Skip to main content
SpringerLink
Log in

EC-MOPSO: an edge computing-assisted hybrid cluster and MOPSO-based routing protocol for the Internet of Vehicles

  • Published:
Annals of Telecommunications Aims and scope Submit manuscript

Abstract

In recent years, the Internet of Vehicles (IoV) has received a lot of attention due to its unique features, such as rapid topology change, specific movement patterns, and variable node density and speed. Providing an efficient routing algorithm is one of the main challenges of these networks. Roadside units (RSU) defined in vehicular Ad hoc network (VANET) architecture can play as an edge computing device and assist in the routing process. They are usually fixed along the roadside or in specific locations such as junctions. On the other hand, bioinspired metaheuristic optimization algorithms are good candidates for this field due to their dynamic nature and ability to consider several parameters simultaneously. Clustering can also be used to reduce complexity. In this paper, an edge computing-assisted cluster-based routing algorithm utilizing multi-objective particle swarm optimization (MOPSO), named EC-MOPSO, is presented for the IoV applications. The proposed method evaluates different particles based on the three objective functions of accumulative delay, number of hops, and number of cluster members. Particles suggesting lower delays, fewer hops, and more members in the same cluster are considered to be superior. RSUs (edges) are responsible for the optimization procedure. Evaluation results show that the proposed method has a significant advantage over similar works in terms of distance traveled, number of hops, latency, packet delivery ratio, and convergence time.

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

Similar content being viewed by others

Data availability

Not applicable.

Code availability

Not applicable.

References

  1. Ullah A, Yao X, Shaheen S, Ning H (2019) Advances in position based routing towards ITS Enabled FoG-Oriented VANET–A Survey. IEEE Trans Intell Transp Syst 21(2):828–840

    Article  Google Scholar 

  2. Zarei M (2020) Traffic-centric mesoscopic analysis of connectivity in VANETs. Comput J 63(2):203–219

    Article  Google Scholar 

  3. Ren M, Zhang J, Khoukhi L et al (2021) A review of clustering algorithms in VANETs. Ann Telecommun 76:581–603

  4. Kaur R, Ramachandran RK, Doss R, Pan L (2021) The importance of selecting clustering parameters in VANETs: a survey. Comput Sci Rev 40:100392

    Article  Google Scholar 

  5. Hajlaoui R, Guyennet H, Moulahi T (2016) A survey on heuristic-based routing methods in vehicular ad-hoc network: technical challenges and future trends. IEEE Sens J 16(17):6782–6792

    Article  Google Scholar 

  6. Azzoug Y, Boukra A (2021) Bio-inspired VANET routing optimization: an overview. Artif Intell Rev 54:1005–1062

  7. Kennedy J (2011) Particle Swarm Optimization. In: Sammut C, Webb GI (eds) Encyclopedia of machine learning. Springer, US, Boston, MA, pp 760–766

    Google Scholar 

  8. Fahad M, Aadil F, Ejaz S, Ali A (2017) Implementation of evolutionary algorithms in vehicular ad-hoc network for cluster optimization. 2017 Intell Syst Conf (IntelliSys) 137–141

  9. Sheikh Sofla M, Haghi Kashani M, Mahdipour E, Faghih Mirzaee R (2021) Towards effective offloading mechanisms in fog computing: a systematic survey. Multimedia Tools and Applications, submitted for publication

  10. Haghi Kashani M, Ahmadzadeh A, Mahdipour E (2021) Load balancing mechanisms in fog computing: a systematic review. IEEE Trans Serv Comput, submitted for publication, 1–19

  11. Songhorabadi M, Rahimi M, Farid AMM, Kashani MH (2020) Fog computing approaches in smart cities: a state-of-the-art review. arXiv preprintarXiv:2011.14732, 1–19

  12. Rahimi M, Songhorabadi M, Haghi Kashani M (2020) Fog-based smart homes: a systematic review. J Netw Comput Appl 153:102531. https://doi.org/10.1016/j.jnca.2020.102531

