Digital Twins in an Ecosystem

Disclaimer: This thesis chapter has been generated using chitragupta. Despite some potential for hallucination, the ideas communicated in this thesis chapter are accurate. Please send your corrections and suggestions to prasad.talasila@gmail.com

From the isolated twin to the ecosystem

Almost every canonical account of the digital twin describes a pair. There is a physical thing, there is a virtual counterpart, and there is a data connection that keeps the second faithful to the first. The five-dimension formulation makes this explicit by naming physical entity, virtual entity, services, data and connections as the constituents of a twin (Tao et al. 2019), and the classification by degree of data integration --- digital model, digital shadow, digital twin --- turns the quality of that single connection into the criterion that separates a twin from its weaker relatives (Kritzinger et al. 2018). The literature that grew around this pair is large, cross-domain and, by now, reasonably well mapped (Semeraro et al. 2021; Dalibor et al. 2022).

The pair, however, is not how twins are encountered in practice once more than one of them exists. A production machine has a twin; so does the line it sits in, the factory that contains the line, the product moving through it, the worker operating it, and the energy contract that prices its consumption. Each of these was very likely built by a different team, using a different toolchain, against a different metamodel, for a different purpose, and --- in the cases that matter most --- under a different owner. The interesting questions stop being about how well one twin mirrors one asset and start being about what happens when many twins have to coexist, exchange information, depend on one another and be assembled into something larger. That shift is what this chapter calls the move from the twin to the ecosystem.

Several research communities arrived at this observation independently and at roughly the same time. Ricci et al. named the phenomenon the Web of Digital Twins and argued that the dominant view in the literature had been "about the virtualisation of individual physical assets in a closed-system perspective", proposing instead an open, distributed and dynamic ecosystem of connected twins acting as an interoperable service-oriented layer for the applications above it (Ricci et al. 2022). Giulianelli et al. put the engineering consequence bluntly: ecosystems virtualise "a multitude of heterogeneous but interrelated physical assets --- possibly belonging to different domains", and at the centre of the resulting challenge sits the capability of integrating twins developed with heterogeneous technologies (Giulianelli et al. 2024). Kuruppuarachchi et al. approached it from the industrial-collaboration side, contrasting the discrete twin that "can create value without depending on other systems" with the composite twin that spans systems and business boundaries, and listing trust, interoperability, governance, ownership, security and privacy as the obstacles to the latter (Kuruppuarachchi, Rea, and McGibney 2022). Michael et al. framed it as a systems-of-systems problem and observed that research on twins as systems-of-systems "largely ignores reusing digital twins" (Michael et al. 2022). Healthcare arrived at the same place by a different road, with the Virtual Human Twin described as "a distributed and collaborative infrastructure" assembled from technologies, data and models contributed by many parties (Viceconti et al. 2024).

The economic pressure behind this is not subtle. Building a single twin requires marshalling a heterogeneous collection of models, data sources, tools and services, and coordinating them is itself a substantial engineering task (Talasila et al. 2024). If every twin is built from scratch, the cost per twin does not fall, and the technology stays what Niederer et al. called artisanal rather than industrial (Niederer et al. 2021). Reuse is the only route to scale, and reuse across teams and organisations is precisely what an ecosystem is for.

Research question

This chapter is organised around one question:

RQ: When digital twins cease to be isolated artefacts and become members of an ecosystem, what capabilities must the surrounding infrastructure provide --- with respect to interoperability, networking, collaboration and hierarchical composition --- and where do current approaches fall short of providing them?

The four terms in that list are not an arbitrary partition. They correspond to four distinct relations that can hold between twins, and the literature treats them as four distinct research programmes with only partially overlapping membership. Interoperability concerns whether two twins can understand each other. Networking concerns whether they can reach each other, and at what cost in latency, bandwidth and fidelity. Collaboration concerns whether they may exchange anything at all, given that they answer to different owners with different interests. Hierarchical composition concerns whether a set of twins can be treated as a single twin at a coarser granularity, and what that new twin is entitled to claim about the system it represents.

A recurring argument of the chapter is that these four are not independent. Interoperability is usually posed as a data-representation problem, but the choice of representation determines what can be composed hierarchically; networking is usually posed as a performance problem, but placement decisions across the edge--cloud continuum decide which collaborations are even feasible; and collaboration is usually posed as a trust problem, but the mechanisms that establish trust --- policy-governed exchange, decentralised identity, provenance --- are exactly the mechanisms that make a composed twin auditable. Treating them separately is how ecosystems end up with four partial solutions that do not fit together.

A running example

Abstract arguments about ecosystems are easy to agree with and hard to test, so the chapter uses one scenario throughout. It is deliberately mundane, and deliberately spans ownership boundaries.

A contract manufacturer operates a production cell. The cell contains a six-axis robot supplied by a robot vendor, a conveyor supplied by a second vendor, and a vision system supplied by a third. The robot vendor ships a twin of the robot as part of its service contract: it carries a kinematic and dynamic model, wear estimates and a maintenance-scheduling service, and the vendor considers the model itself commercially sensitive. The manufacturer has built its own twin of the cell for cycle-time optimisation, and its customer --- an automotive tier-one supplier --- maintains a twin of the product being assembled, for quality traceability. The building the cell sits in has a twin too, operated by the facility owner, which prices energy and manages HVAC. The grid operator would like a twin-level view of the building's flexible load.

Nothing in this arrangement is exotic; variants of it appear throughout the manufacturing and value-chain literature (Bhandal 2024; Gleich et al. 2024; Altamiranda 2024). But every question the chapter asks has a concrete form in it. Can the manufacturer's cell twin interpret the robot twin's wear estimate, given that the two were described in different formats (Section 1.3{reference-type="ref" reference="sec:dte-interop"})? What happens to the cell twin's cycle-time predictions when the plant network drops for eleven seconds and the robot twin's state goes stale (Section 1.4{reference-type="ref" reference="sec:dte-network"})? On what basis does the robot vendor allow its twin to answer the manufacturer's queries without disclosing the model behind them, and what does the tier-one supplier's audit require (Section 1.5{reference-type="ref" reference="sec:dte-collab"})? And is the manufacturer's cell twin --- which aggregates the robot, conveyor and vision twins --- entitled to claim it represents the cell, when the robot twin was validated only for the vendor's own duty cycle (Section 1.6{reference-type="ref" reference="sec:dte-hierarchy"})?

The last question is the one that recurs. It is the easiest to state, the hardest to answer, and the one on which the practical value of the other three depends.

Structure of the chapter

Section 1.2{reference-type="ref" reference="sec:dte-terminology"} takes an inventory of the vocabulary, because the same phenomenon currently travels under at least eight names, and the differences between them are sometimes substantive and sometimes not. Sections 1.3{reference-type="ref" reference="sec:dte-interop"}--1.6{reference-type="ref" reference="sec:dte-hierarchy"} treat interoperability, networking, collaboration and hierarchical composition in turn, each closing with an assessment of what is settled and what is not. Section 1.7{reference-type="ref" reference="sec:dte-synthesis"} synthesises the four into a capability model that answers the research question directly, and Section 1.8{reference-type="ref" reference="sec:dte-agenda"} states the open problems that the synthesis exposes, including three where the literature consulted for this chapter offers no answer at all.

What is a digital twin ecosystem?

An inventory of names

The phenomenon described in Section 1.1{reference-type="ref" reference="sec:dte-intro"} is not short of labels. The following are all in current use, and the differences between them repay attention.

Ecosystem of digital twins.

The most common term, used for a set of twins that are connected, heterogeneous, and not centrally designed. It carries a deliberate biological connotation: members join and leave, adapt to one another, and are not all built by the same party. The healthcare operating suite of Burattini et al. (2023) and the industrial ecosystem of Martinelli et al. (2024) both use the word in this sense. An unfortunate collision arises in environmental applications, where the twinned subject matter is itself an ecological ecosystem, as in the Mar Menor lagoon platform (Ye et al. 2024); the word then denotes the referent rather than the software structure.

Web of Digital Twins (WoDT).

A stronger claim than "ecosystem": it commits to openness, to hypermedia-style discovery, and to the twins forming a service-oriented layer that applications --- including multi-agent systems --- run on top of (Ricci et al. 2022). The commitment is architectural, not merely metaphorical, and it has been carried into concrete toolchains (Giulianelli et al. 2024, 2025).

