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On the designing principles and optimization approaches of bio-inspired self-organized network: a survey

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Abstract

A plethora of studies on self-organization has been carried out in broad areas including chemistry, biology, astronomy, medical science, telecommunications, etc., in both academia and industry. Following the studies on swarm intelligence observed in social species, the artificial self-organized systems are expected to exhibit some intelligent features (e.g., flexibility, robustness, decentralized control, self-evolution, etc.) that may have made social species so successful in the biosphere. In this paper, the application of swarm intelligence in communications networks will be studied, and we survey different aspects of bio-inspired mechanisms and examine various algorithms that have been proposed to improve the performance of artificial systems. Some fundamental self-organized networking (SON) mechanisms, designing principles and optimization approaches for artificial systems will then be investigated, followed by some well-known bio-inspired algorithms (e.g., cooperation, division of labor, distributed network synchronization, load balancing, etc.) as well as their applications to the maintenance/operation/optimization of artificial systems being analyzed. Besides, some new emerging technologies, such as the Self-X capabilities and cognitive machine-to-machine (M2M) optimization for the 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE)/LTE-Advanced systems, are also surveyed. Finally, the remaining challenges to be faced in designing the future heterogeneous systems will be discussed.

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References

  1. Khandekar A, Bhushan N, Ji T, et al. LTE-advanced: heterogeneous networks. In: European Wireless Conference, Lucca, 2010. 978–982

    Google Scholar 

  2. Hämäläinen S, Sanneck H, Sartori C. LTE Self-Organizing Networks (SON): Network Management Automation for Operational Efficiency. Wiley-Blackwell, 20

  3. Dressler F. Self-organization in Sensor and Actor Networks. Wiley, 2007

    Book  Google Scholar 

  4. Prehofer C, Bettstetter C. Self-organization in communication networks: principles and design paradigms. IEEE Commun Mag, 2005, 43: 78–85

    Article  Google Scholar 

  5. Dressler F, Akan O. Bio-inspired networking: from theory to practice. IEEE Commun Mag, 2010, 48: 176–183

    Article  Google Scholar 

  6. EUSTREP SOCRATES Project. Self-optimisation and self-conguration in wireless networks. Deliverable D5.9: final report on self-organisation and its implications in wireless access networks. INFSO-ICT-216284, 2010. 1–135

    Google Scholar 

  7. Saunders S R, Carlaw S, Giustina A, et al. Femtocells: Opportunities and Challenges for Business and Technology. John Wiley & Sons Ltd., 2009

    Google Scholar 

  8. Dixit S, Yanmaz E, Tonguz O K. On the design of self-organized cellular wireless networks. IEEE Commun Mag, 2005, 43: 86–93

    Article  Google Scholar 

  9. Peng M, Liu Y, Wei D, et al. Hierarchical cooperative relay based heterogeneous networks. IEEE Commun Mag, 2011, 18: 48–56

    Google Scholar 

  10. Bonabeau E, Dorigo M, Theraulaz G. Swarm Intelligence: from Natural to Artificial Systems. Oxford University Press, 1999

    MATH  Google Scholar 

  11. Murthy C S R, Manoj B S. Ad Hoc Wireless Networks. New Jersey: Prentice Hall, 2004

    Google Scholar 

  12. Akyildiz I F, Su W, Sankarasubramaniam Y, et al. Wireless sensor networks: a survey. Comput Netw, 2002, 38: 393–422

    Article  Google Scholar 

  13. Arshinov V, Fuchs C. Causality, Emergence, Self-Organisation. NIA-Priroda, 2003

    Google Scholar 

  14. Edmonds B. What is complexity?—The philosophy of complexity per se with application to some examples in evolution. In: Heylighen F, Aerts D, eds. The Evolution of Complexity. Dordrecht: Kluwer, 1999

    Google Scholar 

  15. Kauffman S. The Origins of Order. Oxford: Oxford University Press, 1993

    Google Scholar 

  16. Heylighen F. The growth of structural and functional complexity during evolution. In: Heylighen F, Aerts D, eds. The Evolution of Complexity. Dordrecht: Kluwer, 1999

    Google Scholar 

  17. Collier J. Fundamental properties of self-organization. In: Arshinov V, Fuchs C, eds. Causality, Emergence, Self-Organisation. NIA-Priroda, 2003

    Google Scholar 

  18. Dressler F, Dietrich I, German R, et al. A rule-based system for programming self-organized sensor and actor networks. Comput Netw, 2009, 53: 1737–1750

    Article  MATH  Google Scholar 

  19. Dorigo M, Birattari M, Stutzle T. Ant colony optimization-artificial ants as a computational intelligence technique. IEEE Comput Intell Mag, 2006, 1: 28–39

