Crowd management is the problem of dealing with physical crowds of humans, e.g., for safety reasons or to improve services through crowd-awareness. Information and communication technology can be of great help in supporting the monitoring and management of crowds. Following the “digital twin concept”, a group or crowd of people – as a dynamic physical entity – is amenable to be represented and managed through digital models. Accordingly, in this work we present and develop a general notion of a Crowd Digital Twin (CDT) , intended as the digital twin associated to physical gatherings of humans. We motivate it as a fundamental component of crowd management systems, and investigate the functional and non-functional requirements that such a technical solution should satisfy. Then, we consider what the fundamental components of CDT systems are, and accordingly delineate a reference architecture aimed at flexibly supporting the corresponding physical-digital thread. We also discuss methods and tools that might be useful for supporting implementations of such a CDT concept, and delineate a set of challenges that may serve as a roadmap for future research on the topic.

Crowd management is the problem of dealing with physical crowds of humans, e.g., for safety reasons or to improve services through crowd-awareness. In previous work, the notion of a Crowd Digital Twin (CDT), namely the digital twin associated to physical gatherings of humans, was motivated and proposed as a fundamental component of crowd management systems. In this work, we better characterise the CDT notion, by investigating the functional and non-functional requirements that such a technical solution should satisfy. Then, we consider what are the fundamental components of CDT systems, and accordingly delineate a general architecture aimed at flexibly supporting the corresponding physical-digital thread.

The development of a crowd digital twin offers significant potential for enhancing public safety, urban planning, and event management. A key challenge in creating such a digital twin lies in the efficient and accurate acquisition of crowd-related data, particularly through object detection models deployed on resource-constrained devices. Through a series of experiments, we compare TinyML and Edge approaches in terms of detection accuracy, inferencing time, and resource utilization. Our findings highlight the trade-offs inherent in selecting detection models for crowd digital twin applications, underscoring the importance of aligning model choice with specific deployment needs.

Swarm behaviour engineering is an area of research that seeks to investigate methods and techniques for coordinating computation and action within groups of simple agents to achieve complex global goals like pattern formation, collective movement, clustering, and distributed sensing. Despite recent progress in the analysis and engineering of swarms (of drones, robots, vehicles), there is still a need for general design and implementation methods and tools that can be used to define complex swarm behaviour in a principled way. To contribute to this quest, this article proposes a new field-based coordination approach, called MacroSwarm, to design and program swarm behaviour in terms of reusable and fully composable functional blocks embedding collective computation and coordination. Based on the macroprogramming paradigm of aggregate computing, MacroSwarm builds on the idea of expressing each swarm behaviour block as a pure function, mapping sensing fields into actuation goal fields, e.g., including movement vectors. In order to demonstrate the expressiveness, compositionality, and practicality of MacroSwarm as a framework for swarm programming, we perform a variety of simulations covering common patterns of flocking, pattern formation, and collective decision-making. The implications of the inherent self-stabilisation properties of field-based computations in MacroSwarm are discussed, which formally guarantee some resilience properties and guided the design of the library.

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 ensembles (dynamic groups of cooperating devices) and collective tasks (joint activities carried out by ensembles). In this work, we consider collections of devices interacting with neighbours and that execute in nearly-synchronised sense-compute-interact rounds, where the computation is given by a single program mapping sensing values and incoming messages to output and outcoming messages. To support programming whole computational collectives, we propose the abstraction of a distributed collective process, which can be used to define at once the ensemble formation logic and its collective task. We formalise the abstraction in the eXchange Calculus (XC), a core functional language based on neighbouring values (maps from neighbours to values) where state and interaction is handled through a single primitive, exchange, and provide a corresponding implementation in the FCPP language. Then, we exercise distributed collective processes using two case studies: multi-hop message propagation and distributed monitoring of spatial properties. Finally, we discuss the features of the abstraction and its suitability for different kinds of distributed computing applications.

