Advertisement

PriNergy: a priority-based energy-efficient routing method for IoT systems

  • 16 Accesses

Abstract

The Internet of Things (IoT) devices gather a plethora of data by sensing and monitoring the surrounding environment. Transmission of collected data from the IoT devices to the cloud through relay nodes is one of the many challenges that arise from IoT systems. Fault tolerance, security, energy consumption and load balancing are all examples of issues revolving around data transmissions. This paper focuses on energy consumption, where a priority-based and energy-efficient routing (PriNergy) method is proposed. The method is based on the routing protocol for low-power and lossy network (RPL) model, which determines routing through contents. Each network slot uses timing patterns when sending data to the destination, while considering network traffic, audio and image data. This technique increases the robustness of the routing protocol and ultimately prevents congestion. Experimental results demonstrate that the proposed PriNergy method reduces overhead on the mesh, end-to-end delay and energy consumption. Moreover, it outperforms one of the most successful routing methods in an IoT environment, namely the quality of service RPL (QRPL).

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

Access options

Buy single article

Instant unlimited access to the full article PDF.

US$ 39.95

Price includes VAT for USA

Subscribe to journal

Immediate online access to all issues from 2019. Subscription will auto renew annually.

US$ 199

This is the net price. Taxes to be calculated in checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

References

  1. 1.

    Park S et al (2014) IoT routing architecture with autonomous systems of things. In: 2014 IEEE world forum on internet of things (WF-IoT). IEEE

  2. 2.

    Dhumane A et al (2019) Context awareness in IoT routing. In: 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA). IEEE

  3. 3.

    Kotb Y et al (2019) Cloud-based multi-agent cooperation for iot devices using workflow-nets. J Grid Comput 17:1–26

  4. 4.

    Al Ridhawi I et al (2019) A profitable and energy-efficient cooperative fog solution for IoT services. IEEE Trans Ind Inf

  5. 5.

    Souri A et al (2019) A systematic review of IoT communication strategies for an efficient smart environment. Trans Emerg Telecommun Technol electronic version e3736

  6. 6.

    Kharrufa H, Al-Kashoash H, Kemp A (2019) RPL-based routing protocols in IoT applications: a review. IEEE Sens J 19:5952–5967

  7. 7.

    Zikria YB et al (2018) A survey on routing protocols supported by the Contiki Internet of things operating system. Future Gener Comput Syst 82:200–219

  8. 8.

    Souri A, Norouzi M (2019) A state-of-the-art survey on formal verification of the internet of things applications. J Serv Sci Res 11(1):47–67

  9. 9.

    Musaddiq A, Zikria YB, Kim SW (2018) Energy-aware adaptive trickle timer algorithm for RPL-based routing in the internet of things. In: 2018 28th International Telecommunication Networks and Applications Conference (ITNAC). IEEE

  10. 10.

    Ganesh D, Patil KK, Suresh L (1997) Two to tango: the role of government in fisheries co-management. Marine Policy 105(1):267–292

  11. 11.

    Yahiaoui S et al (2018) An energy efficient and QoS aware routing protocol for wireless sensor and actuator networks. AEU-Int J Electron Commun 83:193–203

  12. 12.

    Raoof A, Matrawy A, Lung C-H (2018) Routing attacks and mitigation methods for RPL-based internet of things. IEEE Commun Surv Tutor 21:1582–1606

  13. 13.

    Souri A, Navimipour NJ, Rahmani AM (2018) Formal verification approaches and standards in the cloud computing: a comprehensive and systematic review. Comput Stand Interfaces 58:1–22

  14. 14.

    Kumar M, Azad M, Agrawal N (2019) Comparative analysis of tree-based data aggregation protocols to maximize lifetime of wireless sensor networks. In: Pervasive computing: a networking perspective and future directions. Springer, pp 153–163

  15. 15.

    Ambigavathi M, Sridharan D (2018) Energy-aware data aggregation techniques in wireless sensor network. In: Advances in power systems and energy management. Springer, pp 165–173

  16. 16.

    Gilbert EPK et al (2018) Trust based data prediction, aggregation and reconstruction using compressed sensing for clustered wireless sensor networks. Comput Electr Eng 72:894–909

  17. 17.

    Petrovic D et al (2003) Data funneling: routing with aggregation and compression for wireless sensor networks. In: Proceedings of the First IEEE International Workshop on Sensor Network Protocols and Applications, 2003. IEEE

  18. 18.

