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Issue No.02 - February (2008 vol.7)
pp: 187-198
Modeling mobility and user behavior is of fundamental importance in testing the performance of protocols for wireless data networks. While several models have been proposed in the literature, none of them can at the same time capture important features such as geographical mobility, user generated traffic, and wireless technology at hand. When collectively considered, these three aspects determine the user-perceived QoS-level, which, in turn, might have an influence on mobility of those users (we call them QoSdriven users) who do not display constrained mobility patterns, but they can decide to move to less congested areas of the network in case their perceived QoS-level becomes unacceptable. In this paper, we introduce the WiQoSM model which collectively considers all the above mentioned aspects of wireless data networks. WiQoSM is composed of i) a user mobility model, ii) a user traffic model, iii) a wireless technology model, and iv) a QoS model. Components i), ii), and iii) provide input to the QoS model, which, in turn, can influence the mobility behavior of QoS-driven users. WiQoSM is very simple to use and configure, and can be used to generate user and traffic traces at the APs composing a wireless data network. WiQoSM is shown to be able to generate traces which resemble statistical features observed in traces extracted from real-world WLAN deployments. Furthermore, WiQoSM has the nice feature of allowing fine tuning of disjoint set of parameters, in order to influence different statistical properties of the generated traces, and of providing the network designer with a high degree of flexibility in choosing network parameters such as number of users and APs, wireless channel technology, traffic mix, and so on. Given the above features, WiQoSM can be a valuable tool in the simulation of wireless data network protocols.
Mobility modeling, user behavior modeling, QoS-driven mobility, wireless data networks
Giovanni Resta, Paolo Santi, "WiQoSM: An Integrated QoS-Aware Mobility and User Behavior Model for Wireless Data Networks", IEEE Transactions on Mobile Computing, vol.7, no. 2, pp. 187-198, February 2008, doi:10.1109/TMC.2007.70728
[1] A. Balachandran, G.M. Voelker, P. Bahl, and P. Venkat Rangan, “Characterizing User Behavior and Network Performance in a Public Wireless LAN,” Proc. ACM Int'l Conf. Measurement and Modeling of Computer Systems (SIGMETRICS '02), pp. 195-205, 2002.
[2] A. Balachandran, P. Bahl, and G.M. Voelker, “Hot-Spot Congestion Relief and User Service Guarantees in Public-Area Wireless Networks,” Proc. Fourth IEEE Workshop Mobile Computing System and Applications (WMCSA '02), 2002.
[3] A. Balachandran, G.M. Voelker, and P. Bahl, “Wireless Hotspots: Current Challenges and Future Directions,” Mobile Networks and Applications, vol. 10, pp. 265-274, 2005.
[4] M. Balazinska and P. Castro, “Characterizing Mobility and Network Usage in a Corporate Wireless Local-Area Network,” Proc. First Int'l Conf. Mobile Systems, Applications, and Services (MobiSys '03), pp. 303-316, 2003.
[5] C. Bettstetter, G. Resta, and P. Santi, “The Node Distribution of the Random Waypoint Mobility Model for Wireless Ad Hoc Networks,” IEEE Trans. Mobile Computing, vol. 2, no. 3, pp. 257-269, July-Sept. 2003.
[6] C. Bettstetter, “Smooth is Better than Sharp: A Random Mobility Model for Simulation of Wireless Networks,” Proc. Fourth ACM Int'l Workshop Modeling, Analysis, and Simulation of Wireless and Mobile Systems (MSWiM '01), pp. 19-27, July 2001.
[7] R. Bruno, M. Conti, and E. Gregori, “Mesh Networks: Commodity Multihop Ad Hoc Networks,” IEEE Comm. Magazine, vol. 43, no. 3, pp. 123-131, Mar. 2005.
[8] T. Camp, J. Boleng, and V. Davies, “Mobility Models for Ad Hoc Network Simulations,” Wireless Comm. and Mobile Computing (WCMC), special issue on mobile ad hoc networking, 2002.
[9] Cisco Aironet 1240AG Data Sheets, , 2007.