Digital twin federation.

Emphasises that members retain autonomy and local authority while participating in a larger whole. Marah and Challenger define it as "a unified, integrated, interconnected, interoperable, and evolving ecosystem that couples and comprises a group of digital twins" and explicitly draw the lineage from the High Level Architecture for distributed simulation (Marah and Challenger 2025). The term travels well into settings with real organisational boundaries, such as urban mobility assessment across municipalities (J. Li et al. 2025) and federated scientific computing infrastructure ("[interTwin]{.nocase}: Advancing Scientific Digital Twins Through AI, Federated Computing and Data," n.d.).

Composite digital twin (CDT).

Used where the emphasis is on crossing business boundaries to produce a single logical twin from parts belonging to different stakeholders, with the attendant trust and governance requirements (Kuruppuarachchi, Rea, and McGibney 2022).

Digital twin system-of-systems.

The software-engineering framing, in which twins of smaller systems become parts of, or communicate with, twins of larger systems, and the classical systems-of-systems concerns about independently developed and independently evolving constituents apply (Michael et al. 2022; Altamiranda 2024).

System of digital twins and services.

Human et al.'s term for an aggregated set of twins coupled with a services network, chosen to keep the services --- not only the twins --- in view as first-class design objects (Human, Basson, and Kruger 2023).

Digital twin continuum / fluid computing.

Framings that foreground the deployment substrate: twins spread across a device--edge--cloud continuum, migrating as conditions change (Barbone et al. 2024; Bedogni et al. 2025).

Digital twin constellation.

Used on the DTaaS platform for the orchestration of components toward the business goals of a twin (Talasila et al. 2025).

Consortium glossaries have attempted to fix some of this vocabulary ("Glossary of Digital Twins by Digital Twin Consortium" n.d.), and the Digital Twin Consortium's interoperability framework leans on the older notions of the digital thread and of system-of-systems to do so (Budiardjo and Migliori 2021). The attempts have not yet converged, and the disagreement is not purely verbal: "federation" implies retained sovereignty, "composite" implies a single resulting twin, and "web" implies open discovery. A design that satisfies one does not automatically satisfy the others.

::: {#tab:dte-terms}

Term Distinctive commitment Representative source

Ecosystem of DTs Heterogeneous, not centrally (Burattini et al. designed, members join and 2023) leave

Web of Digital Open discovery, hypermedia, (Ricci et al. 2022) Twins consumed by agents

DT federation Members retain local (Marah and authority and sovereignty Challenger 2025)

Composite DT Parts from several owners (Kuruppuarachchi, yield one logical twin Rea, and McGibney 2022)

DT Independently developed, (Michael et al. system-of-systems independently evolving 2022) constituents

System of DTs and Services kept as first-class (Human, Basson, and services design objects Kruger 2023)

DT continuum Deployment substrate spans (Barbone et al. device, edge and cloud 2024)

DT constellation Components orchestrated (Talasila et al. toward one twin's business 2025) goal

: Terms in current use for collections of digital twins, and the commitment each one carries beyond "more than one twin". :::

A working definition

For the purposes of this chapter, a digital twin ecosystem is a set of digital twins, built and operated by more than one party, that are able to discover one another, exchange information with defined semantics, and be combined into larger twins or twin-backed services without being redesigned.

Three clauses in that definition are doing work. "More than one party" rules out the case where a single vendor's platform hosts many twins under one authority --- that is a large deployment, not an ecosystem, and it does not face the governance problems that dominate Section 1.5{reference-type="ref" reference="sec:dte-collab"}. "Defined semantics" rules out the case where twins merely exchange bytes; as Combemale et al. observe, if plug-and-play capability "stays at the technical level" then "we lose the semantics" of what was exchanged (Combemale et al. 2025). "Without being redesigned" is the economic clause: an assembly that requires each participating twin to be rewritten is an integration project, not an ecosystem.

Four relations between twins

Given that definition, four relations can hold between two twins $A$ and $B$, and they map onto the four sections that follow.

$A$ can interpret $B$'s description and state. This is interoperability, and it decomposes further into technical, syntactic, semantic, pragmatic, dynamic and organisational levels, a stratification that Combemale et al. adopt from a systematic review of the field (Combemale et al. 2025).
$A$ can reach $B$ within a bounded time. This is the network relation, and it is not a given: twins are real-time-coupled artefacts whose usefulness degrades, sometimes discontinuously, when the coupling does (Vaezi et al. 2022; Frasheri et al. 2023).
$A$ is permitted to obtain what it obtains from $B$, and $B$ is willing to provide it. This is the collaboration relation, and it is where ownership, intellectual property, privacy and liability enter (Kuruppuarachchi, Rea, and McGibney 2022; Michael et al. 2022).
$A$ and $B$, together, constitute a twin $C$ of the composite physical system. This is the composition relation, and it is by some distance the least well understood of the four (Michael et al. 2022; Gill et al. 2024).

A disagreement worth stating early

Before proceeding it is worth naming a fault line that runs through the whole of what follows. One camp treats ecosystem members as essentially passive: a twin is a queryable, subscribable representation of an asset, and the intelligence lives in the applications above the twin layer. The hierarchical industrial ecosystem of Martinelli et al. (2024) is explicit about this, describing the ecosystem as an abstraction layer that decouples applications from the management of physical assets, and the entity-based FIWARE approach of Franke and Koch (2025) shares the assumption. The other camp treats ecosystem members as agents: Rivera et al. argue that realising the vision "will demand DTs with increased autonomy and enhanced ability to monitor, reason about, and react upon relevant phenomena" and propose a reference framework for autonomic and cooperating twins (Rivera et al. 2021); Esterle et al. go further and consider autonomous cyber-physical systems that meet spontaneously and use each other's twins to negotiate safe collaboration at runtime (Esterle et al. 2021); and the Web of Digital Twins is designed explicitly to be consumed by multi-agent systems (Ricci et al. 2022).

This is not a difference of emphasis. If twins are passive, the ecosystem's hard problems are representation and plumbing. If twins are agents, the hard problems include negotiation, conflict resolution and mutual verification of claims, and none of the standardisation work discussed in Section 1.3{reference-type="ref" reference="sec:dte-interop"} addresses those. The chapter returns to the consequence in Section 1.7{reference-type="ref" reference="sec:dte-synthesis"}.

Interoperability

Levels, not a level

Interoperability is routinely spoken of as though it were a single property, and it is not. The stratification used by Combemale et al., drawn from a systematic literature review, distinguishes technical, syntactic, semantic, pragmatic, dynamic and organisational levels (Combemale et al. 2025). The distinctions matter because different parts of the literature solve different levels and then claim the word.

At the technical level, two twins share a transport: MQTT, HTTP, OPC UA. This is essentially solved and is not where ecosystems fail. At the syntactic level, they share a serialisation and a schema, so a message can be parsed. At the semantic level, they agree on what the parsed fields mean --- that this "temperature" is the coolant inlet and is measured in degrees Celsius at the point of entry. At the pragmatic level, they agree on what an exchange does: whether a setpoint message is advice, a request or a command. At the dynamic level, they can continue to agree while both sides evolve. At the organisational level, the exchange is permitted, accountable and contractually meaningful.

The bulk of the digital twin standards effort operates at the syntactic and semantic levels. Sections 1.4{reference-type="ref" reference="sec:dte-network"} and 1.5{reference-type="ref" reference="sec:dte-collab"} argue that the dynamic and organisational levels are where ecosystems actually break.

The standards landscape

The Digital Twin Consortium's interoperability framework organises the problem into seven concepts and insists on two requirements that ecosystems inherit directly: information may originate in multiple domains and so may carry "disparate syntactic and semantic standards that must be correlated within a digital thread", and all information flow must support both unidirectional and bi-directional exchange (Budiardjo and Migliori 2021). The consortium's platform stack framework similarly names a distinct service layer for integration, interoperability and synchronisation (Heaton, n.d.). These are statements of requirement rather than of mechanism; the mechanisms come from the standards themselves.

