Deep-Gap: A deep learning framework for forecasting crowdsourcing supply-demand gap based on imaging time series and residual learning

Mobile crowdsourcing has become easier thanks to the widespread of smartphones capable of seamlessly collecting and pushing the desired data to cloud services. However, the success of mobile crowdsourcing relies on balancing the supply and demand by first accurately forecasting spatially and temporally the supply-demand gap, and then providing efficient incentives to encourage participant movements to maintain the desired balance. In this paper, we propose Deep-Gap, a deep learning approach based on residual learning to predict the gap between mobile crowdsourced service supply and demand at a given time and space. The prediction can drive the incentive model to achieve a geographically balanced service coverage in order to avoid the case where some areas are over-supplied while other areas are under-supplied. This allows anticipating the supply-demand gap and redirecting crowdsourced service providers towards target areas. Deep-Gap relies on historical supply-demand time series data as well as available external data such as weather conditions and day type (e.g., weekday, weekend, holiday). First, we roll and encode the time series of supply-demand as images using the Gramian Angular Summation Field (GASF), Gramian Angular Difference Field (GADF) and the Recurrence Plot (REC). These images are then used to train deep Convolutional Neural Networks (CNN) to extract the low and high-level features and forecast the crowdsourced services gap. We conduct comprehensive comparative study by establishing two supply-demand gap forecasting scenarios: with and without external data. Compared to state-of-art approaches, Deep-Gap achieves the lowest forecasting errors in both scenarios.Comment: Accepted at CloudCom 2019 Conferenc

A Probabilistic Approach for Maximizing Travel Journey WiFi Coverage Using Mobile Crowdsourced Services

A public transport journey planning service often yields multiple alternative journeys plans to get from a source to a destination. In addition to journey preferences, such as connecting time and walking distance, passengers can select the optimal plan based on mobile crowdsourced WiFi coverage available along the journey. This requires discovering mobile crowdsourced WiFi services available along the journey path. However, this task is challenging due to the uncertain availability of discovered services. To enhance the availability of WiFi coverage, we propose a probabilistic approach to discover groups of available crowdsourced WiFi services along with the journey segments. We first analyze the log of their trajectories and use a density estimation technique to discover reference spots representing the frequently visited locations. Then, a joint discrete Fourier transform and autocorrelation analysis are applied to mine the periods of the presence of moving crowdsourced services with respect to each reference spot. A low-complexity cluster analysis based on Jensen-Shannon divergence is then used to mine the periodic movement behaviors of services during the identified periods. Finally, mobile crowdsourced WiFi services that are simultaneously available at intersecting reference spots are grouped. The QoS of discovered groups is computed in terms of availability confidence, failover capacity, aggregated bandwidth capacity, and coverage. Additionally, we propose an algorithm to determine the best public transport journey plan offering based on the QoS of available WiFi service groups along the journey path. We conduct a comprehensive comparative study to validate the effectiveness of the proposed framework.This work was supported by the Qatar National Research Fund (a member of the Qatar Foundation) through the National Priorities Research Program (NPRP) under Grant NPRP9-224-1-049.Scopu