Mirko Viroli

• Associate Professor
• University of Bologna

In the last years, Federated learning (FL) has become a popular solution to train machine learning models in domains with high privacy concerns. However, FL scalability and performance face significant challenges in real-world deployments where data across devices are non-independently and identically distributed (non-IID). The heterogeneity in dat...

Today's distributed and pervasive computing addresses large-scale cyber-physical ecosystems, characterised by dense and large networks of devices capable of computation, communication and interaction with the environment and people. While most research focuses on treating these systems as “composites” (i.e., heterogeneous functional complexes), rec...

In recent advancements in machine learning, federated learning allows a network of distributed clients to collaboratively develop a global model without needing to share their local data. This technique aims to safeguard privacy, countering the vulnerabilities of conventional centralized learning methods. Traditional federated learning approaches o...

Today's distributed and pervasive computing addresses large-scale cyber-physical ecosystems, characterised by dense and large networks of devices capable of computation, communication and interaction with the environment and people. While most research focusses on treating these systems as "composites" (i.e., heterogeneous functional complexes), re...

This chapter addresses coordination challenges in emerging application scenarios, such as cyber-physical systems (CPSs), the Internet of Things (IoT), and edge computing, through a Fluidware-based approach to field-based coordination, showing how data structures distributed in space and time (fields) can be extended towards fluidity. In time, we ch...

The objective of this chapter is to present the vision and overall structure of Fluidware, an approach toward an innovative architectural and programming model to ease the development of flexible and robust large-scale IoT services and applications. The key distinctive idea of Fluidware is to abstract collectives of devices of the IoT fabric as sou...

This chapter describes the Fluidware architecture with emphasis on its two main features, namely a truly full-distributed computing approach and a middleware component devoted to the actual creation, allocation, and management of funnels to computational nodes and to their deployment across the IoT-Edge-Cloud continuum. Particularly, the pulverizat...

In collective systems, a multitude of computational agents coordinate to achieve a system goal beyond their individual capabilities. These systems are typically deployed in dynamic and partially unknown environments, where system designers cannot anticipate all potential situations, events, and faults that agents may experience. For this reason, su...

Self-organisation and collective adaptation are highly desired features for several kinds of large-scale distributed systems including robotic swarms, computational ecosystems, wearable collectives, and Internet-of-Things systems. These kinds of distributed processes, addressing functional and non-functional aspects of complex socio-technical syste...

A recurrent task in coordinated systems is managing (estimating, predicting, or controlling) signals that vary in space, such as distributed sensed data or computation outcomes. Especially in large-scale settings, the problem can be addressed through decentralised and situated computing systems: nodes can locally sense, process, and act upon signal...

Multi Agent Reinforcement Learning (MARL) is an emerging field in machine learning where multiple agents learn, simultaneously and in a shared environment, how to optimise a global or local reward signal. MARL has gained significant interest in recent years due to its successful applications in various domains, such as robotics, IoT, and traffic co...

Swarm behaviour engineering is an area of research that seeks to investigate methods for coordinating computation and action within groups of simple agents to achieve complex global goals like collective movement, clustering, and distributed sensing. Despite recent progress in the study and engineering of swarms (of drones, robots, vehicles), there...

Recent trends like the Internet of Things (IoT) suggest a vision of dense and multi-scale deployments of computing devices in nearly all kinds of environments. A prominent engineering challenge revolves around programming the collective adaptive behaviour of such computational ecosystems. This requires abstractions able to capture concepts like ens...

Recent works in contexts like the Internet of Things (IoT) and large-scale Cyber-Physical Systems (CPS) propose the idea of programming distributed systems by focussing on their global behaviour across space and time. In this view, a potentially vast and heterogeneous set of devices is considered as an "aggregate" to be programmed as a whole, while...

Supported by current socio-scientific trends, programming the global behaviour of whole computational collectives makes for great opportunities, but also significant challenges. Recently, aggregate computing has emerged as a prominent paradigm for so-called collective adaptive systems programming. To shorten the gap between such research endeavours...

The Internet of Things and edge computing are fostering a future of ecosystems hosting complex decentralized computations, deeply integrated with our very dynamic environments. Digitalized buildings, communities of people, and cities will be the next-generation “hardware and platform,” counting myriads of interconnected devices, on top of which int...

A recurrent task in coordinated systems is managing (estimating, predicting, or controlling) signals that vary in space, such as distributed sensed data or computation outcomes. Especially in large-scale settings, the problem can be addressed through decentralised and situated computing systems: nodes can locally sense, process, and act upon signal...

