.png){kind=icon}
.png){kind=icon}
Return
Budget-Aware Edge-First Collaboration for Robust Inference in Sensor–Cloud Networks
.png){kind=icon}
PRE
AI
M
I
I
A
N
DOI：10.1109/mnet.2026.3671221.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Edge-enabled sensing applications in digital health, industrial Internet of Things (IIoT), and extended reality (XR) face a persistent trilemma: lightweight edge models meet strict latency, bandwidth, and energy budgets but degrade under noise and distribution shift; cloud models improve robustness and calibration but can violate tail-latency and privacy constraints; and naïve split inference reduces edge compute at the cost of inflated network usage and unstable tails. We present a deployable, edge-first blueprint for bud-get-aware collaborative training and co-inference between large cloud models and lightweight edge models in sensor-cloud networks. The design has three pillars: (i) a collaborative training workflow that transfers robustness and calibrated confidence from a cloud teacher to an edge student so most inference completes locally; (ii) a confidence-gated, budget-aware router that selects among EDGE, EDGE + CORRECTION, and CLOUD actions using calibrated uncertainty and short-horizon link telemetry while explicitly enforcing P95 latency, bandwidth, and energy budgets; and (iii) privacy scaffolding via trusted execution environments (and optional differential privacy), with all correction or offload overheads accounted end-to-end when invoked. Compact case studies on inertial and physiological sensing workloads demonstrate edge-dominant operation with tight latency tails, small and predictable bandwidth, bounded energy consumption, and improved robustness over non-collaborative baselines. We conclude with practical guidance on system design, scheduling, measurement, and deployment trade-offs for realworld sensor-cloud intelligence.
Keywords:
Edge computing
Sensor systems and applications
Large-scale systems
Industrial Internet of Things
Extended reality
Electronic healthcare
Collaboration
Collaboration
Training
Privacy
Journal
IF:
6.3
Papers:
2.6K
Citations:
1.1W
Organization
Cited Papers