Open AccessMachine Learning in Delay Tolerant Networks: Algorithms, Strategies, and Applications
Author(s) -
Ms Amirthavalli,
S. Ramya
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.a1009.1191s19
Subject(s) - computer science , delay tolerant networking , computer network , network packet , overhead (engineering) , routing (electronic design automation) , network congestion , distributed computing , routing protocol , optimized link state routing protocol , operating system
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