The N×On-Off/D/1 model describes a single server with deterministic service times, fed by N independent sources that each alternate between active ON periods, during which they emit packets, and silent OFF periods. This structure matters because real network traffic is nothing like the smooth, memoryless arrivals assumed in textbook models. Since the landmark measurements of Ethernet traffic by Leland and colleagues in the 1990s, engineers have known that packet streams are bursty and correlated, and that burstiness is precisely what inflates queues and delays. The On-Off source model captures this correlation in a tractable way, making it a workhorse for teletraffic engineering in broadband and wireless systems alike.

Before trusting any simulator, the researchers had to prove it reproduces known results. Their first validation stage used the M/D/1 queue, the classical model with Poisson arrivals and deterministic service, as a baseline. For this model, the asymptotic decay rate of the queue-length tail distribution is known in closed form, giving an exact mathematical benchmark. Across utilization levels rho ranging from 0.7 to 0.95, the simulator matched the theoretical decay rate to within one percent. That level of agreement is the kind of result that separates a credible simulation framework from a toy, and it established the foundation for everything that followed.

The second validation stage tackled the harder target: the Burst Scale Decay Rate, or BSDR, a closed-form approximation that predicts how quickly the probability of large queue buildups decays when many On-Off sources feed the buffer. Here the simulator tracked the approximation within 2.2 to 12.5 percent across the tested configurations. Crucially, the team did not treat that residual gap as an embarrassment. By running multiple independent replications and constructing confidence intervals, they showed that the discrepancy is a systematic property of the closed-form approximation itself, not random simulation noise. In other words, the simulator is more accurate than the approximation it was being tested against, a subtle but important inversion that gives engineers a clearer picture of how much to trust the analytical shortcut.

With the framework validated, the researchers turned to the central question: what does the distribution of per-packet queueing delay actually look like under bursty load? They examined three different ON/OFF configurations at utilization levels from 0.89 to 0.98, deep in the congested regime where wireless networks spend much of their operating life. Each operating point was simulated with five independent replications totaling roughly 700 million packets, an enormous dataset by any statistical standard. The scale matters because the question they were asking, whether a delay distribution follows a particular mathematical form, is exactly the kind of question where insufficient data produces misleading answers.

The verdict came from four independent distance measures: the Kolmogorov-Smirnov statistic, the Anderson-Darling statistic, the coefficient of determination between cumulative distribution functions, and the Bhattacharyya overlap coefficient. All four measures told the same story. As the load increases, the queueing delay distribution steadily approaches a lognormal shape. The Kolmogorov-Smirnov distance shrank to 0.051 at the highest loads, and the fixed-sample Anderson-Darling statistic, which is especially sensitive to the tails of a distribution, dropped by factors of three to seven. For network engineers, this is a genuinely useful regularity: the lognormal distribution, whose parameters can be estimated from a mean and a variance, offers a compact closed-form description of delay behavior that grows more accurate precisely where delays matter most, near saturation.

Yet the study is equally emphatic about the limits of that regularity. Bootstrap-corrected goodness-of-fit tests rejected exact lognormality at every single operating point, no matter how close the visual and distance-based agreement appeared. The authors therefore present the lognormal as an approximation with quantified accuracy rather than an exact law, a distinction that reflects a mature statistical philosophy. To test whether some other two-parameter distribution might do better, they systematically compared the lognormal against Weibull, gamma, log-logistic, and Lomax alternatives. The result was nuanced: at moderate loads the lognormal could lag behind the best competitor for a given configuration, but as load increased it became competitive with, and in some cases the best among, all the two-parameter candidates.

Configuration mattered too. The team’s sensitivity analysis showed that OFF-dominated or symmetric traffic patterns, where the silent period is at least as long as the active period, produced better lognormal agreement at moderate loads. Near saturation, however, this dependence weakened, suggesting that extreme congestion washes out the details of the source structure and drives the delay distribution toward a common heavy-tailed form regardless of how the bursts are shaped. This finding has practical implications for traffic engineering, because it tells operators when they can rely on a simple lognormal fit and when the specifics of their traffic mix demand a more careful analysis.

