Temporal Windows of Integration for Multisensory Wireless Systems as Enablers of Physical AI

TL;DR

This paper introduces a timing-aware approach for Physical AI systems that ensures decision relevance by optimizing delay and reliability in multisensory wireless networks, enabling more coherent real-time physical interactions.

Contribution

It formulates a convex optimization framework to minimize the validity horizon while maintaining reliability, advancing the design of timing-aware Physical AI systems.

Findings

Optimal reliability and delay budgets are derived for multisensory wireless systems.

The proposed method outperforms uniform reliability allocation benchmarks.

The approach ensures more coherent and timely decision-making in Physical AI applications.

Abstract

Physical artificial intelligence (AI) refers to the AI that interacts with the physical world in real time. Similar to multisensory perception, Physical AI makes decisions based on multimodal updates from sensors and devices. Physical AI thus operates with a finite spatial footprint of its sensory tributaries. The multimodal updates traverse heterogeneous and unreliable paths, involving wireless links. Throughput or latency guarantees do not ensure correct decision-making, as misaligned, misordered, or stale inputs still yield wrong inferences. Preserving decision-time coherence hinges on three timing primitives at the network-application interface: (i) simultaneity, a short coincidence window that groups measurements as co-temporal, (ii) causality, path-wise delivery that never lets a consequence precede its precursor, and (iii) usefulness, a validity horizon that drops information too stale to influence the current action. In this work, we focus on usefulness and adopt temporal window of integration (TWI)-Causality: the TWI enforces decision-time usefulness by assuming path-wise causal consistency and cross-path simultaneity are handled upstream. We model end-to-end path delay as the sum of sensing/propagation, computation, and access/transmission latencies, and formulate network design as minimizing the validity horizon under a delivery reliability constraint. In effect, this calibrates delay-reliability budgets for a timing-aware system operating over sensors within a finite spatial footprint. The joint choice of horizon and per-path reliability is cast as a convex optimization problem, solved to global optimality to obtain the minimal horizon and per-path allocation of reliability. This is compared favourably to a benchmark based on uniform-after-threshold allocation. Overall, this study contributes to timing-aware Physical AI in next-generation networks.