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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.

In this paper, we present a study of a Federated Learning (FL) system, based on the use of decentralized architectures to ensure trust and increase reliability. The system is based on the idea that the FL collaborators upload the (ciphered) model parameters on the Inter-Planetary File System (IPFS) and interact with a dedicated smart contract to track their behavior. Thank to this smart contract, the phases of parameter updates are managed efficiently, thereby strengthening data security. We have carried out an experimental study that exploits two different methods of weight aggregation, i.e., a classic averaging scheme and a federated proximal aggregation. The results confirm the feasibility of the proposal., Comment: TRUSTCHAIN workshop

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.

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.

This paper presents an analysis of the role of social media, specifically Twitter, in the context of non-fungible tokens, better known as NFTs. Such emerging technology framing the creation and exchange of digital object, started years ago with early projects such as "CryptoPunks" and since early 2021, has received an increasing interest by a community of people creating, buying, selling NFT's and by the media reporting to the general public. In this work it is shown how the landscape of one class of projects, specifically those used as social media profile pictures, has become mainstream with leading projects such as "Bored Ape Yacht Club", "Cool Cats" and "Doodles". This work illustrates how heterogeneous data was collected from the Ethereum blockchain and Twitter and then analysed using algorithms and state-of-art metrics related to graphs. The initial results show that from a social network perspective, the collections of most popular NFTs can be considered as a single community around NFTs. Thus, while each project has its own value and volume of exchange, on a social level all of them are primarily influenced by the evolution of values and trades of "Bored Ape Yacht Club" collection., Comment: In Proceedings of the ACM International Conference on Information Technology for Social Good (GoodIT'22), September 07--09, 2022, Cyprus

In shaping the Internet of Money, the application of blockchain and distributed ledger technologies (DLTs) to the financial sector triggered regulatory concerns. Notably, while the user anonymity enabled in this field may safeguard privacy and data protection, the lack of identifiability hinders accountability and challenges the fight against money laundering and the financing of terrorism and proliferation (AML/CFT). As law enforcement agencies and the private sector apply forensics to track crypto transfers across ecosystems that are socio-technical in nature, this paper focuses on the growing relevance of these techniques in a domain where their deployment impacts the traits and evolution of the sphere. In particular, this work offers contextualized insights into the application of methods of machine learning and transaction graph analysis. Namely, it analyzes a real-world dataset of Bitcoin transactions represented as a directed graph network through various techniques. The modeling of blockchain transactions as a complex network suggests that the use of graph-based data analysis methods can help classify transactions and identify illicit ones. Indeed, this work shows that the neural network types known as Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT) are a promising AML/CFT solution. Notably, in this scenario GCN outperform other classic approaches and GAT are applied for the first time to detect anomalies in Bitcoin. Ultimately, the paper upholds the value of public-private synergies to devise forensic strategies conscious of the spirit of explainability and data openness.

We present a study on portfolio investments in financial applications. We describe a general modeling and simulation framework and study the impact on the use of different metrics to measure the correlation among assets. In particular, besides the traditional Pearson's correlation, we employ the Detrended Cross-Correlation Analysis (DCCA) and Detrended Partial Cross-Correlation Analysis (DPCCA). Moreover, a novel portfolio allocation scheme is introduced that treats assets as a complex network and uses modularity to detect communities of correlated assets. Weights of the allocation are then distributed among different communities for the sake of diversification. Simulations compare this novel scheme against Critical Line Algorithm (CLA), Inverse Variance Portfolio (IVP), the Hierarchical Risk Parity (HRP). Synthetic times series are generated using the Gaussian model, Geometric Brownian motion, GARCH, ARFIMA and modified ARFIMA models. Results show that the proposed scheme outperforms state of the art approaches in many scenarios. We also validate simulation results via backtesting, whose results confirm the viability of the proposal.

