The adoption of decentralized architectures for health data management offers benefits including patient data sovereignty and elimination of single points of failure, but introduces questions about network overhead compared to traditional centralized systems. This paper presents a network overhead analysis comparing Firebase Real-Time Database with IPFS-based storage via Pinata for mobile health data transmission. We implemented an Android application that collects physiological data from wearable devices and transmits this information to both backends using REST APIs. Our experimental evaluation across eight transmission scales reveals that Firebase demonstrates lower fixed overhead and latency for small payloads, while Pinata exhibits superior scaling characteristics for larger data volumes. A crossover point occurs around 50 records per payload, beyond which the decentralized architecture transmits less total data than the centralized alternative. The results indicate that neither architecture maintains uniform efficiency across all operational scales, with architectural choice depending on expected transaction patterns in the deployment context.

Crowd-sensing has emerged as a powerful data retrieval model, enabling diverse applications by leveraging active user participation. However, data availability and privacy concerns pose significant challenges. Traditional methods like data encryption and anonymization, while essential, may not fully address these issues. For instance, in sparsely populated areas, anonymized data can still be traced back to individual users. Additionally, the volume of data generated by users can reveal their identities. To develop credible crowd-sensing systems, data must be anonymized, aggregated and separated into uniformly sized chunks. Furthermore, decentralizing the data management process, rather than relying on a single server, can enhance security and trust. This paper proposes a system utilizing smart contracts and blockchain technologies to manage crowd-sensing campaigns. The smart contract handles user subscriptions, data encryption, and decentralized storage, creating a secure data marketplace. Incentive policies within the smart contract encourage user participation and data diversity. Simulation results confirm the system's viability, highlighting the importance of user participation for data credibility and the impact of geographical data scarcity on rewards. This approach aims to balance data origin and reduce cheating risks.

Parallel And Distributed Simulation (PADS) is a well-known modeling paradigm that allows for efficient implementation of large simulation models as a collection of interacting entities, called Simulated Entities (SEs). SEs can then be assigned to independent execution units for parallel execution, if possible. Allocating SEs to execution units is one of the most challenging problems in PADS: ideally, highly interacting SEs should be placed on the same execution unit so that all communications are local; however, placing too many SEs on the same processor might degrade performance. Furthermore, many simulation models exhibit non-uniform computation and communication patterns among components, that might change during execution at unpredictable times. In this paper we propose a clustering heuristic that exploits communication locality, with the aim to reduce the communication cost experienced by the PADS during the execution. The heuristic adapts automatically to changing interaction patterns by migrating SEs, and can do so without any user-visible modification of the simulation model; there are, however, some parameters that can be used to tune the heuristic. We perform a large set of computational experiments to assess the effectiveness of the heuristic on a real-world scenario, with the aim of guiding the users in selecting optimal values of the heuristic parameters.

Crowd-sensing is considered a robust model for data collection, yet with challenges related to data availability and privacy. Traditional techniques such as data encryption and anonymization may not fully mitigate these issues, since anonymized data can still be traced back to individual users, and the volume of data generated can reveal user identities. This paper introduces a system that employs smart contracts and blockchain technology to manage crowd-sensing campaigns. The smart contract oversees user subscriptions, data encryption, and decentralized storage, creating a secure data marketplace. Incentive mechanisms within the smart contract promote user participation. Simulation results validate the system’s feasibility, emphasizing the importance of user engagement for data credibility and the impact of geographical data scarcity on rewards.

The rise of blockchain has expanded the possibilities for asset representation, particularly through Non-Fungible Tokens (NFTs), which enable unique connections between digital and physical assets. Despite their potential, existing NFT systems face challenges such as high transaction costs and uncertainties related to the management and transfer of associated physical assets. This paper introduces a decentralized marketplace architecture designed to address these challenges, by making use of blockchain-based smart contracts, decentralized storage solutions, and trustless validation mechanisms. The proposed system separates ownership from possession, ensuring secure and transparent asset transfers. The architecture was then tested in a blockchain-based environment, demonstrating its economic viability. This work paves the way for decentralized asset management across various domains, such as rental services and remote logistics. By addressing existing challenges, the proposed architecture provides a usable and cost-effective solution that links digital and physical asset ecosystems while preserving the fundamental principles of blockchain technology.

Current health data management relies on centralized architectures that create a single point of failure, limit patient autonomy, and increase vulnerability to data breaches and vendor lock-in. This paper presents a decentralized approach to continuous health monitoring through the integration of wearable devices and distributed file systems. We implemented an Android application that collects physiological data and contextual information from a wearable device, storing it on the IPFS via Pinata API. Additionally, we propose a blockchain architecture for role-based access control. Performance evaluation comparing our IPFS-based implementation against Firebase Real-Time database reveals that the resource requirements remain negligible for modern smartphones while achieving significant benefits, including no single point of failure, enhanced data portability, and patient data sovereignty. The results demonstrate that decentralized health data management is technically feasible on mobile devices, offering an alternative approach to traditional centralized health data architectures.

Machine Learning (ML) in distributed environments increasingly deals with sensitive data (like healthcare or financial records) that cannot be centrally stored or processed due to privacy concerns. Federated Learning (FL) addresses this by enabling model training across decentralized devices, but faces significant challenges including system reliability, node failures, and trust issues among participants. Traditional FL approaches often rely on centralized coordinators, creating single points of failure and potential security vulnerabilities. This paper presents a novel approach to FL that leverages smart contracts, blockchain, and decentralized storage to enhance the traceability and reliability of the learning process. Our proposed system architecture is fully decentralized, eliminating single points of failure and promoting cooperation through a rewarding mechanism. Unlike previous approaches that neglect node fault tolerance, we introduce a smart contract based scheme for managing node failures and electing the aggregator node. The presence of the smart contract, executed on a decentralized permissioned blockchain, provides reliability guarantees and eliminates the need for costly distributed algorithms in terms of message exchange. An experimental study is conducted to evaluate various aspects of the FL system. We present results related to the accuracy and effectiveness of the FL system on ML models. We also examine the performance related to the distribution of the weights of the ML model based on the use of IPFS. Furthermore, we analyze the performance of the smart contract in terms of gas consumption. Lastly, we investigate the impact of failures combined with incentive policies and aggregator election algorithms on the FL system. Our findings demonstrate the viability of the proposed approach, paving the way for more robust, reliable, and efficient FL systems.

