Deploying complex Cyber-Physical Systems (CPSs) is challenging due to the gap between abstract design models and their physical implementation. This often requires manual recoding, an error-prone process that breaks the continuity from a verified model to the final deployed system. To bridge this gap, this paper introduces a methodology that enables a direct and seamless transition from a formal computational model to its physical deployment, eliminating the need for manual recoding. The core aim is to use a single, unmodified model for both simulation and real-world operation. We propose a Model-Based Systems Engineering (MBSE) methodology grounded in the Discrete Event System Specification (DEVS) formalism. Its key innovation is the formalization of the Digital Twin (DT) concept as a reusable, executable DEVS coupled model, which explicitly structures the interface between the system’s digital logic and its physical counterpart. The methodology is implemented using the xDEVS simulation engine, whose Real-Time (RT) capabilities and built-in hardware protocol handlers (e.g., Inter-Integrated Circuit (I 2 C), MQTT) allow the formal model to directly control physical components. We demonstrated the methodology by adapting the purely computational DEVS-BLOOM model to a physical emulation controlling a small-scale Unmanned Surface Vehicle (USV). Field tests confirmed the physical USV, operated by the unmodified DEVS model running in real-time, successfully performed its autonomous navigation and monitoring mission. This successful validation is demonstrated using this single-case study as a foundational proof-of-concept. Our approach provides a robust and seamless pathway from a verified computational model to a reliable real-world system. With the formalization of the physical–digital interface inside the model itself, the methodology effectively closes the abstraction–implementation gap in CPS development.

This paper presents a new algorithm for the Discrete EVent System Specification (DEVS) formalism that improves the performance of simulating complex systems by reducing the number of iterations through the model components in each simulation step. It also minimizes unnecessary visits to model components by propagating simulation routines only when necessary. Additionally, we provide two parallel versions of this new simulation algorithm that use work-stealing scheduling and avoid locking mechanisms without compromising the validity of the execution in shared-memory architectures. We implemented the proposed algorithms in the xDEVS simulator and evaluated their performance using the DEVStone synthetic benchmark. The results show that the proposed algorithms outperform state-of-the-art alternatives. For computationally intensive models, parallel implementations achieve high parallelism efficiency. Furthermore, they are more resilient to model complexity than the sequential algorithm, showing better performance for complex models even without computational overhead in state transition functions.

Abstract The advent of edge computing holds immense promise for advancing the digitization of society, ushering in critical applications that elevate the overall quality of life. Yet, the practical implementation of the edge paradigm proves more challenging than anticipated, encountering disruptions primarily due to the constraints of applying conventional cloud‐based strategies at the network's periphery. Increasingly influenced by sustainability commitments, industry regulations currently view edge computing as a potential threat, primarily due to the energy inefficiency of solutions situated in close proximity to data generation sources and the rising density of computing. This paper presents a proactive strategy to transform the perceived threat into an opportunity, steering the sustainable evolution of future edge infrastructures to make them both environmentally and economically competitive for accelerated adoption. The vision outlined addresses key challenges associated with edge deployment and operation, emphasizing energy efficiency, fault‐tolerant automation, and collaborative orchestration. The proposed approach integrates two‐phase immersion cooling, formal modeling, machine learning, and federated management to effectively harness heterogeneity, propelling the sustainability of edge computing. To substantiate the efficacy of this approach, the paper details initial efforts towards establishing the sustainability of an edge infrastructure designed for an Advanced Driver Assistance Systems application.

Cyanobacterial Blooms (CBs) constitute a relevant ecological and public health problem since they often produce toxic metabolites that endanger the lives of many species, and they prevent human water consumption and recreational use. To determine the locations of CBs in lentic water bodies, we present a new planner based on Evolutionary Algorithms (EAs) that optimizes the trajectory of an Autonomous Surface Vehicle (ASV) equipped with a probe capable of detecting CBs. The planner 1) exploits the information provided by a particle transport simulator that determines the CB distribution from the water currents and the inherent CB behavior (in particular, its biological growth and vertical displacements) and 2) is supported by an EA that optimizes the mission duration, the ASV trajectory length, and the contributions of each simulated particle to the predicted cyanobacterial concentration along the ASV trajectory. The planner also ensures the trajectory feasibility from the ASV, probe, and water body perspective; and refines the trajectory shape by increasing the number of the decision variables during the iteration of an EA supported by usual NSGA-II operations. The results over different scenarios show that the planner determines overall good solutions that adapt the ASV trajectory to the evolution of CB distribution.

Different accurate predictive models have been developed to forecast the amount of solar energy produced in a given area. These models are usually run in a centralized manner, considering irradiance inputs taken from a set of sensors that are deployed in that area. CAIDE is a framework that supports the deployment and analysis of solar plants following Model Based System Engineering (MBSE) and Internet of Things (IoT) methodologies. However, the current solution performs the training and inference phases of the solar energy forecasting models in a central way, not taking advantage of the distributed environment modeled by means of CAIDE. This work presents an extension of CAIDE that allows us to distribute the training and inference phases, obtaining performance improvements, and achieving a greater adaptation to the inherently distributed topology of the deployment of the sensors.

Abstract Employing Modeling and Simulation (M&S) extensively to analyze and develop complex systems is the norm today. The use of robust M&S formalisms and rigorous methodologies is essential to deal with complexity. Among them, the Discrete Event System Specification (DEVS) provides a solid framework for modeling structural, behavior and information aspects of any complex system. This gives several advantages to analyze and design complex systems: completeness, verifiability, extensibility, and maintainability. DEVS formalism has been implemented in many programming languages and executable on multiple platforms. In this paper, we describe the features of an M&S framework called xDEVS that builds upon the prevalent DEVS Application Programming Interface (API) for both modeling and simulation layers, promoting interoperability between the existing platform‐specific (C++, Java, Python) DEVS implementations. Additionally, the framework can simulate the same model using sequential, parallel, or distributed architectures. The M&S engine has been reinforced with several strategies to improve performance, as well as tools to perform model analysis and verification. Finally, xDEVS also facilitates systems engineers to apply the vision of model‐based systems engineering (MBSE), model‐driven engineering (MDE), and model‐driven systems engineering (MDSE) paradigms. We highlight the features of the proposed xDEVS framework with multiple examples and case studies illustrating the rigor and diversity of application domains it can support.

Searching for targets from a group of Unmanned Aerial Vehicles (UAVs) is a complex problem, whose applications range from the localization of military targets to search and rescue missions. Determining the best locations to search within the mission scenario requires to consider the dynamics of the UAVs and of its onboard sensors, and the uncertainty of the problem, usually related with the target initial location and dynamics, and with the sensor likelihood. Besides, what is best is not always the same (e.g. it can be maximizing the detection probability and/or minimizing the target detection time, while ensuring communications, smooth trajectories, energy saving, etc). These makes the evaluation of UAVs target-search strategies a complex system itself. In this paper, we tackle this problem using the Discrete Event System Specification (DEVS) to exploit its modular and hierarchical design, and to improve the reusability and scalability of our evaluation system. DEVS also provides simple and clear semantics to manage the complexities of the system, represents an explicit separation between the model specification and the corresponding simulation, and helps us to debug and verify our model, as the results of the paper show.

BACKGROUND: The analysis of health and medical data is crucial for improving the diagnosis precision, treatments and prevention. In this field, machine learning techniques play a key role. However, the amount of health data acquired from digital machines has high dimensionality and not all data acquired from digital machines are relevant for a particular disease. Primary Progressive Aphasia (PPA) is a neurodegenerative syndrome including several specific diseases, and it is a good model to implement machine learning analyses. In this work, we applied five feature selection algorithms to identify the set of relevant features from 18F-fluorodeoxyglucose positron emission tomography images of the main areas affected by PPA from patient records. On the other hand, we carried out classification and clustering algorithms before and after the feature selection process to contrast both results with those obtained in a previous work. We aimed to find the best classifier and the more relevant features from the WEKA tool to propose further a framework for automatic help on diagnosis. Dataset contains data from 150 FDG-PET imaging studies of 91 patients with a clinic prognosis of PPA, which were examined twice, and 28 controls. Our method comprises six different stages: (i) feature extraction, (ii) expertise knowledge supervision (iii) classification process, (iv) comparing classification results for feature selection, (v) clustering process after feature selection, and (vi) comparing clustering results with those obtained in a previous work. RESULTS: Experimental tests confirmed clustering results from a previous work. Although classification results for some algorithms are not decisive for reducing features precisely, Principal Components Analisys (PCA) results exhibited similar or even better performances when compared to those obtained with all features. CONCLUSIONS: Although reducing the dimensionality does not means a general improvement, the set of features is almost halved and results are better or quite similar. Finally, it is interesting how these results expose a finer grain classification of patients according to the neuroanatomy of their disease.

Activity recognition, as an important component of behavioral monitoring and intervention, has attracted enormous attention, especially in Mobile Cloud Computing (MCC) and Remote Health Monitoring (RHM) paradigms. While recently resource constrained wearable devices have been gaining popularity, their battery life is limited and constrained by the frequent wireless transmission of data to more computationally powerful back-ends. This paper proposes an ultra-low power activity recognition system using a novel adaptive compressed sensing technique that aims to minimize transmission costs. Coarse-grained on-body sensor localization and unsupervised clustering modules are devised to autonomously reconfigure the compressed sensing module for further power saving. We perform a thorough heuristic optimization using Grammatical Evolution (GE) to ensure minimal computation overhead of the proposed methodology. Our evaluation on a real-world dataset and a low power wearable sensing node demonstrates that our approach can reduce the energy consumption of the wireless data transmission up to 81.2 and 61.5 percent, with up to 60.6 and 35.0 percent overall power savings in comparison with baseline and a naive state-of-the-art approaches, respectively. These solutions lead to an average activity recognition accuracy of 89.0 percent-only 4.8 percent less than the baseline accuracy-while having a negligible energy overhead of on-node computation.

A Ground Control Station (GCS) is an essential element to supervise and control autonomous vehicles performing complex missions in real time. In the new era of Internet of Things, where systems are highly connected, these missions demand enormous amounts of computational power to correctly manage the coordination of all the vehicles involved. In this scope, the set of Unmanned Vehicles (UVs) included in the mission must achieve more difficult tasks everyday. As a consequence, the development of a robust, reusable and adaptable GCS framework to allow a single operator to monitor and control a team of heterogeneous agents raises a number of research and engineering challenges. In this paper we introduce an adaptive event-driven framework specially designed for GCSs involved in heterogeneous multi-agent missions that takes advantage of two features: 1) it allows the GCS to add or remove both actual or simulated agents in real time, changing the number or types of monitored agents, and 2) from a software design perspective, the graphical user interface dynamically changes its view in order to minimize operators fatigue and mental workload, facilitating the success of the mission in such complex environments. We also show one of the tests performed with the adaptive framework, where after observing how a real UV deployed in a water surface performs successfully a set of previously planned trajectories, we will see how a simulated UV joins the mission in order to fulfill a leader-follower maneuver.

Summary Data centers handle impressive high figures in terms of energy consumption, and the growing popularity of cloud applications is intensifying their computational demand. Moreover, the cooling needed to keep the servers within reliable thermal operating conditions also has an impact on the thermal distribution of the data room, thus affecting to servers' power leakage. Optimizing the energy consumption of these infrastructures is a major challenge to place data centers on a more scalable scenario. Thus, understanding the relationship between power, temperature, consolidation, and performance is crucial to enable an energy‐efficient management at the data center level. In this research, we propose novel power and thermal‐aware strategies and models to provide joint cooling and computing optimizations from a local perspective based on the global energy consumption of metaheuristic‐based optimizations. Our results show that the combined awareness from both metaheuristic and best fit decreasing algorithms allow us to describe the global energy into faster and lighter optimization strategies that may be used during runtime. This approach allows us to improve the energy efficiency of the data center, considering both computing and cooling infrastructures, in up to a 21.74% while maintaining quality of service.

Wearable monitoring devices for ubiquitous health care are becoming a reality that has to deal with limited battery autonomy. Several researchers focus their efforts in reducing the energy consumption of these motes: from efficient micro-architectures, to on-node data processing techniques. In this paper we focus in the optimization of the energy consumption of monitoring devices for the prediction of symptomatic events in chronic diseases in real time. To do this, we have developed an optimization methodology that incorporates information of several sources of energy consumption: the running code for prediction, and the sensors for data acquisition. As a result of our methodology, we are able to improve the energy consumption of the computing process up to 90% with a minimal impact on accuracy. The proposed optimization methodology can be applied to any prediction modeling scheme to introduce the concept of energy efficiency. In this work we test the framework using Grammatical Evolutionary algorithms in the prediction of chronic migraines.