    Google Scholar 

  20. Dorigo M, Blum C. Ant colony optimization theory: a survey. Theor Comput Sci, 2005, 344: 243–278

    Article  MathSciNet  MATH  Google Scholar 

  21. Caro G D, Dorigo M. AntNet: distributed stigmergetic control for communications networks. J Artif Intell Res, 1998, 9: 317–365

    MATH  Google Scholar 

  22. Goyal M, Xie W, Hosseini H, et al. AntSens: an ant routing protocol for large scale wireless sensor networks. In: International Conference on Broadband, Wireless Computing, Communication and Applications (BWCCA), Fukuoka, 2010. 41–48

    Chapter  Google Scholar 

  23. Kuang Z. An multicast routing based on ant colony optimization algorithm for DTN. In: 4th International Conference on Genetic and Evolutionary Computing (ICGEC), Shenzhen, 2010. 354–357

    Google Scholar 

  24. Feng S, Seidel E. Self-organizing networks (SON) in 3GPP long term evolution. Nomor Res, 2008. 1–15

    Google Scholar 

  25. Ramiro J, Hamied K. Self-Organizing Networks (SON): Self-Planning, Self-Optimization and Self-Healing for GSM, UMTS and LTE. Wiley-Blackwell, 2011

    Google Scholar 

  26. Boccuzzi J, Ruggiero M. Femtocells: Design & Application. McGraw-Hill, 2011

    Google Scholar 

  27. Andrews J G, Claussen H, Dohler M, et al. Femtocells: past, present, and future. IEEE J Sel Areas Commun, 2012, 30: 497–508

    Article  Google Scholar 

  28. Dottling M, Osseiran A, Mohr W. Radio Technologies and Concepts for IMT-Advanced. Chichester: Wiley & Sons, 2009

    Book  Google Scholar 

  29. Tanenbaum A S, Steen M V. Distributed Systems: Principles and Paradigms. 2nd ed. Prentice Hall, 2006

    Google Scholar 

  30. Skorin-Kapov N, Tonguz O, Puech N. Self-organization in transparent optical networks: a new approach to security. In: 9th International Conference on Telecommunications, Zagreb, 2007. 7–14

    Google Scholar 

  31. Yeom J S, Tonguz O, Castanon G. Security in all-optical networks: self-organization and attack avoidance. In: Proceedings of IEEE International Conference on Communications (ICC), Marrakech, 2007. 1329–1335

    Google Scholar 

  32. Yang K, Ou S, Guild K, et al. Convergence of ethernet PON and IEEE 802.16 broadband access networks and its QoS-aware dynamic bandwidth allocation scheme. IEEE J Sel Areas Commun, 2009, 27: 101–116

    Article  Google Scholar 

  33. Shen G, Tucker R S, Chae C J. Fixed mobile convergence architectures for broadband access: integration of EPON and WiMAX. IEEE Commun Mag, 2007, 45: 44–50

    Article  Google Scholar 

  34. Cheng Y, Jiang H, Zhuang W, et al. Efficient resource allocation for Chinas 3G/4G wireless networks. IEEE Commun Mag, 2005, 43: 76–83

    Google Scholar 

  35. Ross R M. The evolution of sex-change mechanisms in fishes. Environ Biol Fish, 1990, 29: 81–93

    Article  Google Scholar 

  36. Tonguz O. Biologically inspired solutions to fundamental transportation problems. IEEE Commun Mag, 2011, 49: 106–115

    Article  Google Scholar 

  37. Ballerini M, Cabibbo N, Candelier R, et al. Interaction ruling animal collective behavior depends on topological rather than metric distance: evidence from a field study. P Natl Acad Sci USA, 2008, 105: 1232–1237

    Article  Google Scholar 

  38. Grosan C, Abraham A. Stigmergic Optimization: Inspiration, Technologies and Perspectives. Studies in Computational Intelligence. Berlin: Springer-Verlag, 2006

    Google Scholar 

  39. Thien H, Moelyadi M, Muhammad H. Effects of leaders position and shape on aerodynamic performances of V flight formation. In: Proceedings of the International Conference on Intelligent Unmanned System (ICIUS), Bali, 2007. 43–49

    Google Scholar 

  40. Mills D L. Internet time synchronization: the network time protocol. IEEE Trans Commun, 1991, 39: 1482–1493

    Article  Google Scholar 

  41. Zhang Z, Jiang W, Zhou H, et al. High accuracy frequency offset correction with adjustable acquisition range in OFDM systems. IEEE Trans Wirel Commun, 2005, 4: 228–237