Research trends are pushing artificial intelligence (AI) across the Internet of Things (IoT)–edge–fog–cloud continuum to enable effective data analytics, decision making, as well as the efficient use of resources for QoS targets. Approaches for collective adaptive systems (CASs) engineering, such as aggregate computing, provide declarative programm...

Swarm intelligence leverages collective behaviours emerging from interaction and activity of several “simple” agents to solve problems in various environments. One problem of interest in large swarms featuring a variety of sub-goals is swarm clustering, where the individuals of a swarm are assigned or choose to belong to zero or more groups, also c...

Recent trends in pervasive computing promote the vision of Collective Adaptive Systems (CASs): large-scale collections of relatively simple agents that act and coordinate with no central orchestrator to support distributed applications. Engineering global behaviour out of local activity and interaction, however, is a difficult task, typically addre...

A recurrent task in coordinated systems is managing (estimating, predicting, or controlling) signals that vary in space, such as distributed sensed data or computation outcomes. Especially in large-scale settings, the problem can be addressed through decentralised and situated computing systems: nodes can locally sense, process, and act upon signal...

The engineering of large-scale cyber-physical systems (CPS) increasingly relies on principles from self-organisation and collective computing, enabling these systems to cooperate and adapt in dynamic environments. CPS engineering also often leverages digital twins that provide synchronised logical counterparts of physical entities. In contrast, sen...

Emerging application scenarios, such as cyber-physical systems (CPSs), the Internet of Things (IoT), and edge computing, call for coordination approaches addressing openness, self-adaptation, heterogeneity, and deployment agnosticism. Field-based coordination is one such approach, promoting the idea of programming system coordination declaratively...

A key goal of edge computing is to achieve “distributed sensing” out of data continuously generated from a multitude of interconnected physical devices. The traditional approach is to gather information into sparse collector devices by relying on hop-by-hop accumulation, but issues of reactivity and fragility naturally arise in scenarios with high...

Field-based coordination is a model for expressing the coordination logic of large-scale adaptive systems, composing functional blocks from a global perspective. As for any coordination model, a proper toolchain must be developed to support its adoption across all development phases. Under this point of view, the ScaFi toolkit provides a coordinati...

Space and time are key elements for many computer-based systems and often elevated to first-class abstractions. In tuple-based coordination, Linda primitives have been independently extended with space (with tuples and queries spanning spatial regions) or time information (mostly for tuple scoping). However, recent works in collective adaptive syst...

Research and technology developments on autonomous agents and autonomic computing promote a vision of artificial systems that are able to resiliently manage themselves and autonomously deal with issues at runtime in dynamic environments. Indeed, autonomy can be leveraged to unburden humans from mundane tasks (cf. driving and autonomous vehicles), f...

Collecting statistics from graph-based data is an increasingly studied topic in the data mining community. We argue that they can have great value in the coordination of dynamic IoT systems as well, especially to support complex coordination strategies related to distributed situation recognition. Thanks to a mapping to the field calculus, a distri...

Cyber-physical systems increasingly feature highly-distributed and mobile deployments of devices spread over large physical environments: in these contexts, it is generally very difficult to engineer trustworthy critical services, mostly because formal methods generally hardly scale with the number of involved devices, especially when faults, conti...

Edge computing promotes the execution of complex computational processes without the cloud, i.e., on top of the heterogeneous, articulated, and possibly mobile systems composed of IoT and edge devices. Such a pervasive smart fabric augments our environment with computing and networking capabilities. This leads to a complex and dynamic ecosystem of...

Emerging application scenarios, such as cyber-physical systems (CPSs), the Internet of Things (IoT), and edge computing, call for coordination approaches addressing openness, self-adaptation, heterogeneity, and deployment agnosticism. Field-based coordination is one such approach, promoting the idea of programming system coordination declaratively...

Recent works in contexts like the Internet of Things (IoT) and large-scale Cyber-Physical Systems (CPS) propose the idea of programming distributed systems by focussing on their global behaviour across space and time. In this view, a potentially vast and heterogeneous set of devices is considered as an "aggregate" to be programmed as a whole, while...

Emerging cyber-physical systems, such as robot swarms, crowds of augmented people, and smart cities, require well-crafted self-organizing behavior to properly deal with dynamic environments and pervasive disturbances. However, the infrastructures providing networking and computing services to support these systems are becoming increasingly complex,...