Managing energy efficiency under timing constraints is an interesting and big challenge. This paper proposes an accurate power model in data centers for time-constrained servers in Cloud computing. This model, as opposed to previous approaches, does not only consider the workload assigned to the processing element, but also incorporates the need of considering the static power consumption and, even more interestingly, its dependency with temperature. The proposed model has been used in a multiobjective optimization environment in which the dynamic voltage and frequency scaling and workload assignment have been efficiently optimized.

The migraine disease is a chronic headache presenting symptomatic crisis that causes high economic costs to the national health services, and impacts negatively on the quality of life of the patients. Even if some patients can feel unspecific symptoms before the onset of the migraine, these only happen randomly and cannot predict the crisis precisely. In our work, we have proved how migraine crisis can be predicted with high accuracy from the physiological variables of the patients, acquired by a non-intrusive Wireless Body Sensor Network. In this paper, we derive alternative models for migraine prediction using Grammatical Evolution techniques. We obtain prediction horizons around 20 minutes, which are sufficient to advance the drug intake and avoid the symptomatic crisis. The robustness of the models with respect to sensor failures has also been tackled to allow the practical implementation in the ambulatory monitoring platform. The achieved models are non linear mathematical expressions with low computing overhead during the run-time execution in the wearable devices.

The increasing demand for computational resources has led to a significant growth of data center facilities. A major concern has appeared regarding energy efficiency and consumption in servers and data centers. The use of flexible and scalable server power models is a must in order to enable proactive energy optimization strategies. This paper proposes the use of Evolutionary Computation to obtain a model for server dynamic power consumption. To accomplish this, we collect a significant number of server performance counters for a wide range of sequential and parallel applications, and obtain a model via Genetic Programming techniques. Our methodology enables the unsupervised generation of models for arbitrary server architectures, in a way that is robust to the type of application being executed in the server. With our generated models, we are able to predict the overall server power consumption for arbitrary workloads, outperforming previous approaches in the state-of-the-art.

Different techniques have been proposed to tackle GA-Hard problems. Some techniques work with different encodings and representations, other use reordering operators and several, such as the Evolutionary Mapping Method (EMM), apply genotype-phenotype mappings. EMM uses multiple chromosomes in a single cell for mating with another cell within a single population. Although EMM gave good results, it fails on solving some deceptive problems. In this line, EMMRS (EMM with Replacement and Shift) adds a new operator, consisting on doing a replacement and a shift of some of the bits within the chromosome. Results showed the efficiency of the proposal on deceptive problems. However, EMMRS was not tested with other kind of hard problems. In this paper we have adapted EMMRS for solving the Traveling Salesman Problem (TSP). The encodings and genetic operators for solving the TSP are quite different to those applied on deceptive problems. In addition, execution times recommended the parallelization of the GA. We implemented a GPU parallel version. We present here some preliminary results proving that Evolutionary Mapping Method with Replacement and Shift gives good results not only in terms of quality but also in terms of speedup on its GPU parallel version for some instances of the TSP problem.

SUMMARY The increasing transistor scale integration poses, among others, the thermal‐aware floorplanning problem consisting of how to place the hardware components in order to reduce overheating by dissipation. Because of the huge amount of feasible floorplans, most of the solutions found in the literature include an evolutionary algorithm for, either partially or completely, carrying out the task of floorplanning. Evolutionary algorithms usually have a bottleneck in the fitness evaluation. In the problem of thermal‐aware floorplanning, the layout evaluation by the thermal model takes 99.5% of the computational time for the best floorplanning algorithm proposed so far. The contribution of this paper is to present a parallelization of this evaluation phase in a master‐worker model to achieve a dramatic speed‐up of the thermal‐aware floorplanning process. Exhaustive experimentation was carried out over 3D integrated circuits, with 48 and 128 cores, outperforming previous published works. Copyright © 2012 John Wiley & Sons, Ltd.

