Skip to main content

Advertisement

SpringerLink
Log in

Nature-inspired algorithm-based secure data dissemination framework for smart city networks

  • S. I : Hybridization of Neural Computing with Nature Inspired Algorithms
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Unceasing population growth and urbanization have intensified the traditional systems to deal with citizen lifestyle, environment, economic issues and good governess. New communication technologies have played a vital role in changes traditional urbanization into a smarter and comfort zone for the citizen. Due to various systems and integration of several new standards and systems, the smart cities have suffered from various open challenges related to technologies, system controlling and management, scalability and security concerns. The new concepts of nature-inspired solutions have implemented to deal with smart cities’ challenges by more optimization and performance-oriented methods. Therefore, this paper aims to handle at least three areas of smart cities including smart mobility, smart living and security provision by developing three nature-inspired solutions. The three proposed solutions are dragon clustering mobility in IoV, moth flame electric management for smart living and ant colony-based intrusion detection system for security provision. These solutions are based on a dragonfly, moth flame and ant colony optimization techniques. The proposed solutions are evaluated in a simulation to check the performance. These solutions will help new researchers to explore the nature-inspired solutions to tackle the new and complex systems of smart cities.

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

Similar content being viewed by others

References

  1. Matos A, Pinto B, Barros F, Martins S, Martins J, Au-Yong-Oliveira M (2019) Smart cities and smart tourism: what future do they bring? In: World conference on information systems and technologies, 2019. Springer, pp 358–370

  2. Ismagilova E, Hughes L, Dwivedi YK, Raman KR (2019) Smart cities: advances in research—an information systems perspective. Int J Inf Manag 47:88–100

    Article  Google Scholar 

  3. Qureshi KN, Abdullah AH (2013) A survey on intelligent transportation systems. Middle East J Sci Res 15(5):629–642

    Google Scholar 

  4. Alanazi HO, Abdullah AH, Qureshi KN (2017) A critical review for developing accurate and dynamic predictive models using machine learning methods in medicine and health care. J Med Syst 41(4):69

    Article  Google Scholar 

  5. Bibri SE, Krogstie J (2017) Smart sustainable cities of the future: an extensive interdisciplinary literature review. Sustain Cities Soc 31:183–212

    Article  Google Scholar 

  6. Iqbal S, Abdullah AH, Qureshi KN, Lloret J (2017) Soft-GORA: soft constrained globally optimal resource allocation for critical links in IoT backhaul communication. IEEE Access 6:614–624

    Article  Google Scholar 

  7. Bujok P, Tvrdík J, Poláková R (2019) Comparison of nature-inspired population-based algorithms on continuous optimisation problems. Swarm Evol Comput 50:100490

    Article  Google Scholar 

  8. Mirjalili S, Dong JS (2020) Introduction to nature-inspired algorithms. In: Nature-inspired optimizers. Springer, pp 1–5. https://doi.org/10.1007/978-3-030-12127-3_1

  9. da Costa KA, Papa JP, Lisboa CO, Munoz R, de Albuquerque VH (2019) Internet of things: a survey on machine learning-based intrusion detection approaches. Comput Netw 151:147–157

    Article  Google Scholar 

  10. Guimaraes RR et al (2018) Intelligent network security monitoring based on optimum-path forest clustering. IEEE Netw 33(2):126–131

    Article  Google Scholar 

  11. Khatoun R, Zeadally S (2016) Smart cities: concepts, architectures, research opportunities. Commun ACM 59(8):46–57

    Article  Google Scholar 

  12. Höjer M, Wangel J (2015) Smart sustainable cities: definition and challenges. In: ICT innovations for sustainability. Springer, pp 333–349

  13. Monzon A (2015) Smart cities concept and challenges: bases for the assessment of smart city projects. In: 2015 international conference on smart cities and green ICT systems (SMARTGREENS), 2015. IEEE, pp 1–11

