State estimation from uncertain, distributed observations is central in many cyber-physical applications. While Distributed Particle Filtering (DPF) algorithms address nonlinear and non-Gaussian estimations in distributed settings, most solutions remain tied to specific architectures and communication assumptions, limiting adaptability in open, heterogeneous deployments-most notably, the Internet of Things (IoT). In this paper, we propose a field-based formulation of Distributed Particle Filtering grounded in Aggregate Computing (AC). By expressing estimation and information dissemination as computational fields, our approach decouples the core filtering logic from coordination and data-flow strategies. This enables systematic customisation of key design dimensions, including fusion-center placement and resilience, aggregated measurement functions, as well as the type and scope of information propagation. Through a set of in-silico experiments, we show how diverse DPF configurations can be derived within a unified framework, highlighting trade-offs among accuracy, communication cost, and robustness. Overall, the proposed approach positions AC as an effective abstraction layer for engineering adaptable DPF solutions in open IoT environments.

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