{"ID":2846441,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.01127","arxiv_id":"2511.01127","title":"Neuro-Inspired Task Offloading in Edge-IoT Networks Using Spiking Neural Networks","abstract":"Traditional task offloading strategies in edge computing often rely on static heuristics or data-intensive machine learning models, which are not always suitable for highly dynamic and resource-constrained environments. In this paper, we propose a novel task-offloading framework based on Spiking Neural Networks inspired by the efficiency and adaptability of biological neural systems. Our approach integrates an SNN-based decision module into edge nodes to perform real-time, energy-efficient task orchestration. We evaluate the model under various IoT workload scenarios using a hybrid simulation environment composed of YAFS and Brian2. The results demonstrate that our SNN-based framework significantly reduces task processing latency and energy consumption while improving task success rates. Compared to traditional heuristic and ML-based strategies, our model achieves up to 26% lower latency, 32% less energy consumption, and 25\\% higher success rate under high-load conditions.","short_abstract":"Traditional task offloading strategies in edge computing often rely on static heuristics or data-intensive machine learning models, which are not always suitable for highly dynamic and resource-constrained environments. In this paper, we propose a novel task-offloading framework based on Spiking Neural Networks inspire...","url_abs":"https://arxiv.org/abs/2511.01127","url_pdf":"https://arxiv.org/pdf/2511.01127v1","authors":"[\"Fabio Diniz Rossi\"]","published":"2025-11-03T00:32:19Z","proceeding":"cs.DC","tasks":"[\"cs.DC\"]","methods":"[]","has_code":false}