As AI systems move into areas that have real social consequences, trust stops being something you can attribute to an algorithm in isolation. It starts to look more like an outcome of how the whole system behaves over time, including how feedback loops form, how quickly responses arrive, and how institutions react when things go wrong. In this work, we propose a simulation-based approach to study how trust evolves in AI-enabled socio-technical systems. We use Markovian Agent Models (MAMs) to represent users, recommender algorithms, and oversight bodies as interacting populations that change continuously over time. We apply this framework to a misinformation scenario in online recommender systems, with particular attention to situations where engagement-driven optimization allows harmful content to spread before corrective actions take effect. Trust is modeled explicitly as an evaluative index derived from the system’s harmful and corrective states, not as an abstract notion. The simulations suggest that trust erosion is driven mainly by slow or delayed institutional responses, more than by the behavior of individual algorithms on their own. Experiments that remove or delay parts of the governance machinery show that moderation and policy enforcement cannot replace one another: disabling either leads to long-lasting trust loss, and even moderate delays fail to change the system’s trajectory once harmful dynamics have taken hold. The broader point is that trust is shaped by how feedback and timing interact at the system level, and that formal simulation can make these dependencies visible when testing different governance choices under clearly stated assumptions.

Game engines have become foundational platforms not only for video game development but also for Virtual Reality (VR) applications in fields such as medicine, architecture, and training. The growing dependence on a single engine throughout a project's life cycle introduces the risk of vendor lock-in, a condition in which switching to an alternative platform becomes prohibitively costly. While partial solutions exist at the hardware level—notably the OpenXR standard—no systematic cross-engine evaluation of VR development effort and portability strategies has been established. This work presents a structured comparison of three widely adopted game engines—Unity, Unreal Engine, and Godot—through the implementation of the same VR scene in each platform. A metric called Number of Clicks (NoC) is introduced to quantify implementation effort across engines. The evaluation combines NoC measurements with qualitative analysis of architectural differences, engine-specific XR toolkits, and asset handling. In addition, practical guidelines are derived for structuring VR projects and assets so as to mitigate engine dependency.

Standardized cognitive assessment relies on protocols designed for reproducibility and scoring consistency, yet paper-based and two-dimensional digital interfaces constrain the observable behavioral space and limit ecological validity. This paper presents Cogito, a Virtual Reality (VR) framework designed to support the development and administration of standardized cognitive assessments providing a structured pipeline that encompasses task formalization, real-time interaction tracking, and high-resolution behavioral data acquisition, enabling the systematic measurement of user performance and interaction dynamics within reproducible VR-based cognitive protocols. The system adopts a dual-role workflow in which clinicians configure and store testing sessions, while patients execute the assigned tasks and consult their results. Within this framework, three validated neuropsychological paradigms are implemented: the Tower of Hanoi, the Corsi Block-Tapping Test, and the Token Test. Each task is defined through parameterized configurations that regulate execution constraints and experimental conditions, ensuring controlled and reproducible administration while capturing high-resolution kinematic and interaction telemetry. A within-subject evaluation with 35 participants assessed usability, user experience, and simulator sickness using the System Usability Scale (SUS), the User Experience Questionnaire Short (UEQ-S), and the Simulator Sickness Questionnaire (SSQ) instruments. Results confirm high usability (SUS mean=81.35) , a clearly positive user experience (UEQ-S mean=+1.31), and only mild cybersickness (SSQ median=22.44). These findings support the feasibility of Cogito as a reproducible, data-oriented platform for cognitive assessment in immersive settings.

Novel View Synthesis techniques such as 3D Gaussian Splatting (3DGS) conventionally rely on Structure-from-Motion (SfM) to generate the sparse point cloud required for initialisation. SfM, however, fails systematically on textureless or flat surfaces, producing sparse geometry and visible artefacts. This paper presents a synthetic data generation pipeline that bypasses SfM entirely by decoupling geometric and photometric acquisition. A custom path tracing engine employs a toroidal sensor to capture omnidirectional data and produces a dense, ground-truth point cloud that serves as an optimal initialisation state for 3DGS. We investigate several surface sampling strategies and demonstrate that Colour-Based Importance Sampling outperforms uniform methods by concentrating samples on visually informative regions. Experimental results show that our pipeline eliminates the need for Adaptive Density Control, achieves superior reconstruction quality compared to standard SfM-based initialisations, and matches COLMAP training times despite operating on a significantly denser primitive set.

Autonomous Guided Vehicles are one of the hottest research and industrial topics, with applications spanning from alternative transport, security and logistic. However, safety and security is usually one of the main concerns in this type of technology, limiting their application and introduction. In this paper we will consider the effects that a malicious user can cause on the system, and we propose an analysis technique based on a mean-field model, to be able to quickly obtain estimates of the consequences of an attack, and the impact of the counter-measures.

Traditional surgical training methods often lack the immersive and interactive elements necessary for optimal skill acquisition. Virtual reality (VR) technology has emerged as a transformative tool in surgical education, offering realistic simulations that enhance technical proficiency, psychomotor skills, and cognitive planning. This paper presents a novel collaborative cross-platform VR framework that supports multi-user interaction, enabling trainees and instructors to engage in a shared virtual environment. Structured around a modular digital hospital, the system facilitates real-time training sessions, interactive knowledge sharing, and procedural simulations with integrated feedback mechanisms. A user study demonstrated a significant reduction in errors, improved precision, and faster task completion, underscoring the system’s effectiveness in enhancing surgical training efficiency. Participants rated usability highly, highlighting the system’s intuitive design, engagement, and potential for democratizing surgical education.

The Bounding Volume Hierarchy (BVH) is a fundamental data structure in the ray tracing algorithms to accelerate the detection of ray-scene intersections. The Surface Area Heuristic (SAH), employed during the construction of the BVH, is based on the hypothesis that the ray distribution in the scene is uniform. In this paper, we show that the SAH hypothesis is not valid when importance sampling is used, and propose two novel heuristics. With the Projected Area Heuristic (PAH) we demonstrate how it is possible to estimate better the cost of a BVH. In particular, we replace the approximation of the probability an AABB is hit by a ray, from the ratio between the surface areas of the node and the root of the BVH to the ratio of their projected areas. The plane the AABBs are projected on and the kind of projection (either orthographic or perspective) are chosen based on the local ray distribution. With the Splitting Plane Facing Heuristic (SPFH) we show how we can build higher-quality BVHs by taking into account the knowledge of the ray distribution in a local region of the scene during the selection of the orientation of the plane used to split a node.

Surgical operations must be preformed by skilled and qualified personnel. Training is therefore of paramount importance to master enough skill to succeeds in the tasks without endangering the health of the patients. Combining different types of training activities, can help reaching the goal in a quicker and more effective way. In this scenario, Virtual Reality represents an interesting step, particularly thanks to the reduction in the cost of the equipment that have made their adoption affordable and their availability widespread. In this work, we present our advances in training lung operations with low-cost virtual reality simulation environments.

Cloud platforms offer not only the capacity to facilitate effective and scalable services for third-party applications and business solutions, but also present an opportunity to implement intricate disaster recovery strategies. For instance, a Chief Technical Officer may opt to maintain operations on private systems in order to effectively manage costs, privacy, and security, while simultaneously leveraging the cloud as an autonomous and immediate disaster recovery support. This can be achieved by building a secondary leg of the IT system that functions as an online cold or hot spare, manages workload peaks, or handles a portion of the workload under normal conditions. To assess the effectiveness and cost-effectiveness of such solutions, appropriate models are essential to examine the trade-offs and explore the parameter space of possible alternatives. This paper presents a modeling approach for the design and evaluation of cloud-based recovery setups.

The diffusion of intelligent services and the push for the integration of computing systems and services in the environment in which they operate require a constant sensing activity and the acquisition of different information from the environment and the users. Health monitoring, domotics, Industry 4.0 and environmental challenges leverage the availability of cost-effective sensing solutions that allow both the creation of knowledge bases and the automatic process of them, be it with algorithmic approaches or artificial intelligence solutions. The foundation of these solutions is given by the Internet of Things (IoT), and the substanding Wireless Sensor Networks (WSN) technology stack. Of course, design approaches are needed that enable defining efficient and effective sensing infrastructures, including energy related aspects. In this paper we present a Domain Specific Language for the design of energy aware WSN IoT solutions, that allows domain experts to define sensor network models that may be then analyzed by simulation-based or analytic techniques to evaluate the effect of task allocation and offloading and energy harvesting and utilization in the network. The language has been designed to leverage the SIMTHESys modeling framework and its multiformalism modeling evaluation features.

Smartphones are well fit to be used as head mounted displays for virtual reality, because they already embed the sensors necessary to track the head rotation. The purpose of this study is to find out how much we can improve the user experience by adding three more degrees of freedom to track the user chest rotation, in particular with respect to the perceived motion sickness. With this addition the user can rotate the body independently from the head, meaning that he or she can look in a direction different from the one he or she is facing. The interface is complemented with a technique to match the rotation of the user body and head with his or her virtual avatar. The proposed physical interface is tested on two custom simulations. The first is a virtual museum where the user can walk and look around. The user can control the direction where the avatar is looking with the head, and the direction he or she is facing using the body. The second simulation is a game that uses the chest rotation as a core gameplay mechanic, allowing to control a flying skateboard by moving the body, with the goal of traveling around a circuit to establish the best time.

Modern networking is moving towards exploitation of autonomic features into networks to reduce management effort and compensate the increasing complexity of network infrastructures, e.g. in large computing facilities such the data centers that support cloud services delivery. Autonomicity provides the possibility of reacting to anomalies in network traffic by recognizing them and applying administrator defined reactions without the need for human intervention, obtaining a quicker response and easier adaptation to network dynamics, and letting administrators focus on general system-wide policies, rather than on each component of the infrastructure. The process of defining proper policies may benefit from adopting model-based design cycles, to get an estimation of their effects. In this paper we propose a model-based analysis approach of a simple autonomic router, using Stochastic Petri Nets, to evaluate the behavior of given policies designed to react to traffic workloads. The approach allows a detailed analysis of the dynamics of the policy and is suitable to be used in the preliminary phases of the design cycle for a Software Defined Networks compliant router control plane.

Indoor Object Tracking Systems (IOTS) allow sensing moving objects inside a closed space, where GPS is not available. Besides the most popular use, indoor navigation, IOTS may also contribute to extend the operational range and the possible applications of Virtual Reality (VR) and Augmented Reality (AR) based technologies, such as complex training scenarios or entertainment oriented simulations: in fact, providing devices with a reliable IOTS support adds realism and allows a higher degree of safety and interactivity, that allow a high number of people to take part and collaborate in a simulated scene with a very high degree of physical interaction. In this paper we introduce a novel approach for the optimization and the evaluation of movement tracking in a IOTS based system, oriented to VR/AR applications, with special focus on the training of teams. Our proposal is applied to a case study, an AR application designed to assist business buildings workers in fire extinguisher use training. Performances of our proposal are evaluated by means of a simulation, and results are validated in a test scenario based on Ultra Wide Frequency positioning by means of a simulation scenario fed with real data from anchors.

Data heterogeneity and irregularity are key characteristics of big data applications that often overwhelm the existing software and hardware infrastructures. In such context, the exibility and elasticity provided by the cloud computing paradigm over a natural approach to cost-effectively adapting the allocated resources to the application's current needs. Yet, the same characteristics impose extra challenges to predicting the performance of cloud-based big data applications, a central step in proper management and planning. This paper explores two modeling approaches for performance prediction of cloud-based big data applications. We evaluate a queuing-based analytical model and a novel fast ad-hoc simulator in various scenarios based on different applications and infrastructure setups. Our results show that our approaches can predict average application execution times with 26% relative error in the very worst case and about 12% on average. Moreover, our simulator provides performance estimates 70 times faster than state of the art simulation tools.

Microservice-based software architectures are a recent trend, stemming from solutions that have been designed and experimented in big software companies, that aims to support devops and agile development strategies. The main point is that software architectures, similarly to what happens in SOA, are decomposed into very elementary tasks, that can be developed, maintained and deployed in isolation by small independent teams, and that compose an application by means of simple interactions. The resulting architecture is advocated to be more maintainable, less prone to failures, more agile, but obviously impacts on performances. In this paper we provide a simulation based approach to explore the impact of microservicebased software architectures in terms of performances and dependability, given a desired configuration. Our approach aims at giving a first approximation estimation of the behavior of different classes of microservice-based applications over a given system configuration, to characterize the infrastructure from the point of view of the service provider under a randomly generated realistic overall workload: to the best of our knowledge, there is not any other analogous decision support tool available in literature.

The performance over cost ratio of last generation off the shelf devices enables the design of heterogeneous distributed computing systems capable of supporting the implementation of an immersive Virtual Reality, Internet of Things based training support architecture. In this paper we present our work in progress on a low cost distributed immersive simulation system for the training of teams by means of Virtual Reality and off the shelf mobile and prototyping devices. In this case, performance prediction is crucial, because the generation of the scenario have to be performed in real time and synchronization problems may disrupt the result. The approach is demonstrated by a prototypical case study, that consists in a distributed simulator for the interactive training of groups of people that have to coordinate to face a fire emergency, and features advanced immersivity thanks to CGI-enabled stereoscopic 360 degree 3D Virtual Reality and ad-hoc devised interaction interfaces. In particular, we focus on the subsystem that is related to a single trainee, providing a reference implementation and a performance evaluation oriented model to support the design of the complete system.

Summary Protection and preservation of cultural heritage is an important responsibility for policy makers, public and private institutions, and the citizens themselves. Technologists can make an important contribution by designing monitoring systems for these sites and using the data to prevent incidents. Internet‐of‐things technology offers, for a sustainable price and with significant flexibility, a wide range of different possibilities, fitting different circumstances: from monitoring the environmental parameters of a room in a museum to sensing structural changes in a historical building and to protecting vulnerable artifacts. In this paper, we consider the case of monitoring an extended cultural heritage area: a UNESCO protected site, the center of Matera, an Italian town that will be a European Capital of Culture in 2019. This city is a unique historical settlement, as buildings are partially carved into the rock that constitutes the geological substrate of the area, a local practice used since the prehistoric age. The extent and density of these structures makes the physical protection of the site a big challenge when the expected large crowds of tourists arrive. The objective of the proposed system is to anticipate the threats in a timely manner so that appropriate actions are taken by the authorities thus avoiding damage to the cultural heritage sites. We propose a technique for modeling the performances of the Internet‐of‐things–based monitoring systems that support the planning of incident management in a protected site by exploiting multiple, sparse, heterogeneous, and partially controlled sensors to monitor the behavior of the crowd. The technique is based on the use of Markovian agent models to study the parameters and the dynamics of a scenario, to understand the needs of the monitoring system.

The fashion industry is one of the most flourishing fields for visual applications of IT. Due to the importance of the concept of look in fashion, the most advanced applications of computer graphics and sensing may fruitfully be exploited. The existence of low cost solutions in similar fields, such as the ones that empower the domestic video games market, suggest that analogous low cost solutions are viable and can foster innovation even in small and medium enterprises. In this paper the current state of development of vMannequin, a dynamic, user mimicking, user enacted virtual mannequin software solution, is presented. In order to allow users designing dress concepts, the application simulate the creation and fitting of clothes on virtual models. The interaction is sensor based, in order to both simplify the user interface, and create a richer involvement inside the application.

The effects of networks on the performances of cloud architectures are a very significant issue in designing a data center. The efficiency of data transfers and the overall traffic management are a critical factor that constitutes a potential performance bottleneck, potentially limiting the number of computing nodes that can be installed more than their cost issues. In this paper we present a modeling approach, based on Markovian agents, that allows a performance analysis of network effects in high scale cloud architectures.

Summary The capabilities of multicore processors lead them to be widely adopted in systems at any scale, since their are able to provide more computing power at a lower consumption and dissipation cost. System designers are challenged to a deeper understanding of multicore functioning in order to fully exploit them while keeping the optimal balance between cores utilization and optimal throughput, response time and energy usage. Besides the advancement of general purpose CPUs, the same technological evolution leads to the rise of GPUs, dramatic evolution of graphical coprocessors, that are now affordable, efficient, dedicated computing units, capable of parallel computing and equipped with facilities that make them suited for supporting the main CPU of a system in running ordinary applications. The availability of commercial off‐the‐shelf (COTS) multicore computers, eventually equipped with one or more GPUs, makes them the basic building block of data centers devoted to cloud applications or scientific computing. The way to optimal exploitation of such a wide amount of computing power passes through the ability of matching the best scheduling of hardware resources with the software characteristics of the applications. This requires appropriate models and evaluation methods. Simulation and analytical techniques are essential tools to support the design and the management process of such architectures, but a sound characterization of the workloads is required. Typical workloads consist in multithreaded applications, with different characteristics, that dynamically span over the cores of multiple machines, connected by fast networks. In this paper we propose several parametric performance models for different configurations of multicore machines, with or without GPU support, running multiple class multithreaded applications, aiming to supply a detailed modeling help for complex data centers. Copyright © 2015 John Wiley & Sons, Ltd.

The evaluation of the opportunity of investments on complex production processes is a critical factor in order to enable the balance of risks and potential benefits. There is no out-of-the-box tool that can solve this problem: only the experience of the responsible expert and his knowledgeability of the process can help. Outcome Driven Innovation is an evaluation technique that can support decisions, based on a structured approach to process analysis and on the availability of domain experts: anyway, the need for experts can make the evaluation itself very expensive. In this paper a simulative approach is used to provide an a priori characterization of the conditions that can suggest the opportunity of adopting Outcome Driven Innovation for a process. © ECMS Valeri M. Mladenov, Petia Georgieva, Grisha Spasov, Galidiya Petrova (Editors)

Summary Big Data applications are characterized by a non‐negligible number of complex parallel transactions on a huge amount of data that continuously varies, generally increasing over time. Because of the amount of needed resources, the ideal runtime scenario for these applications is based on complex cloud computing and storage infrastructures, providing a scalable degree of parallelism together with isolation between different applications and resource abstraction. However, such additional abstraction degree also introduces significant complexity in performance modeling and decision making. Potential concurrency of many applications on the same cloud infrastructure has to be evaluated, and, simultaneously, scalability of applications over time has to be studied through proper modeling practices, in order to predict the system behavior as the usage patterns evolve and the load increases. For this purpose, in this paper, we propose an analytic modeling technique based on the use of Markovian Agents and Mean Field Analysis that allows the effective description of different concurrent Big Data applications on a same, multi‐site cloud infrastructure, accounting for mutual interactions, in order to support the careful evaluation of several elements in terms of real costs/risks/benefits for correctly dimensioning and allocating the resources and verifying the existing service level agreements. Copyright © 2014 John Wiley & Sons, Ltd.

Multicore architectures are now available for a wide range of high performance applications, ranging from embedded systems to large scale servers deployed in cloud environments. Multicore architectures are usually subject to two conflicting goals: obtaining a full utilization of the cores while achieving given performance objectives, such as throughput, response time or reduced energy consumption. Moreover, there is a strong interdependence between the software characteristics of the applications, and the underlying CPU architecture. In this scenario, simulation and analytical techniques can provide solid tools to properly design the considered class of systems: however, properly characterize the workload on multithreaded application in multicore environment is not an easy task, and thus is an hot research topic. In this paper we present several models, of increasing complexity, that can characterize multithreaded applications running on multicore architectures.

This paper presents a Monte Carlo-based framework for the estimation of lifetime reliability of multicore systems. Existing mathematical tools either consider only the time to the first failure, or are limited by their intrinsic complexity and high computational time. The proposed framework allows to compute quasi-exact results with a reasonable computational time, without adopting typical (and possibly misleading) simplifications that characterize the existing tools for computing Mean Time To Failure (MTTF). The paper describes the framework with all its mathematical details, assumptions and simplifications; it proves the correctness of the obtained results, by comparing them against the exact ones, and underlines the differences with the simplistic approaches, also discussing time overhead improvements.

This paper proposes a novel approach for remotely sensed image restoration. The main goal of this study is to mitigate two most well-known types of noises from remote sensing images while their important details such as edges are preserved. To this end, a novel method based on partial differential equations is proposed. The parameters used in the proposed algorithm are adaptively set regarding the type of noise and the texture of noisy datasets. Moreover, we propose to apply a segmentation pre-processing step based on Watershed transformation to localize the denoising process. The performance of the restoration techniques is measured using PSNR criterion. For further assessment, we also feed the original/noisy/denoised images into SVM classifier and explore the results.

In this paper, we present ParSim, a software tool designed for modeling parallel applications. The tool reproduces a workload, including its parallel characteristics specified by the users, on the target multicore or multiCPU system, and analyzes the collected data. The objective of ParSim is to close the gap between the "generalized" benchmarks, designed for conventional system architectures, and the peculiar needs of parallel environments based on multicore systems. A simple language for the description of parallel features of an application and specific analysis techniques are provided. We present the working context and principles of ParSim, its architecture and its features, and validate our tool through its application on a case study.

