GRES: Guaranteed Remaining Energy Scheduling of Energy-harvesting Sensors by Quality Adaptation - Sixdenier, Pierre-Louis; Wildermann, Stefan; Teich, Jürgen (Proceedings of the 13th Mediterranean Conference on Embedded Computing (MECO), 2024)
Many battery-powered IoT sensor nodes rely on harvesting energy which must be assumed an unreliable source. Previous works have shown that a sensor node can adapt its power consumption to keep its battery’s state of charge at a sufficient level to achieve perpetual operation by, e.g., dynamically adapting its duty cycle. In this paper, we show that it is also possible to reduce the energy consumed by the operation of a sensor node by controlling the quality of the processed and transmitted data. Moreover, whereas most state-of-the-art methods rely on forecasting energy harvesting, risking loss of service in case of wrong predictions, this paper presents an algorithm called Guaranteed Remaining Energy Scheduling (GRES) which dynamically controls the quality of processed and transmitted data of a sensor node at runtime based on the state of charge of the battery, and providing a guarantee of safe continuous operation at the expense of data quality despite fluctuations in expected harvested energy. In experiments, GRES is evaluated and compared to an approach computing ILP-generated quality schedules for one full day ahead based on the assumption of a perfect harvested energy prediction. It is shown that the latter approach is not only computationally and energy-wise expensive, but also can lead to power shutdowns in case of wrongly predicted harvesting profiles.<br

Hybrid Genetic Reinforcement Learning for Generating Run-Time Requirement Enforcers - Spieck, Jan; Sixdenier, Pierre-Louis; Esper, Khalil; Wildermann, Stefan; Teich, Jürgen (2023 21st ACM-IEEE International Symposium on Formal Methods and Models for System Design (MEMOCODE), 2023)

RAVEN: Reinforcement Learning for Generating Verifiable Run-time Requirement Enforcers for MPSoCs - Esper, Khalil; Spieck, Jan; Sixdenier, Pierre-Louis; Wildermann, Stefan; Teich, Jürgen (Fourth Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2023), 2023)
In embedded systems, applications frequently have to meet non-functional requirements regarding, e.g., real-time or energy consumption constraints, when executing on a given MPSoC target platform. Feedback-based controllers have been proposed that react to transient environmental factors by adapting the DVFS settings or degree of parallelism following some predefined control strategy. However, it is, in general, not possible to give formal guarantees for the obtained controllers to satisfy a given set of non-functional requirements. Run-time requirement enforcement has emerged as a field of research for the enforcement of non-functional requirements at run-time, allowing to define and formally verify properties on respective control strategies specified by automata. However, techniques for the automatic generation of such controllers have not yet been established. In this paper, we propose a technique using reinforcement learning to automatically generate verifiable feedback-based enforcers. For that, we train a control policy based on a representative input sequence at design time. The learned control strategy is then transformed into a verifiable enforcement automaton which constitutes our run-time control model that can handle unseen input data. As a case study, we apply the approach to generate controllers that are able to increase the probability of satisfying a given set of requirement verification goals compared to multiple state-of-the-art approaches, as can be verified by model checkers.<br
Keywords: Verification; Runtime Requirement Enforcement; Reinforcement Learning; Finite State Machines; Model Checking

Seque: Lean and Energy-aware Data Management for IoT Gateways - Sixdenier, Pierre-Louis; Wildermann, Stefan; Ottens, Martin; Teich, Jürgen (IEEE International Conference on Edge Computing and Communications (EDGE), 2023)

SIDAM: A Design Space Exploration Framework for Multi-Sensor Embedded Systems Powered by Energy Harvesting - Sixdenier, Pierre-Louis; Wildermann, Stefan; Ziegler, Daniel; Teich, Jürgen (International Conference on Embedded Computer Systems: Architectures, Modeling and Simulation (SAMOS XXII), 2022)
Multi-sensor embedded systems often consist of a central unit being responsible to manage a heterogeneous set of attached sensors. Particularly when such systems are deployed in areas without access to a static power supply, they have to be powered using energy harvesting to operate autonomously. Objectives such as availability and data loss rate depend on the set of attached sensors, the system configuration (e.g., used PV module, batteries, and data storage), as well as environmental factors such as the location of the deployed system. Moreover, also the employed energy management strategy and its parametrization severely influence the system characteristics. In fact, different strategies can lead to different tradeoffs in terms of the above objectives. In this paper we propose a design methodology to automatically explore the design space of configurations of multi-sensor embedded systems and to determine and configure the best energy management strategy for a given sensor configuration and location. Our methodology includes a real-time analysis and a simulation-based DSE to explore the design space. We investigate a case study from a biomonitoring project and demonstrate the benefits of the proposed design methodology: A system—including its configuration and energy management strategy—has to be tailored to the characteristics of the set of attached sensors and the location it operates. Else designs exhibit suboptimal characteristics when operating at sites or for sensor sets for which they were not optimized.<br

The Base Station - Sixdenier, Pierre-Louis; Reinhold, Lukas; Piotrowski, Krzysztof (Weather stations for biodiversity: a comprehensive approach to an automated and modular monitoring system, 2024)

Towards an Autonomous, Power-Efficient Base Station for Sensor Data Collection - Sixdenier, Pierre-Louis (2nd IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion, ACSOS-C 2021, 2021)
Keywords: Autonomous-system; Data-transmission; Data-compression; Power-management; Iot

WiP paper: Utility-Aware Transmission of Sensor Data on Energy-Harvesting IoT Gateways - Sixdenier, Pierre-Louis; Wildermann, Stefan; Arockiaraj, Jebacyril; Teich, Jürgen (22nd International Conference on Embedded Wireless Systems and Networks (EWSN 2025), 2025)