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INTRODUCTION TO THE SPECIAL ISSUE ON LEARNING, OPTIMIZATION, AND THEORY OF G-NETWORKS

Published online by Cambridge University Press:  29 July 2019

Nihal Pekergin*
Affiliation:
LACL, Faculté des Sciences et Technologie, Université de Paris-Est Créteil, 61 avenue du Général de Gaulle 94010 Créteil, France E-mail: [email protected]
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Abstract

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We introduce the special issue on “Learning, Optimization, and the Theory of G-Networks” of the journal Probability in the Engineering and Informational Sciences that appears in 2019. We first outline some of the applications and developments of G-Networks which motivate the ongoing interest for this area, including some areas which could not be covered in this special issue. We then briefly discuss the contributions presented in the ten papers that are published in this special issue in the context of related work.

Type
Editorial
Copyright
Copyright © Cambridge University Press 2019

References

1.Berl, A., Gelenbe, E., Di Girolamo, M., Giuliani, G., De Meer, H., Dang, M.Q., & Pentikousis, K. (2010). Energy-efficient cloud computing. The Computer Journal 53(7): 10451051.CrossRefGoogle Scholar
2.Boxma, O.J. & Gelenbe, E. (1985). Two symmetric queues with alternating service and switching times, in Models of computer system performance (Proceedings 10th IFIP WG7. 3 International Symposium on Computer Performance Modelling, Measurement and Evaluation, Paris, France, 19–21 December 1984). North-Holland Publishing Company, pp. 409431.Google Scholar
3.Brun, O., Wang, L., & Gelenbe, E. (2016). Big data for autonomic intercontinental overlays. IEEE Journal on Selected Areas in Communications 34(3): 575583.CrossRefGoogle Scholar
4.Brun, O., Yin, Y., Gelenbe, E., Kadioglu, Y.M., Augusto-Gonzalez, J., & Ramos, M. (2018). Deep learning with dense random neural networks for detecting attacks against iot-connected home environments, in Recent Cybersecurity Research in Europe: Proceedings of the 2018 ISCIS Security Workshop, Imperial College London. Lecture Notes CCIS No. 821, Springer Verlag, Vol. 821.Google Scholar
5.Dobson, S., Denazis, S., Fernández, A., Gaïti, D., Gelenbe, E., Massacci, F., Nixon, P., Saffre, F., Schmidt, N., & Zambonelli, F. (2006). A survey of autonomic communications. ACM Transactions on Autonomous and Adaptive Systems (TAAS) 1(2): 223259.CrossRefGoogle Scholar
6.Fayolle, G., Gelenbe, E., & Labetoulle, J. (1977). Stability and optimal control of the packet switching broadcast channel. Journal of the ACM (JACM) 24(3): 375386.CrossRefGoogle Scholar
7.Francois, F. & Gelenbe, E. (2016). Towards a cognitive routing engine for software defined networks, in 2016 IEEE International Conference on Communications (ICC). IEEE, pp. 16.Google Scholar
8.Gelenbe, E. (1989). Random neural networks with negative and positive signals and product form solution. Neural Computation 1(4): 502510.CrossRefGoogle Scholar
9.Gelenbe, E. (1990). 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): 177180.Google Scholar
10.Gelenbe, E. (1991). Product-form queueing networks with negative and positive customers. Journal of Applied Probability 28(3): 656663.CrossRefGoogle Scholar
11.Gelenbe, E. (1993). G-networks by triggered customer movement. Journal of Applied Probability 30(3): 742748.CrossRefGoogle Scholar
12.Gelenbe, E. (1993). Learning in the recurrent random neural network. Neural Computation 5(1): 154164.CrossRefGoogle Scholar
13.Gelenbe, E. (1993). G-networks with signals and batch removal. Probability in the Engineering and Informational Sciences 7(3): 335342.CrossRefGoogle Scholar
