Skip to main content
Springer Nature Link
Log in

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

  • Published:
Wireless Networks Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  4. Shu, Y., Shu, Z., & Luo, B. (2014). Incentive mechanism design for heterogeneous networking routing. Communications and Networks, 16(4), 458–464.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  8. Kinoshita, K., Inoue, N., & Tode, H. (2016). Fair routing for overlapped cooperative heterogeneous wireless sensor networks. IEEE Sensors, 14(1), 3981.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  11. Rashedi, E., Nezamabadi-Pour, H., & Saryazdi, S. (2009). GSA: a gravitational search algorithm. Information Sciences, 179, 2232–2248.

    Article  MATH  Google Scholar 

  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).

  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).

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  19. Rohokale, V. M., & Prasad, N. R. (2010). Receiver sensitivity in opportunistic cooperative internet of things (IoT). Ad Hoc Networks, 49(3), 160–167.

    Article  Google Scholar 

  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.

    Article  MathSciNet  Google Scholar 

  21. Atzori, L., Iera, A., & Morabito, G. (2010). The internet of things: A survey. Computer Networks, 54(15), 2787–2805.

    Article  MATH  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  MathSciNet  MATH  Google Scholar 

  24. Ossama, Y., Marwan, K., & Srinivasan, R. (2006). Node clustering in wireless sensor networks: recent developments and deployment challenges. IEEE Network, 20, 20–25.

    Article  Google Scholar 

  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).

  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.

    MATH  Google Scholar 

  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).

  28. Rashedi, E., Nezamabadi-pour, H., & Saryazdi, S. (2009). GSA: A gravitational search algorithm. Information Sciences, 179, 2232–2248.

    Article  MATH  Google Scholar 

  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.

    Article  MATH  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  34. Hammoudeh, M., & Newman, R. (2015). Adaptive routing in wireless sensor networks: QoS optimisation for enhanced application performance. Information Fusion, 22, 3–15.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  36. Amgoth, T., & Jana, P. K. (2014). Energy-aware routing algorithm for wireless sensor networks. Computers & Electrical Engineering, 41, 357–367.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

  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. 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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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).

  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.

    Article  Google Scholar 

  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.

    MATH  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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).

  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).

  51. Xu, X., Bessis, N., & Cao, J. (2013). An autonomic agent trust model for IoT systems. Procedia Computer Science, 21, 107–113.

    Article  Google Scholar 

  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).

  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).

  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).

  55. Dey, A. K. (2001). Understanding and using context (pp. 1–10). Atlanta: Georgia Institute of Technology.

    Google Scholar 

  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).

  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).

  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).

Download references

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.

Author information

Authors and Affiliations

  1. Smt. Kashibai Navale College of Engineering, Savitribai Phule Pune University, Pune, India

    Amol V. Dhumane

  2. NBN Sinhgad School of Engineering, Pune, India

    Rajesh S. Prasad

Authors
  1. Amol V. Dhumane
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Rajesh S. Prasad
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Amol V. Dhumane.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dhumane, A.V., Prasad, R.S. Multi-objective fractional gravitational search algorithm for energy efficient routing in IoT. Wireless Netw 25, 399–413 (2019). https://doi.org/10.1007/s11276-017-1566-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-017-1566-2

Keywords

pFad - Phonifier reborn

Pfad - The Proxy pFad of © 2024 Garber Painting. All rights reserved.

Note: This service is not intended for secure transactions such as banking, social media, email, or purchasing. Use at your own risk. We assume no liability whatsoever for broken pages.


Alternative Proxies:

Alternative Proxy

pFad Proxy

pFad v3 Proxy

pFad v4 Proxy