The Asset Administration Shell (AAS) is the most institutionally committed of these. Its specification adopts the design principle "keep it simple but do not simplify if it affects interoperability" ("Asset Administration Shell Part - 1," n.d.), and an ecosystem of open-source implementations and submodel templates has grown around it (Jacoby et al. 2023; Zhang et al. 2024). Zhang et al. examine the three AAS types against requirements extracted from popular digital twin definitions and conclude that each type contributes to engineering specific twin components, while leaving open challenges for the implementation of complete twins (Zhang et al. 2024). Ellwein et al.'s systematic mapping shows that even the interaction semantics of the AAS --- what an "active" shell is entitled to do, whether it makes decisions, exercises skills or negotiates --- differ substantially across the sources that describe it (Ellwein et al. 2025). A standard whose own community disagrees about its interaction model is not yet a settled basis for pragmatic-level interoperability.

Alongside the AAS sit the Digital Twins Definition Language, OPC UA, the W3C Web of Things Thing Description, and, in manufacturing, the ISO 23247 series with its reference architecture and its dedicated Part 4 on information exchange (Larsen, Fitzgerald, and Gomes 2024; Shao and Helu 2020; Shao, Frechette, and Srinivasan 2023). Surveys of the standards relevant to twins run to catalogue length ("Summary of IoT, and DT Standards," n.d.), and new open standards continue to be proposed ("Open Digital Twin Standard," n.d.) --- which is itself the finding: the landscape is diverging, not converging.

Three strategies, and the disagreement between them

Faced with plurality, three strategies are visible in the literature, and they are genuinely in tension.

Converge.

Pick one metamodel and make everything speak it. This is the implicit programme of the AAS community, reinforced by European procurement and funding practice that increasingly asks proposals to align with relevant international standards (Zhang et al. 2024). Its attraction is obvious; its cost is that a single metamodel must serve manufacturing, healthcare, civil infrastructure and energy, and the evidence that it can is thin.

Bridge.

Accept plurality and build transformations between metamodels. Schmidt et al. implement and test a generic DTDL-to-AAS mapping, observing first that the existing standards "are incompatible with each other, i.e., they do not have the same (i) syntax, (ii) semantics" (Schmidt et al. 2023). Cavalieri and Salafia map DTDL to OPC UA on the same reasoning (Cavalieri and Gambadoro 2023). Mattila et al. go a step further and work backwards from the task: they identify the data fields actually required for machine-to-machine collaboration, check which description formats carry them, propose additions where they are missing, and demonstrate the resulting mapping by controlling an industrial machine described simultaneously as an AAS document and as a WoT Thing Description (Mattila et al. 2025). That last piece of work is the most convincing evidence in the corpus that bridging is tractable, precisely because it is requirement-driven rather than schema-driven.

Leave.

Abandon the industrial metamodels for Web architecture. Ricci et al. argue for hypermedia-style twins forming an open service layer (Ricci et al. 2022); Burattini et al. propose a core ontology plus a Linked Data approach to state management (Burattini et al. 2024); and Giulianelli et al. turn this into an engineering method and then a toolchain (Giulianelli et al. 2024, 2025). The strategy inherits the Web's strengths in discovery and evolution --- and, as the Web of Things survey documents, also inherits a fragmentation problem of its own that a decade of standardisation has not resolved (Sciullo et al. 2022).

The three strategies are not reconcilable by simply doing all of them. A bridge is only worth building if the endpoints are stable, which converge denies and leave makes irrelevant. The most defensible position visible in the evidence is that convergence should be pursued within domains, where the modelled concepts genuinely coincide, and bridging across them --- but this is an inference from the pattern of results, not a claim any single source in this corpus makes.

Does anyone follow the standards?

A finding that deserves more attention than it usually gets: Ferko et al. compared digital twin architectures documented in the literature against the ISO 23247 reference architecture and reported that, from their own project experience, "standards, and in particular the ISO 23247 standard, are not completely followed" (Ferko et al. 2023). This sits uncomfortably beside the analyses that present the same standard as a generic framework ready for specialisation to new sectors (Shao, Frechette, and Srinivasan 2023; Shao 2021). Both can be true --- a standard can be well designed and poorly adopted --- but for the ecosystem argument the adoption fact dominates the design fact. An ecosystem built on the assumption that members conform to a reference architecture will encounter members that do not.

The same gap between specification and practice appears at platform level. Pfeiffer et al.'s comparison of the modelling capabilities of commercial twin platforms finds substantial differences in what those platforms' metamodels can express (Jérôme Pfeiffer et al. 2022), and the survey of open-source frameworks reaches a similar conclusion by case study (Gil, Mikkelsen, et al. 2024). Efforts toward a unifying reference model for twins of cyber-physical systems (Jerome Pfeiffer et al. 2025) are a response to exactly this fragmentation.

Semantics: ontologies and knowledge graphs

Semantic-level interoperability in this literature almost always means ontologies. Steinmetz et al. build twin models supported by knowledge graphs and note plainly that standards for such modelling are still lacking (Steinmetz, Schroeder, Sulak, et al. 2022). Kamburjan et al. use OWL asset models not merely to describe a twin but to drive its reconfiguration, making the ontology load-bearing rather than documentary (Kamburjan et al. 2022), and extend the idea to declarative lifecycle management in which a twin's self-adaptive behaviour must accommodate physical components that "evolve independently" (Kamburjan et al. 2024). Semantic reflection lifts runtime state into a knowledge graph connected to a domain ontology, giving other parties a principled way to interrogate a twin's current condition rather than a bespoke API (Kamburjan et al. 2025). Ontology also underpins the cognitive-twin programme, where it is treated as the basis for unified knowledge description and for integrating twins across lifecycle phases (Jinzhi et al. 2022), and service-level description has been given the same treatment so that twin services can be discovered and reused rather than rediscovered (Oakes et al. 2024).

The limitation is well known and worth stating: ontologies solve semantic agreement conditional on the parties having agreed on the ontology, which relocates the coordination problem rather than dissolving it. This is why the organisational level, discussed in Section 1.5{reference-type="ref" reference="sec:dte-collab"}, cannot be treated as an afterthought.

The digital thread: interoperability across time

There is a third axis besides "across twins" and "across levels", and it is the one industrial practice cares about most: interoperability across the lifecycle. The digital thread names the requirement that information about an asset --- its design intent, as-built configuration, service history, and eventual disposal --- remain linked and interpretable as the asset moves between the organisations responsible for each phase (Budiardjo and Migliori 2021). Commercial platform vendors use precisely this framing when they describe twins spanning an organisation's ecosystem (Khoshkenar and Nassereddine 2024).

For the running example, the thread is what lets the tier-one supplier trace a quality defect back through the cell twin to the specific robot trajectory and the specific batch of material. The obstacle is that each phase typically has its own authoritative representation, and the mappings between them are lossy. This is acute in civil infrastructure, where the model architectures used at design time and those used in operation come from different traditions entirely, and interoperability solutions have had to be retrofitted (Naderi and Shojaei 2023). Michael et al. list "different lifecycle representations of the original system" as a distinct integration challenge for exactly this reason (Michael et al. 2022), and lifecycle-spanning analytics architectures have been proposed as a partial answer (Marosi et al. 2022). The IoT-side surveys make the complementary point that the technical features and architectural models available for twins differ by lifecycle stage as well as by domain (Minerva, Lee, and Crespi 2020).

The thread also has a governance face. Once information persists across lifecycle phases and organisations, provenance becomes a requirement rather than a nicety --- which is one route by which the digital product passport work (Gleich et al. 2024) and the dataspace work (Schmidt et al. 2025) converge on the same infrastructure.

Behavioural interoperability

Everything above concerns the exchange of state. Ecosystems also need the exchange of behaviour: twin $A$'s simulation must be able to be stepped in concert with twin $B$'s. This is co-simulation, and it has its own mature standards discussion, including the treatment of hybrid continuous-time and discrete-event coupling and the research needs that remain open (Schweiger et al. 2019). Michael et al. list "interoperability of models and simulation environments" as one of their fourteen integration challenges (Michael et al. 2022), and it is arguably the hardest of them: two Functional Mock-up Units may both conform to FMI and still fail to compose because of algebraic loops, incompatible step-size policies or mismatched assumptions about their environment. Tooling has begun to address the mechanics --- CoFMPy provides unified coordination of co-simulation, algebraic-loop resolution and distributed data exchange for FMI-based twins (Friedrich et al. 2025), and Spawn demonstrates automatic run-time coupling of Modelica and EnergyPlus in the buildings domain (Wetter et al. 2024) --- but the composition of validity claims, as opposed to the composition of solvers, remains open and is taken up in Section 1.6{reference-type="ref" reference="sec:dte-hierarchy"}.

