On the self-similar nature of Ethernet traffic (extended version)
IEEE/ACM Transactions on Networking (TON)
Age matters: efficient route discovery in mobile ad hoc networks using encounter ages
Proceedings of the 4th ACM international symposium on Mobile ad hoc networking & computing
Towards realistic mobility models for mobile ad hoc networks
Proceedings of the 9th 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
Building realistic mobility models from coarse-grained traces
Proceedings of the 4th international conference on Mobile systems, applications and services
Power law and exponential decay of inter contact times between mobile devices
Proceedings of the 13th annual ACM international conference on Mobile computing and networking
Distributed community detection in delay tolerant networks
Proceedings of 2nd ACM/IEEE international workshop on Mobility in the evolving internet architecture
SMOOTH: a simple way to model human walks
ACM SIGMOBILE Mobile Computing and Communications Review
Trace-based mobility modeling for multi-hop wireless networks
Computer Communications
STEPS - an approach for human mobility modeling
NETWORKING'11 Proceedings of the 10th international IFIP TC 6 conference on Networking - Volume Part I
On the applicability of mobility metrics for user movement pattern recognition in MANETs
Proceedings of the 11th ACM international symposium on Mobility management and wireless access
| Hi-index | 0.00 |
In addition to being realistic, a mobility model should be easy to understand and use. Unfortunately, most of the simple mobility models proposed thus far are not realistic and most of the realistic mobility models proposed thus far are not simple to use. The main contribution of this work is to present SMOOTH, a new mobility model that is realistic (e.g., SMOOTH is based on several known features of human movement) and is simple to use (e.g., SMOOTH does not have any complex input parameters). We first present SMOOTH. We then validate that SMOOTH imitates human movement patterns present in real mobility traces collected from a range of diverse scenarios. In addition, we compare SMOOTH with the other mobility models developed based on these mobility traces. Thus, with SMOOTH, we provide researchers with a tool that allows them to leverage the statistical features present in real human movement in a simple and easy to understand manner.