Towards modeling complex wireless sensor networks using agents and networks: A systematic approach

Wireless Sensor Networks (WSN) are a fast-emerging area of interest in the domain of large-scale communication networks. However, design of novel WSN applications requires first developing models and performing extensive simulation. Modern large-scale WSNs are expected to be both dynamic as well as random and often spread over a large scale. However, this problem has not previously been addressed in literature. In our previous works, we have demonstrated how Agent-based modeling may be used to effectively model various types of Complex Adaptive Systems such as, but not limited to, Self-organizing Communication infrastructures. In this paper, we present first steps towards providing a comprehensive set of guidelines and tutorial for developing deployment models of WSNs besides modeling routing algorithms using agent-based modeling. Our simulation results demonstrate the effectiveness and ease of use coupled with a short learning curve involved in developing agent-based models of complex WSN applications.

[1]  M. Niazi,et al.  Towards a Methodology for Validation of Centrality Measures in Complex Networks , 2014, PloS one.

[2]  M. A. Niazi,et al.  Sensing Emergence in Complex Systems , 2011, IEEE Sensors Journal.

[3]  Muaz A. Niazi,et al.  Complex adaptive communication networks and environments: Part 2 , 2013, Simul..

[4]  George F. Riley,et al.  The Georgia Tech Network Simulator , 2003, MoMeTools '03.

[5]  U. Wilensky,et al.  Thinking Like a Wolf, a Sheep, or a Firefly: Learning Biology Through Constructing and Testing Computational Theories—An Embodied Modeling Approach , 2006 .

[6]  Muaz A. Niazi,et al.  Cognitive Agent-based Computing-I: A Unified Framework for Modeling Complex Adaptive Systems using Agent-based & Complex Network-based Methods , 2012 .

[7]  Muaz A. Niazi,et al.  Complex adaptive communication networks and environments: Part 1 , 2013, Simul..

[8]  Özgür B. Akan,et al.  Cognitive radio sensor networks , 2009, IEEE Network.

[9]  Kevin Ashton,et al.  That ‘Internet of Things’ Thing , 1999 .

[10]  M. Vetterli,et al.  Lattice sensor networks: capacity limits, optimal routing and robustness to failures , 2004, Third International Symposium on Information Processing in Sensor Networks, 2004. IPSN 2004.

[11]  Muaz A. Niazi,et al.  A Novel Agent-Based Simulation Framework for Sensing in Complex Adaptive Environments , 2011, IEEE Sensors Journal.

[12]  John S. Baras,et al.  ATEMU: a fine-grained sensor network simulator , 2004, 2004 First Annual IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks, 2004. IEEE SECON 2004..

[13]  Chamundeshwari Kalyane.  Data Aggregation and Routing In Wireless Sensor Networks: Optimal and Heuristic Algorithms , 2015 .

[14]  Teerawat Issariyakul,et al.  Introduction to Network Simulator NS2 , 2008 .

[15]  Mohamed F. Younis,et al.  A survey on routing protocols for wireless sensor networks , 2005, Ad Hoc Networks.

[16]  Sergio D. Servetto,et al.  Constrained random walks on random graphs: routing algorithms for large scale wireless sensor networks , 2002, WSNA '02.

[17]  Mani Srivastava,et al.  Energy efficient routing in wireless sensor networks , 2001, 2001 MILCOM Proceedings Communications for Network-Centric Operations: Creating the Information Force (Cat. No.01CH37277).

[18]  A. Varga,et al.  THE OMNET++ DISCRETE EVENT SIMULATION SYSTEM , 2003 .

[19]  Xinjie Chang.  Network simulations with OPNET , 1999, WSC'99. 1999 Winter Simulation Conference Proceedings. 'Simulation - A Bridge to the Future' (Cat. No.99CH37038).

[20]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[21]  K. Shenai,et al.  Cognitive sensor networks , 2008, 2008 26th International Conference on Microelectronics.

[22]  Francisco J. Miguel Quesada.  Gilbert, Nigel; Troitzsch, Klaus (2005). Simulation for the Social Scientist , 2006 .

[23]  Mario Kolberg,et al.  Verification & Validation of Agent Based Simulations using the VOMAS (Virtual Overlay Multi-agent System) Approach , 2009, MALLOW.

[24]  Muaz A. Niazi,et al.  Agent-based tools for modeling and simulation of self-organization in peer-to-peer, ad hoc, and other complex networks , 2009, IEEE Communications Magazine.

[25]  Weiren Shi,et al.  Comparison of OMNET++ and other simulator for WSN simulation , 2008, 2008 3rd IEEE Conference on Industrial Electronics and Applications.

[26]  Mohamed Ibnkahla,et al.  Cognitive approaches in Wireless Sensor Networks: A survey , 2010, 2010 25th Biennial Symposium on Communications.

[27]  Mohamed Khalil Watfa,et al.  BARC: A Battery Aware Reliable Clustering algorithm for sensor networks , 2009, J. Netw. Comput. Appl..

[28]  G. Nigel Gilbert,et al.  Simulation for the social scientist , 1999 .

[29]  Ian F. Akyildiz,et al.  Wireless sensor networks: a survey , 2002, Comput. Networks.

[30]  JAMAL N. AL-KARAKI,et al.  Routing techniques in wireless sensor networks: a survey , 2004, IEEE Wireless Communications.

[31]  Muaz A. Niazi,et al.  Complex Adaptive Systems Modeling: A multidisciplinary Roadmap , 2013, Complex Adapt. Syst. Model..

[32]  Uri Wilensky,et al.  NetLogo: A simple environment for modeling complexity , 2014 .

[33]  Reka Albert,et al.  Mean-field theory for scale-free random networks , 1999 .

[34]  David E. Culler,et al.  TOSSIM: accurate and scalable simulation of entire TinyOS applications , 2003, SenSys '03.