Wireless network multicasting
The changing usage of a mature campus-wide wireless network
Proceedings of the 10th annual international conference on Mobile computing and networking
A community based mobility model for ad hoc network research
REALMAN '06 Proceedings of the 2nd international workshop on Multi-hop ad hoc networks: from theory to reality
The random trip model: stability, stationary regime, and perfect simulation
IEEE/ACM Transactions on Networking (TON)
The Impact of the Mobility Model on Delay Tolerant Networking Performance Analysis
ANSS '07 Proceedings of the 40th Annual Simulation Symposium
Impact of Human Mobility on Opportunistic Forwarding Algorithms
IEEE Transactions on Mobile Computing
Proceedings of the 1st ACM SIGMOBILE workshop on Mobility models
Pervasive and Mobile Computing
Money Circulation, Trackable Items, and the Emergence of Universal Human Mobility Patterns
IEEE Pervasive Computing
Digital Footprinting: Uncovering Tourists with User-Generated Content
IEEE Pervasive Computing
Heterogeneous Community-Based Mobility Model for Human Opportunistic Network
WIMOB '09 Proceedings of the 2009 IEEE International Conference on Wireless and Mobile Computing, Networking and Communications
SOFSEM'06 Proceedings of the 32nd conference on Current Trends in Theory and Practice of Computer Science
Hi-index | 0.00 |
Store-carry-forward communication, which is set as the heart of all routing protocols for mobile disruption-tolerant networks (DTNs), exploits nodes' mobility to bring messages closer to their destinations by exchanging messages across mobile nodes when they meet in close proximity. Understanding the subtle characteristics of human mobility leads to better service and application provisioning for mobile DTNs. We use GPS traces collected from multiple mobile users to empirically study different aspects of human mobility. Various Markov models (first, second and third-order) are estimated from users' mobility data. Based on empirical evidence, second-order Markov models are deemed sufficient to estimate mobile users' future locations accurately. These Markov models permit the design of a new routing algorithm for mobile DTNs capable of more efficiently routing data objects to their destination locations. The relay selection in this routing algorithm is based on mobile users' absorption times to the destination location. Simulations show that the proposed routing algorithm consumes less energy than legacy epidemic routing algorithms without excessive transmission delays.