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Autonomous vehicle-based storage and retrieval systems are commonly used in many fulfillment centers (e.g., e-commerce warehouses), because they allow a high- and flexible-throughput capacity. In these systems, roaming robots transport loads between a storage location and a workstation. Two main variants exist: horizontal , where the robots only move horizontally and use lifts for vertical transport, and a new variant vertical , where the robots can also travel vertically in the rack. This paper builds a framework to analyze the performance of the vertical system and compare its throughput capacity with the horizontal system. We build closed queueing network models for this that, in turn, are used to optimize the design. The results show that the optimal height-to-width ratio of a vertical system is around one. Because a large number of system robots may lead to blocking and delays, we compare the effects of different robot blocking protocols on the system throughput: robot Recirculation and Wait-on-Spot. The Wait-on-Spot policy produces a higher system throughput when the number of robots in the system is small. However, for a large number of robots in the system, the Recirculation policy dominates the Wait-on-Spot policy. Finally, we compare the operational costs of the vertical and horizontal transport systems. For systems with one load/unload (L/U) point, the vertical system always produces a similar or higher system throughput with a lower operating cost compared with the horizontal system with a discrete lift. It also outperforms the horizontal system with a continuous lift in systems with two L/U points.

Traffic network delays seriously affect the air transportation system’s safety, economy, and efficiency, and have always been a global concern. Flight delays usually propagate within airport networks, causing subsequent flights to be delayed. However, existing works lack in considering network causality, and the incorporation of emerging large language models (LLMs). Thus, this paper endeavours to examine the literature on network delay prediction that combines different background knowledge with journal paper publishing data. Particularly, the network delay prediction methods are categorized into four aspects: classic methods without explicit network topology modelling, traditional explicit network-based prediction methods, emerging deep learning methods, and the application of LLMs in transportation. Classic methods without explicit network topology modelling, including statistical analysis, operations research, traditional machine learning and causal inference without network structures, offer interpretable baselines but fail to capture the complexity and nonlinearity of air traffic systems. Traditional explicit network-based prediction methods often approach air traffic systems through frameworks such as complex networks and queuing theory, with an increasing focus on causal relationship analysis. However, these methods fall short in capturing the spatiotemporal dependencies of network delays, particularly in modelling spatiotemporal causality. In contrast, emerging deep learning methods have advanced significantly, enabling the construction of spatiotemporal causal networks and improving the accuracy of network delay prediction. In addition, some future trends are analyzed. It is concluded that graph neural networks with causality and emerging deep learning methods (e.g., spatiotemporal GCN) are identified as essential directions. Moreover, a conceptual AirTraffic LLM is suggested via a novel Spatial-Temporal Causal Large Language Model (STC-LLM) framework for high-precision flight delay prediction, which requires further experimental validation and real-world testing. Nevertheless, issues such as data privacy, model opacity, and high computational costs must be carefully addressed when applying LLMs. Finally, the findings are expected to enhance understanding of delay propagation among researchers, practitioners, and policymakers, while providing insights and guidance to airports, airlines, and air traffic control.

In the past decade, cloud platforms have become a standard across the industry for data storage and operations. Such platforms offer high quality of service in terms of reliability and ease of setup at an effective cost. With exponentially high rates of increase of data storage requirements, data is now increasingly stored in clouds. However, there are limited studies which analyze the processes performing the storage operations. Queueing models offer a very natural way of modeling these storage processes. The data packets waiting for storage form a queue which is served by a storage server. Since data packets are transmitted to the cloud in batches for efficiency, this storage server is modelled as a batch server. The storage server goes into sleep mode in between data transmission periods which are, in turn, modelled as vacations. The storage service is resumed after a vacation if there are enough packets in backlog or enough time has elapsed since last storage. This is modelled as restarting thresholds in our model. Analyzing this model helps us evaluate the quality of service (QoS) of storage processes in terms of measures such as backlog size and probability of a new connection to cloud server. These measures are then used to define a user cost function and QoS constraints, and compute optimal storage parameters.

The application of queueing theory is very broad. Examples include electronic communication systems and devices. New technologies, electronic communication systems, and devices are used by many modern organizations. However, this implies certain requirements and risks. The requirements are, first and foremost, reliability, which accounts for the complexity and interdependence of the system. On the other hand, the stochastic characteristics and complexity of these systems introduce risks related to the demands of reliability control, transmission quality, availability, and security. The research conducted so far is concerned with relatively simple queueing models that require certain assumptions to be made about the stochastic nature of the event stream. This is because complex queueing systems are very difficult to analyze using analytical methods. Hence, this paper attempts to use timed Petri nets in the modeling and performance evaluation of queueing systems belonging to the PQS (Priority Queueing System) group. IntServ and DiffServ architectures are discussed, as well as queueing systems used in quality-of-service assurance. A weighted PQS that eliminates the possibility of blocking lower-priority traffic is investigated. Based on a Petri model, the performance characteristics of the studied system are obtained. The impact of data generation on the system performance was analyzed, showing that temporal Petri nets can be effectively used in the modeling and performance evaluation of PQS systems.

Semi-open queueing networks are suitable for modeling complex manufacturing, health care, and logistics systems. Such networks are different from more well-known open queueing networks because the number of users, that can be serviced in the network simultaneously is restricted by a finite constant. The network loses customers who arrive when its capacity reaches its limit. This paper examined an analytical model characterized by features like the possibility to capture potential correlations in the arrival process by assuming the marked Markov arrival process and modify service rates in the network's nodes depending on the number of users currently processed in the network. A hysteresis strategy for dynamic service rate selection was assumed. Fixing the thresholds of this strategy, the behavior of the network was determined by a continuous-time multidimensional Markov chain with a finite state that is a quasi-birth-and-death process. An explicit formula for the generator of this process was obtained. Expressions for the computation of network performance measures were derived. Numerical results highlight the dependence of some measures on thresholds defining the control policy, and their use to optimize the system is illustrated.

