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Wireless Networks

, Volume 25, Issue 1, pp 399–413 | Cite as

Multi-objective fractional gravitational search algorithm for energy efficient routing in IoT

  • Amol V. DhumaneEmail author
  • Rajesh S. Prasad
Article

Abstract

Nowadays, the Internet of Things (IoT) plays a significant role in the Internet world. The IoT is a system which integrates the computing devices, digital machines provided with unique identifiers which have the ability to transfer the data over the network via the better route. IoT is also expected to generate large amounts of data, the consequent necessity for quick aggregation of the data and process such data more effectively. In this paper, a multi-objective fractional gravitational search algorithm is proposed to find the optimal cluster head for energy efficient routing protocol in IoT network. To extend the lifetime of the node, the Fractional Gravitational Search Algorithm (FGSA) is proposed to find out the optimal cluster head node iteratively in the IoT network model. The cluster head node is selected in FGSA that is evaluated by the fitness function using multiple objectives such as distance, delay, link lifetime and energy, termed as multi-objective FGSA (MOFGSA). The simulation results and performance is analyzed using MATLAB implementation. The performance is compared with existing algorithms like Artificial Bee Colony, Gravitational Search Algorithm and multi-particle swarm immune cooperative algorithm. Thus, the proposed MOFGSA algorithm ensures to prolong the lifetime of IoT nodes.

Keywords

Internet of Things Fractional theory Gravitational search algorithm (GSA) Cluster head selection Multiple objectives 

Notes

Acknowledgements

The authors would like to thank to Dr. Arvind V. Deshpande, Principal, Smt. Kashibai Navale College of Engineering, Pune, Dr. Parikshit N. Mahalle, Head of Computer Engineering Department, Smt. Kashibai Navale College of Engineering, Pune and Dr. Mrs. Jayashree R. Prasad, Professor, Computer Engineering Department, Sinhgad College of Engineering, Pune, India for their constant support and motivation in our work.