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), recent developments in fields such as self-organising systems and swarm robotics have opened up a complementary perspective: treating systems as ‘collectives’ (i.e., uniform, collaborative and self-organising groups of entities). This article explores the motivations, state of the art and implications of this ‘collective computing paradigm’ in software engineering. In particular, it discusses its peculiar challenges, implied by characteristics like distribution, situatedness, large scale and cooperative nature. These challenges outline significant directions for future research in software engineering, touching on aspects such as macro-programming, collective intelligence, self-adaptive middleware, learning/synthesis of collective behaviour, human involvement, safety and security in collective cyber-physical ecosystems.

The term Digital Twin (DT) refers to an emerging concept and technology extending the value of physical assets through the services supported by a bidirectional connection with their digital counterparts. Since its very introduction, the DT concept has been applied to empower several kinds of physical assets across multiple domains, including Industry 4.0, infrastructures, autonomous vehicles, e-health, to name a few. The rise of socio-technical systems and cyber-physical-human systems, which feature humans as key system components, has led to the extension of the DT concept to humans themselves, bringing in the notion of "human digital twin", supporting human-system integration. However, in several contexts, humans are not just isolated entities, but rather form groups or crowds. The peculiar dynamics of such groups have to be carefully considered in various scenarios like emergency management or logistics scenarios. Motivated also by recent trends in social Internet of Things and collective computing, in this paper, we propose the notion of a "crowd digital twin", meant to support services considering the crowd and for the crowd itself. Supporting a bidirectional connection with a physical crowd, this notion is shown to present peculiar requirements, opportunities, and challenges, including low-latency synchronisation, environment-driven prediction, actuation, and validation of emergent behaviour.

Massive deployments of devices across all kinds of environments pose the need for engineering their collaborative, macro-level behaviour. To address this challenge, so-called macro-programming approaches have emerged. A prominent nature-inspired example of macro-programming is aggregate computing. In aggregate computing, macroscopic and self-organising behaviour is declared as a functional manipulation of computational fields. Fields are macro-level abstractions mapping devices to values over time, whose computation details, typically based on an execution model of asynchronous sense–compute–interact rounds, are abstracted. In more than ten years of research, multiple software engineering concerns in aggregate computing systems have been investigated, addressing aspects at the level of the language, execution dynamics, middleware, and deployment. Arguably, the enabling factor for many of such investigations and results is precisely the declarative nature of the aggregate computing paradigm. In this experience report, we analyse aggregate computing through the lenses of declarative programming, and draw significant insights and perspectives related to the engineering of complex adaptive systems.

Recent research on collective adaptive systems and macro-programming has shown the importance of programming abstractions for expressing the self-organising behaviour of ensembles, large and dynamic sets of collaborating devices. These generally leverage the interplay between the execution model and the program logic to steer the global-level emergent behaviour of the system. One notable example is the aggregate process abstraction: in an asynchronous round-based computational model, it allows to specify how aggregate-level computations are spawned, take form or spread on a domain of devices, and ultimately quit. Previous presentations of aggregate processes, however, are given in the formal framework of the field calculus, requiring knowledge of its syntax and articulated semantics. To provide a more accessible and language-agnostic presentation of such an abstraction, in this paper we introduce a general formal framework of collective computational processes (CCP). Specifically, as key contribution, we model and describe the programming interface (spawn construct) and dynamics of CCPs on event structures. Furthermore, we also propose novel algorithms for efficient propagation and termination of CCPs, based on statistics on the information speed and a notion of progressive wave-like closure. Crucially, thanks to our theoretical framework, we can provide optimality guarantees for the proposed algorithms, whose performance, superior to the state of the art, is assessed by simulation. Finally, to show applicability of CCPs, we provide a case study of situated service discovery in peer-to-peer networks.

In recent years, the infrastructure supporting the execution of situated distributed computations evolved at a fast pace. Modern collective adaptive applications – as found in the Internet of Things, swarm robotics, and social computing – are designed to be executed on very diverse devices and to be deployed on infrastructures composed of devices ranging from cloud servers to wearable devices, constituting together a cloud–edge continuum. The availability of such an infrastructure opens to better resource utilisation and performance but, at the same time, introduces new challenges to software designers, as applications must be conceived to be able to adapt to changing deployment domains and conditions. In this paper, we introduce a practical framework for the development of systems based on the concept of pulverisation, meant to neatly separate business logic and deployment concerns, allowing applications to be defined independently of the infrastructure they will execute upon, thus supporting scalability. The framework is based on a domain-specific language capturing, in a declarative fashion: pulverised application components, device capabilities, resource allocation, and (runtime re-) configuration policies. The framework, implemented in Kotlin multiplatform and available as open source, is then evaluated in a small-scale real-world demo and in a city-scale simulated scenario, demonstrating the feasibility of the approach and its potential benefits in achieving better trade-offs between performance and resource utilisation.

