Leveraging particle dynamics in force-fields for network packet routing

Abstract

Scalable and resource-efficient packet routing remains a fundamental challenge in modern communication networks, particularly in emerging Internet-of-Things and sensor network deployments where node capabilities are severely constrained. Existing routing paradigms, which includes source routing, hop-by-hop table-driven forwarding and other state-of-the-art frameworks, face inherent scalability limitations arising from either increasing packet header overhead or growing routing table complexity. This paper introduces Particle Dynamics-based Routing (PDR), a novel routing framework inspired by the controllable trajectories of particles in physical force fields. In PDR, network topology is abstracted as a discretized spatial field in which packets are routed along physics-driven trajectories determined by initial launch parameters and pre-assigned logical force distributions at nodes. This enables forwarding decisions at intermediate nodes without reliance on destination-specific routing tables or path-encoded headers. A gravitational-field realization of the framework is presented to demonstrate how routing complexity can be shifted from core network nodes to edge-based pre-launch computation processes. Through theoretical formulation and simulation-based evaluation, the study shows that PDR can maintain constant packet header size independent of network scale, while supporting trajectory-based routing with reduced forwarding overhead at intermediate nodes. The framework further enables natural privacy semantics by limiting exposure of destination information during packet traversal. These characteristics position PDR as a scalable routing alternative for large-scale and resource-constrained network environments. Beyond its immediate applicability, the proposed paradigm establishes a new trajectory-centric perspective on routing design, which opens opportunities for future extensions involving multi-field routing strategies, hierarchical trajectory composition, and routing in higher-dimensional network abstractions.