Seamless Connectivity and Efficient Resource Allocation in IoT Forensics and Bio-inspired

Volume: 10 | Issue: 02 | Year 2024 | Subscription
International Journal of Telecommunications & Emerging Technologies
Received Date: 09/05/2024
Acceptance Date: 10/04/2024
Published On: 2024-11-18
First Page: 8
Last Page: 15

Journal Menu

By: Sumaira Mushtaq, Ahthasham Sajid, Sajid Iqbal, Malik Muhammad Nadeem, and Fatima Shoaib

1-Student, Department of Information Security, Riphah Institute of Systems Engineering, Riphah International University, Islamabad, Pakistan.
2-Assistant Professor, Department of Information Security, Riphah Institute of Systems Engineering, Riphah International University, Islamabad, Pakistan.
3-Student, Department of Information Security, Riphah Institute of Systems Engineering, Riphah International University, Islamabad, Pakistan.
4-Student, Department of Information Security, Riphah Institute of Systems Engineering, Riphah International University, Islamabad, Pakistan.

Abstract

The convergence of cloud computing and the Internet of Things has reshaped many industries, enabling seamless device connectivity and resource allocation on a massive scale. This paper highlights the challenges bio-inspired Internet of Things networks and Internet of Things forensics pose while delving into the core components of achieving connectivity and resource provisioning within cloud-based Internet of Things environments. Different state-of-the-art technologies have been taken into account to enhance reliability in data transmission while minimizing latency for efficient forensic investigations this includes software-defined networking, 5G mobile networks at the edge, and data offloading capabilities from cognitive radio networks via cloudlet technology. The study delves into load-balancing methods and dynamic resource allocation strategies while considering how machine learning can assist in optimizing distribution. Data integrity, device heterogeneity, and privacy issues are some of the factors that Internet of Things forensics includes in forensic investigations to address complications; it also seeks support from bio-inspired algorithms such as swarm intelligence and genetic algorithms that enhance the performance adaptability of networks. The primary goal of this research is to enhance the safety, efficiency, and reliability of cloud-based Internet of things networks. By reviewing existing literature, in this study, authors have identified several innovative solutions applicable to smart cities, healthcare, and other critical sectors.

Keywords: IoT, cloud computing, artificial intelligence, connectivity networks, SDN, 5G mobile network

Loading

Citation:

How to cite this article: Sumaira Mushtaq, Ahthasham Sajid, Sajid Iqbal, Malik Muhammad Nadeem, and Fatima Shoaib, Seamless Connectivity and Efficient Resource Allocation in IoT Forensics and Bio-inspired. International Journal of Telecommunications & Emerging Technologies. 2024; 10(02): 8-15p.

How to cite this URL: Sumaira Mushtaq, Ahthasham Sajid, Sajid Iqbal, Malik Muhammad Nadeem, and Fatima Shoaib, Seamless Connectivity and Efficient Resource Allocation in IoT Forensics and Bio-inspired. International Journal of Telecommunications & Emerging Technologies. 2024; 10(02): 8-15p. Available from:https://journalspub.com/publication/seamless-connectivity-and-efficient-resource-allocation-in-iot-forensics-and-bio-inspired/
Full Text

Refrences:

