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Applications

  • Christian Müller-Schloer
  • Sven Tomforde
Chapter
  • 609 Downloads
Part of the Autonomic Systems book series (ASYS)

Abstract

Urban traffic is characterised by high dynamics. As a result, the configuration of green durations for traffic lights is a complex task. In addition, the dynamics of the traffic flow require a continuous adaptation of green durations if optimal solutions are targeted. This challenge becomes even more complex if coordination of traffic lights is desired, or route recommendations reflecting the current traffic conditions must be provided to participants. Consequently, OC technology is a promising enabler for highly efficient, robust, and flexible traffic control and management solutions.

Keywords

Dispatchable Power Plants Swarm Robotics Progressive Signal System (PSS) Berkeley Open Infrastructure For Network Computing (BOINC) Hegselmann-Krause Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. [AE12]
    M. Abouelela, M. El-Darieby, Multidomain hierarchical resource allocation for grid applications. J. Electr. Comput. Eng. 2012, 1–8 (2012). ISSN: 2090-0147zbMATHCrossRefGoogle Scholar
  2. [AKM12]
    H. Appelrath, H. Kagermann, C. Mayer (eds.), Future Energy Grid: Migration to the Internet of Energy. Acatech STUDY (Acatech – National Academy of Science and Engineering, Munich, 2012)Google Scholar
  3. [Ali+13]
    V. Alimisis, C. Piacentini, J.E. King, P.C. Taylor, Operation and control zones for future complex power systems, in 2013 IEEE Green Technologies Conference (GreenTech), pp. 259–265 (2013). https://doi.org/10.1109/GreenTech.2013.47
  4. [And+14a]
    G. Anders, A. Schiendorfer, J.-P. Steghöfer, W. Reif, Robust scheduling in a self-organizing hierarchy of autonomous virtual power plants, in 27th International Conference on Architecture of Computing Systems, Workshop Proceedings (ARCS 2014) (IEEE, Piscataway, NJ, 2014), pp. 1–8Google Scholar
  5. [And+14b]
    G. Anders, F. Siefert, J.-P. Steghöfer, W.-G. Reif, Trust-based scenarios – predicting future agent behavior in open self-organizing systems, English, in Self-organizing Systems, ed. by W. Elmenreich, F. Dressler, V. Loreto. Lecture Notes in Computer Science, vol. 8221 (Springer, Berlin/Heidelberg, 2014), pp. 90–102. ISBN: 978-3-642-54139-1Google Scholar
  6. [And+15]
    G. Anders, A. Schiendorfer, F. Siefert, J.-P. Steghöfer, W. Reif, Cooperative resource allocation in open systems of systems. ACM Trans. Auton. Adapt. Syst. 10(2), 11:1–11:44 (2015). ISSN: 1556-4665Google Scholar
  7. [And+16]
    G. Anders, F. Siefert, A. Schiendorfer, H. Seebach, J.-P. Steghöfer, B. Eberhardinger, O. Kosak, W. Reif, Specification and design of trust-based open self-organising systems, in Trustworthy Open Self-organising Systems, ed. by W. Reif, G. Anders, H. Seebach, J.-P. Steghöfer, E. André, J. Hähner, C. Müller-Schloer, T. Ungerer. Autonomic Systems, Vol. 7 (Springer, Berlin, 2016), pp. 17–53. ISBN: 978-3-319-29199-4Google Scholar
  8. [And17]
    G. Anders, Self-organized robust optimization in open technical systems, Ph.D. thesis, Universität Augsburg, 2017Google Scholar
  9. [AS11]
    F. Allerding, H. Schmeck, Organic smart home: architecture for energy management in intelligent buildings, in Proceedings of the 2011 Workshop on Organic Computing (ACM, Karlsruhe, 2011), pp. 67–76. ISBN: 978-1-4503-0736-9CrossRefGoogle Scholar
  10. [ASR15]
    G. Anders, F. Siefert, W. Reif, A heuristic for constrained set partitioning in the light of heterogeneous objectives, English, in Agents and Artificial Intelligence, ed. by B. Duval, J. van den Herik, S. Loiseau, J. Filipe. Lecture Notes in Computer Science, vol. 9494 (Springer, Cham, 2015), pp. 223–244. ISBN: 978-3-319-27946-6Google Scholar
  11. [Ass]
    Association Management Solutions, Request for Comments (RFC), https://www.ietf.org/rfc.html
  12. [Ass+03]
    National Electronic Manufacturers Association, Traffic Controller Assemblies with NTCIP Requirements, Version 02.06, vol. TS 2 (NEMA, Rosslyn, VA, 2003), p. 2003Google Scholar
  13. [Ban+01]
    S. Bandyopadhyay, K. Hasuike, S. Horisawa, S. Tawara, An adaptive MAC and idrectional routing protocol for ad hoc wireless network using ESPAR antenna, in Proceedings of the 2nd ACM International Symposium on Mobile Ad hoc Networking and Computing (ACM, New York, 2001), pp. 243–246Google Scholar
  14. [Baz05]
    A.L. Bazzan, A distributed approach for coordination of traffic signal agents. Auton. Agent. Multi-Agent Syst. 10(1), 131–164 (2005)CrossRefGoogle Scholar
  15. [BDT99]
    E. Bonabeau, M. Dorigo, G. Theraulaz, Swarm Intelligence: from Natural to Artificial Systems, vol. 1 (Oxford University Press, Oxford, 1999)zbMATHGoogle Scholar
  16. [Bel+11]
    R. Belhomme, R. Cerero, G. Valtorta, P. Eyrolles, The ADDRESS project: developing active demand in smart power systems integrating renewables, in 2011 IEEE Power and Energy Society General Meeting (IEEE, Piscataway, NJ, 2011), pp. 1–8Google Scholar
  17. [Ber+10]
    Y. Bernard, L. Klejnowski, J. Hähner, C. Müller-Schloer, Towards trust in desktop grid systems, in Proceeding of CCGrid 2010, pp. 637–642 (2010)Google Scholar
  18. [Ber+11]
    Y. Bernard, L. Klejnowski, E. Çakar, J. Hähner, C. Müller-Schloer, Efficiency and robustness using trusted communities in a trusted desktop grid, in Proceeding of SASO Workshops (IEEE, Michigan, 2011), pp. 21–26Google Scholar
  19. [Ber14]
    Y. Bernard, Trust-aware agents for self-organising computing systems, Ph.D. thesis, Leibniz Universität Hannover, 2014Google Scholar
  20. [BFH86]
    J. Barriere, J. Farges, J. Henry, Decentralization vs hierarchy in optimal traffic control, in IFAC Control in Transportation Systems (1986)Google Scholar
  21. [BK09]
    A.L. Bazzan, F. Klügl, Multi-agent Systems for Traffic and Transportation Engineering (Citeseer, 2009)CrossRefGoogle Scholar
  22. [BL94]
    J.A. Boyan, M.L. Littman, Packet routing in dynamically changing networks: a reinforcement learning approach. Adv. Neural Inf. Proces. Syst. 6, 671–671 (1994)Google Scholar
  23. [Bou+14]
    A. Boudjadar, A. David, J. Kim, K. Larsen, M. Mikuc̆ionis, U. Nyman, A. Skou, Hierarchical scheduling framework based on compositional analysis using uppaal, English, in Formal Aspects of Component Software, ed. by J.L. Fiadeiro, Z. Liu, J. Xue. Lecture Notes in Computer Science, vol. 8348 (Springer, Berlin, 2014), pp. 61–78. ISBN: 978-3-319-07601-0Google Scholar