  14. Farahat I, Tolba A, Elhoseny M, Eladrosy W (2019) Data security and challenges in smart cities. In: Security in smart cities: models, applications, and challenges. Springer, pp 117–142

  15. Qureshi KN, Bashir F, Abdullah AH (2017) Provision of security in vehicular ad hoc networks through an intelligent secure routing scheme. In: 2017 international conference on frontiers of information technology (FIT), 2017. IEEE, pp 200–205

  16. Elmaghraby AS, Losavio MM (2014) Cyber security challenges in smart cities: safety, security and privacy. J Adv Res 5(4):491–497

    Article  Google Scholar 

  17. Höjer M, Wangel J (2015) Smart sustainable cities: definition and challenges. In: ICT innovations for sustainability, Springer, pp 333–349. https://doi.org/10.1007/978-3-319-09228-7_20

  18. Brincat AA, Pacifici F, Martinaglia S, Mazzola F (2019) The internet of things for intelligent transportation systems in real smart cities scenarios. In: 2019 IEEE 5th world forum on internet of things (WF-IoT), 2019. IEEE, pp 128–132

  19. Adart A, Mouncif H, Naimi M (2017) Vehicular ad-hoc network application for urban traffic management based on markov chains. Int Arab J Inf Technol (IAJIT) 14(4A):624–631

    Google Scholar 

  20. Zhu W, Gao D, Zhao W, Zhang H, Chiang H-P (2018) SDN-enabled hybrid emergency message transmission architecture in internet-of-vehicles. Enterp Inf Syst 12(4):471–491

    Article  Google Scholar 

  21. Gagliardi D, Schina L, Sarcinella ML, Mangialardi G, Niglia F, Corallo A (2017) Information and communication technologies and public participation: interactive maps and value added for citizens. Gov Inf Q 34(1):153–166

    Article  Google Scholar 

  22. Vattapparamban E, Güvenç İ, Yurekli Aİ, Akkaya K, Uluağaç S (2016) Drones for smart cities: issues in cybersecurity, privacy, and public safety. In: 2016 international wireless communications and mobile computing conference (IWCMC), 2016. IEEE, pp 216–221

  23. Menouar H, Guvenc I, Akkaya K, Uluagac AS, Kadri A, Tuncer A (2017) UAV-enabled intelligent transportation systems for the smart city: applications and challenges. IEEE Commun Mag 55(3):22–28

    Article  Google Scholar 

  24. Cilliers L, Flowerday S (2017) Factors that influence the usability of a participatory IVR crowdsourcing system in a smart city. S Afr Comput J 29(3):16–30

    Google Scholar 

  25. Boukhechba M, Bouzouane A, Gaboury S, Gouin-Vallerand C, Giroux S, Bouchard B (2017) A novel Bluetooth low energy based system for spatial exploration in smart cities. Expert Syst Appl 77:71–82

    Article  Google Scholar 

  26. Cao Y, Li Y, Liu X, Rehtanz C (2020) Self-sustainable community of electricity prosumers in distribution system. In: Cyber-physical energy and power systems. Springer, 2020, pp 119–138

  27. Kumar S, Dohare U, Kumar K, Prasad D, Qureshi KN, Kharel R (2018) Cybersecurity measures for geocasting in vehicular cyber physical system environments. IEEE Internet Things J 6(4):5916–5926

    Article  Google Scholar 

  28. Qureshi KN, Bashir MU, Lloret J, Leon A (2020) Optimized cluster-based dynamic energy-aware routing protocol for wireless sensor networks in agriculture precision. J Sens. https://doi.org/10.1155/2020/9040395

    Article  Google Scholar 

  29. Qureshi KN, Din S, Jeon G, Piccialli F (2020) Link quality and energy utilization based preferable next hop selection routing for wireless body area networks. Comput Commun 149:382–392

    Article  Google Scholar 

  30. Yaqoob I, Hashem IAT, Mehmood Y, Gani A, Mokhtar S, Guizani S (2017) Enabling communication technologies for smart cities. IEEE Commun Mag 55(1):112–120

