CommuniSense: Crowdsourcing Road Hazards in Nairobi

Nairobi is one of the fastest growing metropolitan cities and a major business and technology powerhouse in Africa. However, Nairobi currently lacks monitoring technologies to obtain reliable data on traffic and road infrastructure conditions. In this paper, we investigate the use of mobile crowdsourcing as means to gather and document Nairobi's road quality information. We first present the key findings of a city-wide road quality survey about the perception of existing road quality conditions in Nairobi. Based on the survey's findings, we then developed a mobile crowdsourcing application, called CommuniSense, to collect road quality data. The application serves as a tool for users to locate, describe, and photograph road hazards. We tested our application through a two-week field study amongst 30 participants to document various forms of road hazards from different areas in Nairobi. To verify the authenticity of user-contributed reports from our field study, we proposed to use online crowdsourcing using Amazon's Mechanical Turk (MTurk) to verify whether submitted reports indeed depict road hazards. We found 92% of user-submitted reports to match the MTurkers judgements. While our prototype was designed and tested on a specific city, our methodology is applicable to other developing cities.Comment: In Proceedings of 17th International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI 2015

A Unified And Green Platform For Smartphone Sensing

Smartphones have become key communication and entertainment devices in people\u27s daily life. Sensors on (or attached to) smartphones can enable attractive sensing applications in different domains, including environmental monitoring, social networking, healthcare, transportation, etc. Most existing smartphone sensing systems are application-specific. How to leverage smartphones\u27 sensing capability to make them become unified information providers for various applications has not yet been fully explored. This dissertation presents a unified and green platform for smartphone sensing, which has the following desirable features: 1) It can support various smartphone sensing applications; 2) It is personalizable; 2) It is energy-efficient; and 3) It can be easily extended to support new sensors. Two novel sensing applications are built and integrated into this unified platform: SOR and LIPS. SOR is a smartphone Sensing based Objective Ranking (SOR) system. Different from a few subjective online review and recommendation systems (such as Yelp and TripAdvisor), SOR ranks a target place based on data collected via smartphone sensing. LIPS is a system that learns the LIfestyles of mobile users via smartPhone Sensing (LIPS). Combining both unsupervised and supervised learning, a hybrid scheme is proposed to characterize lifestyle and predict future activities of mobile users. This dissertation also studies how to use the cloud as a coordinator to assist smartphones for sensing collaboratively with the objective of reducing sensing energy consumption. A novel probabilistic model is built to address the GPS-less energy-efficient crowd sensing problem. Provably-good approximation algorithms are presented to enable smartphones to sense collaboratively without accurate locations such that sensing coverage requirements can be met with limited energy consumption

Let Opportunistic Crowdsensors Work Together for Resource-efficient, Quality-aware Observations

International audienceOpportunistic crowdsensing empowers citizens carrying hand-held devices to sense physical phenomena of common interest at a large and fine-grained scale without requiring the citizens' active involvement. However, the resulting uncontrolled collection and upload of the massive amount of contributed raw data incur significant resource consumption, from the end device to the server, as well as challenge the quality of the collected observations. This paper tackles both challenges raised by opportunistic crowdsensing, that is, enabling the resource-efficient gathering of relevant observations. To achieve so, we introduce the BeTogether middleware fostering context-aware, collaborative crowdsensing at the edge so that co-located crowdsensors operating in the same context, group together to share the work load in a cost- and quality-effective way. We evaluate the proposed solution using an implementation-driven evaluation that leverages a dataset embedding nearly 1 million entries contributed by 550 crowdsensors over a year. Results show that BeTogether increases the quality of the collected data while reducing the overall resource cost compared to the cloud-centric approach

Citizen noise pollution monitoring

Trabajo presentado a la 10th Annual International Conference on Digital Government Research: Social Networks: Making Connections between Citizens, Data and Government, celebrada en Puebla (México) del 17 al 21 de mayo de 2009.In this paper we present a new approach to monitor noise pollution involving citizens and built upon the notions of participatory sensing and citizen science. We enable citizens to measure their personal exposure to noise in their everyday environment by using GPS-equipped mobile phones as noise sensors. The geo-localised measures and user-generated meta-data can be automatically sent and shared online with the public to contribute to the collective noise mapping of cities. Our prototype, called NoiseTube, can be found online.This work was partially supported by the EU under contract IST- 34721 (TAGora). The TAGora project is funded by the Future and Emerging Technologies program (IST-FET) of the European Commission. Matthias Stevens is a Research Assistant of the Fund for Scientific Research, Flanders (Aspirant van het Fonds Wetenschappelijk Onderzoek - Vlaanderen).Peer reviewe

Mapping Participatory Sensing and Community-led Environmental Monitoring Initiatives: Making Sense H2020 CAPS Project