    Karkazis P et al (2012) Design of primary and composite routing metrics for rpl-compliant wireless sensor networks. In: 2012 International Conference on Telecommunications and Multimedia (TEMU). IEEE

  19. 19.

    Wang S et al (2018) CRPD: a novel clustering routing protocol for dynamic wireless sensor networks. Pers Ubiquit Comput 22(3):545–559. https://doi.org/10.1577/1548-8667(1998)010xxaaa0160:FIITDOxxbbb2.0.CO;2

  20. 20.

    Aldabbas H (2018) LPBR: location prediction based routing protocol for mobile IoT systems. In: Proceedings of the 2nd International Conference on Future Networks and Distributed Systems. ACM

  21. 21.

    Han Z, Li Y, Li J (2018) A novel routing algorithm for IoT cloud based on hash offset tree. Future Gener Comput Syst 86:456–463

  22. 22.

    Baker T et al (2015) GreeDi: an energy efficient routing algorithm for big data on cloud. Ad Hoc Netw 35:83–96

  23. 23.

    Baker T et al (2018) GreeAODV: an energy efficient routing protocol for vehicular ad hoc networks. In: Intelligent computing methodologies. Springer, Cham

  24. 24.

    Baker T et al (2013) Energy efficient cloud computing environment via autonomic meta-director framework. In: 2013 Sixth International Conference on Developments in eSystems Engineering

  25. 25.

    Cai Y et al (2018) Software defined status aware routing in content-centric networking. In: 2018 International Conference on Information Networking (ICOIN). IEEE

  26. 26.

    Dhumane AV, Prasad RS (2019) Multi-objective fractional gravitational search algorithm for energy efficient routing in IoT. Wirel Netw 25(1):399–413

  27. 27.

    Preeth SKSL et al (2018) An adaptive fuzzy rule based energy efficient clustering and immune-inspired routing protocol for WSN-assisted IoT system. J Ambient Intell Human Comput 1–13

  28. 28.

    Wang Y, Wang Z (2018) Routing algorithm of energy efficient wireless sensor network based on partial energy level. Clust Comput 22:8629–8638

  29. 29.

    Yuan P et al (2019) Markov decision process-based routing algorithm in hybrid Satellites/UAVs disruption-tolerant sensing networks. IET Commun 13:1415–1424

  30. 30.

    Umamaheswari S, Negi A (2017) Internet of things and RPL routing protocol: a study and evaluation. In: 2017 International Conference on Computer Communication and Informatics (ICCCI). IEEE

  31. 31.

    Gara F et al (2019) A new scheme for RPL to handle mobility in wireless sensor networks. Int J Ad Hoc Ubiquitous Comput 30(3):173–186

  32. 32.

    Issariyakul T, Hossain E (2009) Introduction to network simulator 2 (NS2). In: Introduction to network simulator NS2. Springer, pp 1–18

  33. 33.

    Mohamed B, Mohamed F (2015) Qos routing rpl for low power and lossy networks. Int J Distrib Sens Netw 11(11):971545

  34. 34.

    Salameh HB, Otoum S, Derbas R, Aloqaily M, Al Ridhawi I, Jararweh Y (2020) Intelligent Jamming-aware Routing in Multi-hop IoT-based Opportunistic Cognitive Radio Networks, Ad Hoc Networks, vol. 98

  35. 35.

    Aloqaily M, Al Ridhawi I, Salameh HB, Jararweh Y (2019) Data and Service Management in Densely Crowded Environments: Challenges. Opportunities, and Recent Developments, IEEE Communications Magazine 57(4):81–87

  36. 36.

    Ridhawi IA, Aloqaily M, Boukerche A (2019) Comparing Fog Solutions for Energy Efficiency in Wireless Networks: Challenges and Opportunities. IEEE Wireless Communications 26(6):80–86

  37. 37.

    Balasubramanian V, Otoum S, Aloqaily M, Al Ridhawi I, Jararweh Y ((2020)) Low-Latency Vehicular Edge: A Vehicular Infrastructure Model for 5G. Simul Model Pract Theory 98:101968

Download references

Author information

Correspondence to Alireza Souri.

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

Verify currency and authenticity via CrossMark

Cite this article

Safara, F., Souri, A., Baker, T. et al. PriNergy: a priority-based energy-efficient routing method for IoT systems. J Supercomput (2020). https://doi.org/10.1007/s11227-020-03147-8

Download citation

Keywords

  • Internet of Things
  • Priority-based routing
  • Energy consumption
  • Low-power and lossy networks