Interim assessment

Technical and syntactic interoperability are solved problems dressed up as open ones. Semantic interoperability has good mechanisms and a coordination problem. Pragmatic interoperability --- agreeing on what an exchange commits the parties to --- is barely addressed outside the AAS interaction-type debate (Ellwein et al. 2025) and Combemale et al.'s warning about semantics lost to technical plug-and-play (Combemale et al. 2025). Dynamic interoperability, meaning continued agreement under independent evolution, is named as a challenge (Combemale et al. 2025; Michael et al. 2022) and has, in the material surveyed here, no worked solution.

Networked digital twins

Two readings of "network"

The phrase "digital twin network" is used for two different things, and conflating them causes confusion.

The first reading, dominant in the software-engineering literature, is twins that are networked: the communication fabric over which ecosystem members exchange state and invoke each other's services. The second, dominant in the communications literature, is twins of networks: using a twin to model, predict and optimise a telecommunications network itself. Wu et al.'s survey defines the digital twin network in the second sense, as a network that "utilizes digital twin technology to create the virtual twins of physical objects" and realises co-evolution between physical and virtual spaces (Wu, Zhang, and Zhang 2021), and the 6G literature follows suit (Khan et al. 2022; Apostolakis et al. 2023; Hakiri et al. 2024). This chapter is primarily concerned with the first reading, but the second is not irrelevant: a twin of the network is one of the more plausible ways to give an ecosystem the ability to reason about its own communication substrate.

Vaezi et al. bridge the two readings usefully. Their argument is that "accurate real-time synchronization between the features at a physical system and its DT is essential", from which it follows that "appropriate networking support is a key component to enable future DT development and applications" (Vaezi et al. 2022). The network is not infrastructure that a twin happens to sit on; it is a determinant of whether the artefact is a twin at all.

Connectivity topologies

The most useful structural vocabulary for networked twins comes from Schroeder et al., who specify six connectivity topologies: disconnected, connected, embedded, aggregated, multi-device and combined (Schroeder et al. 2021). The taxonomy is worth walking through because each topology carries different ecosystem consequences.

A disconnected twin has no live link to its asset and is, in Kritzinger et al.'s terms, a digital model (Kritzinger et al. 2018); it can still participate in an ecosystem as a source of design-time knowledge, but its state claims are not current. A connected twin implements the live link explicitly. An embedded twin runs on the physical device itself, which bounds its computational resources but makes it robust to network loss. A multi-device twin serves several devices from shared components --- one database, one web service --- so that "despite having multiple representations, they are hosted on the same computational resource and therefore can be used in a shared way" (Schroeder et al. 2021). The aggregated topology, where a twin is composed of other twins, is treated in Section 1.6{reference-type="ref" reference="sec:dte-hierarchy"}. The combined topology mixes these.

Two observations follow. First, an ecosystem will contain members in all six topologies simultaneously, and a member's topology determines what interaction patterns it can support: an embedded twin on a constrained device cannot serve arbitrary semantic queries. Second, the topology is a deployment property, not a modelling property, which means it can change at runtime --- and the ecosystem's interoperability contracts must survive that change.

The network as a first-class design constraint

Ecosystem architectures written from the software side often assume reachable, low-latency, reliable communication. The literature that has measured this does not.

Frasheri et al. address the situation directly: what should a twin do when it and its physical counterpart get out of sync "as a result of disturbances in the normal operational conditions ... e.g., due to network degradation or temporary network drop"? Their answer is a best-effort protocol comprising user notification, deliberate degradation of the twin to a digital shadow, and explicit recovery mechanisms to re-establish synchronisation (Frasheri et al. 2023). This is, to my reading, the single most important idea in the networking part of the ecosystem literature, and it is under-cited. It says that fidelity is a runtime property that can be lost and regained, that a twin should know when it has lost it, and --- crucially for ecosystems --- that a twin's declared fidelity is something its peers must be able to observe. An ecosystem in which members silently serve stale state as though it were live is worse than one with no ecosystem at all, because the staleness propagates through every composition built on top of it.

Upstream of the network sits the sensing layer, whose quality bounds everything above it (Gomes et al. 2024). Downstream sits the messaging substrate, where publish/subscribe systems dominate and where benchmarking under realistic domain-based workloads is only beginning to be done systematically (Badolato et al. 2026). Between them, quantitative evidence about ecosystem middleware is scarce: Franke and Koch's benchmark of FIWARE as a scalable interface for decentralised twin ecosystems, complete with a candid account of its limitations, is one of the few studies that measures rather than asserts (Franke and Koch 2025).

Placement across the edge--cloud continuum

Where a twin runs is an ecosystem-level decision, not a local one. Early reference models assumed the cloud (Alam and El Saddik 2017). Current work assumes a continuum, and treats placement as dynamic.

Bellavista et al. make the strongest version of this argument with an entanglement-aware middleware: a "highly dynamic and distributed ecosystem where containerized DTs co-evolve with an orchestration middleware" that monitors and reconfigures deployments "in light of application constraints, available resources, and the quality of cyber-physical entanglement" (Bellavista et al. 2024b). The phrase "quality of cyber-physical entanglement" is the important one: it makes the tightness of the twin--asset coupling into a measurable quantity that the infrastructure optimises, rather than an assumption the architecture makes. The same group drives the twin lifecycle along the cloud-to-edge continuum using microservices and serverless techniques (Bellavista et al. 2024a), and related work frames the whole arrangement as a continuum (Barbone et al. 2024) or as fluid computing (Bedogni et al. 2025). Cloud-native platform substrates for many twins exist in open form (Wermann and Wickboldt 2024), and twins have been distributed across nodes within a single open-source framework (Infante et al. 2025).

The performance literature supplies the optimisation problems that placement raises: twin-assisted task offloading with edge collaboration (Liu et al. 2022), latency-aware and security-aware resource scheduling for grid applications at the 5G edge (Zhou et al. 2022), and the broader digital-twin edge-network programme toward 6G (Tang et al. 2022). These are worth reading with a caution: they typically assume a single optimising authority with global visibility, which is exactly what a multi-party ecosystem does not have.

Twins of networks, and why an ecosystem should want one

The second reading of "digital twin network" deserves more than the dismissal it usually gets in software-engineering treatments. The communications community has built a substantial programme around twinning the network itself: Wu et al. survey the modelling, communication, computing and data-processing technologies that make a digital twin network work, and the application scenarios --- manufacturing, aviation, healthcare, 6G networks, intelligent transport and urban intelligence --- in which it has been proposed (Wu, Zhang, and Zhang 2021). The 6G literature treats the twinned network as an architectural component of the network itself, used for what-if evaluation of configurations before they are applied to live infrastructure (Khan et al. 2022; Apostolakis et al. 2023), and the digital twin edge network programme couples this with multi-access edge computing so that routing and resource-management decisions can be studied against the twin rather than the network (Tang et al. 2022). Comprehensive surveys tie the strand to the industrial IoT more broadly (Hakiri et al. 2024; Y. Li and Zhang 2024).

The reason this matters for the present argument is C3 from Section 1.7{reference-type="ref" reference="sec:dte-synthesis"}. Section 1.4{reference-type="ref" reference="sec:dte-network"} argued that an ecosystem member's fidelity is a function of the communication path to its asset, and that peers need to observe it. A twin of the ecosystem's own network is one credible way to supply that observation: it can predict, rather than merely report, that the path to a given twin is about to degrade, which is the difference between a composed twin that flags reduced confidence in advance and one that discovers the problem in its outputs. In the running example, a twin of the plant network would let the manufacturer's cell twin anticipate the eleven-second drop instead of reacting to it.