    Article  Google Scholar 

  42. Zhang Z, Liu J, Long K. Low-complexity cell search with fast PSS identification in LTE. IEEE Trans Veh Technol, 2012, 61: 1719–1729

    Article  Google Scholar 

  43. Zhang Z, Zhao M, Long K, et al. Frequency offset estimation with fast acquisition in OFDM system. IEEE Commun Lett, 2004, 8: 171–173

    Article  Google Scholar 

  44. Gao F, Nallanathan A. Blind maximum likelihood CFO estimation for OFDM systems via polynomial rooting. IEEE Signal Process Lett, 2006, 13: 73–76

    Article  Google Scholar 

  45. Zhang Z, Long K, Zhao M, et al. Joint frame synchronization and frequency offset estimation in OFDM systems. IEEE Trans Broadcasting, 2005, 51: 389–394

    Article  Google Scholar 

  46. Buck J, Buck E. Synchronous fireflies. Sci Amer, 1976, 234: 74–85

    Article  Google Scholar 

  47. Pikovsky A, Rosenblum M, Kurths J. Synchronization-A Universal Concept in Nonlinear Sciences. Cambridge: Cambridge University Press, 2001

    Book  MATH  Google Scholar 

  48. Yates C, Erban R, Escudero C, et al. Inherent noise can facilitate coherence in collective swarm motion. P Natl Acad Sci USA, 2009, 106: 5464–5469

    Article  Google Scholar 

  49. Hong Y W, Cheow L, Scaglione A. A simple method to reach detection consensus in massively distributed sensor networks. In: Proceedings of the International Symposium on Information Theory (ISIT’04), Chicago, 2004. 251

    Google Scholar 

  50. Barbarossa S, Scutari G. Decentralized maximum likelihood estimation for sensor networks composed of nonlinearly coupled dynamical systems. IEEE Trans Signal Process, 2007, 55: 3456–3470

    Article  MathSciNet  Google Scholar 

  51. Peskin C S. Mathematical Aspects of Heart Physiology. New York University, 1975. 268–278

    MATH  Google Scholar 

  52. Mirollo R E, Strogatz S H. Synchronization of pulse-coupled biological oscillators. SIAM J Appl Math, 1990, 50: 1645–1662

    Article  MathSciNet  MATH  Google Scholar 

  53. Gerstner W. Rapid phase locking in systems of pulse-coupled oscillators with delays. Phys Rev Lett, 1996, 76: 1755–1758

    Article  Google Scholar 

  54. Abbott L F. A network of oscillators. J Phys-A-Math Gen, 1990, 23: 3835–3859

    Article  MATH  Google Scholar 

  55. Hong Y W, Scaglione A. A scalable synchronization protocol for large scale sensor networks and its applications. IEEE J Sel Areas Commun, 2005, 23: 1085–1099

    Article  Google Scholar 

  56. Tyrrell A, Auer G, Bettstetter C. Emergent slot synchronization in wireless networks. IEEE Trans Mob Comput, 2010, 9: 719–732

    Article  Google Scholar 

  57. Santini C, Tyrrell A. Investigating the properties of self-organization and synchronization in electronic system. IEEE Trans Nanobiosci, 2009, 8: 237–251

    Article  Google Scholar 

  58. Neuman B C. Scale in Distributed Systems. Readings in Distributed Computing Systems. IEEE Computer Society Press, 1994

    Google Scholar 

  59. Duarte-Melo E, Liu M. Data-gathering wireless sensor networks: organization and capacity. Comput Netw, 2003, 43: 519–537

    Article  MATH  Google Scholar 

  60. Bertsekas D, Tsitsiklis J. Parallel and Distributed Computation: Numerical Methods. 2nd ed. Nashua: Athena Scientific, 1989

    MATH  Google Scholar 

  61. Giridhar A, Kumar P. Toward a theory of in-network computation in wireless sensor networks. IEEE Commun Mag, 2006, 44: 98–107

    Article  Google Scholar 

  62. Gupta P, Kumar P R. The capacity of wireless networks. IEEE Trans Inform Theory, 2000, 46: 388–404

    Article  MathSciNet  MATH  Google Scholar 

  63. Dressler F, Akan O B. A survey on bio-inspired networking. Comput Netw, 2010, 54: 881–900

    Article  MATH  Google Scholar 

  64. Wang X, Chen G. Complex networks: small worlds, scale-free and beyond. IEEE Circuits Syst Mag, 2003, 3: 6–20

    Article  Google Scholar 

  65. Zhuang W, Ismail M. Cooperation in wireless communication networks. IEEE Wirel Commun, 2012, 19: 10–20

    Article  Google Scholar 

  66. Iwata A, Chiang C C, Pei G, et al. Scalable routing strategies for ad hoc wireless networks. IEEE J Sel Areas Commun, 1999, 17: 1369–1379

