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November 12, 2025SensorsOpen Access

Stochastic Geometric-Based Modeling for Partial Offloading Task Computing in Edge-AI Systems

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Authors

HSHamid SaeediANAli Nouruzi

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Overview

Simulation reveals improved robustness and task management in Edge-AI systems through resource allocation and spatial correlation.

Key Points

  • Joint optimization problem enhances resource allocation while ensuring robustness against non-i.i.d. distributions.
  • Simulation results show a clustering accuracy of 99.2%, which is 15% higher than the baseline.
  • Projected gradient descent method effectively addresses the NP-hard problem of task computing and resource allocation.
  • Cooperation between selected users as edge servers and central server enhances performance and reliability in distributed Edge-AI systems.

Cite This Study

Saeedi et al. (2025) studied this question.

synapsesocial.com/papers/69252e83c0ce034ddc355972https://doi.org/10.3390/s25226892
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Distributed Task Offloading in Cooperative Mobile Edge Computing Networks2024 · 4 citations
  2. 2Heterogeneous Task Edge Offloading and Resource Optimization Strategy for Intelligent Scenarios2025
  3. 3Mobility-Aware Cooperative Optimization for Task Offloading and Resource Allocation in Multi-Edge Computing2026
  4. 4Joint Optimization for Dependency-Aware Computation-Offloading and Cooperative Service-Caching in Edge Computing2026
  5. 5Online Multi-Task Offloading for Semantic-Aware Edge Computing Systems2024