Time-Sensitive Networking (TSN) ensures reliable traffic delivery in industrial automation, multimedia, and automotive systems. While effective in wired networks, wireless TSN (WTSN) faces challenges like delays and interference, complicating traffic scheduling. This paper presents a data-driven approach to improve WTSN management by addressing the residual service time (RST) problem, which increases link latency. Tested in a Wi-Fi TSN-based environment, the proposed WTSN digital twin framework preserves TSN traffic guarantees while significantly reducing RST and hence link latency.

Time-Sensitive Networking (TSN) plays a crucial role in ensuring determinism and low latency, vital for the demands of industrial applications. Integrating the benefits of wire-less networks, including mobility, presents a significant challenge in such environments. In this study, we propose a novel solution to address this challenge by introducing handover capabilities into wireless Time-Sensitive Networking (W-TSN). Through real-world development and testing, we present an optimized approach for minimizing handover delay and leveraging machine learning to select the optimal handover time and space moment in a two-dimensional environment, with low effect on time-sensitive traffic. Our findings demonstrate that our mechanism reduces handover delay below 10 milliseconds and optimizes the handover moment selection, leading to improvements in critical network parameters such as bandwidth and jitter.

Time-sensitive networking (TSN) is proposed to support deterministic communication for industrial automation use cases. To harvest the wireless communication flexibility, TSN features have been extended to the wireless domain as well. One of the key TSN features is the ability to assign transmission schedules to different traffic flows in the network with the aim of reducing time slot access delay on each network node. In the wireless domain, this becomes even more challenging due to the shared medium, lower reliability, and slower transmission rates compared to wired systems, reducing the available time resources. In this paper we look at utilizing genetic algorithms to support scheduling of traffic flows from different wireless end devices in a shared schedule. Two optimization functions are defined. The first optimization is based on minimizing the overall shared air time between different end nodes, while the second optimization is based on maximizing time slots that can be used without any interference. Both optimizations aim to reduce the collision probability. With these initial results, we identify the best parameters for genetic algorithms and examine the initial population's impact on overall performance. We show that a fully randomized initial population does not achieve the highest fitness value, even after several generations.

End-to-end low-latency deterministic communication, next to high-reliability communication, is one of the key features that communication systems are expected to provide for industrial systems. To achieve time-sensitive networking (TSN), a set of standards have already been designed and deployed for wired industrial communication systems, coexisting or replacing other long-living technologies such as Fieldbus, Profibus, or Modbus. Wireless time-sensitive networking (W-TSN) is getting traction with the development of the newest WiFi generation (IEEE 802.11be) as well as advances in cellular networking. One of the challenges in W-TSN is scheduling and isolation of time-critical traffic in the shared wireless medium. In this paper we present a solution, called dynamic traffic classification, to give faster dedicated access to the wireless medium for packets of highly-time-sensitive flows, that can be generated randomly. Dynamic traffic classification utilizes so-called shadow queues implemented in FPGA-based WiFi baseband SDR platform, openwifi, to prioritize channel access of certain packets over others. We show that the channel access latency in the case of dynamic traffic classification does not depend on the scheduling cycle, but on the distribution of dedicated time slots inside the schedule cycle. As such we achieve to decrease the end-to-end latency by 75% in case of longer communication cycles with wider space between communication time slots.

The emergence of Time-Sensitive Networking (TSN) has enabled network determinism to a new level, offering high reliability and bounded latency for critical communications. However, the unpredictable nature of traffic generation also poses new challenges to TSN. While TSN is designed to maintain backward compatibility with the 802.1 standards, many end nodes may not be equipped to understand TSN. This can result in a less deterministic TSN, and suboptimal resource utilization, mainly driven by Residual Service Time (RST). To address these challenges, this study proposes three scheduling mechanisms to reduce RST: q-learning, active time slot update, and polynomial forecasting. Real-world data captured from our wireless-TSN (W-TSN) evaluation kit is used to compare the proposed approaches in terms of one-way latency. The results show that the machine learning approach outperforms the other methods in terms of overall latency. However, it is less effective in identifying the optimal time slot position compared to the other methods.

Industrial communication systems provide deterministic and reliable communication between various industrial components. In the past several decades, different communication technologies (Fieldbus, Real-Time Ethernet (RTE)) were used to achieve such determinism. Recently, Time-Sensitive Networking (TSN) is being utilized in industrial environments to support end-to-end low latency deterministic communication by providing mechanisms for accurate time synchronization, traffic scheduling/shaping, and reliability. With many use cases requiring portability and seamless mobility, such features are being developed for wireless networks as well, expanding the time-sensitive communication to the wireless domain. Wireless TSN’s aim is to provide wired TSN-like features, achieving wired-wireless interoperability and flattening the automation system pyramid. In this paper, we present an integration between the wireless TSN and PROFINET. We show that the safety-related applications can be supported seamlessly, providing deterministic communication and reliability under best-effort traffic load in the wireless network. The solution is evaluated in terms of the achieved end-to-end latency and the probability of failure per hour of the fail-safe communication. It is shown that by using wireless time-sensitive networking with dedicated time slots per traffic flow a safety integrity level up to grade 4 can be achieved.

Time-sensitive networking (TSN) is widely used in industrial environments to support low-latency deterministic communications. Innovation to bring time-sensitive networking to wireless networks is getting traction. Besides enabling real-time and deterministic communications, Wireless Time-Sensitive Networks (W- TSN) should provide flexibility and easy deploy-ment, key characteristic requirements for industrial networks. Nevertheless, current research in this field focuses on adapting wired TSN features to the wireless world, namely accurate time synchronization and traffic scheduling, essential processes for wireless end devices such as automated and impact-less association procedure are not considered until now. This work proposes a W - TSN impactless association procedure that provides time synchronization and traffic scheduling for prospect W - TSN clients during the association phase by utilization of beacons. As such, prospect clients can perform association procedure in a controlled fashion avoiding collisions with other, already-associated, W - TSN clients. The presented procedure is designed, implemented, and tested in a real-world scenario on top of a wireless Software Defined Radio (SDR) platform with the IEEE802.11 standard. The results show high accuracy synchro-nization on client frame transmissions even with challenging scheduling timeslots of 128 μs.

Low power wide area networks (LPWAN) such as long range wide area networks (LoRaWAN), provide several advantages on monitoring systems development in forested environments due to its simple set-up, low cost, low power consumption, and wide coverage. Regarding the coverage area, the transmission in forested environments can be highly attenuated by foliage and must be defined to optimize the number of nodes. This paper discusses an empirical study of LoRa with LoRaWAN transmission range in riparian forests, based on path-loss modeling, using both received signal strength indicator (RSSI) and signal-to-noise-ratio (SNR). The measurements have been conducted in the riparian forest of three local rivers at urban, semi-urban, and rural environments located in the city of Cuenca, Ecuador. The measurement results found that there is a significant distribution difference among measurement places, a high correlation between two banks of the same river, a higher standard deviation in urban measurements and a larger coverage in rural areas.