    Article  Google Scholar 

  67. Saleem K, Fisal N, Abdullah M S, et al. Proposed nature inspired self-organized secure autonomous mechanism for WSNs. In: Asian Conference on Intelligent Information and Database Systems, Quang Binh Province, Vietnam, 2009. 277–282

    Google Scholar 

  68. Yang H, Shu J, Meng X, et al. SCAN: self-organized network-layer security in mobile ad hoc networks. IEEE J Sel Areas Commun, 2006, 24: 261–273

    Article  Google Scholar 

  69. Boudriga N. Security of Mobile Communications. CRC Press, Taylor & Francis Group, 2010

    Google Scholar 

  70. Balasubramaniam S, Botvich D, Donnelly W, et al. Biologically inspired self-governance and self-organisation for autonomic networks. In: Proceedings of the 1st International Conference on Bio Inspired Models of Network, Information and Computing Systems. Cavalese: ACM, 2006. 30

    Google Scholar 

  71. Boonma P, Suzuki J. MONSOON: a coevolutionary multiobjective adaptation framework for dynamic wireless sensor networks. In: Proceedings of the 41st Hawaii International Conference on System Sciences (HICSS), Big Island, 2008

    Google Scholar 

  72. Mazhar N, Farooq M. BeeAIS: artificial immune system security for nature inspired, MANET routing protocol, BeeAdHoc. In: Proceedings of the 6th International Conference, Santos, 2007. 370–381

    Google Scholar 

  73. Pathan A S K. Security of Self-Organizing Networks: MANET, WSN, WMN, VANET. CRC Press, Taylor & Francis Group, 2011

    Google Scholar 

  74. De Lemos R, Timmis J, Ayara M, et al. Immune-inspired adaptable error detection for automated teller machines. IEEE Trans Syst Man Cybern Part C-Appl Rev, 2007, 37: 873–886

    Article  Google Scholar 

  75. Lee C, Suzuki J. SWAT: a decentralized self-healing mechanism for wormhole attacks in wireless sensor networks. In: Xiao Y, Chen H, Li F, eds. Handbook on Sensor Networks. World Scientific, 2010

    Google Scholar 

  76. Castanon G, Razo-Zapata I, Mex C, et al. Security in all-optical networks: failure and attack avoidance using self-organization. In: 2nd ICTON Mediterranean Winter (ICTON-MW’08), Marrakech, 2008. 1–5

    Chapter  Google Scholar 

  77. Lauber P. Bats: Wings in the Night. New York: Random House, 1968

    Google Scholar 

  78. Glass A M, Brewster R L, Abdulaziz N K. Modelling of CSMA/CA protocol by simulation. Electr Lett, 1988, 24: 692–694

    Article  Google Scholar 

  79. Barcelo J, Inaltekin H, Bellalta B. Obey or play: asymptotic equivalence of slotted aloha with a game theoretic contention model. IEEE Commun Lett, 2011, 15: 623–625

    Article  Google Scholar 

  80. Guo L, Cao J, Yu H, et al. Path-based routing provisioning with mixed shared protection in WDM mesh networks. J Lightwave Technol, 2006, 24: 1129–1141

    Article  Google Scholar 

  81. Li Y, Wang J, Qiao C, et al. Integrated fiber-wireless (FiWi) access networks supporting inter-ONU communications. J Lightwave Technol, 2010, 28: 714–724

    Article  Google Scholar 

  82. Korowajczuk L. LTE, WIMAX and WLAN Network Design, Optimization and Performance Analysis. John Wiley & Sons, 2011

    Google Scholar 

  83. Morley R, Ekberg G. Cases in chaos: complexity-based approaches to manufacturing. In: Park J, Toomey E, Wolf J, eds. Embracing Complexity: A Colloquium on the Application of Complex Adaptive Systems to Business. Cambridge: The Ernst & Young Center for Business Innovation, 1998. 97–702

    Google Scholar 

  84. Hubaux J P, Gross T, Le Boudec J Y, et al. Toward self-organized mobile ad hoc networks: the terminodes project. IEEE Commun Mag, 2001, 39: 118–124

    Article  Google Scholar 

  85. Perkins C E, Bhagwat P. Highly dynamic destination-sequenced distance-vector routing (DSDV) for mobile computers. Comput Commun Rev, 1994, 24: 234–244

    Article  Google Scholar 

  86. Ko Y, Vaidya N H. Location-aided routing (LAR) mobile ad hoc networks. In: MOBICOM 98, Dallas, 1998

    Google Scholar 

  87. Clausen T, Jacquet P. Optimized link state routing protocol (OLSR). IETF RFC 3626, 2003

    Google Scholar 

  88. Perkins C, Royer E. Ad hoc on-demand distance vector routing. In: 2nd IEEE Workshop on Mobile Computing Systems and Applications, New Orleans, 1999. 90–100

    Google Scholar 

  89. Deneubourg J L, Pasteels J M, Verhaeghe J C. Probabilistic behaviour in ants: a strategy of errors? J Theor Biol, 1983, 105: 259–271

    Article  Google Scholar 

  90. Caro G D, Ducatelle F, Gambardella L. Anthocnet: an adaptive nature-inspired algorithm for routing in mobile ad hoc networks. Eur Trans Telecommun, 2005, 16: 443–455

    Article  Google Scholar 

  91. Zhu Y, Zhang J Y, Li L, et al. Multiple ant colony routing optimization based on cloud model for wsn with longchain structure. In: 6th International Conference on Wireless Communications Networking and Mobile Computing (WiCOM), Chengdu, 2010. 1–4

    Google Scholar 

  92. Sim K M, Sun W H. Multiple ant-colony optimization for network routing. In: Proceedings of 1st International Symposium on Cyberworld, Tokyo, 2002. 277–281

    Google Scholar 

  93. Zhang Z, Long K, Wang J. Self-organization paradigms and optimization approaches for cognitive radio technologies: a survey. IEEE Wirel Commun, 2013, 20: 36–42

    Article  Google Scholar 

  94. Hoque M, Hong X. BioStaR: a bio-inspired stable routing for cognitive radio networks. In: International Conference on Computing, Networking and Communications (ICNC), Maui, HI, 2012. 402–406

    Chapter  Google Scholar 

  95. Huang X L, Wang G, Hu F, et al. Stability-capacity-adaptive routing for high-mobility multihop cognitive radio networks. IEEE Trans Veh Technol, 2011, 60: 2714–2729

    Article  Google Scholar 

  96. Liu Y, Grace D. Cognitive routing metrics with adaptive weight for heterogeneous ad hoc networks. In: IET Seminar on Cognitive Radio and Software Defined Radios: Technologies and Techniques, London, 2008. 1–5

    Google Scholar 

  97. Zhang G, Ding C, Gu J, et al. An adaptive multi-path routing algorithm in cognitive wireless mesh networks. In: 7th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM), Wuhan, 2011. 1–4

    Google Scholar 

  98. Niyato D, Xiao L, Wang P. Machine-to-machine communications for home energy management system in smart grid. IEEE Commun Mag, 2011, 49: 53–59

    Article  Google Scholar 

  99. Zhang Y, Yu R, Nekovee M, et al. Cognitive machine-to-machine communications: visions and potentials for the smart grid. IEEE Network, 2012, 26: 6–13

    Article  Google Scholar 

  100. Hu R, Qian Y, Chen H H, et al. Recent progress in machine-to-machine communications. IEEE Commun Mag, 2011, 49: 24–26

    Article  Google Scholar 

  101. Tang Z, Liu B, Zhao B, et al. Building practical self organization networks on heterogeneous wireless modems. In: 5th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS), Seoul, 2011. 636–643

    Google Scholar 

  102. Ma Z, Krings A. Insect population inspired wireless sensor networks: a unified architecture with survival analysis, evolutionary game theory, and hybrid fault models. In: International Conference on BioMedical Engineering and Informatics, Sanya, 2008. 636–643

    Google Scholar 

  103. Tsang P H, Lin F S, Chen C W. Maximization of network survival time in the event of intelligent and malicious attacks. In: Proceedings of IEEE International Conference on Communications (ICC), Beijing, 2008. 1722–1726

    Google Scholar 

  104. Castillo J. The survival of communications in ad hoc and M2M networks: the fundamentals design of architecture and radio technologies used for low-power communication NOMOHI devices. In: International Symposium in Information Technology (ITSim), Kuala Lumpur, 2010. 666–671

    Google Scholar 

  105. Han Z, Wang Z, Liu K. A resource allocation framework with credit system and user autonomy over heterogeneous wireless network. In: IEEE Global Telecommn Conference (GLOBECOM), San Francisco, 2003. 977–981

    Chapter  Google Scholar 

  106. Robson S K, Traniello J F A. Resource assesment recruitment behavior, and organization of cooperative prey retrieval in the ant formica schaufussi (hymenoptera: formicidae). J Insect Behav, 1998, 11: 1–22

    Article  Google Scholar 

  107. Kube C R. Collective robotics: from local perception to global action. Dissertation for the Doctoral Degree. University of Alberta, 1997

    Google Scholar 

  108. Dorigo M, Bonabeau E, Theraulaz G. Ant algorithms and stigmergy. Future Gener Comput Syst, 2000, 16: 851–871

    Article  Google Scholar 

  109. Blum C, Sampels M. An ant colony optimization algorithm for shop scheduling problems. J Math Model Alg, 2004, 3: 285–308

    Article  MathSciNet  MATH  Google Scholar 

  110. Bullnheimer B. Ant colony optimization in vehicle routing. Dissertation for the Doctoral Degree. University of Vienna, 1999

    Google Scholar 

  111. Mondada F, Franzi E, Lenne P. Mobile robot miniaturization: a tool for investigation in control algorithms. In: Proceedings of 3rd International Symposium on Experimental Robotics (ISER’93), Kyoto, 1993. 501–513

    Google Scholar 

  112. Jing Y, Hassibi B. Distributed space-time coding in wireless relay networks. IEEE Trans Wirel Commun, 2006, 5: 3524–3536

    Article  Google Scholar 

  113. Du J, Xiao M, Skoglund M. Cooperative network coding strategies for wireless relay networks with backhaul. IEEE Trans Commun, 2011, 59: 2502–2514

    Article  Google Scholar 

  114. Zhang Z, Tellambura C, Schober R. Improved OFDMA uplink transmission via cooperative relaying in the presence of frequency offsets-part I: ergodic information rate analysis. Eur Trans Telecommun, 2010, 21: 224–240

    Article  Google Scholar 

  115. Zhang Z, Tellambura C, Schober R. Improved OFDMA uplink transmission via cooperative relaying in the presence of frequency offsets-part II: outage information rate analysis. Eur Trans Telecommun, 2010, 21: 241–250

    Article  Google Scholar 

  116. Jovicic A, Viswanath P. Cognitive radio: an information-theoretic perspective. IEEE Trans Inform Theory, 2009, 55: 3945–3958

    Article  MathSciNet  Google Scholar 

  117. Ahmed M, Vorobyov S. Collaborative beamforming for wireless sensor networks with gaussian distributed sensor nodes. IEEE Trans Wirel Commun, 2009, 8: 638–643

    Article  Google Scholar 

  118. Sawahashi M, Kishiyama Y, Morimoto A, et al. Coordinated multipoint transmission/reception techniques for LTEadvanced. IEEE Wirel Commun, 2010, 17: 26–34

    Article  Google Scholar 

  119. Hao X, Cheung M H, Wong V, et al. A coalition formation game for energy-efficient cooperative spectrum sensing in cognitive radio networks with multiple channels. In: IEEE Global Telecommn Conference (GLOBECOM), Kathmandu, 2011. 1–6

    Google Scholar 

  120. Pantisano F, Bennis M, Saad W, et al. Cooperative interference alignment in femtocell networks. In: IEEE Global Telecommn Conference (GLOBECOM), Kathmandu, 2011. 1–6

    Google Scholar 

  121. Da B, Zhang R. Cooperative interference control for spectrum sharing in OFDMA cellular systems. In: Proceedings of IEEE International Conference on Communications (ICC), Kyoto, 2011. 1–5

    Google Scholar 

  122. Maruta K, Ohta A, Iizuka M, et al. Iterative inter-cluster interference cancellation for cooperative base station systems. In: Vehicular Technology Conference (VTC Spring), Yokohama, 2012. 1–5

    Google Scholar 

  123. Camazine S, Deneubourg J, Franks N, et al. Self-Organization in Biological Systems. Princeton University Press, 2003

    MATH  Google Scholar 

  124. Tovey C A. Honey bee algorithm: a biologically inspired approach to internet server optimization. Eng Enterp, Spring 2004. 13–15

    Google Scholar 

  125. Seeley T D, Towne W F. Tactics of dance choice in honey bees: do foragers compare dances? Behav Ecol Sociobiol, 1992, 30: 59–69

    Article  Google Scholar 

  126. Farooq M. From the wisdom of the hive to intelligent routing in telecommunication networks: a step towards intelligent network management through natural engineering. Dissertation for Doctoral Degree. University of Dortmund, 2006

    Google Scholar 

  127. Zhang H, Qiu X, Meng L, et al. Achieving distributed load balancing in self-organizing LTE radio access network with autonomic network management. In: IEEE GLOBECOM Workshops (GC Wkshps), Miami, 2010. 454–459

