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Delay Tolerant Networking to Extend Connectivity in Rural Areas Using Public Transport Systems: Design And Analysis

Salah Abdeljabar, Marco Zennaro, Mohamed-Slim Alouini

TL;DR

This work tackles the digital divide in rural areas by deploying a Delay Tolerant Networking framework that uses informal public transport as mobile data mules to extend Internet-like connectivity. It develops a probabilistic mobility model based on renewal theory, derives a tractable Mean Peak Age of Information (MPAoI) approximation, and analyzes data-rate performance, all validated with real-world mobility traces from Nouakchott, Accra, and Addis Ababa. The main contributions include a comprehensive mobility- and QoS-focused analytical framework, a closed-form approximation for AoI metrics, and a deployment-cost optimization that determines the minimum number of mobile DTN nodes to meet specified performance targets. The results demonstrate that leveraging existing transportation networks can meaningfully improve data delivery speed and information freshness in resource-constrained rural settings, with practical guidance for scalable deployment.

Abstract

In today's digital age, access to the Internet is essential, yet a significant digital divide exists, particularly in rural areas of developing nations. This paper presents a Delay Tolerant Networking (DTN) framework that utilizes informal public transportation systems, such as minibus taxis, as mobile data mules to enhance connectivity in these underserved regions. We develop a probabilistic model to capture the randomness in vehicle mobility, including travel times and contact durations at bus stops. Key performance metrics are analyzed, including average data transmission rate and Peak Age of Information (PAoI), to assess the effectiveness of the proposed system. An analytical approximation for the Mean PAoI (MPAoI) is derived and validated through simulations. Case studies from real-world datasets in Nouakchott, Accra, and Addis Ababa demonstrate the practical applicability and scalability of our framework. The findings indicate that leveraging existing transportation networks can significantly bridge the digital divide by providing reliable internet-like connectivity to remote areas.

Delay Tolerant Networking to Extend Connectivity in Rural Areas Using Public Transport Systems: Design And Analysis

TL;DR

This work tackles the digital divide in rural areas by deploying a Delay Tolerant Networking framework that uses informal public transport as mobile data mules to extend Internet-like connectivity. It develops a probabilistic mobility model based on renewal theory, derives a tractable Mean Peak Age of Information (MPAoI) approximation, and analyzes data-rate performance, all validated with real-world mobility traces from Nouakchott, Accra, and Addis Ababa. The main contributions include a comprehensive mobility- and QoS-focused analytical framework, a closed-form approximation for AoI metrics, and a deployment-cost optimization that determines the minimum number of mobile DTN nodes to meet specified performance targets. The results demonstrate that leveraging existing transportation networks can meaningfully improve data delivery speed and information freshness in resource-constrained rural settings, with practical guidance for scalable deployment.

Abstract

In today's digital age, access to the Internet is essential, yet a significant digital divide exists, particularly in rural areas of developing nations. This paper presents a Delay Tolerant Networking (DTN) framework that utilizes informal public transportation systems, such as minibus taxis, as mobile data mules to enhance connectivity in these underserved regions. We develop a probabilistic model to capture the randomness in vehicle mobility, including travel times and contact durations at bus stops. Key performance metrics are analyzed, including average data transmission rate and Peak Age of Information (PAoI), to assess the effectiveness of the proposed system. An analytical approximation for the Mean PAoI (MPAoI) is derived and validated through simulations. Case studies from real-world datasets in Nouakchott, Accra, and Addis Ababa demonstrate the practical applicability and scalability of our framework. The findings indicate that leveraging existing transportation networks can significantly bridge the digital divide by providing reliable internet-like connectivity to remote areas.

Paper Structure

This paper contains 22 sections, 2 theorems, 17 equations, 6 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Motivation
  3. Related Works
  4. Article Outline
  5. Notation Summary
  6. System Model
  7. Overview
  8. Considered Scenario
  9. DTN Data Mule Model
  10. Contact Time
  11. Travel Time
  12. Inter-arrival time of vehicles
  13. Performance Metrics
  14. Wireless Link Data Rate
  15. Downlink/Uplink Data Transmission Rate
  16. ...and 7 more sections

Key Result

Lemma 1

The data size for uplink/downlink is proportional to the time needed to pick up the data package from region A (or B) and deliver it to region B (or A). Thus, the transmitted data size for the $v$-th vehicle during contact time is given by: where the complementary cumulative distribution function (CCDF) of the data size is: where $c_1$ and $c_2$ are the minimum and maximum contact times at the b

Figures (6)

  • Figure 1: The considered DTN scenario, where DTN modules carried via informal public transport means are used to connect the city and rural networks.
  • Figure 2: A sample realization of AoI and PAoI for a system with two DTN-equipped vehicles. The peaks $A_1, A_2, \text{\ldots }$ correspond to the PAoI values.
  • Figure 3: MAoI, MPAoI, and MPAoI approximation as described in Equation \ref{['Equ:MPAoI_approximation']}. The simulation was averaged over $10^5$ time units. The maximum approximation error represents the largest observed difference between the analytical approximation in Equation \ref{['Equ:MPAoI_approximation']} and the simulation-based MPAoI across all vehicle configurations tested (1 to 20 vehicles). The red dashed line represents the average time required for a vehicle to complete a one-way trip from the village to the city ($T_{oneway}$).
  • Figure 4: MPAoI and mean data transmission rate for different mean round-trip times and DTN-equipped vehicles in the network.
  • Figure 5: Network performance metrics (Transmission Rate and MPAoI) for the routes (a) between Nouakchott University and Bamako Crossroads, and (b) Dodowa and Accra Central, considering varying numbers of DTN data mules.
  • ...and 1 more figures

Theorems & Definitions (4)

  • Lemma 1: Mean Transmitted Data Size
  • Lemma 2: Mean Data Transmission Rate
  • Definition 1: Age of Information
  • Definition 2: Peak Age of Information