References

  1. 1.
    Ding, Y., Hu, Y., Hao, K., & Cheng, L. (2015). MPSICA: An intelligent routing recovery scheme for heterogeneous wireless sensor networks. Information Science, 308, 49–60.CrossRefGoogle Scholar
  2. 2.
    Gaddour, O., Koubaa, A., & Abid, M. (2015). Quality-of-service aware routing for static and mobile IPv6-based low-power and lossy sensor networks using RPL. Ad Hoc Networks, 33, 233–256.CrossRefGoogle Scholar
  3. 3.
    Hoang, D. C., Kumar, R., & Panda, S. K. (2013). Realisation of a cluster-based protocol using fuzzy C-means algorithm for wireless sensor networks. IET Wireless Sensor Systems, 3(3), 163–171.CrossRefGoogle Scholar
  4. 4.
    Shu, Y., Shu, Z., & Luo, B. (2014). Incentive mechanism design for heterogeneous networking routing. Communications and Networks, 16(4), 458–464.CrossRefGoogle Scholar
  5. 5.
    Leu, J.-S., Chen, C.-F., & Hsu, K.-C. (2013). Improving heterogeneous SOA-based IoT message stability by shortest processing time scheduling. IEEE Transactions on Services Computing, 7(1), 1.CrossRefGoogle Scholar
  6. 6.
    Turkanovic, M., Brumen, B., & Holbl, M. (2014). A novel user authentication and key agreement scheme for heterogeneous ad hoc wireless sensor networks based on the internet of things notion. Ad Hoc Networks, 20, 96–112.CrossRefGoogle Scholar
  7. 7.
    Li, F., & Xiong, P. (2013). Practical secure communication for integrating wireless sensor networks into the internet of things. IEEE Sensors, 13(10), 3677–3684.CrossRefGoogle Scholar
  8. 8.
    Kinoshita, K., Inoue, N., & Tode, H. (2016). Fair routing for overlapped cooperative heterogeneous wireless sensor networks. IEEE Sensors, 14(1), 3981.CrossRefGoogle Scholar
  9. 9.
    Lin, Y., Zhang, J., Chung, H. S.-H., Ip, W. H., & Li, Y. (2012). An ant colony optimization approach for maximizing the lifetime of heterogeneous wireless sensor networks. IEEE Transactions on Systems, Man and Cybernetics-Part C: Applications and Reviews, 42(3), 408–420.CrossRefGoogle Scholar
  10. 10.
    Mahmoud, M. M. E. A., Lin, X., & Shen, X. S. (2013). Secure and reliable routing protocols for heterogeneous multi-hop wireless networks. IEEE Transactions on Parallel and Distributed Systems, 26(4), 1140–1153.CrossRefGoogle Scholar
  11. 11.
    Rashedi, E., Nezamabadi-Pour, H., & Saryazdi, S. (2009). GSA: a gravitational search algorithm. Information Sciences, 179, 2232–2248.CrossRefzbMATHGoogle Scholar
  12. 12.
    Christin, D., Reinhardt, A., Mogre, P. S., & Steinmetz, R. (2009). Wireless sensor networks and the internet of things: selected challenges. In Proceedings of the 8th GI/ITG KuVS Fachgesprach “Drahtlose Sensornetze” (pp. 31–34).Google Scholar
  13. 13.
    Gururaja, N, & Dr. Brahmananda, S. H. (2014). Lifetime maximization in heterogeneous wireless sensor networks using multipath routing technique. Scientific and Research Publications, 4(5).Google Scholar
  14. 14.
    Yao, Y., Cao, Q., & Vasilakos, A. V. (2014). EDAL: An energy-efficient, delay-aware, and lifetime-balancing data collection protocol for heterogeneous wireless sensor networks. IEEE Transactions on Networking, 23(3), 810–823.CrossRefGoogle Scholar
  15. 15.
    Al-Hamadi, H., & Chen, I.-R. (2013). Redundancy management of multipath routing for intrusion tolerance in heterogeneous wireless sensor networks. IEEE Transactions on Network and Service Management, 10(2), 189–203.CrossRefGoogle Scholar
  16. 16.
    Kumar, R., & Kumar, D. (2016). Multi-objective fractional artificial bee colony algorithm to energy aware routing protocol in wireless sensor network. Wireless Networks, 22(5), 1461–1474.CrossRefGoogle Scholar
  17. 17.
    Deniz, F., Bagci, H., Korpeoglu, I., & Yazıcı, A. (2016). An adaptive, energy-aware and distributed fault-tolerant topology-control algorithm for heterogeneous wireless sensor networks. Ad Hoc Networks, 44, 104–117.CrossRefGoogle Scholar
  18. 18.
    Presser, M., & Barnaghi, P. M. (2009). The SENSEI project: Integrating the physical world with the digital world of the network of the future. IEEE Communications Magazine, 47(4), 1–4.CrossRefGoogle Scholar
  19. 19.
    Rohokale, V. M., & Prasad, N. R. (2010). Receiver sensitivity in opportunistic cooperative internet of things (IoT). Ad Hoc Networks, 49(3), 160–167.CrossRefGoogle Scholar
  20. 20.
    Zou, Z., Mendoza, D. S., Wang, P., Zhou, Q., Mao, J., Jonsson, F., et al. (2011). A low-power and flexible energy detection IR-UWB receiver for RFID and wireless sensor networks. IEEE Transactions on Circuits and Systems I: Regular Papers, 58(7), 1470–1482.MathSciNetCrossRefGoogle Scholar