Distributed collective systems are systems formed by homogeneous dynamic collections of devices acting in a shared environment to pursue a joint task or goal. Typical applications emerge in the context of wireless sensor networks, robot swarms, groups of wearable-augmented people, and computing infrastructures. Programming such systems is notoriously hard, due to requirements of scalability, concurrency, faults, and difficulty in making desired collective behaviour ultimately emerge: ad-hoc languages and mechanisms have been proposed threads like spatial computing, macro-programming, and field-based coordination. In this paper we present the eXchange Calculus (XC), formalising a tiny set of key mechanisms, usable across many different languages and platforms, allowing to express the overall interactive behaviour of distributed collective systems in a declarative way. In this approach, computation (executed in asynchronous rounds), communication (which is neighbour-based), and state over time, are all expressed by a single declarative construct, called exchange. We provide a formalisation of XC in terms of syntax, device-level and network-level semantics, prove a number of properties of the calculus, and discuss applicability considering a smart city scenario. XC is implemented as a DSL in Scala and in C＋＋, with different trade-offs in terms of productivity and platform targetting.

Collectiveness is an important property of many systems-both natural and artificial. By exploiting a large number of individuals, it is often possible to produce effects that go far beyond the capabilities of the smartest individuals or even to produce intelligent collective behavior out of not-so-intelligent individuals. Indeed, collective intelligence, namely, the capability of a group to act collectively in a seemingly intelligent way, is increasingly often a design goal of engineered computational systems-motivated by recent technoscientific trends like the Internet of Things, swarm robotics, and crowd computing, to name only a few. For several years, the collective intelligence observed in natural and artificial systems has served as a source of inspiration for engineering ideas, models, and mechanisms. Today, artificial and computational collective intelligence are recognized research topics, spanning various techniques, kinds of target systems, and application domains. However, there is still a lot of fragmentation in the research panorama of the topic within computer science, and the verticality of most communities and contributions makes it difficult to extract the core underlying ideas and frames of reference. The challenge is to identify, place in a common structure, and ultimately connect the different areas and methods addressing intelligent collectives. To address this gap, this article considers a set of broad scoping questions providing a map of collective intelligence research, mostly by the point of view of computer scientists and engineers. Accordingly, it covers preliminary notions, fundamental concepts, and the main research perspectives, identifying opportunities and challenges for researchers on artificial and computational collective intelligence engineering.

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 abstracting away the details of individual behaviour and exchange of messages, which are expressed declaratively. One such a paradigm, known as aggregate programming, builds on computational models inspired by field-based coordination. Existing models such as the field calculus capture interaction with neighbours by a so-called "neighbouring field" (a map from neighbours to values). This requires ad-hoc mechanisms to smoothly compose with standard values, thus complicating programming and introducing clutter in aggregate programs, libraries and domain-specific languages (DSLs). To address this key issue we introduce the novel notion of "computation against a neighbour", whereby the evaluation of certain subexpressions of the aggregate program are affected by recent corresponding evaluations in neighbours. We capture this notion in the neighbours calculus (NC), a new field calculus variant which is shown to smoothly support declarative specification of interaction with neighbours, and correspondingly facilitate the embedding of field computations as internal DSLs in common general-purpose programming languages -- as exemplified by a Scala implementation, called ScaFi. This paper formalises NC, thoroughly compares it with respect to the classic field calculus, and shows its expressiveness by means of a case study in edge computing, developed in ScaFi.