Smart contracts are self-executing digital contracts that run on a blockchain network. They enable the automation and decentralization of various operations and have become increasingly popular in recent years. However, smart contracts are susceptible to vulnerabilities, and their deployment without proper security testing can result in severe consequences, such as financial losses and reputational damage. In this article, we explore the use of deep learning techniques, particularly Convolutional Neural Networks (CNNs), for detecting and classifying vulnerabilities in smart contracts deployed on the Ethereum main net. We compare different kinds of neural architectures, i.e., a baseline LSTM, multiple 1D CNNs working on the smart contracts’ bytecode, a Vision Transformer (Swin v2 Tiny), and various 2D CNNs that work on RGB images obtained from the bytecode (i.e., ResNet-50, ResNeXt-50, Inception v3, and EfficientNetv2 Small). We provide an in-depth analysis of these techniques to classify a dataset of smart contracts we have collected. Our study shows that the use of deep neural networks can represent a promising technique to automatically assess smart contracts’ correctness and classify potential vulnerabilities. According to our experiments, the ResNet 1D CNN working directly on the smart contract bytecode offers the best results in terms of classification capabilities. Moreover, due to the unbalanced sizes of the different classes, the classification resulted in more effectiveness for the unchecked calls and reentrancy vulnerability classes while still providing good results for others.

Mobile Crowdsensing (MCS) is a paradigm where a crowdsourcer recruits a set of workers through a campaign to collect data using sensors in their mobile device. This process greatly reduces the costs of data collection processes; however, most of the historically proposed systems are centralized. Since this makes the MCS platform a single point of failure, there is an increasing interest in decentralized blockchain-based solutions; regardless, most of the current proposals have a vertical focus and do not account for the heterogeneity of MCS. We propose a decentralized high-level architecture for MCS, based on Distributed Ledger Technology (DLT), that is adaptable to most MCS deployments. We then implement our architecture using the IOTA protocols and evaluate its performance over a real deployment in terms of scalability, showing its advantages over classic blockchains for MCS data.

Money laundering in cryptocurrencies is a significant concern, as it facilitates and conceals crime and can distort markets and the broader financial system. To combat this issue, researchers have turned to techniques to develop effective Anti-Money Laundering (AML) frameworks. The findings contribute to the ongoing efforts to promote social good by reducing the impact of criminal activities on society. By preventing money laundering, we can also help to combat other criminal activities such as drug trafficking, corruption, and terrorism. This paper focuses on the use of Graph Neural Networks (GNNs) to classify cryptocurrencies transactions. Specifically, the study employs Graph Convolutional Networks (GCNs), Graph Attention Networks (GAT), the Chebyshev spatial convolutional neural network (ChebNet), and GraphSAGE network to classify Bitcoin transactions. The study finds that ChebNet, GraphSAGE and a variant of GAT outperform other methods and improve upon the state of the art in terms of recall and F1 scores, thus suggesting that they can be more reliable in identifying illicit transactions.

Mobile devices entered people's lives by leaps and bounds, offering various applications relying on private third-party entities to manage their users' data. Centralized control of personal health data endangers the privacy of the users directly involved. In the future, there will likely be a trend toward decentralizing the health data collection, relieving central entities of this task. This comes with several challenges in a decentralized environment, such as avoiding a single point of failure to guarantee data availability. The following work proposes an architecture based on Distributed Ledger Technology to allow users to decide on their data while ensuring availability by employing social networks. We will outline the mechanisms behind data storage and the implications of using smart contracts in the architecture. In concluding the work, we show the developed architecture and results deriving from its assessment, highlighting possible use cases applied to the specific health data management context.

Multilevel modeling and simulation (M&S) is becoming increasingly relevant due to the benefits that this methodology offers. Multilevel models allow users to describe a system at multiple levels of detail. From one side, this can make better use of computational resources, since the more detailed and time-consuming models can be executed only when/where required. From the other side, multilevel models can be assembled from existing components, cutting down development and verification/validation time. A downside of multilevel M&S is that the development process becomes more complex due to some recurrent issues caused by the very nature of multilevel models: how to make sub-models interoperate, how to orchestrate execution, how state variables are to be updated when changing scale, and so on. In this paper, we address some of these issues by presenting a set of design patterns that provide a systematic approach for designing and implementing multilevel models. The proposed design patterns cover multiple aspects, including how to represent different levels of detail, how to combine incompatible models, how to exchange data across models, and so on. Some of the patterns are derived from the general software engineering literature, while others are specific to the multilevel M&S application area.

The rise of Internet-of-Things enables the development of smart applications devoted to improving the quality of life in urban and rural areas, thus fostering the creation of smart territories. However, some dislocated areas are underprivileged in providing such services due to the lack, inefficiency, or excessive cost of Internet access. Opportunistic networking techniques might aid in surmounting these problems. In this article, we propose a framework that relies on an untrusted Data Mule to carry data from an offline source to an online destination. In particular, we present a framework that enables the communication between different actors and a reward mechanism using Distributed Ledger Technologies, Smart Contracts, and Decentralized File Storage. The protocol involved in bringing a Client’s message online and getting back a response is thoroughly explained in all its steps and then discussed on the most important trust and security issues. Finally, we evaluate such a protocol and the whole framework through a series of communication latency tests, an analysis of the Smart Contract usage, and simulations in which buses act as Data Mules. Our results suggest the feasibility of our proposal in a smart territory scenario.

