Skip to main content
SpringerLink
Log in

Performance, Energy Savings and Security: An Introduction

  • Conference paper
  • First Online:
Modelling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNCCN,volume 12527))

  • 385 Accesses

Abstract

The International Symposia on the Modelling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS) have a 28 year-long distinguished record, and we introduce the papers that were presented at the MASCOTS 2020 workshop. We also briefly review recent work of one of the founders of this series and organizer of this year’s conference on the occasion of his 75th birthday, covering recent research on the Performance of Systems and Networks encompassing a broad view that includes Quality of Service, Energy Consumption and Security.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. The 24th International Symposium on Computer and Information Sciences, ISCIS 2009, North Cyprus, 14–16 September 2009. IEEE (2009)

    Google Scholar 

  2. Lent, R.: Contact holdups and their impact for overlay delay tolerant networks. In: Calzarossa, M.C., et al. (eds.) MASCOTS 2020. LNCS, vol. 12527, pp. 92–101. Springer, Cham (2021)

    Google Scholar 

  3. Filus, K., Siavvas, M., Domanska, J., Gelenbe, E.: The random neural network as a bonding model for software vulnerability prediction. In: Calzarossa, M.C., et al. (eds.) MASCOTS 2020. LNCS, vol. 12527, pp. 102–116. Springer, Cham (2021)

    Google Scholar 

  4. Filus, K., Domanska, J., Gelenbe, E.: A random neural network for attack detection. In: Calzarossa, M.C., et al. (eds.) MASCOTS 2020. LNCS, vol. 12527, pp. 79–91. Springer, Cham (2021)

    Google Scholar 

  5. Aaro, G., Roos, D., Carlsson, N.: Toolset for run-time dataset collection of deep-scene information. In: Calzarossa, M.C., et al. (eds.) MASCOTS 2020. LNCS, vol. 12527, pp. 224–236. Springer, Cham (2021)

    Google Scholar 

  6. Abdelbaki, H., Gelenbe, E., El-Khamy, S.E.: Random neural network decoder for error correcting codes. In: International Joint Conference on Neural Networks, IJCNN 1999, vol. 5, pp. 3241–3245. IEEE (1999)

    Google Scholar 

  7. Abdelbaki, H., Gelenbe, E., Kocak, T.: Matched neural filters for EMI based mine detection. In: International Joint Conference on Neural Networks, IJCNN 1999, vol. 5, pp. 3236–3240. IEEE (1999)

    Google Scholar 

  8. Abdelbaki, H., Gelenbe, E., Koçak, T., El-Khamy, S.E.: Random neural network filter for land mine detection. In: Proceedings of the Sixteenth National Radio Science Conference, NRSC 1999, pp. C43–1. IEEE (1999)

    Google Scholar 

  9. Abdelrahman, O.H., Gelenbe, E.: Search in non-homogenous random environments? ACM SIGMETRICS Per. Eval. Rev. 39(3), 37–39 (2011)

    Article  Google Scholar 

  10. Abdelrahman, O.H., Gelenbe, E.: Packet delay and energy consumption in non-homogeneous networks. Comput. J. 55(8), 950–964 (2012)

    Article  Google Scholar 

  11. Abdelrahman, O.H., Gelenbe, E.: Time and energy in team-based search. Phys. Rev. E 87(3), 032125 (2013)

    Article  Google Scholar 

  12. Abdelrahman, O.H., Gelenbe, E.: Signalling storms in 3g mobile networks. In: 2014 IEEE International Conference on Communications, ICC 2014, Sydney, Australia, 10–14 June 2014, pp. 1017–1022. IEEE (2014). https://doi.org/10.1109/ICC.2014.6883453

  13. Abdelrahman, O.H., Gelenbe, E., Gorbil, G., Lent, R. (eds.): Information Sciences and Systems 2015. LNEE, vol. 363. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-22635-4

    Book  Google Scholar 

  14. Adeel, A., Larijani, H., Ahmadinia, A.: Random neural network based cognitive engines for adaptive modulation and coding in LTE downlink systems. Comput. Electr. Eng. 57, 336–350 (2017). https://doi.org/10.1016/j.compeleceng.2016.11.005

  15. Aguilar, J., Gelenbe, E.: Task assignment and transaction clustering heuristics for distributed systems. Inf. Sci. 97(1), 199–219 (1997)

    Article  Google Scholar 

  16. Ahmad, J., Larijani, H., Emmanuel, R., Mannion, M., Javed, A., Phillipson, M.: Energy demand prediction through novel random neural network predictor for large non-domestic buildings. In: 2017 Annual IEEE International Systems Conference, SysCon 2017, Montreal, QC, Canada, 24–27 April 2017, pp. 1–6. IEEE (2017). https://doi.org/10.1109/SYSCON.2017.7934803

  17. Akinwande, O., Gelenbe, E.: A reinforcement learning approach to adaptive forwarding in named data networking. In: Czachórski, T., Gelenbe, E., Grochla, K., Lent, R. (eds.) ISCIS 2018. CCIS, vol. 935, pp. 211–219. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00840-6_23

    Chapter  Google Scholar 

  18. Asaad Althoubi, R.A., Peyravi, H.: Tail latency in datacenter networks. In: Calzarossa, M.C., et al. (eds.) MASCOTS 2020. LNCS, vol. 12527, pp. 254–272. Springer, Cham (2021)

    Google Scholar 

  19. Atalay, V., Gelenbe, E.: Parallel algorithm for colour texture generation using the random neural network model. Int. J. Pattern Recogn. Artif. Intell. 6(02n03), 437–446 (1992)

    Google Scholar 

  20. Atalay, V., Gelenbe, E., Yalabik, N.: The random neural network model for texture generation. Int. J. Pattern Recogn. Artif. Intell. 6(01), 131–141 (1992)

    Article  Google Scholar 

  21. Atmaca, T., Kamli, A., Kuaban, G.S., Czachorski, T.: Performance evaluation of the packet aggregation mechanism of an N-green metro network node. In: Calzarossa, M.C., et al. (eds.) MASCOTS 2020. LNCS, vol. 12527, pp. 62–78. Springer, Cham (2021)

    Google Scholar 

  22. Augusto-Gonzalez, J., et al.: From internet of threats to internet of things: a cyber security architecture for smart homes. In: 2019 IEEE 24th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), pp. 1–6. IEEE (2019)

    Google Scholar 

  23. Aykanat, C., Dayar, T., Körpeoğlu, İ. (eds.): ISCIS 2004. LNCS, vol. 3280. Springer, Heidelberg (2004). https://doi.org/10.1007/b101749

    Book  Google Scholar 

  24. Badel, M., Gelenbe, E., Leroudier, J., Potier, D., Lenfant, J.: Adaptive optimization of the performance of a virtual memory computer. ACM SIGMETRICS Perf. Eval. Rev. 3(4), 188 (1974)

    Article  Google Scholar 

  25. Badel, M., Gelenbe, E., Leroudier, J., Potier, D.: Adaptive optimization of a time-sharing system’s performance. Proc. IEEE 63(6), 958–965 (1975)

    Article  Google Scholar 

  26. Baldini, G., et al.: IoT network risk assessment and mitigation: the SerIoT approach (2020)

    Google Scholar 

  27. Basterrech, S., Mohamed, S., Rubino, G., Soliman, M.A.: Levenberg-Marquardt training algorithms for random neural networks. Comput. J. 54(1), 125–135 (2011). https://doi.org/10.1093/comjnl/bxp101

  28. Berl, A., et al.: Energy-efficient cloud computing. Comput. J. 53(7), 1045–1051 (2010)

    Article  Google Scholar 

  29. Bi, H., Desmet, A., Gelenbe, E.: Routing emergency evacuees with cognitive packet networks. In: Gelenbe, E., Lent, R. (eds.) Information Sciences and Systems 2013. LNEE, vol. 264, pp. 295–303. Springer, Cham (2013). https://doi.org/10.1007/978-3-319-01604-7_29

    Chapter  Google Scholar 

  30. Bi, H., Gelenbe, E.: A cooperative emergency navigation framework using mobile cloud computing. In: Czachórski, T., Gelenbe, E., Lent, R. (eds.) Information Sciences and Systems 2014, pp. 41–48. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-09465-6_5

    Chapter  Google Scholar 

  31. Bi, H., Gelenbe, E.: A survey of algorithms and systems for evacuating people in confined spaces. Electronics 8(6), 711 (2019)

    Article  Google Scholar 

  32. Brun, O., Wang, L., Gelenbe, E.: Big data for autonomic intercontinental overlays. IEEE J. Sel. Areas Commun. 34(3), 575–583 (2016)

    Article  Google Scholar 

  33. Brun, O., Yin, Y., Augusto-Gonzalez, J., Ramos, M., Gelenbe, E.: IoT attack detection with deep learning. In: ISCIS Security Workshop (2018)

    Google Scholar 

  34. Brun, O., Yin, Y., Gelenbe, E.: Deep learning with dense random neural network for detecting attacks against IoT-connected home environments. Proc. Comput. Sci. 134, 458–463 (2018)