No source consulted here closes this loop explicitly --- the network-twin literature and the twin-ecosystem literature are largely disjoint, citing different venues and different foundational papers. Connecting them is a modest and, on the evidence assembled here, unclaimed research opportunity.

Interim assessment

Networking is the best-instrumented of the four concerns --- it inherits decades of measurement culture --- and the worst-integrated with the others. The concepts that matter most for ecosystems are observable fidelity under degradation (Frasheri et al. 2023) and entanglement quality as an orchestration signal (Bellavista et al. 2024b). Neither appears in the interoperability standards discussed in Section 1.3{reference-type="ref" reference="sec:dte-interop"}: no widely used twin description format has a field for "how stale is this, and how would you know". That omission is a concrete, fixable gap, and it is one of the recommendations in Section 1.8{reference-type="ref" reference="sec:dte-agenda"}.

Collaborative digital twins

Two senses of collaboration

"Collaborative digital twin" also covers two distinct ideas, and both belong in this chapter.

The first is twins that collaborate: twins of separate assets cooperating at runtime to achieve something neither could alone. Esterle et al. describe autonomous cyber-physical systems that meet spontaneously and use twin models "aiming to improve collaboration and mutual safety", proposing an architecture for self-integration and self-improvement at runtime (Esterle et al. 2021), and extend the idea to autonomous reconfiguration (Esterle, Frasheri, and Larsen 2024). Rivera et al. supply the reference framework for autonomic and cooperating twins (Rivera et al. 2021). Gil et al. address the modelling problem for cooperative robotic systems, extending information-modelling approaches to include behaviour and using an ontology to express the semantic relationships between the cooperating parts (Gil et al. 2023).

The second is people and organisations collaborating through twins: the twin as the shared artefact across an organisational boundary. This is Kuruppuarachchi et al.'s composite twin spanning "systems and business boundaries" (Kuruppuarachchi, Rea, and McGibney 2022), and it is where supply chains (Bhandal 2024), cross-municipal urban assessment (J. Li et al. 2025) and cross-institutional health infrastructure (Viceconti et al. 2024) live.

The two senses interact badly in one specific way. Runtime collaboration between twins requires low-friction, low-latency interaction. Cross-boundary collaboration requires policy checks, provenance and consent. An ecosystem that supports both has to reconcile a microsecond budget with a legal review, and the literature has not, in the sources consulted, produced a design that does so convincingly.

The non-functional obstacles

Kuruppuarachchi et al.'s list is the best short statement of what stops collaboration: trust, interoperability, governance, ownership, security and privacy (Kuruppuarachchi, Rea, and McGibney 2022). Michael et al.'s longer enumeration of integration challenges reaches the same places from the engineering side, naming protection of intellectual property, privacy aspects of data, and rights and roles in the integrated twin as distinct problems alongside the purely technical ones (Michael et al. 2022). Combemale et al. add prioritisation --- whose objective wins when two twins disagree --- and place privacy controls and ethics controls in the same list as the technical challenges rather than in an appendix (Combemale et al. 2025).

Note what this implies for engineering practice. Intellectual-property protection is not a matter of encrypting a channel; it is a matter of a twin being able to answer useful questions about an asset without revealing the model that answers them. That is a design constraint on the twin's interface, which means it belongs in the interoperability discussion of Section 1.3{reference-type="ref" reference="sec:dte-interop"} and is largely absent from it.

Dataspaces and sovereignty

The most developed institutional answer to cross-organisational exchange is the dataspace. Schmidt et al. describe dataspaces of the International Data Spaces family as facilitating "sovereign, policy-governed data exchange across organizations" and address the integration of AAS-based twins into them --- reporting that this integration "remains challenging due to manual processes, synchronization issues, and varying implementations" (Schmidt et al. 2025). Singh et al. develop the edge-driven, cross-domain version of the same idea, motivated by the observation that data generated in one domain is routinely needed in another and that such sharing "across edges" requires data sovereignty (Singh, Meratnia, et al. 2024; Singh, Beliatis, and Presser 2024), and connect it to business ecosystem growth and monetisation (Singh,.., et al. 2024). Gleich et al. give a worked artefact: an AAS-based Digital Product Passport delivered as a Gaia-X service, intended to demonstrate "safe and trustworthy data exchange and collaboration across value chain" (Gleich et al. 2024). Interoperable analytics reference architectures for twin-aided manufacturing address the same boundary from the processing side (Marosi et al. 2022).

Dataspaces supply what Section 1.3{reference-type="ref" reference="sec:dte-interop"} called organisational interoperability: identity, policy, usage control and auditability. What they do not yet supply is the pragmatic level. Knowing that party $B$ is permitted to read a temperature series does not establish what $B$ is entitled to conclude from it, or what obligation $A$ incurs if the series is wrong.

Trust, identity and the disappearing perimeter

An ecosystem has no defensible perimeter. Every twin is a networked service with a data path to a physical asset, which is why the security surveys treat twins as a distinctive threat class rather than as ordinary software (Alcaraz and Lopez 2022; Qureshi et al. 2025). Kulik et al. ground this in the structural fact: twin-enabled systems "depend on communications and networking, and so face a range of threats" (Kulik, Kazemi, and Larsen 2024), and the IIoT surveys reach the same conclusion from the deployment side (Xu et al. 2023).

Zero-trust architecture is the natural fit for a perimeterless ecosystem (Rose et al. 2020), and its practical precondition in a multi-domain setting is decentralised identity: authentication and authorisation that work when resources are shared across a computing continuum spanning administrative domains (Bernabé Murcia et al. 2025). Permissioned distributed ledgers appear in the edge-twin literature as a mechanism for accountable multi-party learning (Lu et al. 2021). Twins also cut the other way, as instruments for security rather than only as targets (Qureshi et al. 2025).

A recurring pattern deserves separate mention because it dissolves rather than manages the sharing problem: parties collaborate on a model without exchanging the data that trained it. Lu et al. combine communication-efficient federated learning with a permissioned blockchain for twin edge networks (Lu et al. 2021). Kim et al. pose federated twins as a scheduling problem and solve it with temporal graph neural networks and deep reinforcement learning (Kim et al. 2025). Marah and Challenger's federation roadmap positions this within a broader programme of integration, collaboration and coordination across an ecosystem of federated twins (Marah and Challenger 2025), and research infrastructures have adopted federated computing and data as the organising principle for scientific twins ("[interTwin]{.nocase}: Advancing Scientific Digital Twins Through AI, Federated Computing and Data," n.d.).

The limitation is scope. Federated learning solves collaborative statistical model-building. It does nothing for the case where the shared artefact is a physics-based model, a control decision, or a claim about the current state of a shared asset --- which is most of what twins do.

Humans in the ecosystem

Industry 5.0 framings insist that people are ecosystem participants rather than users of it. Villani et al. present a twin-driven human-centric ecosystem on exactly this premise (Villani et al. 2025), and Tóth et al. propose a collaboration architecture whose stated aim is "integrating various innovative agents (human, AI, IoT, robot) in a plant-level collaboration process through a generic semantic definition" backed by a knowledge graph (Tóth et al. 2023). Human twins are being built in clinical settings from multimodal data to support practitioner decision-making (Azevedo et al. 2024), which raises the privacy and consent questions of Section 1.5{reference-type="ref" reference="sec:dte-collab"} in their sharpest form. At the institutional scale, the social and human dimensions of twin technologies in formal and informal institutional settings are themselves a research object (Alexandridis and LaFontaine 2024), and the urban-twin literature has begun to push back on technocratic framings with a more sceptical account of what city twins have actually achieved and what remains conceptually unresolved (Bettencourt 2024). That scepticism is a useful corrective to the ecosystem enthusiasm of the rest of the field.

Collaboration at city and sector scale

The manufacturing setting of the running example understates the governance problem, because a supply chain at least has contracts. Two settings push further.

The first is the city. Urban twins are proposed as integrations across mobility, energy, water, buildings and emergency services, each already owned and operated by a different municipal or private body. Modular, adaptive architectures have been developed to make such integration tractable for real-time urban management (Herath et al. 2024), and methods exist for accelerating twin development specifically in the smart-city setting (McKee and Dokter 2024). Cross-municipal federation has been given a functional architecture for mobility assessment (J. Li et al. 2025), and the value case has been worked through for flexible building loads coordinated across a network of participants (Reynolds, Sabri, and Lee 2024). Against this, Bettencourt et al. supply the necessary corrective: the conceptual challenges for urban twins are not the same as the technical ones, and the field's achievements to date are more modest than its rhetoric (Bettencourt 2024). The institutional-settings literature makes the parallel point that twins land in formal and informal institutions whose existing decision rights they do not automatically respect (Alexandridis and LaFontaine 2024).

