Recent trends in computer science have promoted new ways to think about software at both design time and run time. Next-generation intelligent pervasive systems will be modelled as ecosystems of Digital Twins (DTs), that are the virtual counterparts of physical objects. Digital Twins have emerged as a revolutionary approach to managing and optimising the lifecycle of physical assets in pervasive computing applications. By creating a digital counterpart, that mirrors a physical entity or process and encapsulates computational intelligence or simulation tools, stakeholders can continuously monitor, analyse, and predict the behaviour of the real-world counterpart in real time. This continuous feedback loop enables enhanced decision-making, efficiency improvements, and proactive maintenance, thereby driving innovation and competitiveness in various industries. In particular, the convergence of IoT, AI, and big data analytics has accelerated the development and adoption of DTs in several application domains. IoT devices provide the necessary sensory input, AI offers advanced data processing, reasoning and decision-making capabilities, and big data analytics facilitates the handling of large volumes of data generated by physical and digital entities. Moreover, different simulation techniques are exploited in this context, being complementary approaches that can be integrated as tools with analytics and predictive properties. This synergy is critical in realising the full potential of Digital Twins, making it possible to create highly responsive, intelligent, and autonomous systems. As such, they are becoming increasingly vital in a wide range of sectors, from manufacturing to the energy industry, healthcare, and transportation.

Rarely we can think of assets as fully isolated from their surroundings. This is especially true when we start applying the DT concept not only to devices or products but possibly to people and processes we want to track in our domain. Following this trend, Digital Twins Ecosystems can be envisioned to not be limited to the scope of an individual twin but to engineer the integration of several DTs to get a holistic view of the target domain. However, the design and implementation of Digital Twin Ecosystems are not without challenges. Issues such as data security, privacy, and ethical considerations are paramount, especially given the sensitive nature of the data involved. Ensuring interoperability and scalability of Digital Twin solutions across different platforms and industries also presents significant hurdles. Integrating and coordinating different approaches to analyse the whole ecosystem behaviour and possibly predict its evolution is another open issue. Addressing these challenges requires a concerted effort from the research community and industry practitioners.