    Article  Google Scholar 

  35. Buyya, R., et al.: A manifesto for future generation cloud computing: research directions for the next decade. ACM Comput. Surv. (CSUR) 51(5), 1–38 (2019)

    Article  Google Scholar 

  36. Chabridon, S., Gelenbe, E.: Scheduling of distributed tasks for survivability of the application. Inf. Sci. 97(1&2), 179–198 (1997). https://doi.org/10.1016/S0020-0255(96)00177-6

  37. Chesnais, A., Gelenbe, E., Mitrani, I.: On the modeling of parallel access to shared data. Commun. ACM 26(3), 196–202 (1983)

    Article  MATH  Google Scholar 

  38. Chouhan, A.S., Sridhar, V., Rao, S.: Service provider strategies in telecommunications markets: analytical and simulation analysis. Sadanha 46(1), 2333–2335 (2021)

    Google Scholar 

  39. Collen, A., et al.: GHOST - safe-guarding home IoT environments with personalised real-time risk control. In: Gelenbe, E., et al. (eds.) Euro-CYBERSEC 2018. CCIS, vol. 821, pp. 68–78. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-95189-8_7

    Chapter  Google Scholar 

  40. Cramer, C., Gelenbe, E.: Video quality and traffic QoS in learning-based subsampled and receiver-interpolated video sequences. IEEE J. Sel. Areas Commun. 18(2), 150–167 (2000). https://doi.org/10.1109/49.824788

  41. Cramer, C., Gelenbe, E., Bakircioglu, H.: Video compression with random neural networks. In: International Workshop on Neural Networks for Identification, Control, Robotics, and Signal/Image Processing. Proceedings, pp. 476–484. IEEE (1996)

    Google Scholar 

  42. Cramer, C., Gelenbe, E., Bakircloglu, H.: Low bit-rate video compression with neural networks and temporal subsampling. Proc. IEEE 84(10), 1529–1543 (1996)

    Article  Google Scholar 

  43. Czachorski, T., Gelenbe, E., Kuaban, G.S., Marek, D.: A time-dependent routing model of software defined networks. In: The Second International Workshop on Stochastic Modeling and Applied Research of Technology: SMARTY 2020, 16–20 August 2020. Karelian Research Center, Russian Academy of Sciences, Petrozavodsk (2020)

    Google Scholar 

  44. Czachórski, T., Gelenbe, E., Lent, R. (eds.): Information Sciences and Systems 2014. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-09465-6

    Book  Google Scholar 

  45. Czachorski, T., Gelenbe, E., Marek, D.: Software defined network dynamics via diffusions. In: Calzarossa, M.C., et al. (eds.) MASCOTS 2020. LNCS, vol. 12527, pp. 29–47. Springer, Cham (2021)

    Google Scholar 

  46. Czachorski, T., Gelenbe, E., Sulla, K.G., Marek, D.: Transient behaviour of a network router. In: 2020 43rd International Conference on Telecommunications and Signal Processing (TSP), pp. 1–5. IEEE (2020)

    Google Scholar 

  47. Czachórski, T., Gelenbe, E., Grochla, K., Lent, R. (eds.): ISCIS 2018. CCIS, vol. 935. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00840-6

    Book  Google Scholar 

  48. Desmet, A., Gelenbe, E.: Graph and analytical models for emergency evacuation. In: 2013 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), pp. 523–527. IEEE (2013)

    Google Scholar 

  49. Desmet, A., Gelenbe, E.: Interoperating infrastructures in emergencies. In: Gelenbe, E., Lent, R. (eds.) Computer and Information Sciences III, pp. 123–130. Springer, London (2013). https://doi.org/10.1007/978-1-4471-4594-3_13

    Chapter  Google Scholar 

  50. Di Ferdinando, A., Lent, R., Gelenbe, E.: A framework for autonomic networked auctions. In: Proceedings of the 2007 Workshop on INnovative SERvice Technologies, pp. 1–10 (2007)

    Google Scholar 

  51. Dimakis, N., Filippoupolitis, A., Gelenbe, E.: Distributed building evacuation simulator for smart emergency management. Comput. J. 53(9), 1384–1400 (2010)

    Article  Google Scholar 

  52. Dobson, S., et al.: A survey of autonomic communications. ACM Trans. Auton. Adap. Syst. (TAAS) 1(2), 223–259 (2006)

    Article  Google Scholar 

  53. Domanska, J., Gelenbe, E., Czachorski, T., Drosou, A., Tzovaras, D.: Research and innovation action for the security of the internet of things: the SerIoT project. In: Gelenbe, E., et al. (eds.) Euro-CYBERSEC 2018. CCIS, vol. 821, pp. 101–118. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-95189-8_10

    Chapter  Google Scholar 

  54. Du, J., Gelenbe, E., Jiang, C., Han, Z., Ren, Y.: Auction-based data transaction in mobile networks: data allocation design and performance analysis. IEEE Trans. Mobile Comput. 19(5), 1040–1055 (2019)

    Article  Google Scholar 

  55. Du, J., Gelenbe, E., Jiang, C., Zhang, H., Han, Z., Ren, Y.: Data transaction modeling in mobile networks: contract mechanism and performance analysis. In: GLOBECOM 2017-2017 IEEE Global Communications Conference, pp. 1–6. IEEE (2017)

    Google Scholar 

  56. Du, J., Gelenbe, E., Jiang, C., Zhang, H., Ren, Y.: Contract design for traffic offloading and resource allocation in heterogeneous ultra-dense networks. IEEE J. Sel. Areas Commun. 35(11), 2457–2467 (2017)

    Article  Google Scholar 

  57. Du, J., Gelenbe, E., Jiang, C., Zhang, H., Ren, Y., Poor, H.V.: Peer prediction-based trustworthiness evaluation and trustworthy service rating in social networks. IEEE Trans. Inf. Foren. Sec. 14(6), 1582–1594 (2018)

    Article  Google Scholar 

  58. Du, J., Jiang, C., Gelenbe, E., Han, Z., Ren, Y., Guizani, M.: Networked data transaction in mobile networks: a prediction-based approach using auction. In: 2018 14th International Wireless Communications & Mobile Computing Conference (IWCMC), pp. 201–206. IEEE (2018)

    Google Scholar 

  59. Du, J., Jiang, C., Gelenbe, E., Xu, L., Li, J., Ren, Y.: Distributed data privacy preservation in IoT applications. IEEE Wirel. Commun. 25(6), 68–76 (2018)

    Article  Google Scholar 

  60. Du, J., Jiang, C., Gelenbe, E., Zhang, H., Ren, Y.: Traffic offloading in software defined ultra-dense networks. In: Ultra-Dense Networks: Principles and Applications, p. 164 (2020)

    Google Scholar 

  61. Du, J., Jiang, C., Gelenbe, E., Zhang, H., Ren, Y., Quek, T.Q.: Double auction mechanism design for video caching in heterogeneous ultra-dense networks. IEEE Trans. Wireless Commun. 18(3), 1669–1683 (2019)

    Article  Google Scholar 

  62. Evmorfos, S., Vlachodimitropoulos, G., Bakalos, N., Gelenbe, E.: Neural network architectures for the detection of SYN flood attacks in IoT systems. In: Proceedings of the 13th ACM International Conference on PErvasive Technologies Related to Assistive Environments, pp. 1–4. No. 69. ACM (2020). https://doi.org/10.1145/3389189.3398000

  63. Fayolle, G., Gelenbe, E., Labetoulle, J.: Stability and optimal control of the packet switching broadcast channel. J. ACM (JACM) 24(3), 375–386 (1977)

    Article  MathSciNet  MATH  Google Scholar 

  64. Fayolle, G., Gelenbe, E., Labetoulle, J., Bastin, D.: The stability problem of broadcast packet switching computer networks. Acta Informatica 4(1), 49–53 (1974)

    Article  MATH  Google Scholar 

  65. Filippoupolitis, A., et al.: PerNEM 2014: the fourth international workshop on pervasive networks for emergency management, 2014-welcome and committees welcome message from the PernEM’14 co-chairs. In: Proceedings PerCOM 2014 (2014)

    Google Scholar 

  66. Filippoupolitis, A., et al.: Distributed agent-based building evacuation simulator (2012)

    Google Scholar 

  67. Filippoupolitis, A., Gorbil, G., Gelenbe, E.: Spatial computers for emergency management. In: 2011 Fifth IEEE Conference on Self-Adaptive and Self-Organizing Systems Workshops, pp. 61–66. IEEE (2011)

    Google Scholar 

  68. Filippoupolitis, A., Gorbil, G., Gelenbe, E.: Pervasive emergency support systems for building evacuation. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 525–527. IEEE (2012)

    Google Scholar 

  69. Filippoupolitis, A., Hey, L., Loukas, G., Gelenbe, E., Timotheou, S.: Emergency response simulation using wireless sensor networks. In: AMBI-SYS 2008: Proceedings of the 1st International Conference on Ambient Media and Systems, pp. 1–7, February 2008