The second is the research infrastructure, where scientific twins are being built on federated computing and federated data because no single institution holds either ("[interTwin]{.nocase}: Advancing Scientific Digital Twins Through AI, Federated Computing and Data," n.d.), and where the health-domain equivalent is explicitly framed as a distributed and collaborative infrastructure assembled from contributed technologies, data and models (Viceconti et al. 2024).

What both settings share is that the ecosystem's governance predates its technology. The participants already have decision rights, funding lines and accountability structures, and the twin ecosystem must fit them rather than replace them. This is the strongest practical argument for the federation framing over the composite framing (Marah and Challenger 2025): it is the only one of the terms in Table 1.1{reference-type="ref" reference="tab:dte-terms"} that treats retained local authority as a design requirement rather than an obstacle.

The supply side: marketplaces and reuse

If ecosystems are justified by reuse, someone must supply the reusable parts. The Change2Twin project provides the most concrete evidence available here: a catalogue of twin enabling technologies documenting the fragmentation of the tool landscape ("Tools and Libraries Catalogue," n.d.), a marketplace design ("Change2Twin Marketplace Design," n.d.), and a review of that marketplace in operation ("Change2Twin Marketplace Review," n.d.). HUBCAP demonstrated the adjacent case for model-based design of cyber-physical systems, providing collaboration infrastructure aimed specifically at small and medium enterprises that cannot absorb the fixed costs alone (Larsen et al. 2022). Value-creation studies show the same logic at the application level, with buildings-as-batteries value realised through a mass-customisation network of participants rather than by any single twin (Reynolds, Sabri, and Lee 2024).

The commercial platform literature adopts the ecosystem word freely, typically meaning an organisation-wide deployment connected by a digital thread (Khoshkenar and Nassereddine 2024; Parle et al. 2024). That is the weaker sense identified in Section 1.2{reference-type="ref" reference="sec:dte-terminology"}: one authority, many twins.

Interim assessment

Collaboration has the clearest problem statement of the four concerns (Kuruppuarachchi, Rea, and McGibney 2022; Michael et al. 2022) and the most institutional machinery pointed at it (Schmidt et al. 2025; Gleich et al. 2024; Singh, Meratnia, et al. 2024). What it lacks is a connection to the technical layers: dataspace policy is expressed over datasets, whereas twins expose services, simulations and control affordances, and no source consulted here expresses usage policy over those. Section 1.8{reference-type="ref" reference="sec:dte-agenda"} returns to this.

Hierarchical and composed digital twins

Aggregation as a topology

The hierarchical case has a clean statement in Schroeder et al.'s aggregated topology: "the digital twin is composed of other digital twins ... It can be seen as a hierarchical system, where the parent transparently provides all functionalities of its children, while possibly adding more functionalities" (Schroeder et al. 2021). Their worked example is the standard one: the twin of a production line aggregates the twins of its machines and products, and the aggregation of production lines yields the twin of the factory. They also note an attractive practical consequence --- embedded twins with limited on-device capability can be aggregated into a twin with greater computational resources --- and a structural one, that a one-to-one physical--cyber correspondence can be maintained at every level of the hierarchy simultaneously.

Martinelli et al. build this out into a running industrial ecosystem "exploiting twin relationships and hierarchies to build a digitalised replica of the whole manufacturing system structure", with the stated capabilities of data augmentation, actionability, navigability and composability, and they measure the resource consumption their hierarchy imposes (Martinelli et al. 2024). Human et al. approach the same object as a design problem, giving a six-step framework for a system of aggregated twins and services running from needs analysis through physical-system decomposition and service allocation to verification and validation (Human, Basson, and Kruger 2023). Both are valuable, and both assume a single designing authority --- which returns the problem to Section 1.5{reference-type="ref" reference="sec:dte-collab"} as soon as the levels of the hierarchy belong to different owners.

Horizontal integration versus vertical composition

The most useful analytical distinction comes from Michael et al., who separate horizontal integration of digital twin parts --- assembling the models, services, data, APIs, access rights and views that make up one twin at one level --- from vertical composition of digital twins --- embedding a twin into a larger twin (Michael et al. 2022). Their fourteen challenges divide accordingly, and several are worth restating because they are usually skipped.

Horizontally, a single asset typically has multiple legitimate views: a car twin "may feature a driver's view, a maintenance view, an insurance view, and a producer's view", each with different models, services and data (Michael et al. 2022). When several twins of the same asset exist, composing them is subject to all the usual difficulties "with the twist of all DTs of the same original system aspiring to be the true DT for their respective (potentially overlapping) views". Multi-purpose twin engineering addresses part of this at design time (Heithoff et al. 2024), and a product-line architecture for twins offers variability management as an alternative to per-instance composition (Jérôme Pfeiffer et al. 2023).

Vertically, the challenges are sharper. Michael et al. observe that a twin being embedded may record data "on different levels of abstraction or in incompatible granularity", that its services and behaviours "might contradict the behaviors of the DT it should be embedded in", and that the models themselves may be incompatible (Michael et al. 2022). To these they add different lifecycle representations of the original system, conflicting constraints and requirements, hierarchical functional abstraction, composition of interfaces both twin-to-twin and twin-to-CPS, and the interoperability of models and simulation environments already discussed. Granularity is a first-class design parameter here, and has been treated as such (Steinmetz, Schroeder, Rodrigues, et al. 2022), as has the transition from simple to complex twins across multiple scales and scenarios (Jia, Wang, and Zhang 2022).

Set that list beside Schroeder et al.'s statement that the parent "transparently provides all functionalities of its children" (Schroeder et al. 2021) and the tension is plain. Transparent provision presumes that children's functionalities do not conflict, that their data are commensurable, and that their behaviours compose. Michael et al. document that in practice none of these holds (Michael et al. 2022). The aggregated topology is a description of what one wants, not an account of how to get it.

What composition must preserve

The deeper question is what a composed twin is entitled to claim. If twins $A$ and $B$ are each validated representations of their assets, the twin $C$ formed from them is not thereby a validated representation of the composite system. Two reasons stand out.

The first is emergence. Grieves and Vickers framed the twin's purpose partly as mitigating "unpredictable, undesirable emergent behavior in complex systems" (Grieves and Vickers 2017). Emergent behaviour is by construction not present in the constituents, so a composition that merely federates constituent models cannot exhibit it. Composing twins to study emergence requires modelling the interactions, not just the parts --- which is why behavioural interoperability (Section 1.3{reference-type="ref" reference="sec:dte-interop"}) is load-bearing rather than incidental.

The second is validity. Each constituent twin was validated within an assumed operating envelope. Composition can move a constituent outside its envelope without anything in the composed system detecting it. Combemale et al.'s worked example makes this concrete: two instances of a room twin come to share a single irrigation tank, which "renders the current level estimation of the predictive model invalid", forcing evolution of the predictive model or the extraction of a separate tank twin that must then be composed with both room twins (Combemale et al. 2025). The National Academies report on foundational research gaps identifies verification, validation and uncertainty quantification for twins as a first-order open problem (Foundational Research Gaps and Future Directions for Digital Twins 2024); at ecosystem scale, where constituents evolve independently and their validation evidence is often proprietary, the problem is strictly harder and correspondingly less studied.

No source consulted for this chapter provides a formal account of when composition preserves validity, or a mechanism by which a composed twin can compute its own confidence from its constituents'. That is a genuine gap, and it is stated here as such rather than papered over.

Composition over the lifecycle, and the reporting precondition

A composed twin is not assembled once. Its constituents are replaced, upgraded and retired on schedules set by their owners, which makes composition a lifecycle activity rather than a build step. The systems-of-systems literature on asset lifecycle management makes this explicit for capital-intensive industries, where asset twins must survive decades of component turnover and where SoS concepts are recruited precisely to handle constituents that change independently (Altamiranda 2024). The declarative, ontology-driven approach to twin lifecycle management is a direct response to constituents that "evolve independently" (Kamburjan et al. 2024), and the multi-scale modelling work addresses the related problem that a composite may need to be examined at several granularities in different scenarios (Jia, Wang, and Zhang 2022).

