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.
Context-aware computing refers to a paradigm in which applications sense aspects of the environment and use this information to adjust their behavior in response to changing circumstances. In this paper, we present a formal model and notation (Context UNITY) for expressing quintessential aspects of context-aware computations; existential quantification, for instance, proves to be highly effective in capturing the notion of discovery in open systems. Furthermore, Context UNITY treats context in a manner that is relative to the specific needs of an individual application and promotes an approach to context maintenance that is transparent to the application .
We would like to thank Simon Coates (UCL Librarian) for helping with initial searches. We would also like to thank Hannah Bains (UK Health Visitor and International Board Certified Lactation Consultant) and Vicki Rich (Vermont Breastfeeding Consultant and Doula) for their comments on the manuscript.
Abstract. Context-aware computing refers to a computing paradigm in which the behavior of individual components is determined by the circumstances in which they find themselves to an extent that greatly exceeds the typical system/environment interaction pattern common to most modern computing. The environment has an exceedingly powerful impact on a particular application component either because the latter needs to adapt in response to changing external conditions or because it relies on resources whose availability is subject to continuous change. In this paper we seek to develop a systematic understanding of the quintessential nature of context-aware computing by constructing a formal model and notation for expressing context-aware computations. We start with the basic premise that, in its most extreme form, context should be made manifest in a manner that is highly local in appearance and decoupled in fact. Furthermore, we assume a notion of context that is relative to the needs of each individual component, and we expect context-awareness to be maintained in a totally transparent manner with minimal programming effort. We construct the model from first principles, seek to root our decisions in these formative assumptions, and make every effort to preserve minimality of concepts and elegance of notation.
Queries are convenient abstractions for the discovery of information and services, as they offer content-based information access. In distributed settings, query semantics are well-defined, e.g., they often satisfy ACID transactional properties. In a dynamic network setting, however, achieving transactional semantics becomes complex due to the openness and unpredictability. In this paper, we propose a query processing model for mobile ad hoc and sensor networks suitable for expressing a wide range of query semantics; the semantics differ in the degree of consistency with which results reflect the state of the environment during execution. We introduce several distinct notions of consistency and formalize them. A practical contribution of this paper is a protocol for query processing that automatically assesses and adaptively provides an achievable degree of consistency given the state of the operational environment throughout its execution. The protocol attaches an assessment of the achieved guarantee to returned query results, allowing precise reasoning about a query with a range of possible semantics.
The ability to recognize social gestures opens the door for the development of enhanced pervasive computing applications that are responsive to users' social interactions. In this paper, we explore the feasibility of using a smartband for social gesture recognition. We apply logistic regression, a supervised machine learning technique, to accelerometer data collected in a study of 32 users performing 12 social gestures. Our experimental results show promise for recognizing social gestures with a smartband; our simple approach achieves an average accuracy of 86% for classification of social gestures.
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