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Due to the rapid development of online retailers, there is a great demand for package express shipping services, which causes traffic congestion, resource consumption, and environmental pollution (e.g., carbon emission). However, there is still a large amount of under-utilized capacity in the public transportation systems during off-peak hours. In this paper, we investigate the same-day package distribution using crowdsourced public transportation systems (CPTSs). Specifically, given a number of packages and the timetable of available CPTSs trips, we optimize the schemes of delivering the packages using the under-utilized capacity of the CPTS trips, without impacting the quality of passenger experience. To estimate the amount of under-utilized capacity of each trip across any two adjacent stations, we propose the passenger transit model based on the history data. To assign the under-utilized capacity of each trip to the package deliveries, we develop the minimum limitation delivery (MLD) method, which only utilizes the minimum amount of under-utilized capacity of the whole trip to deliver packages. However, the available capacity is not fully utilized at most stations by MLD. Therefore, we further propose the adaptive limitation delivery (ALD) method, which loads as many packages as possible, until the volume of loaded packages reaches the available capacity in theory. The experimental results and theoretical analysis show that both MLD and ALD could distribute packages efficiently. Moreover, given a set of packages, scheduling of ALD only consumes about 67% time compared to the scheduling of MLD, with a little higher risk of impacting passengers.

Localization is an essential service in most of the wireless sensor network (WSN) applications. This paper focuses on utilizing the moving passive event to achieve the network localizability in an initially nonlocalizable network. The hybrid TDOA and distance measurement is firstly used in the network localizability. We propose the sufficient conditions in terms of node localizability and network localizability, respectively. Besides, a more promising TDOA estimation approach, which is approximately synchronization-free, is proposed to well support our localization approach. We design a corresponding algorithm in distributed manner for the implementation of our approach. Compared with the current works, our approach requires no extra hardware cost and any adjustment of the current topology of the network and hence is more feasible in practice. We evaluate the performance of our approach via extensive simulations on a one-thousand-node network. Simulation results show that our TDOA estimation approach achieves the accuracy of 0.1 milliseconds. More than 90% nodes can be localized within 0.04 r , where r denotes the ranging radius of a sensor.

Information retrieval applications have to publish their output in the form of ranked lists. Such a requirement motivates researchers to develop methods that can automatically learn effective ranking models. Many existing methods usually perform analysis on multidimensional features of query-document pairs directly and don't take users' interactive feedback information into account. They thus incur the high computation overhead and low retrieval performance due to an indefinite query expression. In this paper, we propose a Virtual Feature based Logistic Regression (VFLR) ranking method that conducts the logistic regression on a set of essential but independent variables, called virtual features (VF). They are extracted via the principal component analysis (PCA) method with the user's relevance feedback. We then predict the ranking score of each queried document to produce a ranked list. We systematically evaluate our method using the LETOR 4.0 benchmark datasets. The experimental results demonstrate that the proposal outperforms the state-of-the-art methods in terms of the Mean Average Precision (MAP), the Precision at position k (P@k), and the Normalized Discounted Cumulative Gain at position k (NDCG@k).