The need for effective simulation techniques, when studying the performance of portfolio investments in financial applications, was recognized since it was observed that backtesting typically introduces significant bias. However, while Monte Carlo simulations are commonly used in this application scenario, up to now no general frameworks have been proposed. This paper describes a general modeling and simulation framework that is used to study how allocation schemes perform when different synthetic time series generation models are employed. Moreover, we devised a novel portfolio allocation scheme where assets are nodes of a complex network and communities of correlated assets are detected and measured by means of modularity. Allocation is than obtained by equally distributing weights among different communities. We compare this novel scheme against state-of-the-art approaches in various scenarios, under Gaussian, Geometric Brownian motion and ARFIMA generation models. Results show that the proposed scheme outperforms the others in many scenarios.

The Internet Computer Protocol is a new generation blockchain that aims to provide better security and scalability than the traditional blockchain solutions. In this paper, this innovative distributed computing architecture is introduced, modeled and then simulated by means of an agent-based simulation. The result is a digital twin of the current Internet Computer, to be exploited to drive future design and development optimizations, investigate its performance, and evaluate the resilience of this distributed system to some security attacks. Preliminary performance measurements on the digital twin and simulation scalability results are collected and discussed. The study also confirms that agent-based simulation is a prominent simulation strategy to develop digital twins of complex distributed systems.

Multilevel modeling is increasingly relevant in the context of modelling and simulation since it leads to several potential benefits, such as software reuse and integration, the split of semantically separated levels into sub-models, the possibility to employ different levels of detail, and the potential for parallel execution. The coupling that inevitably exists between the sub-models, however, implies the need for maintaining consistency between the various components, more so when different simulation paradigms are employed (e.g., sequential vs parallel, discrete vs continuous). In this paper we argue that multilevel modelling is well suited for the simulation of human mobility, since it naturally leads to the decomposition of the model into two layers, the “micro” and “macro” layer, where individual entities (micro) and long-range interactions (macro) are described. In this paper we investigate the challenges of multilevel modeling, and describe some preliminary results using prototype implementations of multilayer simulators in the context of epidemic diffusion and vehicle pollution.

Many services that are taken for granted in smart cities are not even remotely available in dislocated areas, i.e. "smart territories". With the aim to offer a practical and secure way to transport data in such constrained scenarios, we focus on the problem of incentivizing to Data Mules, i.e. devices dedicated to enable communication even in the absence of the Internet. We combine decentralized technologies and State-Channels to verify the correct behavior of participants in an offline scenario.

Many services that are taken for granted in smart cities are not even remotely available in dislocated areas yet, due to the lack of or too costly wide area network connectivity. With the aim to offer a practical and secure way to transport data and allow for communications in such constrained scenarios, we focus on the problem of incentivizing to data mules, i.e. devices dedicated to enable the data transfer even in the absence of the Internet. Our solution combines the use of several distributed technologies for verifying the correct behavior of all the partici-pants and incentivize them. We focus on the use of state channels to support the flow of smart-contract-based tokens as a form of payment, in a condition where participants communicate only with others in physical proximity. Furthermore, we validate the viability of the application through the simulation of peer-to-peer interactions between the participants. In this work we achieve positive results in terms of communication latency and percentage of client nodes which are able to benefit from the system.

We investigate the use of deep learning to classify smart contract code vulnerabilities. We use different variants of Convolutional Neural Networks (CNNs) and a Long Short-Term Memory (LSTM) neural network. Five classes of vulnerabilities were employed. Our results suggest that the CNNs are able to provide a good level of accuracy, thus showing the viability of the proposed approach.

Abstract The number of users approaching the world of cryptocurrencies exploded in the last years, and consequently the daily interactions on their underlying distributed ledgers have intensified. In this paper, we analyze the flow of these digital transactions in a certain period of time, trying to discover important insights on the typical use of these technologies by studying, through complex network theory, the patterns of interactions in four prominent and different Distributed Ledger Technologies (DLTs), namely Bitcoin, DogeCoin, Ethereum, Ripple. In particular, we describe the Distributed Ledger Network Analyzer (DiLeNA), a software tool for the investigation of the transactions network recorded in DLTs. We show that studying the network characteristics and peculiarities is of paramount importance, in order to understand how users interact in the DLT. For instance, our analyses reveal that all transaction graphs exhibit small world properties.

Dealing with a growing amount of data is a crucial challenge for the future of information and communication technologies. More and more devices are expected to transfer data through the Internet, therefore new solutions have to be designed in order to guarantee low latency and efficient traffic management. In this paper, we propose a solution that combines the edge computing paradigm with a decentralized communication approach based on Peer-to-Peer (P2P). According to the proposed scheme, participants to the system are employed to relay messages of other devices, so as to reach a destination (usually a server at the edge of the network) even in absence of an Internet connection. This approach can be useful in dynamic and crowded environments, allowing the system to outsource part of the traffic management from the Cloud servers to end-devices. To evaluate our proposal, we carry out some experiments with the help of LUNES, an open source discrete events simulator specifically designed for distributed environments. In our simulations, we tested several system configurations in order to understand the impact of the algorithms involved in the data dissemination and some possible network arrangements.