Perhaps the most consequential result concerns the tails, the extreme delays that determine whether a system meets the stringent latency budgets of 5G, the tactile internet, and ultra-reliable low-latency communications. Above a utilization of roughly 0.95, the fitted lognormal consistently overestimated the empirical 95th, 99th, and 99.9th percentile delays in every configuration tested, at P99.9 by factors ranging from 3.6 to 9.6 times the observed values. At moderate loads, by contrast, the sign of the tail error depended on the traffic configuration, sometimes underestimating the true risk. The authors frame the heavily loaded overestimation as a virtue: the lognormal acts as an empirically conservative closed-form tool for delay budgeting, erring on the safe side exactly in the regime where network dimensioning decisions are made.

The broader significance of the work lies in its marriage of large-scale simulation with rigorous uncertainty quantification. Rather than declaring a new universal law of network delay, the study delivers something more useful: a validated simulator, a calibrated understanding of when the BSDR approximation can be trusted, and a quantified recipe for using the lognormal as a delay-budgeting tool with known error bounds. As wireless networks absorb everything from factory robots to remote surgery, the difference between a guessed delay distribution and a statistically verified one translates directly into either wasted capacity or violated service guarantees. This research gives the engineers building that future a sharper instrument, and a candid account of its precision.

Subject of Research: Queueing-theoretic modeling and statistical validation of packet delay distributions in bursty wireless network traffic

Article Title: Validation of burst scale decay rate and analysis of lognormal delay distribution in NxOn-Off/D/1 queueing models for wireless network performance

Article References: Enisoglu, R., Inan, B. A., & Ugurlu, S. Y. (2026). Validation of burst scale decay rate and analysis of lognormal delay distribution in NxOn-Off/D/1 queueing models for wireless network performance. Cluster Computing, 29(13), Article 780. https://doi.org/10.1007/s10586-026-06591-y

Image Credits: AI Generated

DOI: 10.1007/s10586-026-06591-y

Keywords: queueing theory, N×On-Off/D/1 model, burst scale decay rate, lognormal distribution, packet delay, QoS provisioning, wireless networks, tail probability estimation, goodness-of-fit testing, network dimensioning, discrete-event simulation, 5G latency

At its core, pyjevsim is built upon the Discrete Event System Specification (DEVS) formalism, which provides a rigorous mathematical foundation for modeling dynamic systems. The previous version, 2.1.2, established the initial HLA integration, but 2.2.0 expands this capability by offering reproducible comparisons between standalone and federated simulation trajectories. The software utilizes JPype for Python-to-Java interactions with Pitch pRTI and Portico, while the gorti connector leverages a separate Python SDK to interface with an rtid service. This multi-backend approach ensures that users can select the most appropriate runtime infrastructure for their specific simulation requirements, whether they prioritize performance, compatibility, or experimental flexibility.

The architectural design of pyjevsim 2.2.0 emphasizes the separation of concerns between model behavior and runtime infrastructure. StructuralModel groups child models and defines their port couplings, while BehaviorModel defines the atomic behavior of individual components. The SysExecutor coordinates the scheduling of events, and the BehaviorExecutor reports the next event time and invokes model output and transition functions. For HLA-controlled execution, the HLAExecutor extends the BehaviorExecutor to connect bound model ports to the RTIConnector. External bindings use HLAInteraction and HLAAttribute descriptors to associate ports with Federation Object Model (FOM) defined interactions and object attributes, specifying inbound, outbound, or bidirectional exchange. This design keeps RTI-specific calls outside the model classes, ensuring that the simulation logic remains independent of the underlying distributed infrastructure.

One of the most notable features of this release is the in-process execution of RTI connectors. Unlike previous tools such as DEVSim++ ME, which required a separate KHLA Adaptor process to access RTI services, pyjevsim runs its connectors within the Python simulation process. This eliminates the need for inter-process communication overhead and simplifies the deployment of federated simulations. The create_rti function selects the RTI backend based on user configuration, and the connectors handle the encoding of outgoing payloads and the decoding of incoming data. This streamlined approach allows for more efficient data exchange between federates, which is critical for simulations involving high-frequency interactions or large-scale data sets.

To validate the effectiveness of the new connectors, the research team conducted functional tests using two deterministic anti-torpedo simulation (atsim) scenarios. These scenarios involved one surface ship, one torpedo, and four decoys, with each scenario running for 30 logical ticks. The standalone reference execution used a single SysExecutor, while the federated configuration utilized ship-side and torpedo-side HLA_TIME federates running in separate threads within a single Python process. The atsim driver exchanged platform object attributes through publication and reflection between simulation steps, ensuring that the state of each object was accurately synchronized across the federation. The tests were designed to verify that the federated execution could reproduce the application-state traces of the standalone execution with high precision.