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 signals, and coordinate with neighbours to implement collective strategies. Accordingly, in this work we devise distributed coordination strategies for the estimation of a spatial phenomenon through collaborative adaptive sampling. Our design is based on the idea of dynamically partitioning space into regions that compete and grow/shrink to provide accurate aggregate sampling. Such regions hence define a sort of virtualised space that is "fluid", since its structure adapts in response to pressure forces exerted by the underlying phenomenon. We provide an adaptive sampling algorithm in the field-based coordination framework, and prove it is self-stabilising and locally optimal. Finally, we verify by simulation that the proposed algorithm effectively carries out a spatially adaptive sampling while maintaining a tuneable trade-off between accuracy and efficiency.

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 programming models and tools for dealing with the uncertainty and the complexity that may arise from scale, heterogeneity, and dynamicity. Crucially, aggregate computing architecture allows for “pulverization”: applications can be decomposed into many deployable micromodules that can be spread across the ICT infrastructure, thus allowing multiple potential deployment configurations for the same application logic. This article studies the deployment architecture of aggregate-based edge services and its implications in terms of performance and cost. The goal is to provide methodological guidelines and a model-based toolchain for the generation and simulation-based evaluation of potential deployments. First, we address this subject methodologically by proposing an approach based on deployment code generators and a simulation phase whose obtained solutions are assessed with respect to their performance and costs. We then tailor this approach to aggregate computing applications deployed onto an IoT–edge–fog–cloud infrastructure, and we develop a corresponding toolchain based on Protelis and EdgeCloudSim. Finally, we evaluate the approach and tools through a case study of edge multimedia streaming, where the edge ecosystem exhibits intelligence by self-organizing into clusters to promote load balancing in large-scale dynamic settings.

We focus on the online multi-object k -coverage problem (OMOkC), where mobile robots are required to sense a mobile target from k diverse points of view, coordinating themselves in a scalable and possibly decentralised way. There is active research on OMOkC, particularly in the design of decentralised algorithms for solving it. We propose a new take on the issue: Rather than classically developing new algorithms, we apply a macro-level paradigm, called aggregate computing , specifically designed to directly program the global behaviour of a whole ensemble of devices at once. To understand the potential of the application of aggregate computing to OMOkC, we extend the Alchemist simulator (supporting aggregate computing natively) with a novel toolchain component supporting the simulation of mobile robots. This way, we build a software engineering toolchain comprising language and simulation tooling for addressing OMOkC. Finally, we exercise our approach and related toolchain by introducing new algorithms for OMOkC; we show that they can be expressed concisely, reuse existing software components and perform better than the current state-of-the-art in terms of coverage over time and number of objects covered overall.

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 from a global perspective, in terms of functional manipulation and evolution in "space and time" of distributed data structures called fields. More specifically regarding time, in field-based coordination (as in many other distributed approaches to coordination) it is assumed that local activities in each device are regulated by a fair and unsynchronised fixed clock working at the platform level. In this work, we challenge this assumption, and propose an alternative approach where scheduling is programmed in a natural way (along with usual field-based coordination) in terms of causality fields, each enacting a programmable distributed notion of a computation "cause" (why and when a field computation has to be locally computed) and how it should change across time and space. Starting from low-level platform triggers, such causality fields can be organised into multiple layers, up to high-level, collectively-computed time abstractions, to be used at the application level. This reinterpretation of time in terms of articulated causality relations allows us to express what we call "time-fluid" coordination, where scheduling can be finely tuned so as to select the triggers to react to, generally allowing to adaptively balance performance (system reactivity) and cost (resource usage) of computations. We formalise the proposed scheduling framework for field-based coordination in the context of the field calculus, discuss an implementation in the aggregate computing framework, and finally evaluate the approach via simulation on several case studies.

Recent works in the context of large-scale adaptive systems, such as those based on opportunistic IoT-based applications, promote aggregate programming, a development approach for distributed systems in which the collectivity of devices is directly targeted, instead of individual ones.This makes the resulting behaviour highly insensitive to network size, density, and topology, and as such, intrinsically robust to failures and changes to working conditions (e.g., location of computational load, communication technology, and computational infrastructure).Most specifically, we argue that aggregate programming is particularly suitable for building models and simulations of complex large-scale reactive MASs.Accordingly, in this paper we describe SCAFI (Scala Fields), a Scala-based API and DSL for aggregate programming, and its integration with the ALCHEMIST simulator, and usage scenarios in the context of smart mobility.