Energy saving on DTN using trajectory inference model

Delay or Disruption Tolerant Networks (DTN) are characterized by long delays and intermittent connectivity, requiring efficient energy consumption for increasing the mobile nodes lifetime. The movements of nodes modify the network topology, changing the number of connection opportunities between nodes. This paper proposes a new technique for energy saving on DTN by using a trajectory inference model for mobile nodes powered by machine learning techniques. The objective of this work is to reduce the energy consumption of DTN using a mobility prediction method. Experimental results indicate more than 47% of energy saving on data communication applying the trajectory inference model.

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