    Google Scholar 

  70. Fourneau, J.M., Gelenbe, E.: G-networks with adders. Future Internet 9(3), 34 (2017)

    Article  Google Scholar 

  71. Fourneau, J., Gelenbe, E., Suros, R.: G-networks with multiple classes of negative and positive customers. Theor. Comput. Sci. 155(1), 141–156 (1996). https://doi.org/10.1016/0304-3975(95)00018-6

  72. Francois, F., Abdelrahman, O.H., Gelenbe, E.: Impact of signaling storms on energy consumption and latency of LTE user equipment. In: 2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium on Cyberspace Safety and Security, and 2015 IEEE 12th International Conference on Embedded Software and Systems, pp. 1248–1255. IEEE (2015)

    Google Scholar 

  73. Francois, F., Abdelrahman, O.H., Gelenbe, E.: Towards assessment of energy consumption and latency of LTE UEs during signaling storms. In: Abdelrahman, O.H., Gelenbe, E., Gorbil, G., Lent, R. (eds.) Information Sciences and Systems 2015. LNEE, vol. 363, pp. 45–55. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-22635-4_4

    Chapter  Google Scholar 

  74. Fröhlich, P., Gelenbe, E.: Optimal fog services placement in SDN IoT network using random neural networks and cognitive network map. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds.) ICAISC 2020. LNCS (LNAI), vol. 12415, pp. 78–89. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-61401-0_8

    Chapter  Google Scholar 

  75. Frohlich, P., Gelenbe, E., Nowak, M.P.: Smart SDN management of fog services. In: GIOTS 2020: Global IoT Summit 2020, IEEE Communications Society, Dubin, Ireland, 1–5 June 2020. TechRxiv (2020)

    Google Scholar 

  76. Gelenbe, E.: Réseaux neuronaux aléatoires stables. Comptes rendus de l’Académie des sciences. Série 2, Mécanique, Physique, Chimie, Sciences de l’univers, Sciences de la Terre 310(3), 177–180 (1990)

    Google Scholar 

  77. Gelenbe, E.: Current research on cybersecurity in Europe. In: Gelenbe, E., et al. (eds.) Recent Cybersecurity Research in Europe: Proceedings of the 2018 ISCIS Security Workshop, Imperial College London. Lecture Notes CCIS No. 821. Springer (2018)

    Google Scholar 

  78. Gelenbe, E., Batty, F.: Minimum graph vertex covering with the random neural network. In: Computer Science and Operations Research, pp. 139–147. Pergamon, Amsterdam (1992)

    Google Scholar 

  79. Gelenbe, E., Hussain, K.F., Kaptan, V.: Simulating autonomous agents in augmented reality. J. Syst. Softw. 74(3), 255–268 (2005)

    Article  Google Scholar 

  80. Gelenbe, E., Koubi, V., Pekergin, F.: Dynamical random neural network approach to the traveling salesman problem. Proc. IEEE Syst. Man Cybern. Conf. 2, 630–635 (1993)

    Article  Google Scholar 

  81. Gelenbe, E., Lent, R.: Mobile ad-hoc cognitive packet networks. In: Proceedings of the IEEE ASWN, pp. 2–4 (2002)

    Google Scholar 

  82. Gelenbe, E.: A unified approach to the evaluation of a class of replacement algorithms. IEEE Trans. Comput. 100(6), 611–618 (1973)

    Article  MathSciNet  MATH  Google Scholar 

  83. Gelenbe, E.: On approximate computer system models. J. ACM (JACM) 22(2), 261–269 (1975)

    Article  MathSciNet  MATH  Google Scholar 

  84. Gelenbe, E.: Probabilistic models of computer systems. Acta Informatica 12(4), 285–303 (1979)

    Article  MathSciNet  Google Scholar 

  85. Gelenbe, E.: Random neural networks with negative and positive signals and product form solution. Neural Comput. 1(4), 502–510 (1989)

    Article  Google Scholar 

  86. Gelenbe, E.: Stability of the random neural network model. Neural Comput. 2(2), 239–247 (1990)

    Article  Google Scholar 

  87. Gelenbe, E.: G-nets and learning recurrent random networks. In: Proceedings of the International Conference on Artificial Neural Networks, Brighton, England (1992)

    Google Scholar 

  88. Gelenbe, E.: Learning in the recurrent random neural network. Neural Comput. 5(1), 154–164 (1993)

    Article  Google Scholar 

  89. Gelenbe, E.: G-networks: a unifying model for neural and queueing networks. Ann. Oper. Res. 48(5), 433–461 (1994)

    Article  MathSciNet  MATH  Google Scholar 

  90. Gelenbe, E.: The first decade of G-networks. Eur. J. Oper. Res. 126(2), 231–232 (2000)

    Article  MATH  Google Scholar 

  91. Gelenbe, E. (ed.): International Symposium on Computer and Information Sciences. CRC Press (2002)

    Google Scholar 

  92. Gelenbe, E.: Sensible decisions based on QoS. Comput. Manage. Sci. 1(1), 1–14 (2003)

    Article  MATH  Google Scholar 

  93. Gelenbe, E.: Quality of service in ad hoc networks. Ad Hoc Netw. 2(3), 203 (2004)

    Article  Google Scholar 

  94. Gelenbe, E.: Analysis of automated auctions. In: Levi, A., Savaş, E., Yenigün, H., Balcısoy, S., Saygın, Y. (eds.) ISCIS 2006. LNCS, vol. 4263, pp. 1–12. Springer, Heidelberg (2006). https://doi.org/10.1007/11902140_1

    Chapter  Google Scholar 

  95. Gelenbe, E.: Users and services in intelligent networks. IEE Proc. Intell. Transp. Syst. 153(3), 213–220 (2006)

    Article  Google Scholar 

  96. Gelenbe, E.: Analytical solution of gene regulatory networks. In: 2007 IEEE International Fuzzy Systems Conference, pp. 1–6. IEEE (2007)

    Google Scholar 

  97. Gelenbe, E.: Dealing with software viruses: a biological paradigm. Inf. Secur. Tech. Rep. 12(4), 242–250 (2007)

    Article  MathSciNet  Google Scholar 

  98. Gelenbe, E.: A diffusion model for packet travel time in a random multi-hop medium. ACM Trans. Sensor Netw. 3(2), 10-es (2007)

    Google Scholar 

  99. Gelenbe, E.: A diffusion model for packet travel time in a random multihop medium. ACM Trans. Sensor Netw. (TOSN) 3(2), 10 (2007)

    Article  Google Scholar 

  100. Gelenbe, E.: Steady-state solution of probabilistic gene regulatory networks. Phys. Rev. E 76(1), 031903 (2007)

    Article  Google Scholar 

  101. Gelenbe, E.: Steady-state solution of probabilistic gene regulatory networks. Phys. Rev. E 76(3), 031903 (2007)

    Article  Google Scholar 

  102. Gelenbe, E.: Network of interacting synthetic molecules in steady-state. Proc. Royal Soc. A 464, 2219–2228 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  103. Gelenbe, E.: Network of interacting synthetic molecules in steady state. Proc. Royal Soc. A Math. Phys. Eng. Sci. 464(2096), 2219–2228 (2008)

    MathSciNet  MATH  Google Scholar 

  104. Gelenbe, E.: Analysis of single and networked auctions. ACM Trans. Internet Technol. (TOIT) 9(2), 8 (2009)

    Article  Google Scholar 

  105. Gelenbe, E.: Steps toward self-aware networks. Commun. ACM 52(7), 66–75 (2009)

    Article  Google Scholar 

  106. Gelenbe, E.: Search in unknown random environments. Phys. Rev. E 82, 061112 (2010)

    Article  Google Scholar 

  107. Gelenbe, E.: Special issue on G-networks and their applications. Perform. Eval. 67, 415–416 (2010)

    Google Scholar 

  108. Gelenbe, E.: Introduction to the special issue on G-networks and the random neural network (2011)

    Google Scholar 

  109. Gelenbe, E.: Energy packet networks: adaptive energy management for the cloud. In: CloudCP 2012: Proceedings of the 2nd International Workshop on Cloud Computing Platforms, pp. 1–5. ACM (2012). https://doi.org/10.1145/2168697.2168698

  110. Gelenbe, E.: Energy packet networks: ICT based energy allocation and storage. In: Rodrigues, J.J.P.C., Zhou, L., Chen, M., Kailas, A. (eds.) GreeNets 2011. LNICST, vol. 51, pp. 186–195. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33368-2_16

    Chapter  Google Scholar 

  111. Gelenbe, E.: Energy packet networks: smart electricity storage to meet surges in demand. In: Proceedings of the 5th International ICST Conference on Simulation Tools and Techniques, pp. 1–7. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering) (2012)

    Google Scholar 

  112. Gelenbe, E.: Natural computation. Comput. J. 55(7), 848–851 (2012)

    Article  Google Scholar 

  113. Gelenbe, E.: Adaptive management of energy packets. In: 2014 IEEE 38th International Computer Software and Applications Conference Workshops, pp. 1–6. IEEE (2014)

    Google Scholar 

  114. Gelenbe, E.: Error and energy when communicating with spins. In: 2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP), pp. 784–787. IEEE, December 2014. https://doi.org/10.1109/GlobalSIP.2014.7032226

