3,180 research outputs found
A survey of urban drive-by sensing: An optimization perspective
Pervasive and mobile sensing is an integral part of smart transport and smart
city applications. Vehicle-based mobile sensing, or drive-by sensing (DS), is
gaining popularity in both academic research and field practice. The DS
paradigm has an inherent transport component, as the spatial-temporal
distribution of the sensors are closely related to the mobility patterns of
their hosts, which may include third-party (e.g. taxis, buses) or for-hire
(e.g. unmanned aerial vehicles and dedicated vehicles) vehicles. It is
therefore essential to understand, assess and optimize the sensing power of
vehicle fleets under a wide range of urban sensing scenarios. To this end, this
paper offers an optimization-oriented summary of recent literature by
presenting a four-step discussion, namely (1) quantifying the sensing quality
(objective); (2) assessing the sensing power of various fleets (strategic); (3)
sensor deployment (strategic/tactical); and (4) vehicle maneuvers
(tactical/operational). By compiling research findings and practical insights
in this way, this review article not only highlights the optimization aspect of
drive-by sensing, but also serves as a practical guide for configuring and
deploying vehicle-based urban sensing systems.Comment: 24 pages, 3 figures, 4 table
IoT-based air quality monitoring systems for smart cities: A systematic mapping study
The increased level of air pollution in big cities has become a major concern for several organizations and authorities because of the risk it represents to human health. In this context, the technology has become a very useful tool in the contamination monitoring and the possible mitigation of its impact. Particularly, there are different proposals using the internet of things (IoT) paradigm that use interconnected sensors in order to measure different pollutants. In this paper, we develop a systematic mapping study defined by a five-step methodology to identify and analyze the research status in terms of IoT-based air pollution monitoring systems for smart cities. The study includes 55 proposals, some of which have been implemented in a real environment. We analyze and compare these proposals in terms of different parameters defined in the mapping and highlight some challenges for air quality monitoring systems implementation into the smart city context
Spatial modelling of air pollution for open smart cities
A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Geographic Information SystemsHalf of the world’s population already lives in cities, and by 2050 two-thirds of the
world’s population are expected to further move into urban areas. This urban growth
leads to various environmental, social and economic challenges in cities, hampering
the Quality of Life (QoL). Although recent trends in technologies equip us with
various tools and techniques that can help in improving quality of life, air pollution
remains the ‘biggest environmental health risk’ for decades, impacting individuals’
quality of life and well-being according to World Health Organisation (WHO). Many
efforts have been made to measure air quality, but the sparse arrangement of
monitoring stations and the lack of data currently make it challenging to develop
systems that can capture within-city air pollution variations. To solve this, flexible
methods that allow air quality monitoring using easily accessible data sources at the
city level are desirable. The present thesis seeks to widen the current knowledge
concerning detailed air quality monitoring by developing approaches that can help
in tackling existing gaps in the literature. The thesis presents five contributions
which address the issues mentioned above. The first contribution is the choice of
a statistical method which can help in utilising existing open data and overcoming
challenges imposed by the bigness of data for detailed air pollution monitoring.
The second contribution concerns the development of optimisation method which
helps in identifying optimal locations for robust air pollution modelling in cities.
The third contribution of the thesis is also an optimisation method which helps
in initiating systematic volunteered geographic information (VGI) campaigns for
detailed air pollution monitoring by addressing sparsity and scarcity challenges
of air pollution data in cities. The fourth contribution is a study proposing the
involvement of housing companies as a stakeholder in the participatory framework
for air pollution data collection, which helps in overcoming certain gaps existing in
VGI-based approaches. Finally, the fifth contribution is an open-hardware system that
aids in collecting vehicular traffic data using WiFi signal strength. The developed
hardware can help in overcoming traffic data scarcity in cities, which limits detailed
air pollution monitoring. All the contributions are illustrated through case studies
in Muenster and Stuttgart. Overall, the thesis demonstrates the applicability of the developed approaches for enabling air pollution monitoring at the city-scale under
the broader framework of the open smart city and for urban health research
Air Quality Prediction in Smart City's Information System
The introduction of new technology and computational power enables more data usages in a city. Such a city is called a smart city that records more data related to daily life activities and analyzes them to provide better services. Such data acquisition and analysis must be conducted quickly to support real-time information sharing and support other decision-making processes. Among such services, an information system is used to predict the air quality to ensure people's health in the city. The objective of this study is to compare various machine learning techniques (e.g., random forest, decision tree, neural network, naïve Bayes, etc.) when predicting the air quality in a city. For the comparison, we perform the removal of records with empty values, data division into training and testing datasets, and application of the k-fold cross-validation method. Numerical experiments are performed using a given online dataset. The results show that the three best methods are random forest, Gradient Boosting, and k-nearest neighbors with precision, recall, and f1-score values more than 0.63
Quality of Information in Mobile Crowdsensing: Survey and Research Challenges
Smartphones have become the most pervasive devices in people's lives, and are
clearly transforming the way we live and perceive technology. Today's
smartphones benefit from almost ubiquitous Internet connectivity and come
equipped with a plethora of inexpensive yet powerful embedded sensors, such as
accelerometer, gyroscope, microphone, and camera. This unique combination has
enabled revolutionary applications based on the mobile crowdsensing paradigm,
such as real-time road traffic monitoring, air and noise pollution, crime
control, and wildlife monitoring, just to name a few. Differently from prior
sensing paradigms, humans are now the primary actors of the sensing process,
since they become fundamental in retrieving reliable and up-to-date information
about the event being monitored. As humans may behave unreliably or
maliciously, assessing and guaranteeing Quality of Information (QoI) becomes
more important than ever. In this paper, we provide a new framework for
defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the
current state-of-the-art on the topic. We also outline novel research
challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN
City Hub:a cloud based IoT platform for Smart Cities
Cloud based Smart City hubs are an attractive approach to addressing some of the complex issues faced when deploying PaaS infrastructure for Smart Cities. In this paper we introduce the general notion of IoT hubs and then discusses our work to generalize our IoT hub as a Smart City PaaS. Two key issues are identified, support for hybrid public/private cloud and interoperability. We briefly describe our approach to these issues and discuss our experiences deploying two cloud-based Smart City hubs, one in the UK and the other in Canada
- …