The validation process required exact agreement between the sorted records of logical tick, object identifier, and position, with coordinates formatted to ten significant digits. Each of the six objects in the simulation contributed one position record per tick, yielding 180 records per scenario. Pitch pRTI, Portico, and experimental gorti were each invoked five times, and all 180 records per scenario matched the standalone reference in every invocation across all three backends. This level of reproducibility demonstrates the robustness of the pyjevsim 2.2.0 architecture and its ability to maintain simulation integrity even when distributed across multiple runtime environments. The versioned source release includes the complete atsim launchers, port-binding configuration guide, and reference traces, allowing other researchers to repeat these comparisons and verify the results independently.

The software also supports various execution modes, including virtual-time, real-time, and HLA-controlled execution. In HLA-controlled execution, the Federate.run_until method requests time advances through the RTIConnector and passes each granted time to the step method of the SysExecutor. The positive lookahead argument sets the increment between requested times, which is distinct from the backend’s HLA regulating lookahead. The execution core processes events up to each granted time, including zero-time cascades, and simultaneous internal and external events invoke the model’s confluent transition. This mechanism ensures that the simulation progresses in a controlled manner, respecting the time constraints imposed by the HLA infrastructure while maintaining the logical consistency of the discrete event model.

From a technical perspective, pyjevsim 2.2.0 requires Python 3.10 or higher and the dill library for serialization. Additional runtime requirements depend on the selected RTI implementation, version, and connector. The version-pinned validation documentation provides backend-specific requirements and tested configurations, ensuring that users can set up their environments with confidence. The software is released under the MIT License, which encourages its use in both academic and commercial settings. The developer documentation, available on Read the Docs, provides detailed guidance on model composition, execution, and HLA integration, making it accessible to a wide range of users with varying levels of expertise in discrete event simulation.

The implications of pyjevsim 2.2.0 extend beyond the immediate benefits of simplified HLA integration. By providing a Python-based environment for DEVS simulation and state journaling, the software opens new possibilities for research in distributed systems, cyber-physical systems, and complex adaptive systems. The ability to compare standalone and federated trajectories allows researchers to study the impact of distribution on simulation outcomes, which is crucial for understanding the scalability and performance of large-scale simulations. Furthermore, the inclusion of the experimental gorti connector, an open-source IEEE 1516-2010 Run-Time Infrastructure written in Go, demonstrates the software’s commitment to exploring new technologies and maintaining compatibility with emerging standards in the simulation community.

As the field of discrete event simulation continues to evolve, tools like pyjevsim play a critical role in bridging the gap between theoretical models and practical applications. The release of version 2.2.0 represents a significant step forward in this effort, providing researchers with a powerful and flexible platform for conducting distributed simulations. By streamlining simulation workflows and ensuring reproducibility, pyjevsim 2.2.0 sets a new standard for software tools in the field, encouraging further innovation and collaboration among researchers worldwide. The ongoing development of the software, including plans to extend FOM datatype coverage and evaluate communication performance across physical hosts, promises to further enhance its capabilities and relevance in the coming years.

Subject of Research: Python-based discrete event simulation with High Level Architecture integration

Article Title: Version 2.2.0 – pyjevsim: Streamlining simulation workflows using journaling in python-based discrete event simulation environments

Article References: Kim, D., Yang, S., Jang, S., & Choi, C. (2026). Version 2.2.0 – pyjevsim: Streamlining simulation workflows using journaling in python-based discrete event simulation environments. SoftwareX, 36, Article 103047. https://doi.org/10.1016/j.softx.2026.103047

Image Credits: AI Generated

DOI: 10.1016/j.softx.2026.103047

Keywords: discrete event simulation, High Level Architecture, Python, DEVS, federated simulation, software release, RTI connectors, simulation reproducibility, JPype, gorti, Portico, Pitch pRTI

The research, led by Partheeb Mostafiz, Debolina Debnath and Kazi Md Tanvir Anzum, focuses on fast-moving consumer goods with extremely short shelf lives, using packaged bread as its case study. Bread has a commercial shelf life of just 120 hours, and retail scanner evidence indicates that bakery lines exhibit the highest expiration rates of any perishable category, with more than 8% of perishable units expiring before sale and bakery products frequently exceeding 12%. Distributors of such products face a chronic dilemma: overstocking secures service levels but converts edible product into end-of-life waste, while understocking protects margin at the cost of lost sales and customer defection. The problem is sharpest in the traditional-trade channel that dominates South Asian retail, where neighbourhood outlets typically operate without electronic point-of-sale systems, real-time inventory visibility or formal demand forecasting, so replenishment quantities are set judgementally days before demand is realised.

The team’s answer is a dual-channel clearance mechanism they call the Near-Food Channel, or NFC. At a fixed audit point 72 hours after production, any inventory in excess of an optimised threshold, termed the Protection Level, is physically transferred to a separate discount retailer. Because clearance stock is spatially and commercially insulated from the full-price shelf, it cannot cannibalise fresh sales, the very outcome that has discouraged distributors from adopting markdown pricing in the first place. Existing markdown models typically assume that discounted units remain on the primary shelf, where they erode full-price revenue; the new design removes that penalty by construction rather than by pricing cleverness.

Determining how much inventory to protect required a stochastic mixed-integer non-linear programme, solved in GAMS using sample average approximation over 300 Monte Carlo scenarios, and then validated in a Rockwell Arena 14.0 discrete-event simulation of the full 120-hour product lifecycle. Crucially, the two behavioural parameters that govern the policy were estimated from primary data rather than assumed. A structured survey of 217 traditional-trade bread purchasers in Dhaka yielded a freshness decay penalty of 0.72, fitted by non-linear least squares to a power-decay model of willingness to pay, and a clearance-channel price elasticity of 2.85, obtained by log-log regression of conversion rates on relative price. Both parameters entered the models as normal distributions with a standard deviation of 0.05, so that estimation uncertainty propagated through every scenario and replication rather than being suppressed.

The empirical setting was a high-volume retail node in Jatrabari, Dhaka, comprising 20 neighbourhood outlets served by a single distributor. Daily footfall across the catchment was recorded, tested for normality and represented as a normal distribution with a mean of 1,040 customers and a standard deviation of 30, while individual inter-arrival times within the trading day were generated as exponential variates conditional on the realised daily total. The distributor buys each unit at 40 Bangladeshi taka and sells fresh product at 50 taka, the clearance retailer operates on a 10% margin, and reverse logistics cost 0.5 taka per transferred unit. Under the conventional baseline, the manufacturer grants a 2% wastage allowance that reduces the distributor’s effective unit cost to 39.2 taka, an arrangement the authors describe as an implicit subsidy for waste.

The numerical results are striking. In the conventional single-channel system, the model identifies a sharp waste threshold at 2,126 units, the mean quantity the fresh channel absorbs over the lifecycle: below it the entire order sells, and above it every additional unit ordered is wasted. At the maximum tested stocking level of 3,000 units, the baseline produces 874 wasted loaves, a 29.13% waste rate, and a distributor loss of 11,300 taka, with profit turning negative from 2,750 units onward. Both dual-channel scenarios, one applying a constant 30% markdown throughout the clearance window and the other a stepped 20% on Day 4 followed by a further 10% on Day 5, eliminated physical waste entirely across the full range of stocking levels from 1,700 to 3,000 units.

The financial consequences are equally significant. Under the constant markdown, distributor profit stabilises between roughly 19,400 and 20,900 taka at stocking levels where the conventional structure would have lost money, while the stepped schedule performs better still, delivering 20,346 taka at maximum overstock and generating 17% more gross clearance revenue, 23,210 taka against 19,810 taka. The advantage of the stepped policy stems from simple arithmetic: most clearance volume moves on Day 4 at only a 20% reduction, returning 36.36 taka per unit to the distributor rather than the 31.82 taka recovered at a 30% cut, and the deeper discount applies only to the residual. Total ecosystem value rises from 106,300 taka under the conventional structure to more than 142,000 taka under the stepped policy, with the gains drawn from waste that previously accrued to no one rather than redistributed between parties.

