This paper proposes a model-based rejuvenation approach that embeds rejuvenation strategies into a stochastic attack–defense model aligned with the Cyber Kill Chain phases. It enables phase-aware reasoning to determine how system refreshes can effectively shorten attacker dwell time and reduce overall exposure. A continuous-time Markov chain (CTMC) jointly captures adversary progression and defender-triggered rejuvenation. The framework links system and service level objectives (SLO) to assess availability and security. Rejuvenation is treated as a timer optimization problem, looking for the refresh rates that satisfy SLO constraints while balancing risk reduction against refresh overhead. Grounded in the classical notion of rejuvenation as periodic return to a known-good state, the proposal demonstrates, through a numerical case study on SQL injection, that tuned timers can bound attacker opportunity windows and improve availability and security, meeting system and service SLO.

Sustainable urban planning manages cities to protect the environment and uses resources wisely for the needs of urban daily life. The Internet of Things (IoT) optimizes the usage of resources and the consideration of urban utilities, such as congestion and pollution. Furthermore, Blockchain supports transparent data collection and securely storing data. Immutability is a fundamental feature of Blockchain, which keeps the data unchanged; however, researchers have proposed mutable Blockchains for data modifications by putting away security. This paper studies mutable Blockchains for sustainable IoT urban planning. It aims to discuss the productivity of mutable Blockchain in IoT-driven Sustainable Urban Planning. To this end, it discusses the challenges of mutable blockchains, presents the analytical modeling in practical applications, and examines the security of mutable Blockchains in public and centralized private Blockchains. Our main goal is to discuss their potential and limitations in IoT-based urban planning environments. The analysis shows that mutable Blockchains reduce computational costs but increase security risks. Our study explains the need for carefully governed mutability to support privacy and adaptability in IoT-based urban planning.

Hyperspectral imaging (HSI) has proven to be a powerful tool for capturing detailed spectral and spatial information across diverse applications. Despite the advancements in deep learning (DL) and Transformer architectures for HSI classification, challenges such as computational efficiency and the need for extensive labeled data persist. This letter introduces WaveMamba, a novel approach that integrates wavelet transformation with the spatial-spectral Mamba (SSMamba) architecture to enhance HSI classification. WaveMamba captures both local texture patterns and global contextual relationships in an end-to-end trainable model. The Wavelet-based enhanced features are then processed through the state-space architecture to model spatial-spectral relationships and temporal dependencies. The experimental results indicate that WaveMamba surpasses existing models, achieving an accuracy improvement of 4.5% on the University of Houston dataset and a 2.0% increase on the Pavia University dataset.

Hyperspectral images (HSIs) capture detailed spectral–spatial information across hundreds of contiguous bands, enabling precise material identification in domains such as environmental monitoring, agriculture, and urban analysis. However, the high dimensionality and spectral variability inherent to HSIs present significant challenges for effective feature extraction and classification. This letter introduces EnergyFormer (EF), a transformer-based framework designed to overcome these limitations through three key innovations: 1) multihead energy attention (MHEA), which formulates an energy optimization mechanism to selectively enhance discriminative spectral–spatial features; 2) Fourier positional embedding (FoPE), which adaptively models long-range spectral and spatial dependencies; and 3) enhanced convolutional block attention module (ECBAM), which emphasizes informative wavelength bands and spatial structures for robust representation learning. Extensive experiments on the WHU-Hi-HanChuan, Salinas, and Pavia University datasets demonstrate that EF achieves superior classification performance with overall accuracies of 99.28%, 98.63%, and 98.72%, respectively, outperforming leading CNN-, transformer-, and Mamba-based models.

Hyperspectral image classification (HSIC) is a challenging task due to the high dimensionality of hyperspectral data, the complex interplay of spatial and spectral features, and the scarcity of annotated samples. Existing approaches, mainly based on tokenization-based feature extraction, introduce artificial segmentation, increasing computational cost, and may lead to information loss. To address these issues, a novel framework, Byte Latent Mamba with Knowledge Distillation (BLM-KD), overcoming explicit tokenization by directly learning byte-level spectral-spatial representations from raw hyperspectral data, is proposed. The Byte Latent Mamba architecture learns compact and expressive byte-level features through an end-to-end convolutional encoder, preserving spectral continuity and spatial structure. A structured State Space Model (SSM) is integrated to model long-range spatial-spectral dependencies efficiently via learned dynamic state transitions. Additionally, an adaptive knowledge distillation (KD) strategy is adopted, where a high-capacity teacher model selectively transfers salient features to a lightweight student model, driven by a temperature-controlled weighting schedule. This ensures robust generalization with reduced model complexity. A patch-based preprocessing scheme also excludes irrelevant zero-labeled samples, refining the training process. Extensive experiments conducted on multiple real-world hyperspectral benchmarks demonstrate that BLM-KD outperforms existing state-of-the-art methods in both classification accuracy and computational efficiency.

Multihead self-attention and cross-attention mechanisms often suffer from computational inefficiencies, limited scalability, and suboptimal contextual understanding, particularly in hyperspectral image (HSI) classification. These mechanisms struggle to effectively capture long-range dependencies while maintaining computational feasibility due to the quadratic complexity of self-attention. To address these challenges, this work proposes PolicyMamba, a spectral-spatial mamba model enhanced with a localized policy attention mechanism. This mechanism reduces computational overhead by restricting attention to nonoverlapping localized regions and enforcing sparsity constraints, ensuring that only the most informative interactions are retained. A hierarchical aggregation strategy further integrates patch-wise attention outputs, preserving spectral-spatial correlations across scales. In addition, a sliding window patch process enhances local feature continuity while mitigating information loss. The PolicyMamba framework integrates spectral-spatial token generation, token enhancement, localized attention, and state transition modules, significantly improving HSI feature representation. Extensive experiments demonstrate that PolicyMamba achieves superior classification accuracy, outperforming conventional and state-of-the-art methods in land cover classification (LCC) by efficiently modeling intricate dependencies in HSI data.