    Article  Google Scholar 

  13. Shah VS (2018) Multi-agent cognitive architecture-enabled IoT applications of mobile edge computing. Ann Telecommun 73(7):487–497

    Article  Google Scholar 

  14. Katiyar A, Singh D, Yadav RS (2020) State-of-the-art approach to clustering protocols in vanet: a survey. Wireless Netw 26(7):5307–5336

    Article  Google Scholar 

  15. Maadani M, Motamedi SA (2016) A comprehensive DCF performance analysis in noisy industrial wireless networks. Int J Commun Syst 29(11):1720–1739

    Article  Google Scholar 

  16. Hashemi S, Zarei M (2021) Internet of Things backdoors: resource management issues, security challenges, and detection methods. Trans  Emerg Telecommun Technol 32(2):e4142

    Article  Google Scholar 

  17. Ali I, Hassan A, Li F (2019) Authentication and privacy schemes for vehicular ad hoc networks (VANETs): a survey. Veh Commun 16:45–61

    Article  Google Scholar 

  18. Sakiz F, Sen S (2017) A survey of attacks and detection mechanisms on intelligent transportation systems: VANETs and IoV. Ad Hoc Netw 61:33–50

    Article  Google Scholar 

  19. Hasrouny H, Samhat AE, Bassil C, Laouiti A (2017) VANet security challenges and solutions: a survey. Veh Commun 7:7–20

    Article  Google Scholar 

  20. Amouee E, Zanjireh MM, Bahaghighat M, Ghorbani M (2020) A new anomalous text detection approach using unsupervised methods. Facta universitatis-series: Electron Energ 33(4):631–653

    Google Scholar 

  21. Hajikarimi A, Bahaghighat M (2022) Optimum outlier detection in Internet of things industries using autoencoder. In: Khosravy M, Gupta N, Patel N (eds) Frontiers in nature-inspired industrial optimization. Springer Tracts in Nature-Inspired Computing. Springer, Singapore. https://doi.org/10.1007/978-981-16-3128-3_5

  22. Bahaghighat M, Abedini F, Xin Q, Zanjireh MM, Mirjalili S (2021) Using machine learning and computer vision to estimate the angular velocity of wind turbines in smart grids remotely. Energy Reports

  23. Shamseen A, Zanjireh MM, Bahaghighat M, Xin Q (2021) Developing a parallel classifier for mining in big data sets. IIUM Eng J 22(2):119–134

    Article  Google Scholar 

  24. Haghi Kashani M, Madanipour M, Nikravan M, Asghari P, Mahdipour E (2021) A systematic review of IoT in healthcare: applications, techniques, and trends. J Netw Comput Appl 192:103164

    Article  Google Scholar 

  25. Maadani M, Motamedi SA, Safdarkhani H (2011) Delay-reliability trade-off in MIMO-enabled IEEE 802.11-based wireless sensor and actuator networks. Procedia Comput Sci 5:945–950

    Article  Google Scholar 

  26. Karimi Y, Haghi Kashani M, Akbari M, Mahdipour E (2021) Leveraging big data in smart cities: a systematic review. Concurrency and Computation: Practice and Experience, submitted for publication

  27. Naumov V, Gross 2007 TR Connectivity-aware routing (CAR) in vehicular ad-hoc networks. In IEEE INFOCOM 2007–26th IEEE Int Conf Comput Commun, (pp. 1919–1927): IEEE

  28. Yang Q, Lim A, Li S, Fang J, Agrawal P (2010) ACAR: adaptive connectivity aware routing for vehicular ad hoc networks in city scenarios. Mob Networks Appl 15(1):36–60

    Article  Google Scholar 

  29. Zarei M, Rahmani AM, Samimi H (2017) Connectivity analysis for dynamic movement of vehicular ad hoc networks. Wirel Netw 23(3):843–858

    Article  Google Scholar 

  30. Zhang W, Chen Y, Yang Y, Wang X, Zhang Y, Hong X et al (2012) Multi-hop connectivity probability in infrastructure-based vehicular networks. IEEE J Sel Areas Commun 30(4):740–747