    Article  Google Scholar 

  31. Awan KM et al (2019) A priority-based congestion-avoidance routing protocol using IoT-based heterogeneous medical sensors for energy efficiency in healthcare wireless body area networks. Int J Distrib Sens Netw 15(6):1550147719853980

    Article  Google Scholar 

  32. Nathali Silva B, Khan M, Han KJWC (2017) Big data analytics embedded smart city architecture for performance enhancement through real-time data processing and decision-making. Wirel Commun Mob Comput. https://doi.org/10.1155/2017/9429676

    Article  Google Scholar 

  33. Dorigo M, Stützle T (2019) Ant colony optimization: overview and recent advances. In: Handbook of metaheuristics. Springer, 2019, pp 311–351

  34. Fahad M et al (2018) Grey wolf optimization based clustering algorithm for vehicular ad-hoc networks. Comput Electr Eng 70:853–870

    Article  Google Scholar 

  35. Aadil F, Ahsan W, Rehman ZU, Shah PA, Rho S, Mehmood I (2018) Clustering algorithm for internet of vehicles (IoV) based on dragonfly optimizer (CAVDO). J Supercomput 74(9):4542–4567

    Article  Google Scholar 

  36. Ebadinezhad S, Dereboylu Z, Ever E (2019) Clustering-based modified ant colony optimizer for internet of vehicles (CACOIOV). Sustainability 11(9):2624

    Article  Google Scholar 

  37. Mohanakrishnan U, Ramakrishnan B (2020) MCTRP: an energy efficient tree routing protocol for vehicular ad hoc network using genetic whale optimization algorithm. Wirel Pers Commun 110:185–206

    Article  Google Scholar 

  38. Kumar PM, Devi U, Manogaran G, Sundarasekar R, Chilamkurti N, Varatharajan R (2018) Ant colony optimization algorithm with internet of vehicles for intelligent traffic control system. Comput Netw 144:154–162

    Article  Google Scholar 

  39. Rida N, Ouadoud M, Hasbi A (2020) Ant colony optimization for real time traffic lights control on a single intersection. Int J Interact Mob Technol 14(02):196–214

    Article  Google Scholar 

  40. Hariz MB, Said D, Mouftah HT (2019) Mobility traffic model based on combination of multiple transportation forms in the smart city. In: 2019 15th international wireless communications & mobile computing conference (IWCMC), 2019. IEEE, pp 14–19

  41. Lakshmanaprabu S, Shankar K, Ilayaraja M, Nasir AW, Vijayakumar V, Chilamkurti N (2019) Random forest for big data classification in the internet of things using optimal features. Int J Mach Learn Cybern 2019:1–10

    Google Scholar 

  42. Hassan MK, El Desouky AI, Badawy MM, Sarhan AM, Elhoseny M, Gunasekaran M (2019) EoT-driven hybrid ambient assisted living framework with naïve Bayes–firefly algorithm. Neural Comput Appl 31(5):1275–1300

    Article  Google Scholar 

  43. Ullah I, Hussain S (2019) Time-constrained nature-inspired optimization algorithms for an efficient energy management system in smart homes and buildings. Appl Sci 9(4):792

    Article  Google Scholar 

  44. Butt AA, et al (2017) Energy efficiency using genetic and crow search algorithms in smart grid. In: International conference on P2p, parallel, grid, cloud and internet computing, 2017. Springer, pp 63–75

  45. Ochoa A, Oliva D (2018) Smart traffic management to support people with color blindness in a Smart City. In: 2018 IEEE Latin American conference on computational intelligence (LA-CCI), 2018. IEEE, pp 1–8

  46. Famila S, Jawahar A, Sariga A, Shankar K (2019) Improved artificial bee colony optimization based clustering algorithm for SMART sensor environments. Peer Peer Netw Appl 2019:1–9