Layered queues are a canonical form of extended queueing network for systems with nested multiple resource possession, in which successive depths of nesting define the layers. The model has been applied to most modern distributed systems, which use different kinds of client-server and master-slave relationships, and scales up well. The layered queueing network (LQN) model is described here in a unified fashion, including its many more extensions to match the semantics of sophisticated practical distributed and parallel systems. These include efficient representation of replicated services, parallel and quorum execution, and dependability analysis under failure and reconfiguration. The full LQN model is defined here and its solver is described. A substantial case study to an air traffic control system shows errors (compared to simulation) of a few percent. The LQN model is compared to other models and solutions, and is shown to cover all their features.

RabbitMQ, an implementation of Advanced Message Queuing Protocol (AMQP), is a very popular message middleware. It supports concurrency, guarantees sequential consistency, and enables independent applications and services to communicate. Consequently, it is of great significance to ensure the secure communication of RabbitMQ. Therefore, Kerberos, a network authentication protocol, is introduced to combine with RabbitMQ to address this security issue. In this paper, we apply formal methods to model and verify RabbitMQ with Kerberos. By utilizing UPPAAL, RabbitMQ is abstracted to timed automata. Further, we validate the constructed model with the simulator in UPPAAL. On this basis, we verify whether RabbitMQ meets some basic but essential properties, including Reachability of Data, Concurrency, Sequence Consistency and Heartbeat Mechanism. Additionally, the security property Secure Communication is verified as well. From the verification results via UPPAAL, it can be found that RabbitMQ can totally cater for these properties and it maintains secure communication under the umbrella of Kerberos.

The Peak Age of Information (PAoI) quantifies the freshness of updates used in cyber-physical systems (CPSs), realized within the Internet of Things (IoT) paradigm, encompassing devices, networks, and control algorithms. Consequently, PAoI is a critical metric for real-time applications enabled by Ultra-Reliable Low Latency Communication (URLLC). While highly useful for system evaluation, the direct analysis of this metric is complicated by the correlation between the random variables constituting the PAoI. Thus, it is often evaluated using only the mean value rather than the full distribution. Furthermore, since CPS communication technologies like Wi-Fi or DECT-2020 involve multiple processing stages, modeling them as tandem queueing systems is essential for accurate PAoI analysis. In this paper, we develop an analytical model for a DECT-2020 network segment represented as a two-phase tandem queueing system, enabling detailed PAoI analysis via Laplace–Stieltjes transforms (LST). We circumvent the dependence between generation and sojourn times by classifying updates into four mutually exclusive groups. This approach allows us to derive the LST of the PAoI and determine the exact Probability Density Function (PDF) for M|M|1→M|M|1 system. We also calculate the mean and variance of the PAoIs and validate our results through numerical experiments. Additionally, we evaluate the impact of different service time distributions on PAoI variability. These findings contribute to the theoretical understanding of PAoI in tandem queueing systems and provide practical insights for optimizing DECT-2020-based communication systems.

Fifth-generation (5G) New Radio (NR) systems are expected to support multiple traffic classes including enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine-type communications (mMTC) at the same air interface. This functionality is assumed to be implemented by utilizing the network slicing concept. According to the 3rd Generation Partnership Project (3GPP), the efficient support of this feature requires statistical multiplexing and, at the same time, traffic isolation between slices. In this paper, we formulate and solve a mathematical model for a class of Radio Access Network (RAN) slicing algorithms that simultaneously include resource reservation and a priority-based service discipline allowing us to incur fine granularity in the service processes of different traffic aggregates. The system is based on a queueing model and allows parametrization by accounting for the specifics of wireless channel impairments. As metrics of interest, we utilize K-class session drop probability, K-class session pre-emption probability, and system resource utilization. To showcase the capabilities of the model, we also compare performance guarantees provided for URLLC, eMBB, and mMTC traffic when multiplexed over the same NR radio interface. Our results demonstrate that the performance trade-off is dictated by the offered traffic load of the highest priority sessions: (i) when it is small, mixed reservation/priority scheme outperforms the full reservation mechanism; (ii) for overloaded conditions, full reservations provides better traffic isolation. The mixed strategy is beneficial to traffic aggregates with short-lived lightweight sessions, such as URLLC and mMTC, while the reservation only scheme works better for elastic eMBB traffic. The most important feature is that the mixed strategy allows resource utilization to be improved up to 95%, which is 10–15% higher compared to the reservation-only scheme while still providing isolation between traffic types.

The IoT offered an enormous number of services with the help of multiple applications so it faces various security-related problems and also heavy malicious attacks. Initially, the IoT data are gathered from the standard dataset as Message Queuing Telemetry Transport (MQTT) set. Further, the collected data are undergone the pre-processing stage, which is accomplished by using data cleaning and data transformation. The resultant processed data is given into two models named (i) Autoencoder with Deep Belief Network (DBN), in which the optimal features are selected from Autoencoder with the aid of Modified Archimedes Optimization Algorithm (MAOA). Further, the optimal features are subjected to the AL-DBN model, where the first classified outcomes are obtained with the parameter optimization of MAOA. Similarly, (ii) Long Short-Term Memory (LSTM) with DBN, in this model, the optimal features are chosen from LSTM with the aid of MAOA. Consequently, the optimal features are subjected into the AL-DBN model, where the second classified outcomes are acquired. Finally, the average score is estimated by two outcomes to provide the final classified result. Thus, the findings reveal that the suggested system achieves outstanding results to detect the attack significantly.