@article {Drosatos2014,
	title = {{P}rivacy-{P}reserving {C}omputation of {P}articipatory {N}oise {M}aps in the {C}loud},
	journal = {Journal of Systems and Software},
	volume = {92},
	year = {2014},
	note = {http://www.sciencedirect.com/science/article/pii/S0164121214000430},
	month = {June},
	pages = {170{\~A}{\textcent}{\^a}{\quotesinglbase}{\textlnot}{\^a}{\texteuro}{\oe}183},
	abstract = {Participatory sensing is a crowd-sourcing technique which relies both on active contribution of citizens and on their location and mobility patterns. As such, it is particularly vulnerable to privacy concerns, which may seriously hamper the large-scale adoption of participatory sensing applications. In this paper, we present a privacy-preserving system architecture for participatory sensing contexts which relies on cryptographic techniques and distributed computations in the cloud. Each individual user is represented by a personal software agent, which runs in the cloud. The system enables individuals to aggregate and analyse sensor data by performing collaborative distributed computations among multiple agents. No personal data is disclosed to anyone, including the cloud service providers. The distributed computation proceeds by having agents execute a cryptographic protocol based on a homomorphic encryption scheme in order to aggregate data. We show that our architecture is secure in the Honest-But-Curious model both for the users and the cloud service providers. Our approach was implemented and validated on top of the NoiseTube system, which enables participatory sensing of noise. In particular, we repeated several mapping experiments carried out with NoiseTube, and show that our system is able to produce identical outcomes in a privacy-preserving way. We experimented with real and artificially generated data sets, and present a live demo running on a heterogeneous set of commercial cloud providers. The results show that our approach goes beyond a proof-of-concept and can actually be deployed in a real-world setting. To the best of our knowledge this system is the first operational privacy-preserving approach for participatory sensing. While validated in terms of NoiseTube, our approach is suitable for just about any participatory sensing system, even beyond the domain of environmental monitoring.},
	keywords = {Citizen science, Cloud computing, Environmental monitoring, Noise mapping, Participatory sensing, Privacy-preserving computation},
	issn = {0164-1212},
	doi = {10.1016/j.jss.2014.01.035},
	author = {George Drosatos and Pavlos S. Efraimidis and Ioannis N. Athanasiadis and Matthias Stevens and Ellie D{\textquoteright}Hondt}
}
@article {DHondt2013,
	title = {{P}articipatory noise mapping works! {A}n evaluation of participatory sensing as an alternative to standard techniques for environmental monitoring},
	journal = {Pervasive and Mobile Computing},
	volume = {9},
	number = {5},
	year = {2013},
	note = {<ce:title>Special issue on Pervasive Urban Applications</ce:title>},
	pages = {681-694},
	abstract = {Participatory sensing enables a person-centric collection of environmental measurement data with, in principle, high granularity in space and time. In this paper we provide concrete proof that participatory techniques, when implemented properly, can achieve the same accuracy as standard mapping techniques. We do this through a citizen science experiment for noise map- ping a 1 km$^{2}$ area in the city of Antwerp using NoiseTube, a participatory sensing framework for monitoring ambient noise. At the technical side, we set up measuring equipment in accordance with official norms insofar as they apply, also carrying out extensive calibration experiments. At the citizen side, we collaborated with up to 10 volunteers from a citizen-led Antwerp-based action group. From the data gathered we construct purely measurement-based noise maps of the target area with error margins of about 5dB, comparable to those of official simulation-based noise maps. We also report on a survey evaluating NoiseTube, as a system for participative grassroots noise mapping campaigns, from the user perspective.},
	keywords = {Citizen science, Environmental policy, Mobile phones, Noise mapping, Participatory sensing},
	issn = {1574-1192},
	doi = {http://dx.doi.org/10.1016/j.pmcj.2012.09.002},
	url = {http://www.sciencedirect.com/science/article/pii/S1574119212001137},
	author = {Ellie D{\textquoteright}Hondt and Matthias Stevens and An Jacobs}
}
@conference {Drosatos2012,
	title = {{A} privacy-preserving cloud computing system for creating participatory noise maps},
	booktitle = {36th Annual IEEE International Computer Software and Applications Conference (COMPSAC 2012; July 16-20, 2012, Izmir, Turkey)},
	year = {2012},
	month = {July},
	pages = {581{\~A}{\textcent}{\^a}{\quotesinglbase}{\textlnot}{\^a}{\texteuro}{\oe}586},