    Google Scholar 

  47. Madan S, Goswami P (2019) A novel technique for privacy preservation using k-anonymization and nature inspired optimization algorithms. Available at SSRN 3357276, 2019

  48. Elhoseny M, Shankar K, Lakshmanaprabu S, Maseleno A, Arunkumar N (2018) Hybrid optimization with cryptography encryption for medical image security in internet of things. Neural Comput Appl 2018:1–15

    Google Scholar 

  49. Madan S, Goswami P (2018) A privacy preserving scheme for big data publishing in the cloud using k-anonymization and hybridized optimization algorithm. In: 2018 international conference on circuits and systems in digital enterprise technology (ICCSDET), 2018. IEEE, pp 1–7

  50. Wang Y, Zhang M, Shu W (2018) An emerging intelligent optimization algorithm based on trust sensing model for wireless sensor networks. EURASIP J Wirel Commun Netw 2018(1):145

    Article  Google Scholar 

  51. Tyagi M, Manoria M, Mishra B (2019) A framework for data storage security with efficient computing in cloud. In: International conference on advanced computing networking and informatics, 2019. Springer, pp 109–116

  52. Garg S, Kaur K, Batra S, Kaddoum G, Kumar N, Boukerche A (2020) A multi-stage anomaly detection scheme for augmenting the security in IoT-enabled applications. Fut Gener Comput Syst 104:105–118

    Article  Google Scholar 

  53. Qureshi KN, Abdullah AH, Kaiwartya O, Ullah F, Iqbal S, Altameem A (2016) Weighted link quality and forward progress coupled with modified RTS/CTS for beaconless packet forwarding protocol (B-PFP) in VANETs. Telecommun Syst: 1–16

  54. Hernafi Y, Ahmed MB, Bouhorma M (2017) ACO and PSO algorithms for developing a new communication model for VANET applications in smart cities. Wirel Pers Commun 96(2):2039–2075

    Article  Google Scholar 

  55. Hadded M, Zagrouba R, Laouiti A, Muhlethaler P, Saidane LA (2015) A multi-objective genetic algorithm-based adaptive weighted clustering protocol in vanet. In: 2015 IEEE congress on evolutionary computation (CEC), 2015. IEEE, pp 994–1002

  56. Mirjalili S (2016) Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput Appl 27(4):1053–1073

    Article  MathSciNet  Google Scholar 

  57. Iwai N et al (2017) Examination of the link between life stages uncovered the mechanisms by which habitat characteristics affect odonates. Ecosphere 8(9):e01930

    Article  Google Scholar 

  58. Bonabeau E, Dorigo M, Marco DDRDF, Theraulaz G, Théraulaz G (1999) Swarm intelligence: from natural to artificial systems, vol 1. Oxford University Press, Oxford

    Book  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Bahria University, Islamabad, Pakistan

    Kashif Naseer Qureshi

  2. Dipartimento di Informatica, Università degli Studi di Milano, Milan, Italy

    Awais Ahmad

  3. Department of Mathematics and Applications “Renato Caccioppoli”, University of Naples Federico II, Naples, Italy

    Francesco Piccialli & Giampaolo Casolla

  4. School of Electronic Engineering, Xidian University, Xi’an, China

    Gwanggil Jeon

  5. Department of Embedded Systems Engineering, Incheon National University, Incheon, Korea

    Gwanggil Jeon

Authors
  1. Kashif Naseer Qureshi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Awais Ahmad
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Francesco Piccialli
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Giampaolo Casolla
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Gwanggil Jeon
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Francesco Piccialli or Gwanggil Jeon.

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

Qureshi, K.N., Ahmad, A., Piccialli, F. et al. Nature-inspired algorithm-based secure data dissemination framework for smart city networks. Neural Comput & Applic 33, 10637–10656 (2021). https://doi.org/10.1007/s00521-020-04900-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-020-04900-z

Keywords

Search

Navigation