  24. [BP76]
    E. Balas, M.W. Padberg, Set partitioning: a survey. SIAM Rev. 18(4), 710–760 (1976). ISSN: 00361445MathSciNetzbMATHCrossRefGoogle Scholar
  25. [BPL10]
    M.R. Barbosa, E.S. Pires, A.M. Lopes, Optimization of parallel manipulators using evolutionary algorithms, in Soft Computing Models in Industrial and Environmental Applications, 5th International Workshop (SOCO 2010) (Springer, Berlin, 2010), pp. 79–86Google Scholar
  26. [Bra+13]
    M. Brambilla, E. Ferrante, M. Birattari, M. Dorigo, Swarm robotics: a review from the swarm engineering perspective. Swarm Intell. 7(1), 1–41 (2013)CrossRefGoogle Scholar
  27. [BS96]
    T. Back, H.-P. Schwefel, Evolutionary computation: an overview, in Proceedings of IEEE International Conference on Evolutionary Computation, 1996 (IEEE, Piscataway, NJ, 1996), pp. 20–29Google Scholar
  28. [BST13]
    M. van den Briel, P. Scott, S. Thiébaux, Randomized load control: a simple distributed approach for scheduling smart appliances, in Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI 2013), ed. by F. Rossi (IJCAI/AAAI, Palo Alto, CA, 2013)Google Scholar
  29. [CA07]
    C. Chang, H.K. Aghajan, Linear dynamic data fusion techniques for face orientation estimation in smart camera networks, in 2007 First ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC 2007, Vienna, 25–28 Sept 2007, pp. 44–51 (2007). https://doi.org/10.1109/ICDSC20074357504
  30. [Cap+05]
    G. Caprari, A. Colot, R. Siegwart, J. Halloy, J.-L. Deneubourg, Animal and robot mixed societies: building cooperation between microrobots and cockroaches. IEEE Robot. Autom. Mag. 12(2), 58–65 (2005)CrossRefGoogle Scholar
  31. [Cap16]
    F. Capitanescu, Critical review of recent advances and further developments needed in AC optimal power flow. Electr. Power Syst. Res. 136, 57–68 (2016)CrossRefGoogle Scholar
  32. [Cas+04]
    J. Casillas, B. Carse, L. Bull, B. Carse, Fuzzy XCS: an accuracy- based fuzzy classifier system, in Proceedings of the XII Congreso Espanol Sobre Tecnologia y Logica Fuzzy (ESTYLF 2004) (Citeseer, 2004), pp. 369–376Google Scholar
  33. [CBT02]
    F. Cupillard, F. Brémond, M. Thonnat, Group behavior recognition with multiple cameras, in 6th IEEE Workshop on Applications of Computer Vision (WACV 2002), 3–4 Dec 2002, Orlando, FL, pp. 177–183 (2002). https://doi.org/10.1109/ACV.2002.1182178
  34. [CF10]
    C. Castelfranchi, R. Falcone, Trust Theory: A Socio-Cognitive and Computational Model, vol. 18 (Wiley, Chichester, 2010)zbMATHCrossRefGoogle Scholar
  35. [CGD08]
    S.-B. Cools, C. Gershenson, B. D’Hooghe, Self-organizing traffic lights: a realistic simulation, in Advances in Applied Self-organizing Systems (Springer, London, 2008), pp. 41–50CrossRefGoogle Scholar
  36. [CGD13]
    S.-B. Cools, C. Gershenson, B. D’Hooghe, Self-organizing traffic lights: a realistic simulation, in Advances in Applied Self-organizing Systems (Springer, London, 2013), pp. 45–55CrossRefGoogle Scholar
  37. [Cha+11]
    G. Chalkiadakis, V. Robu, R. Kota, A. Rogers, N.R. Jennings, Cooperatives of distributed energy resources for efficient virtual power plants, in Proceedings of the 10th International Conference on Autonomous Agents and Multiagent Systems, vol. 2 (International Foundation for Autonomous Agents and Multiagent Systems, Taipei, 2011), pp. 787–794Google Scholar
  38. [CHM08]
    E. Cakar, J. Hähner, C. Müller-Schloer, Investigation of generic observer/controller architectures in a traffic scenario, in GI Jahrestagung (2), pp. 733–738 (2008)Google Scholar
  39. [CHM10]
    Y. Chaaban, J. Hähner, C. Müller-Schloer, Towards robust hybrid central/self-organizing multi-agent systems, in Proceedings of the 2nd International Conference on Agents and Artificial Intelligence, pp. 341–346 (2010). ISBN: 978-989-674-022-1, https://doi.org/10.5220/0002761003410346
  40. [Coh03]
    B. Cohen, Incentives build robustness in BitTorrent, in Proceedings of the 1st Workshop on Economics of Peer-to-Peer Systems, Berkeley (2003)Google Scholar
  41. [CR07]
    A.M. Cheriyadat, R.J. Radke, Automatically determining dominant motions in crowded scenes by clustering partial feature trajectories, in 2007 First ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC 2007, Vienna, 25–28 Sept 2007, pp. 52–58 (2007). http://doi.org/10.1109/ICDSC.2007.4357505
  42. [DS04]
    M. Dorigo, E. Şahin, Guest editorial. Auton. Robot. 17(2), 111–113 (2004)CrossRefGoogle Scholar
  43. [Dür01]
    P. Dürr, Integration des ÖPNV in die dynamische Fahrwegsteuerung des Straßenverkehrs-Steuerungsverfahren Darvin. SCHRIFTENR LEHRST VERKEHRSPLANUNG U VERKEHRSWES TU MUENCHEN, vol. 11 (2001)Google Scholar
  44. [Ege07]
    K. Eger, Simulation of BitTorrent Peer-to-Peer (P2P) Networks inns-2 (2007). http://www.tuharburg.de/et6/research/bittorrentsim/index.html Google Scholar
  45. [Fal99]
    K. Fall, Network emulation in the vint/NS simulator, in Proceedings of the The Fourth IEEE Symposium on Computers and Communications (IEEE Computer Society, Washington, 1999), p. 244Google Scholar
  46. [Far+00]
    A. Farago, A.D. Myers, V.R. Syrotiuk, G.V Zaruba, Meta-MAC protocols: automatic combination of MAC protocols to optimize per formance for unknown conditions. IEEE J. Sel. Areas Commun. 18(9), 1670–1681 (2000)Google Scholar
  47. [FKL90]
    J. Farges, I. Khoudour, J. Lesort, PRODYN: on site evaluation, in Third International Conference on Road Traffic Control, 1990 (IET, London, 1990), pp. 62–66Google Scholar
  48. [FM08]
    D. Floreano, C. Mattiussi, Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies (MIT press, Cambridge, 2008)Google Scholar
  49. [Foo+05]
    M.C. Foo, H.B. Lim, Y. Zeng, V.T. Lam, R. Teo, G.W. Ng, Impact of distributed resource allocation in sensor networks, in 2005 International Conference on Intelligent Sensors, Sensor Networks and Information Processing (IEEE, Piscataway, NJ, 2005), pp. 69–74Google Scholar
  50. [Fra+03]
    N.R. Franks, A. Dornhaus, J.P. Fitzsimmons, M. Stevens, Speed versus accuracy in collective decision making. Proc. R. Soc. Lond. B 270(1532), 2457–2463 (2003)CrossRefGoogle Scholar
  51. [Fre+15]
    S. Frey, A. Diaconescu, D. Menga, I.-B. Demeure, A generic holonic control architecture for heterogeneous multiscale and multiobjective smart microgrids. ACM Trans. Auton. Adapt. Syst. 10(2), 9:1–9:21 (2015). ISSN: 1556-4665. https://doi.org/10.1145/2700326
  52. [Ger07]