14.Gelenbe, E. (1996). Genetic algorithms with analytical solution, in Proceedings of the 1st annual conference on genetic programming. MIT Press, 1996, pp. 437443.Google Scholar
15.Gelenbe, E. (1997). A class of genetic algorithms with analytical solution. Robotics and Autonomous Systems 22(1): 5964.CrossRefGoogle Scholar
16.Gelenbe, E. (2007). A diffusion model for packet travel time in a random multihop medium. ACM Transactions on Sensor Networks (TOSN) 3(2): 10.CrossRefGoogle Scholar
17.Gelenbe, E. (2007). Dealing with software viruses: a biological paradigm. Information Security Technical Report 12(4): 242250.CrossRefGoogle Scholar
18.Gelenbe, E. (2007). Steady-state solution of probabilistic gene regulatory networks. Physical Review E 76(3): 031903.CrossRefGoogle ScholarPubMed
19.Gelenbe, E. (2009). Analysis of single and networked auctions. ACM Transactions on Internet Technology (TOIT) 9(2): 8.CrossRefGoogle Scholar
20.Gelenbe, E. (2009). Steps toward self-aware networks. Communications of the ACM 52(7): 6675.CrossRefGoogle Scholar
21.Gelenbe, E. (2012). Energy packet networks: adaptive energy management for the cloud, in Proceedings of the 2nd International Workshop on Cloud Computing Platforms. ACM, p. 1.Google Scholar
22.Gelenbe, E. (2012). Energy packet networks: Ict based energy allocation and storage. In Rodrigues, J.J.P.C., Zhou, L., Chen, M., & Kailas, A. (eds), Green communications and networking, Berlin, Heidelberg: Springer, pp. 186195.CrossRefGoogle Scholar
23.Gelenbe, E. (2015). Synchronising energy harvesting and data packets in a wireless sensor. Energies 8(1): 356369.CrossRefGoogle Scholar
24.Gelenbe, E. & Abdelrahman, O.H. (2018). An energy packet network model for mobile networks with energy harvesting. IEICE Nonlinear Theory and Its Applications 9(3): 115, doi: 10.1587/nolta.9.1.CrossRefGoogle Scholar
25.Gelenbe, E. & Ceran, E.T. (2015). 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
26.Gelenbe, E. & Ceran, E.T. (2016). Energy packet networks with energy harvesting. IEEE Access 4: 13211331.CrossRefGoogle Scholar
27.Gelenbe, E. & Hussain, K. (2002). Learning in the multiple class random neural network. IEEE Transactions on Neural Networks 13(6): 12571267. [Online]. Available: http://dx.doi.org/10.1109/TNN.2002. 804228.CrossRefGoogle ScholarPubMed
28.Gelenbe, E. & Loukas, G. (2007). A self-aware approach to denial of service defence. Computer Networks 51(5): 12991314.CrossRefGoogle Scholar
29.Gelenbe, E. & Mahmoodi, T. (2011). Energy-aware routing in the cognitive packet network, in ENERGY 2011, The First International Conference on Smart Grids, Green Communications and IT Energy-aware Technologies, pp. 712.Google Scholar
30.Gelenbe, E. & Morfopoulou, C. (2011). A framework for energy-aware routing in packet networks. The Computer Journal 54(6): 850859.CrossRefGoogle Scholar
31.Gelenbe, E. & Muntz, R.R. (1976). Probabilistic models of computer systems: Part i (exact results). Acta Informatica 7(1): 3560.CrossRefGoogle Scholar
32.Gelenbe, E. & Pujolle, G. (1982). Introduction aux réseaux de files d'attente, Paris: Eyrolles.Google Scholar
33.Gelenbe, E. & Sevcik, K. (1979). Analysis of update synchronization for multiple copy data bases. IEEE Transactions on Computers 28(10): 737747.CrossRefGoogle Scholar
34.Gelenbe, E. & Stafylopatis, A. (1991). Global behavior of homogeneous random neural systems. Applied Mathematical Modelling 15(10): 534541.CrossRefGoogle Scholar
35.Gelenbe, E. & Wu, F.-J. (2013). Future research on cyber-physical emergency management systems. Future Internet 5(3): 336354.CrossRefGoogle Scholar
36.Gelenbe, E. & Yin, Y. (2016). Deep learning with random neural networks, in 2016 International Joint Conference on Neural Networks (IJCNN). IEEE, pp. 16331638.Google Scholar
