ConTST: Predicting Mobile Subnetwork Encounters in Dynamic Factories with Time Series Transformers - Müller, Kilian; Franchi, Norman (2024 IEEE 100th Vehicular Technology Conference, 2024)
Subnetworks with different levels of autonomy are an integral part of 6G, helping to improve local connectivity, resilience, and security. These are especially interesting for cooperative robotics on shop floors, where multiple robots can come together and directly exchange task-specific data in near real-time without stressing the parent network. The same sub-network capabilities are currently implemented into Automated Guided Vehicles (AGVs), allowing non-time-critical data from near monitoring equipment to be offloaded via AGVs instead of streaming them directly to the shop floor's parent network, relieving the parent network in the process. However, to do so, an efficient approach is needed, which (1) can predict future encounters between AGVs and monitoring nodes on a node-to-node basis with minimal traffic overhead, (2) does not require heavy computation like machine learning on highly constraint nodes, and (3) does not need access to AGVs' subnetwork controllers and routing tables.Introducing ConTST (CONnection forecasting with Time Series Transformers), a semi-passive, probabilistic approach for predicting future data offloading possibilities to autonomous mobile subnetworks on a node-to-node basis from only successfully received bundles in the network edge. We utilize a single, small probabilistic time series transformer model in the network edge, minimal additional metadata per bundle, and some tracing bundles to successfully estimate future encounters from past encounters. Nodes optimize their offloading strategy greedily by only utilizing precomputed encounter tables from the edge services, making this approach compatible even with the most constrained IoT devices. We extensively evaluate our approach on our shop floor simulator and show that it is resilient against outliers, capable of predicting future encounters, and can reliably optimize overall offloading Quality of Service (QoS) metrics like bundle latency or lost bundle ratio.<br

FLINT: Performance-Aware In-Network Computing for Dynamic 6G Sub-Networks - Müller, Kilian; Zhang, Jiajing; Vossiek, Paul; Shafaei, Sina; Fitzek, Frank H.P.; Franchi, Norman (IEEE International Conference on Communications (ICC), 2025)

Hybrid Predictive Modeling for Single-Sensor Data Consistency in Industrial IoT - Ruhland, Tim; Müller, Kilian; Lübke, Maximilian; Tobola, Andreas; Franchi, Norman (21st International Conference on Distributed Computing in Smart Systems and the Internet of Things, 2025)

NimbleNet: Serverless Computing for the Extreme Edge in Factory Environments - Müller, Kilian; Seidler, Maximilian; Ulbrich, Peter; Franchi, Norman (WoSC10 '24: Proceedings of the 10th International Workshop on Serverless Computing, 2024)
Modern factories are moving towards modular layouts with more generic production cells, allowing rapid production customization. Here, the cell's edge nodes exhibit considerable heterogeneity, ranging from simple microcontrollers to fully-fledged computing systems, which mandates that assembly-specific serverless functions used by Automated Guided Vehicles (AGVs) navigating through and linking the production cells are platform-independent. These moving manufacturing processes call for an adapted communication and distribution infrastructure to maintain low response times while minimizing network traffic. We present NimbleNet, a lightweight distribution and orchestration approach for serverless functions, specifically tailored for IoT devices in industrial factory settings. NimbleNet leverages lightweight sandboxing such as WebAssembly, dynamic dependency management, and a neighbor-first caching strategy to enable efficient, platform-independent deployment of tasks at the extreme edge. Our simulations and real-world evaluations demonstrate that our approach facilitates the dynamic deployment and execution of tasks on resource-constrained devices while optimizing the network load within a mesh network configuration.

TinyEP: TinyML-enhanced Energy Profiling for Extreme Edge Devices - Müller, Kilian; Weidner, Johannes; Franchi, Norman; Wägemann, Peter (IEEE Access, 2024)
The widespread integration of the Internet of Things (IoT) into daily operations has made optimizing energy consumption in low-power edge devices increasingly important. This is especially critical when battery-powered IoT platforms, like sensor networks, are provided to third parties to run custom sensing and perception applications. In such cases, two main requirements emerge: (A) accurate measurement of task energy consumption to fairly distribute maintenance and operations costs among parties and (B) reliable identification of tasks with high power consumption fluctuations to prevent interference with sensitive measurement campaigns. However, the need for compact, cost-effective, and energy-efficient power measurement solutions often compromises dynamic range, resolution, and overall measurement quality in these platforms. To address these challenges, we introduce TinyEP, a system that leverages machine learning on highly constrained microcontrollers (TinyML) to enhance the quality of power traces recorded by limited measurement hardware on highly constrained devices in real-time. Our approach incorporates online learning in the final layer of the model, combined with sparse feedback from an existing battery fuel gauge IC, to automatically adapt to new environments and compensate for concept drift. We evaluated the robustness of TinyEP under extreme conditions, demonstrating its effective operation within a temperature range of –40 °C to 80 °C. Our results show that TinyEP improves the Mean Absolute Percentage Error (MAPE) in recorded power traces and reduces the MAPE in task energy measurements from over 2% to under 0.8% across all tested scenarios. Additionally, it significantly decreases residual variance during task energy measurement