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The increasing deployment of deep neural networks (DNNs) in cyber-physical systems (CPS) enhances perception fidelity, but imposes substantial computational demands on execution platforms, posing challenges to real-time control deadlines. Traditional distributed CPS architectures typically favor on-device inference to avoid network variability and contention-induced delays on remote platforms. However, this design choice places significant energy and computational demands on the local hardware. In this work, we revisit the assumption that cloud-based inference is intrinsically unsuitable for latency-sensitive control tasks. We demonstrate that, when provisioned with high-throughput compute resources, cloud platforms can effectively amortize network and queueing delays, enabling them to match or surpass on-device performance for real-time decision-making. Specifically, we develop a formal analytical model that characterizes distributed inference latency as a function of the sensing frequency, platform throughput, network delay, and task-specific safety constraints. We instantiate this model in the context of emergency braking for autonomous driving and validate it through extensive simulations using real-time vehicular dynamics. Our empirical results identify concrete conditions under which cloud-based inference adheres to safety margins more reliably than its on-device counterpart. These findings challenge prevailing design strategies and suggest that the cloud is not merely a feasible option, but often the preferred inference location for distributed CPS architectures. In this light, the cloud is not as distant as traditionally perceived; in fact, it is closer than it appears. 

Real-time cyber-physical systems (CPS) rely on Perception-Cognition-Actuation (PCA) pipelines to enable autonomous observation, decisionmaking, and action execution. Closed-loop PCA systems utilize feedback-driven control to iteratively adapt actions in response to real-time environmental changes whereas open-loop PCA systems execute single actions without iterative feedback. The overall performance of these systems is inherently tied to the models selected for each pipeline component. Recent advancements in neural networks, particularly for perception tasks, have substantially enhanced CPS capabilities but have introduced significant complexity into the PCA pipeline. While traditional research [1] often evaluates perception models in static, controlled settings, it fails to account for the cascading latency and accuracy trade-offs that manifest across interconnected PCA modules in dynamic, real-time applications. Additionally, the proliferation of distributed device-edge-cloud architectures [2] has expanded computational possibilities but introduced new challenges in balancing latency and accuracy with resource constraints. The holistic impact of model selection, deployment platforms, and network conditions on application performance in real-time scenarios remains under-explored. 

This paper investigates the challenges posed by delays in Closed-Loop Sense-Act Systems in the context of Adversarial Internet of Things (IoT) applications. Prior work focused on studying the impact of delays on a single resource-constrained platform. To capitalize on the capabilities of different computing platforms, this work investigates the adaptation of control placement to optimize application performance in distributed settings. An Adaptive Control Placement (ACP) strategy is introduced, which dynamically switches between a local controller with lower accuracy and a cloud controller with higher accuracy based on network dynamics, optimizing overall application performance. The effectiveness of the ACP strategy is evaluated using a simulated Vehicle Following application in the PyBullet simulator. The results demonstrate that in terms of a time-to-complete (TTC) metric, the ACP strategy consistently outperforms strategies that use a fixed combination of controller type and location (e.g., PID at Local and MPC at Cloud) across various deadline scenarios.