Tracking Topology Dynamicity for Link Prediction in Intermittently Connected Wireless Networks

TL;DR

This paper introduces a tensor-based temporal link prediction method for intermittently connected wireless networks, leveraging human behavior patterns to improve prediction accuracy in Delay Tolerant Networks.

Contribution

It proposes a novel tensor-based technique for predicting links in DTNs using behavior similarity, validated through distributed implementation and comparison with existing metrics.

Findings

Tensor-based method effectively predicts future links.

Distributed implementation performs well with local information.

Outperforms some existing link prediction metrics.

Abstract

Through several studies, it has been highlighted that mobility patterns in mobile networks are driven by human behaviors. This effect has been particularly observed in intermittently connected networks like DTN (Delay Tolerant Networks). Given that common social intentions generate similar human behavior, it is relevant to exploit this knowledge in the network protocols design, e.g. to identify the closeness degree between two nodes. In this paper, we propose a temporal link prediction technique for DTN which quantifies the behavior similarity between each pair of nodes and makes use of it to predict future links. We attest that the tensor-based technique is effective for temporal link prediction applied to the intermittently connected networks. The validity of this method is proved when the prediction is made in a distributed way (i.e. with local information) and its performance is compared to well-known link prediction metrics proposed in the literature.