This report presents a summary of the state of the art in urban participatory sensing and community-led environmental monitoring, the types of engagement approaches typically followed, contextual examples of current developments in this field, and current challenges and opportunities for successful interventions. The goal is to better understand the field and possible options for reflection and action around it, in order to better inform future conceptual and practical developments inside and outside the Making Sense project.JRC.I.2-Foresight, Behavioural Insights and Design for Polic

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

Location-based Social Network for Cities & Neighbourhood Sustainable Development

Online Social Network (OSN) is categorized as Web 2.0 which is defined by O'Reilly in 2004, is the idea of mutually maximizing collective intelligence and added value for each participant by dynamic information sharing and creation. Current trend sbows that the next big thing in OSN is Location-based Social Networking (LBSN) which is the composite of OSN and Location-based Service (LBS). The goal of this paper is to study on Malaysian online social behaviour and to explore what are the key technologies of LBSN to support the development of neighbourhoods where residents feel a sense of connection to their local community and ability to engage in that community. Problems and opportunities identified are: I) Lack of research has been done to nnderstand Malaysian online social behavior in the context of cities & neighbourhood development, 2) Modem societies are said to lives in a condition of individualism and 3) Malaysia has strong networked community and there are a number of social Application Programming Interface (API) which provide a great opportunities for developers to create an application which can support the idea of smart, liveable and sustainable cities. The objectives of the research are: 1) To study on Malaysian social behavior in using Location-based Social Network (LBSN) , motivation for participation and pattern of use, 2) To identifY and understand key technologies of LBSN, and 3) To design an engaging LBSN which leverage on key technologies for neighbourhood and cities' sustainable development. Survey instrument is used as data collection tool to investigate the Malaysian online social behaviour and gauge their views on civil issues such as crime in their residential. Interview also is carried out to the owner of existing crime mapping system to identifY the gaps and opportunities for improvements. This research discovers that Malaysians are socially active in online community network and have strong civic conscious to make our neighbourhood works better. Govermnent shonld look forward into open data for beneficial of public. With proper neighbourhood planning, it will contribute to sustainable community which can help country's development

An Elastic Hybrid Sensing Platform: Architecture and Research Challenges

© 2016 Published by Elsevier B.V. The dynamic provisioning of hybrid sensing services that integrates both WSN and MPS is a promising, yet challenging concept. It does not only widen the spatial sensing coverage, but it also enables different types of sensing nodes to collaboratively perform sensing tasks and complement each other. Furthermore, it allows for the provisioning of a new category of services that was not possible to implement in pure WSN or MPS networks. Offering a hybrid sensing platform as a service results in several benefits including, but no limited to, efficient sharing and dynamic management of sensing nodes, diversification and reuse of sensing services, as well as combination of many sensing paradigms to enable data to be collected from different sources. However, many challenges need to be resolved before such architecture can be feasible. Currently, the deployment of sensing applications and services is a costly and complex process, which also lacks automation. This paper motivates the need for hybrid sensing, sketches an early architecture, and identifies the research issues with few hints on how to solve them. We argue that a sensing platform that reuses the virtualization and cloud computing concepts will help in addressing many of these challenges, and overcome the limitations of today\u27s deployment practices

From Sensor to Observation Web with Environmental Enablers in the Future Internet

This paper outlines the grand challenges in global sustainability research and the objectives of the FP7 Future Internet PPP program within the Digital Agenda for Europe. Large user communities are generating significant amounts of valuable environmental observations at local and regional scales using the devices and services of the Future Internet. These communities’ environmental observations represent a wealth of information which is currently hardly used or used only in isolation and therefore in need of integration with other information sources. Indeed, this very integration will lead to a paradigm shift from a mere Sensor Web to an Observation Web with semantically enriched content emanating from sensors, environmental simulations and citizens. The paper also describes the research challenges to realize the Observation Web and the associated environmental enablers for the Future Internet. Such an environmental enabler could for instance be an electronic sensing device, a web-service application, or even a social networking group affording or facilitating the capability of the Future Internet applications to consume, produce, and use environmental observations in cross-domain applications. The term ?envirofied? Future Internet is coined to describe this overall target that forms a cornerstone of work in the Environmental Usage Area within the Future Internet PPP program. Relevant trends described in the paper are the usage of ubiquitous sensors (anywhere), the provision and generation of information by citizens, and the convergence of real and virtual realities to convey understanding of environmental observations. The paper addresses the technical challenges in the Environmental Usage Area and the need for designing multi-style service oriented architecture. Key topics are the mapping of requirements to capabilities, providing scalability and robustness with implementing context aware information retrieval. Another essential research topic is handling data fusion and model based computation, and the related propagation of information uncertainty. Approaches to security, standardization and harmonization, all essential for sustainable solutions, are summarized from the perspective of the Environmental Usage Area. The paper concludes with an overview of emerging, high impact applications in the environmental areas concerning land ecosystems (biodiversity), air quality (atmospheric conditions) and water ecosystems (marine asset management)