  1. Xiao Z, Song W, Chen Q. Dynamic resource allocation using virtual machines for cloud computing environment. IEEE Trans Parall Distrib Syst. 2012;24(6):1107–17.
  2. Patel R, Patel S. Survey on resource allocation strategies in cloud computing. Int J Engin Res Technol. 2013;2(2)1–5.
  3. Oriwoh E, Jazani D, Epiphaniou G, Sant P. Internet of things forensics: challenges and approaches. In: 9th IEEE International Conference on Collaborative computing: Networking, Applications and Worksharing. IEEE. 2013 Oct 20. pp. 608–615.
  4. Lee K. Security threats in cloud computing environments. Int J Sec Appl. 2012;6(4):25–32.
  5. Dressler F, Akan OB. A survey on bio-inspired networking. Comp Net. 2010;54(6):881–900.
  6. Kennedy J, Eberhart R. Particle swarm optimization. In: Proceedings of ICNN’95-international conference on neural networks. IEEE. 1995 Nov 27;4:1942–1948.
  7. MacDermott A, Baker T, Shi Q. IoT forensics: challenges for the IoA era. In: 2018 9th IFIP International Conference on New Technologies, Mobility and Security (NTMS). IEEE. 2018 Feb 26. pp. 1–5.
  8. Masdari M, Gharehpasha S, Ghobaei-Arani M, Ghasemi V. Bio-inspired virtual machine placement schemes in cloud computing environment: taxonomy, review, and future research directions. Clust Comp. 2020;23(4):2533–63.
  9. Beloglazov A, Abawajy J, Buyya R. Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Fut Gen Comp Syst. 2012;28(5):755–68.
  10. Conti M, Dehghantanha A, Franke K, Watson S. Internet of things security and forensics: challenges and opportunities. Fut Gen Comp Syst. 2018; 78:544–6.
  11. Mazhar MS, Saleem Y, Almogren A, Arshad J, Jaffery MH, Rehman AU, Shafiq M, Hamam H. Forensic analysis on internet of things (IoT) device using machine-to-machine (M2M) framework. Electronics. 2022;11(7):1126.
  12. Holland JH. Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press; 1992.
  13. Kodali RK, Swamy G, Lakshmi B. An implementation of IoT for healthcare. In: 2015 IEEE Recent Advances in Intelligent Computational Systems (RAICS). IEEE. 2015 Dec 10. pp. 411–416.
  14. Kreutz D, Ramos FM, Verissimo PE, Rothenberg CE, Azodolmolky S, Uhlig S. Software-defined networking: a comprehensive survey. Proc IEEE. 2014;103(1):14–76.
  15. Merkel D. Docker: lightweight linux containers for consistent development and deployment. Linux J. 2014;239(2):2.
  16. Joshi S, Kumari U. Load balancing in cloud computing: challenges & issues. In: 2016 2nd International Conference on Contemporary Computing and Informatics (IC3I). IEEE. 2016 Dec 14. pp. 120–125.
  17. Perumal S, Norwawi NM, Raman V. Internet of Things (IoT) digital forensic investigation model: top-down forensic approach methodology. In: 2015 Fifth International Conference on Digital Information Processing and Communications (ICDIPC). IEEE. 2015 Oct 7. pp. 19–23.
  18. Simou S, Kalloniatis C, Kavakli E, Gritzalis S. Cloud forensics solutions: a review. In: Advanced Information Systems Engineering Workshops: CAiSE 2014 International Workshops, Thessaloniki, Greece. Springer International Publishing. 2014 June 16–20. pp. 299–309.
  19. Schmidhuber J. Deep learning in neural networks: an overview. Neural Net. 2015;61:85–117.
  20. Shi W, Cao J, Zhang Q, Li Y, Xu L. Edge computing: vision and challenges. IEEE IoT J. 2016;3(5):637–46.
  21. Zanella A, Bui N, Castellani A, Vangelista L, Zorzi M. Internet of things for smart cities. IEEE IoT J. 2014;1(1):22–32.
  22. Zawoad S, Hasan R. Faiot: towards building forensics aware eco system for the internet of things. In: 2015 IEEE International Conference on Services Computing. IEEE. 2015 Jun 27. pp. 279–284.
  23. Liu A, Zhang Q, Xu S, Feng H, Chen XB, Liu W. QBIoT: a quantum blockchain framework for IoT with an improved proof-of-authority consensus algorithm and a public-key quantum signature. Comput Mat Continua. 2024;80(1).
  24. Al-Fuqaha A, Guizani M, Mohammadi M, Aledhari M, Ayyash M. Internet of things: a survey on enabling technologies, protocols, and applications. IEEE Commun Surv Tutor. 2015;17(4):2347–76.

 Previous Article
Next Article