Advertisement

GSOMCR: Multi-Constraint Genetic-Optimized QoS-Aware Routing Protocol for Smart Grids

  • Razieh Rastgoo
  • Vahid Sattari-Naeini
Research Paper

Abstract

Smart grid (SG) includes the various communication networks and manages them intelligently and effectively. One of the important challenges of the SG is routing optimization. In this paper, we propose a routing protocol, namely Genetic-based Stable Optimization Multi-Constrained Routing (GSOMCR), using the seven parameters that show the Quality of Service (QoS) guaranteed in the SG. We collect the suggested parameters in one network cost function and optimize the function by Genetic Algorithm. Appropriate parameterization of GA is very important in convergence of fitness function, used in GSOMCR. We applied the novelty of Direction-Based Crossover (DBC) operator in proposed GA. DBC uses the values of fitness function to find the best direction for function to converge. Instead of using random initializing in GA, we use the GSOMCR to show the initial population in proposed GA. Comparison of the simulation results of the GSOMCR by the other protocols shows the improvement of the network performance in routing optimization of SG.

Keywords

Smart grid QoS Performance optimization Artificial intelligence Genetic algorithm Stability factor 

References

  1. Ahn CW, Ramakrishna RS (2002) A genetic algorithm for shortest path routing problem and the sizing of populations. IEEE Trans Evol Comput 6:566–579CrossRefGoogle Scholar
  2. Barbancho J, Leon C, Molina FJ, Barbancho A (2007a) Using artificial intelligence in routing schemes for wireless networks. Comput Commun 30:2802–2811.  https://doi.org/10.1016/j.comcom.2007.05.023 CrossRefGoogle Scholar
  3. Barbancho J, Leon C, Molina FJ, Barbancho A (2007b) Using artificial intelligence in routing schemes for wireless networks. Comput Commun 30:2802–2811.  https://doi.org/10.1016/j.comcom.2007.05.023 CrossRefGoogle Scholar
  4. Bou-Harb E, Fachkha C, Pourzandi M, Debbabi M, Assi C (2013) Communication security for Smart Grid distribution networks. IEEE Commun Mag 51:42–49CrossRefGoogle Scholar
  5. Bueno MLP, Oliveira GMB (2010) Multi-cast flow routing: Evaluation of heuristics and multi-objective evolutionary algorithms. In: IEEE Congress on Evolutionary Computation (CEC), Barcelona 2010Google Scholar
  6. Deb K, Deb D (2014) Analyzing mutation schemes for real-parameter genetic algorithms. Int J Artif Intell Soft Comput 4:1–28CrossRefGoogle Scholar
  7. Ebrahimi M, Tenhunen H, Dehyadegari M (2013a) Fuzzy-based ADAPTIVE ROUTING ALGORITHM FOR NETWORKS-ON-CHIP. J Syst Architect 59:516–527CrossRefGoogle Scholar
  8. Ebrahimi M, Tenhunen H, Dehyadegari M (2013b) Fuzzy-based adaptive routing algorithm for networks-on-chip. J Syst Architect 59:516–527CrossRefGoogle Scholar
  9. Gao J, Xiao Y, Liu J, Liang W, Chen CLP (2011) A survey of communication/networking in Smart Grids. Future Gen Comput Syst 28:391–404.  https://doi.org/10.1016/j.future.2011.04.014 CrossRefGoogle Scholar
  10. Hosseini SA, Abyaneh HA, Sadeghi SHH, Razavi F, Karami M (2015) Presenting a new method for identifying fault location in micro-grids, using harmonic impedance. IJST Trans Electr Eng 39:167–182Google Scholar
  11. Jahromi AE, Rad ZB (2012) Optimal topological design of power communication networks using genetic algorithm. Sci Iran 20(3):945–957Google Scholar
