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.

Multilevel modeling is a design technique that allows complex models to be specified in terms of simpler ones. An obvious advantage of multilevel modeling is the possibility of reusing existing models, therefore reducing development cost. However, multilevel modeling entails more than the mere application of the ubiquitous principle of decomposition. Indeed, in a multilevel model the same entity may be described at different levels of detail during execution. This allows, for example, to focus on areas of interest by switching to a more detailed model, and revert back to a faster but less accurate one when possible. In this paper we propose GEMMA (GEneric Multilevel Modeling Abstraction), a methodology inspired by DEVS that can help the designer to produce sound and correct multilevel models. We propose a practical realization of GEMMA as a concrete software architecture that can be instantiated in different programming languages. We illustrate a simple use case where different sub-models written in different programming languages are combined according to the GEMMA methodology. Our ultimate goal is to bridge the gap between theory and practice of multilevel modeling, to foster a broader adoption of this useful technique.

Multilevel modeling and simulation (M&S) is emerging as a methodology for the study of complex systems that consist of multiple interacting components. While many simulation models are based on a monolithic architecture, with a single building block that reproduces all aspects of the simulation, in multilevel models different sub-components are in charge of reproducing semantically different aspects of the system. The advantages of multilevel M&S are multiple. First, a complex model built upon independent and reusable blocks might be faster to implement, as the development phase can be carried out in parallel, and to update, as changes will affect only a local portion of the model. Then, it is possible to make use of existing implementations, employing models that have already been tested and proved to be efficient for a specific goal, thus saving considerable time for implementation and the testing. Finally, a multilevel approach allows the developers to use the most suitable modeling paradigm for each part of the system. However, significant challenges exist: identifying the “best” partitioning of the model is not always straightforward, and the interaction among sub-models might be an issue. There is no definition of multilevel modeling that is agreed upon by the scientific community [1]. In fact, some authors refer to this concept with multiple terms (e.g., multiscale [2] or multi-resolution [3] modeling), while others use the expression “multilevel modeling” to indicate M&S schemes that do not necessarily require the presence of multiple cooperating models [4]. Specifically, the following types of models are referred as multilevel in the research community: •Models where different components are in charge of representing semantically different elements of a system [5]. •Models where the system is represented at different levels of detail [6]. This scenario occurs when it is computationally infeasible to carry out simulations where the whole system is described at the maximum level of precision. Thus, a reasonable approach is to find an appropriate trade-off between accuracy and use of computational resources, for leveraging the benefits of both fine-grained and coarse-grained simulators. In these frameworks, accurate micro models describing the most important parts of the simulation coexist with macro models that represent the simulated system with a greater focus on computational efficiency, at the cost of a reduced accuracy. Usually, these types of models are referred to as multiscale models. •Logistic regression models that take information at both individual and aggregate level (e.g., individual and aggregate) [7].

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 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.

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.

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.