In industrial environments, publish/subscribe is essential for reliable and efficient machine-to-machine communication. Established protocols like Message Queuing Telemetry Transport (MQTT) and Advanced Message Queuing Protocol (AMQP), which implement this messaging pattern, have architectural limitations that hinder their ability to guarantee hard real-time requirements. To address this limitation, we propose combining MQTT for Sensor Networks (MQTT-SN), a lightweight UDP-based variant of MQTT, with Time-Sensitive Networking (TSN), a set of IEEE standards that enhance Ethernet with real-time capabilities. The resulting protocol, designated as MQTT-TSN, enables time-triggered publish/subscribe using the Time-Aware Shaper (TAS) and guarantees hard real-time compliance for MQTT-SN messages. In this work, we present a conceptual framework for the mapping of real-time MQTT-SN traffic to periodic time-triggered (TT) streams, incorporating configuration and adaptation at runtime. A case study demonstrates the feasibility and limitations of our approach in a simplified scenario.

Time-Sensitive Networking (TSN) extends Ethernet to enable deterministic networking making it suitable for latency-sensitive industrial applications. To ensure bounded end-to-end delays and minimal jitter, a network-wide communication schedule with exclusively reserved transmission slots is enforced for time-triggered hard realtime traffic. While numerous scheduling methodologies exist for generating such schedules, their practical applicability remains uncertain. In this paper, we present a testbed for evaluating the real-world suitability of TSN schedules. We describe the hardware and software setup and demonstrate our evaluation methodology.

In Time-Sensitive Networking (TSN), Time-Aware Shaping facilitates the convergence of deterministic and lowpriority traffic in Ethernet networks. The allocation of bandwidth for low-priority traffic is crucial when scheduling timetriggered (TT) streams to prevent starvation and packet loss caused by queue congestion. This issue is particularly pertinent in online scheduling scenarios, where the deletion of streams can lead to schedule fragmentation. To prevent this issue, the time slots of the TT streams must be compressed to provide larger contiguous gaps for low-priority traffic in the schedule. In this paper, we present a novel heuristic approach for compressing schedules, which facilitates the grouping of time slots into units, thereby increasing the bandwidth for low-priority traffic and improving the schedulability of future streams. The proposed heuristic can be tailored to specific use cases through adaptive weighting, allowing a comprehensive evaluation of time slots according to the properties of the associated time slot units, TT streams, and the network.

The proliferation of wireless mobile devices has led to a growing interest in indoor localization applications, such as context-aware services in healthcare. However, the inherent complexities of multipath and non-line-of-sight (NLoS) in indoor environments pose significant challenges to conventional localization methods, often resulting in compromised accuracy. In this paper, we propose a localization approach for wireless systems that uses self-supervised learning and dissimilarity metrics derived from Received Signal Strength Indicator (RSSI) and Angle-of-Arrival (AoA) data to increase the accuracy of indoor localization in NLoS environments. Recent developments showed that self-supervised machine learning with channel state information (CSI) can accurately estimate positions without ground truth data. Accessing the CSI data can be challenging, so we present an approach that uses the more accessible RSSI values and AoA angles. Our evaluation of various neural network architectures and training parameters indicates that employing dissimilarity metrics derived from AoA and RSSI with selfsupervised learning is a promising approach.

Time-Sensitive Networking (TSN) facilitates the implementation of realtime data traffic with deterministic delay and jitter in Ethernet networks. In its time-triggered communication variant, network paths as well as transmission time slots along the paths need to be planned and reserved for all data streams, respectively. Conventionally, such a feasible (i. e., realtime compliant) network schedule is computed and optimized offline, installed upon deployment, and cannot be changed anymore at runtime without stopping the communication. The latter becomes unsuitable for future industrial use cases with a growing demand for flexibility including schedule adjustments. In this paper, we present an online scheduler capable to incrementally integrate new streams at runtime and to adapt the existing schedule, if necessary, while retaining the realtime guarantees given to active streams. For this purpose, the scheduling heuristic identifies streams whose adjustment requires minimal reconfiguration efforts. In a thorough evaluation, we analyze the scheduler's trade-off between resource usage and schedule quality showing its suitability for diverse application scenarios.