In this paper we describe LUNES-Blockchain, an agent-based simulator of blockchains that relies on Parallel and Distributed Simulation (PADS) techniques to obtain high scalability. The software is organized as a multi-level simulator that permits to simulate a virtual environment, made of many nodes running the protocol of a specific Distributed Ledger Technology (DLT), such as the Bitcoin or the Ethereum blockchains. This virtual environment is executed on top of a lower-level Peer-to-Peer (P2P) network overlay, which can be structured based on different topologies and with a given number of nodes and edges. Functionalities at different levels of abstraction are managed separately, by different software modules and with different time granularity. This allows for accurate simulations, where (and when) it is needed, and enhances the simulation performance. Using LUNES-Blockchain, it is possible to simulate different types of attacks on the DLT. In this paper, we specifically focus on the P2P layer, considering the selfish mining, the 51% attack and the Sybil attack. For which concerns selfish mining and the 51% attack, our aim is to understand how much the hash-rate (i.e. a general measure of the processing power in the blockchain network) of the attacker can influence the outcome of the misbehaviour. On the other hand, in the filtering denial of service (i.e. Sybil Attack), we investigate which dissemination protocol in the underlying P2P network makes the system more resilient to a varying number of nodes that drop the messages. The results confirm the viability of the simulation-based techniques for the investigation of security aspects of DLTs., Comment: To appear in Simulation Modelling Practice and Theory (Elsevier)

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., Comment: To appear in the Proceedings of the 2021 IEEE/ACM 25th International Symposium on Distributed Simulation and Real Time Applications (DS-RT)

Recently, a new generation of P2P systems capable of addressing data integrity and authenticity has emerged for the development of new applications for a "more" decentralized Internet, i.e., Distributed Ledger Technologies (DLT) and Decentralized File Systems (DFS). However, these technologies still have some unanswered issues, mostly related to data lookup and discovery. In this paper, first, we propose a Distributed Hash Table (DHT) system that efficiently manages decentralized keyword-based queries executed on data stored in DFS. Through a hypercube logical layout, queries are efficiently routed among the network, where each node is responsible for a specific keywords set and the related contents. Second, we provide a framework for the governance of the above network, based on a Decentralized Autonomous Organization (DAO) implementation. We show how the use of smart contracts enables organizational decision making and rewards for nodes that have actively contributed to the DHT. Finally, we provide experimental validation of an implementation of our proposal, where the execution of the same protocol for different logical nodes of the hypercube allows us to evaluate the efficiency of communication within the network.

In distributed environments, such as distributed ledgers technologies and other peer-to-peer architectures, communication represents a crucial topic. The ability to efficiently disseminate contents is strongly influenced by the type of system architecture, the protocol used to spread such contents over the network and the actual dynamicity of the communication links (i.e. static vs. temporal nets). In particular, the dissemination strategies either focus on achieving an optimal coverage, minimizing the network traffic or providing assurances on anonymity (that is a fundamental requirement of many cryptocurrencies). In this work, the behaviour of multiple dissemination protocols is discussed and studied through simulation. The performance evaluation has been carried out on temporal networks with the help of LUNES-temporal, a discrete event simulator that allows to test algorithms running on a distributed environment. The experiments show that some gossip protocols allow to either save a considerable number of messages or to provide better anonymity guarantees, at the cost of a little lower coverage achieved and/or a little increase of the delivery time., Comment: To appear in the Proceedings of the 2021 Annual Modeling and Simulation Conference (ANNSIM 2021)

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.

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.

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.

