A fire detection system requires accurate and fast mechanisms to make the right decision in a fire situation. Since most commercial fire detection systems use a simple sensor, their fire recognition accuracy is deficient because of the limitations of the detection capability of the sensor. Existing proposals, which use rule-based algorithms or image-based machine learning can hardly adapt to the changes in the environment because of their static features. Since the legacy fire detection systems and network services do not guarantee data transfer latency, the required need for promptness is unmet. In this paper, we propose a new fire detection system with a multifunctional artificial intelligence framework and a data transfer delay minimization mechanism for the safety of smart cities. The framework includes a set of multiple machine learning algorithms and an adaptive fuzzy algorithm. In addition, Direct-MQTT based on SDN is introduced to solve the traffic concentration problems of the traditional MQTT. We verify the performance of the proposed system in terms of accuracy and delay time and found a fire detection accuracy of over 95%. The end-to-end delay, which comprises the transfer and decision delays, is reduced by an average of 72%.

Time-sensitive networking (TSN) technologies have garnered attention for supporting time-sensitive communication services, with recent interest extending to the wireless domain. However, adapting TSN to wireless areas faces challenges due to the competitive channel utilization in IEEE 802.11, necessitating exclusive channels for low-latency services. Additionally, traditional TSN scheduling algorithms may cause significant transmission delays due to dynamic wireless characteristics, which must be addressed. This paper proposes a wireless TSN model of IEEE 802.11 networks for the exclusive channel access and a novel time-sensitive traffic scheduler, named the wireless intelligent scheduler (WISE), based on deep reinforcement learning. We designed a deep reinforcement learning (DRL) framework to learn the repetitive transmission patterns of time-sensitive traffic and address potential latency issues from changing wireless conditions. Within this framework, we identified the most suitable DRL model, presenting the WISE algorithm with the best performance. Experimental results indicate that the proposed mechanisms meet up to 99.9% under the various wireless communication scenarios. In addition, they show that the processing delay is successfully limited within the specific time requirements and the scalability of TSN streams is guaranteed by the proposed mechanisms.

With the difficulty of collecting desirable training data due to the heterogeneities of IoT sensors in various buildings and the scarcity of fire events, it is time consuming and expensive to apply data-driven deep learning approaches to fire detection systems in specific building environments. Simulation-based learning has been actively researched to mitigate data scarcity problems by reproducing potential fire events. Since simulation-based learning mainly depends on synthetic training data, trained deep learning models may generate erroneous predictions in real-world scenarios that are unlike any of the training samples. In this paper, we propose a trustworthy building fire detection framework based on a multioutput encoder-decoder network, named MEDNet, which is designed for the practical usage of simulation-based learning in building fire detection. The fundamental steps of our approach are (1) modeling and simulating fire events to create realistic synthetic data that reflect data from actual buildings, (2) predicting a fire event and dissimilarities between real input data and synthetic training data based on the trained MEDNet model, and (3) operating a switching mechanism to use a knowledge-based method that does not depend on synthetic training data when dissimilarities exist. Finally, we perform simulation experiments based on a real building compartment where the proposed framework is compared with conventional time-series classification networks on various evaluation datasets. The proposed framework is trustworthy in practical usage because MEDNet with a switching mechanism achieves a 36.65% higher F1-score than conventional time-series classification networks and generates false-positive predictions lower than 0.02% even in unpredictable scenarios.

The concept of digital twin (DT), which has supported the optimization of physical system across various industries, now extends into federated digital twin (fDT) to manage complex, interconnected systems. Recent research and standardization efforts in DT have expanded their focus from optimizing individual physical twins to coordinating DT functionalities to optimize the services provided by set of physical twins. In fDT-based DT coordination, scheduling techniques that determine when to utilize available DTs efficiently are critical. However, the variable processing speeds of DT functionalities, as traceable from digital twins, present significant challenges in deriving optimal schedules. Traditional scheduling algorithms, which assume static processing speeds for machines (corresponding to DTs), encounter limitations in generating optimal schedules. These limitations are further exacerbated in scenarios that demand the flexibility and scalability inherent to fDT. To address this challenge, this paper proposes the fDT-DT scheduling framework. This framework effectively represents relationships among DTs using temporal heterogeneous graphs and generates flexible and scalable schedules by incorporating temporal graph neural networks and deep reinforcement learning to account for variable processing speeds. Experimental results demonstrate that, in scenarios where multiple DTs exhibit diverse speed profiles, the proposed approach improves makespan by 16.9% and reduces tardiness by 76.2%, demonstrating both flexibility and superior performance. Furthermore, even in untrained scenarios with increasing numbers of fDTs and DTs, it outperforms baseline algorithms on both metrics, confirming its scalability.

The evolution of virtual reality technology allows users to immerse themselves into virtual environments, providing a new experience that is impossible in the real world. The appearance of cyber-physical systems and the Internet of things makes humans to understand and control the real world in detail. The integration of virtual reality into cyber-physical systems and the Internet of things may induce innovative education services in the near future. In this paper, we propose a novel, a virtual reality-based cyber-physical education system for efficient education in a virtual reality on a mobile platform, called VR-CPES. VR-CPES can integrate the real world into virtual reality using cyber-physical systems technology, especially using digital twin. We extract essential service requirements of VR-CPES in terms of delay time in the virtual reality service layer. In order to satisfy the requirements of the network layer, we design a new, real-time network technology interworking software, defined as network and time-sensitive network. A gateway function for the interworking is developed to make protocol level transparency. In addition, a path selection algorithm is proposed to make flexible flow between physical things and cyber things. Finally, a simulation study will be conducted to validate the functionalities and performance in terms of packet loss and delay as defined in the requirements.

The explosive demand for wireless communications has intensified the complexity of spectrum dynamics, particularly within unlicensed bands. To promote efficient spectrum utilization and minimize interference during communication, spectrum sensing needs to evolve to a stage capable of detecting multidimensional spectrum states. Signal identification, which identifies each device’s signal source, is a potent method for deriving the spectrum usage characteristics of wireless devices. However, most existing signal identification methods mainly focus on signal classification or modulation classification, thus offering limited spectrum information. In this paper, we propose DSINet, a multitask learning-based deep signal identification network for advanced spectrum sensing systems. DSINet addresses the deep signal identification problem, which involves not only classifying signals but also deriving the spectrum usage characteristics of signals across various spectrum dimensions, including time, frequency, power, and code. Comparative analyses reveal that DSINet outperforms existing shallow signal identification models, with performance improvements of 3.3% for signal classification, 3.3% for hall detection, and 5.7% for modulation classification. In addition, DSINet solves four different tasks with a 65.5% smaller model size and 230% improved computational performance compared to single-task learning model sets, providing meaningful results in terms of practical use.