The Community for Technology Leaders
RSS Icon
Issue No.09 - September (2011 vol.10)
pp: 1327-1344
Zhu Han , University of Houston, Houston
Tamer Başar , University of Illinois at Urbana-Champaign, Urbana
Mérouane Debbah , SUPÉLEC, Gif-sur-Yvette, Paris
Are Hjørungnes , UNIK - University of Oslo, Kjeller
Autonomous wireless agents such as unmanned aerial vehicles, mobile base stations, cognitive devices, or self-operating wireless nodes present a great potential for deployment in next-generation wireless networks. While current literature has been mainly focused on the use of agents within robotics or software engineering applications, this paper proposes a novel usage model for self-organizing agents suitable for wireless communication networks. In the proposed model, a number of agents are required to collect data from several arbitrarily located tasks. Each task represents a queue of packets that require collection and subsequent wireless transmission by the agents to a central receiver. The problem is modeled as a hedonic coalition formation game between the agents and the tasks that interact in order to form disjoint coalitions. Each formed coalition is modeled as a polling system consisting of a number of agents, designated as collectors, which move between the different tasks present in the coalition, collect and transmit the packets. Within each coalition, some agents might also take the role of a relay for improving the packet success rate of the transmission. The proposed hedonic coalition formation algorithm allows the tasks and the agents to take distributed decisions to join or leave a coalition, based on the achieved benefit in terms of effective throughput, and the cost in terms of polling system delay. As a result of these decisions, the agents and tasks structure themselves into independent disjoint coalitions which constitute a Nash-stable network partition. Moreover, the proposed coalition formation algorithm allows the agents and tasks to adapt the topology to environmental changes, such as the arrival of new tasks, the removal of existing tasks, or the mobility of the tasks. Simulation results show how the proposed algorithm allows the agents and tasks to self-organize into independent coalitions, while improving the performance, in terms of average player (agent or task) payoff, of at least 30.26 percent (for a network of five agents with up to 25 tasks) relatively to a scheme that allocates nearby tasks equally among agents.
Multiagent wireless networks, game theory, hedonic coalitions, task allocation, ad hoc networks.
Zhu Han, Tamer Başar, Mérouane Debbah, Are Hjørungnes, "Hedonic Coalition Formation for Distributed Task Allocation among Wireless Agents", IEEE Transactions on Mobile Computing, vol.10, no. 9, pp. 1327-1344, September 2011, doi:10.1109/TMC.2010.242
[1] M. Debbah, “Mobile Flexible Networks: The Challenges Ahead,” Proc. Int'l Conf. Advanced Technologies for Comm., Oct. 2008.
[2] W. Saad, Z. Han, M. Debbah, A. Hjørungnes, and T. Başar, “A Game-Based Self-Organizing Uplink Tree for VoIP Services in IEEE 802.16j Networks,” Proc. IEEE Int'l Conf. Comm., June 2009.
[3] D. Niyato, E. Hossein, and Z. Han, Dynamic Spectrum Access in Cognitive Radio Networks. Cambridge Univ., 2009.
[4] B. Kauffmann, F. Baccelli, A. Chaintreau, V. Mhatre, K. Papagiannaki, and C. Diot, “Measurement-Based Self Organization of Interfering 802.11 Wireless Access Networks,” Proc. IEEE INFOCOM, May 2007.
[5] A. Akella, G. Judd, P. Steenkiste, and S. Seshan, “Self Management in Chaotic Wireless Deployments,” Proc. ACM MobiCom, Sept. 2005.
[6] S. Yousefi, E. Altman, R. El-Azouzi, and M. Fathy, “Connectivity in Vehicular Ad Hoc Networks in Presence of Wireless Mobile Base-Stations,” Proc. Seventh Int'l Conf. ITS Telecomm., June 2007.
[7] M.M.B. Tariq, M. Ammar, and E. Zegura, “Message Ferry Route Design for Sparse Ad Hoc Networks with Mobile Nodes,” Proc. ACM MobiHoc, May 2006.
[8] Y. Shi and Y.T. Hou, “Theoretical Results on Base Station Movement Problem for Sensor Network,” Proc. IEEE INFOCOM, Apr. 2008.
[9] B. Gerkey and M.J. Mataric, “A Formal Framework for the Study of Task Allocation in Multi-Robot Systems,” Int'l J. Robotics Research, vol. 23, no. 9, pp. 939-954, Sept. 2004.
[10] M. Alighanbari and J. How, “Robust Decentralized Task Assignment for Cooperative UAVs,” Proc. Am. Inst. of Aeronautics and Astronautics (AIAA) Guidance, Navigation, and Control Conf., Aug. 2006.
[11] D.M. Stipanovic, P.F. Hokayem, M.W. Spong, and D.D. Siljak, “Cooperative Avoidance Control for Multi-Agent Systems,” J. Dynamic Systems, Measurement, and Control, vol. 129, no. 5, pp. 699-706, Sept. 2007.
[12] Q. Chen, M. Hsu, U. Dayal, and M. Griss, “Multi-Agent Cooperation, Dynamic Workflow and XML for e-Commerce Automation,” Proc. Int'l Conf. Autonomous Agents, June 2000.
[13] O. Shehory and S. Kraus, “Methods for Task Allocation via Agent Coalition Formation,” Artificial Intelligence, vol. 101, pp. 165-200, May 1998.