There is a precondition here that is easy to overlook. A twin can only be reused in a composition by a party that did not build it if that party can determine what it does, what it assumes and how it was validated. Gil et al. argue for exactly this, proposing a systematic reporting framework for twins and evaluating it on a cooperative robotics case (Gil, Oakes, et al. 2024); complementary tooling makes twin structure and provenance continuously visible to stakeholders rather than documenting it once at handover (Fitzgerald, Gomes, and Larsen 2024). In the running example, the robot vendor's twin is only composable into the manufacturer's cell twin if it reports the duty cycle over which it was validated --- and that report is the same artefact the manufacturer would need in order to detect, later, that the cell has drifted outside it. Documentation is usually treated as a courtesy in this literature. For ecosystems it is a functional requirement.

Automating composition

If composition is to happen at ecosystem scale it cannot be manual. Gill et al. propose a pipeline for automating the composition of twins within systems-of-systems, and are explicit about the precondition: "a formal semantic model is necessary, based on domain-specific standards, that clearly and unambiguously describes all relevant DT information", supported by a meta-model, with a top-level domain ontology selected to integrate the constituents and ensure their interoperability (Gill et al. 2024). Their pipeline includes automated generation of composed twin behaviour from newly composed twin functions --- the step that distinguishes genuine composition from mere aggregation of dashboards.

Amadeo et al. approach the same target from the Internet-of-Everything side, observing that existing twin architectures are application-driven verticals and that "a general-purpose modeling approach is lacking", and proposing a user-centric composition model (Amadeo et al. 2024). Kamburjan et al.'s declarative lifecycle management provides machinery of a different kind: ontology-driven self-adaptation for twins whose physical components "evolve independently" (Kamburjan et al. 2024), which is the dynamic-interoperability problem of Section 1.3{reference-type="ref" reference="sec:dte-interop"} attacked at the composition layer. Design-pattern catalogues supply the reusable structural vocabulary that such automation needs to target (Tekinerdogan and Verdouw 2020).

Platform support for composition

The platform literature has converged on reuse as the mechanism. Van Schalkwyk and Isaacs argue the industrial case for the composable twin, which "offers re-use of effort, accelerated time to results, general applicability, and the dynamic range to address both simple and complex issues within an enterprise at scale", paired with lean, minimum-viable-product delivery practice (Schalkwyk and Isaacs 2023). Talasila et al. give the platform realisation: a Digital Twin as a Service platform that manages reusable assets --- models, data, functions and tools --- and creates composable twins from them, then makes those twins available as a service to other users, with asset management, storage, compute provisioning, communication, monitoring and execution as platform responsibilities and with two case studies as evaluation (Talasila et al. 2025). The same lineage supplies the account of why building a twin from scratch is expensive enough to justify the machinery (Talasila et al. 2024). OpenTwins pursues compositional twins in open source (Robles, Martín, and Díaz 2023) and has been extended to distributed operation (Infante et al. 2025).

The broader as-a-service framing has an architecture reference model (Aheleroff et al. 2021), cross-domain instantiations that report reduced implementation effort and operating cost through open modelling standards (Zech et al. 2024), arguments for platform openness as an ecosystem precondition (Grübel et al. 2023), and now a survey that organises platform requirements into a component-based taxonomy of core and supportive components with associated performance metrics, while noting that prior studies "mostly remain domain-specific, thereby limiting the potential for generalisation" (Duran et al. 2026).

Interim assessment

Hierarchical composition is where the ecosystem programme is furthest from delivery. There is a good structural vocabulary (Schroeder et al. 2021; Tekinerdogan and Verdouw 2020), a good design method under single authority (Human, Basson, and Kruger 2023), running hierarchical systems (Martinelli et al. 2024), platform machinery for asset reuse (Talasila et al. 2025; Robles, Martín, and Díaz 2023; Schalkwyk and Isaacs 2023), and a clear statement of what automation would require (Gill et al. 2024). What is missing is semantics: an account of what a composed twin means, when composition is legitimate, and how the composite's validity relates to its constituents'.

Synthesis: what an ecosystem demands of its infrastructure

Answering the research question

Reading the four concerns together, the capabilities an ecosystem requires of its infrastructure can be stated as eight, each with a maturity that the preceding sections have argued for.

C1 --- Identity and discovery.

Every twin needs a stable, resolvable identity and a discoverable description, so that peers can find it without out-of-band coordination. Web and hypermedia approaches provide this natively (Ricci et al. 2022; Giulianelli et al. 2025); AAS registries provide it within their ecosystem (Jacoby et al. 2023). Across ecosystems, decentralised identity is the emerging answer (Bernabé Murcia et al. 2025). Maturity: good within ecosystems, weak across them.

C2 --- Semantic description of state and capability.

A peer must be able to determine what a twin represents, what it can be asked, and what its answers mean. Mechanisms are plentiful (Mattila et al. 2025; Burattini et al. 2024; Steinmetz, Schroeder, Sulak, et al. 2022; Oakes et al. 2024); agreement on which to use is not (Schmidt et al. 2023; Ferko et al. 2023). Maturity: mechanisms good, coordination poor.

C3 --- Declared and observable fidelity.

A peer must be able to determine how current and how trustworthy a twin's state is, including under degradation. The concept exists (Frasheri et al. 2023) and orchestration middleware treats entanglement quality as a signal (Bellavista et al. 2024b), but no widely used description format carries it. Maturity: recognised, not standardised.

C4 --- Bounded, adaptive communication.

Reachability with known latency characteristics, and graceful behaviour when those characteristics fail (Vaezi et al. 2022; Frasheri et al. 2023; Badolato et al. 2026). Maturity: good, well instrumented.

C5 --- Placement and orchestration across a continuum.

Deciding where each twin runs, and moving it when conditions change (Bellavista et al. 2024b, 2024a; Wermann and Wickboldt 2024; Barbone et al. 2024). Maturity: good under one authority, unaddressed across several.

C6 --- Policy, sovereignty and accountability.

Expressing and enforcing what may be shared, with whom, for what purpose, with an audit trail (Schmidt et al. 2025; Gleich et al. 2024; Singh, Meratnia, et al. 2024; Rose et al. 2020). Maturity: institutionally strong for data, absent for services, simulations and control affordances.

C7 --- Behavioural composability.

Coupling constituent models so that a composed twin can simulate the composite system (Schweiger et al. 2019; Friedrich et al. 2025; Wetter et al. 2024; Gill et al. 2024). Maturity: solver-level tooling good, semantics absent.

C8 --- Reusable assets and a supply side.

Models, data, functions and tools that can be found, licensed and reassembled (Talasila et al. 2025; Schalkwyk and Isaacs 2023; "Change2Twin Marketplace Review," n.d.; Larsen et al. 2022). Maturity: platforms exist, market thin.

::: {#tab:dte-capabilities}

1	`Capability Concern Assessment`

C1 Identity and Interoperability Good within an ecosystem, discovery weak across ecosystems

C2 Semantic Interoperability Mechanisms good, description of coordination poor state and
capability

C3 Declared and Network / Recognised, not observable fidelity composition standardised --- falls between concerns

C4 Bounded, adaptive Network Good, well instrumented communication

C5 Placement and Network / Good under one authority, orchestration collaboration unaddressed across across a continuum several

C6 Policy, sovereignty Collaboration Strong for data, absent and accountability for services and control

C7 Behavioural Composition Solver-level tooling composability good, semantics absent

C8 Reusable assets and Composition / Platforms exist, market a supply side collaboration thin

: Capabilities an ecosystem demands of its infrastructure, the concern each principally serves, and an assessment of maturity based on the evidence reviewed in Sections 1.3{reference-type="ref" reference="sec:dte-interop"}--1.6{reference-type="ref" reference="sec:dte-hierarchy"}. :::

The direct answer to the research question is therefore that the four concerns are unevenly served. Networking (C4, C5) is the most mature. Interoperability (C1, C2) has good mechanisms held back by a coordination failure that is political rather than technical. Collaboration (C6) has strong institutional machinery aimed at the wrong granularity --- datasets rather than twin services. Composition (C7) has tooling without semantics. And one capability, C3, falls between all four and is claimed by none of them.

