Access and mobility of wireless PDA users
ACM SIGMOBILE Mobile Computing and Communications Review
Routing in a delay tolerant network
Proceedings of the 2004 conference on Applications, technologies, architectures, and protocols for computer communications
The changing usage of a mature campus-wide wireless network
Proceedings of the 10th annual international conference on Mobile computing and networking
Pocket switched networks and human mobility in conference environments
Proceedings of the 2005 ACM SIGCOMM workshop on Delay-tolerant networking
Learning to detect events with Markov-modulated poisson processes
ACM Transactions on Knowledge Discovery from Data (TKDD)
A socio-aware overlay for publish/subscribe communication in delay tolerant networks
Proceedings of the 10th ACM Symposium on Modeling, analysis, and simulation of wireless and mobile systems
ACM SIGMOBILE Mobile Computing and Communications Review
Bubble rap: social-based forwarding in delay tolerant networks
Proceedings of the 9th ACM international symposium on Mobile ad hoc networking and computing
Pervasive and Mobile Computing
Exploiting Self-Reported Social Networks for Routing in Ubiquitous Computing Environments
WIMOB '08 Proceedings of the 2008 IEEE International Conference on Wireless & Mobile Computing, Networking & Communication
From opportunistic networks to opportunistic computing
IEEE Communications Magazine
Bluetooth and Wi-Fi wireless protocols: a survey and a comparison
IEEE Wireless Communications
Reaching for the clouds: contextually enhancing smartphones for energy efficiency
Proceedings of the 2nd ACM workshop on High performance mobile opportunistic systems
Opportunistic Networks: A Taxonomy of Data Dissemination Techniques
International Journal of Virtual Communities and Social Networking
| Hi-index | 0.00 |
An opportunistic network is composed of human-carried mobile devices that interact in a store-carry-and-forward fashion. A mobile node stores data and carries it around; when it encounters another node, it may decide to forward the data if the encountered node is the destination or has a better chance of bringing the data closer to the destination. In order to obtain efficient routing in such a network, we should be able to predict the future behavior of a node. This would help the algorithm decide if the data contained by the node should be further carried or forwarded, and to which node it is to be forwarded. In this paper, we present a mobile interaction trace collected at the University Politehnica of Bucharest in the spring of 2012, and analyze it in terms of the predictability of encounters and contact durations. We show that there is a regular pattern in the contact history of a node and then we prove that, by modelling the time series as a Poisson distribution, we can efficiently predict the number of contacts per time unit in the future. These assumptions are demonstrated both on the trace presented in this paper, as well as on a different trace recorded in another type of environment, showing that predictability doesn't happen only in strict and controlled situations.