Emergency preparedness to accidental chemical spills from tankers in Istanbul Strait

Istanbul Strait is one of the most important and dangerous maritime passage in the world. In this study, the hazards for possible accidents of the tankers carrying various chemicals through the Istanbul Strait were investigated and a significant risk was identified due to the intensive transportation of the chemicals. The purpose of this work is to define some risk control options in order to establish an efficient management system which can minimize the probability of accidents and hazardous effects of possible chemical spills to human life and environment. The risk is assessed by using the Formal Safety Assessment Methodology of the International Maritime Organization. Following this methodology hazards of accidents were identified through a questionnaire which is applied to a group of experts focussed on a passage of Istanbul Strait. In addition to this, a frequency analysis of the accidents was carried out on the defined sections along the strait using the accident database in order to determine the geographical distribution of the type and cause of the accidents. On the other hand, the maritime traffic of the Istanbul Strait was simulated using computer based software in order to investigate the effects of the local traffic on the passage. As a conclusion of the simulation the hot spots were defined as the potential locations for collisions. Also the consequences of such probable accidents were evaluated by using different dispersion modelling software for the spilled chemicals. As a result, a comprehensive management system for preparedness and response to chemical spills in the Istanbul Strait were proposed by taking into account the current management system and response equipment. Furthermore, a detailed economic analysis of the proposed system was also performed

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). 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Automobile at Grand Canyon

Black and white print of automobile parked near an overlook

Collusion of operators in wireless spectrum markets

The liberalization of wireless spectrum markets has been envisioned as a method for improving mobile communication services and accommodating the increasing traffic volumes of wireless users. It has been assumed that competition among operators will foster optimal utilization of wireless spectrum and ensure the provision of cost-efficient wireless services to users. However, spectrum markets often function inefficiently due to collusion of the operators. Although it is illegal and detrimental to users, such phenomena are often observed in real life as, for example, in the form of implicit price fixing. This results in a de-facto monopoly in the spectrum market to the detriment of the users. In this paper, we consider a general wireless spectrum market where a set of operators sell bandwidth to a large population of users. We use an evolutionary game model to capture the user dynamics in the presence of limited market information and analyze the interaction of the operators using coalitional game theory. We define a partition formation game in order to rigorously study the conditions that render the grand coalition (emergence of monopoly) stable under various stability notions. The results obtained provide a foundation for effective measures against operator collusion by altering the underlying motivations rather than fighting against the symptoms through law enforcement. © 2012 IFIP

Competition and regulation in a wireless operator market: An evolutionary game perspective

We consider a communication market where a set of wireless operators compete over a large common pool of users. The latter have a reservation utility of U0 units or, equivalently, an alternative option to satisfy their communication needs. The operators must satisfy these minimum requirements in order to attract the users. In this setting, we analyze how the users select operators and how the operators compete for the users. We identify the critical system parameters and study how they affect the market operation. We model the users decisions and interaction as an evolutionary game and the competition among the operators as a noncooperative pricing game which is proved to be a potential game. For each set of prices selected by the operators, the evolutionary game attains a different stationary point. We show that the outcome of both games depends on the reservation utility of the users and the amount of spectrum W the operators have at their disposal. We express the market welfare and the revenue of the operators as functions of these two parameters. Accordingly, we consider the scenario where a regulating agency is able to intervene and change the outcome of the market by tuning W and/or U0. Different regulators may have different objectives and criteria according to which they intervene. We analyze the various possible regulation methods and discuss their requirements, implications and impact on the market. © 2012 Univ of Avignon