  115. Gelenbe, E.: A sensor node with energy harvesting. ACM SIGMETRICS Perform. Eval. Rev. 42(2), 37–39 (2014)

    Article  Google Scholar 

  116. Gelenbe, E.: Synchronising energy harvesting and data packets in a wireless sensor. Energies 8(1), 356–369 (2015). https://doi.org/10.3390/en8010356

    Article  Google Scholar 

  117. Gelenbe, E.: Agreement in spins and social networks. ACM SIGMETRICS Perform. Eval. Rev. 44(2), 15–17 (2016)

    Article  Google Scholar 

  118. Gelenbe, E., Abdelrahman, O.H.: An energy packet network model for mobile networks with energy harvesting. Nonlinear Theory Appl. 9(3), 1–15 (2018). https://doi.org/10.1587/nolta.9.1. IEICE 2018

  119. Gelenbe, E., Bi, H.: Emergency navigation without an infrastructure. Sensors 14(8), 15142–15162 (2014)

    Article  Google Scholar 

  120. Gelenbe, E., Campegiani, P., Czachórski, T., Katsikas, S.K., Komnios, I., Romano, L., Tzovaras, D.: Security in computer and information sciences: First international ISCIS security workshop 2018, EURO-CYBERSEC 2018, London, UK, 26–27 February 2018, revised selected papers (2018)

    Google Scholar 

  121. Gelenbe, E., Cao, Y.: Autonomous search for mines. Eur. J. Oper. Res. 108(2), 319–333 (1998)

    Article  MATH  Google Scholar 

  122. Gelenbe, E., Caseau, Y.: The impact of information technology on energy consumption and carbon emissions. Ubiquity 2015, 1–15 (2015)

    Article  Google Scholar 

  123. Gelenbe, E., gce Ceran, E.T.: Central or distributed energy storage for processors with energy harvesting. In: The Fourth International Conference on Sustainable Internet and ICT for Sustainability. IEEE, April 2015

    Google Scholar 

  124. Gelenbe, E., Domanska, J., Frohlich, P., Nowak, M., Nowak, S.: Self-aware networks that optimize security, QoS and energy. Proc. IEEE 108(7) (2020, accepted for publication )

    Google Scholar 

  125. Gelenbe, E., Feng, Y., Krishnan, K.R.R.: Neural network methods for volumetric magnetic resonance imaging of the human brain. Proc. IEEE 84(10), 1488–1496 (1996)

    Article  Google Scholar 

  126. Gelenbe, E., Feng, Y., Ranga, K., Krishnan, R.: Neural networks for volumetric MR imaging of the brain. In: International Workshop on Neural Networks for Identification, Control, Robotics, and Signal/Image Processing. Proceedings, pp. 194–202. IEEE (1996)

    Google Scholar 

  127. Gelenbe, E., Fourneau, J.M.: Random neural networks with multiple classes of signals. Neural Comput. 11(4), 953–963 (1999). https://doi.org/10.1162/089976699300016520

    Article  Google Scholar 

  128. Gelenbe, E., Fourneau, J.: G-networks with resets. Perform. Eval. 49(1/4), 179–191 (2002)

    Article  MATH  Google Scholar 

  129. Gelenbe, E., et al.: IoT network attack detection and mitigation. In: The 9th Mediterranean Conference on Embedded Computing (MECO 2020), Budva, Montenegro, 8–11 June 2020, pp. 1–6 (2020). https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9134241&isnumber\(\ldots \)

  130. Gelenbe, E., Gellman, M., Loukas, G.: An autonomic approach to denial of service defence. In: Sixth IEEE International Symposium on a World of Wireless Mobile and Multimedia Networks, WoWMoM 2005, pp. 537–541. IEEE (2005)

    Google Scholar 

  131. Gelenbe, E., Gellman, M., Su, P.: Self-awareness and adaptivity for quality of service. In: Proceedings of the Eighth IEEE International Symposium on Computers and Communication (ISCC 2003), pp. 3–9. IEEE (2003)

    Google Scholar 

  132. Gelenbe, E., Gesbert, D., Gunduz, D., Külah, H., Uysal-Biyikoglu, E.: Energy harvesting communication networks: optimization and demonstration (the e-crops project). In: 2013 24th Tyrrhenian International Workshop on Digital Communications-Green ICT (TIWDC), pp. 1–6. IEEE (2013)

    Google Scholar 

  133. Gelenbe, E., Ghanwani, A., Srinivasan, V.: Improved neural heuristics for multicast routing. IEEE J. Sel. Areas Commun. 15(2), 147–155 (1997). https://doi.org/10.1109/49.552065

  134. Gelenbe, E., Ghanwani, A., Srinivasan, V.: Improved neural heuristics for multicast routing. IEEE J. Sel. Areas Commun. 15(2), 147–155 (1997)

    Google Scholar 

  135. Gelenbe, E., et al.: NEMESYS: enhanced network security for seamless service provisioning in the smart mobile ecosystem. In: Gelenbe, E., Lent, R. (eds.) Information Sciences and Systems 2013. LNEE, vol. 264, pp. 369–378. Springer, Cham (2013). https://doi.org/10.1007/978-3-319-01604-7_36

    Chapter  Google Scholar 

  136. Gelenbe, E., et al.: Security for smart mobile networks: the NEMESYS approach. In: 2013 International Conference on Privacy and Security in Mobile Systems (PRISMS), pp. 1–8. IEEE (2013)

    Google Scholar 

  137. Gelenbe, E., Gorbil, G., Wu, F.J.: Emergency cyber-physical-human systems. In: 2012 21st International Conference on Computer Communications and Networks (ICCCN), pp. 1–7. IEEE (2012)

    Google Scholar 

  138. Gelenbe, E., Gündüz, D.: Optimum power level for communications with interference. In: 2013 24th Tyrrhenian International Workshop on Digital Communications-Green ICT (TIWDC), pp. 1–6. IEEE (2013)

    Google Scholar 

  139. Gelenbe, E., Györfi, L.: Performance of auctions and sealed bids. In: Bradley, J.T. (ed.) EPEW 2009. LNCS, vol. 5652, pp. 30–43. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-02924-0_3

    Chapter  Google Scholar 

  140. Gelenbe, E., Hebrail, G.: A probability model of uncertainty in data bases. In: Proceedings of the Second International Conference on Data Engineering, pp. 328–333. IEEE Computer Society (1986)

    Google Scholar 

  141. Gelenbe, E., Hussain, K.: Learning in the multiple class random neural network. IEEE Trans. Neural Netw. 13(6), 1257–1267 (2002). https://doi.org/10.1109/TNN.2002.804228

  142. Gelenbe, E., Hussain, K., Kaptan, V.: Simulating autonomous agents in augmented reality. J. Syst. Softw. 74(3), 255–268 (2005)

    Google Scholar 

  143. Gelenbe, E., Hussain, K.F.: Learning in the multiple class random neural network. IEEE Trans. Neural Netw. 13(6), 1257–1267 (2002)

    Article  Google Scholar 

  144. Gelenbe, E., Iasnogorodski, R.: A queue with server of walking type (autonomous service). Annales de l’institut Henri Poincaré (B) Probabilités et Statistiques 16(1), 63–73 (1980)

    Google Scholar 

  145. Gelenbe, E., Kammerman, P., Lam, T.: Performance considerations in totally mobile wireless. Perform. Eval. 36, 387–399 (1999)

    Article  MATH  Google Scholar 

  146. Gelenbe, E., Kaptan, V., Wang, Yu.: Biological metaphors for agent behavior. In: Aykanat, C., Dayar, T., Körpeoğlu, İ. (eds.) ISCIS 2004. LNCS, vol. 3280, pp. 667–675. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30182-0_67

    Chapter  Google Scholar 

  147. Gelenbe, E., Kazhmaganbetova, Z.: Cognitive packet network for bilateral asymmetric connections. IEEE Trans. Indus. Inform. 10(3), 1717–1725 (2014). https://doi.org/10.1109/TII.2014.2321740

  148. Gelenbe, E., Koçak, T.: Area-based results for mine detection. IEEE Trans. Geosci. Remote Sens. 38(1), 12–24 (2000)

    Google Scholar 

  149. Gelenbe, E., Koçak, T., Wang, R.: Wafer surface reconstruction from top-down scanning electron microscope images. Microelectron. Eng. 75(2), 216–233 (2004)

    Article  Google Scholar 

  150. Gelenbe, E., Koubi, V., Pekergin, F.: Dynamical random neural network approach to the traveling salesman problem. In: International Conference on Systems, Man and Cybernetics. Systems Engineering in the Service of Humans, Conference Proceedings, pp. 630–635. IEEE (1993)

    Google Scholar 

  151. Gelenbe, E., Kurinckx, A.: Random injection control of multiprogramming in virtual memory. IEEE Trans. SE Softw. Eng. 4(1), 2–17 (1978)

    Article  MATH  Google Scholar 

  152. Gelenbe, E., Labed, A.: ESPRIT LTR project 8144 LYDIA load balancing and G-networks: design, implementation and evaluation. Technical report, IHEI, Univ. René Descartes, Paris V (1996)