The confluence of Large Language Models (LLMs) and Dataspaces presents a captivating prospect for the future of data science and AI, fostering new avenues for data exploration, analysis, and knowledge extraction. This paper explores both patterns arising from the Dataspaces-LLMs convergence: Dataspaces applied to LLMs (DS4LLM) and LLMs applied to Dataspaces (LLM4DS), further investigating the latter. Dataspaces, a conceptual framework for data-centric systems, are poised to revolutionize data management and sharing, indeed. They provide a structured and secure environment for managing and integrating data from multiple sources. Traditional approaches to data management, nevertheless, are bottom-up, often hindering the timely use of data. This paper advocates for a top-down perspective, prioritizing data collection and provisioning to other data management tasks that have to be thus delivered on purpose and in a timely manner a-posteriori, after data (re)source discovery. To bridge the gap between these two approaches, LLMs emerge as a powerful tool able to unlock the value hidden within vast data repositories and Dataspaces. By LLMs, data management tasks such as filtering, cleaning, aggregation, integration, and augmentation can be automated into LLM4DS workflows. This research specifies some LLM4DS prompt templates that can be tailored to specific use cases for on demand and on purpose (a-posteriori) data management requiring post-scheme. A case study on a medical Dataspace demonstrates the practical application and suitability of the proposed LLM4DS approach to the problem at hand.

Conventional active learning approaches for hyperspectral image classification (HSIC) have limitations such as incrementally growing training sets without considering class structure and heterogeneity within existing and new samples. Additionally, there is limited research leveraging both spectral and spatial information jointly, and stopping criteria are not well established. This study presents a novel fuzzybased spatial-spectral Within and Between method (FLG) for preserving local and global class discriminative information. The method first explores spatial fuzziness to identify misclassified samples. It then computes total within-class and between-class information locally and globally. This information is integrated into a discriminative objective function to selectively query heterogeneous samples, mitigating randomness among training data. Experimental results on benchmark Hyperspectral datasets demonstrate the FLG improves classification accuracy across generative, extreme learning machine, and sparse multinomial logistic regression models by jointly exploiting spectral and spatial information to expand labeled training sets strategically.

Cardiovascular diseases are currently the major causes of death globally. Among the strategies to prevent cardiovascular issues, the automated classification of heart sound abnormalities is an efficient way to detect early signs of cardiac conditions leading to heart failure or other, even asymptomatic, complications, quite effective for timely interventions. Despite the significant improvements in this field, there are still limitations due to the lack of solutions, available data-sets and poor (mainly binary - normal vs abnormal) classification models and algorithms. This paper presents a Medical Cyber-Physical System (MCPS) for the automatic classification of heart valve diseases onsite, in a timely manner. The proposed MCPS, indeed, can be deployed into personal and mobile devices, addressing the limitations of existing solutions for patients, healthcare practitioners, and researchers, through an efficient and easy accessible tool. It combines different neural network models trained on a new Italian dataset of 132 adult patients covering 9 heart sound categories (1 normal and 8 abnormal), also validated against two main open-access (Physionet/CinC Challenge 2016 and Korean) datasets. The overall MCPS performance (time, processing and energy resource utilization) and the high accuracy of the models (up to 98%) demonstrated the feasibility of the proposed solution, even with few data. The dataset supporting the findings of this paper is available upon request to the authors.

In Transformer-based hyperspectral image classification (HSIC), predefined positional encodings (PEs) are crucial for capturing the order of each input token. However, their typical representation as fixed-dimensional learnable vectors makes it challenging to adapt to variable-length input sequences, thereby limiting the broader application of Transformers for HSIC. To address this issue, this study introduces an implicit conditional PEs (CPEs) scheme in a Transformer for HSIC, conditioned on the input token’s local neighborhood. The proposed spatial–spectral Transformer (SSFormer) integrates spatial–spectral information and enhances classification performance by incorporating a CPE mechanism, thereby increasing the Transformer layers’ capacity to preserve contextual relationships within the HSI data. Moreover, SSFormer ensembles the cross attention between patches and proposed learnable embeddings. This enables the model to capture global and local features simultaneously while addressing the constraint of limited training samples in a computationally efficient manner. Extensive experiments on publicly available HSI benchmarking datasets were conducted to validate the effectiveness of the proposed SSFormer model. The results demonstrated remarkable performance, achieving the classification accuracies of 97.7% on the Indian Pines dataset and 96.08% on the University of Houston dataset.

A Smart Factory exploits information and communication technologies (ICT) to improve the production process and the working environment, usually addressing safety concerns. To this concern, factory-grade fire protection systems are governed by several procedures and standards whose application often becomes definitely challenging when the factory premises are dispersed across multiple administrative domains. In such contexts, the Smart Factory approach can prove very effective in the management and coordination of the factory-level fire protection system. However, a catastrophic event may compromise the ICT infrastructure, affecting communication among factory domains and therefore its smart services. A strategy to cope with the latter may be the introduction of mechanisms to handle data analysis on-site for a prompt response while enabling seamless data distribution and processing among neighboring (federated) ICT infrastructures and emergency operators. In this work, a novel software-defined approach for the adaptive management of a Smart Factory infrastructure is proposed, centered around business logic rewiring and reconfiguration at runtime across different factory domains. Thereby, even in the case of catastrophic (e.g., potentially disruptive) events, working devices of the emergency system can go on with their operations, including transferring data to rescuers and others emergency control systems. To demonstrate the effectiveness of the proposed software-defined factory approach, a federated fire protection system operating in an industrial setting is implemented as a case study, able to promptly react and adapt to infrastructure-critical fires and their consequences by leveraging all information and computing facilities pooled over cloud/fog/edge devices spanning the premises.

Hyperspectral images (HSIs) are used in a large number of real-world applications. HSI classification (HSIC) is a challenging task due to high interclass similarity, high intraclass variability, overlapping, and nested regions. The 2-D convolutional neural network (CNN) is a viable classification approach since HSIC depends on both spectral–spatial information. The 3-D CNN is a good alternative for improving the accuracy of HSIC, but it can be computationally intensive due to the volume and spectral dimensions of HSI. Furthermore, these models may fail to extract quality feature maps and underperform over the regions having similar textures. This work proposes a 3-D CNN model that utilizes both spatial–spectral feature maps to improve the performance of HSIC. For this purpose, the HSI cube is first divided into small overlapping 3-D patches, which are processed to generate 3-D feature maps using a 3-D kernel function over multiple contiguous bands of the spectral information in a computationally efficient way. In brief, our end-to-end trained model requires fewer parameters to significantly reduce the convergence time while providing better accuracy than existing models. The results are further compared with several state-of-the-art 2-D/3-D CNN models, demonstrating remarkable performance both in terms of accuracy and computational time.

The Internet of Things continuously generates avalanches of raw sensor data to be transferred to the Cloud for processing and storage. Due to network latency and limited bandwidth, this vertical offloading model, however, fails to meet requirements of time-critical data-intensive applications which must act upon generated data with minimum time delays. To address such a limitation, this article proposes a novel distributed architecture enabling stream data processing at the edge of the network, broadening the principle of enabling processing closer to data sources adopted by Fog and Edge Computing. Specifically, this architecture extends the Apache NiFi stream processing middleware with support for run-time clustering of heterogeneous edge devices, such that computational tasks can be horizontally offloaded to peer devices and executed in parallel. As opposed to vertical offloading on the Cloud, the proposed solution does not suffer from increased network latency and is thus able to offer 5-25 times faster response time, as demonstrated by the experiments on a run-time license plate recognition system.

Wastewater treatment is a critical process in urban and industrial settlements aiming to clean and protect the water as well as the overall environment. Wastewater management systems are conceived explicitly for purifying wastewater, providing clean water efficiently, but this is a hard task due to frequent and quite unpredictable fluctuations of inlet wastewater flows, arising from (random) rain water or (periodical, e.g. day-night) sewage sources, sometimes also leading to failures and outages. To ensure the quality of the clean water out above a threshold and keep the overall system operating, this paper proposes the smart wastewater intelligent management system (SWIMS). It monitors and controls inlet and outlet flows as well as the water quality and parts of the plant as a cyber-physical system (CPS), starting from an Environmental Internet of Things (EIoT) platform. The data generated from the treatment plant is collected in an information system hosted by a server together with an intelligent system that processes this information in a real-time fashion and provides the feedback for optimizing the plant to maintain a good quality of water over time. Such an intelligent system exploits deep learning approaches to control the behaviour of the wastewater treatment system through anomaly detection, supporting decision making on it. SWIMS has been implemented in a real case study deployed in Briatico, Italy. The data and results collected from such a case study are presented, analyzed and discussed in this paper, demonstrating the feasibility and the effectiveness of the SWIMS solution.

The primary goal of a wastewater treatment system is to take care of the environment as well as of people health by purifying sewage water. In urban and industrial environments, wastewater management is non-trivial since it has to deal with abnormal fluctuations in incoming water flows (due to rainwater or human and industrial sewage) that may cause failures and outages to the entire purification process. This paper proposes a solution based on a smart system to ensure the clean water quality by keeping the wastewater treatment system efficient. It is able to constantly and real time monitoring both the purity of the water and the inlet and outlet flows enforcing on them proper policies based on the monitored values thus acting as a cyber-physical system (CPS). The raw data, generated by an Environmental Internet of Things (EIoT) platform part of a real case study implemented in Briatico (Italy), is collected and hosted in a server that can process and manage real-time information about the plant.

The SARS-COV-2 imposed dramatically changes in our lives, habits, lifestyles, relationships, jobs. A strategic sector heavily affected by the pandemic is the education one. Although several distance learning initiatives supported the training courses activities, practical sessions have been greatly penalized. In this paper, a solution to remotely perform experiments on an IoT device, namely an Arduino Uno R3 board, is proposed. The novelty of our approach is to remotely allow not only the software deployment over a specific hardware configuration, but also to set the hardware configuration of the IoT device.

Smart Building/Environment management is an interesting topic that, although widely investigated in the literature, has not seen wide adoption in real case studies. Current Smart Building solutions provide facilities for: i) automatically managing HVAC (Heating, Ventilation and Air Conditioning) systems; ii) energy management, iii) building automation. Current solutions are tightly coupled with the underlying IT infrastructure of the building and cannot be rearranged according to the events, catastrophic or otherwise, that may modify the building structure (e.g., a disruption to sections of a building). In this work, we present a novel approach for Smart Buildings (we refer to it as "Software Defined Building 2.0") to customize and reprogram IT infrastructure powering buildings, this way enabling cooperation among Cyber-Physical Systems (CPSs) operating within. We analyze an architecture enabling this approach, evaluating overhead management and its impact on systems’ performance.

Summary The Internet of Things is underpinned by the global penetration of network‐connected smart devices continuously generating extreme amounts of raw data to be processed in a timely manner. Supported by Cloud and Fog/Edge infrastructures – on the one hand, and Big Data processing techniques – on the other, existing approaches, however, primarily adopt a vertical offloading model that is heavily dependent on the underlying network bandwidth. That is, (constrained) network communication remains the main limitation to achieve truly agile IoT data management and processing. This paper aims to bridge this gap by defining Clustered Edge Computing – a new approach to enable rapid data processing at the very edge of the IoT network by clustering edge devices into fully functional decentralized ensembles, capable of workload distribution and balancing to accomplish relatively complex computational tasks. This paper also proposes ECStream Processing that implements Clustered Edge Computing using Stream Processing techniques to enable dynamic in‐memory computation close to the data source. By spreading the workload among a cluster of collocated edge devices to process data in parallel, the proposed approach aims to improve performance, thereby supporting agile data management. The experimental results confirm that such a distributed in‐memory approach to data processing at the very edge of an IoT network can outperform currently adopted Cloud‐enabled architectures, and has the potential to address a wide range of IoT‐related data‐intensive time‐critical scenarios.

Modern transportation systems, such as computer networks, have become increasingly faster, aiming to “shorten” distances and travel time. This trend allows thinking about new services and induces to reconsider existing ones starting from new technologies, as for Intelligent Transportation Systems. Thereby, an all-encompassing scenario laying at the intersection of several domains, including manufacturing, logistics, traveling, insurance, maintenance, and trading, with the transportation one, can be envisioned. The building block for the resulting transportation ecosystem is an information system that is able to gather and connect all involved stakeholders and domains around the concept of mobility and vehicle to address complex multifaceted problems in an efficient and trustworthy way. This paper proposes the adoption of distributed ledgers to implement such a vehicle-centric information system, distributing data across the network while ensuring trustworthiness. Starting from the vehicle lifecycle immutable and certified information, new services for cross-cuttingly addressing diversity and complexity in the transportation ecosystem can be implemented. The proposed solution merges Multichain and MongoDB technologies to achieve a trade-off between trustworthiness and performance, storing only the metadata in the Multichain network. Its effectiveness is demonstrated by an example on a vehicle trading service showing an overhead for the proposed solution below 18% against a pure MongoDB one on I/O operations.

Enabling data processing at the network edge, as close to the actual source of data as possible, is a challenging, yet realistic goal to be achieved by the Internet of Things (IoT), which still primarily relies on the Cloud for data processing. By further extending the Fog and Edge computing principles, recent research advancements enabled aggregation of computing resources from multiple edge devices to support data-intensive task processing using Big Data clustering middleware. The use of these existing solutions, however, is hindered by the heterogeneous, dynamic, mobile, resource-constrained, and time-critical nature of IoT ecosystems. More specifically, a particularly challenging goal is to discover, select, and cluster suitable edge devices - on the one hand, and decompose and allocate data-intensive tasks with respect to discovered resources - on the other. To address this challenge, this paper introduces a novel decentralized architecture for clustering heterogeneous edge devices and executing data-intensive IoT workflows. The proposed approach first breaks down a complex workflow into simpler tasks, then discovers and selects suitable edge devices, and finally allocates the tasks to the selected nodes, connecting them to recompose the original workflow. The proposed approach benefits from an intelligent mapping algorithm that takes into account available cluster resources and processing demands to efficiently allocate fine-grained tasks to selected nodes. To support the clusterisation process, the proposed solution relies on a unified semantic knowledge base that provides a common vocabulary of terms for modelling task requirements and edge device properties, as well as enables automated task grouping and match-making for device discovery and selection, using built-in reasoning capabilities.

In this innovative practice full paper we present the implementation of the distant laboratory for the Internet of Things teaching and training. The recent outbreak of the SARS-COV-2 virus and related COVID-19 pandemic throughout the world has caused governments across the world to shut down schools and universities, to slow down the spread of the coronavirus that is causing the disease. As a result, some universities and schools have switched from physical classrooms to virtual or online classrooms. This approach is working well for theoretical subjects and courses, but it is not straight forward in the case of laboratory subjects and courses that require access to hardware resources. The IOT-OPEN.EU remote laboratory infrastructure presented in this paper is a timely solution. In this paper, we present current advances in distant learning, distant laboratory models, and the IOT-OPEN.EU remote laboratory implemented as part of the IOT-OPEN.EU ERASMUS+ project, along with short analysis on current advances in distant learning, where students are interacting with physical hardware remote way.

In this work, we propose a low-cost Smart and Healthy Intelligent Room System (SHIRS), able to monitor Indoor Air Quality (IAQ) by enhancing edge-based computation. SHIRS exploits the ability to run Machine Learning (ML) algorithms to infer humans presence (headcount) from environmental data analysis. Experimental results show the validity of the proposed approach, demonstrate the potential of edge-based computing and push towards the adoption of smart integrated Cloud-IoT frameworks for environmental monitoring and control.

Global warming and climate changes are due to several factors, not least vehicle and transportation emissions. Smart City technologies can provide mechanisms for emission (greenhouse gases, particles) containment that may significantly impact on the environment. This paper proposes a solution, based on an intelligent cruise control system, allowing a vehicle to interact with the Smart City infrastructure facilities for cutting down its emissions on the planned route while saving fuel. The proposed approach aims at implementing a (virtually) continuous green wave for a vehicle lowering its emissions by modulating the speed only considering local traffic congestion and traffic light information provided by the Smart City infrastructure, without actuating on the latter. A green-green (green2) wave also reducing the fuel consumption and ensuring a good trade off with travel time. To demonstrate the effectiveness of the proposed solution, a power train model of a c-segment car traveling on while interacting with Smart City facilities has been implemented and evaluated, providing significant insights.

The Internet of Things (IoT) and Robotics cannot be considered two separate domains these days. The Internet of Robotics Things (IoRT) is a concept that has been recently introduced to describe the integration of robotics technologies in IoT scenarios. As a consequence, these two research fields have started interacting, and thus linking research communities. In this paper we intend to make further steps in converging the two communities and broaden the discussion on the development of this interdisciplinary field. The paper provides overview, analysis and challenges of possible solutions for the Internet of Robotic Things, discussing the issues of the IoRT architecture, and the integration of smart environments and robotic applications.

The vision encompassing Smart and Software-defined Buildings (SSDB) is becoming more popular and its implementation is now more accessible due to the widespread adoption of the Internet of Things (IoT) infrastructure. Some of the most important applications sustaining this vision are energy management, environmental comfort, safety and surveillance. This paper surveys IoT and SSB technologies and their cooperation towards the realization of smart spaces. We propose a four-layer reference architecture and we organize related concepts around it. This conceptual frame is useful to identify the current literature on the topic and to connect the dots into a coherent vision of the future of residential and commercial buildings.

The ways of giving, receiving, processing and storing knowledge are changing rapidly. In a few years, in fact, the Web has radically changed our daily lives, making it indispensable for the most disparate reasons, from work to communication and learning. The availability of unlimited knowledge more and more often doesn't correspond to an improvement in school productivity of the new generations. All the problems stem from the fact that the social and educational systems have not evolved with the same speed as the technology. Moreover, the economic crisis has negatively affected this problem. Today it is increasingly difficult for adults to be present at home and help their children do their homework activities in the early school years. In this paper, we present and discuss our Android mobile application we called "Virtual Study Partner". This mobile application has to be considered as the first output of a wider research activity that aims to provide study support to young people in school-age by integrating both machine learning concepts and Cloud technologies. Virtual Study Partner consists of a cognitive training tool which enables and integrates the experimental Learning Framework that we are developing at the University of Messina.

A Smart City can be envisioned as an ecosystem of smart environments that can be federated to interact one another, making infrastructure suitable to host innovative services for citizens and improve quality of city life. In such a context, interoperability and the presence of different administrative domains are the main challenges. In this paper, we propose to extend the Software-Defined City paradigm to both I/O and networking functions for implementing this vision. Through a motivating use case, we show how this approach can help in making city infrastructure a fully programmable ecosystem of resources that can be rewired any time at will.