Plenty of research on smart mobility is currently devoted to the inclusion of novel decentralized software architectures to these systems, due to the inherent advantages in terms of transparency, traceability, trustworthiness. MOVO is a decentralized application (dApp) for smart mobility. It includes: (i) a module for collecting data from vehicles and smartphones sensors; (ii) a component for interacting with Distributed Ledger Technologies (DLT) and Decentralized File Storages (DFS), for storing and validating sensor data; (iii) a module for "offline" interaction between devices. The dApp consists of an Android application intended for use inside a vehicle, which helps the user/driver collect contextually generated data (e.g. a driver’s stress level, an electric vehicle’s battery level), which can then be shared through the use of DLT (i.e., IOTA DLT and Ethereum smart contracts) and DFS (i.e., IPFS). The third module consists of an implementation of a communication channel that, via Wi-Fi Direct, allows two devices to exchange data and payment information with respect to DLT (i.e. cryptocurrency and token) assets. In this paper, we describe the main software components and provide an experimental evaluation that confirms the viability of the MOVO dApp in real mobility scenarios.

Distributed Ledger Technologies (DLT) and Decentralized File Storages (DFS) are becoming increasingly used to create common, decentralized and trustless infrastructures where participants interact and collaborate in Peer-to-Peer interactions. A prominent use case is represented by decentralized data marketplaces, where users are consumers and providers at the same time, and trustless interactions are required. However, data in DLTs and DFS are usually unstructured and there are no efficient mechanisms to query a certain type of data for the search in the market. In this paper, we propose the use of a Distributed Hash Table (DHT) as a layer on top of DLTs where, once the data are acquired and stored in the ledger, these can be searched through multiple keyword based queries, thanks to the lookup functionalities offered by the DHT. The DHT network is a hypercube overlay structure, organized for an efficient processing of multiple keyword-based queries. We provide the architecture of such solution for a decentralized data marketplace and an analysis based on a simulation that proves the viability of the proposed approach.

The European Space Agency (ESA) Atlantic Regional Initiative seeks to positively benefit the Atlantic region and end-user communities through Earth Observation (EO) applications and services. This paper presents the planned research and development of a number of services that will be available and tailored for end-users in the scope of Atlantic Cities: Smart, Sustainable and Secure Ports and Protecting the Ocean.

DTLS is a protocol that provides security guarantees to Internet communications. It can operate on top of both TCP and UDP transport protocols. Thus, it is particularly suited for peer-to-peer and distributed multimedia applications. The same holds if the endpoints are mobile devices. In this scenario, mechanisms are needed to surmount possible network disconnections, often arising due to the mobility or the scarce resources of devices, that can jeopardize the quality of the communications. Session resumption is thus a main issue to deal with. To this aim, we propose a fast reconnection scheme that employs non-connected sockets to quickly resume DTLS communication sessions. The proposed scheme is assessed in a performance evaluation that confirms its viability.

This paper presents a system architecture to promote the development of smart transportation systems. Thanks to the use of distributed ledgers and related technologies, it is possible to create, store and share data generated by users through their sensors, while moving. In particular, IOTA and IPFS are used to store and certify data (and their related metadata) coming from sensors or by the users themselves. Ethereum is exploited as the smart contract platform that coordinates the data sharing and provisioning. The necessary privacy guarantees are provided by the usage of Zero Knowledge Proof. We show some results obtained from some use case scenarios that demonstrate how such technologies can be integrated to build novel smart services and to promote social good in user mobility.

This paper presents an architecture, based on Distributed Ledger Technologies (DLTs) and Decentralized File Storage (DFS) systems, to support the use of Personal Information Management Systems (PIMS). DLT and DFS are used to manage data sensed by mobile users equipped with devices with sensing capability. DLTs guarantee the immutability, traceability and verifiability of references to personal data, that are stored in DFS. In fact, the inclusion of data digests in the DLT makes it possible to obtain an unalterable reference and a tamper-proof log, while remaining compliant with the regulations on personal data, i.e. GDPR. We provide an experimental evaluation on the feasibility of the use of DFS. Three different scenarios have been studied: i) a proprietary IPFS approach with a dedicated node interfacing with the data producers, ii) a public IPFS service and iii) Sia Skynet. Results show that through proper configuration of the system infrastructure, it is viable to build a decentralized Personal Data Storage (PDS).

This paper presents an architecture of a Personal Information Management System, in which individuals can define the access to their personal data by means of smart contracts. These smart contracts, running on the Ethereum blockchain, implement access control lists and grant immutability, traceability and verifiability of the references to personal data, which is stored itself in a (possibly distributed) file system. A distributed authorization mechanism is devised, where trust from multiple network nodes is necessary to grant the access to the data. To this aim, two possible alternatives are described: a Secret Sharing scheme and Threshold Proxy Re-Encryption scheme. The performance of these alternatives is experimentally compared in terms of execution time. Threshold Proxy Re- Encryption appears to be faster in different scenarios, in particular when increasing message size, number of nodes and the threshold value, i.e. number of nodes needed to grant the data disclosure.

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Summary In this paper, we analyze the Ethereum blockchain using the complex networks modeling framework. Accounts acting on the blockchain are represented as nodes, while the interactions among these accounts, recorded on the blockchain, are treated as links in the network. Using this representation, it is possible to derive interesting mathematical characteristics that improve the understanding of the actual interactions happening in the blockchain. Not only, by looking at the history of the blockchain, it is possible to verify if radical changes in the blockchain evolution happened.

Social media are nowadays used by people to talk about any kind of topic, either private or not, and among others health problems. Concerning health, on social media one can find large communities discussing about chronic diseases. Here people look for advise, information, support, and so on. In this paper we take into consideration the Italian Twitter community interested in diabetes, a chronic blood disease. Our aim is to understand which are the main conversation topics in this community. Thus, we analyzed about 9K tweets written in Italian that contained the world {\em diabete} (diabetes) and performed both a hashtag analysis and a lexicon analysis, the latter by means of ad-hoc dictionaries. Results show that this is a calm community interested into several distinct topics (e.g., disease related information gathering, recipes for diabetes patients, fundraising campaigns), none of which is really predominant over the others.

