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Multi-Agent Technology to Improve the Internet of Things Routing Algorithm using Ant Colony Optimization


  • Department of Computer Science and Engineering, Sathyabama University, Chennai – 600119, Tamil Nadu, India


Objective: To improve the Internet of Things routing algorithm using ant colony optimization based on multi agent technology. Methods: The IOT environment contains various types of networks and every network could use a special sort of ACO algorithmic program. This vogue depends on network’s specs, status, and needs. This IOT environment had several intersections between completely different networks that result from various coverage areas, this intersections are known as overlapped areas. A Dual agent is used to generate an optimized routing algorithm in overlapped areas. The effectiveness of the proposed routing algorithmic program is measured in various terms and they are delay time, packet loss ratio, throughput, overhead of management bits, and energy consumption ratio. Findings: Network Simulator NS-2 is employed to evaluate the proposed algorithmic program performance. We have planned our routing algorithm to enhance our packet delivery rate and avoid the overlapped intersections victimization the multi-agent technology. With efficiency it will scale back delay and improves the packet delivery ratio with minimum route price. Applications: The proposed routing algorithm uses an ACO algorithm to obtain the best routing path and it will maximize the network lifetime with minimizing data gathering delay in WSN. The performance of routing protocols will increase with increasing the packet delivery ratio.


ACO Ant Colony Optimization, IOT-Internet of Things, Multi-Agent Technology, NS-2 - Network Simulator 2

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  • Omar Said. Analysis, design and simulation of Internet of Things routing algorithm based on ant colony optimization.International Journal of Communication Systems.2016. Published online in Wiley Online Library (
  • Zhang GP and Gong WT. The Research of Access Control Based on UCON in the Internet of Things. Journal of Software. 2011; 6(4):724-31. Crossref.
  • Ying Lu and Wen Hu. Study on the Application of Ant Colony Algorithm in the Route of Internet of Things.
  • International Journal of Smart Home. 2013 May; 7(3).
  • Abdolreza Mohajerani, Davood Gharavian. An ant colony optimization based routing algorithm for extending network lifetime in wireless sensor networks. New York: Springer Science+Business Media. 2015.
  • Caro G, Dorigo M. Two ant colony algorithms for besteffort routing in datagram networks. USA: The Proceeding of 10th IASTED International Conference on Parallel and Distributed Computing and Systems (PDCS ‘98). 1998 September 2; p. 541-6.
  • Al-Karaki J, Kamal A. Routing techniques in wireless sensor networks: a survey. IEEE Wireless Communications. 2004; 11(6):6-28. The Network Simulator - ns-2. 2008. Available from: Crossref. Accessed: 2 August 2016.
  • Dayong Y, Zhang M, Yang Y. A multi-agent framework for packet routing in wireless sensor networks. Journal of Sensors. 2015; 15(5):10026-47. Crossref. PMid:25928063 PMCid:PMC4481995.
  • Lifetime Improvement of WSN by Trust Level based Ant Colony Optimization.
  • Mallikarjun Talwar. Routing techniques and protocols for Internet of things: a survey. Proceeding of NCRIET-2015 & Indian Journal of Scientific Research. 2015; 12(1):417-23.Bhalki, Bidar, India: Organized by Department of E&CE, Bheemanna Khandrre Institute of Technology.
  • Wang F. Research on Location Selecting for Logistics Distribution Center Based on Ant Colony Algorithm.Journal of Convergence Information Technology. 2012; 7(16):255-62. 11. Emad Elbeltagi, Tarek Hegazy, Donald Grierson.Comparison among five evolutionary-based optimized algorithm. Advanced Engineering Informatics. 2005 January 19; 19:43-53. Crossref.
  • Correia F and Vazao T. Simple ant routing algorithm strategies for a (Multipurpose) MANET model. Ad Hoc Networks. 2010; 8(8):810-23. Crossref.
  • Misra S, Dhurandher SK and Obaidat MS. A low- overhead fault-tolerant routing algorithm for mobile ad hoc networks: A scheme and its simulation analysis. Simulation Modelling Practice and Theory. 2010; 18(5):637-49. Crossref.
  • Stutzle T, Hoos HH. MAX MIN Ant System. Future Generation Computer Systems. 2000; 16:889-914. Crossref.
  • Dorigo M, Gambardella LM. Ant Colony System : A Cooperative Learning Approach to the Traveling Salesman Problem. IEEE Transactions on Evolutionary Computation.1997; 1(1):53-66.
  • Hu X, Zhang J and Li Y. Orthogonal methods based ant colony search for solving continuous optimization problems.Journal of Computer Science and Technology. 2008; 23(1):2-18. Crossref.
  • Gupta DK, Arora Y, Singh UK, Gupta JP. Recursive Ant Colony Optimization for estimation of parameters of a function. Recent Advances in Information Technology (RAIT). 2012 March 15-17; p. 448-54. Crossref.
  • Gupta DK, Gupta JP, Arora Y, Shankar U. Recursive ant colony optimization: a new technique for the estimation of function parameters from geophysical field data. Near Surface Geophysics. 2013; 11(3):325-39. Crossref.


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