The dynamic memory manager (DMM) is a key element whose customization for a target application reports great benefits in terms of execution time, memory usage and energy consumption. Previous works presented algorithms to automatically obtain custom DMMs for a given application. Nevertheless, those approaches are based on grammatical evolution where the fitness is built as an aggregate objective function, which does not completely exploit the search space, returning the designer the DMM solution with best fitness. However, this approach may not find solutions that could fit in a concrete hardware platform due to a very low value of one of the objectives while the others remain high, which may represent a high fitness. In this work we present the first multi-objective optimization methodology applied to DMM optimization where the Pareto dominance is considered, thus providing the designer with a set of non-dominated DMM implementations on each optimization run. Our results show that the multi-objective optimization provides Pareto-optimal alternatives due to a better exploitation of the search space obtaining better hypervolume values than the aggregate objective function approach.

Heat removal and power density distribution delivery have become two major reliability concerns in 3D stacked technology. Additionally, the placement of Through-Silicon-Vias (TSVs) for connecting different layers is one of the key issues in 3D technology. Although a few recent works have considered thermal-aware placement of cores in chip multi-processor architectures, the concepts of 3D and TSVs have not been conveniently incorporated. Therefore, new suitable exploration methods for the 3D thermal-aware floorplaning problem need to be developed. In this paper we analyze the benefits of two different exploration techniques for the floorplanning problem: Multi-Objective Genetic Algorithm (MOGA) and a Mixed Integer Linear Program (MILP). We present a novel algorithm that uses MILP to minimize average temperature in the 3D chip, whereas uses MOGA to insert TSVs, connecting the layers while the total wire length is minimized. Our experiments with two different 3D chips show that our algorithm achieves 10% reduction in the maximum temperature and thermal gradient.

Current general-purpose memory allocators do not provide sufficient speed or flexibility for modern high-performance applications. To optimize metrics like performance, memory usage and energy consumption, software engineers often write custom allocators from scratch, which is a difficult and error-prone process. In this paper, we present a flexible and efficient simulator to study Dynamic Memory Managers (DMMs), a composition of one or more memory allocators. This novel approach allows programmers to simulate custom and general DMMs, which can be composed without incurring any additional runtime overhead or additional programming cost. We show that this infrastructure simplifies DMM construction, mainly because the target application does not need to be compiled every time a new DMM must be evaluated. Within a search procedure, the system designer can choose the "best" allocator by simulation for a particular target application. In our evaluation, we show that our scheme will deliver better performance, less memory usage and less energy consumption than single memory allocators.

Technology scaling has offered advantages to embedded systems, such as increased performance, more available memory and reduced energy consumption. However, scaling also brings a number of problems like reliability degradation mechanisms. The intensive activity of devices and high operating temperatures are key factors for reliability degradation in latest technology nodes. Focusing on embedded systems, the memory is prone to suffer reliability problems due to the intensive use of dynamic memory on wireless and multimedia applications. In this work we present a new approach to automatically design dynamic memory managers considering reliability, and improving performance, memory footprint and energy consumption. Our approach, based on Grammatical Evolution, obtains a maximum improvement of 39% in execution time, 38% in memory usage and 50% in energy consumption over state-of-the-art dynamic memory managers for several real-life applications. In addition, the resulting distributions of memory accesses improve reliability. To the best of our knowledge, this is the first proposal for automatic dynamic memory manager design that considers reliability.

DEVS Modeling and Simulation (M&S) has multiple implementations with several computer languages such asJava, C# or C++. Therefore emerges the need of a distributed platform to provide interoperability mechanics for simulationand encourage reusability of legacy simulations and integration of diversified DEVS models. In this paper, we apply a recentlyproposed interoperability standard for DEVS M&S throughout a renewed version of DEVS/SOA. The main goal of thisweb oriented framework is to connect heterogeneous DEVS simulation elements in a transparent, open, and scalable way.We define a DEVS/SOA implementation that embodies Service Oriented Architecture (SOA) within WSDL standard to describethe simulator and coordinator interfaces and SOAP standard to support communication operations between them. This arrangementallows frontend user applications to lead simulations without local access to modeling components. Furthermore, weillustrate a real military based example of DEVS simulation interoperability among Java and .NET based DEVS models.Experiments in two different examples show that when the simulation is distributed using DEVS/SOA, we obtain a speedup of 22% in average.