Research on data confidentiality, integrity and availability is gaining momentum in the ICT community, due to the intrinsically insecure nature of the Internet. While many distributed systems and services are now based on secure communication protocols to avoid eavesdropping and protect confidentiality' the techniques usually employed in distributed simulations do not consider these issues at all. This is probably due to the fact that many real-world simulators rely on monolithic, offline approaches and therefore the issues above do not apply. However, the complexity of the systems to be simulated, and the rise of distributed and cloud based simulation, now impose the adoption of secure simulation architectures. This paper presents a solution to ensure both anonymity and confidentiality in distributed simulations. A performance evaluation based on an anonymized distributed simulator is used for quantifying the performance penalty for being anonymous. The obtained results show that this is a viable solution.

Summary This paper deals with the use of hybrid simulation to build and compose heterogeneous simulation scenarios that can be proficiently exploited to model and represent the Internet of things (IoT). Hybrid simulation is a methodology that combines multiple modalities of modeling/simulation. Complex scenarios are decomposed into simpler ones, each one being simulated through a specific simulation strategy. All these simulation building blocks are then synchronized and coordinated. This simulation methodology is an ideal one to represent IoT setups, which are usually very demanding, due to the heterogeneity of possible scenarios arising from the massive deployment of an enormous amount of sensors and devices. We present a use case concerned with the distributed simulation of smart territories, a novel view of decentralized geographical spaces that, thanks to the use of IoT, builds ICT services to manage resources in a way that is sustainable and not harmful to the environment. Three different simulation models are combined together, namely, an adaptive agent‐based parallel and distributed simulator, an OMNeT++ based discrete event simulator, and a script‐language simulator based on MATLAB. Results from a performance analysis confirm the viability of using hybrid simulation to model complex IoT scenarios.

This paper presents a methodology for simulating the Internet of Things (IoT) using multi-level simulation models. With respect to conventional simulators, this approach allows us to tune the level of detail of different parts of the model without compromising the scalability of the simulation. As a use case, we have developed a two-level simulator to study the deployment of smart services over rural territories. The higher level is base on a coarse grained, agent-based adaptive parallel and distributed simulator. When needed, this simulator spawns OMNeT++ model instances to evaluate in more detail the issues concerned with wireless communications in restricted areas of the simulated world. The performance evaluation confirms the viability of multi-level simulations for IoT environments.

Mobility in urban environments is an undisputed key factor that can affect citizens’ well-being and quality of life. This is particularly relevant for those people with disabilities or with reduced mobility who have to face the presence of barriers in urban areas. In this scenario, the availability of information about such architectural elements (together with facilities) can greatly support citizens’ mobility by enhancing their independence and their abilities in conducting daily outdoor activities. With this in mind, we have designed and developed mobile Pervasive Accessibility Social Sensing (mPASS), a system that provides users with personalized paths, computed on the basis of their own preferences and needs, with a customizable and accessible interface. The system collects data from crowdsourcing and crowdsensing to map urban and architectural accessibility by providing reliable information coming from different data sources with different levels of trustworthiness. In this context, reliability can be ensured by properly managing crowdsourced and crowdsensed data, combined when possible with authoritative datasets, provided by disability rights organizations and local authorities. To demonstrate this claim, in this article we present our trustworthiness model and discuss results we have obtained by simulations.

Discrete Event Simulation is a widely used technique that is used to model and analyze complex systems in many fields of science and engineering. The increasingly large size of simulation models poses a serious computational challenge, since the time needed to run a simulation can be prohibitively large. For this reason, Parallel and Distributes Simulation techniques have been proposed to take advantage of multiple execution units which are found in multicore processors, cluster of workstations or HPC systems. The current generation of HPC systems includes hundreds of thousands of computing nodes and a vast amount of ancillary components. Despite improvements in manufacturing processes, failures of some components are frequent, and the situation will get worse as larger systems are built. In this paper we describe FT-GAIA, a software-based fault-tolerant extension of the GAIA/ARTIS parallel simulation middleware. FT-GAIA transparently replicates simulation entities and distributes them on multiple execution nodes. This allows the simulation to tolerate crash-failures of computing nodes, furthermore, FT-GAIA offers some protection against Byzantine failures since synchronization messages are replicated as well, so that the receiving entity can identify and discard corrupted messages. We provide an experimental evaluation of FT-GAIA on a running prototype. Results show that a high degree of fault tolerance can be achieved, at the cost of a moderate increase in the computational load of the execution units.

This paper presents an approach to model melodies (and music pieces in general) as networks. Notes of a melody can be seen as nodes of a network that are connected whenever these are played in sequence. This creates a directed graph. By using complex network theory, it is possible to extract some main metrics, typical of networks, that characterize the piece. Using this framework, we provide an analysis on a set of guitar solos performed by main musicians. The results of this study indicate that this model can have an impact on multimedia applications such as music classification, identification, and automatic music generation.

This paper discusses the need to devise novel strategies to create smart services specifically designed to non-metropolitan areas, i.e. countrysides. These solutions must be viable, cheap an should take into consideration the different nature of countrysides, that cannot afford the deployment of services designed for smart cities. These solutions would have an important social impact for people leaving in these countrysides, and might slow down the constant migration of citizens towards metropolis. In this work, we focus on communication technologies and practical technological/software distributed architectures. An important aspect for the real deployment of these “smart shires” is their simulation. We show that “priority-based broadcast” schemes over ad-hoc networks can represent an effective communication substrate to be used in a software middleware promoting the creation of applications for smart shire scenarios.

This paper discusses on the need to focus on effective and cheap communication solutions for the deployment of smart services in countrysides. We present the main wireless technologies, software architectures and protocols that need to be exploited, such as multihop, multipath communication and mobility support through multihoming. We present Always Best Packet Switching (ABPS), an operation mode to perform network handover in a seamless way without the need to change the current network infrastructure and configuration. This is in accordance with the need of having cheap solutions that may work in a smart shire scenario. A simulation assessment confirms the effectiveness of our approach.