    Google Scholar 

  153. Gelenbe, E., Labed, A.: G-networks with multiple classes of signals and positive customers. Eur. J. Oper. Res. 108(2), 293–305 (1998). https://doi.org/10.1016/S0377-2217(97)00371-8

    Article  MATH  Google Scholar 

  154. Gelenbe, E., Labed, A.: G-networks with multiple classes of signals and positive customers. Eur. J. Oper. Res. 108(2), 293–305 (1998)

    Article  MATH  Google Scholar 

  155. Gelenbe, E., Lenfant, J., Potier, D.: Analyse d’un algorithme de gestion simultanée mémoire centrale - disque de pagination. Acta Informatica 3, 321–345 (1974). https://doi.org/10.1007/BF00263587

  156. Gelenbe, E., Lenfant, J., Potier, D.: Response time of a fixed-head disk to transfers of variable length. SIAM J. Comput. 4(4), 461–473 (1975). https://doi.org/10.1137/0204039

  157. Gelenbe, E., Lent, R.: Power-aware ad hoc cognitive packet networks. Ad Hoc Netw. 2(3), 205–216 (2004)

    Article  Google Scholar 

  158. Gelenbe, E., Lent, R.: Optimising server energy consumption and response time. Theoret. Appl. Inform. 24, 257–270 (2012)

    Article  Google Scholar 

  159. Gelenbe, E., Lent, R.: Trade-offs between energy and quality of service. In: 2012 Sustainable Internet and ICT for Sustainability (SustainIT), pp. 1–5. IEEE (2012)

    Google Scholar 

  160. Gelenbe, E., Lent, R. (eds.): Computer and Information Sciences III. 27th International Symposium on Computer and Information Sciences, Paris, France, October 3–4, 2012. Springer, London (2013). https://doi.org/10.1007/978-1-4471-4594-3

    Book  Google Scholar 

  161. Gelenbe, E., Lent, R. (eds.): Information Sciences and Systems 2013. 2013 - Proceedings of the 28th International Symposium on Computer and Information Sciences, ISCIS 2013, Paris, France, October 28–29, Lecture Notes in Electrical Engineering, vol. 264. Springer, London (2013). https://doi.org/10.1007/978-3-319-01604-7

    Book  Google Scholar 

  162. Gelenbe, E., Lent, R., Douratsos, M.: Choosing a local or remote cloud. In: 2012 Second Symposium on Network Cloud Computing and Applications, pp. 25–30. IEEE (2012)

    Google Scholar 

  163. Gelenbe, E., Lent, R., Montuori, A., Xu, Z.: Cognitive packet networks: QoS and performance. In: 10th IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunications Systems, MASCOTS 2002. Proceedings, pp. 3–9. IEEE (2002)

    Google Scholar 

  164. Gelenbe, E., Lent, R., Nunez, A.: Self-aware networks and QoS. Proc. IEEE 92(9), 1478–1489 (2004)

    Article  Google Scholar 

  165. Gelenbe, E., Lent, R., Sakellari, G. (eds.): Computer and Information Sciences II. Springer, London (2012). https://doi.org/10.1007/978-1-4471-2155-8

    Book  Google Scholar 

  166. Gelenbe, E., Lent, R., Sakellari, G., Sacan, A., Toroslu, I.H., Yazici, A. (eds.): Computer and Information Sciences - Proceedings of the 25th International Symposium on Computer and Information Sciences, London, UK, 22–24 September 2010. LNEE, vol. 62. Springer, London (2010). https://doi.org/10.1007/978-90-481-9794-1

  167. Gelenbe, E., Lent, R., Xu, Z.: Design and performance of cognitive packet networks. Perform. Eval. 46(2), 155–176 (2001)

    Article  MATH  Google Scholar 

  168. Gelenbe, E., Lent, R., Xu, Z.: Measurement and performance of a cognitive packet network. Comput. Netw. 37(6), 691–701 (2001)

    Article  MATH  Google Scholar 

  169. Gelenbe, E., Lent, R., Xu, Z.: Towards networks with cognitive packets. In: Goto, K., Hasegawa, T., Takagi, H., Takahashi, Y. (eds.) Performance and QoS of Next Generation Networking, pp. 3–17. Springer, London (2001). https://doi.org/10.1007/978-1-4471-0705-7_1

    Chapter  Google Scholar 

  170. Gelenbe, E., Liu, P.: QoS and routing in the cognitive packet network. In: Sixth IEEE International Symposium on a World of Wireless Mobile and Multimedia Networks, WoWMoM 2005, pp. 517–521. IEEE (2005)

    Google Scholar 

  171. Gelenbe, E., Loukas, G.: A self-aware approach to denial of service defence. Comput. Netw. 51(5), 1299–1314 (2007)

    Article  MATH  Google Scholar 

  172. Gelenbe, E., Mahmoodi, T.: Energy-aware routing in the cognitive packet network. Energy, pp. 7–12 (2011)

    Google Scholar 

  173. Gelenbe, E., Mahmoodi, T.: Distributed energy-aware routing protocol. In: Gelenbe, E., Lent, R., Sakellari, G. (eds.) Computer and Information Sciences II, pp. 149–154. Springer, London (2012). https://doi.org/10.1007/978-1-4471-2155-8_18

    Chapter  Google Scholar 

  174. Gelenbe, E., Mahmoodi, T., Morfopoulou, C.: Energy aware routing in packet networks. E-Energy (2010)

    Google Scholar 

  175. Gelenbe, E., Mang, X., Önvural, R.: Diffusion based statistical call admission control in ATM. Perform. Eval. 27, 411–436 (1996)

    Article  MATH  Google Scholar 

  176. Gelenbe, E., Mang, X., Onvural, R.: Bandwidth allocation and call admission control in high-speed networks. IEEE Commun. Mag. 35(5), 122–129 (1997)

    Article  Google Scholar 

  177. Gelenbe, E., Mao, Z.H., Li, Y.D.: Function approximation by random neural networks with a bounded number of layers. Differ. Equ. Dynam. Syst. 12(1–2), 143–170 (2004)

    MathSciNet  MATH  Google Scholar 

  178. Gelenbe, E., Mao, Z.W., Li, Y.D.: Function approximation with spiked random networks. IEEE Trans. Neural Netw. 10(1), 3–9 (1999)

    Article  MATH  Google Scholar 

  179. Gelenbe, E., Marin, A.: Interconnected wireless sensors with energy harvesting. In: Gribaudo, M., Manini, D., Remke, A. (eds.) ASMTA 2015. LNCS, vol. 9081, pp. 87–99. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-18579-8_7

    Chapter  Google Scholar 

  180. Gelenbe, E., Morfopoulou, C.: Routing and G-networks to optimise energy and quality of service in packet networks. In: Hatziargyriou, N., Dimeas, A., Tomtsi, T., Weidlich, A. (eds.) E-Energy 2010. LNICST, vol. 54, pp. 163–173. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-19322-4_18

    Chapter  Google Scholar 

  181. Gelenbe, E., Morfopoulou, C.: A framework for energy-aware routing in packet networks. Comput. J. 54(6), 850–859 (2011)

    Article  Google Scholar 

  182. Gelenbe, E., Muntz, R.R.: Probabilistic models of computer systems. Part I Exact Results. Acta Informatica 7(1), 35–60 (1976)

    Article  MathSciNet  MATH  Google Scholar 

  183. Gelenbe, E., Ngai, E.: Adaptive random re-routing for differentiated QoS in sensor networks. Comput. J. 53(7), 1052–1061 (2010)

    Article  Google Scholar 

  184. Gelenbe, E., Ngai, E.C.H.: Adaptive QoS routing for significant events in wireless sensor networks. In: 2008 5th IEEE International Conference on Mobile Ad Hoc and Sensor Systems, pp. 410–415. IEEE (2008)

    Google Scholar 

  185. Gelenbe, E., Núñez, A.: Self-aware networks and quality of service. In: Kaynak, O., Alpaydin, E., Oja, E., Xu, L. (eds.) ICANN/ICONIP -2003. LNCS, vol. 2714, pp. 901–908. Springer, Heidelberg (2003). https://doi.org/10.1007/3-540-44989-2_107

    Chapter  Google Scholar 

  186. Gelenbe, E., Oklander, B.: Cognitive users with useful vacations. In: 2013 IEEE International Conference on Communications Workshops (ICC), pp. 370–374. IEEE (2013)

    Google Scholar 

  187. Gelenbe, E., Pavloski, M.: Performance of a security control scheme for a health data exchange system. In: IEEE International Black Sea Conference on Communications and Networking, 26–29 May 2020. Virtual Conference (2020)

    Google Scholar 

  188. Gelenbe, E., Potier, D., Brandwajn, A., Lenfant, J.: Gestion Optimale d’un Ordinateur Multiprogramme a Memoire Virtuelle. In: Conti, R., Ruberti, A. (eds.) Optimization Techniques, Part II. LNCS, vol. 4, pp. 132–143. Springer, Heidelberg (1973). https://doi.org/10.1007/3-540-06600-4_12