A recently proposed approach to the rigorous engineering of collective adaptive systems is the aggregate computing paradigm, which operationalises the idea of expressing collective adaptive behaviour by a global perspective as a functional composition of dynamic computational fields (i.e., structures mapping a collection of individual devices of a...

Many Collect ive Adaptive Systems (CASs) exist in nature: think of ant colonies, where large collectives of ants operate autonomously but interact with other ants and the environment to provide resilient global behaviors that sustain their colony. Following scientific studies that were aimed at understanding and predicting the evolution of these sy...

In software engineering, knowledge about recurrent problems, along with blueprints of associated solutions for diverse design contexts, are often captured in so-called design patterns. Identifying design patterns is particularly valuable for novel and still largely unexplored application contexts such as the Internet of Things, Cyber-Physical Syste...

Emerging application scenarios, such as cyber-physical systems (CPSs), the Internet of Things (IoT), and edge computing, call for coordination approaches addressing openness, self-adaptation, heterogeneity, and deployment agnosticism. Field-based coordination is one such approach, promoting the idea of programming system coordination declaratively...

One of the key coordination problems in physically-deployed distributed systems, such as mobile robots, wireless sensor networks, and IoT systems in general, is to provide notions of “distributed sensing” achieved by the strict, continuous cooperation and interaction among individual devices. An archetypal operation of distributed sensing is data s...

Field-based coordination has been proposed as a model for coordinating collective adaptive systems, promoting a view of distributed computations as functions manipulating data structures spread over space and evolving over time, called computational fields. The field calculus is a formal foundation for field computations, providing specific constru...

We advocate a novel concept of dependable intelligent edge systems (DIES) i.e., the edge systems ensuring a high degree of dependability (e.g., security, safety, and robustness) and autonomy because of their applications in critical domains. Building DIES entail a paradigm shift in architectures for acquiring, storing, and processing potentially la...

Aggregate computing is an emerging approach to the engineering of complex coordination for distributed systems, based on viewing system interactions in terms of information propagating through collectives of devices, rather than in terms of individual devices and their interaction with their peers and environment. The foundation of this approach is...

Runtime verification is a computing analysis paradigm based on observing a system at runtime (to check its expected behaviour) by means of monitors generated from formal specifications. Distributed runtime verification is runtime verification in connection with distributed systems: it comprises both monitoring of distributed systems and using distr...

Runtime verification is a computing analysis paradigm based on observing a system at runtime (to check its expected behaviour) by means of monitors generated from formal specifications. Distributed runtime verification is runtime verification in connection with distributed systems: it comprises both monitoring of distributed systems and using distr...

The objective of this paper is to present the vision and structure of Fluidware, an approach towards an innovative programming model to ease the development of flexible and robust large-scale IoT services and applications. The key distinctive idea of Fluidware is to abstract collectives of devices of the IoT fabric as sources, digesters, and target...

Design patterns are key in software engineering, for they capture the knowledge of recurrent problems and associated solutions in specific design contexts. Emerging distributed computing scenarios, such as the Internet of Things, Cyber-Physical Systems, and Edge Computing, define a novel and still largely unexplored application context, where ident...

Engineering distributed applications and services in emerging and open computing scenarios like the Internet of Things, cyber-physical systems and pervasive computing, calls for identifying proper abstractions to smoothly capture collective behaviour, adaptivity, and dynamic injection and execution of concurrent distributed activities. Accordingly,...

Recent work in the area of coordination models and collective adaptive systems promotes a view of distributed computations as functions manipulating computational fields (data structures spread over space and evolving over time), and introduces the field calculus as a formal foundation for field computations. With the field calculus, evolution (tim...

Technological advances have recently fostered the Internet of Things vision, in which systems of situated entities perceive and act upon the world, and interact with one another to provide novel kinds of services, which are inherently cyber-physical, increasingly contextual and opportunistic in nature, and possibly span different scales and domains...

Computational fields have been proposed as an effective abstraction to fill the gap between the macro-level of distributed systems (specifying a system’s collective behaviour) and the micro-level (individual devices’ actions of computation and interaction to implement that collective specification), thereby providing a basis to better facilitate th...

The impressive development of wearable computing and augmented/mixed reality technologies that has been occurring in recent years allows for devising ICT systems that can bring a disruptive innovation in how emergency medical operations take place. In this paper the authors describe first explorations in that direction, represented by a distributed...

