Emerging cyber-physical systems, such as robot swarms, crowds of augmented people, and smart cities, require well-crafted self-organizing behavior to properly deal with dynamic environments and pervasive disturbances. However, the infrastructures providing networking and computing services to support these systems are becoming increasingly complex, layered and heterogeneous—consider the case of the edge–fog–cloud interplay. This typically hinders the application of self-organizing mechanisms and patterns, which are often designed to work on flat networks. To promote reuse of behavior and flexibility in infrastructure exploitation, we argue that self-organizing logic should be largely independent of the specific application deployment. We show that this separation of concerns can be achieved through a proposed “pulverization approach”: the global system behavior of application services gets broken into smaller computational pieces that are continuously executed across the available hosts. This model can then be instantiated in the aggregate computing framework, whereby self-organizing behavior is specified compositionally. We showcase how the proposed approach enables expressing the application logic of a self-organizing cyber-physical system in a deployment-independent fashion, and simulate its deployment on multiple heterogeneous infrastructures that include cloud, edge, and LoRaWAN network elements.

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.

Context. Self-organising and collective computing approaches are increasingly applied to large-scale cyber-physical systems (CPS) enabling them to adapt and cooperate in dynamic environments. Also, in CPS engineering, digital twins are often leveraged to provide synchronised logical counterparts of physical entities, whereas in sensor networks the different-but-related concept of virtual device is used to abstract groups of sensors.Vision. We envision the design concept of "augmented collective digital twin" that captures digital twins at a collective level extended with purely virtual devices. We argue that this concept can foster the engineering of self-organising CPS by providing a holistic, declarative, and integrated system view.Method. From a review and proposed taxonomy of logical devices comprehending both digital twins and virtual devices, we reinterpret a meta-model for self-organising CPSs and discuss how it can support augmented collective digital twins. We illustrate the approach in a crowd-aware navigation scenario, where virtual devices are opportunistically integrated into the system to enhance spatial coverage, improving navigation capabilities.Conclusion. By integrating physical and virtual devices, the novel notion of augmented collective digital twin paves the way to self-improving system functionality and intelligent use of resources in self-organising CPSs.