    C. Gershenson, Design and Control of Self-organizing Systems (CopIt ArXives, Mexico, 2007)Google Scholar
  53. [GL07]
    N. Georganopoulos, T. Lewis, A framework for dynamic link and network layer protocol optimisation, in 2007 16th IST Mobile and Wireless Communications Summit (IEEE, Piscataway, NJ, 2007), pp. 1–5Google Scholar
  54. [GV+06]
    P. Goyal, H.M. Vin, C. Shen, P.J. Shenoy, A reliable, adaptive network protocol for video transport, in Proceedings of IEEE INFOCOM ’96. Fifteenth Annual Joint Conference of the IEEE Computer Societies. Networking the Next Generation, vol. 3 (1996), pp. 1080–1090.  doi:10.1109/INFCOM.1996.493051
  55. [GR14]
    J.J. Grefenstette, C.L. Ramsey, An approach to anytime learning, in Machine Learning: Proceedings of the Ninth International Conference, pp. 189–195 (2014)Google Scholar
  56. [GV16]
    V. Gerling, S. von Mammen, Robotics for self-organised construction, in IEEE International Workshops on Foundations and Applications of Self* Systems (IEEE, Piscataway, NJ, 2016), pp. 162–167Google Scholar
  57. [GZG10]
    Z. Gao, D. Zhang, Y. Ge, Design optimization of a spatial six degree-of-freedom parallel manipulator based on artificial intelligence approaches. Robot. Comput. Integr. Manuf. 26(2), 180–189 (2010)CrossRefGoogle Scholar
  58. [H+02]
    R. Hegselmann, U. Krause et al., Opinion dynamics and bounded confidence models, analysis, and simulation. J. Artif. Soc. Soc. Simul. 5(3), 1–32 (2002). http://jasss.soc.surrey.ac.uk/5/3/2.html Google Scholar
  59. [Ham+15]
    H. Hamann, M. Wahby, T. Schmickl, P. Zahadat, D. Hofstadler, K. Stoy, S. Risi, A. Faina, F. Veenstra, S. Kernbach et al., Flora robotica- mixed societies of symbiotic robot-plant bio-hybrids, in 2015 IEEE Symposium Series on Computational Intelligence (IEEE, Piscataway, NJ, 2015), pp. 1102–1109CrossRefGoogle Scholar
  60. [Ham10]
    H. Hamann, Space-Time Continuous Models of Swarm Robotic Systems: Supporting Global-to-Local Programming, vol. 9 (Springer, Berlin, 2010)Google Scholar
  61. [Han06]
    M. Handley, Why the internet only just works. BT Technol. J. 24(3), 119–129 (2006)CrossRefGoogle Scholar
  62. [HB09]
    P.V. Hentenryck, R. Bent, Online Stochastic Combinatorial Optimization (MIT Press, Cambridge, 2009). ISBN: 978-0262513470zbMATHGoogle Scholar
  63. [Hei00]
    W.B. Heinzelman, Application-Specific Protocol Architectures for Wireless Networks (Massachusetts Institute of Technology, 2000)Google Scholar
  64. [Her+13]
    L. Hernández, C. Baladrón, J.M. Aguiar, B. Carro, A. Sánchez-Esguevillas, J. Lloret, D. Chinarro, J.J. Gomez-Sanz, D. Cook, A multi-agent system architecture for smart grid management and forecasting of energy demand in virtual power plants. IEEE Commun. Mag. 51(1), 106–113 (2013). ISSN: 0163-6804. https://doi.org/10.1109/MCOM.2013.6400446 CrossRefGoogle Scholar
  65. [HF89]
    J.-J. Henry, J.-L. Farges, PRODYN, in Control, Computers, Communications in Transportation, ed. by J.-P. Perrin (1989)Google Scholar
  66. [HFT84]
    J.-J. Henry, J.L. Farges, J. Tuffal, The PRODYN real time traffic algorithm, in Conference on Control in IFACIFIPIFORS (1984)Google Scholar
  67. [HHM10]
    K.A. Hummel, A. Hess, H. Meyer, Mobilität im, Future Internet. Informatik-Spektrum 33(2), 143–159 (2010)CrossRefGoogle Scholar
  68. [Hil+00]
    M.A. Hiltunen, R.D. Schlichting, C.A. Ugarte, G.T. Wong, Survivability through customization and adaptability: the cactus approach, in Proceedings DARPA Information Survivability Conference and Exposition, 2000, vol. 1 (IEEE, Piscataway, NJ, 2000), pp. 294–307Google Scholar
  69. [HL04]
    B. Horling, V. Lesser, A survey of multi-agent organizational paradigms. Knowl. Eng. Rev. 19(4), 281–316 (2004). ISSN: 0269-8889. https://doi.org/10.1017/S0269888905000317 CrossRefGoogle Scholar
  70. [HLL05]
    D. Helbing, S. Lämmer, J.-P. Lebacque, Self-organized control of irregular or perturbed network traffic, in Optimal Control and Dynamic Games (Springer, Dordrecht, 2005), pp. 239–274zbMATHGoogle Scholar
  71. [HLS13]
    C. Hinrichs, S. Lehnhoff, M. Sonnenschein, COHDA: a combinatorial optimization heuristic for distributed agents. in International Conference on Agents and Artificial Intelligence (Springer, Berlin, 2013), pp. 23–39Google Scholar
  72. [Hof+08a]
    M. Hoffmann, M. Wittke, Y. Bernard, R. Soleymani, J. Hähner, DMCtrac: distributed multi camera tracking, in 2008 Second ACM/IEEE International Conference on Distributed Smart Cameras, Stanford, CA, 7–11 Sept 2008, pp. 1–10 (2008). http://doi.org/10.1109/ICDSC.2008.4635684
  73. [Hof+08b]
    M. Hoffmann, M. Wittke, J. Hähner, C. Müller-Schloer, Spatial partitioning in self-organizing smart camera systems. J. Sel. Top. Sign. Proces. 2(4), 480–492 (2008). http://doi.org/10.1109/JSTSP.2008.2001308 CrossRefGoogle Scholar
  74. [HST16]
    J. Hähner, K. Streit, S. Tomforde, Cellular traffic offloading through network-assisted ad-hoc routing in cellular networks, in 2016 IEEE Symposium on Computers and Communication (IEEE, Piscataway, NJ, 2016), pp. 469–476Google Scholar
  75. [HTH10]
    B. Hurling and S. Tomforde and J. Hähner, A generic architecture for restricted on-line learning of network protocol parameters, in Proceedings of the first Workshop on Self-Adaptive Networking (SAN’10), held in conjunction with 4th IEEE International Conference on Self-Adaptive and Self-Organising Systems (SASO’10) (Budapest, 2010)Google Scholar
  76. [HTH11]
    B. Hurling, S. Tomforde, J. Hähner, Organic network control, in Organic Computing A Paradigm Shift for Complex Systems, ed. by C. Müller-Schloer, H. Schmeck, T. Ungerer. Autonomic Systems (Birkhäuser, Basel, 2011), Chapter 6.1.11, pp. 611–612Google Scholar
  77. [HJR09]
    K.-C. Huang, X. Jing, D. Raychaudhuri, Mac protocol adaptation in cognitive radio networks: an experimental study, in International Conference on Computer Communications and Networks (2009), pp. 1–6Google Scholar
  78. [INE14]
    INET community, The INET Framework (2014). https://inet.omnetpp.org
  79. [Jae+12]
    U. Jaenen, U. Feuerhake, T. Klinger, D. Muhle, J. Haehner, M. Sester, C. Heipke, QTRAJECTORIES: improving the quality of object tracking using self-organizing camera networks, in ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences, vol. I-4, pp. 269–274 (2012)CrossRefGoogle Scholar
  80. [Jän+11]
    U. Jänen, M. Huy, C. Grenz, J. Hähner, M. Hoffmann, Distributed three-dimensional camera alignment in highly-dynamical prioritized observation areas, in 2011 Fifth ACM/IEEE International Confer ence on Distributed Smart Cameras, Ghent, 22–25 Aug 2011, pp. 1–6 (2011). http://doi.org/10.1109/ICDSC.2011.6042904