    Chapter  Google Scholar 

  189. Gelenbe, E., Pujolle, G.: Introduction aux réseaux de files d’attente. Eyrolles (1982)

    Google Scholar 

  190. Gelenbe, E., Rosenberg, C.: Queues with slowly varying arrival and service processes. Manage. Sci. 36(8), 928–937 (1990)

    Article  MathSciNet  MATH  Google Scholar 

  191. Gelenbe, E., Sakellari, G., D’arienzo, M.: Admission of QoS aware users in a smart network. ACM Trans. Auton. Adap. Syst. (TAAS) 3(1), 1–28 (2008)

    Google Scholar 

  192. Gelenbe, E., Sakellari, G., Filippoupolitis, A.: PerNEM 2012: second international workshop on pervasive networks for emergency management 2012, committees and welcome. In: Proceedings of the PerCOM 2012 (2012)

    Google Scholar 

  193. Gelenbe, E., Sakellari, G., Filippoupolitis, A.: PerNEM 2013: third international workshop on pervasive networks for emergency management 2013-committees and welcome. In: Proceedings of the PerCOM 2013 (2013)

    Google Scholar 

  194. Gelenbe, E., Seref, E., Xu, Z.: Simulation with learning agents. Proc. IEEE 89(2), 148–157 (2001)

    Article  Google Scholar 

  195. Gelenbe, E., Sevcik, K.: Analysis of update synchronization for multiple copy data bases. IEEE Trans. Comput. 28(10), 737–747 (1979)

    Article  MathSciNet  MATH  Google Scholar 

  196. Gelenbe, E., Shachnai, H.: On g-networks and resource allocation in multimedia systems. Eur. J. Oper. Res. 126(2), 308–318 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  197. Gelenbe, E., Silvestri, S.: Optimisation of power consumption in wired packet networks. In: Bartolini, N., Nikoletseas, S., Sinha, P., Cardellini, V., Mahanti, A. (eds.) QShine 2009. LNICST, vol. 22, pp. 717–729. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-10625-5_45

    Chapter  Google Scholar 

  198. Gelenbe, E., Silvestri, S.: Reducing power consumption in wired networks. In: 2009 24th International Symposium on Computer and Information Sciences, pp. 292–297. IEEE (2009)

    Google Scholar 

  199. Gelenbe, E., Stafylopatis, A.: Global behavior of homogeneous random neural systems. Appl. Math. Model. 15(10), 534–541 (1991)

    Article  MATH  Google Scholar 

  200. Gelenbe, E., Sungur, M., Cramer, C., Gelenbe, P.: Traffic and video quality with adaptive neural compression. Multimedia Syst. 4(6), 357–369 (1996)

    Article  Google Scholar 

  201. Gelenbe, E., Timotheou, S.: Random neural networks with synchronized interactions. Neural Comput. 20(9), 2308–2324 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  202. Gelenbe, E., Timotheou, S.: Synchronized interactions in spiked neuronal networks. Comput. J. 51(6), 723–730 (2008)

    Article  MATH  Google Scholar 

  203. Gelenbe, E., Timotheou, S., Nicholson, D.: Fast distributed near-optimum assignment of assets to tasks. Comput. J. 53(9), 1360–1369 (2010)

    Article  Google Scholar 

  204. Gelenbe, E., Velan, K.: An approximate model for bidders in sequential automated auctions. In: Håkansson, A., Nguyen, N.T., Hartung, R.L., Howlett, R.J., Jain, L.C. (eds.) KES-AMSTA 2009. LNCS (LNAI), vol. 5559, pp. 70–79. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-01665-3_8

    Chapter  Google Scholar 

  205. Gelenbe, E., Velan, K.: Mathematical models of automated auctions. In: Hakansson, A., Hartung, R. (eds.) Agent and Multi-Agent Systems in Distributed Systems-Digital Economy and E-Commerce, pp. 137–161. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-35208-9_8

    Chapter  Google Scholar 

  206. Gelenbe, E., Wang, L.: Tap: a task allocation platform for the EU FP7 PANACEA project. In: Advances in Service-Oriented and Cloud Computing: Workshops of ESOCC 2015, Taormina, Italy, 15–17 September 2015, Revised Selected Paper, vol. 567, p. 425 (2016)

    Google Scholar 

  207. Gelenbe, E., Wang, Y.: A mathematical approach for mission planning and rehearsal. In: Defense and Security Symposium, pp. 62490Q–62490Q. International Society for Optics and Photonics (2006)

    Google Scholar 

  208. Gelenbe, E., Wang, Y.: Modelling large scale autonomous systems. In: 2006 9th International Conference on Information Fusion, pp. 1–7. IEEE (2006)

    Google Scholar 

  209. Gelenbe, E., Wu, F.J.: Distributed networked emergency evacuation and rescue. In: 2012 IEEE International Conference on Communications (ICC), pp. 6334–6338. IEEE (2012)

    Google Scholar 

  210. Gelenbe, E., Wu, F.J.: Large scale simulation for human evacuation and rescue. Comput. Math. Appl. 64(12), 3869–3880 (2012)

    Article  Google Scholar 

  211. Gelenbe, E., Wu, F.J.: Sensors in cyber-physical emergency systems. In: IET Conference on Wireless Sensor Systems (WSS 2012), pp. 1–7. IET (2012)

    Google Scholar 

  212. Gelenbe, E., Wu, F.J.: Future research on cyber-physical emergency management systems. Future Internet 5(3), 336–354 (2013)

    Article  Google Scholar 

  213. Gelenbe, E., Xu, Z., Seref, E.: Cognitive packet networks. In: 11th IEEE International Conference on Conference Tools with Artificial Intelligence. Proceedings, pp. 47–54. IEEE (1999)

    Google Scholar 

  214. Gelenbe, E., et al. (eds.): Euro-CYBERSEC 2018. CCIS, vol. 821. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-95189-8

    Book  Google Scholar 

  215. Georgiopoulos, M., et al.: A sustainable model for integrating current topics in machine learning research into the undergraduate curriculum. IEEE Trans. Educ. 52(4), 503–512 (2009)

    Article  Google Scholar 

  216. Ghalut, T., Larijani, H.: Non-intrusive method for video quality prediction over LTE using random neural networks (RNN). In: 9th International Symposium on Communication Systems, Networks & Digital Signal Processing, CSNDSP 2014, Manchester, UK, 23–25 July 2014, pp. 519–524. IEEE (2014). https://doi.org/10.1109/CSNDSP.2014.6923884

  217. Ghalut, T., Larijani, H.: Content-aware and QOE optimization of video stream scheduling over LTE networks using genetic algorithms and random neural networks. J. Ubiquit. Syst. Perv. Netw. 9(2), 21–33 (2018). https://doi.org/10.5383/JUSPN.09.02.003

  218. Gorbil, G., Abdelrahman, O.H., Gelenbe, E.: Storms in mobile networks. In: Proceedings of the 10th ACM Symposium on QoS and Security for Wireless and Mobile Networks, pp. 119–126. ACM (2014)

    Google Scholar 

  219. Görbil, G., Abdelrahman, O.H., Gelenbe, E.: Storms in mobile networks. In: Mueller, P., Foschini, L., Yu, R. (eds.) Proceedings of the 10th ACM Symposium on QoS and Security for Wireless and Mobile Networks, Q2SWinet 2014, Montreal, QC, Canada, 21–22 September 2014, pp. 119–126. ACM (2014). http://doi.acm.org/10.1145/2642687.2642688

  220. Gorbil, G., Abdelrahman, O.H., Pavloski, M., Gelenbe, E.: Modeling and analysis of RRC-based signalling storms in 3G networks. IEEE Trans. Emerg. Topics Comput. 4(1), 113–127 (2016)

    Article  Google Scholar 

  221. Gorbil, G., Filippoupolitis, A., Gelenbe, E.: Intelligent navigation systems for building evacuation. In: Gelenbe, E., Lent, R., Sakellari, G. (eds.) Computer and Information Sciences II, pp. 339–345. Springer, London (2011). https://doi.org/10.1007/978-1-4471-2155-8_43

    Chapter  Google Scholar 

  222. Görbil, G., Gelenbe, E.: Design of a mobile agent-based adaptive communication middleware for federations of critical infrastructure simulations. In: Rome, E., Bloomfield, R. (eds.) CRITIS 2009. LNCS, vol. 6027, pp. 34–49. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-14379-3_4

    Chapter  Google Scholar 

  223. Gorbil, G., Gelenbe, E.: Opportunistic communications for emergency support systems. Procedia Comput. Sci. 5, 39–47 (2011)

    Article  Google Scholar 

  224. Gorbil, G., Gelenbe, E.: Disruption tolerant communications for large scale emergency evacuation. In: 2013 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), pp. 540–546. IEEE (2013)

    Google Scholar 

  225. Grochla, K., et al.: LP WAN gateway location selection using modified k-dominating set algorithm. In: Calzarossa, M.C., et al. (eds.) MASCOTS 2020. LNCS, vol. 12527, pp. 209–223. Springer, Cham (2021)

    Google Scholar 

  226. Hasselquist, D., Lindström, C., Korzhitskii, N., Carlsson, N., Gurtov, A.: Quic throughput and fairness over dual connectivity. In: Calzarossa, M.C., et al. (eds.) MASCOTS 2020. LNCS, vol. 12527, pp. 175–190. Springer, Cham (2021)

