Abstract. Mobile phones have evolved from devices that are just used for voice and text communication to platforms that are able to capture and transmit a range of data types (image, audio, and location). The adoption of these increasingly capable devices by society has enabled a potentially pervasive sensing paradigm- participatory sensing. A coordinated participatory sensing system engages individuals carrying mobile phones to explore phenomena of interest using in situ data collection. For participatory sensing to succeed, several technical challenges need to be solved. In this paper, we discuss one particular issue: developing a recruitment framework to enable organizers to identify well-suited participants for data collections based on geographic and temporal availability as well as participation habits. This recruitment system is evaluated through a series of pilot data collections where volunteers explored sustainable processes on a university campus.

Abstract — Our problem formulation is as follows. Given a probabilistic graph G and routing algorithm A, we wish to determine a delivery subgraph G[A] of G with at most k edges, such that the probability Conn2(G[A]) that there is a path from source s to destination t in a graph H chosen randomly from the probability space defined by G[A] is maximized. To the best of our knowledge, this problem and its complexity has not been addressed in the literature. Also, there is the corresponding distributed version of the problem where the delivery subgraph G[A] is to be constructed distributively, yielding a routing protocol. Our proposed solution to this routing problem is multi-fold: First, we prove the hardness of our optimization problem of finding a delivery subgraph that maximizes the delivery probability and discuss the hardness of computing the objective function Conn2(G[A]) (which is not the hardness of Conn2(G[A]) itself); Second, we present an algorithm to approximate Conn2(G[A]) and compare it with an optimal algorithm; Third, we model mobility using a Semi-Markov Chain to estimate the pairwise user contact probabilities; and Fourth, we propose an edgeconstrained routing protocol (EC-SOLAR-KSP) for intermittently connected networks based on the insights obtained from the first step and the contact probabilities computed in the third step. We then highlight the protocol’s novelty and effectiveness by comparing it with a probabilistic routing protocol, and an epidemic routing protocol proposed in literature for intermittently connected networks. I.

Abstract—We propose profile-cast, a novel behavior-oriented service representing a new paradigm of communication in mobile networks. Our study is motivated by the tight user-network coupling in future mobile societies. In such a paradigm, messages are sent to sender-specified behavioral profiles, instead of explicit IDs. Our paper provides a systematic framework in providing such services in two phases. First, user behavioral profiles are constructed based on traces collected from two large wireless networks, and their spatiotemporal stability is analyzed. Our analysis shows that user behavioral profiles are surprisingly stable. The similarity of the behavioral profile of a user to its future behavioral profile is above 0.75 for one week, remaining above 0.6 for five weeks, while the correlation coefficient of the similarity metrics between a user pair at different time instants is above 0.62 for a

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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.