Abstract
This paper provides an overview of the SUPERIOT project, an EU SNS JU (Smart Networks and Services Joint Undertaking) initiative focused on developing truly sustainable IoT systems. The SUPERIOT concept is based on a unique holistic approach to sustainability, proactively developing sustainable solutions considering the design, implementation, usage and disposal/reuse stages. The concept exploits radio and optical technologies to provide dual-mode wireless connectivity and dual-mode energy harvesting as well as dual-mode IoT node positioning. The implementation of the IoT nodes or devices will maximize the use of sustainable printed electronics technologies, including printed components, conductive inks and substrates. The paper describes the SUPERIOT concept, covering the key technical approaches to be used, promising scenarios and applications, project goals and demonstrators which will be developed to the proof-of-concept stage. In addition, the paper briefly discusses some important visions on how this technology may be further developed in the future.

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Abstract
Digital twins have been emerging as a hybrid approach that combines the benefits of simulators with the realism of experimental testbeds. The accurate and repeatable set-ups replicating the dynamic conditions of physical environments, enable digital twins of wireless networks to be used to evaluate the performance of next-generation networks. In this paper, we propose the Position-based Machine Learning Propagation Loss Model (P-MLPL), enabling the creation of fast and more precise digital twins of wireless networks in ns-3. Based on network traces collected in an experimental testbed, the P-MLPL model estimates the propagation loss suffered by packets exchanged between a transmitter and a receiver, considering the absolute node's positions and the traffic direction. The P-MLPL model is validated with a test suite. The results show that the P-MLPL model can predict the propagation loss with a median error of 2.5 dB, which corresponds to 0.5x the error of existing models in ns-3. Moreover, ns-3 simulations with the P-MLPL model estimated the throughput with an error up to 2.5 Mbit/s, when compared to the real values measured in the testbed.

Abstract