Leveraging Contact Pattern to Predict Future Contact Pattern in Mobile Networks
With advances in the Internet and mobile technology, and decreasing cost of mobile devices, large scale pervasive networks are now ubiquitous in solving many earlier service limitations. Here, the challenge lies in its underlying temporal graph. It introduces technical limitations in efficient routing, maximal coverage with minimal latency, data offloading, to effective dissemination over mobile networks or mobility induced dynamic networks. Efficient solution to these interrelated problems lies in the novel prediction strategies for most accurate future contacts (links or interactions), their future contact time etc. In contrast to the existing strategies that consider either network structure or regular pattern and periodic nature of contacts, we propose a novel stochastic Poisson process model (variants of cascaded non-homogeneous Poisson process) that employ multi-recurrent, dependent contact pattern as its basis. We predict number of contacts relative to a node and over all nodes in any future interval, future contact time over a user and a pair of users. Finally, we validate our model with a widely used empirical data set from mobile network, and compare our model with doubly recurrent and homogeneous Poisson process model to conclude the superiority of our prediction model.
S. Das et al., "Leveraging Contact Pattern to Predict Future Contact Pattern in Mobile Networks," Proceedings of the 8th ACM International Workshop on Hot Topics in Planet-Scale mObile computing and online Social Networking (2016, Paderborn, Germany), pp. 13-18, Association for Computing Machinery (ACM), Jul 2016.
The definitive version is available at https://doi.org/10.1145/2944789.2944870
8th ACM International Workshop on Hot Topics in Planet-Scale mObile computing and online Social Networking, HotPOST '16 (2016: Jul. 5-8, Paderborn, Germany)
Intelligent Systems Center
Keywords and Phrases
Forecasting; Mobile computing; Mobile devices; Poisson distribution; Social networking (online); Social sciences computing; Stochastic models; Stochastic systems; Wireless networks; Dynamic network; Homogeneous Poisson process; Mobile Technology; Network structures; Non homogeneous poisson process; Number of contacts; Pervasive networks; Technical limitations; Mobile telecommunication systems; Contact Prediction; Mobility Induced Dynamic Networks
International Standard Book Number (ISBN)
Article - Conference proceedings
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01 Jul 2016