    Google Scholar 

  227. Hasselquist, D., Wahl, C., Bergdal, O., Carlsson, N.: Hypothesis-based comparison of ipv6 and ipv4 path distances. In: Calzarossa, M.C., et al. (eds.) MASCOTS 2020. LNCS, vol. 12527, pp. 191–208. Springer, Cham (2021)

    Google Scholar 

  228. Hey, L., Gelenbe, E.: Adaptive packet prioritisation for large wireless sensor networks. Telecommun. Syst. 48(1–2), 125–150 (2011)

    Article  Google Scholar 

  229. Hocaoglu, A.K., Gader, P.D., Gelenbe, E., Kocak, T.: Optimal linear combination of order statistics filters and their relationship to the delta-operator. In: AeroSense 1999, pp. 1323–1329. International Society for Optics and Photonics (1999)

    Google Scholar 

  230. Hussain, K.F., Kaptan, V.: Modeling and simulation with augmented reality. Int. J. Oper. Res. 38(2), 89–103 (2004)

    MathSciNet  MATH  Google Scholar 

  231. Hussain, K.F., Radwan, E., Moussa, G.S.: Augmented reality experiment: drivers’ behavior at an unsignalized intersection. IEEE Trans. Intell. Transp. Syst. 14(2), 608–617 (2013)

    Article  Google Scholar 

  232. Hussain, K.F., Bassyouni, M.Y., Gelenbe, E.: Accurate and energy-efficient classification with spiking random neural network. Probability in the Engineering and Informational Sciences (2019)

    Google Scholar 

  233. Jr., E.G.C., Gelenbe, E., Plateau, B.: Optimization of the number of copies in a distributed data base. IEEE Trans. Softw. Eng. 7(1), 78–84 (1981). https://doi.org/10.1109/TSE.1981.234510. http://doi.ieeecomputersociety.org/10.1109/TSE.1981.234510

  234. Kieffer, A., Maillé, P., Tuffin, B.: Non-neutrality with users deciding differentiation: a satisfying option?’. In: Calzarossa, M.C., et al. (eds.) MASCOTS 2020. LNCS, vol. 12527, pp. 119–128. Springer, Cham (2021)

    Google Scholar 

  235. Kim, G., Gelenbe, E.: Analysis of an automated auction with concurrent multiple unit acceptance capacity. In: Al-Begain, K., Fiems, D., Knottenbelt, W.J. (eds.) ASMTA 2010. LNCS, vol. 6148, pp. 382–396. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13568-2_27

    Chapter  Google Scholar 

  236. Kim, H.S., Gelenbe, E.: G-networks based two layer stochastic modeling of gene regulatory networks with post-translational processes. Interdisc. Bio Central 3(2), 8-1 (2011)

    Article  Google Scholar 

  237. Kim, H., Atalay, R., Gelenbe, E.: G-network modelling based abnormal pathway detection in gene regulatory networks. In: Gelenbe, E., Lent, R., Sakellari, G. (eds.) Computer and Information Sciences II, pp. 257–263. Springer, London (2011). https://doi.org/10.1007/978-1-4471-2155-8_32

    Chapter  Google Scholar 

  238. Kim, H., Gelenbe, E.: Stochastic gene expression model base gene regulatory networks. In: Lee, J.H., Lee, H., Kim, J.S. (eds.) EKC 2009, pp. 235–244. Springer, Berlin (2010). https://doi.org/10.1007/978-3-642-13624-5_22

    Chapter  Google Scholar 

  239. Kim, H., Gelenbe, E.: Reconstruction of large-scale gene regulatory networks using Bayesian model averaging. In: 2011 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 202–207. IEEE (2011)

    Google Scholar 

  240. Kim, H., Gelenbe, E.: Reconstruction of large-scale gene regulatory networks using Bayesian model averaging. IEEE Trans. NanoBiosci. 11(3), 259–265 (2012). https://doi.org/10.1109/TNB.2012.2214233

    Article  Google Scholar 

  241. Kim, H., Gelenbe, E.: Stochastic gene expression modeling with hill function for switch-like gene responses. IEEE/ACM Trans. Comput. Biol. Bioinform. 9(4), 973–979 (2012). https://doi.org/10.1109/TCBB.2011.153

    Article  Google Scholar 

  242. Kim, H., Park, T., Gelenbe, E.: Identifying disease candidate genes via large-scale gene network analysis. Int. J. Data Mining Bioinform. 10(2), 175–188 (2014). https://doi.org/10.1504/IJDMB.2014.064014

    Article  Google Scholar 

  243. Kim, H., Park, T., Gelenbe, E.: Identifying disease candidate genes via large-scale gene network analysis. Int. J. Data Mining Bioinform. 10(2), 175–188 (2014)

    Article  Google Scholar 

  244. Kokuti, A., Gelenbe, E.: Directional navigation improves opportunistic communication for emergencies. Sensors 14(8), 15387–15399 (2014)

    Article  Google Scholar 

  245. Kolodiej, J., Khan, S., Gelenbe, E., Talbi, E.: Scalable optimization in grid, cloud, and intelligent network computing. Concur. Comput. Pract. Experience 25(12), 1719–1721 (2013)

    Article  Google Scholar 

  246. Kulandai, A.D.R., J, S., Rose, J., Schwarz, T.: Balanced gray codes for reduction of bit-flips in phase change memories. In: Calzarossa, M.C., et al. (eds.) MASCOTS 2020. LNCS, vol. 12527, pp. 159–171. Springer, Cham (2021)

    Google Scholar 

  247. Lent, R., Abdelrahman, O.H., Gorbil, G., Gelenbe, E.: Fast message dissemination for emergency communications. In: 2010 8th IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), pp. 370–375. IEEE (2010)

    Google Scholar 

  248. Levi, A., Savaş, E., Yenigün, H., Balcısoy, S., Saygın, Y. (eds.): ISCIS 2006. LNCS, vol. 4263. Springer, Heidelberg (2006). https://doi.org/10.1007/11902140

    Book  Google Scholar 

  249. Li, J., Mishra, D., Seneviratne, A.: Network traffic classification using wifi sensing. In: Calzarossa, M.C., et al. (eds.) MASCOTS 2020. LNCS, vol. 12527, pp. 48–61. Springer, Cham (2021)

    Google Scholar 

  250. Liu, P., Gelenbe, E.: Recursive routing in the cognitive packet network. In: 3rd International Conference on Testbeds and Research Infrastructure for the Development of Networks and Communities, TridentCom 2007, pp. 1–6. IEEE (2007)

    Google Scholar 

  251. Mclean, R., Karamollahi, M., Williamson, C.: Measurement and modeling of tumblr traffic. In: Calzarossa, M.C., et al. (eds.) MASCOTS 2020. LNCS, vol. 12527, pp. 237–253. Springer, Cham (2021)

    Google Scholar 

  252. Nalin, M.: The European cross-border health data exchange roadmap: case study in the Italian setting. J. Biomed. Inform. 94, 103183 (2019)

    Google Scholar 

  253. Natsiavas, P., et al.: Comprehensive user requirements engineering methodology for secure and interoperable health data exchange. BMC Med. Inform. Decis. Mak. 18(1), 85 (2018)

    Article  Google Scholar 

  254. Ngai, E.C.H., Gelenbe, E., Humber, G.: Information-aware traffic reduction for wireless sensor networks. In: 2009 IEEE 34th Conference on Local Computer Networks, pp. 451–458. IEEE (2009)

    Google Scholar 

  255. Oeke, G., Loukas, G.: A denial of service detector based on maximum likelihood detection and the random neural network. Comput. J. 50(6), 717–727 (2007)

    Article  Google Scholar 

  256. Oke, G., Loukas, G., Gelenbe, E.: Detecting denial of service attacks with bayesian classifiers and the random neural network. In: 2007 IEEE International Fuzzy Systems Conference, pp. 1–6. IEEE (2007)

    Google Scholar 

  257. Oklander, B., Gelenbe, E.: Optimal behaviour of smart wireless users. In: Gelenbe, E., Lent, R. (eds.) Information Sciences and Systems 2013, pp. 87–95. Springer, Cham (2013). https://doi.org/10.1007/978-3-319-01604-7_9

    Chapter  Google Scholar 

  258. Ören, T.I., Numrich, S.K., Uhrmacher, A.M., Wilson, L.F., Gelenbe, E.: Agent-directed simulation: challenges to meet defense and civilian requirements. In: Proceedings of the 32nd Conference on Winter Simulation, pp. 1757–1762. Society For Computer Simulation International (2000)

    Google Scholar 

  259. Pankratova, E., Farkhadov, M., Gelenbe, E.: Research of heterogeneous queueing system SM—M\(^{(n)}|\infty \). In: Dudin, A., Nazarov, A., Kirpichnikov, A. (eds.) ITMM 2017. CCIS, vol. 800, pp. 122–132. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68069-9_10