37.Gelenbe, E. & Zhang, Y. (2019). IEEE Systems Journal, pp. 111.Google Scholar
38.Gelenbe, E., Glynn, P., & Sigman, K. (1991). Queues with negative arrivals. Journal of Applied Probability 28(1): 245250.CrossRefGoogle Scholar
39.Gelenbe, E., Mang, X., & Önvural, R. (1996). Diffusion based statistical call admission control in atm. Performance Evaluation 27: 411436.CrossRefGoogle Scholar
40.Gelenbe, E., Lent, R., & Nunez, A. (2004). Self-aware networks and qos. Proceedings of the IEEE 92(9): 14781489.CrossRefGoogle Scholar
41.Gelenbe, E., Liu, P., & Lainé, J. (2006). Genetic algorithms for route discovery. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 36(6): 12471254.CrossRefGoogle ScholarPubMed
42.Gorbil, G. & Gelenbe, E. (2011). Opportunistic communications for emergency support systems. Procedia Computer Science 5: 3947.CrossRefGoogle Scholar
43.Hussain, K.F., Bassyouni, M.Y., & Gelenbe, E. (2019). Accurate and energy-efficient classification with spiking random neural network: Corrected and expanded version, Arxiv.CrossRefGoogle Scholar
44.Kadioglu, Y.M. & Gelenbe, E. (2018). Product-form solution for cascade networks with intermittent energy. IEEE Systems Journal 99: 110.Google Scholar
45.Kim, H. & Gelenbe, E. (2010). Stochastic gene expression model base gene regulatory networks, in EKC 2009 Proceedings of the EU-Korea Conference on Science and Technology. Springer Berlin Heidelberg, pp. 235244.Google Scholar
46.Kim, H. & Gelenbe, E. (2012). Stochastic gene expression modeling with hill function for switch-like gene responses. IEEE/ACMTransactions on Computational Biology and Bioinformatics 9(4): 973979, [Online]. Available: http://doi.ieeecomputersociety.org/10.1109/TCBB.2011.153.Google ScholarPubMed
47.Kim, H. & Gelenbe, E. (2012). Reconstruction of large-scale gene regulatory networks using bayesian model averaging. IEEE Transactions on NanoBioscience 11(3): 259265.Google ScholarPubMed
48.Kim, H., Atalay, R., & Gelenbe, E. (2011). G-network modelling based abnormal pathway detection in gene regulatory networks, in Computer and Information Sciences: 26th International Symposium on Computer and Information Sciences. Springer Verlag, p. 257.Google Scholar
49.Kim, H., Park, T., & Gelenbe, E. (2014). Identifying disease candidate genes via large-scale gene network analysis. International Journal of Data Mining and Bioinformatics 10(2): 175188.CrossRefGoogle ScholarPubMed
50.Oke, G., Loukas, G., & Gelenbe, E. (2007). Detecting denial of service attacks with bayesian classifiers and the random neural network, in 2007 IEEE International Fuzzy Systems Conference. IEEE, pp. 16.Google Scholar
51.Pernici, B., Aiello, M., vom Brocke, J., Donnellan, B., Gelenbe, E., & Kretsis, M. (2012). What is can do for environmental sustainability: a report from caise2011 panel on green and sustainable is. Communications of the Association for Information Systems 30(1): 18.CrossRefGoogle Scholar
52.Phan, H., Sterberg, M.J., & Gelenbe, E. (2012). Aligning protein-protein interaction networks using random neural networks. Bioinformatics and Biomedicine (BIBM), 2012 IEEE International Conference on IEEE, pp. 16.CrossRefGoogle Scholar
53.Walrand, J. (1998). An introduction to queueing networks, Englewood Cliffs, N.J.: Prentice Hall.Google Scholar
54.Wang, L. & Gelenbe, E. (2018). Adaptive dispatching of tasks in the cloud. IEEE Transactions on Cloud Computing 6(1): 3345.CrossRefGoogle Scholar
55.Wang, L., Brun, O., & Gelenbe, E. (2016). Adaptive workload distribution for local and remote clouds, in 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, pp. 003 984003 988.Google Scholar