[14] R. Beard, D. Kingston, M. Quigley, D. Snyder, R. Christiansen, W. Johnson, T. McLain, and M. Goodrich, “Autonomous Vehicle Technologies for Small Fixed-Wing UAVs,” J. Aerospace Computing, Information, and Comm., vol. 2, no. 1, pp. 92-108, Jan. 2005.
[15] Z. Han, A. Swindlehurst, and K.J. Liu, “Optimization of MANET Connectivity via Smart Deployment/Movement of Unmanned Air Vehicles,” IEEE Trans. Vehicular Technology, vol. 58, no. 7, pp. 3533-3546, Sept. 2009.
[16] D.L. Gu, G. Pei, H. Ly, M. Gerla, B. Zhang, and X. Hong, “UAV Aided Intelligent Routing for Ad Hoc Wireless Network in Single Area Theater,” Proc. IEEE Wireless Comm. and Networking Conf., pp. 1220-1225, Sept. 2000.
[17] K. Xu, X. Hong, M. Gerla, H. Ly, and D.L. Gu, “Landmark Routing in Large Wireless Battlefield Networks Using UAVs,” Proc. IEEE Military Comm. Conf., pp. 230-234, Oct. 2001.
[18] D.L. Gu, H. Ly, X. Hong, M. Gerla, G. Pei, and Y. Lee, “C-ICAMA, A Centralized Intelligent Channel Assigned Multiple Access for Multi-Layer Ad-Hoc Wireless Networks with UAVs,” Proc. IEEE Wireless Comm. and Networking Conf., pp. 879-884, Sept. 2000.
[19] J. Proakis, Digital Communications, fourth ed., McGraw-Hill, 2001.
[20] R.B. Myerson, Game Theory, Analysis of Conflict. Harvard Univ., Sept. 1991.
[21] H. Takagi, Analysis of Polling Systems. MIT, Apr. 1986.
[22] H. Levy and M. Sidi, “Polling Systems: Applications, Modeling, and Optimization,” IEEE Trans. Comm., vol. 38, no. 10, pp. 1750-1760, Oct. 1990.
[23] Y. Li, H.S. Panwar, and J. Shao, “Performance Analysis of a Dual Round Robin Matching Switch with Exhaustive Service,” Proc. IEEE Global Telecomm. Conf., Nov. 2002.
[24] V. Vishnevsky and O. Semenova, “The Power-Series Algorithm for Two-Queue Polling System with Impatient Customers,” Proc. Int'l Conf. Telecom., June 2008.
[25] D. Applegate, R.M. Bixby, V. Chvatal, and W.J. Cook, The Traveling Salesman Problem: A Computational Study. Princeton Univ., 2006.
[26] L. Kleinrock, “Power and Deterministic Rules of Thumb for Probabilistic Problems in Computer Communications,” Proc. IEEE Int'l Conf. Comm., June 1979.
[27] W.K. Ching, “A Note on the Convergence of Asynchronous Greedy Algorithm with Relaxation in a Multiclass Queuing Environment,” IEEE Comm. Letters, vol. 3, no. 2, pp. 34-36, Feb. 1999.
[28] E. Altman, T. Başar, and R. Srikant, “Nash Equilibria for Combined Flow Control and Routing in Networks: Asymptotic Behavior for a Large Number of Users,” IEEE Trans. Automatic Control, vol. 47, no. 6, pp. 917-930, June 2002.
[29] V. Vukadinovic and G. Karlsson, “Video Streaming in 3.5G: On Throughput-Delay Performance of Proportional Fair Scheduling,” Proc. IEEE Int'l Symp. Modeling, Analysis and Simulation of Computer and Telecom. Systems, Sept. 2006.
[30] D. Ray, A Game-Theoretic Perspective on Coalition Formation. Oxford Univ., Jan. 2007.
[31] G. Demange and M. Wooders, Group Formation in Economics. Cambridge Univ., 2006.
[32] A. Bogomonlaia and M. Jackson, “The Stability of Hedonic Coalition Structures,” Games and Economic Behavior, vol. 38, pp. 201-230, Jan. 2002.
[33] E. Diamantoudi and L. Xue, “Farsighted Stability in Hedonic Games,” Social Choice and Welfare, vol. 21, pp. 39-61, Jan. 2003.
[34] J. Drèze and J. Greenberg, “Hedonic Coalitions: Optimality and Stability,” Econometrica, vol. 48, pp. 987-1003, Jan. 1980.
[35] S. Shellhammer, A. Sadek, and W. Zhang, “Technical Challenges for Cognitive Radio in TV White Space Spectrum,” Proc. Information Theory and Applications Workshop (ITA '09), Feb. 2009.
[36] G. Calhoun, M.H. Drape, M.F. Abernathy, M. Patzek, and F. Delgado, “Synthetic Vision System for Improving Unmanned Aerial Vehicle Operator Situation Awareness,” Proc. SPIE Conf., Mar. 2005.
[37] M. Brohede and S.F. Andler, “Distributed Simulation Communication through an Active Real-Time Database,” Proc. NASA Goddard/IEEE Software Eng. Workshop, Dec. 2002.
[38] L. Wainfan, Challenges in Virtual Collaboration: Videoconferencing Audioconferencing and Computer-Mediated Communications. RAND Corporation, July 2005.
[39] W. Saad, Z. Han, T. Başar, M. Debbah, and A. Hjørungnes, “A Selfish Approach to Coalition Formation among Unmanned Aerial Vehicles in Wireless Networks,” Proc. Int'l Conf. Game Theory for Networks (GameNets '99), May 2009.
17 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool