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

Context-aware Background Application Scheduling in Interactive Mobile Systems

Department of Computer Science and EngineeringEach individual's usage behavior on mobile devices depend on a variety of factors such as time, location, and previous actions. Hence, context-awareness provides great opportunities to make the networking and the computing capabilities of mobile systems to be more personalized and more efficient in managing their resources. To this end, we first reveal new findings from our own Android user experiment: (i) the launching probabilities of applications follow Zipf's law, and (ii) inter-running and running times of applications conform to log-normal distributions. We also find contextual dependencies between application usage patterns, for which we classify contexts autonomously with unsupervised learning methods. Using the knowledge acquired, we develop a context-aware application scheduling framework, CAS that adaptively unloads and preloads background applications for a joint optimization in which the energy saving is maximized and the user discomfort from the scheduling is minimized. Our trace-driven simulations with 96 user traces demonstrate that the context-aware design of CAS enables it to outperform existing process scheduling algorithms. Our implementation of CAS over Android platforms and its end-to-end evaluations verify that its human involved design indeed provides substantial user-experience gains in both energy and application launching latency.ope