A one-at-a-time sensitivity analysis confirmed that the profitability benefit is robust. Doubling and even quadrupling the reverse-logistics cost, and raising the clearance retailer’s margin from 10% to 20%, left waste mitigation efficiency at 100% and reduced distributor profit by at most 7.7%, a consequence of the fact that the recovered salvage value exceeds the transfer cost by a factor of more than sixty at every markdown depth tested. The binding constraints proved to be demand-side rather than cost-side: a 10% reduction in catchment footfall was the single most damaging perturbation, cutting waste mitigation efficiency to 90%, while a 20% increase in the freshness decay penalty reduced it to 92.3%. Even under the worst perturbation, however, distributor profit remained positive, ranging from 5,483 to 23,973 taka against the conventional baseline loss of 11,300 taka at the same stocking level.

Beyond the numbers, the study carries a broader theoretical claim: waste reduction and profitability are complementary rather than competing objectives once the channel structure is right. Prior simulation-based studies achieved waste reductions of 15 to 30% through single-channel discounting and treated the residual as an irreducible cost of service. The present results suggest that this residual is largely an artefact of channel design, because physically separating clearance stock from full-price stock removes the cannibalisation penalty that bounds markdown policies. The framework also dissolves the distributor’s incentive to under-order: with the payoff function flattened and never turning negative, the ordering rule can shift from a conservative quantile of the demand distribution to a service-driven one, and the manufacturer recovers the full wholesale price of 40 taka per unit, worth approximately 2.92 million taka annually to a producer shipping 10,000 units per day.

The practical appeal of the mechanism lies in its low-tech implementability. Because the Protection Level can be expressed as a lookup table indexed by the initial order quantity, front-line warehouse staff can execute the audit and transfer from a printed schedule, requiring no real-time analytics, connectivity or point-of-sale integration, a decisive consideration in exactly the traditional-trade settings where data-intensive waste-reduction approaches have failed to take hold. The authors argue the framework transfers to packaged dairy, ready-to-eat meals, cut produce and pre-packed protein, subject to re-estimation of the behavioural parameters and adjustment of the audit timing. At policy level, they suggest that wastage allowances written into manufacturer-distributor contracts could be discouraged, and that an explicit regulatory category for near-expiry sale with mandatory date disclosure would reduce the legal uncertainty that currently deters clearance retailing. Given that retail accounts for roughly 13% of the 931 million tonnes of food wasted globally each year, a profitable, subsidy-free intervention of this kind offers emerging economies a low-capital complement to the cold-chain investments that have long dominated food-loss strategy.

Subject of Research: Stochastic dual-channel clearance routing to prevent food waste in perishable FMCG supply chains

Article Title: A stochastic dual-channel clearance strategy for food waste prevention in perishable FMCG supply chains

Article References: Mostafiz, P., Debnath, D., & Anzum, K. M. T. (2026). A stochastic dual-channel clearance strategy for food waste prevention in perishable FMCG supply chains. Cleaner Engineering and Technology, 34, Article 101310. https://doi.org/10.1016/j.clet.2026.101310

Image Credits: AI Generated

DOI: 10.1016/j.clet.2026.101310

Keywords: food waste, perishable supply chains, FMCG, markdown pricing, simulation-optimisation, discrete-event simulation, protection level, clearance channel, reverse logistics, cleaner production, Bangladesh, bakery

The framework, developed by researchers at the Technical University of Košice in the Slovak Republic, integrates six distinct concerns that the literature has typically handled separately: heterogeneous industrial data, a discrete-event simulation-based digital twin, MQTT and OPC UA communication protocols, external predictive analytics, operational ESG-oriented evaluation, and planner decision support. Rather than treating each as a standalone project, the authors arrange them into a six-layer, event-driven architecture in which information flows between layers as discrete events, triggering analysis, prediction, and ultimately human-approved decisions on the factory floor.

At the heart of the proposal is the idea that events, not periodic reports, should drive production planning and control. In an event-driven design, every meaningful occurrence on the shop floor, such as a machine fault, a material shortage, or a completed batch, becomes a message that propagates through the architecture. Lightweight publish-subscribe protocols like MQTT can carry high-frequency telemetry from sensors to cloud services, while OPC UA provides the standardized, semantically rich interface that industrial equipment uses to expose its state. Prior performance studies of these protocols, cited in the paper, show they are well suited to exchanging data between industrial plants and cloud servers, which makes them natural candidates for the communication backbone of a digital twin.

The digital twin itself is built on discrete-event simulation, a modeling technique that represents a production system as a chronological sequence of events and state changes. This choice matters because discrete-event simulation can answer the questions production planners actually ask: what happens to throughput if a machine goes down for two hours, how a rush order reshapes the schedule, or where buffers will overflow. By coupling the simulation model to live industrial data streams, the twin becomes a predictive instrument rather than a static mirror, allowing planners to test candidate schedules and interventions in silico before committing resources on the floor.

