HelPal: A Search System for Mobile Crowd Service
Proliferation of ubiquitous mobile devices makes location based services prevalent. Mobile users are able to volunteer as providers of specific services and in the meanwhile to search these services. For example, drivers may be interested in tracking available nearby users who are willing to help with motor repair or are willing to provide travel directions or first aid. With the diffusion of mobile users, it is necessary to provide scalable means of enabling such users to connect with other nearby users so that they can help each other with specific services. Motivated by these observations, we design and implement a general location based system HelPal for mobile users to provide and enjoy instant service, which is called mobile crowd service. In this demo, we introduce a mobile crowd service system featured with several novel techniques. We sketch the system architecture and illustrate scenarios via several cases. Demonstration shows the user-friendly search interface for users to conveniently find skilled and qualified nearby service providers.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
A Deep Learning Spatiotemporal Prediction Framework for Mobile Crowdsourced Services
This papers presents a deep learning-based framework to predict crowdsourced service availability spatially and temporally. A novel two-stage prediction model is introduced based on historical spatio-temporal traces of m…
Deep LearningGeneral ClassificationPredictionTime Series+1Evaluating Collaborative and Autonomous Agents in Data-Stream-Supported Coordination of Mobile Crowdsourcing
Mobile crowdsourcing refers to systems where the completion of tasks necessarily requires physical movement of crowdworkers in an on-demand workforce. Evidence suggests that in such systems, tasks often get assigned to c…
PredictionMobile Coverage Analysis using Crowdsourced Data
Effective assessment of mobile network coverage and the precise identification of service weak spots are paramount for network operators striving to enhance user Quality of Experience (QoE). This paper presents a novel f…
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 balan…
Time SeriesTime Series AnalysisCaring Without Sharing: A Federated Learning Crowdsensing Framework for Diversifying Representation of Cities
Mobile Crowdsensing has become main stream paradigm for researchers to collect behavioral data from citizens in large scales. This valuable data can be leveraged to create centralized repositories that can be used to tra…
Federated LearningPrivacy Preserving