Complex Optimization Method of Routing Information Flows in Self-organized Networks

2017;
: pp. 76 - 87
1
Lviv Polytechnic National University
2
Lviv Polytechnic National University
3
Lviv Polytechnic National University

Modified routing algorithms are presented based on basic meta-heuristic algorithms: ant colony optimization, genetic and simulated annealing to determine the best route for information flows in self-organized networks. An ant colony optimization is based on the use of the probability parameter for the transition between the nodes located between the source node and the receiving node. To solve the problem of optimization of routing in a simulated annealing, its modification is proposed by adding or removing a transit node based on the coverage of the reaching range of neighboring nodes. As a target function for estimating a route, the QoS parameter is considered — the time of data delivery from the source node to the receiving node. For the first time, a routing algorithm is proposed based on a combination of proposed modified algorithms, where, from a set of best routes, formed by a modified annealing simulation algorithm, the choice of the best route according to the criterion of the time of data transmission is made by using a modified ant algorithm. For simulation an algorithm for generating traffic of a self-organized network is presented. The considered algorithms of routing allow to reduce the time of data transmission between the source node and the receiving node, which increases the efficiency of routing information flows in self- organized networks. It is shown that an important condition for efficient routing in self- organized networks is the reduction of the number of transit nodes between the source node and the node-coordinator.

1. Kumar Singh, Vinay, Sharma Vidushi (2013), “Hybrid genetic algorithm based approach for energy efficient routing in wireless sensor nets”, International journal of emerging technologies in computational and applied sciences (IJETCAS), pp. 408–413. 2. Sharawi, M., Aly Saroit, I., El-Mandy, H., Emary, E. (2013), “Routing wireless sensor networks based on soft computing paradigm”, International journal on soft computing. Artificial intelligence and applications(IISCAI), vol. 2, No. 4, August. 3. Emelyanov V. V., Kureychik V. V., Kureychik V. M. (2003), “Teoriya i praktika evolyutsionnogo modelirovaniya”, M.: FIZMATLIT, 432 p. 4. Latif, Kudr, Skobtsov Yu. A. (2014), Geneticheskiy algoritm marshrutizatsii besprovodnyih sensornyih setey. Problemi Informatsiynih tehnologIy, No.1, pp. 85–91. 5. Wook Ahn, Ch., Ramakrishna, R. S. (2002). A genetic algorithm for shortest path routing problem and the sizing of populations. IEEE transactions on evolutionary computation 6 (6), pp. 566–579. 6. Shtovba S. D. (2003) Muravinyie algoritmyi. Exponenta Pro. Matematika v prilozheniyah, No.4, pp. 70–75. 7. Kovalenko A.M. (2011) Razrabotka algoritma napravlennoy marshrutizatsii dlya besprovodnyih sensornyih setey. Trudyi Odesskogo politehnicheskogo universiteta, Vyip. 1(35), pp. 151–154. 8. Gendreau M. (1994) Metaheuristics for the vehicle routing problem. Technical Report CRT-963, Centre de Recherche sur les Transports. — Universit de Montral. 9. Ingber, L. (1993) Simulated annealing: Practice versus theory. Mathematical and Computer Modeling 18(11), pp. 29–57. 10. Klymash Y., Kaidan M., Strykhalyuk B. (2018) Modified routing algorithms for self-organized network. Advanced Trends in Radioelektronics, Telecommunications and Computer Engineering Мodern, Proceedings of the 14th International Conference TCSET’2018 (Lviv-Slavske, Ukraine February 20–24, 2018). 11. Ipatov A. V. (2011) Modifitsirovannyiy metod imitatsii otzhiga v zadache marshrutizatsii transporta. Tr. IMM UrO RAN, 2011, Tom 17, No. 4, pp. 121–125.