  21. 21.
    Atzori, L., Iera, A., & Morabito, G. (2010). The internet of things: A survey. Computer Networks, 54(15), 2787–2805.CrossRefzbMATHGoogle Scholar
  22. 22.
    Guidoni, D. L., Hojo Souza, F. S., Ueyama, J., & Aparecido Villas, L. (2014). RouT: A routing protocol based on topologies for heterogeneous wireless sensor networks. IEEE Latin America Transactions, 12(4), 812–817.CrossRefGoogle Scholar
  23. 23.
    Shen, B., Wang, Z., & Hung, Y. S. (2010). Distributed consensus H-infinity filtering in sensor networks with multiple missing measurements: The finite-horizon case. Automatica, 46(10), 1682–1688.MathSciNetCrossRefzbMATHGoogle Scholar
  24. 24.
    Ossama, Y., Marwan, K., & Srinivasan, R. (2006). Node clustering in wireless sensor networks: recent developments and deployment challenges. IEEE Network, 20, 20–25.CrossRefGoogle Scholar
  25. 25.
    Karlof, C. & Wagner, D. (2003). Secure routing in sensor networks: Attacks and countermeasures. In Proceedings of the IEEE 1st international workshop sensor network protocols applications, Vol 1, (pp. 113–127).Google Scholar
  26. 26.
    Qing, L., Zhu, Q.-X., & Wang, M.-W. Z. C. (2006). A distributed energy-efficient clustering algorithm for heterogeneous wireless sensor networks. Journal of Software, 29(12), 2230–2237.zbMATHGoogle Scholar
  27. 27.
    Li, L. & Zuo, M. (2009). A dynamic adaptive routing protocol for heterogeneous wireless sensor network. In Proceedings of international conference on networks security, wireless communications and trusted computing, vol. 1 (pp. 666–669).Google Scholar
  28. 28.
    Rashedi, E., Nezamabadi-pour, H., & Saryazdi, S. (2009). GSA: A gravitational search algorithm. Information Sciences, 179, 2232–2248.CrossRefzbMATHGoogle Scholar
  29. 29.
    Solteiro Pires, E. J., Tenreiro Machado, J. A., de Moura Oliveira, P. B., Boaventura Cunha, J., & Mendes, L. (2010). Particle swarm optimization with fractional-order velocity. Nonlinear Dynamics, 61, 295–301.CrossRefzbMATHGoogle Scholar
  30. 30.
    Hoang, D. C., Yadav, P., Kumar, R., & Panda, S. K. (2014). Real-time implementation of a harmony search algorithm-based clustering protocol for energy-efficient wireless sensor networks. IEEE Transactions on Industrial Informatics, 10(1), 774–783.CrossRefGoogle Scholar
  31. 31.
    Han, Z., Wu, J., Zhang, J., Liu, L., & Tian, K. (2014). A general self-organized tree-based energy-balance routing protocol for wireless sensor network. IEEE Transactions on Nuclear Science, 61(2), 732–770.CrossRefGoogle Scholar
  32. 32.
    Gautam, N., & Pyun, J.-Y. (2010). Distance aware intelligent clustering protocol for wireless sensor networks. Journal of Communications and Networks, 12(2), 122–129.CrossRefGoogle Scholar
  33. 33.
    Lee, J. S., & Cheng, W. L. (2012). Fuzzy-logic-based clustering approach for wireless sensor networks using energy predication. IEEE Sensors Journal, 12(9), 2891–2897.CrossRefGoogle Scholar
  34. 34.
    Hammoudeh, M., & Newman, R. (2015). Adaptive routing in wireless sensor networks: QoS optimisation for enhanced application performance. Information Fusion, 22, 3–15.CrossRefGoogle Scholar
  35. 35.
    Yan, F., Yeung, A. K. H., Joseph, A. C., & Chen, G. (2015). Degree-energy-based local random routing strategies for sensor networks. Communications in Nonlinear Science and Numerical Simulation, 20(1), 250–262.CrossRefGoogle Scholar
  36. 36.
    Amgoth, T., & Jana, P. K. (2014). Energy-aware routing algorithm for wireless sensor networks. Computers & Electrical Engineering, 41, 357–367.CrossRefGoogle Scholar
  37. 37.
    Karaboga, D., Okdem, S., & Ozturk, C. (2012). Cluster based wireless sensor network routing using artificial bee colony algorithm. Wireless Network, 18(7), 847–860.CrossRefGoogle Scholar
  38. 38.
    Chen, R. -C., Chang, W. -L., Shieh, C. -F., Zou, C. C. (2012). Using hybrid artificial bee colony algorithm to extend wireless sensor network lifetime. In Proceedings of third international conference on innovations in bio-inspired computing and applications.Google Scholar
  39. 39.
    Singh, B., & Lobiyal, D. K., (2012). A novel energy-aware cluster head selection based on particle swarm optimization for wireless sensor networks. Human-Centric Computing and Information Sciences. doi: 10.1186/2192-1962-2-13.
  40. 40.