The second major contribution is what the authors call a four-layer transformation logic, and it is here that the framework makes its most distinctive claim. Digital technologies, the authors argue, do not directly improve environmental, social, or governance outcomes. Instead, they influence ESG-relevant results only through changes in production planning and control processes and in managerial evaluation. A sensor network by itself reduces nothing; it is the rescheduled batch, the avoided machine failure, or the reweighted performance indicator, approved by a human planner, that translates digital capability into measurable sustainability impact. This mediating logic is intended to correct what the authors see as a common weakness in the literature, where digitalization and sustainability are linked loosely without specifying the causal pathway.

To make the framework concrete, the paper illustrates it with a hypothetical manufacturing scenario, a complete event trace showing how messages move through the six layers, and formal definitions of measurable indicators. The indicator definitions are designed to be operational, meaning they specify exactly what is measured, from what data, and how the resulting values feed into ESG-oriented evaluation. This level of specification is what separates a genuine decision-support architecture from a conceptual diagram: planners can, in principle, implement the indicators directly and audit how each recommendation was derived from underlying events.

Human oversight is built into the architecture rather than bolted on. The framework explicitly positions planner decision support as the final layer, so that predictive outputs and ESG evaluations arrive as recommendations that a human decision-maker reviews and approves before any change to production planning takes effect. This design reflects the broader Industry 5.0 movement, which, as the European policy literature cited in the paper emphasizes, seeks a sustainable, human-centric, and resilient industry rather than full autonomy. The authors position their work in this tradition, drawing on recent scholarship that frames Industry 5.0 as a corrective to the technology-first ethos of Industry 4.0.

The intellectual lineage of the framework is broad. It builds on established digital twin reference models and six-layer architectural patterns from the manufacturing literature, on systematic reviews of machine learning applications in production lines and predictive quality, and on a growing body of work connecting intelligent manufacturing to ESG performance, including empirical studies of Chinese manufacturing firms and recent analyses asking whether smarter production is also greener. By synthesizing these strands, the authors aim to provide what they describe as an explicit integration of technical event flows with human-approved, ESG-oriented production planning and control decisions, something they argue no single existing framework delivers.

The authors are candid about the limits of the current study. The framework is derived through a structured conceptual synthesis, and the paper does not report implemented or empirically validated performance results. No datasets were generated or analyzed, and the hypothetical scenario exists to illustrate the architecture, not to prove it. The authors list as future work a set of concrete validation steps: discrete-event simulation experiments, construction of an MQTT and OPC UA testbed, expert assessment, and industrial validation in real manufacturing settings. Until those studies are complete, the framework should be read as a rigorous design proposal and a research agenda rather than a demonstrated solution.

Even so, the timing of the proposal is significant. Manufacturers worldwide face simultaneous pressure to digitize operations and to report credible environmental, social, and governance performance, and regulators and investors increasingly demand that sustainability claims rest on verifiable operational data. A framework that traces a straight line from a sensor event, through a predictive simulation, to a planner-approved scheduling decision and a defined ESG indicator offers exactly the kind of auditable causal chain that both engineers and sustainability officers need. If the planned testbed and industrial validation bear out the design, event-driven digital twins could become the connective tissue that finally unifies the smart factory’s many brains, turning streams of industrial telemetry into decisions that are simultaneously faster, more predictive, and demonstrably more sustainable.

Subject of Research: An event-driven digital twin framework for predictive production planning and ESG-oriented decision support in smart manufacturing

Article Title: A Conceptual Framework for Event-Driven Digital Twin-Based Predictive Production Planning and ESG-Oriented Decision Support in Smart Manufacturing

Article References: Karakai, M., & Bobko, D. (2026). A Conceptual Framework for Event-Driven Digital Twin-Based Predictive Production Planning and ESG-Oriented Decision Support in Smart Manufacturing. Mobile Networks and Applications. https://doi.org/10.1007/s11036-026-02535-3

Image Credits: AI Generated

DOI: 10.1007/s11036-026-02535-3

Keywords: digital twin, smart manufacturing, Industry 5.0, discrete-event simulation, MQTT, OPC UA, predictive production planning, ESG, decision support, industrial IoT, sustainability, conceptual framework