Machine Learning in Delay Tolerant Networks: Algorithms, Strategies, and Applications
R. Amirthavalli1, S. Thanga Ramya2
1Ms. R. Amirthavalli, Department of CSE, Velammal Engineering College, Chennai (Tamil Nadu), India.
2Dr. S. Thanga Ramya, Department of IT, RMD Engineering College, Chennai (Tamil Nadu), India.
Manuscript received on 22 November 2019 | Revised Manuscript received on 03 December 2019 | Manuscript Published on 14 December 2019 | PP: 34-38 | Volume-9 Issue-1S November 2019 | Retrieval Number: A10091191S19/2019©BEIESP | DOI: 10.35940/ijitee.A1009.1191S19
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Delay Tolerant Networks (DTNs) has intermittent connectivity, nodes in the network experience a long delay in the delivery of packets, and the nodes are sparsely distributed. DTN is deployed in the applications where human intervention is least like underwater communication, interplanetary communication, disaster management, tracking wildlife, etc. Any changes in the environment affect the deployed sensor nodes, so it is required that the sensor nodes adapt to these environmental changes. Machine-Learning (ML) techniques can be deployed to overcome such difficulty. ML improves the network lifetime. ML in DTN facilitates routing by adapting to the network changes, mitigates congestion, reduces overhead. This paper provides a survey of ML techniques used in DTN. To the best of our knowledge, this work is the first of its kind to survey ML techniques in DTN.
Keywords: Delay Tolerant Networks, Machine Learning, Routing, Supervised Learning.
Scope of the Article: Machine Learning