	abstract = {Participatory sensing is a crowd-sourcing technique which relies both on active contribution of citizens and on their location and mobility patterns. As such, it is particularly vulnerable to privacy concerns, which may seriously hamper the large-scale adoption of participatory sensing applications. In this paper, we present a privacy-preserving system architecture for participatory sensing contexts which relies on cryptographic techniques and distributed computations in the cloud. Each individual is represented by a personal software agent, which is deployed on one of the popular commercial cloud computing services. The system enables individuals to aggregate and analyse sensor data by performing a collaborative distributed computation among multiple agents. No personal data is disclosed to anyone, including the cloud service providers. The distributed computation proceeds by having agents execute a cryptographic protocol based on a homomorphic encryption scheme in order to aggregate data. We show formally that our architecture is secure in the Honest-But-Curious model both for the users and the cloud providers. Our approach was implemented and validated on top of the NoiseTube system [1], [2], which enables participatory sensing of noise. In particular, we repeated several mapping experiments carried out with NoiseTube, and show that our system is able to produce identical outcomes in a privacy-preserving way. We experimented with real and simulated data, and present a live demo running on a heterogeneous set of commercial cloud providers. The results show that our approach goes beyond a proof-of-concept and can actually be deployed in a real-world setting. To the best of our knowledge this system is the first operational privacy-preserving approach for participatory sensing. While validated in terms of NoiseTube, our approach is useful in any setting where data aggregation can be performed with efficient homomorphic cryptosystems.},
	issn = {0730-3157},
	doi = {10.1109/COMPSAC.2012.78},
	author = {George Drosatos and Pavlos S. Efraimidis and Ioannis N. Athanasiadis and Ellie D{\textquoteright}Hondt and Matthias Stevens},
	editor = {Xiaoying Bai and Fevzi Belli and Elisa Bertino and Carl K. Chang and Atilla El\c ci and Cristina Seceleanu and Haihua Xie and Mohammad Zulkernine}
}
@conference {DHondt2011a,
	title = {{P}articipatory noise mapping},
	booktitle = {Demo Proceedings of the 9th International Conference on Pervasive Computing (Pervasive 2011; June 12-15, 2011, San Francisco, CA, USA)},
	year = {2011},
	month = {June},
	pages = {33{\~A}{\textcent}{\^a}{\quotesinglbase}{\textlnot}{\^a}{\texteuro}{\oe}36},
	abstract = {Participatory sensing allows for a person-centric approach to gathering environmental measurement data with, in principle, high gran- ularity in space and time. Here we report on a citizen science experiment for noise mapping a 1 km2 area in the city of Antwerp using NoiseTube, a participatory sensing framework for monitoring ambient noise. At the technical side, we set up measuring equipment in accordance with offi- cial norms insofar as they apply, also carrying out extensive calibration experiments. At the citizen side, we collaborated with up to 10 volunteers from a citizen-led Antwerp-based action group. From the data gathered we construct purely measurement-based noise maps of the targeted area with error margins of about 5dB, comparable to those of official simulation-based noise maps.},
	url = {http://soft.vub.ac.be/Publications/2011/vub-soft-tr-11-03.pdf},
	author = {Ellie D{\textquoteright}Hondt and Matthias Stevens},
	editor = {Rafael {\textquoteleft}Tico{\textquoteright} Ballagas and Daniela K. Rosner}
}
@conference {DHondt2010,
	title = {{C}ommunity memories for sustainable urban living},
	booktitle = {INNOV 2010 {\~A}{\textcent}{\^a}{\quotesinglbase}{\textlnot}{\^a}{\texteuro}{\oe} Proceedings of the 1st International Multi-Conference on Innovative Developments in ICT (Athens, Greece, July 29-31, 2010)},
	year = {2010},
	note = {Poster displayed at ICGREEN 2010 {\~A}{\textcent}{\^a}{\quotesinglbase}{\textlnot}{\^a}{\texteuro}{\oe} International Conference on Green Computing (part of INNOV 2010)},
	month = {July},
	pages = {77{\~A}{\textcent}{\^a}{\quotesinglbase}{\textlnot}{\^a}{\texteuro}{\oe}80},
	abstract = {We propose a particular approach by which computer science can aid on the technological side of sustainable development, which at the same time contributes to raising people{\textquoteright}s awareness of the issues at hand. In particular we develop a so-called community memory for urban environmental measurement surveys, focusing on noise, microclimate and atmospheric pollution. Our goal is to gather data both in a quantitative way, using participatory mobile sensing, and in a qualitative way, by social tagging. In this way we provide a method for environmental monitoring which is complementary to current modelling approaches.},
	keywords = {-postnote},
	isbn = {978-989-8425-15-7},
	url = {http://soft.vub.ac.be/Publications/2010/vub-tr-soft-10-16.pdf},
	author = {Ellie D{\textquoteright}Hondt and Matthias Stevens},
	editor = {Boris Shishkov and George A. Tsihrintzis and Maria Virvou}
}
@conference {Stevens2010a,
	title = {{C}rowdsourcing of {P}ollution {D}ata using {S}martphones},
	booktitle = {Workshop on Ubiquitous Crowdsourcing, held at Ubicomp {\textquoteright}10 (September 26-29, 2010, Copenhagen, Denmark)},
	year = {2010},
	month = {September},