To enable and support smart environments, a recent ICT trend promotes pushing computation from the remote Cloud as close to data sources as possible, resulting in the emergence of the Fog and Edge computing paradigms. Together with Cloud computing, they represent a stacked architecture, in which raw datasets are first pre-processed locally at the Edge and then vertically offloaded to the Fog and/or the Cloud. However, as hardware is becoming increasingly powerful, Edge devices are seen as candidates for offering data processing capabilities, able to pool and share computing resources to achieve better performance at a lower network latency—a pattern that can be also applied to Fog nodes. In these circumstances, it is important to enable efficient, intelligent, and balanced allocation of resources, as well as their further orchestration, in an elastic and transparent manner. To address such a requirement, this article proposes an OpenStack-based middleware platform through which resource containers at the Edge, Fog, and Cloud levels can be discovered, combined, and provisioned to end users and applications, thereby facilitating and orchestrating offloading processes. As demonstrated through a proof of concept on an intelligent surveillance system, by converging the Edge, Fog, and Cloud, the proposed architecture has the potential to enable faster data processing, as compared to processing at the Edge, Fog, or Cloud levels separately. This also allows architects to combine different offloading patterns in a flexible and fine-grained manner, thus providing new workload engineering patterns. Measurements demonstrated the effectiveness of such patterns, even outperforming edge clusters.

The cover image, by Rustem Dautov et al., is based on the Research Article Metropolitan Intelligent Surveillance Systems for Urban Areas by Harnessing IoT and Edge Computing Paradigms, https://doi.org/10.1002/spe.2586. Photo Credit: Rustem Dautov.

Summary Recent technological advances led to the rapid and uncontrolled proliferation of intelligent surveillance systems (ISSs), serving to supervise urban areas. Driven by pressing public safety and security requirements, modern cities are being transformed into tangled cyber‐physical environments, consisting of numerous heterogeneous ISSs under different administrative domains with low or no capabilities for reuse and interaction. This isolated pattern renders itself unsustainable in city‐wide scenarios that typically require to aggregate, manage, and process multiple video streams continuously generated by distributed ISS sources. A coordinated approach is therefore required to enable an interoperable ISS for metropolitan areas, facilitating technological sustainability to prevent network bandwidth saturation. To meet these requirements, this paper combines several approaches and technologies, namely the Internet of Things, cloud computing, edge computing and big data, into a common framework to enable a unified approach to implementing an ISS at an urban scale, thus paving the way for the metropolitan intelligent surveillance system (MISS). The proposed solution aims to push data management and processing tasks as close to data sources as possible, thus increasing performance and security levels that are usually critical to surveillance systems. To demonstrate the feasibility and the effectiveness of this approach, the paper presents a case study based on a distributed ISS scenario in a crowded urban area, implemented on clustered edge devices that are able to off‐load tasks in a “horizontal” manner in the context of the developed MISS framework. As demonstrated by the initial experiments, the MISS prototype is able to obtain face recognition results 8 times faster compared with the traditional off‐loading pattern, where processing tasks are pushed “vertically” to the cloud.

In the era of digital economies, data can be considered as the new commodity, fueling the next-generation software services and applications. Increasing amounts of data, generated on a daily basis by various domains, such as social networks, stock exchanges, the Internet of Things, and cyber-physical systems, are soon expected to exceed the yottabyte1frontier. To process this overwhelming amount, Big Data solutions are being developed to enable a new generation of data-centric/data-intensive applications (DIAs) and services. However, many of such applications currently fail to meet the increasingly demanding data management requirements. In particular, proper techniques and tools to support architects and developers in DIA design are required to cope with these pressing Big Data challenges. This paper makes an initial step in this direction, aiming at reducing the gap between the architects and DIAs they have to develop. The proposed approach extends the conventional Big Data process workflow with a way of capturing and modeling the `three Vs' of Big Data (i.e. volume, velocity, and variety) to provide useful insights on the overall process, knowing the behavior of its individual components. Starting from the V-attributes of the Big Data process components, the proposed framework provides an estimation of its V-metrics by evaluating a performance model generated from the process. To demonstrate the feasibility and the effectiveness of the approach, a case study on a computer vision DIA is reported.

© 2017 by SCITEPRESS - Science and Technology Publications, Lda. All Rights Reserved. In this paper we present the results of simulation of exitatory Hebbian and inhibitory "sombrero" learning of a hardware architecture based on organic memristive elements and operational amplifiers implementing an artificial neuron we recently proposed. This is a first step towards the deployment on robots of a bioplausible simulation, currently developed in the neuro-biologically inspired cognitive architecture (NeuCogAr) implementing basic emotional states or affects in a computational system, in the context of our "Robot dream" project. The long term goal is to re-implement dopamine, serotonin and noradrenaline pathways of NeuCogAr in a memristive hardware.

Smart Cities are the perfect ecosystem where IoT technology could be applied. In particular, to allow a direct interaction with objets scattered over a city (e.g, parking slots, light posts, gates) a powerful and scalable architecture has to be envisioned. In this paper, using Bluetooth Low Energy as underlying technology, we extend the concept of Personal Area Networks to include smart city objects so that a user can easily interact with them in a geolocalized and real time manner. A prototype implementing a smart gate has been developed in our University Campus in order to demonstrate the feasibility of the proposed approach.

As embedded and mobile systems grow pervasive in people lives and expand their reach, paradigms related to mobile crowdsensing (MCS) are going to play an ever more prominent role. Innovative methodologies and applications able to unlock such a huge potential are required. A domain where MCS really fits is mobility and transportation. However, for a full exploitation of this paradigm in intelligent transportation systems (ITS), distributed and self-management capabilities for the involved nodes-vehicles have to be provided. This paper is a first step in this direction, laying out an optimization system by exploiting feedback-driven patterns in a distributed-opportunistic way. In this sense, collective intelligence and stigmergic, swarm-based paradigms are adapted to an innovative decentralized MCS pattern toward new approaches in ITS. Their effectiveness is demonstrated through a traffic engineering case study, where route planning services are designed according to the proposed approach and then modeled and evaluated by Markovian agents.

To kickstart the process of morphing Messina into a "smart" city, an explicit mission for the crowdfunded #SmartME project, it is essential to set up an infrastructure of smart devices embedding sensors and actuators, to be scattered all over the urban area. An horizontal framework coupled with the Fog computing approach, by moving logic toward the "extreme" edge of the Internet where data needs to be quickly elaborated, decisions made, and actions performed, is a suitable solution for data- intensive services with time-bound constraints as those usually required by citizens. This is especially true in the context of IoT and Smart City where thousands of smart objects, vehicles, mobiles, people interact to provide innovative services. We thus designed Stack4Things as an OpenStack-based framework spanning the Infrastructure-as-a-Service and Platform-as-a-Service layers. We present some of the core Stack4Things functionalities implementing a Fog computing approach towards a run- time "rewireable" Smart City paradigm, by outlining node management and contextualization mechanisms, also describing its usage in terms of already supported and developed verticals, as well as a specific example related to environmental data collection through #SmartME.