This paper describes a simulation study on security attacks over Distributed Ledger Technologies (DLTs). We specifically focus on attacks at the underlying peer-to-peer layer of these systems, that is in charge of disseminating messages containing data and transaction to be spread among all participants. In particular, we consider the Sybil attack, according to which a malicious node creates many Sybils that drop messages coming from a specific attacked node, or even all messages from honest nodes. Our study shows that the selection of the specific dissemination protocol, as well as the amount of connections each peer has, have an influence on the resistance to this attack., Comment: Proceedings of the 3rd Workshop on Cryptocurrencies and Blockchains for Distributed Systems (CryBlock 2020)

This paper describes the Distributed Ledger Network Analyzer (DiLeNA), a new software tool for the analysis of the transactions network recorded in Distributed Ledger Technologies (DLTs). The set of transactions in a DLT forms a complex network. Studying its characteristics and peculiarities is of paramount importance, in order to understand how users interact in the distributed ledger system. The tool design and implementation is introduced and some results are provided. In particular, the Bitcoin and Ethereum blockchains, i.e. the most famous and used DLTs at the time of writing, have been analyzed and compared., Comment: Proceeding of the 3rd Workshop on Cryptocurrencies and Blockchains for Distributed Systems (CryBlock 2020)

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)., Comment: To appear in the Proceedings of the 25th IEEE Symposium on Computers and Communications (ISCC 2020)

The aim of this paper is to understand whether Distributed Ledger Technologies (DLTs) are ready to support complex services, such as those related to Intelligent Transportation Systems (ITS). In smart transportation services, a huge amount of sensed data is generated by a multitude of vehicles. While DLTs provide very interesting features, such as immutability, traceability and verifiability of data, some doubts on the scalability and responsiveness of these technologies appear to be well-founded. We propose an architecture for ITS that resorts to DLT features. Moreover, we provide experimental results of a real test-bed over IOTA, a promising DLT for IoT. Results clearly show that, while the viability of the proposal cannot be rejected, further work is needed on the responsiveness of DLT infrastructures., Comment: Proceedings of the 3rd Workshop on Cryptocurrencies and Blockchains for Distributed Systems (CryBlock 2020)

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., Comment: Proceedings of the IEEE Consumer Communications and Networking Conference 2020 (CCNC 2020)

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., Comment: Proceedings of the IEEE Consumer Communications and Networking Conference 2020 (CCNC 2020)

In this paper we focus on Internet-based simulation, a form of distributed simulation in which a set of execution units that are physically located around the globe work together to run a simulation model. This setup is very challenging because of the latency/variability of communications. Thus, clever mechanisms must be adopted in the distributed simulation, such as the adaptive partitioning of the simulated model and load balancing strategies among execution units. We simulate a wireless model over a real Internet-based distributed simulation setup, and evaluate the scalability of the simulator with and without the use of adaptive strategies for both communication overhead reduction and load-balancing enhancement. The results confirm the viability of our approach to build Internet-based simulations., Comment: Proceedings of the Proceedings of the 2019 Winter Simulation Conference (WSC 2019)

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.

In this paper, we describe LUNES-Blockchain, an agent-based simulator of blockchains that is able to exploit Parallel and Distributed Simulation (PADS) techniques to offer a high level of scalability. To assess the preliminary implementation of our simulator, we provide a simplified modelling of the Bitcoin protocol and we study the effect of a security attack on the consensus protocol in which a set of malicious nodes implements a filtering denial of service (i.e. Sybil Attack). The results confirm the viability of the agent-based modelling of blockchains implemented by means of PADS., Comment: Proceedings of the 19-th Asia Simulation Conference (AsiaSim 2019)

Crowdfunding has become a popular form of collective funding, in which small donations or investments, made by groups of people, support the development of new projects in exchange of free products or different types of recognition. Social network sites, on the other hand, promote user cooperation and currently are at the basis of any individuals cyber-interactions. In this paper, we present LikeStarter, a blockchain-based decentralized platform that combines social interactions with crowdfunding mechanisms, allowing any user to raise funds while becoming popular in the social network. Being built over the Ethereum blockchain, LikeStarter is structured as a Decentralized Autonomous Organization (DAO), that fosters crowdfunding without the intervention of any central authority, and recognizes the active role of donors, enabling them to support artists or projects, while making profits., Comment: Proceedings of the 2st Workshop on Cryptocurrencies and Blockchains for Distributed Systems (CryBlock'19). Paris, France, 29 April, 2019