The rapid adoption of mobile devices that are able to capture and transmit a wide variety of sensing modalities (media and location) has enabled a new data collection paradigm- participatory sensing. Participatory sensing initiatives organize individuals to gather sensed information using mobile devices through cooperative data collection. A major factor in the success of these data collection projects is sustained, high quality participation. However, since data capture requires a time and energy commitment from individuals, incentives are often introduced to motivate participants. In this work, we investigate the use of micro-payments as an incentive model. We define a set of metrics that can be used to evaluate the effectiveness of incentives and report on findings from a pilot study using various micro-payment schemes in a university campus sustainability initiative. Author Keywords Participatory sensing, incentives, mobile sensing systems

System energy models are important for energy optimization and management in mobile systems. However, existing system energy models are built in a lab setting with the help from a second computer. Not only are they labor-intensive; but also they do not adequately account for the great diversity in the hardware and usage of mobile systems. Moreover, existing system energy models are intended for energy estimation for time intervals of one second or longer; they do not provide the required rate for fine-grain use such as per-application energy accounting. In this work, we study a self-modeling paradigm in which a mobile system automatically generates its energy model without any external assistance. Our solution, Sesame, leverages the possibility of self power measurement through the smart battery interface and employs a suite of novel techniques to achieve accuracy and rate much higher than that of the smart battery interface. We report the implementation and evaluation of Sesame on a laptop and a smartphone. The experiment results show that Sesame is able to generate system energy models of 95 % accuracy at one estimation per second and of 88 % accuracy at one estimation per 10 ms, without any external assistance. Two fiveday field studies with four laptop and four smartphones users further demonstrate the effectiveness, efficiency, and noninvasiveness of Sesame.

Future pervasive computing scenarios will be characterized by an increasing diversity and dynamics of services and of contextual data sources, and by an increasing exploitation of crowdsourcing for social sensing and human computation. Accordingly, the role of middleware should no longer be limited to facilitating interactions and compositions via discovery and orchestration, but should approach that of a recommendation engine capable of dynamically and adaptively planning patterns of service interaction and composition on a best-effort basis. Along these lines, this position paper firstly elaborates on the limitations of traditional middleware infrastructures in meeting the new requirements of the emerging pervasive computing scenarios. Then, it introduces two case study scenarios to motivate and clarify the concepts expressed. Finally, it identifies some key research challenges for future pervasive middleware infrastructures.

The first decade of the century witnessed a proliferation of devices with sensing and communication capabilities in the possession of the average individual. Examples range from camera phones and wireless GPS units to sensor-equipped, networked fitness devices and entertainment platforms (such as Wii). Social networking platforms emerged, such as Twitter, that allow sharing information in real time. The unprecedented deployment scale of such sensors and connectivity options usher in an era of novel data-driven applications that rely on inputs collected by networks of humans or measured by sensors acting on their behalf. These applications will impact domains as diverse as health, transportation, energy, disaster recovery, intelligence, and warfare. This paper surveys the important opportunities in human-centric sensing, identifies challenges brought about by such opportunities, and describes emerging solutions to these challenges. 1.

This paper proposes Convenience Probe, a participatory sensing tool to collect large-scale consumer flow behaviors from everyday mobile phones. We hope to use Convenience Probe to collect real consumer flow data that will help convenience store chains in store location assessment.

Abstract—Ubiquitous smartphones are increasingly becoming the dominant platform for collaborative sensing. Smartphones, with their ever richer set of sensors, are being used to enable collaborative driver-assistance services like traffic advisory and road condition monitoring. To enable such services, the smartphones ’ GPS, accelerometer and gyro sensors have been widely used. On the contrary, smartphone cameras, despite being very powerful sensors, have largely been neglected. In this paper, we introduce a collaborative sensing platform that exploits the cameras of windshield-mounted smartphones. To demonstrate the potential of this platform, we propose several services that it can support, and prototype SignalGuru, a novel service that leverages windshield-mounted smartphones and their cameras to collaboratively detect and predict the schedule of traffic signals, enabling Green Light Optimal Speed Advisory (GLOSA) and other novel applications. Results from two deployments of SignalGuru, using iPhones in cars in Cambridge (MA, USA) and Singapore, show that traffic signal schedules can be predicted accurately. On average, SignalGuru comes within 0.66s, for pre-timed traffic signals and within 2.45s, for traffic-adaptive traffic signals. Feeding SignalGuru’s predicted traffic schedule to our GLOSA application, our vehicle fuel consumption measurements show savings of 20.3%, on average.

Wireless networks and personalized mobile devices are deeply integrated and embedded in our lives. Such wide adoptions of new technologies will impact user behavior and in turn will affect network performance. It is imperative to characterize the fundamental structure of wireless user behavior in order to model, manage, leverage and design efficient mobile networks. One major challenge in characterizing user behavior stems from the significant size and complexity of user behavioral data. Without summarization and dimension reduction, the sheer amount of data does not provide much useful information. The key contribution of the paper is a novel similarity metric based on a matrix representation of mobility preferences and its decomposition. This method provides an efficient way to reduce important spatiotemporal dynamics in user mobility into a few eigen-behavior vectors. This also facilitates nodes to exchange their mobility summaries and determine their mutual similarity locally. Without any assumption on the properties of user population, we use unsupervised learning (clustering) techniques to classify WLAN users. Such a user grouping scheme based on learned user behavior is crucial for applications relying on the usage context of each mobile device (e.g., participatory sensing, social-relationship-aware message forwarding). In this study, using our systematic TRACE approach, we analyze wireless users ’ behavioral patterns by extensively mining wireless network logs from two major university campuses to showcase its efficacy. While our findings partly validate intuitive repetitive behavioral trends and user grouping, it is surprising to find the qualitative commonalities and striking consistency of user behavior from the two universities. We discover multi-modal user behavior for more than 60 % of the users, and there are hundreds of distinct groups with unique behavioral patterns in both campuses. The sizes of the major groups follow a power-law distribution. I.

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