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

A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities

Mobile crowdsensing (MCS) has gained significant attention in recent years and has become an appealing paradigm for urban sensing. For data collection, MCS systems rely on contribution from mobile devices of a large number of participants or a crowd. Smartphones, tablets, and wearable devices are deployed widely and already equipped with a rich set of sensors, making them an excellent source of information. Mobility and intelligence of humans guarantee higher coverage and better context awareness if compared to traditional sensor networks. At the same time, individuals may be reluctant to share data for privacy concerns. For this reason, MCS frameworks are specifically designed to include incentive mechanisms and address privacy concerns. Despite the growing interest in the research community, MCS solutions need a deeper investigation and categorization on many aspects that span from sensing and communication to system management and data storage. In this paper, we take the research on MCS a step further by presenting a survey on existing works in the domain and propose a detailed taxonomy to shed light on the current landscape and classify applications, methodologies, and architectures. Our objective is not only to analyze and consolidate past research but also to outline potential future research directions and synergies with other research areas

Low-latency Data Uploading in D2D-enabled Cellular Networks

指導教員：姜　暁

Understanding real-world phenomena from human-generated sensor data

Nowadays, there is an increasing data availability. Smartphones, wearable devices, social media, web browsing information and sales recordings are only few of the newly available information sources. Analysing this kind of information is an important step towards understanding human behaviour. In this dissertation, I propose novel techniques for uncovering the complex dependencies between factors extracted from raw sensor data and real-world phenomena and I demonstrate the potential of utilising the vast amount of human digital traces in order to better understand human behaviour and factors influenced by it. In particular, two main problems are considered: 1) whether there is a dependency between social media data and traded assets prices and 2) how smartphone sensor data can be used to understand factors that influence our stress level. In this thesis, I focus on uncovering the structural dependencies among factors of interest rather than on the detection of mere correlation. Special attention is given on enhancing the reliability of the findings by developing techniques that can better handle the specific characteristics of the examined datasets. Although the developed approaches are motivated by specific problems related to human-generated sensor data, they are general and can be applied in any dataset with similar characteristics