  81. [Jän+15]
    U. Jänen, C. Grenz, S. Edenhofer, A. Stein, J. Brehm, J. Hähner, Task execution in distributed smart systems, in Proceedings Internet and Distributed Computing Systems – 8th International Conference, IDCS 2015, Windsor, 2–4 Sept 2015, pp. 103–117 (2015). http://doi.org/10.1007/978-3-319-23237-9_10
  82. [Kan+14a]
    J. Kantert, S. Bödelt, S. Edenhofer, S. Tomforde, J. Hähner, C. Müller-Schloer, Interactive simulation of an open trusted desktop grid system with visualisation in 3D, in Proceedings of the Eighth IEEE International Conference on Self-adaptive and Self-organizing Systems. (Best Demo Award) (IEEE, London, 2014), pp. 191–192Google Scholar
  83. [Kan+14b]
    J. Kantert, L. Klejnowski, Y. Bernard, C. Müller-Schloer, Influence of norms on decision making in trusted desktop grid systems: making norms explicit, in Proceedings of the 6th International Conference on Agents and Artificial Intelligence, vol. 2 (SciTePress, Angers, 2014), pp. 278–283Google Scholar
  84. [Kan+15a]
    J. Kantert, S. Edenhofer, S. Tomforde, J. Hähner, C. Müller-Schloer, Defending autonomous agents against attacks in multi-agent systems using norms, in Proceedings of the 7th International Conference on Agents and Artificial Intelligence (SciTePress, Lisbon, 2015), pp. 149–156Google Scholar
  85. [Kan+15b]
    J. Kantert, S. Edenhofer, S. Tomforde, J. Hähner, C. Müller-Schloer, Detecting and isolating inconsistently behaving agents using an intelligent control loop, in Proceedings of the 12th International Conference on Informatics in Control, Automation and Robotics (SciTePress, Colmar, 2015), pp. 246–253Google Scholar
  86. [Kan+15c]
    J. Kantert, S. Edenhofer, S. Tomforde, J. Hähner, C. Müller-Schloer, Norm-based system control in distributed low-power sensor networks, in Poster Session Proceedings of the 28th International Conference on Architecture of Computing Systems, pp. 13–14 (2015)Google Scholar
  87. [Kan+15d]
    J. Kantert, S. Edenhofer, S. Tomforde, C. Müller-Schloer, Representation of trust and reputation in self-managed computing systems, in Proceedings of the 13th IEEE International Conference on Dependable, Autonomic and Secure Computing (IEEE, Liverpool, 2015), pp. 1827–1834Google Scholar
  88. [Kan+15e]
    J. Kantert, C. Ringwald, G. von Zengen, S. Tomforde, L. Wolf, C. Müller-Schloer, Enhancing RPL for robust and efficient routing in challenging environments, in Proceedings of the Ninth IEEE International Conference on Self-adaptive and Self-organizing Systems Workshops (IEEE, Cambridge, 2015), pp. 7–12Google Scholar
  89. [Kan+15f]
    J. Kantert, H. Spiegelberg, S. Tomforde, J. Hähner, C. Müller-Schloer, Distributed rendering in an open self-organised trusted desktop grid, in Proceeding of the 12th IEEE International Conference on Autonomic Computing (IEEE, Grenobles, 2015), pp. 267–272Google Scholar
  90. [Kan+16a]
    J. Kantert, S. Edenhofer, S. Tomforde, C. Müller-Schloer, Improving reliability and reducing overhead in low-power sensor networks using trust and forgiveness, in Proceeding of the 13th IEEE International Conference on Autonomic Computing (IEEE, Würzburg, 2016), pp. 325–333Google Scholar
  91. [Kan+16b]
    J. Kantert, S. Edenhofer, S. Tomforde, C. Müller-Schloer, Improving reliability and reducing overhead in low-power sensor networks using trust and forgiveness, in Proceeding of the 13th IEEE International Conference on Autonomic Computing (IEEE, Würzburg, 2016), pp. 325–333Google Scholar
  92. [Kan+16c]
    J. Kantert, M. Kauder, S. Edenhofer, S. Tomforde, C. Müller-Schloer, Detecting colluding attackers in distributed grid systems, in Proceedings of the 8th International Conference on Agents and Ar tificial Intelligence, vol. 1 (SciTePress, Rome, 2016), pp. 198–206. ISBN: 978-989-758-172-4CrossRefGoogle Scholar
  93. [Kan+16d]
    J. Kantert, F. Reinhard, G. von Zengen, S. Tomforde, L. Wolf, C. Müller-Schloer, Combining trust and ETX to provide robust wireless sensor networks, in Proceedings of the 29th International Conference on Architecture of Computing Systems Workshops, ed. by A.L. Varbanescu (VDE Verlag GmbH, Berlin/Offenbach, 2016), Chapter 16, pp. 1–7. ISBN: 978-3-8007-4157-1Google Scholar
  94. [Kan+16e]
    J. Kantert, R. Scharrer, S. Tomforde, S. Edenhofer, C. Müller-Schloer, Runtime clustering of similarly behaving agents in open organic computing systems, in Proceedings of the 29th Inter national Conference on Architecture of Computing Systems, ed. by F. Hannig, J. Cardoso, T. Pionteck, D. Fey, W. Schröder Preikschat, J. Teich. LNCS, vol. 9637 (Springer, Nuremberg, 2016), pp. 321–333. ISBN: 978-3-319-30694-0Google Scholar
  95. [Kan+16f]
    J. Kantert, S. Tomforde, M. Kauder, R. Scharrer, S. Edenhofer, J. Hähner, C. Müller-Schloer, Controlling negative emergent behavior by graph analysis at runtime. ACM Trans. Auton. Adapt. Syst. 11(2), 7:1–7:34 (2016). ISSN: 1556-4665Google Scholar
  96. [KH06]
    W.H. Kwon, S.H. Han, Receding Horizon Control: Model Predictive Control for State Models (Springer, Dordrecht, 2006)Google Scholar
  97. [KHO12]
    J. Kotlarski, B. Heimann, T. Ortmaier, Influence of kinematic redundancy on the singularity-free workspace of parallel kinematic machines. Front. Mech. Eng. 7(2), 120–134 (2012)CrossRefGoogle Scholar
  98. [Kle14]
    L. Klejnowski, Trusted community: a novel multiagent organisation for open distributed systems, Ph.D. thesis, Leibniz Universität Hannover, 2014. http://edok01.tib.uni-hannover.de/edoks/e01dh11/668667427.pdf
  99. [Kos+15]
    O. Kosak, G. Anders, F. Siefert, W. Reif, An approach to robust resource allocation in large-scale systems of systems, in 2015 IEEE 9th International Conference on Self-adaptive and Self-organizing Systems, pp. 1–10 (2015)Google Scholar
  100. [Kot+10]
    J. Kotlarski, T. Do Thanh, B. Heimann, T. Ortmaier, Optimization strategies for additional actuators of kinematically redundant parallel kinematic machines, in 2010 IEEE International Conference on Robotics and Automation (IEEE, Piscataway, NJ, 2010), pp. 656–661Google Scholar
  101. [Kri14]
    J.B. Kristensen, Big Buck Bunny 3D Rendering Exploration, http://bbb3d.ren-derfarming.net/explore.html. Accessed: 2015-02-18. Blender Foundation, 2014
  102. [KS12]