    Chapter  MATH  Google Scholar 

  260. Pavloski, M., Görbil, G., Gelenbe, E.: Bandwidth usage—based detection of signaling attacks. In: Abdelrahman, O.H., Gelenbe, E., Gorbil, G., Lent, R. (eds.) Information Sciences and Systems 2015. LNEE, vol. 363, pp. 105–114. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-22635-4_9

    Chapter  Google Scholar 

  261. Pernici, B., Aiello, M., Vom Brocke, J., Donnellan, B., Gelenbe, E., Kretsis, M.: What is can do for environmental sustainability: a report from CAiSE’11 panel on green and sustainable is. Commun. Assoc. Inf. Syst. 30(1), 18 (2012)

    Google Scholar 

  262. Phan, H.T., Stemberg, M.J., Gelenbe, E.: Aligning protein-protein interaction networks using random neural networks. In: 2012 IEEE International Conference on Bioinformatics and Biomedicine, pp. 1–6. IEEE (2012)

    Google Scholar 

  263. Potier, D., Gelenbe, E., Lenfant, J.: Adaptive allocation of central processing unit quanta. J. ACM 23(1), 97–102 (1976). https://doi.org/10.1145/321921.321932

  264. Qureshi, A., Larijani, H., Ahmad, J., Mtetwa, N.: A novel random neural network based approach for intrusion detection systems. In: 2018 10th Computer Science and Electronic Engineering Conference, CEEC 2018, University of Essex, Colchester, UK, 19–21 September 2018, pp. 50–55. IEEE (2018). https://doi.org/10.1109/CEEC.2018.8674228

  265. Radhakrishnan, K., Larijani, H.: Evaluating perceived voice quality on packet networks using different random neural network architectures. Perform. Eval. 68(4), 347–360 (2011). https://doi.org/10.1016/j.peva.2011.01.001

  266. Robert, S., Zertal, S., Couve, P.: Demonstration of shaman: a flexible framework for auto-tuning hpc systems. In: Calzarossa, M.C., et al. (eds.) MASCOTS 2020. LNCS, vol. 12527, pp. 147–158. Springer, Cham (2021)

    Google Scholar 

  267. Rubino, G., Tirilly, P., Varela, M.: Evaluating users’ satisfaction in packet networks using random neural networks. In: Kollias, S.D., Stafylopatis, A., Duch, W., Oja, E. (eds.) ICANN 2006, Part I. LNCS, vol. 4131, pp. 303–312. Springer, Heidelberg (2006). https://doi.org/10.1007/11840817_32

    Chapter  Google Scholar 

  268. Sakellari, G., Gelenbe, E.: Adaptive resilience of the cognitive packet network in the presence of network worms. In: Proceedings of the NATO Symposium on C3I for Crisis, Emergency and Consequence Management, pp. 11–12 (2009)

    Google Scholar 

  269. Sakellari, G., Hey, L., Gelenbe, E.: Adaptability and failure resilience of the cognitive packet network. In: DemoSession of the 27th IEEE Conference on Computer Communications (INFOCOM 2008), Phoenix, Arizona, USA (2008)

    Google Scholar 

  270. Sakellari, G., Leung, T., Gelenbe, E.: Auction-based admission control for self-aware networks. In: Gelenbe, E., Lent, R., Sakellari, G. (eds.) Computer and Information Sciences II, pp. 223–230. Springer, London (2011). https://doi.org/10.1007/978-1-4471-2155-8_28

    Chapter  Google Scholar 

  271. Sakellari, G., Morfopoulou, C., Mahmoodi, T., Gelenbe, E.: Using energy criteria to admit flows in a wired network. In: Gelenbe, E., Lent, R. (eds.) Computer and Information Sciences III, pp. 63–72. Springer, London (2013). https://doi.org/10.1007/978-1-4471-4594-3_7

    Chapter  Google Scholar 

  272. Serrano, W., Gelenbe, E.: The random neural network in a neurocomputing application for web search. Neurocomputing 280, 123–134 (2018)

    Article  Google Scholar 

  273. Serrano, W., Gelenbe, E.: Deep learning clusters in the cognitive packet network. Neurocomputing 396, 406–428 (2020)

    Article  Google Scholar 

  274. Serrano, W., Gelenbe, E., Yin, Y.: The random neural network with deep learning clusters in smart search. Neurocomputing 396, 394–405 (2020)

    Article  Google Scholar 

  275. Siavvas, M., Gelenbe, E., Kehagias, D., Tzovaras, D.: static analysis-based approaches for secure software development. In: Gelenbe, E., et al. (eds.) Euro-CYBERSEC 2018. CCIS, vol. 821, pp. 142–157. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-95189-8_13

    Chapter  Google Scholar 

  276. Staffa, M., et al.: KONFIDO: an OpenNCP-based secure eHealth data exchange system. In: Gelenbe, E., et al. (eds.) Euro-CYBERSEC 2018. CCIS, vol. 821, pp. 11–27. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-95189-8_2

    Chapter  Google Scholar 

  277. Staffa, M., et al.: An openNCP-based solution for secure eHealth data exchange. J. Netw. Comput. Appl. 116, 65–85 (2018)

    Article  Google Scholar 

  278. Timotheou, S.: A novel weight initialization method for the random neural network. Neurocomputing 73(2), 160–168 (2009)

    Article  Google Scholar 

  279. Tomak, J., Gorlatch, S.: Measuring performance of fault management in a legacy system: An alarm system study. In: Calzarossa, M.C., et al. (eds.) MASCOTS 2020. LNCS, vol. 12527, pp. 129–146. Springer, Cham (2021)

    Google Scholar 

  280. Velan, K., Gelenbe, E.: Analysing bidder performance in randomised and fixed-deadline automated auctions. In: Jędrzejowicz, P., Nguyen, N.T., Howlet, R.J., Jain, L.C. (eds.) KES-AMSTA 2010. LNCS (LNAI), vol. 6071, pp. 42–51. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13541-5_5

    Chapter  Google Scholar 

  281. Wang, L., Brun, O., Gelenbe, E.: Adaptive workload distribution for local and remote clouds. In: 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 003984–003988. IEEE (2016)

    Google Scholar 

  282. Wang, L., Gelenbe, E.: An implementation of voice over IP in the cognitive packet network. In: Czachórski, T., Gelenbe, E., Lent, R. (eds.) Information Sciences and Systems 2014, pp. 33–40. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-09465-6_4

    Chapter  Google Scholar 

  283. Wang, L., Gelenbe, E.: Adaptive dispatching of tasks in the cloud. IEEE Trans. Cloud Comput. 6(1), 33–45 (2018)

    Article  Google Scholar 

  284. Witkowski, M., White, G., Louvieris, P., Gorbil, G., Gelenbe, E., Dodd, L.: High-level information fusion and mission planning in highly anisotropic threat spaces. In: 2008 11th International Conference on Information Fusion, pp. 1–8. IEEE (2008)

    Google Scholar 

  285. Yazıcı, A., Şener, C. (eds.): ISCIS 2003. LNCS, vol. 2869. Springer, Heidelberg (2003). https://doi.org/10.1007/b14229

    Book  MATH  Google Scholar 

  286. Yolum, I., Güngör, T., Gürgen, F., Özturan, C. (eds.): ISCIS 2005. LNCS, vol. 3733. Springer, Heidelberg (2005). https://doi.org/10.1007/11569596

    Book  Google Scholar 

  287. Yu, C.M., Ni, G.K., Chen, I.Y., Gelenbe, E., Kuo, S.Y.: Top-k query result completeness verification in tiered sensor networks. IEEE Trans. Inf. Forensics Secur. 9(1), 109–124 (2014)

    Article  Google Scholar 

  288. Zhu, Q., Gelenbe, E., Qiao, Y.: Adaptive prefetching algorithm in disk controllers. Perform. Eval. 65(5), 382–395 (2008)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Engineering Department, Yaşar University, Üniversite Caddesi No: 37-39 Ag̃açlı Yol, 35100, Bornova, Izmir, Turkey

    Ufuk Çag̃layan

Authors
  1. Ufuk Çag̃layan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ufuk Çag̃layan .

Editor information

Editors and Affiliations

  1. Department of Industrial and Information Engineering, University of Pavia, Pavia, Italy

    Maria Carla Calzarossa

  2. Institute of Theoretical and Applied Informatics, Gliwice, Poland

    Erol Gelenbe

  3. Polish Academy of Sciences, Institute of Theoretical and Applied Informatics, Gliwice, Poland

    Krysztof Grochla

  4. University of Houston, Houston, TX, USA

    Ricardo Lent

  5. Institute of Theoretical and Applied Informatics of the Polish Academy of Sciences, Gliwice, Poland

    Tadeusz Czachórski

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Çag̃layan, U. (2021). Performance, Energy Savings and Security: An Introduction. In: Calzarossa, M.C., Gelenbe, E., Grochla, K., Lent, R., Czachórski, T. (eds) Modelling, Analysis, and Simulation of Computer and Telecommunication Systems. MASCOTS 2020. Lecture Notes in Computer Science(), vol 12527. Springer, Cham. https://doi.org/10.1007/978-3-030-68110-4_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-68110-4_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-68109-8

  • Online ISBN: 978-3-030-68110-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation