Complex adaptive communication networks and ...

Simulation

Complex adaptive communication networks and environments: Part 2

Simulation: Transactions of the Society for Modeling and Simulation International 89(7) 787–789 Ó 2013 The Society for Modeling and Simulation International DOI: 10.1177/0037549713497714 sim.sagepub.com

Muaz A Niazi1 and Amir Hussain2

Complexity in Communication Networks Due to recent rapid advancements in social, pervasive and mobile communication network technologies, the topologies as well as interaction of components in modern networks1 often involve complex communication of personal as well as sensory data. An exponential increase in human usage of networks can result in a set of unprecedented as well as unpredictable effects, not just on the network structure but also as a reflection back on the lives of individual human users and the society. As a result, modern communication networks tend to exhibit properties associated with living or lifelike artificial systems, often classified as Complex Adaptive Systems (CAS)2. CAS are systems with numerous nonlinear interacting components often leading to emergent phenomena. CAS are considered as a special class of systems because it is often impossible to model them using traditional analytical techniques due to a lack of linearity as well as a high number of variables (or agents) in the system. This often results in a system with characteristics that are unpredictable if evaluated based solely on an examination of the individual components3. In any domain, the absence of well-established modeling and simulation techniques makes it difficult to quantify or classify problems or present solutions in that domain. Being able to model and simulate the environment4 and not just the network5 gives designers the ability to predict outcomes as well as to perform a systematic simluationbased validation6 of real-world network deployments. Modeling can be particularly useful in the domain of online and offline7 social networks, both of which have shown extensive growth in the recent past as modeled by Zhu et al8. While simulation of computer networks has always played an important role in the design and development of networks as well as protocols and algorithms9, due to the above-mentioned increase in the scale and order of complexity, there is a need for newer and more effective techniques and paradigms for modeling and simulation of large-scale networks. As a follow-up to the first part of the special issue on Complex Adaptive COmmunicatiOn Networks and environmentS (CACOONS)10, this second part presents a selection of four peer-reviewed papers

on the use of two complexity-related multidisciplinary modeling and simulation techniques, namely, agent-based modeling (ABM)11 and complex networks–based modeling (CN)12.

Papers The following selection of accepted papers demonstrates the usefulness of these techniques13, which have previously been used to model life-related CAS, to also be effective for modeling complexities in modern, large-scale communication and social networks14. In the first article, ‘‘A co-simulation method as an enabler for joint analysis and design of MAS-based electrical power protection and communication’’ by Weilin Li, Min Luo, Lin Zhu, Antonello Monti and Ferdinanda Ponci, presents the use of multiagent system protection as a potential method for distributed control in modern electrical power systems. The paper proposes a co-simulation solution for power systems, communication networks and a multiagent system based on the extended capability of VPNET, a co-simulation platform. Simulation results show the utility of VPNET in exploring the design tradeoffs between protection strategies and communication in the design phase. The paper ‘‘A framework of multilayer social networks for communication behavior with agent-based modeling’’ by Yuanzhneg Ge, Liang Liu, Xiaogang Qiu, Hongbin Song, Yong Wang, and Kedi Huang presents agent-based modeling and simulation for developing a simulation framework of multilayer social networks to model highresolution interactions between agents. The agent model contains three components: social networks, demographicbased population and schedule-based behavior. The simulation results show a high correlation between social networks and transmission of influenza, and demonstrate that 1

Bahria University, Islamabad, Pakistan University of Stirling, Scotland, UK

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Corresponding author: Muaz A. Niazi, Bahria University, Islamabad, Pakistan. Email: [email protected]

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Simulation: Transactions of the Society for Modeling and Simulation International 89(7)

individual-based social network models can reproduce and analyze complex interacting behavior. The paper ‘‘Reputation-based cluster head elections in wireless sensor networks’’ by Gicheol Wang and Gihwan Cho proposes a scheme which securely elects cluster heads in a wireless sensor network by detecting and excluding intelligent attackers. The proposed scheme greatly enhances the non-manipulability and agreement property of cluster head election results compared to other schemes, even in the presence of message loss. The final paper, ‘‘An optimal distributed trigger counting algorithm for large-scale networked systems’’ by Seokhyun Kim, Jaeheung Lee, Yongsu Park and Yookun Cho, proposes a solution to the distributed trigger counting (DTC) problem related to the detection of triggers in largescale distributed systems that have general characteristics of CAS. The authors propose a randomized algorithm, termed TreeFill, and also carry out an evaluation by means of an agent-based simulation model using NetLogo. The simulation results show that TreeFill only uses about 54%69% of the messages used in the previous work: CoinRand. The maximum number of received messages in each node of TreeFill is also smaller than in previous studies. Acknowledgments The guest editors would like to acknowledge the efforts of the Editor-in-Chief Levent Yilmaz as well as Vicki Pate for their efforts in making this special issue possible.

