Introspection-Based Periodicity Awareness Model for Intermittently Connected Mobile Networks
Recently, context awareness in Intermittently Connected Mobile Networks (ICMNs) has gained popularity in order to discover social similarities among mobile entities. Nevertheless, most of the contextual methods depend on network knowledge obtained with unrealistic scenarios. Mobile entities should have a self-knowledge determination in order to estimate their activity routines in a group of communities. This paper presents a periodicity awareness model which relies on introspective spatiotemporal observations. In this model, hourly, daily, and weekly locations of mobile entities are being tracked to predict future trajectories and periodicities within a targeted time period. Realistic simulations are utilized to analyze the predictions in weekly observation sets. The results show that a reasonable accuracy with an increasing level of determination can be obtained which does not require global network knowledge. In this regard, the presented model can give insights for any type of ICMN objectives.
KeywordsIntermittently-connected mobile networks social networks context- awareness periodicity awareness model spatiotemporal correlations
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