Network Performance Estimator with Applications to Route Selection for IoT Multimedia Applications

TL;DR

This paper introduces MAPE, a real-time, deterministic network performance estimator for IoT multimedia traffic in wireless networks, enabling improved route selection with high accuracy and efficiency.

Contribution

The paper presents MAPE, the first deterministic, simulation-based estimator that provides real-time throughput, loss, and delay estimates considering multi-rate flows and interference.

Findings

MAPE offers real-time performance estimates for IoT multimedia traffic.

MAPE achieves higher accuracy than existing deterministic simulators.

MAPE operates faster than stochastic network simulators.

Abstract

Estimating the performance of multimedia traffic is important in numerous contexts, including routing and forwarding, QoS provisioning, and adaptive video streaming. This paper proposes a network performance estimator which aims at providing, in quasi real-time, network performance estimates for IoT multimedia traffic in IEEE 802.11 multihop wireless networks. To our knowledge, the proposed multimedia-aware performance estimator, or MAPE, is the first deterministic simulation-based estimator that provides real-time per-flow throughput, packet loss and delay estimates while considering inter-flow interference and multi-rate flows, typical of multimedia traffic. Our experimental results indicate that MAPE is able to provide network performance estimates that can be used by IoT multimedia services, notably to inform real-time route selection in IoT video transmission, at a fraction of the execution time when compared to stochastic network simulators. When compared to existing deterministic simulators, MAPE yields higher accuracy at comparable execution times due to its ability to consider multi-rate flows.