Skip to main content

Advertisement

SpringerLink
Log in

Fuzzy based Load Balancing in Sensor Cloud: Multi-Agent Approach

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

In recent years, sensor cloud showing its impact in the networking field and also it is being used for most of the applications. The major concern for sensor cloud is its lifetime because yet nodes are operated with limited energy, bandwidth and exhaust very soon affecting the whole network. To make reliable communication and optimizing the whole system for improving network lifetime, load balancing concept is included in sensor cloud. The fuzzy optimizer is responsible for finding the deserving cluster head and precise next hop node for information transmission. Fuzzy method is conducted at the sensor cloud server and the information is shared to sink node for physical network setup. Based on the importance of the information coming from physical network, it can be saved into priority or non priority servers using information classification technique leading to load balancing at server also. Agents are triggered into the network for information collection along with truthful information delivery ratio in a minimum time. The proposed method is compared with existing popular methods to check the load balancing capacity, and it is found that the proposed work is far better than existing methods with respect to response time, delay, energy consumption, minimum packets transmission and network lifetime.

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

Access this article

Log in via an institution

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
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  1. Leila Ben Saad, Bernard Tourancheau. (2015). Lifetime Optimization of Sensor-Cloud Systems. In: IEEE 2015 7th International Conference on New Technologies, Mobility and Security (NTMS) (pp. 1–5).

  2. Nanda, M., & Singh, U. K. (2016). A survey on wireless sensor network technologies recent advances and applications. International research journal of engineering and technology (IRJET), 3(7), 1381–1384.

    Google Scholar 

  3. Chawla, H. (2014). Some issues and challenges of Wireless Sensor Networks. International Journal of Advanced Research in Computer Science and Software Engineering, 4(7), 236–239.

    Google Scholar 

  4. Alamri, A., Ansari, W. S., Hassan, M. M., Hossain, M. S., Alelaiwi, A., & Hossain, M. A. (2013). A survey on sensor-cloud: architecture, applications, and approaches. International Journal of Distributed Sensor Networks, 9(2), 1–18.

    Article  Google Scholar 

  5. Dinh, H. T., Lee, C., Niyato, D., & Wang, P. (2013). A survey of mobile cloud computing: architecture, applications, and approaches. Wireless communications and mobile computing, 13(18), 1587–1611.

    Article  Google Scholar 

  6. Barker, S. K., & Shenoy, P. (2010, February). Empirical evaluation of latency-sensitive application performance in the cloud. In: Proceedings of the first annual ACM SIGMM conference on Multimedia systems (pp. 35–46).

  7. Kacimi, R., Dhaou, R., & Beylot, A. L. (2013). Load balancing techniques for lifetime maximizing in wireless sensor networks. Ad hoc networks, 11(8), 2172–2186.

    Article  Google Scholar 

  8. Wang, J., Ma, T., Cho, J., & Lee, S. (2011). An energy efficient and load balancing routing algorithm for wireless sensor networks. Computer Science and Information Systems, 8(4), 991–1007.

    Article  Google Scholar 

  9. Megharaj, G., & Mohan, K. G. (2016). A survey on load balancing techniques in cloud computing. IOSR Journal of Computer Engineering., 18(2), 55–61.

    Google Scholar 

  10. Kushwaha, M., & Gupta, S. (2015). Various schemes of load balancing in distributed systems-a review. International Journal of Scientific Research Engineering and Technology (IJSRET), 4(7), 2278–2882.

    Google Scholar 

  11. Zhiwei, Z., & Xuebo, L. (2018, August). Multi-parameter NCS scheduling based on fuzzy neural network. In: 2018 IEEE International Conference on Smart Internet of Things (SmartIoT) (pp. 192–197). IEEE.

  12. 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 

  13. Sangulagi, P., & Sutagundar, A. V. (2018). Agent based resource management in Sensor Cloud. International Journal of Emerging Technologies and Innovative Research (JETIR), 5(8), 331–336.

    Google Scholar 

  14. Sutagundar, A. V., & Manvi, S. S. (2008, November). Agent based approach to information fusion in wireless sensor networks. In: TENCON 2008–2008 IEEE Region 10 Conference (pp. 1–6). IEEE.

  15. Wooldridge, M. (2009). An introduction to multiagent systems. John Wiley & Sons.

  16. Singh, A., Juneja, D., & Malhotra, M. (2015). Autonomous agent based load balancing algorithm in cloud computing. Procedia Computer Science, 45, 832–841.