    Google Scholar 

  128. Son H, Lee S, Kim S C, et al. Soft load balancing over heterogeneous wireless networks. IEEE Trans Veh Technol, 2008, 57: 2632–2638

    Article  Google Scholar 

  129. NEC. Self organizing network: NEC’s proposals for next-generation radio network management. NEC White Paper, 2009. 1–5

    Google Scholar 

  130. Motorola. LTE operations and maintenance strategy using self-organizing networks to reduce OPEX. MotorolaWhite Paper, 2009. 1–7

    Google Scholar 

  131. Nokia. Self-organizing network (SON): introducing the nokia siemens networks SON suite-an efficient, future-proof platform for SON, Nokia Siemens Networks, 2009. 1–16

    Google Scholar 

  132. Hu H, Zhang J, Zheng X, et al. Self-configuration and self-optimization for LTE networks. IEEE Commun Mag, 2010, 48: 94–100

    Article  Google Scholar 

  133. 3GPP. Self-Configuring and Self-Optimizing Network Use Cases and Solutions. Technical Report TR 36.902 v.1.2.0, 2009

    Google Scholar 

  134. NGMN. Use cases related to self-organising network, overall description. http://www.ngmn.org, 2007

    Google Scholar 

  135. 4WARD. EU FP7 Project. http://www.4ward-project.eu/

  136. End-to-End Efficiency (E3). EU FP7 project. https://ict-e3.eu/

  137. EU FP7 Project. Fibre-optic networks for distributed extendible heterogeneous radio architectures and service provisioning (FUTON). http://www.ict-futon.eu/default.aspx

  138. 3GPP. Self-configuring and self-optimizing network use cases and solutions. TS 36.902, 2009

    Google Scholar 

  139. 3GPP. Evolved universal terrestrial radio access (E-UTRA) and evolved universal terrestrial radio access network (E-UTRAN), overall description, Stage 2, Release 8. TS 36.300 v.8.8.0, 2009

    Google Scholar 

  140. Eisenblatter A, Turke U, Schmelz C. Self-configuration in LTE radio networks: automatic generation of eNodeB parameters. In: 73rd IEEE Vehicular Technology Conference (VTC Spring), Yokohama, 2011. 1–3

    Google Scholar 

  141. Xu L, Sun C, Li X, et al. The methods to implementate self optimisation in LTE system. In: IEEE ICCTA, Beijing, 2009. 381–385

    Google Scholar 

  142. Amirijoo M, Frenger P, Gunnarsson F, et al. On self-optimization of the random access procedure in 3G long term evolution. In: IFIP/IEEE International Symposium on Integrated Network Management-Workshops (IM’09), New York, 2009. 177–184

    Chapter  Google Scholar 

  143. Ul Islam M N, Mitschele-Thiel A. Reinforcement learning strategies for self-organized coverage and capacity optimization. In: IEEE Wireless Communications and Networking Conference (WCNC), Shanghai, 2012. 2818–2823

    Chapter  Google Scholar 

  144. 3GPP. Technical specification group radio access network. E-UTRAN X2 Application Protocol (X2AP), TS 36.423, v.9.6.0, Release 10. http://www.3gpp.org/ftp/Specs/latest/Rel-10/36series/36423-a20.zip, 2011

    Google Scholar 

  145. De la Roche G, Ladanyi A, Lopez-Perez D, et al. Self-organization for LTE enterprise femtocells. In: IEEE Global Telecommunication Conference (GLOBECOM), Miami, 2010. 674–678

    Google Scholar 

  146. Baker M. From LTE-advanced to the future. IEEE Commun Mag, 2012, 50: 116–120

    Article  Google Scholar 

  147. Akyildiz I, Lee W Y, Vuran M, et al. Next generation/dynamic spectrum access/cognitive radio wireless networks. Comput Netw, 2006, 50: 2127–2159

    Article  MATH  Google Scholar 

  148. Dottling M, Viering I. Challenges in mobile network operation: towards self-optimizing networks. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Taipei, 2009. 3609–3612

    Google Scholar 

  149. Marchetti N, Prasad N, Johansson J, et al. Self-organizing networks: state-of-the-art, challenges and perspectives. In: 8th International Conference on Communications (COMM), Bucharest, 2010. 503–508

    Google Scholar 

  150. 3GPP. Telecommunication management; self-organizing networks (SON); self-healing concepts and requirements (Release 10). TS32.541, 2010

    Google Scholar 

  151. Boonma P, Suzuki J. MONSOON: a coevolutionary multiobjective adaptation framework for dynamic wireless sensor networks. In: Proceedings of the 41st Annual Hawaii International Conference on System Sciences (HICSS), Waikoloa, HI, 2008. 497

