Abstract Distributed runtime verification (DRV) addresses the problem of checking the correctness of distributed systems during execution, coping with partial knowledge, dynamic topologies, and the absence of global time. These challenges are particularly prominent in proximity-based networks, such as those arising in IoT and Far Edge computing scenarios, where large numbers of devices interact through local communication. This tutorial presents an approach to DRV based on Aggregate Programming (AP), a paradigm for designing distributed collective systems via high-level abstractions over computational fields. We show how temporal and spatial properties (expressed in past-CTL and SLCS, respectively) can be systematically compiled into aggregate monitors grounded in the eXchange Calculus and executed using the FCPP C++ framework and simulator for AP. The tutorial combines conceptual foundations with practical guidance: participants learn how to specify spatio-temporal properties, generate corresponding monitors, and execute them in a 3D simulation environment. Examples are drawn from ongoing industrial collaborations and research projects, which we use to illustrate realistic monitoring scenarios and motivate open challenges for AP-based DRV.

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.