A Location-Based Mobile Crowdsensing Framework Supporting a Massive Ad Hoc Social Network Environment

摘要

This article addresses one of the key challenges of engaging a massive ad hoc crowd by providing sustainable incentives. The incentive model is based on a context-aware cyber-physical spatio-temporal serious game with the help of a mobile crowd sensing mechanism. To this end, this article describes a framework that can create an ad hoc social network of millions of people and provide context-aware serious-game services as an incentive. While interacting with different services, the massive crowd shares a rich trail of geo-tagged multimedia data, which acts as a crowdsourcing eco-system. The incentive model has been tested on the mass crowd at the Hajj since 2014. From our observations, we conclude that the framework provides a sustainable incentive mechanism that can solve many real-life problems such as reaching a person in a crowd within the shortest possible time, isolating significant events, finding lost individuals, handling emergency situations, helping pilgrims to perform ritual events based on location and time, and sharing geo-tagged multimedia resources among a community of interest within the crowd. The framework allows an ad hoc social network to be formed within a very large crowd, a community of interests to be created for each person, and information to be shared with the right community of interests. We present the communication paradigm of the framework, the serious game incentive model, and cloud-based massive geo-tagged social network architecture.