References 1. Albert R, Jeong H and Baraba´si A-L. Error and attack tolerance of complex networks. Nature 2000; 406: 378–382. 2. Niazi MA. Complex adaptive systems modeling: a multidisciplinary roadmap. Complex Adaptive Systems Modeling 2013; 1: 1. 3. Holland J. Studying complex adaptive systems. Journal of Systems Science and Complexity 2006; 19: 1–8. 4. Vizzari G, Manenti L and Crociani L. Adaptive pedestrian behaviour for the preservation of group cohesion. Complex Adaptive Systems Modeling 2013; 1: 7. 5. Niazi MA and Hussain A. Sensing emergence in complex systems. IEEE Sensors Journal 2011; 11: 2479–2480. 6. Niazi MAK. Towards a Novel Unified Framework for Developing Formal, Network and Validated Agent-Based Simulation Models of Complex Adaptive Systems [PhD thesis]. Stirling, UK: University of Stirling, Stirling; 2011. 7. Niazi MA and Hussain A. Social network analysis of trends in the consumer electronics domain. Presented at: 2011 IEEE International Conference on Consumer Electronics (ICCE); January 9–12, 2011; Las Vegas, NV. 8. Zhu K, Li W and Fu X. Modeling population growth in online social networks. Complex Adaptive Systems Modeling 2013; 1: 14. 9. Kim T, Hwang MH and Kim D. DEVS/NS-2 environment: an integrated tool for efficient networks modeling and

simulation. The Journal of Defense Modeling and Simulation 2008; 5: 33–60. 10. Niazi MA and Hussain A. Complex adaptive communication networks and environments: part 1. Simulation 2013; 89: 559–561. 11. Railsback SF, Lytinen SL and Jackson SK. Agent-based simulation platforms: review and development recommendations. Simulation 2006; 82: 609–623. 12. Morris JF, O’Neal JW and Deckro RF. A random graph generation algorithm for the analysis of social networks [published online ahead of print June 11, 2013]. The Journal of Defense Modeling and Simulation; doi: 10.1177/ 1548512912450370. 13. Sayama H, Pestov I, Schmidt J, et al. Modeling complex systems with adaptive networks. Computers and Mathematics with Applications 2013; 65: 1645–1664. 14. Zhang X and Riley GF. Scalability of an ad hoc on-demand routing protocol in very large-scale mobile wireless networks. Simulation 2006; 82: 131–142.

Author biographies Muaz A. Niazi is a senior member of the IEEE and is associated with various IEEE societies. He is a member of the IEEE CIS task forces on Intelligent Agent and Organic computing. Dr. Niazi is the founding editor-in-chief of SpringerOpen/Biomed Central’s Complex Adaptive Systems Modeling, an Open Access Journal, as well as IGI Global’s International Journal of Privacy and Health Information Management. He also serves as an Associate Editor for Springer Cognitive Computation and Wiley’s Transactions on Emerging Telecommunication Technologies. With an undergraduate honors degree in Electrical Engineering, Dr. Niazi has an MS and a PhD in computer sciences from Boston University and the University of Stirling, respectively, in addition to a senior postdoctoral research fellowship from the COSIPRA Lab at Stirling. Dr. Niazi’s research is focused on the modeling, simulation and engineering of complex adaptive systems using cognitive agent-based computing, which is a unified framework based on using a combination agentbased and complex network-based approaches for modeling various types of complex systems in multidisciplinary areas such as communication networks, life sciences, social sciences and others. Dr. Niazi currently serves as a full professor and director of research at Bahria University, Pakistan. Amir Hussain obtained his BEng (with the highest 1st class honours) and PhD (with a resulting international patent on novel neural network architectures and algorithms) from the University of Strathclyde in Glasgow in 1992 and 1997, respectively. Following a postdoctoral research fellowship at the University of Paisley (1996-1998) and a research lectureship at the University of Dundee in Scotland (1998-2000), he joined the University of Stirling in 2000, where he is currently professor of computing science and founding director of the Cognitive Signal-Image and Control Processing Research (COSIPRA) Laboratory. He has (co)authored/edited more than a dozen books and over 200 papers to date in leading international journals and refereed conference proceedings. Since 2003, he has generated over e2m in


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research income (as principal investigator), including from UK research councils, EU FP6/7, international charities and industry. He is founding editor-in-chief of both Cognitive Computation journal and SpringerBriefs in Cognitive Computation, is associate editor for the IEEE Transactions on Neural Networks and Learning Systems and serves on the editorial board of a number of other journals. He has served as invited speaker, general/

program (co)chair and organizing/programme committee member for over 50 leading international conferences to date. He is founding general co-chair of the annual International Conference on Brain Inspired Cognitive Systems (BICS’2004-2013) and the IEEE ICEIS’2006. He is chair of the IEEE UK and Republic of Ireland (RI) Industry Applications Society Chapter and a Fellow of the UK Higher Education Academy.
