Network performance estimator with applications to route selection for IoT multimedia applications

Abstract

Estimating the performance of multimedia (MM) traffic is important in numerous contexts, including routing and forwarding, quality of service (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 MM traffic in IEEE 802.11 multihop wireless networks. To our knowledge, the proposed MM-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 multirate flows, typical of MM traffic. Our experimental results indicate that MAPE is able to provide network performance estimates that can be used by IoT MM 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 multirate flows.