    A. Kamper, H. Schmeck, Adaptives verteiltes Lastmanagement in Bilanzkreisen. Informatik-Spektrum 35(2), 102–111 (2012). ISSN: 0170-6012CrossRefGoogle Scholar
  103. [KTM15]
    J. Kantert, S. Tomforde, C. Müller-Schloer, Measuring self-or ganisation in distributed systems by external observation, in Proceedings of the 28th International Conference on Architecture of Computing Systems Workshops (VDE Verlag GmbH, Berlin, 2015), pp. 1–8. ISBN: 978-3-8007-3657-7Google Scholar
  104. [Kun03]
    T. Kunz, Reliable multicasting in MANETs, Ph.D. thesis, Carleton University, 2003Google Scholar
  105. [KWK05]
    K. Kok, C. Warmer, R. Kamphuis, PowerMatcher: multiagent control in the electricity infrastructure, in Proceedings of the 4th International Joint Conference on Autonomous Agents and Multiagent Systems (ACM, New York, 2005), pp. 75–82. ISBN: 1-59593-093-0CrossRefGoogle Scholar
  106. [L+06]
    H.B. Lim, M.C. Foo, Y. Zeng et al., An adaptive distributed resource allocation scheme for sensor networks, in International Conference on Mobile Ad-Hoc and Sensor Networks (Springer, Berlin, 2006), pp. 770–781Google Scholar
  107. [Läm07]
    S. Lämmer, Reglerentwurf zur dezentralen online-steuerung von lichtsignalanlagen in straßennetzwerken, Technische Universität Dresden (2007)Google Scholar
  108. [Li+02]
    S.-T. Li, H.-C. Hsieh, L.-Y. Shue, W.-S. Chen, PDA watch for mobile surveillance services, in Proceedings of the IEEE Workshop on Knowledge Media Networking (IEEE Computer Society, Washington, 2002), pp. 49. ISBN: 0-7695-1778-1Google Scholar
  109. [LMB16]
    M. Luces, J.K. Mills, B. Benhabib, A review of redundant parallel kinematic mechanisms. J. Intell. Robot. Syst. 86(2), 175–198 (2017)CrossRefGoogle Scholar
  110. [LMG04]
    K. Lerman, A. Martinoli, A. Galstyan, A review of probabilistic macroscopic models for swarm robotic systems, in International Workshop on Swarm Robotics (Springer, New York, 2004), pp. 143–152Google Scholar
  111. [LSS12]
    T. Logenthiran, D. Srinivasan, T.Z. Shun, Demand side management in smart grid using heuristic optimization. IEEE Trans. Smart Grid 3(3), 1244–1252 (2012). ISSN: 1949-3053CrossRefGoogle Scholar
  112. [Man+00]
    H.C. Manual et al., Transportation Research Board, vol. 113 (National Re Search Council, Washington, 2000)Google Scholar
  113. [Mau+15]
    I. Mauser, C. Hirsch, S. Kochanneck, H. Schmeck, Organic architecture for energy management and smart grids, in Proceedings of the 12th IEEE International Conference on Autonomic Computing (IEEE, Piscataway, NJ, 2015), pp. 101–108Google Scholar
  114. [McA+12]
    S.D.J. McArthur, P.C. Taylor, G.W. Ault, J.E. King, D. Athanasiadis, V.D. Alimisis, M. Czaplewski, The autonomic power system network operation and control beyond smart grids, in 2012 3rd IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe), pp. 1–7 (2012). https://doi.org/10.1109/ISGTEurope.2012.6465807
  115. [McC+16]
    T.L. McCluskey, A. Kotsialos, J.P. Müller, F. Klügl, O. Rana, R. Schumann, Autonomic Road Transport Support Systems (Springer, Cham, 2016)CrossRefGoogle Scholar
  116. [MEA04]
    A. Martinoli, K. Easton, W. Agassounon, Modeling swarm robotic systems: a case study in collaborative distributed manipulation. Int. J. Robot. Res. 23(4–5), 415–436 (2004)CrossRefGoogle Scholar
  117. [MPR01]
    H. Miranda, A. Pinto, L. Rodrigues, Appia, a flexible protocol kernel supporting multiple coordinated channels, in 21st International Conference on Distributed Computing Systems, 2001 (IEEE, Piscataway, NJ, 2001), pp. 707–710Google Scholar
  118. [MRW12]
    S.C. Müller, U. Häger, C. Rehtanz, H.F. Wedde, Application of self-organizing systems in power systems control, in Proceedings Product-Focused Software Process Improvement: 13th International Conference PROFES 2012, Madrid, 13–15 June 2012, ed. by O. Dieste, A. Jedlitschka, N. Juristo (Springer, Berlin/Heidelberg, 2012), pp. 320–334. ISBN: 978-3-642-31063-8. https://doi.org/10.1007/978-3-642-31063-8_25 CrossRefGoogle Scholar
  119. [MTH16]
    S. von Mammen, S. Tomforde, J. Hähner, An organic computing approach to self-organizing robot ensembles. Front. Robot. AI 3, 67 (2016)Google Scholar
  120. [Nie+13]
    S. Niemann, J. Kotlarski, T. Ortmaier, C. Müller-Schloer, Reducing the optimization problem for the efficient motion planning of kinematically redundant parallel robots, in 2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (IEEE, Piscataway, NJ, 2013), pp. 618–624Google Scholar
  121. [Nie+14]
    A. Nieße, S. Beer, J. Bremer, C. Hinrichs, O. Lünsdorf, M. Sonnenschein, Conjoint dynamic aggregation and scheduling methods for dynamic virtual power plants, in Federated Conference on Computer Science and Information Systems, 2014, pp. 1505–1514 (2014). https://doi.org/10.15439/2014F76 CrossRefGoogle Scholar
  122. [NJ13]
    K. Nichols, V. Jacobson, Controlled delay active queue management, in Work in Progress (2013)Google Scholar
  123. [Noa07]
    A. Noack, Unified quality measures for clusterings, layouts, and orderings of graphs, and their application as software design criteria, Ph.D. thesis, Brandenburg University of Technology, pp. 1–289 (2007)Google Scholar
  124. [Off03]
    Office of Electric Transmission and Distribution, “Grid 2030” – a National Vision for Electricity’s Second 100 Years, Tech. Rep., US Department of Energy, 2003Google Scholar
  125. [ORo87]
    J. O’Rourke, Art Gallery Theorems and Algorithms (Oxford University Press, New York, 1987). ISBN: 0-19-503965-3zbMATHGoogle Scholar
  126. [Öst06]
    F. Österlind, A sensor network simulator for the contiki OS, Tech. Rep., Swedish Institute of Computer Science, May 2006Google Scholar
  127. [Pad04]
    N. Padhy, Unit commitment: a bibliographical survey. IEEE Trans. Power Syst. 19(2), 1196–1205 (2004). ISSN: 0885-8950CrossRefGoogle Scholar
  128. [PK01]
    K. Pahlavan, P. Krishnamurthy, Principles of Wireless Networks: A Unified Approach (Prentice Hall PTR, Upper Saddle River, NJ, 2001)Google Scholar
  129. [PM02]
    I. Pavlidis, V. Morellas, Two examples of indoor and outdoor surveillance systems: motivation, design, and testing, in Video-Based Surveillance Systems: Computer Vision and Distributed Processing, ed. by P. Remagnino, G.A. Jones, N. Paragios, C.S. Regazzoni (Springer, Boston, 2002), pp. 39–50. ISBN: 978-1-4615-0913-4. http://doi.org/10.1007/978-1-4615-0913-4_3 CrossRefGoogle Scholar
  130. [Pou+05]
    J. Pouwelse, P. Garbacki, D. Epema, H. Sips, The bittorrent P2P file-sharing system: measurements and analysis, in International Workshop on Peer-to-Peer Systems (Springer, Boston, 2005), pp. 205–216Google Scholar
