Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Mehrban, A.; Moudoud, H.; Brik, B.; Houda, Z. A. El
Enhancing Spectral Efficiency and Resilience in SAGIN-ITS: An IRS-Assisted Semantic Offloading Framework Journal Article
In: IEEE Internet of Things Magazine, vol. 9, no. 4, pp. 30–36, 2026, ISSN: 25763180 (ISSN).
Abstract | Links | BibTeX | Tags: Air grounds, Bandwidth, Computation offloading, Federated learning, federated learning (FL), IEEE Standards, Integrated networks, Intelligent reflecting surface, Intelligent reflecting surfaces (IRS), Intelligent transportation system, Intelligent transportation systems, intelligent transportation systems (ITS), Intelligent vehicle highway systems, Learning systems, Reflecting surface, Semantic offloading, Semantics, Sensor networks, Sensors data, Signal encoding, Space-air-ground integrated network, space-air-ground integrated networks (SAGIN), Traffic congestion, Transmissions
@article{mehrbanEnhancingSpectralEfficiency2026,
title = {Enhancing Spectral Efficiency and Resilience in SAGIN-ITS: An IRS-Assisted Semantic Offloading Framework},
author = {A. Mehrban and H. Moudoud and B. Brik and Z. A. El Houda},
url = {https://www.scopus.com/pages/publications/105036681552?origin=resultslist},
doi = {10.1109/MIOT.2026.3676349},
issn = {25763180 (ISSN)},
year = {2026},
date = {2026-01-01},
journal = {IEEE Internet of Things Magazine},
volume = {9},
number = {4},
pages = {30–36},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {In dense Vehicle-to-Everything (V2X) scenarios within Space-Air-Ground Integrated Networks (SAGIN), connected autonomous cars (CAVs) generate a large volume of sensor data, which leads to significant bandwidth congestion. There is a risk to safety because of the poor Packet Success Rate (PSR) and dangerous transmission delays caused by this congestion. To address this issue, we introduce SemantIRS, a framework that integrates semantic offloading with Intelligent Reflecting Surface (IRS)-assisted links to achieve high network resilience, when compute resources permit and lower latency. SemantIRS deploys lightweight Large Language Models (LLMs) as encoders to compress massive sensor data at the edge of the SAGIN network before transmission through IRS-assisted links. Intent-Based Networking (IBN) and Federated Learning (FL) are also integrated to automate resilience policies and preserve distributed offloading privacy throughout SAGIN layers, respectively. We conduct our experiments using a 12-state hardware benchmark of three LLM encoders (Phi-2, Llama-2-7B-Chat, and Mistral-7B-Instruct-v0.2) on four compute tiers (TPU v6e-1, A100, L4, and Intel Xeon CPU). The measured inference latencies are then fed into an IEEE 802.11p airtime simulator. Our obtained results show that, in congested network conditions, SemantIRS improves the packet success rate from 1.7% to 66.4% while reducing transmission energy by 90%. © 2026 IEEE.},
keywords = {Air grounds, Bandwidth, Computation offloading, Federated learning, federated learning (FL), IEEE Standards, Integrated networks, Intelligent reflecting surface, Intelligent reflecting surfaces (IRS), Intelligent transportation system, Intelligent transportation systems, intelligent transportation systems (ITS), Intelligent vehicle highway systems, Learning systems, Reflecting surface, Semantic offloading, Semantics, Sensor networks, Sensors data, Signal encoding, Space-air-ground integrated network, space-air-ground integrated networks (SAGIN), Traffic congestion, Transmissions},
pubstate = {published},
tppubtype = {article}
}
In dense Vehicle-to-Everything (V2X) scenarios within Space-Air-Ground Integrated Networks (SAGIN), connected autonomous cars (CAVs) generate a large volume of sensor data, which leads to significant bandwidth congestion. There is a risk to safety because of the poor Packet Success Rate (PSR) and dangerous transmission delays caused by this congestion. To address this issue, we introduce SemantIRS, a framework that integrates semantic offloading with Intelligent Reflecting Surface (IRS)-assisted links to achieve high network resilience, when compute resources permit and lower latency. SemantIRS deploys lightweight Large Language Models (LLMs) as encoders to compress massive sensor data at the edge of the SAGIN network before transmission through IRS-assisted links. Intent-Based Networking (IBN) and Federated Learning (FL) are also integrated to automate resilience policies and preserve distributed offloading privacy throughout SAGIN layers, respectively. We conduct our experiments using a 12-state hardware benchmark of three LLM encoders (Phi-2, Llama-2-7B-Chat, and Mistral-7B-Instruct-v0.2) on four compute tiers (TPU v6e-1, A100, L4, and Intel Xeon CPU). The measured inference latencies are then fed into an IEEE 802.11p airtime simulator. Our obtained results show that, in congested network conditions, SemantIRS improves the packet success rate from 1.7% to 66.4% while reducing transmission energy by 90%. © 2026 IEEE.