding algorithm and fills the gap between theoretical modelling of opportunistic networking and real deployment of citizen-centric data collection system. According to the results from a 3-day real-life experiment, OppNet shows consistent performance in terms of number of hops and time delay. Moreover, the underlying social structure can be clearly identified by analysing social contact data collected through OppNet

Incentive Mechanisms for Participatory Sensing: Survey and Research Challenges

Participatory sensing is a powerful paradigm which takes advantage of smartphones to collect and analyze data beyond the scale of what was previously possible. Given that participatory sensing systems rely completely on the users' willingness to submit up-to-date and accurate information, it is paramount to effectively incentivize users' active and reliable participation. In this paper, we survey existing literature on incentive mechanisms for participatory sensing systems. In particular, we present a taxonomy of existing incentive mechanisms for participatory sensing systems, which are subsequently discussed in depth by comparing and contrasting different approaches. Finally, we discuss an agenda of open research challenges in incentivizing users in participatory sensing.Comment: Updated version, 4/25/201

Cyber–Physical–Social Frameworks for Urban Big Data Systems: A Survey

The integration of things’ data on the Web and Web linking for things’ description and discovery is leading the way towards smart Cyber–Physical Systems (CPS). The data generated in CPS represents observations gathered by sensor devices about the ambient environment that can be manipulated by computational processes of the cyber world. Alongside this, the growing use of social networks offers near real-time citizen sensing capabilities as a complementary information source. The resulting Cyber–Physical–Social System (CPSS) can help to understand the real world and provide proactive services to users. The nature of CPSS data brings new requirements and challenges to different stages of data manipulation, including identification of data sources, processing and fusion of different types and scales of data. To gain an understanding of the existing methods and techniques which can be useful for a data-oriented CPSS implementation, this paper presents a survey of the existing research and commercial solutions. We define a conceptual framework for a data-oriented CPSS and detail the various solutions for building human–machine intelligence

Noise mapping based on participative measurements

The high temporal and spatial granularities recommended by the European regulation for the purpose of environmental noise mapping leads to consider new alternatives to simulations for reaching such information. While more and more European cities deploy urban environmental observatories, the ceaseless rising number of citizens equipped with both a geographical positioning system and environmental sensors through their smartphones legitimates the design of outsourced systems that promote citizen participatory sensing. In this context, the OnoM@p system aims at offering a framework for capitalizing on crowd noise data recorded by inexperienced individuals by means of an especially designed mobile phone application. The system fully rests upon open source tools and interoperability standards defined by the Open Geospatial Consortium. Moreover, the implementation of the Spatial Data Infrastructure principle enables to break up as services the various business modules for acquiring, analysing and mapping sound levels. The proposed architecture rests on outsourced processes able to filter outlier sensors and untrustworthy data, to cross- reference geolocalised noise measurements with both geographical and statistical data in order to provide higher level indicators, and to map the collected and processed data based on web services

TCitySmartF: A comprehensive systematic framework for transforming cities into smart cities

A shared agreed-upon definition of "smart city" (SC) is not available and there is no "best formula" to follow in transforming each and every city into SC. In a broader inclusive definition, it can be described as an opportunistic concept that enhances harmony between the lives and the environment around those lives perpetually in a city by harnessing the smart technology enabling a comfortable and convenient living ecosystem paving the way towards smarter countries and the smarter planet. SCs are being implemented to combine governors, organisations, institutions, citizens, environment, and emerging technologies in a highly synergistic synchronised ecosystem in order to increase the quality of life (QoL) and enable a more sustainable future for urban life with increasing natural resource constraints. In this study, we analyse how to develop citizen- and resource-centric smarter cities based on the recent SC development initiatives with the successful use cases, future SC development plans, and many other particular SC development solutions. The main features of SC are presented in a framework fuelled by recent technological advancement, particular city requirements and dynamics. This framework - TCitySmartF 1) aims to aspire a platform that seamlessly forges engineering and technology solutions with social dynamics in a new philosophical city automation concept - socio-technical transitions, 2) incorporates many smart evolving components, best practices, and contemporary solutions into a coherent synergistic SC topology, 3) unfolds current and future opportunities in order to adopt smarter, safer and more sustainable urban environments, and 4) demonstrates a variety of insights and orchestrational directions for local governors and private sector about how to transform cities into smarter cities from the technological, social, economic and environmental point of view, particularly by both putting residents and urban dynamics at the forefront of the development with participatory planning and interaction for the robust community- and citizen-tailored services. The framework developed in this paper is aimed to be incorporated into the real-world SC development projects in Lancashire, UK