[10] I. Haratcherev, J. Taal, K. Langendoen, R. Legendijk, and H. Sips, “Automatic IEEE 802.11 Rate Control for Streaming Applications,” Wireless Comm. and Mobile Computing, vol. 5, pp. 421-437, 2005.
[11] M. Heusse, F. Rousseau, G. Berger-Sabbatel, and A. Duda, “Performance Anomaly of 802.11b,” Proc. IEEE INFOCOM, 2003.
[12] W. Hsu, K. Merchant, H. Shu, C. Hsu, and A. Helmy, “Preference-Based Mobility Model and the Case for Congestion Relief in WLANs Using Ad Hoc Networks,” Proc. IEEE Vehicular Technology Conf. (VTC '04), 2004.
[13] R. Jain, D. Lelescu, and M. Balakrishnan, “Model T: An Empirical Model for User Registration Patterns in a Campus Wireless LAN,” Proc. ACM MobiCom, pp. 170-184, 2005.
[14] A. Jardos, E.M. Belding-Royer, K. Almeroth, and S. Suri, “Towards Realistic Mobility Models for Mobile Ad Hoc Networks,” Proc. ACM MobiCom, pp. 217-229, 2003.
[15] D.B. Johnson and D.A. Maltz, “Dynamic Source Routing in AdHoc Wireless Networks,” Mobile Computing, pp. 153-181, 1996.
[16] M. Kim and D. Kotz, “Modeling Users' Mobility among WiFi Access Points,” Proc. Int'l Workshop Wireless Traffic Measurements and Modeling (WiTMeMo '05), 2005.
[17] M. Kim, D. Kotz, and S. Kim, “Extracting a Mobility Model from Real User Traces,” Proc. IEEE INFOCOM, 2006.
[18] D. Kotz and K. Essien, “Characterizing Usage of a Campus-Wide Wireless Network,” Proc. ACM MobiCom, pp. 107-118, 2002.
[19] J.Y. LeBoudec and M. Vojnovic, “Perfect Simulation and Stationarity of a Class of Mobility Models,” Proc. IEEE INFOCOM, pp.2743-2754, 2005.
[20] J.K. Lee and J. Hou, “Modeling Steady-State and Transient Behaviors of User Mobility: Formulation, Analysis, and Application,” Proc. ACM MobiHoc, pp. 85-96, 2006.
[21] D. Lelescu, U. Kozat, R. Jain, and M. Balakrishnan, “Model T++: An Empirical Joint Space-Time Registration Model,” Proc. ACM MobiHoc, pp. 61-72, 2006.
[22] M. McGuire, “Stationary Distributions of Random Walk Mobility Models for Wireless Ad Hoc Networks,” Proc. ACM MobiHoc, pp.90-98, 2005.
[23] M. Musolesi, S. Hailes, and C. Mascolo, “An Ad Hoc Mobility Model Founded on Social Network Theory,” Proc. Seventh ACM Int'l Workshop Modeling, Analysis, and Simulation of Wireless and Mobile Systems (MSWiM '04), pp. 20-24, 2004.
[24] W. Navidi and T. Camp, “Stationary Distributions for the Random Waypoint Mobility Model,” IEEE Trans. Mobile Computing, vol. 3, no. 1, pp. 99-108, Jan.-Mar. 2004.
[25] M. Satyanarayanan, “Pervasive Computing: Vision and Challenges,” IEEE Personal Comm., vol. 8, no. 4, pp. 10-17, Aug. 2001.
[26] D. Tang and M. Baker, “Analysis of a Local-Area Wireless Network,” Proc. ACM MobiCom, pp. 1-10, 2000.
[27] C. Tuduce and T. Gross, “A Mobility Model Based on WLAN Traces and Its Validation,” Proc. IEEE INFOCOM, pp. 664-674, 2005.
[28] J. Yoon, M. Liu, and B. Noble, “Random Waypoint Considered Harmful,” Proc. IEEE INFOCOM, pp. 1312-1321, Apr. 2003.
[29] J. Yoon, M. Liu, and B. Noble, “Sound Mobility Models,” Proc. ACM MobiCom, pp. 205-216, Sept. 2003.
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