This paper introduces a new model of artificial cognitive architecture for intelligent systems, the Neuromodulating Cognitive Architecture (NEUCOGAR). The model is bio mimetically inspired and adapts the neuromodulators role of human brains into computational environments. This way we aim at achieving more efficient Artificial Intelligence solutions based on the biological inspiration of the deep functioning of human brain, which is highly emotional. The analysis of new data obtained from neurology, psychology philosophy and anthropology allows us to generate a mapping of monoamine neuro modulators and to apply it to computational system parameters. Artificial cognitive systems can then better perform complex tasks (regarding information selection and discrimination, attention, innovation, creativity) as well as engaging in affordable emotional relationships with human users.

Summary Performance and dependability evaluation plays a key role in the design of a broad range of systems, especially when strict requirements need to be met. This is particularly challenging in distributed contexts, where several components may interact among themselves by influencing each other. In this paper, we present an analytical method that allows the study of a class of systems where different operating conditions alternate by changing the stochastic behavior of the system components but still preserving the continuity of the performance and dependability quantities to investigate. The proposed solution technique, based on phase type distributions, Kronecker algebra, and ad‐hoc fitting algorithms, can be applied for the analytical evaluation of a wide class of distributed systems. Examples are provided to show the usefulness and the applicability of the methodology, characterizing and investigating different performance and dependability aspects of three distributed computing systems, that is, a connection‐oriented network, an Internet of Things application, and an Infrastructure‐as‐a‐Service Cloud. Copyright © 2014 John Wiley & Sons, Ltd.

In this paper, we focus on the reliability and availability analysis of Web service (WS) compositions, orchestrated via the Business Process Execution Language (BPEL). Starting from the failure profiles of the services being composed, which take into account multiple possible failure modes, latent errors, and propagation effects, and from a BPEL process description, we provide an analytical technique for evaluating the composite process' reliability-availability metrics. This technique also takes into account BPEL's advanced composition features, including fault, compensation, termination, and event handling. The method is a design-time aid that can help users and third party providers reason, in the early stages of development, and in particular during WS selection, about a process' reliability and availability. A non-trivial case study in the area of travel management is used to illustrate the applicability and effectiveness of the proposed approach.

Many current endeavors dealing with IoT and Cloud topics suggest leveraging sensing resources as mere data producers whereas the authors follow a Sensing and Actuation as a Service (SAaaS) approach. Among core ideas of SAaaS there's the enablement of an Infrastructure-oriented (i.e. IaaS-like) provisioning model for sensors and actuators alike. Such model matches technological requirements and constraints arising within certain application domains inspired by research about Future Internet topics. Aim of this paper is to explain why adopting the SAaaS paradigm for pervasively available and geographically distributed IT infrastructure is key, especially when field deployment of custom functionality is needed. Smart devices featuring always-on connectivity are the backbone of the IoT vision, and core building blocks of such infrastructure. Cloud-powered monitoring of urban areas and public facilities are among the most compelling scenarios to be found in IoT, and we are here focusing on these scenarios for two use cases. Actors, workflows and activities are described here in order to explain the main interactions and follow through outlining the use cases.

Cloud computing is a promising paradigm able to rationalize the use of hardware resources by means of virtualization. Virtualization allows to instantiate one or more virtual machines (VMs) on top of a single physical machine managed by a virtual machine monitor (VMM). Similarly to any other software, a VMM experiences aging and failures. Software rejuvenation is a proactive fault management technique that involves terminating an application, cleaning up the system internal state, and restarting it to prevent the occurrence of future failures. In this work, we propose a technique to model and evaluate the VMM aging process and to investigate the optimal rejuvenation policy that maximizes the VMM availability under variable workload conditions. Starting from dynamic reliability theory and adopting symbolic algebraic techniques, we investigate and compare existing time-based VMM rejuvenation policies. We also propose a time-based policy that adapts the rejuvenation timer to the VMM workload condition improving the system availability. The effectiveness of the proposed modeling technique is demonstrated through a numerical example based on a case study taken from the literature.

This special issue deals with open problems related to Cloud computing assessment, and the interest raised in the scientific community is confirmed by the high quality of the papers received, which were selected after a review process started on July 2012 and ended on February 2013. Some relevant numbers are: 43 total submissions, nine papers accepted (approximately 0.2 acceptance ratio) after three rounds of reviews by more than 190 reviewers involved in the process. We would like to thank the authors of all the submitted papers for the high quality of their scientific contributions, and the prompt and timely reaction to the continual requests from the editors. We also thank all reviewers for their dedication and timeless efforts, which allowed us to select very high quality papers and respect the strict deadlines we have imposed. Different nonfunctional properties are considered in the special issue, mainly investigating Clouds behavior from the different perspectives of security and performance. For this reason we decided to split the special issue into two parts: the first that deals with security-related aspects (part I), and the second related to performance aspects (part II) of Cloud systems. Part I of this special issue addresses security aspects of Cloud computing such as trustworthiness, privacy, security vulnerabilities, countermeasures and threats to both physical (hardware) and logical (software, applications, data) architecture and infrastructure.

The articles in this special section focus on the measuremnet and analysis of cloud computing applications.

WS-BPEL language has become the industrial standard to design and orchestrate modular applications, formalizing service compositions and business relationships among providers and consumers. Once service level agreements (SLAs) among the parties are established, effective tools for evaluating appropriate measurements have to be developed to meet the requirements. However, the design of quality of service (QoS)-guaranteed composed Web services (WSes) still requires several efforts. This work aims at proposing a complete method to study the QoS of a composed WS at design time, i.e., when the process is specified by using WS-BPEL. Starting from the nonfunctional properties of the WS to compose, we propose a technique to derive non-Markovian stochastic Petri net (NMSPN) models from WS-BPEL processes, with the final goal of evaluating parameters such as the service time distribution and the service reliability. To demonstrate the effectiveness of the proposed method and to validate the obtained model, a nontrivial example implementing a travel agency flight reservation process, exposed as a synchronous composed WS, is investigated.