    Attea, B. A., & Khalil, E. A. (2012). A new evolutionary based routing protocol for clustered heterogeneous wireless sensor networks. Applied Soft Computing, 12(7), 1950–1957.CrossRefGoogle Scholar
  41. 41.
    Sharma, S., & Jena, S. K. (2015). Cluster based multipath routing protocol for wireless sensor networks. Newsletter, ACM SIGCOMM Computer Communication Review, 45(2), 14–20.CrossRefGoogle Scholar
  42. 42.
    Jin, R.-C., Gao, T., Song, J.-Y., Zou, J.-Y., & Wang, L.-D. (2013). Passive cluster-based multipath routing protocol for wireless sensor networks. Wireless Networks, 19(8), 1851–1866.CrossRefGoogle Scholar
  43. 43.
    Zhong, C., Mo, Y., Zhao, J., Lin, C., & Lu, X. (2014). Secure clustering and reliable multi-path route discovering in wireless sensor networks. In Proceedings of 2014 sixth international symposium on parallel architectures, algorithms and programming (PAAP), (pp. 130–134).Google Scholar
  44. 44.
    Ganesan, D., Govindan, R., Shenker, S., & Estrin, D. (2001). Highly-resilient energy-efficient multipath routing in wireless sensor networks. ACM SIGMOBILE Mobile Computing and Communications Review (MC2R), 5, 11–25.CrossRefGoogle Scholar
  45. 45.
    De, S., Qiao, C., & Wu, H. (2003). Meshed multipath routing with selective forwarding: an efficient strategy in wireless sensor networks. Wireless Sensor Networks, 43, 481–497.zbMATHGoogle Scholar
  46. 46.
    Chen, S., Xu, H., Liu, D., Hu, B., & Wang, H. (2014). A vision of IoT: Applications, challenges, and opportunities with China perspective. IEEE Internet of Things Journal, 1(4), 349–359.CrossRefGoogle Scholar
  47. 47.
    Li, X., Lu, R., Liang, X., Shen, X., Chen, J., & Lin, X. (2011). Smart community: An internet of things application. IEEE Communications Magazine, 49(11), 68–75.CrossRefGoogle Scholar
  48. 48.
    Lake, D., Milito, R., Morrow, M., & Vargheese, R. (2014). Internet of things: architectural framework for ehealth security. Journal of ICT Standardization, River Publishers., 1, 301–328.CrossRefGoogle Scholar
  49. 49.
    Leo, M., Battisti, F., Carli, M., & Neri, A. (2014). A federated architecture approach for Internet of Things security. In Proceedings of Euro med telco conference (EMTC) (pp. 1–5).Google Scholar
  50. 50.
    Kothmay, T., Schmitt, C., Hu, W., Brunig M., & Carle, G. (2012). A DTLS based end-to-end security architecture for the Internet of Things with two-way authentication. In Proceedings of IEEE 37th conference on local computer networks workshops (LCN workshops), (pp. 956–963).Google Scholar
  51. 51.
    Xu, X., Bessis, N., & Cao, J. (2013). An autonomic agent trust model for IoT systems. Procedia Computer Science, 21, 107–113.CrossRefGoogle Scholar
  52. 52.
    Shafagh, H., Hithnawi, A., Dröscher, A., Duquennoy, S., & Hu, W. (2015). Poster: Towards encrypted query processing for the Internet of Things. In Proceedings of the 21st annual international conference on mobile computing and networking, (pp. 251–253).Google Scholar
  53. 53.
    Fan, K., Liang, C., Li, H., & Yang, Y. (2014). LRMAPC: A lightweight RFID mutual authentication protocol with cache in the reader for IoT. In Proceedings of IEEE international conference on computer and information technology (pp. 276–280).Google Scholar
  54. 54.
    Dhumane, A., Prasad, R., & Prasad, J. (2016). Routing issues in Internet of Things: A aurvey. In Proceedings of international multi conference of engineers and computer scientists, vol. 1, (pp. 1–9).Google Scholar
  55. 55.
    Dey, A. K. (2001). Understanding and using context (pp. 1–10). Atlanta: Georgia Institute of Technology.Google Scholar
  56. 56.
    Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd annual Hawaii international conference on system sciences, IEEE, vol. 2, (pp. 1–10).Google Scholar
  57. 57.
    Handy, M. J., Haase, M. & Timmermann, D. (2002). Low energy adaptive clustering hierarchy with deterministic cluster-head selection. In 4th international workshop on mobile and wireless communications network, (pp. 368–372).Google Scholar
  58. 58.
    Farooq, M. O., Dogar, A. B., & Shah, G. A. (2010). MR-LEACH: Multi-hop routing with low energy adaptive clustering hierarchy. In Proceedings of fourth international conference on sensor technologies and applications, Venice (pp. 262–268).Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Smt. Kashibai Navale College of EngineeringSavitribai Phule Pune UniversityPuneIndia
  2. 2.NBN Sinhgad School of EngineeringPuneIndia

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