The couplings between concerns

The claim in Section 1.1.1{reference-type="ref" reference="sec:dte-rq"} that the four concerns are not independent can now be made precise, and three couplings matter most.

Fidelity couples networking to composition. A composed twin's trustworthiness is bounded by the least current of its constituents. Since currency is a network property (Frasheri et al. 2023) and composition is a modelling operation (Gill et al. 2024), the composition layer must be able to read a network-level property that the description formats do not expose. This is the concrete form of the C3 gap.

Policy couples collaboration to interoperability. Michael et al. list intellectual-property protection and rights and roles as integration challenges (Michael et al. 2022); dataspaces express policy over data (Schmidt et al. 2025). Twins expose services and control affordances, so the policy language and the interface description must be the same artefact. They currently are not.

Placement couples networking to collaboration. Orchestration middleware reconfigures deployment to preserve entanglement quality (Bellavista et al. 2024b), but moving a twin can move data across a jurisdictional or organisational boundary. Sovereignty constraints (Singh, Meratnia, et al. 2024) are therefore constraints on the placement optimiser, and none of the offloading and scheduling formulations reviewed here (Liu et al. 2022; Zhou et al. 2022; Kim et al. 2025) carries them.

Passive layer or agent society, revisited

The disagreement flagged in Section 1.2{reference-type="ref" reference="sec:dte-terminology"} determines which capabilities are sufficient. Under the passive reading (Martinelli et al. 2024; Franke and Koch 2025), C1--C8 are close to complete: twins publish, applications consume, and intelligence is external. Under the agent reading (Rivera et al. 2021; Esterle et al. 2021; Ricci et al. 2022), at least two further capabilities are needed --- a means for twins to make and evaluate commitments to one another, and a means to resolve conflicting objectives, which Combemale et al. raise as prioritisation (Combemale et al. 2025). Neither appears in any standard reviewed here.

The choice is not merely academic, because the two readings imply different description formats. A passive twin needs to describe what it is; an agent twin needs to describe what it will do, and under what conditions it will refuse.

Open problems

Six problems follow from the synthesis. The first three are supported by explicit statements in the literature; the last three are gaps where the corpus consulted for this chapter offers no answer, and they are labelled as such rather than dressed in a citation that does not support them.

P1 --- A fidelity field in twin descriptions.

Twin description formats should carry machine-readable currency and confidence, so that peers and compositions can reason about staleness. The concept is available (Frasheri et al. 2023; Bellavista et al. 2024b); the standards work is not done (Mattila et al. 2025; Zhang et al. 2024).

P2 --- Usage policy over twin services, not only data.

Dataspace machinery should be extended from datasets to service invocations, simulation runs and control affordances (Schmidt et al. 2025; Michael et al. 2022).

P3 --- Sovereignty-aware placement.

Orchestration objectives should include jurisdiction and ownership as hard constraints alongside latency and cost (Bellavista et al. 2024b; Singh, Meratnia, et al. 2024).

P4 --- Compositional validity (gap).

No source consulted here supplies a formal account of when composing validated twins yields a valid composite, nor a mechanism for a composed twin to derive its own uncertainty from its constituents'. Gill et al. argue that a formal semantic model is necessary (Gill et al. 2024) and the National Academies report names verification, validation and uncertainty quantification as foundational gaps (Foundational Research Gaps and Future Directions for Digital Twins 2024), but the compositional result itself does not exist in this corpus.

P5 --- Liability and contract (gap).

The synced corpus contains no treatment of legal liability, contractual allocation of risk, or regulatory status for cross-organisational twin ecosystems. Given that composite twins are proposed for safety-relevant decisions in manufacturing, health and urban management, this absence is itself a finding.

P6 --- Comparative ecosystem benchmarks (gap).

Beyond the FIWARE benchmark (Franke and Koch 2025), the publish/subscribe workload study (Badolato et al. 2026) and the resource measurements accompanying one hierarchical prototype (Martinelli et al. 2024), there is little comparative measurement of ecosystem middleware. Claims about scalability in this field are, for the most part, architectural rather than empirical.

Summary

This chapter asked what changes when digital twins become members of an ecosystem rather than isolated artefacts, and specifically what interoperability, networking, collaboration and hierarchical composition demand of the surrounding infrastructure.

The answer developed here is that the four demands are unequally met and badly connected. Interoperability has a rich set of mechanisms --- description standards, transformations between them, ontologies and Linked Data --- and a coordination failure about which to use, with three mutually incompatible strategies (converge, bridge, leave) in simultaneous pursuit and evidence that the flagship reference architecture is not in fact followed (Ferko et al. 2023). Networking is the most mature concern and contributes the chapter's most under-appreciated idea, that fidelity is a runtime property which can be lost, observed and recovered (Frasheri et al. 2023). Collaboration has the clearest problem statement (Kuruppuarachchi, Rea, and McGibney 2022; Michael et al. 2022) and the most institutional machinery, aimed one level too low: at datasets rather than at the services, simulations and control affordances that twins actually expose. Hierarchical composition has structural vocabulary (Schroeder et al. 2021), running systems (Martinelli et al. 2024), platform support for asset reuse (Talasila et al. 2025; Schalkwyk and Isaacs 2023) and a clear statement of what automation would require (Gill et al. 2024), but no semantics for what a composed twin is entitled to claim.

Two conclusions follow for the remainder of this thesis. First, the ecosystem problem is not solved by any single layer: a platform that gets reuse right but cannot express sovereignty, or a standard that gets semantics right but cannot express staleness, will not compose into a working ecosystem. Second, the capability that falls between all four concerns --- declared, observable fidelity --- is both the cheapest to fix and the one on which the credibility of every composition rests. It is taken up in the chapters that follow.

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Digital Twins in an Ecosystem

From the isolated twin to the ecosystem

Research question

A running example

Structure of the chapter

What is a digital twin ecosystem?

An inventory of names

Ecosystem of digital twins.

Web of Digital Twins (WoDT).

Digital twin federation.

Composite digital twin (CDT).

Digital twin system-of-systems.

System of digital twins and services.

Digital twin continuum / fluid computing.

Digital twin constellation.

A working definition

Four relations between twins

A disagreement worth stating early

Interoperability

Levels, not a level

The standards landscape

Three strategies, and the disagreement between them

Converge.

Bridge.

Leave.

Does anyone follow the standards?

Semantics: ontologies and knowledge graphs

The digital thread: interoperability across time

Behavioural interoperability

Interim assessment

Networked digital twins

Two readings of "network"

Connectivity topologies

The network as a first-class design constraint

Placement across the edge--cloud continuum

Twins of networks, and why an ecosystem should want one

Interim assessment

Collaborative digital twins

Two senses of collaboration

The non-functional obstacles

Dataspaces and sovereignty

Trust, identity and the disappearing perimeter

Collaborating without sharing

Humans in the ecosystem

Collaboration at city and sector scale

The supply side: marketplaces and reuse

Interim assessment

Hierarchical and composed digital twins

Aggregation as a topology

Horizontal integration versus vertical composition

What composition must preserve

Composition over the lifecycle, and the reporting precondition

Automating composition

Platform support for composition

Interim assessment

Synthesis: what an ecosystem demands of its infrastructure

Answering the research question

C1 --- Identity and discovery.

C2 --- Semantic description of state and capability.

C3 --- Declared and observable fidelity.

C4 --- Bounded, adaptive communication.

C5 --- Placement and orchestration across a continuum.

C6 --- Policy, sovereignty and accountability.

C7 --- Behavioural composability.

C8 --- Reusable assets and a supply side.

The couplings between concerns

Passive layer or agent society, revisited

Open problems

P1 --- A fidelity field in twin descriptions.

P2 --- Usage policy over twin services, not only data.

P3 --- Sovereignty-aware placement.

P4 --- Compositional validity (gap).

P5 --- Liability and contract (gap).

P6 --- Comparative ecosystem benchmarks (gap).

Summary