Fusion of Opportunistic Networks with Machine Learning: Present and Future

Authors

  • Sonal Beniwal Associate Professor, BPSMV, Khanpur Kalan, Gohana, Sonipat, Haryana, India
  • Puneet Garg Associate Professor, KIET Group of Institutions, Delhi NCR, Ghaziabad, India
  • Rakesh Rajpal Professor, SAITM, Gurugram Delhi NCR, Haryana, India
  • Neetu Sharma Professor, Galgotias University Greater Noida, Uttar Pradesh, India
  • Harish Kumar Mittal Professor, BMIET, Sonepat, Delhi NCR, India

DOI:

https://doi.org/10.63278/10.63278/mme.v31.1

Keywords:

Hyperspectral Imaging, HIS in tealeaves.

Abstract

Opportunistic Networks (OppNets) are mobile ad hoc networks characterized by intermittent connectivity and the lack of a guaranteed end-to-end path between source and destination. Nodes in an OppNet employ a store-carry-forward strategy – messages are stored and carried by mobile nodes until a communication opportunity arises, at which point they are forwarded. This paradigm enables data delivery in challenging environments (disaster areas, remote regions, etc.) where conventional infrastructure is absent, but it also introduces high delays and uncertainty. Machine Learning (ML) has emerged as a powerful tool to improve OppNet performance by exploiting patterns in node mobility, contact frequency, and context. This paper provides an extensive survey of the state-of-the-art in merging ML with OppNets and discusses future developments. In this paper, we analyze how ML algorithms have enhanced message delivery rates, reduced delays, and improved decision-making in OppNets (often outperforming traditional protocols by significant margins), as illustrated by recent results in the literature. Key challenges at this fusion include data sparsity, computational constraints on mobile devices, privacy/security concerns, and the need for realistic testing.

References

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Published

2025-01-22

How to Cite

Beniwal, Sonal, Puneet Garg, Rakesh Rajpal, Neetu Sharma, and Harish Kumar Mittal. 2025. “Fusion of Opportunistic Networks With Machine Learning: Present and Future”. Metallurgical and Materials Engineering 31 (1):311-24. https://doi.org/10.63278/10.63278/mme.v31.1.

Issue

Vol. 31 No. 1 (2025)

Section

Research

License

Copyright (c) 2025 K. Subramanian, Kangkan Talukdar, Mamidi H Varshith, Krtyush Kumar

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