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

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

Health Participatory Sensing Networks for Mobile Device Public Health Data Collection and Intervention

The pervasive availability and increasingly sophisticated functionalities of smartphones and their connected external sensors or wearable devices can provide new data collection capabilities relevant to public health. Current research and commercial efforts have concentrated on sensor-based collection of health data for personal fitness and personal healthcare feedback purposes. However, to date there has not been a detailed investigation of how such smartphones and sensors can be utilized for public health data collection. Unlike most sensing applications, in the case of public health, capturing comprehensive and detailed data is not a necessity, as aggregate data alone is in many cases sufficient for public health purposes. As such, public health data has the characteristic of being capturable whilst still not infringing privacy, as the detailed data of individuals that may allow re-identification is not needed, but rather only aggregate, de-identified and non-unique data for an individual. These types of public health data collection provide the challenge of the need to be flexible enough to answer a range of public health queries, while ensuring the level of detail returned preserves privacy. Additionally, the distribution of public health data collection request and other information to the participants without identifying the individual is a core requirement. An additional requirement for health participatory sensing networks is the ability to perform public health interventions. As with data collection, this needs to be completed in a non-identifying and privacy preserving manner. This thesis proposes a solution to these challenges, whereby a form of query assurance provides private and secure distribution of data collection requests and public health interventions to participants. While an additional, privacy preserving threshold approach to local processing of data prior to submission is used to provide re-identification protection for the participant. The evaluation finds that with manageable overheads, minimal reduction in the detail of collected data and strict communication privacy; privacy and anonymity can be preserved. This is significant for the field of participatory health sensing as a major concern of participants is most often real or perceived privacy risks of contribution

From MANET to people-centric networking: Milestones and open research challenges

In this paper, we discuss the state of the art of (mobile) multi-hop ad hoc networking with the aim to present the current status of the research activities and identify the consolidated research areas, with limited research opportunities, and the hot and emerging research areas for which further research is required. We start by briefly discussing the MANET paradigm, and why the research on MANET protocols is now a cold research topic. Then we analyze the active research areas. Specifically, after discussing the wireless-network technologies, we analyze four successful ad hoc networking paradigms, mesh networks, opportunistic networks, vehicular networks, and sensor networks that emerged from the MANET world. We also present an emerging research direction in the multi-hop ad hoc networking field: people centric networking, triggered by the increasing penetration of the smartphones in everyday life, which is generating a people-centric revolution in computing and communications

Listening to the forest and its curators: lessons learnt from a bioacoustic smartphone application deployment

Our natural environment is complex and sensitive, and is home to a number of species on the verge of extinction. Surveying is one approach to their preservation, and can be supported by technology. This paper presents the deployment of a smartphone-based citizen science biodiversity application. Our findings from interviews with members of the biodiversity community revealed a tension between the technology and their established working practices. From our experience, we present a series of general guidelines for those designing citizen science apps Full Citation Moran, Stuart, Pantidi, Nadia, Rodden, Tom, Chamberlain, Alan, Griffiths, Chloe, Zilli, Davide, Merrett, Geoff V. and Rogers, Alex (2014) Listening to the forest and its curators: lessons learnt from a bioacoustic smartphone application deployment. In, ACM CHI Conference on Human Factors in Computing Systems, Toronto, CA, 26 Apr - 01 May 2014. (doi:10.1145/2556288.255702)

Privacy in the Smart City - Applications, Technologies, Challenges and Solutions

Many modern cities strive to integrate information technology into every aspect of city life to create so-called smart cities. Smart cities rely on a large number of application areas and technologies to realize complex interactions between citizens, third parties, and city departments. This overwhelming complexity is one reason why holistic privacy protection only rarely enters the picture. A lack of privacy can result in discrimination and social sorting, creating a fundamentally unequal society. To prevent this, we believe that a better understanding of smart cities and their privacy implications is needed. We therefore systematize the application areas, enabling technologies, privacy types, attackers and data sources for the attacks, giving structure to the fuzzy term “smart city”. Based on our taxonomies, we describe existing privacy-enhancing technologies, review the state of the art in real cities around the world, and discuss promising future research directions. Our survey can serve as a reference guide, contributing to the development of privacy-friendly smart cities

Dynamic Deployment of Sensing Experiments in the Wild Using Smartphones

Part 1: Full Research PapersInternational audienceWhile scientific communities extensively exploit simulations to validate their theories, the relevance of their results strongly depends on the realism of the dataset they use as an input. This statement is particularly true when considering human activity traces, which tend to be highly unpredictable. In this paper, we therefore introduce APISENSE, a distributed crowdsensing platform for collecting realistic activity traces. In particular, APISENSE provides to scientists a participative platform to help them to easily deploy their sensing experiments in the wild. Beyond the scientific contributions of this platform, the technical originality of APISENSE lies in its Cloud orientation and the dynamic deployment of scripts within the mobile devices of the participants.We validate this platform by reporting on various crowdsensing experiments we deployed using Android smartphones and comparing our solution to existing crowdsensing platforms

Sensafety: Crowdsourcing the Urban Sense of Safety

Today, community initiatives to improve the urban quality of life can be conducted in a more focused way because local authorities and urban planners are able to reveal urban hotspots through the investigation of location-annotated crime and accident data. However, urban areas, which according to well-recorded incident data are characterized by a high level of public safety, but which are generally perceived by citizens as unsafe, remain undiscovered and therefore untreated. This work presents Sensafety, a citizen-centric crowdsourcing approach that enables users by means of a mobile application to report their personal feeling of safety anytime and at any site. Sensafety’s goal is to reveal a comprehensive and complete picture of the perceived safety in urban environments in order to identify blind spots that have not been further investigated due to lack of data. To encourage citizens to participate and contribute, Sensafety’s mobile application offers different ways to explore and experience the collected data depending on the user’s location. This paper gives a detailed description of Sensafety’s integrated concept and outlines the major technical and non-technical findings