Characterizing and Evaluation :Temporal properties of real and synthetic datasets for DTN

TL;DR

This paper analyzes the temporal properties of real and synthetic datasets in Delay Tolerant Networks to improve message dissemination by understanding mobility patterns and their impact on network connectivity.

Contribution

It introduces a characterization and evaluation framework for temporal properties of mobility datasets, aiding in the design of better DTN algorithms.

Findings

Real and synthetic datasets exhibit distinct temporal properties.

Temporal distance and diameter influence message delivery efficiency.

Synthetic datasets can be evaluated for realism using the proposed metrics.

Abstract

Nodes movements play a significant role in disseminating messages in the sparse mobile ad-hoc network. In the network scenarios, where traditional end-to-end paths do not exist, mobility creates opportunities for nodes to connect and communicate when they are encountered. A series of encountering opportunities spread a message among many nodes and eventually deliver to the destination. Further improvements to the performance of message delivery can come from exploiting temporal mobility properties. It is modeled as time varying graph, where, moving nodes are considered as vertices and contact opportunity to other nodes as an edge. The paper discusses about characterization and design of the temporal algorithm. Then, evaluating temporal distance, diameter and centrality of real and synthetic mobility data sets.