6 research outputs found
Dynamic capacity provision for wireless sensors connectivity: A profit optimization approach
[EN] We model a wireless sensors' connectivity scenario mathematically and analyze it using capacity provision mechanisms, with the objective of maximizing the profits of a network operator. The scenario has several sensors' clusters with each one having one sink node, which uploads the sensing data gathered in the cluster through the wireless connectivity of a network operator. The scenario is analyzed both as a static game and as a dynamic game, each one with two stages, using game theory. The sinks' behavior is characterized with a utility function related to the mean service time and the price paid to the operator for the service. The objective of the operator is to maximize its profits by optimizing the network capacity. In the static game, the sinks' subscription decision is modeled using a population game. In the dynamic game, the sinks' behavior is modeled using an evolutionary game and the replicator dynamic, while the operator optimal capacity is obtained solving an optimal control problem. The scenario is shown feasible from an economic point of view. In addition, the dynamic capacity provision optimization is shown as a valid mechanism for maximizing the operator profits, as well as a useful tool to analyze evolving scenarios. Finally, the dynamic analysis opens the possibility to study more complex scenarios using the differential game extension.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Spanish Ministry of Economy and Competitiveness through project TIN2013-47272-C2-1-R; AEI/FEDER, UE through project TEC2017-85830-C2-1-P; and co-supported by the European Social Fund BES-2014-068998.Sanchis-Cano, Á.; Guijarro, L.; Condoluci, M. (2018). Dynamic capacity provision for wireless sensors connectivity: A profit optimization approach. International Journal of Distributed Sensor Networks (Online). 14(4):1-14. https://doi.org/10.1177/1550147718772544S114144Weiser, M. (1991). The Computer for the 21st Century. Scientific American, 265(3), 94-104. doi:10.1038/scientificamerican0991-94Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems, 29(7), 1645-1660. doi:10.1016/j.future.2013.01.010Perera, C., Zaslavsky, A., Christen, P., & Georgakopoulos, D. (2013). Sensing as a service model for smart cities supported by Internet of Things. Transactions on Emerging Telecommunications Technologies, 25(1), 81-93. doi:10.1002/ett.2704Wang, N., Hossain, E., & Bhargava, V. K. (2016). Joint Downlink Cell Association and Bandwidth Allocation for Wireless Backhauling in Two-Tier HetNets With Large-Scale Antenna Arrays. IEEE Transactions on Wireless Communications, 15(5), 3251-3268. doi:10.1109/twc.2016.2519401Chowdhury, M. Z., Jang, Y. M., & Haas, Z. J. (2013). Call admission control based on adaptive bandwidth allocation for wireless networks. Journal of Communications and Networks, 15(1), 15-24. doi:10.1109/jcn.2013.000005Nan, G., Mao, Z., Yu, M., Li, M., Wang, H., & Zhang, Y. (2014). Stackelberg Game for Bandwidth Allocation in Cloud-Based Wireless Live-Streaming Social Networks. IEEE Systems Journal, 8(1), 256-267. doi:10.1109/jsyst.2013.2253420Zhu, K., Niyato, D., Wang, P., & Han, Z. (2012). Dynamic Spectrum Leasing and Service Selection in Spectrum Secondary Market of Cognitive Radio Networks. IEEE Transactions on Wireless Communications, 11(3), 1136-1145. doi:10.1109/twc.2012.010312.110732Vamvakas, P., Tsiropoulou, E. E., & Papavassiliou, S. (2017). Dynamic Provider Selection & Power Resource Management in Competitive Wireless Communication Markets. Mobile Networks and Applications, 23(1), 86-99. doi:10.1007/s11036-017-0885-yNiyato, D., Hoang, D. T., Luong, N. C., Wang, P., Kim, D. I., & Han, Z. (2016). Smart data pricing models for the internet of things: a bundling strategy approach. IEEE Network, 30(2), 18-25. doi:10.1109/mnet.2016.7437020Guijarro, L., Pla, V., Vidal, J. R., & Naldi, M. (2016). Maximum-Profit Two-Sided Pricing in Service Platforms Based on Wireless Sensor Networks. IEEE Wireless Communications Letters, 5(1), 8-11. doi:10.1109/lwc.2015.2487259Romero, J., Guijarro, L., Pla, V., & Vidal, J. R. (2017). Price competition between a macrocell and a small-cell service provider with limited resources and optimal bandwidth user subscription: a game-theoretical model. Telecommunication Systems, 67(2), 195-209. doi:10.1007/s11235-017-0331-2Al Daoud, A., Alanyali, M., & Starobinski, D. (2010). Pricing Strategies for Spectrum Lease in Secondary Markets. IEEE/ACM Transactions on Networking, 18(2), 462-475. doi:10.1109/tnet.2009.2031176Do, C. T., Tran, N. H., Huh, E.-N., Hong, C. S., Niyato, D., & Han, Z. (2016). Dynamics of service selection and provider pricing game in heterogeneous cloud market. Journal of Network and Computer Applications, 69, 152-165. doi:10.1016/j.jnca.2016.04.012Tsiropoulou, E. E., Vamvakas, P., & Papavassiliou, S. (2017). Joint Customized Price and Power Control for Energy-Efficient Multi-Service Wireless Networks via S-Modular Theory. IEEE Transactions on Green Communications and Networking, 1(1), 17-28. doi:10.1109/tgcn.2017.2678207Sanchis-Cano, A., Romero, J., Sacoto-Cabrera, E., & Guijarro, L. (2017). Economic Feasibility of Wireless Sensor Network-Based Service Provision in a Duopoly Setting with a Monopolist Operator. Sensors, 17(12), 2727. doi:10.3390/s17122727Weber, T. A. (2011). Optimal Control Theory with Applications in Economics. doi:10.7551/mitpress/9780262015738.001.0001Mandjes, M. (2003). Pricing strategies under heterogeneous service requirements. Computer Networks, 42(2), 231-249. doi:10.1016/s1389-1286(03)00191-9Shariatmadari, H., Ratasuk, R., Iraji, S., Laya, A., Taleb, T., Jäntti, R., & Ghosh, A. (2015). Machine-type communications: current status and future perspectives toward 5G systems. IEEE Communications Magazine, 53(9), 10-17. doi:10.1109/mcom.2015.7263367Ng, C.-H., & Soong, B.-H. (2008). Queueing Modelling Fundamentals. doi:10.1002/9780470994672Mendelson, H. (1985). Pricing computer services: queueing effects. Communications of the ACM, 28(3), 312-321. doi:10.1145/3166.3171Altman, E., Boulogne, T., El-Azouzi, R., Jiménez, T., & Wynter, L. (2006). A survey on networking games in telecommunications. Computers & Operations Research, 33(2), 286-311. doi:10.1016/j.cor.2004.06.005Belleflamme, P., & Peitz, M. (2015). Industrial Organization. doi:10.1017/cbo9781107707139Reynolds, S. S. (1987). Capacity Investment, Preemption and Commitment in an Infinite Horizon Model. International Economic Review, 28(1), 69. doi:10.2307/2526860Barron, E. N. (2013). Game Theory. doi:10.1002/9781118547168Sandholm, W. (2009). Pairwise Comparison Dynamics and Evolutionary Foundations for Nash Equilibrium. Games, 1(1), 3-17. doi:10.3390/g1010003Schlag, K. H. (1998). Why Imitate, and If So, How? Journal of Economic Theory, 78(1), 130-156. doi:10.1006/jeth.1997.234