This paper deals with the use of self-organizing protocols to improve the reliability of dynamic Peer-to-Peer (P2P) overlay networks. We present two approaches, that employ local knowledge of the 2nd neighborhood of nodes. The first scheme is a simple protocol requiring interactions among nodes and their direct neighbors. The second scheme extends this approach by resorting to the Edge Clustering Coefficient (ECC), a local measure that allows to identify those edges that connect different clusters in an overlay. A simulation assessment is presented, which evaluates these protocols over uniform networks, clustered networks and scale-free networks. Different failure modes are considered. Results demonstrate the viability of the proposal.

In this paper we argue that the set of wireless, mobile devices (e.g., portable telephones, tablet PCs, GPS navigators, media players) commonly used by human users enables the construction of what we term a “digital ecosystem”, i.e., an ecosystem constructed out of so-called “digital organisms” (see below), that can foster the development of novel distributed services. In this context, a human user equipped with his/her own mobile devices, can be though of as a “digital organism” (DO), a subsystem characterized by a set of peculiar features and resources it can offer to the rest of the ecosystem for use from its peer DOs. The internal organization of the DO must address issues of management of its own resources, including power consumption. Inside the DO and among DOs, peer-to-peer interaction mechanisms can be conveniently deployed to favor resource sharing and data dissemination. Throughout this paper, we show that most of the solutions and technologies needed to construct a digital ecosystem are already available. What is still missing is a framework (i.e., mechanisms, protocols, services) that can support effectively the integration and cooperation of these technologies. In addition, in the following we show that that framework can be implemented as a middleware subsystem that enables novel and ubiquitous forms of computation and communication. Finally, in order to illustrate the effectiveness of our approach, we introduce some experimental results we have obtained from preliminary implementations of (parts of) that subsystem.

In this paper we discuss the design and experimental evaluation of a middleware architecture that enables Service Level Agreement (SLA)-driven dynamic configuration, management and optimization of cloud resources and services. This architecture has been designed in order to respond effectively to the Quality of Service (QoS) requirements of the cloud customer applications. Typically, an application can be hosted in an execution platform constructed out of (real and virtual) cloud resources. In this context, the application QoS requirements can be specified in a SLA that binds the application to its hosting platform. Our architecture incorporates a load balancer that distributes the computational load across the platform resources, and monitors the QoS the platform delivers. If this deviates from that specified in the SLA, so as to violate it, the platform is reconfigured dynamically in order to incorporate additional resources from the cloud. In contrast, if the SLA is honored and platform resources result unused, platform reconfiguration occurs to release those unused resources.

We describe a cross-layer architecture we are developing in order to offer mobility support to wireless devices executing multimedia applications which require seamless communications. This architecture is based on the use of pairs of proxies, which enable the adaptive and concurrent use of different network interfaces during the communications. A cloud computing environment is used as the infrastructure to set up (and release) dynamically the proxies on the server-side, in accordance with the pay-as-you-go principle of cloud based services.

Unstructured peer-to-peer systems represent viable candidates for supporting distributed applications with frequent node failures. This paper presents a framework to study the reliability of evolving overlay networks in presence of faulty nodes. We focus on the node degree distribution, an important metric to understand whether the topology of the overlay can be maintained in presence of peer churns. From the degree distribution, other metrics can be estimated such as the diameter of the net. Two protocols are considered, which differ in the way peers attach to other nodes. Numerical results are compared with those obtained through simulation. They confirm that the proposed model can be factually exploited to adapt the peers' behavior and preserve the desired topology of the overlay.

We model peer-to-peer live streaming as a multistage congestion game where certain strategy restrictions allow, at equilibrium, to minimize both streaming duration and congestion. We also propose a distributed algorithm (ConGaS) that can be easily executed at peers, enabling them to coordinate toward streaming optimization. Finally, ConGaS is compared against two other dissemination policies through experimental evaluation, and simulations confirm the viability and efficacy of the former.

Intervehicle communication (IVC) is emerging in research prominence for the interest that it is generating in all major car manufacturers and for the benefits that its inception will produce. The specific features of IVC will allow the deployment of a wide set of possible applications, which span from road safety to entertainment. Even if, on the one hand, these applications share the common need for fast multihop message propagation, on the other hand, they possess distinct characteristics in terms of generated network traffic. The state of the art of current research only proposes solutions specifically designed for a single application (or class) that is not directly extendable to a general IVC context. Instead, we claim that a privileged architecture exists, which is able to support the whole spectrum of application classes. To this aim, we propose a novel IVC architecture that adapts its functionalities to efficiently serve applications by quickly propagating their messages over a vehicular network. We conducted an extensive set of experiments that demonstrate the efficacy of our approach. As representative case studies, we considered two application classes that, for their network traffic characteristics, are at the opposite boundaries of the application spectrum: safety and entertainment.

Detecting cheaters in peer-to-peer multiplayer online games (MOGs) may be more effective than avoiding the cheats. This is particularly true for fast paced, real-time MOGs, since the classic approaches to prevent time cheats usually complicate the communication protocols among peers. This results in a loss of responsiveness among players during the game session. We present a cheating detection scheme, named AC/DC (Algorithm for Cheating Detection by Cheating), that faces a specific time cheat, i.e., the look-ahead cheat. The idea is that of exploiting a counterattack approach to determine if the suspected node waits for messages coming from other peers before generating its cheated events. During the counterattack, the controlling node delays the transmission of newly generated events to the suspected node. This allows to determine if the suspected node waits for these events before generating its own ones. In this paper, a novel version of the AC/DC approach is presented which exploits a control scheme that measures the statistical correlation between the additional delay employed by the controlling peer and the network latencies from the controlled peer. Simulation results confirm the viability of the approach.

