Unmanned aerial vehicles (UAVs) are considered to be excellent candidates of airborne relays or base stations for the 3-D ad hoc networks. They can be promptly deployed to serve large bursts of communication traffic in cellular networks or provide timely network support in wireless sensor networks (WSNs). It is desirable but challenging to dynamically find the optimal deployment strategy of UAVs in the air to provide a better Quality of Service (QoS) for a UAV-assisted wireless network. In this article, we propose a novel Gibbs-sampling distributed algorithm (GSDA) to dynamically optimize the UAVs’ locations when they serve as airborne base stations for ground users. In our proposed GSDA, channel capacity is adopted as the objective function and a distributed approach is employed such that each UAV is able to optimize its location independently and asynchronously. Furthermore, we propose a polynomial-regression-based predictor to make use of users’ moving trajectories and take advantage of the predicted users’ future locations to expedite the convergence of the GSDA. Meanwhile, we also compare our proposed GSDA with the existing distributed genetic algorithm. The asynchronization of UAV location updates and the location errors are also investigated to evaluate the robustness of the GSDA. Simulation results demonstrate that our proposed novel GSDA is quite robust and superior to the existing distributed genetic algorithm.

Identifying a set of diversified users who are local residents in a city is an important task for a wide spectrum of applications such as target ads of local business, surveys and interviews, and personalized recommendations. While many previous studies have investigated the problem of identifying the local users in a given area using online social network information (e.g., geotagged posts), few methods have been developed to solve the diversified user identification problem. In this paper, we propose a new analytical framework, Diversified Local Users Finder (DLUF), to accurately identify a set of diversified local users using a principled approach. In particular, the DLUF scheme first defines a new distance metric that measures the diversity between local users from physical dimension. The DLUF scheme then provides a solution to find the set of local users with maximum diversity. The performance of DLUF scheme is compared to several representative baselines using two real world datasets obtained from Foursquare application. We observe that the DLUF scheme accurately identifies the local users with a great diversity and significantly outperforms the compared baselines.

Structural variation is important in disease etiology and ecological adaptation. Prior work has focused on using either only short paired-end reads or a hybrid approach that combines long and short reads to detect structural variants. Few methods have focused solely on using long reads. Here, we aim to detect a specific type of structural variation, large inversions, using only raw PacBio long reads. We propose a new breakpoint detection approach that is complementary to current state-of-the-art methods and models inversion detection as a Max-Cut problem. We show that this new approach is powerful for detecting large inversions when compared with popular structural variation detection tools. In particular, it yields high Positive Predictive Value (PPV), relatively good sensitivity for detecting large inversions, and shows potential for detecting inversions in more complex genomes. Our software is publicly available at https://bitbucket.org/NDBL/invdet.

Crowdsourcing has emerged as an important data collection paradigm in participatory and human-centric sensing applications. While many crowdsourcing studies focus on sensing and recovering the status of the physical world, this paper investigates the problem of profiling the crowd sensors (i.e., humans). In particular, we study the problem of accurately inferring the home locations of people from the noisy and sparse crowdsourcing data they contribute. In this study, we propose a semi-supervised framework, Where Are You From (WAYF), to accurately infer the home locations of people by explicitly exploring the localness of people and the dependency between people based on their check-in behaviors under a rigorous analytical framework. We perform extensive experiments to evaluate the performance of our scheme and compared it to the state-of-the-art techniques using three real world data traces collected from Foursquare. The results showed the effectiveness of our scheme in accurately profiling the home locations of people.

Users' home location is important information for many advanced information services in big data applications (e.g., localized recommendation, target ads of local business and urban planning). In this paper, we study the problem of accurately inferring the home locations of people from the noisy and sparse data they voluntarily share on online social media. Previous studies have developed supervised learning approaches to predict a person's home location in a city. However, the accuracy of these techniques largely depends on a high quality training dataset, which is difficult and expensive to obtain in practice. In this study, we propose a new analytical framework, Unsupervised Home Location Inference (UHLI), to accurately infer the home locations of people using a set of principle approaches. In particular, the UHLI scheme addresses the critical challenges of using sparse and noisy online social media data and derives an optimal solution to the home location inference problem. We evaluated the performance of our scheme and compared it to the state-of-the-art baselines using three real world data traces collected from Foursquare. The results showed that our scheme can accurately infer the home location of people and significantly outperform the state-of-the-art baselines.