  131. [Pro+08]
    H. Prothmann, F. Rochner, S. Tomforde, J. Branke, C. Müller-Schloer, H. Schmeck, Organic control of traffic lights, in International Conference on Autonomic and Trusted Computing (Springer, Berlin, 2008), pp. 219–233CrossRefGoogle Scholar
  132. [Pro+09]
    H. Prothmann, J. Branke, H. Schmeck, S. Tomforde, F. Rochner, J. Hahner, C. Muller-Schloer, Organic traffic light control for urban road networks. Int. J. Auton. Adapt. Commun. Syst. 2(3), 203–225 (2009)CrossRefGoogle Scholar
  133. [Pro+11a]
    H. Prothmann, H. Schmeck, S. Tomforde, J. Lyda, J. Hahner, C. Muller-Schloer, J. Branke, Decentralised route guidance in or ganic traffic control, in 2011 Fifth IEEE International Conference on Self-adaptive and Self-organizing Systems (IEEE, Piscataway, NJ, 2011), pp. 219–220CrossRefGoogle Scholar
  134. [Pro+11b]
    H. Prothmann, S. Tomforde, J. Branke, J. Hähner, C. Müller-Schloer, H. Schmeck, Organic traffic control, in Organic Computing – A Paradigm Shift for Complex Systems (Springer, Basel, 2011), pp. 431–446CrossRefGoogle Scholar
  135. [Pro+12]
    H. Prothmann, S. Tomforde, J. Lyda, J. Branke, J. Hähner, C. Müller-Schloer, H. Schmeck, Self-organised routing for road networks, in International Workshop on Self-organizing Systems (Springer, New York, 2012), pp. 48–59Google Scholar
  136. [PRS07]
    D. Pudjianto, C. Ramsay, G. Strbac, Virtual power plant and system integration of distributed energy resources. IET Renew. Power Gener. 1(1), 10–16 (2007)CrossRefGoogle Scholar
  137. [PSA11]
    J. Pitt, J. Schaumeier, A. Artikis, The axiomatisation of socio-economic principles for self-organising systems, in 2011 Fifth IEEE International Conference on Self-adaptive and Self-organizing Systems (IEEE, Michigan, 2011), pp. 138–147Google Scholar
  138. [Ram+12]
    S.D. Ramchurn, P. Vytelingum, A. Rogers, N.R. Jennings, Putting the “Smarts” into the smart grid: a grand challenge for artificial intelligence. Commun. ACM 55(4), 86–97 (2012)CrossRefGoogle Scholar
  139. [RB15]
    C. Rosinger, S. Beer, Glaubwürdigkeit in dynamischen Wirkleistungsverbünden’. Informatik-Spektrum 38(2), 103–110 (2015). ISSN: 1432-122X. https://doi.org/10.1007/s00287-014-0856-7 CrossRefGoogle Scholar
  140. [RB91]
    D.I. Robertson, R.D. Bretherton, Optimizing networks of traffic signals in real time: the SCOOT method. IEEE Trans. Veh. Technol. 40(1), 11–15 (1991)CrossRefGoogle Scholar
  141. [Rei+16]
    W. Reif, G. Anders, H. Seebach, J.-P. Steghöfer, E. André, J. Hähner, C. Müller-Schloer, T. Ungerer (eds.), Trustworthy Open Self-organising Systems. Autonomic Systems, vol. 7 (Springer, Cham, 2016), pp. 89–126. ISBN: 978-3-319-29199-4Google Scholar
  142. [Ren+98]
    R.V. Renesse, K. Birman, M. Hayden, A. Vaysburd, D. Karr, Building adaptive systems using ensemble. Softw. Pract. Exp. 28(9), 963–979 (1998)CrossRefGoogle Scholar
  143. [RMK13]
    L. Rani, M. Mam, S. Kumar, Economic load dispatch in thermal power plant taking real time efficiency as an additional constraints. Int. J. Eng. Res. Technol. 2(7) (2013). ISSN: 2278-0181Google Scholar
  144. [RRL07]
    L. Rosa, L. Rodrigues, A. Lopes, Appia to R-appia: refactoring a protocol composition framework for dynamic reconfiguration, Tech. Rep. (2007)Google Scholar
  145. [SB01]
    P. Sudame, B. Badrinath, On providing support for protocol adaptation in mobile wireless networks. Mob. Netw. Appl. 6(1), 43–55 (2001)zbMATHCrossRefGoogle Scholar
  146. [Sch+13a]
    A. Schiendorfer, J.-P. Steghöfer, A. Knapp, F. Nafz, W. Reif, Constraint relationships for soft constraints, English, in Research and Development in Intelligent Systems XXX, ed. by M. Bramer, M. Petridis (Springer, Cham, 2013), pp. 241–255. ISBN: 978-3-319-02620-6CrossRefGoogle Scholar
  147. [Sch+13b]
    T. Schmickl, S. Bogdan, L. Correia, S. Kernbach, F. Mondada, M. Bodi, A. Gribovskiy, S. Hahshold, D. Miklic, M. Szopek et al., AS-SISI: mixing animals with robots in a hybrid society, in Conference on Biomimetic and Biohybrid Systems (Springer, New York, 2013), pp. 441–443Google Scholar
  148. [Sch+15a]
    A. Schiendorfer, G. Anders, J.-P. Steghöfer, W. Reif, Abstraction of heterogeneous supplier models in hierarchical re-source allocation, in Transactions on Computational Collective IntelligenceXX, ed. by N.T. Nguyen, R. Kowalczyk, B. Duval, J. van den Herik, S. Loiseau, J. Filipe. Lecture Notes in Computer Science, vol. 9420, (Springer, Cham, 2015), pp. 23–53Google Scholar
  149. [Sch+15b]
    A. Schiendorfer, A. Knapp, J.-P Steghöfer, G. Anders, F. Siefert, W. Reif, Partial valuation structures for qualitative soft constraints, English, in Software Services, and Systems, ed. by R.D. Nicola, R. Hennicker. Lecture Notes in Computer Science, vol. 8950 (Springer, Cham, 2015), pp. 115–133Google Scholar
  150. [Sch+15c]
    A. Schiendorfer, C. Lassner, G. Anders, R. Lienhart, W. Reif, Active learning for efficient sampling of control models of collectives, in IEEE 9th International Conference on Self-adaptive and Self-organizing Systems, 2015, pp. 51–60 (2015)Google Scholar
  151. [Sco+13]
    P. Scott, S. Thiébaux, M. van den Briel, P. Van Hentenryck, Residential demand response under uncertainty, English, in Principles and Practice of Constraint Programming, ed. by C. Schulte. Lecture Notes in Computer Science, vol. 8124 (Springer, Berlin/Heidelberg, 2013), pp. 645–660. ISBN: 978-3-642-40626-3. https://doi.org/10.1007/978-3-642-40627-0_48
  152. [SD80]
    A.G. Sims, K.W. Dobinson, The sydney coordinated adaptive traffic (SCAT) system philosophy and benefits. IEEE Trans. Veh. Technol. 29(2), 130–137 (1980)CrossRefGoogle Scholar
  153. [Set10]
    B. Settles, Active learning literature survey, Computer Sciences Technical Report 1648, University of Wisconsin–Madison, 2010. http://burrsettles.com/pub/settles.activelearning.pdf
  154. [Sie+07]
    M. Siekkinen, V. Goebel, T. Plagemann, K.-A. Skevik, M. Banfield, I. Brusic, Beyond the future internet–requirements of autonomic networking architectures to address long term future networking challenges, in 11th IEEE International Workshop on Future Trends of Distributed Computing Systems (IEEE, Piscataway, NJ, 2007), pp. 89–98Google Scholar
  155. [SIF07]
    J.A. Short, D.G. Infield, L.L. Freris, Stabilization of grid frequency through dynamic demand control. IEEE Trans. Power Syst. 22(3), 1284–1293 (2007)CrossRefGoogle Scholar
