MDC Enhanced IoT Networks: Network Modeling and Performance Analysis

TL;DR

This paper develops an analytical framework combining queueing theory and stochastic geometry to evaluate the performance of MDC-enhanced IoT networks, focusing on coverage, delay, and energy metrics.

Contribution

It introduces a novel analytical model for MDC-enhanced IoT networks, deriving closed-form expressions for key performance metrics and providing insights for system optimization.

Findings

MDC velocity has minimal impact on coverage probability.

Optimal sensor density and contact radius can minimize delay.

The framework quantifies effects of system parameters on performance.

Abstract

As a promising architecture, Mobile Data Collector (MDC) enhanced Internet of Things (IoT) exhibits broad prospects in efficient data collection and data aggregation especially for sparse deployment scenarios. Combining the tools from queueing theory and stochastic geometry, we propose an analytical framework to study the network performance of an MDC enhanced IoT network, in terms of coverage probability, end-to-end delay and energy consumption. We derive the closed-form expressions for average contact and inter-contact time between a sensor and its associated MDC. By modeling the data collection system between a sensor and its associated MDCs as an M/G/1 queue system with vacations and general limited (G-limited) service, we first derive the queueing delay at the tagged sensor, and further obtain the end-to-end delay. The proposed analytical framework enables us to quantify the effect on network performance of key system parameters, such as MDC velocity, packet arrival rate, densities of sensors and MDCs, and contact radius. This study reveals that the MDC velocity has little impact on the coverage probability, and provides guidelines to minimize the end-to-end delay by optimizing the density and contact radius of sensors, and the velocity and density of MDCs.