    Article  Google Scholar 

  31. Maadani M, Motamedi SA 2011 EDCA delay analysis of spatial diversity in IEEE 802.11-based real-time wireless sensor and actuator networks. In 2011 8th International Symposium on Wireless Communication Systems,(675–679): IEEE

  32. O'Driscoll A, Pesch D 2013 An infrastructure enhanced geographic routing protocol for urban vehicular environments. In 2013 IEEE 5th International Symposium on Wireless Vehicular Communications (WiVeC),(pp. 1–5): IEEE

  33. Aadil F, Bajwa KB, Khan S, Chaudary NM, Akram A (2016) CACONET: ant colony optimization (ACO) based clustering algorithm for VANET. PloS one 11(5):e0154080

    Article  Google Scholar 

  34. Kashani MH, Zarrabi H, Javadzadeh G 2017 A new metaheuristic approach to task assignment problem in distributed systems. In 2017 IEEE 4th International Conference on Knowledge-Based Engineering and Innovation (KBEI), Tehran, Iran, 22-22 Dec. 2017(pp. 0673-0677). https://doi.org/10.1109/KBEI.2017.8324882

  35. Lin D, Kang J, Squicciarini A, Wu Y, Gurung S, Tonguz O (2016) MoZo: a moving zone based routing protocol using pure V2V communication in VANETs. IEEE Trans Mob Comput 16(5):1357–1370

    Article  Google Scholar 

  36. Fekair MEA, Lakas A, Korichi A 2016 CBQoS-Vanet: cluster-based artificial bee colony algorithm for QoS routing protocol in VANET. In 2016 International conference on selected topics in mobile & wireless networking (MoWNeT),(pp. 1–8): IEEE

  37. Zarei M, Rahmani AM (2016) Renewal process of information propagation in delay tolerant VANETs. Wireless Pers Commun 89(4):1045–1063

    Article  Google Scholar 

  38. Li G, Boukhatem L, Wu J (2017) Adaptive quality-of-service-based routing for vehicular ad hoc networks with ant colony optimization. IEEE Trans Veh Technol 66(4):3249–3264

    Article  Google Scholar 

  39. Joshua CJ, Duraisamy R, Varadarajan V (2019) A reputation based weighted clustering protocol in VANET: a multi-objective firefly approach. Mobile Networks and Applications 24(4):1199–1209

    Article  Google Scholar 

  40. Goswami V, Verma S, Singh V 2017 A novel hybrid GA-ACO based clustering algorithm for VANET. In 2017 3rd International Conference on Advances in Computing, Communication & Automation (ICACCA)(Fall), (pp. 1–6): IEEE

  41. Abuashour A, Kadoch M (2017) Performance improvement of cluster-based routing protocol in VANET. Ieee access 5:15354–15371

    Article  Google Scholar 

  42. Maadani M, Motamedi SA, Safdarkhani H 2011 An adaptive rate and coding scheme for MIMO-enabled IEEE 802.11-based Soft-Real-Time wireless sensor and actuator networks. In 2011 3rd International Conference on Computer Research and Development,(Vol. 1, pp. 439–443): IEEE

  43. Chen C, Liu L, Qiu T, Wu DO, Ren Z (2019) Delay-aware grid-based geographic routing in urban VANETs: a backbone approach. IEEE/ACM Trans Networking 27(6):2324–2337

    Article  Google Scholar 

  44. Maadani M, Motamedi SA, Soltani M (2012) EDCA Delay Analysis of Spatial Multiplexing in IEEE802. 11-Based Wireless Sensor and Actuator Networks. Int J Inf Electron Eng 2(3):318

    Google Scholar 

  45. Haider S, Abbas G, Abbas ZH, Baker T (2019) DABFS: a robust routing protocol for warning messages dissemination in VANETs. Comput Commun 147:21–34

    Article  Google Scholar 

  46. Zhang D, Zhang T, Liu X (2019) Novel self-adaptive routing service algorithm for application in VANET. Appl Intell 49(5):1866–1879