  156. [SM05]
    T. Schöler, C. Müller-Schloer, An observer/controller architecture for adaptive reconfigurable stacks, in International Conference on Architecture Of Computing Systems (Springer, Berlin, 2005), pp. 139–153Google Scholar
  157. [Son+12]
    M. Sonnenschein, H.-J. Appelrath, L. Hofmann, M. Kurrat, S. Lehnhoff, C. Mayer, A. Mertens, M. Uslar, A. Nieße and, M. Tröschel, Dezentrale und selbstorganisierte koordination in smart grids, in Tagungsband VDE–Kongress 2012 (VDE, Berlin, 2012)Google Scholar
  158. [Spä86]
    H. Späth, Anticlustering: maximizing the variance criterion. Control. Cybern. 15(2), 213–218 (1986)zbMATHGoogle Scholar
  159. [Ste+13a]
    J.-P. Steghöfer, G. Anders, F. Siefert, W. Reif, A system of systems approach to the evolutionary transformation of power management systems, in Proceedings of INFORMATIK 2013 – Workshop on “Smart Grids”. Lecture Notes in Informatics, vol. P-220 (Bonner Köllen, Bonn, 2013)Google Scholar
  160. [Ste+13b]
    J.-P. Steghöfer, P. Behrmann, G. Anders, F. Siefert, W. Reif, HiS-PADA: self-organising hierarchies for large-scale multi-agent systems, in Proceedings of the Ninth International Conference on Autonomic and Autonomous Systems, pp. 71–76 (2013). ISBN: 978-1-61208-257-8Google Scholar
  161. [Ste+14]
    J.-P. Steghöfer, G. Anders, W. Reif, J. Kantert, C. Müller-Schloer, An effective implementation of norms in trust-aware open self-organising systems, in Proceedings of the Eighth IEEE International Conference on Self-adaptive and Self-organizing Systems Workshops (IEEE, London, 2014), pp. 76–77Google Scholar
  162. [STH13]
    M. Sommer, S. Tomforde, J. Hähner, Using a neural network for forecasting in an organic traffic control management system, in ESOS (2013)Google Scholar
  163. [STH15]
    M. Sommer, S. Tomforde, J. Hähner, Learning a dynamic recombination strategy of forecast techniques at runtime, in 2015 IEEE International Conference on Autonomic Computing (IEEE, Piscataway, NJ, 2015), pp. 261–266CrossRefGoogle Scholar
  164. [STH16a]
    M. Sommer, S. Tomforde, J. Hähner, An organic computing approach to resilient traffic management, in Autonomic Road Transport Support Systems (Springer, Cham, 2016), pp. 113–130CrossRefGoogle Scholar
  165. [STH16b]
    M. Sommer, S. Tomforde, J. Hähner, Forecast-augmented route guidance in urban traffic networks based on infrastructure obser vations, in Proceedings of the International Conference on Vehicle Technology and Intelligent Transport Systems, pp. 177–186 (2016). ISBN: 978-989-758-185-4. https://doi.org/10.5220/0005741901770186
  166. [Str+14]
    P. Ströhle, E.H. Gerding, M.M. de Weerdt, S. Stein, V. Robu, Online mechanism design for scheduling non-preemptive jobs under uncertain supply and demand, in Proceedings of the 13th International Conference on Autonomous Agents and Multiagent Systems (International Foundation for Autonomous Agents and Multiagent Systems, Richland, 2014), pp. 437–444. ISBN: 978-1-4503-2738-1Google Scholar
  167. [Tan02]
    A.S. Tanenbaum, Computer Networks, 4th edn. (Recording for the Blind and Dyslexic, Princeton, NJ, 2002)zbMATHGoogle Scholar
  168. [TCH09]
    S. Tomforde, E. Cakar, J. Hähner, Dynamic control of network protocols-a new vision for future self-organising networks, in ICINCO-ICSO, pp. 285–290 (2009)Google Scholar
  169. [TH04]
    M. Treiber, D. Helbing, Visualisierung der fahrzeugbezogenen und verkehrlichen Dynamik mit und ohne Beeinflussungs-Systemen, in SimVis, pp. 323–334 (2004)Google Scholar
  170. [TH11]
    S. Tomforde, J. Hähner, Organic network control: turning standard protocols into evolving systems, in Biologically Inspired Networking and Sensing: Algorithms and Architectures (IGI, 2011), pp. 11–35Google Scholar
  171. [THH11]
    S. Tomforde, B. Hurling, J. Hähner, Adapting parameters of mobile adhoc network protocols to changing environments, in Informatics in Control, Automation and Robotics – Selected Papers from the International Conference on Informatics in Control, Automation and Robotics 2010, ed. by J. Andrade-Cetto, J.-L. Ferrier, J. Filipe. Lecture Notes in Electrical Engineering Series, vol. 81 (Springer, Berlin, 2011), pp. 91–104Google Scholar
  172. [Thi+13]
    S. Thiébaux, C. Coffrin, H. Hijazi, J. Slaney, Planning with MIP for supply restoration in power distribution systems, in Proceedings of the Twenty-Third International Joint Conference on ArtificialIntelligence (AAAI Press, Beijing, 2013), pp. 2900–2907. ISBN: 978-1-57735-633-2Google Scholar
  173. [Thu12]
    P. Thubert, Objective Function Zero for the Routing Protocol for Low-Power and Lossy Networks (RPL). RFC 6552 (Proposed Standard). Internet Engineering Task Force, Mar. 2012. http://www.ietf.org/rfc/rfc6552.txt
  174. [Tia+08]
    Y.-L. Tian, L. Brown, A. Hampapur, M. Lu, A. Senior, C.-f. Shu, IBM smart surveillance system (S3): event based video surveillance system with an open and extensible framework. Mach. Vis. Appl. 19(5–6), 315–327 (2008). ISSN: 0932-8092. http://doi.org/10.1007/s00138-008-0153-z CrossRefGoogle Scholar
  175. [TOH14]
    S. Tomforde, A. Ostrovsky, J. Hähner, Load-aware reconfiguration of LTE-antennas dynamic cell-phone network adaptation using organic network control, in 2014 11th International Conference on Informatics in Control, Automation and Robotics (ICINCO), vol. 1 (IEEE, Piscataway, NJ, 2014), pp. 236–243Google Scholar
  176. [Tom+08]
    S. Tomforde, H. Prothmann, F. Rochner, J. Branke, J. Hähner, C. Müller-Schloer, H. Schmeck, Decentralised progressive signal systems for organic traffic control, in 2008 Second IEEE International Conference on Self-adaptive and Self-organizing Systems (IEEE, Piscataway, NJ, 2008), pp. 413–422CrossRefGoogle Scholar
  177. [Tom+09]
    S. Tomforde, M. Hoffmann, Y. Bernard, L. Klejnowski, J. Hähner, Beiträge der 39. Jahrestagung der Gesellschaft für Informatik e.V. (GI), held in Lübeck, Germany (28.9.-2.10.2009), ed. by S. Fischer, E. Maehle, R. Reischuk (Gesellschaft für Informatik e.V. (GI), 2009), pp. 3177–3192Google Scholar
  178. [Tom+10a]
    S. Tomforde, H. Prothmann, J. Branke, J. Hähner, C. Müller-Schloer, H. Schmeck, Possibilities and limitations of decentralised traffic control systems, in The 2010 International Joint Conference on Neural Networks (IEEE, Piscataway, NJ, 2010), pp. 1–9Google Scholar
  179. [Tom+10b]