    Chapter  Google Scholar 

  152. Laiho J, Raivio K, Lehtimaki P. Coordination of groups of mobile autonomous agents using nearest neighbour rules. IEEE Trans Wirel Commun, 2005, 4: 930–942

    Article  Google Scholar 

  153. Salfner F, Lenk M, Malek M. A survey of online failure prediction methods. ACM Comput Surv, 2010, 42: 3

    Article  Google Scholar 

  154. Barco R, Lazaro P, Diez L, et al. Continuous versus discrete model in autodiagnosis systems for wireless networks. IEEE Trans Mob Comput, 2008, 7: 673–681

    Article  Google Scholar 

  155. Amirijoo M, Jorguseski L, Litjens T, et al. Cell outage compensation in LTE networks: algorithms and performance assessment. In: IEEE International Workshop on Self-Organizing Networks, Budapest, 2011. 1–5

    Google Scholar 

  156. Zheng Y, Xiao C. Improved models for the generation of multiple uncorrelated Rayleigh fading waveforms. IEEE Commun Lett, 2002, 6: 256–258

    Article  Google Scholar 

  157. Laiho J, Wacker A, Novasad T. Radio Network Planning and Optimization for UMTS. 2nd ed. New York: John Wiley & Sons, 2006

    Google Scholar 

  158. Zhu H, Karachontzitis S, Toumpakaris D. Low-complexity resource allocation and its application to distributed antenna systems. IEEE Wirel Commun, 2010, 17: 44–50

    Article  Google Scholar 

  159. Zhu H, Wang J. Chunk-based resource allocation in OFDMA systems-part I: chunk allocation. IEEE Trans Commun, 2009, 57: 2734–2744

    Article  Google Scholar 

  160. Zhu H, Wang J. Chunk-based resource allocation in OFDMA systems-part II: joint chunk, power and bit allocation. IEEE Trans Commun, 2012, 60: 499–509

    Article  Google Scholar 

  161. Wang J, Chen J. Performance of wideband CDMA with complex spreading and imperfect channel estimation. IEEE J Sel Areas Commun, 2001, 19: 152–163

    Article  Google Scholar 

  162. Gotsis A, Lioumpas A, Alexiou A. M2M scheduling over LTE: challenges and new perspectives. IEEE Veh Technol Mag, 2012, 7: 34–39

    Article  Google Scholar 

  163. Lopez-Perez D, Ladanyi A, Juttner A, et al. Optimization method for the joint allocation of modulation schemes, coding rates, resource blocks and power in self-organizing LTE networks. In: Proceedings of IEEE INFOCOM, Shanghai, 2011. 111–115

    Google Scholar 

  164. Haykin S. Cognitive radio: brain-empowered wireless communications. IEEE J Sel Areas Commun, 2005, 23: 201–220

    Article  Google Scholar 

  165. Fadlullah Z M, Fouda M M, Kato N, et al. Toward intelligent machine-to-machine communications in smart grid. IEEE Commun Mag, 2011, 49: 60–65

    Article  Google Scholar 

  166. Lawton G. Machine-to-machine technology gears up for growth. Computer, 2004, 37: 12–15

    Article  Google Scholar 

  167. Chang K, Soong A, Tseng M, et al. Global wireless machine-to-machine standardization. IEEE Int Comput, 2011, 15: 64–69

    Article  Google Scholar 

  168. Lien S Y, Chen K C, Lin Y. Toward ubiquitous massive accesses in 3GPP machine-to-machine communications. IEEE Commun Mag, 2011, 49: 66–74

    Article  Google Scholar 

  169. Zheng K, Hu F, Wang W, et al. Radio resource allocation in LTE-advanced cellular networks with M2M communications. IEEE Commun Mag, 2012, 50: 184–192

    Article  Google Scholar 

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Authors and Affiliations

  1. Institute of Advanced Network Technology and New Services (ANTS), University of Science and Technology Beijing (USTB), Beijing, 100083, China

    ZhongShan Zhang, Wei Huangfu, KePing Long, Xu Zhang, XiaoYuan Liu & Bin Zhong

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  1. ZhongShan Zhang
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  2. Wei Huangfu
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  4. Xu Zhang
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Correspondence to Wei Huangfu.

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Zhang, Z., Huangfu, W., Long, K. et al. On the designing principles and optimization approaches of bio-inspired self-organized network: a survey. Sci. China Inf. Sci. 56, 1–28 (2013). https://doi.org/10.1007/s11432-013-4894-6

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