8,100 research outputs found

    Providing Efficient Privacy-Aware Incentives for Mobile Sensing

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    Abstract—Mobile sensing relies on data contributed by users through their mobile device (e.g., smart phone) to obtain useful information about people and their surroundings. However, users may not want to contribute due to lack of incentives and concerns on possible privacy leakage. To effectively promote user participation, both incentive and privacy issues should be addressed. Existing work on privacy-aware incentive is limited to special scenario of mobile sensing where each sensing task needs only one data report from each user, and thus not appropriate for generic scenarios in which sensing tasks may require multiple reports from each user (e.g., in environmental monitoring applications). In this paper, we propose a privacy-aware incentive scheme for general mobile sensing, which allows each sensing task to collect one or multiple reports from each user as needed. Besides being more flexible in task management, our scheme has much lower computation and communication cost compared to the existing solution. Evaluations show that, when each node only contributes data for a small fraction of sensing tasks (e.g, due to the incapability or disqualification to generate sensing data for other tasks), our scheme runs at least one order of magnitude faster. I

    Incentive Mechanisms for Participatory Sensing: Survey and Research Challenges

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    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

    Emotions in context: examining pervasive affective sensing systems, applications, and analyses

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    Pervasive sensing has opened up new opportunities for measuring our feelings and understanding our behavior by monitoring our affective states while mobile. This review paper surveys pervasive affect sensing by examining and considering three major elements of affective pervasive systems, namely; “sensing”, “analysis”, and “application”. Sensing investigates the different sensing modalities that are used in existing real-time affective applications, Analysis explores different approaches to emotion recognition and visualization based on different types of collected data, and Application investigates different leading areas of affective applications. For each of the three aspects, the paper includes an extensive survey of the literature and finally outlines some of challenges and future research opportunities of affective sensing in the context of pervasive computing
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