Cloud computing is among the hottest trends in ICT, aiming at providing on-demand computing and storage resources with guarantees on the quality of service. A limit of current Cloud implementations is the absence of mechanisms to effectively manage inputs from the physical world. Our idea is to move towards a pervasive Cloud, providing facilities and solutions able to interact with the surrounding environment enabling development of new and value added services. In this vision also mobile devices, such as PDAs, usually equipped with several sensors and actuators, have to be included in the overall picture. Mobile devices and their respective owners can decide whether, how and when to contribute to the Cloud, thus introducing further unknowns. In order to deal with all such issues, in this paper we propose a solution that gives way to the Sensing and Actuation as a Service (SAaaS) paradigm, a step towards the creation of a Cloud of sensors and actuators. This paper mainly focuses on the implementation of the underlying infrastructure at the basis of the SAaaS. An ad-hoc architecture and some preliminary background on this challenging vision are provided and discussed.

In this paper, we present an evaluation methodology to validate the performance of a UML model, representing a software architecture. The proposed approach is based on open and well-known standards: UML for software modeling and the OMG Profile for Schedulability, Performance, and Time Specification for the performance annotations into UML models. Such specifications are collected in an intermediate model, called the Performance Context Model (PCM). The intermediate model is translated into a performance model which is subsequently evaluated. The paper is focused on the mapping from the PCM to the performance domain. More specifically, we adopt Petri nets as the performance domain, specifying a mapping process based on a compositional approach we have entirely implemented in the ArgoPerformance tool. All of the rules to derive a Petri net from a PCM and the performance measures assessable from the former are carefully detailed. To validate the proposed technique, we provide an in-depth analysis of a web application for music streaming.

Cloud computing is the "new hot" topic in IT. It combines the maturity of Web technologies (networking, APIs, semantic Web 2.0, languages, protocols and standards such as WSDL, SOAP, REST, WS-BPEL, WS-CDL, IPSEC, etc.), the robustness of geographically distributed computing paradigm (Network, Internet and Grid computing) and self-management capabilities (Autonomic computing), with the capacity to manage quality of services by monitoring, metering, quantifying and billing computing resources and costs (Utility computing). Those have made possible and cost-effective for businesses, small and large, to completely host data- and application-centers virtually... in the Cloud. Our idea of Cloud proposes a new dimension of computing, in which everyone, from single users to communities and enterprises, can, on one hand, share resources and services in a transparent way and, on the other hand, have access to and use such resources and services adaptively to their requirements. Such an enhanced concept of Cloud, enriching the original one with Volunteer computing and interoperability challenges, has been proposed and synthesized in Cloud@Home. The complex infrastructure implementing Cloud@Home has to be supported by an adequate distributed middleware able to manage it. In order to develop such a complex distributed software, in this paper we apply software engineering principles such as rigor, separation of concerns and modularity. Our idea is, starting from a software engineering approach, to identify and separate concerns and tasks, and then to provide both the software middleware architecture and the hardware infrastructure following the hw/sw co-design technique widely used in embedded systems. In this way we want to primarily identify and specify the Cloud@Home middleware architecture and its deployment into a feasible infrastructure; secondly, we want to propose the development process we follow, based on hardware/software co-design, in distributed computing contexts, demonstrating its effectiveness through Cloud@Home.

Service Oriented Architecture (SOA) is the most important and effective software paradigm to design Internet-based services. Using the SOA technology, value-added services can be easily deployed as a combination of existing Web services. In this context, WS-BPEL language has become the SOA industrial standard. To allow services to be composed, business relationships between providers and consumers have to be adequately managed. This implies that a formal definition of Quality of Service (QoS) is agreed and that effective tools for its measurement have to be developed. However, the design of QoS guaranteed composed Web services still requires several efforts due to the highly distributed nature of such software applications. This work aims at proposing a methodology to evaluate Web service performance at the earliest design phase. We present a novel technique to translate WS-BPEL processes into non-Markovian stochastic Petri nets with the final goal to evaluate parameters such as service time distribution and service reliability. The obtained model can be numerically solved through automatic tools, allowing to investigate the service behavior under different operating conditions and thus helping software engineers to develop QoS-guaranteed software solutions.

Computing systems are becoming the heart of modern technology, implementing critical tasks usually demanded to and implying human interactions. This highlights the problem of dependability in computer science contexts. High availability computing/clusters is a possible solution in such cases, implementing standby redundancy as a trade-off between dependability and costs. From the engineering perspective, this implies the use of specific techniques and tools for adequately evaluating the reliability/availability of high availability clusters, also taking into account dependencies among nodes (standby, repair, etc.) and the effect of wear and tear into such nodes, especially when failure and repair times are not exponentially distributed. The solution proposed in this paper is based on the use of phase type distributions and Kronecker algebra. In fact, we represent the reliability and maintainability of each component by specific phase type distributions, whose interactions describe the system availability. This latter is thus modeled by an expanded Markov chain expressed in terms of Kronecker algebra in order to face the state space explosion problem of expansion techniques and to represent the memory policies related to the aging process. More specifically, the paper firstly details the technique and then applies it to the evaluation of a standby redundant system representing a high availability cluster taken as example with the aim of demonstrating its effectiveness. Moreover, in order to show the potentiality of the technique, different maintenance strategies are evaluated and therefore compared.

The spread of worldwide networks and the technological trend are feeding the progress of network and distributed computing in different directions (grid, cloud, autonomic, ubiquitous, pervasive, volunteer, etc). With regard to information, great amount of data widely (geographically) spread over the network require adequate management, to ensure availability for authorized users only, confidentiality and integrity of information and data or, summarizing, security.In order to adequately address security problems such as insider attacks and identity thefts in network-distributed environments, in this work we propose a lightweight cryptography algorithm, combining the strong and highly secure asymmetric cryptography technique with the symmetric cryptography. The algorithm we propose implements a whole secure file system, which preserves and ensures the security of both data and file system structures (directory, links, etc). In the paper we describe in detail the secure distributed file system structure and the algorithms implementing its interface operations.In order to demonstrate the effectiveness of the proposed approach, we also describe its implementation into a Grid (gLite) environment.