    Article  Google Scholar 

  17. Wajgi, D., & Thakur, N. V. (2012). Load balancing based approach to improve lifetime of wireless sensor network. International Journal of Wireless & Mobile Networks, 4(4), 155–167.

    Article  Google Scholar 

  18. Sreenivasamurthy, S., & Obraczka, K. (2018, September). Clustering for load balancing and energy efficiency in IoT applications. In: 2018 IEEE 26th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS) (pp. 319–332). IEEE.

  19. Kumar, N., Kumar, M., & Patel, R. B. (2010). Coverage and connectivity aware neural network based energy efficient routing in wireless sensor networks. International journal on applications of graph theory in wireless ad hoc networks and sensor networks, 2(1), 45–60.

    Article  Google Scholar 

  20. Jiang, H., Sun, Y., Sun, R., & Xu, H. (2013). Fuzzy-logic-based energy optimized routing for wireless sensor networks. International Journal of Distributed Sensor Networks, 9(8), 1–8.

    Article  Google Scholar 

  21. Kashyap, P. K. (2019). Genetic-fuzzy based load balanced protocol for WSNs. International Journal of Electrical & Computer Engineering, 9(2), 1168–1183.

    Google Scholar 

  22. Cui, X., Huang, X., Ma, Y., & Meng, Q. (2019). A load balancing routing mechanism based on SDWSN in smart city. Electronics, 8(3), 1–12.

    Article  Google Scholar 

  23. Selvakumar, K., & Pattabirani, G. (2019). A clustered fuzzy and dynamically well organized load balancing algorithm (CFDLB) for network life time enhancement in wireless sensor networks. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 8(4), 473–479.

    Google Scholar 

  24. Bouadem, N., Kacimi, R., & Tari, A. (2018, April). B-ďWSP selection algorithm: A load balancing convergecast for WSNs. In: 2018 Wireless Days (WD) (pp. 101–103). IEEE.

  25. Abdulasik, A., & Suriyakrishnaan, K. (2017, May). Improvement of network lifetime with security and load balancing mobile data clustering for wireless sensor networks. In: 2017 Third International Conference on Sensing, Signal Processing and Security (ICSSS) (pp. 463–468). IEEE.

  26. Baviskar, Y. S., Patil, S. C., & Govind, S. B. (2015, December). Energy efficient load balancing algorithm in cloud based wireless sensor network. In: 2015 International Conference on Information Processing (ICIP) (pp. 464–467). IEEE.

  27. Pramanick, M., Chowdhury, C., Basak, P., Al-Mamun, M. A., & Neogy, S. (2015, February). An energy-efficient routing protocol for wireless sensor networks. In: 2015 Applications and Innovations in Mobile Computing (AIMoC) (pp. 124–131). IEEE.

  28. Zhao, F., Xu, Y., & Li, R. (2012). Improved LEACH routing communication protocol for a wireless sensor network. International Journal of Distributed Sensor Networks, 8(12), 649609.

    Article  Google Scholar 

  29. Beiranvand, Z., Patooghy, A., & Fazeli, M. (2013, May). I-LEACH: An efficient routing algorithm to improve performance & to reduce energy consumption in Wireless Sensor Networks. In The 5th Conference on Information and Knowledge Technology (pp. 13–18). IEEE.

  30. Geetha, V. A., Kallapur, P. V., & Tellajeera, S. (2012). Clustering in wireless sensor networks: Performance comparison of leach & leach-c protocols using ns2. Procedia Technology, 4, 163–170.

    Article  Google Scholar 

Download references

Acknowledgements

The authors thank AICTE Delhi for the support and the BEC Bagalkot for doing the work. The work is funded by AICTE grant for carrying out the project “Resource Management in Internet of Things” Ref. No. File No. 8-40/RIFD/RPS/POLICY1/2016-17 dated August 02, 2017.

Author information

Authors and Affiliations

  1. Bheemanna Khandre Institute of Technology, Bhalki, Karnataka, India

    Prashant Sangulagi

  2. Basaveshwar Engineering College, Bagalkot, Karnataka, India

    Ashok Sutagundar

Authors
  1. Prashant Sangulagi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ashok Sutagundar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Prashant Sangulagi.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sangulagi, P., Sutagundar, A. Fuzzy based Load Balancing in Sensor Cloud: Multi-Agent Approach. Wireless Pers Commun 117, 1685–1710 (2021). https://doi.org/10.1007/s11277-020-07941-8

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-020-07941-8

Keywords

Search

Navigation