Analysis of a local-area wireless network
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
Smooth is better than sharp: a random mobility model for simulation of wireless networks
MSWIM '01 Proceedings of the 4th ACM international workshop on Modeling, analysis and simulation of wireless and mobile systems
Characterizing user behavior and network performance in a public wireless LAN
SIGMETRICS '02 Proceedings of the 2002 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Analysis of a campus-wide wireless network
Proceedings of the 8th annual international conference on Mobile computing and networking
Hot-Spot Congestion Relief in Public-Area Wireless Networks
WMCSA '02 Proceedings of the Fourth IEEE Workshop on Mobile Computing Systems and Applications
Proceedings of the 9th annual international conference on Mobile computing and networking
Towards realistic mobility models for mobile ad hoc networks
Proceedings of the 9th annual international conference on Mobile computing and networking
The Node Distribution of the Random Waypoint Mobility Model for Wireless Ad Hoc Networks
IEEE Transactions on Mobile Computing
An ad hoc mobility model founded on social network theory
MSWiM '04 Proceedings of the 7th ACM international symposium on Modeling, analysis and simulation of wireless and mobile systems
Stationary distributions of random walk mobility models for wireless ad hoc networks
Proceedings of the 6th ACM international symposium on Mobile ad hoc networking and computing
Characterizing mobility and network usage in a corporate wireless local-area network
Proceedings of the 1st international conference on Mobile systems, applications and services
Automatic IEEE 802.11 rate control for streaming applications: Research Articles
Wireless Communications & Mobile Computing - Radio Link and Transport Protocol Engineering for Future-Generation Wireless Mobile Data Networks
Modeling users' mobility among WiFi access points
WiTMeMo '05 Papers presented at the 2005 workshop on Wireless traffic measurements and modeling
Model T: an empirical model for user registration patterns in a campus wireless LAN
Proceedings of the 11th annual international conference on Mobile computing and networking
Model T++: an empirical joint space-time registration model
Proceedings of the 7th ACM international symposium on Mobile ad hoc networking and computing
Proceedings of the 7th ACM international symposium on Mobile ad hoc networking and computing
Wireless hotspots: current challenges and future directions
Mobile Networks and Applications - Special issue: Wireless mobile wireless applications and services on WLAN hotspots
Stationary Distributions for the Random Waypoint Mobility Model
IEEE Transactions on Mobile Computing
Mesh networks: commodity multihop ad hoc networks
IEEE Communications Magazine
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Congestion is expected to become a prominent problem to deal with as the popularity of wireless data networks continues to increase. However, this problem can in principle be mitigated if a fraction of the network users could decide to move to another location in case their perceived QoS degrades. To account for this, we propose an extension of the well-known RWP model called QoS-RWP, in which users are divided into mobile users displaying constrained movement patterns, and QoS-driven users who are mainly stationary, but they can decide to move to a better location to improve their QoS level. Another enhancement of QoS-RWP with respect to the original RWP model is that waypoints are chosen according to an access point (AP) popularity metric, which reflects the recently observed phenomenon that different APs in a wireless data network display very different degrees of popularity among users. The QoS-RWP model also accounts for different classes of load offered to the network by the users, and for different channel access methods. Based on QoS-RWP, we perform a simulation-based analysis of network usage under different combinations of network parameters such as the number of users, number of APs, relative fraction of QoS-driven users, and channel access method. Our investigation discloses interesting insights on network usage, and shows that our model is able to capture important properties observed in real-world network deployments.