Big Data applications represent an emerging field, which have proved to be crucial in business intelligence and in massive data management. Big Data promises to be the next big thing in the development of strategical computer applications, even if it requires considerable investment and an accurate resource planning, as the architectures needed to perform at the requisite speed need to scale easily on to a large number of computing nodes. Appropriate management of such architectures benefits from the availability of performance models, to allow developers and administrators to take informed decisions, saving time and experimental work. This paper presents a dedicated modeling language showing firstly how it is possible to ease the modeling process and secondly how the semantic gap between modeling logic and the domain can be reduced.

To efficiently manage resources and provide guaranteed services, today's computing systems monitor and collect a large number of resource usages, for example the average and time series of CPU utilization. However, little is known about the analytical distribution of resource usages, which are the crucial parameters to infer performance metrics defined in service level agreements (SLAs), such as response times and throughputs. In this paper, we aim to characterize the entire distribution of CPU utilization via stochastic reward models. In particular, we first study and derive the probability density function of the utilization of widely known and applied queuing systems, namely Poisson processes, Markov modulated Poisson processes and time-varying Poisson processes. Secondly, we apply our proposed analysis on characterizing the CPU usage of live production systems, and simulated queuing systems. Evaluation results show that analytical characterization of the selected queueing models can capture the utilization distribution of a wide range of real-life systems well, and we argue the robustness of our methodology to further infer system performance metrics.

We study the delay performance of a sensor network, whose nodes access the medium by using the unslotted MAC protocol specified by the IEEE 802.15.4 standard. Unlike previous works, which focus on the average throughput and delay analysis, we develop a detailed model that allows us to obtain the delivery delay distribution of messages sent by concurrently contending sensors toward a central controller. We carry out a transient analysis that is of particular interest when sensor networks are deployed to provide k-coverage for real-time applications, and we study both single- and multi-hop network topologies. We validate our analytical results against simulation results obtained through ns2.

The design and the requirements of modern computer-based systems have reached a complexity level that calls for the use of models for the verification of non functional requirements since the beginning of their design cycle. Such systems are however too complex to be modeled directly in a simple unstructured formal language like Queueing Networks or Petri Nets. SIMTHESys (Structured Infrastructure for Multiformalism modeling and Testing of Heterogeneous formalisms and Extensions for SYStems) is a novel approach to multiformalism compositional modeling, that is based on the possibility of freely specifying the dynamics of the elements of a formal modeling language in an open framework. This is obtained by the application of consolidated metamodeling foundations to the description of models, together with the concept of behavior as a bridge between formalism dynamics and solution techniques. In this paper the main concepts of the SIMTHESys approach are presented, together with a running example of how SIMTHESys copes with performance evaluation of multiformalism models.

We study the delay performance of a sensor network, whose nodes access the medium by using the unslotted MAC protocol specified by the IEEE 802.15.4 standard. Unlike previous works which focus on the average throughput and delay analysis, we develop a detailed model that allows us to obtain the delivery delay distribution of messages sent by concurrently contending sensors toward a central controller. We carry out a transient analysis, which is of particular interest when sensor networks are deployed to provide k-coverage for real-time applications, and validate our analytical results against simulation results obtained through ns2.

The paper discusses a Dynamic Markovian Agent Model obtained by adding mobility to a recently introduced new formalism suitable for the analysis of large scale systems, composed by a population of interacting entities, called Markovian Agents (MA). The differential equations describing the evolution of the MA density in time and space are derived, and their numerical solution is briefly sketched. An application to the analysis of the flow of vehicles in a road tunnel is discussed, together with the evaluation of the probability of collision against a fixed obstacle.

Power management in battery operated sensor networks, is a hot topic addressed by many ongoing researches. One of the most commonly employed technique consists in turning on and of the power of the radio unit to reduce the power consumption. In this paper we exploit the modelling power of Interacting Markovian agent to evaluate the performance of four different on-off strategies. A Markovian agent (MA) is an entity whose behaviour is described by a continuous time Markov chain (CTMC) and that is able to interact with other MA's by sending and receiving messages. A perceived message may induce a state transition in an MA according to a perception function than can depend on the geographical location of the MA's, on the message routing strategy and on the transmission property of the medium. We represent each sensor by an MA and we show how MA's can interact to produce the collective behaviour of the sensor network.