    Article  Google Scholar 

  47. Serhani A, Naja N, Jamali A (2020) AQ-Routing: mobility-, stability-aware adaptive routing protocol for data routing in MANET–IoT systems. Clust Comput 23(1):13–27

    Article  Google Scholar 

  48. Zarei M, Rahmani AM (2017) Analysis of vehicular mobility in a dynamic free-flow highway. Vehicular Communications 7:51–57

    Article  Google Scholar 

  49. Ram A, Mishra MK (2020) Density-connected cluster-based routing protocol in vehicular ad hoc networks. Ann Telecommun 75(7):319–332

    Article  Google Scholar 

  50. Maadani M, Motamedi SA, Noshari MM 2011 Delay analysis and improvement of IEEE 802.11 e-based soft-real-time wireless industrial networks: using an open-loop spatial multiplexing scheme. In 2011 International Symposium on Computer Networks and Distributed Systems (CNDS),(pp. 17–22): IEEE

  51. Saiáns-Vázquez JV, López-Nores M, Blanco-Fernández Y, Ordóñez-Morales EF, Bravo-Torres JF, Pazos-Arias JJ (2018) Efficient and viable intersection-based routing in VANETs on top of a virtualization layer. Ann Telecommun 73(5):317–328

    Article  Google Scholar 

  52. Jin Y, Okabe T, Sendhoff B 2003 Solving three-objective optimization problems using evolutionary dynamic weighted aggregation: results and analysis. In Genetic and Evolutionary Computation Conference,(pp. 636–637): Springer

  53. Medeiros DS, Hernandez DA, Campista MEM, Aloysio de Castro PP (2019) Impact of relative speed on node vicinity dynamics in VANETs. Wirel Netw 25(4):1895–1912

    Article  Google Scholar 

  54. de Medeiros DS, Campista MEM, Mitton N, de Amorim MD, Pujolle G (2017) The power of quasi-shortest paths: ρ-geodesic betweenness centrality. IEEE Trans Netw Sci Eng 4(3):187–200

    Article  MathSciNet  Google Scholar 

  55. Mattos DMF, Duarte OCMB (2016) AuthFlow: authentication and access control mechanism for software defined networking. Ann Telecommun 71(11):607–615

    Article  Google Scholar 

  56. Quincozes SE, Albuquerque C, Passos D, Mossé D (2021) A survey on intrusion detection and prevention systems in digital substations. Comput Netw 184:107679

    Article  Google Scholar 

  57. Lopez MA, Mattos DMF, Duarte OCM (2016) An elastic intrusion detection system for software networks. Ann Telecommun 71(11):595–605

    Article  Google Scholar 

  58. Wang W, Mosse D, Papadopoulos AV (2020) Packet priority assignment for wireless control systems of multiple physical systems. J Syst Archit 107:101708

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Shahr-e-Qods Branch, Islamic Azad University, Tehran, Iran

    Melody Jamalzadeh, Mohsen Maadani & Mojdeh Mahdavi

Authors
  1. Melody Jamalzadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohsen Maadani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mojdeh Mahdavi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

The paper is based on Melody Jamalzadeh’s MSc. thesis. Mohsen Maadani (the corresponding author) and Mojdeh Mahdavi are the thesis supervisor and advisor, respectively. All authors contributed to the idea development, algorithm design, analytical method verification, implementation of the research and simulation, analysis of the results, and writing of the manuscript.

Corresponding author

Correspondence to Mohsen Maadani.

Ethics declarations

Informed consent

Not applicable.

Conflict of interest

The authors declare no competing interests.

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

Jamalzadeh, M., Maadani, M. & Mahdavi, M. EC-MOPSO: an edge computing-assisted hybrid cluster and MOPSO-based routing protocol for the Internet of Vehicles. Ann. Telecommun. 77, 491–503 (2022). https://doi.org/10.1007/s12243-021-00892-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12243-021-00892-6

Keywords

Search

Navigation