We present SPAWC (signal parser and well-formed composer), a system for the generation of complex melodies, chords and sounds, based on a simple interaction of the performer with an input instrument, i.e., a modulator. Via modulator, the performer generates inputs which are manipulated by SPAWC and transformed into melodies, chords or complex sounds. Real/virtual instruments, as well as a set of (pre or live) recorded audio samples can be utilized to produce the final output. In this paper, we describe all the system components working inside SPAWC. The final output is dynamically produced based on the utilized carrier, i.e., the exploited real/virtual instrument or the set of audio samples loaded on SPAWC. A real installation of SPAWC has been developed by exploiting a theremin as the modulator. Real experiences have demonstrated that SPAWC can be utilized by artists to produce interactive music generation experiences. Concurrently, the system can be employed for entertainment and educational purposes. The software architecture of SPAWC is designed to allow the distribution of its software components over a network. This would make possible to dynamically exploit different carriers available on the net, and promote novel forms of artistic interactions among users over the Internet.

Multimedia and Web 2.0 can meet up. Despite the success they have obtained in last years, these two domains present some important limitations. In particular, current Web 2.0 based technologies, such as wikis, blogs, podcasts, commonly resort to sequential combinations of contents, rather than exploiting dynamic and synchronized compositions of discrete and continuous media resources. On the other hand, tools for multimedia content authoring and distribution usually lack cooperativeness, interactivity, use of adequate metatagging structures and application mash-up. This paper discusses how by merging these two worlds it is possible to inherit the advantages while removing their limitations. We describe a system exemplar we developed for the cooperative creation, manipulation and sharing of rich media contents.

As the technology available on cars is increasing, a wide range of applications, from safety to entertainment, are becoming available for this scenario. Many of these applications involve interactive multihop broadcast of messages over a certain area-of-interest. To this aim, specific solutions have been proposed to deal with the high mobility and density of a vehicular network scenario. In particular, we developed a simple, yet effective, solution for allowing cars to estimate their communication range through including few parameters in exchanged application messages. Then, this range estimation is exploited to reduce the number of hops and the delay required by a broadcast message to reach all the vehicles in the area-of-interest. Since so far the evaluation of our solution has been focused on simulations, we have now created a real testbed. Here, we report on results achieved through this real testbed, showing that they confirm the efficacy of our solution in estimating vehicles' transmission range and choosing the most appropriate forwarder for transmitted messages.

Internet and the latest correlated technologies, such as Web 2.0, represent inventions that have changed the world with their social contribution. Yet, digital divide and cyber-balkanization are Internet's discriminatory phenomena well known by field experts. In addition, we want now to raise the attention on the actual employment of these technologies: the vast potential within the Internet is currently dispersed in millions of applications devoted to frivolous and egocentric utilizations. Being (almost) the only use people have made of the most pervasive communication system ever made, this represents an unacceptable waste of opportunities. To revert this trend, we propose a novel utilization paradigm of Internet technologies which is aimed at liberating part of resources that are captured but not used by egocentric applications to give them back to a possible common (and altruistic) use.

The technology supporting multiplayer online games (MOGs) has greatly improved in the past few years. This represents great news both for players all over the world and for researchers that are struggling behind tough problems in real-time distributed systems. Indeed, MOGs represent a peculiar class of distributed systems, sharing features, requirements, challenges, and thereby also feasible solutions. To this aim, we focus on issues and state-of art solutions related to a crucial aspect in MOG deployment: the synchronization of its nodes. In particular, we review techniques able to guarantee that the system evolves in a quick and consistent way and highlight how they can be made more effective by exploiting specific game features. To further support this claim, we also present results from an experimental evaluation we performed.

This paper presents a novel tangible user interface for the dynamic control and manipulation of multiple video streams. Streams can be activated, triggered and managed based on the physical use of markers attached to cubes on a table top, whose movement is recognized by a camera placed on top of the working area. Markers can be associated to a single video stream, as well as a group of streams. Videostreams can be played out, paused, stopped, put in foreground, maximized or minimized, thanks to the use of our tangible interface; audio volumes of different streams can be controlled. Needless to say, streams can be distributed at different servers, or dynamically generated at distributed nodes in real-time. We claim that such an interface results as quite intuitive and easy-to-use, and can be employed in several videostreaming-based application domains, such as tele-conferencing, video-on-demand, video mixing. Results coming from an experimental assessment confirm the viability of our approach.

The large diffusion of e-learning technologies represents a great opportunity for underserved segments of population. This is particularly true for people with disabilities for whom digital barriers should be overstepped with the aim of reengaging them back into society to education. In essence, before a mass of learners can be engaged in a collective educational process, each single member should be put in the position to enjoy accessible and customized educational experiences, regardless of the wide diversity of their personal characteristics and technological equipment. To respond to this demand, we developed LOT (Learning Object Transcoder), a distributed PHP-based service-oriented system designed to deliver flexible and customized educational services for a multitude of learners, each with his/her own diverse preferences and needs. The main novelty of LOT amounts to a broking service able to manage the transcoding activities needed to convert multimedia digital material into the form which better fits a given student profile. Transcoding activities are performed based on the use of Web service technologies. Experimental results gathered from several field trials with LOT (available online at http://137.204.74.83/∼lot/) have confirmed the viability of our approach.