New portable consumer embedded devices must execute multimedia applications (e.g., 3D games, video players and signal processing software, etc.) that demand extensive memory accesses and memory usage at a low energy consumption. Moreover, they must heavily rely on Dynamic Memory (DM) due to the unpredictability of the input data and system behavior. Within this context, consistent design methodologies that can tackle efficiently the complex DM behavior of these multimedia applications are in great need. In this article, we present a novel design framework, based on genetic programming, which allows us to design custom DM management mechanisms, optimizing memory accesses, memory use and energy consumption for the target embedded system. First, we describe the large design space of DM management decisions for multimedia embedded applications. Then, we propose a suitable way to traverse this design space using grammatical evolution and construct custom DM managers that minimize the DM used by these highly dynamic applications. As a result, our methodology achieves significant improvements in memory accesses (23% less on average), memory usage (38% less on average) and energy consumption (reductions of 21% on average) in real case studies over the current state-of-the-art DM managers used for these types of dynamic applications. To the best of our knowledge, this is the first approach to efficiently design DM managers for embedded systems using evolutionary computation and grammar evolution.

New multimedia embedded applications are becoming increasingly dynamic. Thus, they cannot only rely on static data allocation, and must employ Dynamically-allocated Data Types (DDTs) to store their data and efficiently use the limited physical resources of embedded devices. However, the optimization of the DDTs for each target embedded system is a very time-consuming process due to the large design space of possible DDTs implementations and selection for the memory hierarchy of each specific embedded device. Thus, new suitable exploration methods for embedded design metrics (memory accesses, usage and power consumption) need to be developed. In this paper we analyze the benefits of two different exploration techniques for DDTs optimization: Multi-Objective Particle Swarm Optimization (MOPSO) and a Mixed Integer Linear Program (MILP). Furthermore, we propose a novel MOPSO exploration method, OMOPSO*, which uses MILP solutions, as reference points, to guide a MOPSO exploration and reach solutions closer to the real Pareto front of solutions. Our experiments with two real-life embedded applications show that our algorithm achieves 40% better coverage and set of solutions than state-of-the-art optimization methods for DDTs (MOGAs and other MOPSOs).

ESL

New multimedia embedded applications are increasingly dynamic, and rely on Dynamically-allocated Data Types (DDTs) to store their data. The optimization of DDTs for each target embedded system is a time-consuming process due to the large design space of possible DDTs implementations. Thus, suitable exploration methods for embedded design metrics (memory accesses, memory usage and power consumption) need to be developed. In this work we present a detailed analysis of the characteristics of different types of Multi-Objective Evolutionary Algorithms (MOEAs) to tackle the optimization of DDTs in multimedia applications and compare them with other state-of-the-art heuristics. Our results with state-of-the-art MOEAs in two object-oriented multimedia embedded applications show that more sophisticated MOEAs (SPEA2 and NSGA-II) offer better solutions than simple schemes (VEGA). Moreover, the suitable sophisticated scheme varies according to the available exploration time, namely, NSGA-II outperforms SPEA2 in the first set of solutions (300-500 generations), while SPEA2 offers better solutions afterwards.

This paper presents a new method for solving discrete deceptive problems using a genotype to phenotype mapping where a new replacement and shift operator is applied. The method is evaluated using different deceptive problems. Experimental results show how our method obtains a speed-up of 94% with respect to other approaches.

We solve the problem of the distribution of petroleum products through oil pipelines networks. This problem is modeled and solved using two techniques: A heuristic method, a multiobjective evolutionary algorithm and mathematical programming. In the multiobjective evolutionary algorithm, several objective functions are defined to express the goals of the solutions as well as the preferences among them. Some constraints are included as hard objective functions and some are evaluated through a repairing function to avoid infeasible solutions. In the mathematical programming approach the multiobjective optimization is solved using the constraint method in mixed integer linear programming. Some constraints of the mathematical model are nonlinear, so they are linearized. The results obtained with both methods for three concrete networks are presented. They are compared with a hybrid solution, where we use the results obtained by mathematical programming as the seed of the evolutionary algorithm.