    S. Tomforde, I. Zgeras, J. Hähner, C. Müller-Schloer, Adaptive control of sensor networks, in International Conference on Auto- nomic and Trusted Computing (Springer, Berlin, 2010), pp. 77–91Google Scholar
  180. [Tom+11a]
    S. Tomforde, H. Prothmann, J. Branke, J. Hähner, M. Mnif, C. Müller-Schloer, U. Richter, H. Schmeck, Observation and control of or ganic systems, in Organic Computing – A Paradigm Shift for Complex Systems, ed. by C. Müller-Schloer, H. Schmeck, T. Ungerer (Birkhäuser, Basel, 2011), pp. 325–338CrossRefGoogle Scholar
  181. [Tom+11b]
    S. Tomforde, H. Prothmann, J. Branke, J. Hähner, M. Mnif, C. Müller-Schloer, U. Richter, H. Schmeck, Observation and control of or ganic systems, in Organic Computing – A Paradigm Shift for Complex Systems (Springer, Basel, 2011), pp. 325–338CrossRefGoogle Scholar
  182. [Tom+15]
    S. Tomforde, J. Kantert, S. von Mammen, J. Hähner, Cooperative self-optimisation of network protocol parameters at runtime, in Proceedings of the 12th International Conference on Informatics inControl, Automation, and Robotics (ICINCO’15), held 21–23 July 2015 in Colmar, pp. 123–130 (2015)Google Scholar
  183. [Tom12]
    S. Tomforde, Runtime Adaption of Technical Systems, ed. by S. Tomforde (Südwestdeutscher Verlag für Hochschulschriften, Stuttgart, 2012)Google Scholar
  184. [Tri16]
    M. Tribastone, Challenges in quantitative abstractions for collective adaptive systems. arXiv preprint arXiv:1607.02966 (2016)Google Scholar
  185. [Trö10]
    M. Tröschel, Aktive Einsatzplanung in holonischen virtuellen Kraftwerken. OlWIR, Oldenburger Verlag für Wirtschaft, Informatik und Recht, 2010Google Scholar
  186. [UCT09]
    Union for the Coordination of Transmission of Electricity, UCTE Operation Handbook – Policy 1: Load-Frequency Control and Performance, Tech. Rep. UCTE OH P1. Union for the Coordination of Transmission of Electricity, 2009Google Scholar
  187. [Val98]
    V. Valev, Set partition principles revisited. Advances in Pattern Recognition. LNCS, vol. 1451 (Springer, Berlin, 1998), pp. 875–881. ISBN: 978-3-540-64858-1Google Scholar
  188. [VBM96]
    R. Van Renesse, K.P. Birman, S. Maffeis, Horus: a flexible group communication system. Commun. ACM 39(4), 76–83 (1996)CrossRefGoogle Scholar
  189. [Vel+06]
    S. Velipasalar, J. Schlessman, C. Chen, W.H. Wolf, J. Singh, SCCS: a scalable clustered camera system for multiple object tracking communicating via message passing interface, in Proceedings of the 2006 IEEE International Conference on Multimedia and Expo, ICME 2006, 9–12 July 2006, Toronto, Ontario, pp. 277–280 (2006). http://doi.org/10.1109/ICME.2006.262452
  190. [VHD15]
    G. Valentini, H. Hamann, M. Dorigo, Efficient decision-making in a self-organizing robot swarm: on the speed versus accuracy trade-off, in Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems. International Foundation for Autonomous Agents and Multiagent Systems (ACM, New York, 2015), pp. 1305–1314Google Scholar
  191. [VL03]
    T. Van Dam, K. Langendoen, An adaptive energy-efficient MAC protocol for wireless sensor networks, in Proceedings of the 1st International Conference on Embedded Networked Sensor Systems (ACM, New York, 2003), pp. 171–180Google Scholar
  192. [VO09]
    M. Vasirani, S. Ossowski, Exploring the potential of multiagent learning for autonomous intersection control, in Multi-Agent Systems for Traffic and Transportation, pp. 280–290 (2009)Google Scholar
  193. [VR06]
    S. Velastin, P. Remagnino, Intelligent Distributed Video Surveillance Systems (Professional Applications of Computing) (Institution of Engineering and Technology, London, 2006). ISBN: 0863415040CrossRefGoogle Scholar
  194. [Web58]
    F. Webster, Traffic signal settings, Road research technical paper H.M. Stationery Office, 1958. https://booksgoogle.co.uk/books?id=c9QOQ4jXK5cC Google Scholar
  195. [Wed12]
    H.F. Wedde. DEZENT – a cyber-physical approach for providing affordable regenerative electric energy in the near future, in 38thEUROMICRO Conference on Software Engineering and Advanced Applications, pp. 241–249 (2012)Google Scholar
  196. [WGH11]
    M. Wittke, C. Grenz, J. Hähner, Towards organic active vision systems for visual surveillance, in Proceedings Architecture of Computing Systems ARCS 2011 24th International Conference Como, 24–25 Feb 2011, pp. 195–206 (2011). http://doi.org/10.1007/978-3-642-19137-4_17
  197. [Wil95]
    S.W. Wilson, Classifier fitness based on accuracy. Evol. Comput. 3(2), 149–175 (1995)CrossRefGoogle Scholar
  198. [Wit+11]
    M. Wittke, A. del Amo Jimenez, S. Radike, C. Grenz, J. Hähner, ENRA: event-based network reconfiguration algorithm for Active Camera Networks, in 2011 Fifth ACM/IEEE International Conference on Distributed Smart Cameras, Ghent, 22–25 Aug 2011, pp. 1–6 (2011). http://doi.org/10.1109/ICDSC.2011.6042936
  199. [WPN14]
    J. Werfel, K. Petersen, R. Nagpal, Designing collective behavior in a termite-inspired robot construction team. Science 343(6172), 754–758 (2014)CrossRefGoogle Scholar
  200. [WS04]
    S. Whiteson, P. Stone, Towards autonomic computing: adaptive network routing and scheduling, in Proceedings International Conference on Autonomic Computing 2004 (IEEE, Piscataway, NJ, 2004), pp. 286–287CrossRefGoogle Scholar
  201. [WV04]
    Y. Wang, J. Vassileva, Trust-based community formation in peer-to-peer file sharing networks, in Proceedings on Web Intelligence (IEEE, Beijing, 2004), pp. 341–348Google Scholar
  202. [Ye+01]
    T. Ye, D. Harrison, B. Mo, B. Sikdar, H.T. Kaur, S. Kalyanaraman, B. Szymanski, K. Vastola, Traffic management and network control using collaborative on-line simulation, in IEEE International Conference on Communications, 2001, vol. 1 (IEEE, Piscataway, NJ, 2001), pp. 204–209Google Scholar
  203. [YK01]
    T. Ye, S. Kalyanaraman, An adaptive random search alogrithm for optimizing network protocol parameters, Tech. Rep. No. 1 (Reens-selaer Polytechnic Institute, US), Citeseer, 2001Google Scholar
  204. [ZBH91]
    H. Zackor, F. Busch, W. Höpfl, Entwicklung eines Verfahrens zur adaptiven koordinierten Steuerung von Lichtsignalanlagen. Forschung Straßenbau und Straßenverkehrstechnik, vol. 607 (Bundesanst für Strassenwesen, Bergisch Gladbach, 1991)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Christian Müller-Schloer
    • 1
  • Sven Tomforde
    • 2
  1. 1.Institute of Systems EngineeringLeibniz Universität HannoverHannoverGermany
  2. 2.Intelligent Embedded Systems GroupUniversität KasselKasselGermany

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