The virtual reality simulator used in the e-DISTRICT CiPro Pilot-Project, in the context of the EUprogramme Leonardo da Vinci II, allows the realization of an Internet based Collaborative Virtual Environment and it can be used for Education and Training for Civil Protection Operators, and for Research purposes in new operational protocols and their effectiveness (which would be very expensive in case of real drills, and also very difficult to organize). In this article we focus on the goals of the e- District CiPro Project, and we present the virtual reality simulator. We also address the importance of the detail level of the scenarios and we consider their customization and their integration with other software that simulates the phase of treatment in hospital.

We consider the problem of self-deployment and relocation in mobile wireless networks, where nodes are both sensors and actuators. We propose a unified, distributed algorithm that has the following features. During deployment, our algorithm yields a regular tessellation of the geographical area with a given node density, called monitoring configuration. Upon the occurrence of a physical phenomenon, network nodes relocate themselves so as to properly sample and control the event, while maintaining the network connectivity. Then, as soon as the event ends, all nodes return to the monitoring configuration. To achieve these goals, we use a virtual force-based strategy, which proves to be very effective even when compared to an optimal centralized solution.

We focus on a vehicular network supporting safety applications, and we present an application and a channel access mechanism for efficient multihop broadcasting. We study the performance of the proposed solution by developing an analytical framework, which provides several metrics relevant to message dissemination. Analytical results are compared with the performance obtained through ns

Sparse network of fixed or mobile wireless devices, where most of the time there does not exist a complete path from a source to a destination are often referred to as delay tolerant networks. The store-carry-and-forward principle, according to which messages can be stored at mobile nodes moving around the network area before being forwarded to the destination, allows the transmission of messages in such systems. In this paper, we use an analytical framework to study delay tolerant networks, based on asCSL model checking techniques. In particular we focus on the case in which fixed sensors exhibit on-off behavior to overcome battery capacity limitations.

The behavior of large-scale wireless sensor networks has been shown to be surprisingly complex and difficult to analyze, both by empirical experiment and simulation. In this paper we develop a new analytical model of the behavior of wireless sensor networks, based on a fluid approach, i.e., we represent the sensor network by a continuous fluid entity distributed on the network area. The model accounts for node energy consumption, channel contention, as well as traffic routing; thus, it is well suited for describing the properties of sensor networks and understanding their complex behavior.

In this paper a new algorithm for the transient solution of a sub-class of deterministic stochastic Petri nets (DSPN) is proposed. The technique can be applied to DSPNs comprising only deterministic and immediate transitions and such that in each tangible marking only one deterministic transition is enabled. The algorithm does not require any additional restriction on the deterministic transition delays that can have any positive real value. Most of the optimized algorithms presented in the literature are based on an efficient solution of the equations governing the stochastic process associated with the DSPN; the new algorithm we propose is based on an efficient combinatorial analysis of the paths within the state space underlying the DSPN, instead.

The DrawNet++ project addresses the compositional construction of dependability models. Its main goals are to provide: a) a GUI to any graph-based formalism; b) a support to the design process of dependability models, according to concepts inspired by object orientation (OO); c) a user friendly front-end for different classes of analysis/simulation tools.

Fluid stochastic (or hybrid) Petri nets with flush-out arcs are Petri net-based models with two classes of places: discrete places that carry a natural number of distinct objects (tokens), and fluid places that hold a positive amount of fluid, represented by a real number. For this kind of formalisms, equations can be automatically derived from the model. Such equations, however, are often too complex to be solved analytically and simple discretization techniques usually can be successfully applied only to simple cases. In this paper, we present a particular solution technique for transient solution that makes use of the Kronecker-algebra.

Describes a method for the simulation of fluid stochastic Petri nets (FSPNs). The FSPNs are a promising formalism for modeling hybrid dynamic systems, i.e. systems having both discrete and continuous components that evolve over time. Unfortunately, an analytical evaluation of performance measures for such nets requires the solution of a complex system of integro-differential equations whose numerical analysis often becomes a formidable task. One possible way for computing such performance measures is the use of simulative approaches. Because of the mixed (discrete and continuous) state space, the simulation of FSPNs models poses some interesting challenges, which are addressed in this paper. The paper provides a simulative approach for deriving performance measures for a class of FSPNs. The techniques described in the paper are included in a simulation tool for this class of FSPNs.