Aggregate Computing is a promising paradigm for coordinating large numbers of possibly situated devices, typical of scenarios related to the Internet of Things, smart cities, drone coordination, and mass urban events. Currently, little work has been devoted to study and improve security in aggregate programs, and existing works focus solely on appl...

In recent years, we are witnessing a growing interest in large-scale situated systems, such as those falling under the umbrella of pervasive computing, Cyber-Physical Systems, and the Internet of Things. The actor model is a natural choice for designing and implementing such systems, thanks to the ability of actors to address distribution, autonomy...

The Internet of Things (IoT) is emerging as a ubiquitous and dense ecosystem in which novel devices and smart objects interoperate to establish smart cities, smart buildings, etc. In such application contexts, a plethora of innovative services are expected to stand out, deeply impacting our daily routine. In particular, real IoT drivers will be cyb...

Recent trends such as the Internet of Things and pervasive computing demand for novel engineering approaches able to support the specification and scalable runtime execution of adaptive behaviour for large collections of interacting devices. Aggregate Computing is one such approach, formally founded in the field calculus, which enables programming...

Scenarios like wireless network networks, Internet of Things, and pervasive computing, promote full distribution of computation as well as opportunistic, peer-to-peer interactions between devices spread in the environment. In this context, computing estimated distances between devices in the network is a key component, commonly referred to as the g...

We introduce Spatial Tuples, an extension of the basic tuple‐based model for distributed multi‐agent system coordination where (a) tuples are conceptually placed in regions of the physical world and possibly move anchored to a mobile computational device, (b) the behaviour of standard Linda coordination primitives is extended so as to depend on the...

Collecting statistic from graph-based data is an increasingly studied topic in the data mining community. We argue that these statistics have great value as well in dynamic IoT contexts: they can support complex computational activities involving distributed coordination and provision of situation recognition. We show that the HyperANF algorithm fo...

Collecting statistic from graph-based data is an increasingly studied topic in the data mining community. We argue that these statistics have great value as well in dynamic IoT contexts: they can support complex computational activities involving distributed coordination and provision of situation recognition. We show that the HyperANF algorithm fo...

Recent trends such as the Internet of Things and pervasive computing demand for novel engineering approaches able to support the specification and scalable runtime execution of adaptive behaviour of large collections of interacting devices. Aggregate computing is one such approach, formally founded in the field calculus, which enables programming o...

Services have a paramount importance for unfolding the potential of the future Internet of Things (IoT), a dense, open, cyberphysical ecosystem in which humans, conventional computing systems and daily objects straightforwardly interoperate. By summarizing our previous contributions in such novel research context, this paper promotes our vision of...

Collective adaptive systems are an emerging class of networked computational systems particularly suited for application domains such as smart cities, complex sensor networks, and the Internet of Things. These systems tend to feature large-scale, heterogeneity of communication model (including opportunistic peer-to-peer wireless interaction) and re...

Collective adaptive systems are an emerging class of networked computational systems, particularly suited in application domains such as smart cities, complex sensor networks, and the Internet of Things. These systems tend to feature large scale, heterogeneity of communication model (including opportunistic peer-to-peer wireless interaction), and r...

We introduce Spatial Tuples, an extension of the basic tuplebased model for distributed multi-agent system coordination where (i) tuples are conceptually placed in the physical world and possibly move, (ii) the behaviour of coordination primitives may depend on the spatial properties of the coordinating agents, and (iii) the tuple space can be conc...

A key problem when coordinating the behaviour of spatially situated networks, like those typically found in the Internet of Things (IoT), is adaptation to changes impacting network topology, density, and heterogeneity. Computational goals for such systems, however, are often dependent on geometric properties of the continuous environment in which t...

Internet of Thing (IoT) is transforming our physical world into a giant information system, daily providing novel, advanced, cyberphysical services. Differently from conventional computing services (e.g., web-services, and ubiquitous services) that are usually loosely impacted by context-awareness, co-location or transience, Internet of Things (IoT...

With the constant increase in the number of interconnected devices in today networks, more and more computations can be described by spatial computing abstractions. In this context, distances can be estimated in a fully-distributed way by the so-called gradient self-organisation pattern: it is a basic building block also for large-scale system coor...

The impressive development of wearable computing and augmented/mixed reality technologies that has been occurring in recent years allows for devising ICT systems that can bring a disruptive innovation in how emergency medical operations take place. In this paper the authors describe first explorations in that direction, represented by a distributed...