	abstract = {In this paper we present our research into participatory sensing based solutions for the collection of data on urban pollution and nuisance. In the past 2 years we have been involved in the NoiseTube project which explores a crowdsourcing approach to measuring and mapping urban noise pollution using smartphones. By involving the general public and using off-the-shelf smartphones as noise sensors, we seek to provide a low cost solution for citizens to measure their personal exposure to noise in their everyday environment and participate in the creation of collective noise maps by sharing their geo-localized and annotated measurements with the community. We believe our work represents an interesting example of the novel mobile crowdsourcing applications which are enabled by ubiquitous computing systems. Furthermore we believe the NoiseTube system, and the currently ongoing validation experiments, provide an illustrative context for some of the open challenges faced by creators of ubiquitous crowdsourcing applications and services in general. We will also take the opportunity to present the insights we gained into some of the challenges.},
	url = {http://soft.vub.ac.be/Publications/2010/vub-tr-soft-10-15.pdf},
	author = {Matthias Stevens and Ellie D{\textquoteright}Hondt},
	editor = {Maja Vukovic and Soundar Kumara and Ohad Greenshpan}
}
@inbook {Stevens2010,
	title = {{N}oise{T}ube \& {B}rus{S}ense: {P}articipatory sensing for sustainable urban life},
	booktitle = {We Can Change the Weather: 100 Cases in Energy Efficacy to Start Doing So},
	series = {Crosstalks},
	year = {2010},
	pages = {106{\~A}{\textcent}{\^a}{\quotesinglbase}{\textlnot}{\^a}{\texteuro}{\oe}107},
	publisher = {VUBPress/ASP Editions},
	organization = {VUBPress/ASP Editions},
	abstract = {In order to guarantee our future wellbeing, society urgently needs to evolve towards a sustainable lifestyle. A core issue is our unsustainable use of the earth{\textquoteright}s atmosphere. Particularly in urban areas, already home to half of the world{\textquoteright}s population, air and noise pollution are major problems. Efforts to monitor and limit pollution are hampered by the inadequacy of current measuring methods as well as the lack of awareness of the average citizen. Our research explores how computer science can improve both issues.},
	isbn = {978-9054876922},
	url = {http://crosstalks.vub.ac.be/publications/changetheweather/intro.html},
	author = {Matthias Stevens and Ellie D{\textquoteright}Hondt},
	editor = {Marleen Wynants and Sara Engelen}
}
@conference {Vallejos2009,
	title = {{C}ontext-aware {R}esource {S}haring for {P}eople-centric {S}ensing},
	booktitle = {First International Workshop on Software Research and Climate Change (WSRCC-1; October 26, 2009, Orlando, FL, USA)},
	year = {2009},
	note = {Part of Onward!\ 2009, co-located with OOPSLA 2009},
	month = {October},
	abstract = {In order to tackle the problem of climate change we crucially need to develop a sustainable society. This requires everyone to become aware of the precariousness of the situation, while at the same time a major scientific effort is needed. A core issue standing in the way of sustainability is that of environmental pollution. People-centric sensing is an increasingly popular approach for monitoring pollution in urban environments [5]. This model relies on mobile computing devices to enable ordinary citizens to assess their exposure to pollution factors in their everyday activities. A such, this sensing approach yields individualised data with a higher spatio-temporal granularity than systems based on sparse and stationary sensors [9], while the active involvement of citizens in the monitoring increases their awareness of environmental issues. However, people-centric sensing is hampered by the still limited capacities of current mobile devices, in terms of processing power and battery autonomy as well as the absence of specialised environmental sensors, limiting participation to those who have access to expensive or adapted devices. Our claim is that in an approach like people-centric sensing, where the participation of the people directly impacts the quality and quantity of the data collected, such restrictions should be alleviated. Indeed, given the urgency of the environmental challenges we face, we cannot wait for new generations of mobile devices to provide better hardware and software capacities, and become affordable for the public at large. In this work, we explore an extension to people-centric sensing that enables people to share and use resources available in range, in an opportunistic way [6]. The sensing process in this case is no longer constrained by the capacities of specific mobile devices, but instead can be distributed among the available resources (e.g. other mobile devices, sensors or indoor/outdoor appliances). Data gathering, processing and publishing can then be accomplished by resources that are dynamically discovered and selected according to their suitability, availability, and user preferences.},
	url = {http://www.cs.toronto.edu/wsrcc/WSRCC1/Papers.html},
	author = {Jorge Vallejos and Matthias Stevens and Ellie D{\textquoteright}Hondt and Nicolas Maisonneuve and Wolfgang De Meuter and Theo D{\textquoteright}Hondt and Luc Steels},
	editor = {Steve Easterbrook}
}
