Abstract
The upsurge of social networks, mobile devices, Internet or Web-enabled services have enabled unprecedented level of human participation in pervasive computing which is coined as crowdsourcing. The pervasiveness of computing devices leads to a fast varying computing where it is imperative to have a model for catering the dynamic environment. The challenge of efficiently distributing context information in logical-clustering in crowdsourcing scenarios can be countered by the scalable MediaSense PubSub model. MeidaSense is a proven scalable PubSub model for static environment. However, the scalability of MediaSense as PubSub model is further challenged by its viability to adjust to the dynamic nature of crowdsourcing. Crowdsourcing does not only involve fast varying pervasive devices but also dynamic distributed and heterogeneous context information. In light of this, the paper extends the current MediaSense PubSub model which can handle dynamic logical-clustering in crowdsourcing. The results suggest that the extended MediaSense is viable for catering the dynamism nature of crowdsourcing, moreover, it is possible to predict the near-optimal subscription matching time and predict the time it takes to update (insert or delete) context-IDs along with existing published context-IDs. Furthermore, it is possible to foretell the memory usage in MediaSense PubSub model.
Highlights
The penetration of pervasive devices is escalating and the rate of proliferation is always on the rise
The evaluation can be divided into three parts: (i) PubSub for the context-IDs sharing in logical-clustering for which each published context-ID is matched for subscription, and (ii) PubSub for logical-sink synchronization for which all the changes are published to the other physical-sinks, and (iii) dynamic behavior of MediaSense
In current MediaSense, if we want to share context-IDs each context-ID would need to be registered as Universal Context Identifier (UCI)
Summary
The penetration of pervasive devices is escalating and the rate of proliferation is always on the rise. Social-networks are anticipated to contribute to this cause as well, for example, a tweet feed can be considered as sensor data [11] This surge of social networks, mobile devices, Internet or Web-enabled services have enabled unprecedented level of human participation in crowdsourcing which has been branded as “human-in-the-loop-sensing” or citizen sensor networks [12, 13]. This phenomenon has allowed us to encounter vast amount of real-time crowdsourced data from distributed context sources. In a nut-shell, the followings are the properties and requirements for crowdsourcing: People
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