  12. Kardani-Moghaddam S, Entezari-maleki R, Movaghar A (2014) A cost efficient two-level market model for task scheduling problem in grid environment. IJST Trans Electr Eng 38:73–90Google Scholar
  13. Kim Y, Thottan M, Kolesnikov V, Wonsuck L (2010) A secure decentralized data-centric information infrastructure for Smart Grid. IEEE Commun Mag 48:58–65CrossRefGoogle Scholar
  14. Li H, Zhang W (2010) QoS Routing in Smart Grid. In: IEEE Global Telecommunications Conference, Miami, FL, 2010 (GLOBECOM 2010)Google Scholar
  15. Navarro J, Zaballos A, Sancho-Asensio A, Ravera G, Armendariz-Iñigo JE (2012) The information system of INTEGRIS: INTelligent Electrical GRId sensor communications. Ind Inform IEEE Trans 99:1548–1560Google Scholar
  16. Peltokangas R and Sorsa A (2008) Real-coded genetic algorithms and nonlinear parameter identification. Control Engineering Laboratory Report, No 34, 2008Google Scholar
  17. Rastgoo R, Sattari-Naeini V (2014) A Neuro-Fuzzy QoS-aware routing protocol for Smart Grids. In: 22’nd Iranian Conference on Electrical Engineering (ICEE), 2014Google Scholar
  18. Rastgoo R, Sattari-Naeini V (2016) Tuning parameters of the QoS-aware routing protocol for smart grids using genetic algorithm. Appl Artif Intell 30:52–76.  https://doi.org/10.1080/08839514.2016.1138794 CrossRefGoogle Scholar
  19. Riedl A, Schupke DA (2007) Routing optimization in IP networks utilizing additive and concave link metrics. IEEE/ACM Trans Netw 15:1136–1148CrossRefGoogle Scholar
  20. Singh A, Sundar S (2011) An artificial bee colony algorithm for the minimum routing cost spanning tree problem. Soft Comput 15:2489–2499CrossRefGoogle Scholar
  21. Temel Ş, Gungor VC, Koçak T (2014) Routing protocol design guidelines for smart grid environments. Comput Netw (COMNET) 60:160–170.  https://doi.org/10.1016/j.bjp.2013.11.009 CrossRefGoogle Scholar
  22. Vallejo A, Zaballos A, Selga JM, Dalmau J (2012) Next generation QoS control architectures for distribution smart grid communication networks. IEEE Comm Mag 50:128–134CrossRefGoogle Scholar
  23. Wang W, Xu Y, Khanna M (2011) A survey on the communication architectures in smart grid. Comput Netw 55:3604–3629.  https://doi.org/10.1016/j.comnet.2011.07.010 CrossRefGoogle Scholar
  24. Xue G, Sen A, Zhang W, Tang J, Thulasiraman K (2007) Finding a path subject to many additive QoS constraints. IEEE/ACM Trans Netw 15:201–211CrossRefGoogle Scholar
  25. Yan Y, Qian Y, Sharif H, Tipper D (2012) A survey on smart grid communication infrastructures: motivations, requirements and challenges. IEEE Commun Surveys Tutor 15:5–20CrossRefGoogle Scholar
  26. Zaballos A, Vernet D, Selga JM (2013) A genetic QoS-aware routing protocol for the smart electricity networks. Int J Distrib Sensor Netw.  https://doi.org/10.1155/2013/135056 Google Scholar
  27. Zengin A, Sarjoughian H, Ekiz H (2013a) Discrete-event modeling of swarm intelligence based routing in network systems. Inf Sci 222:81–98CrossRefGoogle Scholar
  28. Zengin A, Sarjoughian H, Ekiz H (2013b) Discrete-event modeling of swarm intelligence based routing in network systems. Inf Sci 222:81–98CrossRefGoogle Scholar

Copyright information

© Shiraz University 2018

Authors and Affiliations

  1. 1.Department of Computer EngineeringShahid Bahonar University of KermanKermanIran

Personalised recommendations