Today's reliability methodologies and tools need to assess increasingly large, complex systems. Moreover, in order to adequately evaluate the system dependability it is necessary to take into the right consideration the system dynamics, which depends on the components' dynamics, on the interdependencies arising among such components (load-sharing, standby redundancy, interferences, etc) and on their reliability relationships, that can also be variable (phased-mission systems). Combinatorial techniques can be adopted in case the system's units are two-state (Boolean) and stochastically independent. Otherwise, it is requested to recur to lower level techniques and formalisms, such as: state space methods, or simulation. This paper analyzes the dependability of large, complex systems affected by dependent/dynamic behaviors. Starting from dynamic reliability block diagrams, a notation we developed by extending the reliability block diagrams, we detail how this modeling approach captures dynamic reliability behaviors. In order to demonstrate the effectiveness of such technique, we investigate some common/specific dynamic reliability/availability behaviors, providing the guidelines for their representation and evaluation.

Only commercial cloud solutions have been implemented so far, offering computing resources and services for renting. Some interesting projects, such as Nimbus, OpenNEbula, Reservoir, work on cloud. One of their aims is to provide a cloud infrastructure able to provide and share resources and services for scientific purposes. The encouraging results of volunteer computing projects in this context and the flexibility of the cloud, suggested to address our research efforts towards a combined new computing paradigm we named [email protected] one hand it can be considered as a generalization of the @homephilosophy, knocking down the barriers of volunteer computing, and also allowing to share more general services. On the other hand, Cloud@Home can be considered as the enhancement of the grid-utility vision of cloud computing. In this new paradigm, userspsila hosts are not passive interface to cloud services anymore, but they can interact (free or by charge) with other clouds. In this paper we present the Cloud@Home paradigm, highlighting its contribution to the actual state of the art on the topic of distributed and cloud computing. We detail the functional architecture and the core structure implementing such paradigm, demonstrating how it is really possible to build up a Cloud@Home infrastructure.

Dependability evaluation is an important often-mandatory step in designing and analyzing (critical) systems. Introducing control and/or computing devices to automate processes increases the system complexity, with an impact on the overall dependability. This occurs as a consequence of interferences, dependencies, and other similar effects that cannot be adequately managed through formalisms such as reliability block diagrams (RBDs), fault trees (FTs), and reliability graphs (RGs), since the statistical independence assumption is not satisfied. In addition, more enhanced notations such as dynamic FTs (DFTs) might not be adequate to represent all the behavioral aspects of dynamic systems. To overcome these problems, we developed a new formalism derived from RBD: the dynamic RBD (DRBD). DRBD exploits the concept of dependence as the building block to represent dynamic behaviors, allowing us to compose the dependencies and adequately manage the arising conflicts by means of a priority algorithm. In this paper, we explain how we can use the DRBD notation by specifying a practical methodology. Starting from the system knowledge, the proposed methodology drives to the overall system reliability evaluation through the entire phases of modeling and analysis. Such a technique is applied to an example taken from the literature, consisting of a distributed computing system.

Nowadays, computers and networks represent the heart of a great part of modern technologies. Computing systems are widely used in many application areas, and they are desired to achieve various complex and safety-critical missions. As consequence, greater attention is lavished on performance and dependability evaluation of computing systems. This brings to the specification of precise techniques and models, that consider and evaluate aspects before (consciously or unconsciously) approximated or ignored at all. On the other hand, the increasing importance assumed by such systems is translated in terms of tighter and tighter constraints, requirements and/or policies (QoS, fault tolerance, maintenance, redundancy, etc.) according to the systems' criticism. The evaluation must therefore take into great account such dynamic behaviors, carefully identifying and quantifying dependencies among devices. In this paper we face the problem of individuating and evaluating the most common dynamic behaviors and dependencies affecting fault tolerant computing systems. We propose some models to represent such aspects in terms of reliability/availability, basing on dynamic reliability block diagrams (DRBD), a new formalism derived from RBD we developed. In this way we want to provide the guidelines for adequately evaluating fault tolerant computing system reliability/availability.

Dependability evaluation is an important, often indispensable, step in (critical) systems design and analysis processes. The introduction of control and/or computing systems to automate processes increases the overall system complexity and therefore has an impact in terms of dependability. Moreover it is of interest to evaluate redundancy and maintenance policies. In those cases it is not possible to recur to notations as reliability block diagrams (RBD), fault trees (FT) or reliability graphs (RG) to represent the system, since the statistical independence assumption is not satisfied. Also more enhanced formalisms as dynamic FT (DFT) could result not adequate to the goal. To overcome those problems we developed a new formalism derived from RBD: the dynamic RBD (DRBD). In this paper we explain how to use the DRBD notation in system modeling and analysis, coming inside a methodology that, starting from the system structure, drives to the overall system availability evaluation following modeling and analysis phases. To do this we use an example drawn from literature consisting of a multiprocessor distributed computing system, also comparing our approach with the DFT one.

Abstract WebSPN is a modeling tool for the analysis of non‐Markovian stochastic Petri nets that we developed some years ago. Its solution algorithm is based on a discretization of time and an approximation of non‐exponentially distributed firing time transitions by means of the phase‐type distributions. In order to solve the problems related to the management of the state space (which can become very large) we parallelized the solution algorithm through the MPICH libraries. To improve the MPICH parallel implementation with an adequate security management, a more efficient load distribution and fault‐tolerance capabilities, in this paper we propose the porting of WebSPN from the MPI to the Grid computational paradigm. In addition to a better flexibility in accessing computational and storage resources, one of the main advantages is the introduction of a fault recovery system to detect and recover from potential machine faults. The resulting new tool is named GridSPN. Copyright © 2006 John Wiley & Sons, Ltd.

Nowadays the great part of devices or systems we commonly use are often driven or managed by microchips and computers: cars, music players, phones, trains, planes, .... A consolidated trend of technology is to substitute mechanical with electronic parts, analogical with digital devices or controls, and so on. In this context, features like security, availability and reliability, usually summarized under the concept of dependability, are receiving higher attention. The dependability analysis, especially for what regards critical parts as computing systems or subsystems, is becoming more strategic: specific requirements and explicit or tighter constraints have to be satisfied. Even though this fact, there is a lack of suitable tools to properly model and analyze these aspects, with particular reference to reliability. To fill this gap, we propose the dynamic reliability block diagram (DRBD) modeling tool derived from the reliability block diagram (RBD) formalism. The DRBD permits to model the dynamic reliability behavior of a system through dependence models, exploited to represent dynamics behaviors as redundancy, load sharing, multiple, probabilistic and common failure mode. In this paper, the DRBD expressiveness and other capabilities, are illustrated through the analysis of a complex distributed computing system taken as example