Intelligent TDMA heuristic scheduling by taking into account physical layer interference for an industrial IoT environment

In an Internet of Things environment, where multiple mobile devices are brought together, it is not always possible to serve all these devices simultaneously. We developed an intelligent Time Division Multiple Access (TDMA) scheduler which allows to plan the individual packets of the different streams in such a way that everyone can be served by taking into account the interference on the physical layer. The scheduler is applied in a realistic industrial environment and evaluated based on the maximum link latency, the channel occupancy, and the jitter. Two strategies are compared: one where the packets are sequentially allocated, and one periodically. Our results show that the periodically allocated strategy performs the best for the maximum link latency (for a packet size below 1200 bytes) and for the jitter. The channel occupancy is similar for both strategies. Furthermore, the performance can be improved by using a higher number of channels. Compared to classic Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA), the channel occupancy and the jitter are reduced up to 69.9 and 99.9%, respectively. Considering the maximum link latency, the proposed TDMA strategies perform significantly better than the worst case CSMA/CA (up to 99.8%), however, when assuming a best case CSMA/CA scenario, CSMA/CA performs better. Furthermore, we clearly show that there are cases where it is not possible to plan all streams when using CSMA/CA while this becomes feasible when applying the proposed TDMA strategies

Fluid flow queue models for fixed-mobile network evaluation

A methodology for fast and accurate end-to-end KPI, like throughput and delay, estimation is proposed based on the service-centric traffic flow analysis and the fluid flow queuing model named CURSA-SQ. Mobile network features, like shared medium and mobility, are considered defining the models to be taken into account such as the propagation models and the fluid flow scheduling model. The developed methodology provides accurate computation of these KPIs, while performing orders of magnitude faster than discrete event simulators like ns-3. Finally, this methodology combined to its capacity for performance estimation in MPLS networks enables its application for near real-time converged fixed-mobile networks operation as it is proven in three use case scenarios