The use of adaptive systems provides means to ensure \nanytime, anywhere and any device entertainment experiences for \nheterogeneous players. In this paper, we present an approach to \ndynamically transcode edutainment contents and to facilitate \nuser interactions in context-aware scenarios. We focus on those \ntypologies of interactive online applications, for which \nedutainment activities are structured as a combination of \ndelivery of rich media contents interleaved with interactive \nchoices made by the users (e.g., multimedia quizzes). As a matter \nof facts, today’s edutainment contents are mostly produced to be \ndelivered through TV or broadband networks. Thus, typically \nthey are not encoded to be used in nomadic contexts. It turns out \nthat the use of an adaptation process (based on user profiles) \nresults as an important tool to face with the poor computational \nand networking capabilities of (mobile) devices. Moreover, a \nhigher level of accessibility can be guaranteed to users with \ndisabilities, since contents are transcoded based on user \npreferences. Results gathered from a real evaluation assessment \nshow the viability of our solution.

Abstract — As the technology available on cars is increasing, a wide range of applications, from safety to entertainment, are becoming factually accessible to passengers. Many of these applications involves a one-to-many transmission model where a single car broadcasts a message that has to be forwarded, even with multiple hops, in a very short time to all the other cars located within a range of few kilometers from the source. Since the high mobility and density of a car network scenario, specific solutions need to be devised to choreograph a fast-delivery multihop broadcast. To this aim, we developed a practical and efficient technique that allows cars to estimate their communication range with the help of a very limited message exchange and exploit this information to reduce the number of transmissions, as well as the hops to be traversed, and hence the time, required by a broadcasted message to reach all the cars following the sender within a certain distance.

Successes and failures during rescue operations after hurricane Katrina and the Twin Towers attack demonstrated the importance of supporting first responders with adequate means to perform their operations in an effective and safe way. From a networking point of view, one of the main challenges is that of providing first responders with multimedia information about the emergency as soon as possible, even from a remote location. To this aim, we designed an inter-vehicular communication system able to quickly discover and transmit real time multimedia information from around a crisis area to approaching first responders' vehicles. As vehicular communications are highly variable in nature, we endowed our system with a transmission range estimator that is put to good use to reduce the number of hops that a video triggering message sent by a vehicle will experience to reach its destination. Experimental results demonstrate the efficacy of our scheme in reducing the message delivery time and the traffic generated.

Abstract — Ensuring fairness among players engaged in online games is a challenging task. Yet, it is a fundamental requirement that can make the difference between having customers that persist or desist in using this kind of application. Answering to this demand, we present here an event delivery mechanism among mirrored game servers able to effectively uplift the fairness degree during game sessions through the heterogenesis of ends in targeting interactivity. We also provide extensive results that sustain our claim.

TV still remains the preferred medium for entertainment. With the introduction of interactive digital television (IDTV), new digital services can be now conveyed through this communication technology. In particular, bringing the Web to IDTV may represent an attractive opportunity to inject new fuel into the TV world, thus creating a wider and accessible container of digital services for entertainment. Yet, this raises several technical issues that need to be addressed. While a lot of research has been done with the aim of adjusting the content of a Web page so that it fits to a target computing environment, scarce efforts have been devoted to the specific TV target. Further, effective interfaces for digital TV must be devised which guarantee the same level of usability available through common PCs. In particular, contents must be structured such that: i) their management is facilitated on low-end set top boxes, and ii) their visualization is made effective for standard TV displays. In this context, we propose a new technique for the automatic transcoding of Web contents which enables their delivery over IDTV broadcast channels and allows a proper visualization on IDTV screens. Our system is based on the digital video broadcasting-multimedia home platform (DVB-MHP) standard. We also developed a client application which permits to browse our transcoded contents on a TV set via the remote control. Results gathered from real world experiments demonstrate the viability of our approach

Time cheats represent some of the most crucial issues in online gaming. Since they act on timing properties of generated game events, these malicious schemes are particularly difficult to thwart when distributed games are deployed over peer-to-peer architectures. Indeed, the absence of a global clock shared among peers enables cheaters to see into the future by waiting for events generated by other peers before generating its own ones (lookahead cheat). This may give an unfair advantage to the cheater. We consider a version of lookahead cheat generalized in the context of real-time (i.e., not round-based) games. To face this time cheat, we present AC/DC, an Algorithm for Cheating Detection by Cheating. This algorithm enables to detect cheaters based on monitoring of network latencies. The basic idea is that of conducting against each suspected peer a sort of cheating counterattack, by delaying events before notifying them to the (hypothetic) cheater. This permits to detect whether that peer waits for these events before generating its own ones. Our claim is that an approach based on the monitoring of communication patterns among peers allows cheat detection without affecting the performances of the game.

The union of Interactive Digital TV (IDTV) and Web promotes the development of new interactive multimedia services, enjoyable while watching TV even on the new handheld digital TV receivers. Yet, several design constraints complicate the deployment of this new pattern of services. Indeed, for a suitable presentation on a TV set, Web contents must be structured in such a way that they can be effectively displayed on TV screens via low-end Set Top Boxes (STBs). Moreover, usable interfaces for IDTV platforms are needed which ensure a smooth access to contents. Our claim is that the distribution of Web contents over the IDTV broadcast channels may bring IDTV to a new life. A failure of this attempt may put IDTV on a progressive track towards irrelevance. We propose a system for the distribution of Web contents towards IDTV under the Digital Video Broadcasting - Multimedia Home Platform (DVB-MHP) standard. Our system is able to automatically transcode Web contents and ensure a proper visualization on IDTV. The system is endowed with a client application which permits to easily browse contents on the TV via a remote control. Real assessments have confirmed the effectiveness for such an automatic online service able to reconfigure Web contents for an appropriate distribution and presentation on IDTV.

Since the expansion of their market and their challenging requirements, massively multiplayer online games are gaining increasing attention in the scientific community. One of the key factors in this kind of application is represented by the ability to rapidly deliver game events among the various players over the network. Employing in this context mirrored game server architectures and adapting RED (random early detection) techniques borrowed from network queuing management, we are able to show sensible benefits in upholding interactivity and scalability, whilst preserving game state consistency and game evolution fluency at the player's side