A novel traffic steering scheme that jointly considers real-time network conditions and MEC compute availability to optimize service instance selection and dynamically associates mobile clients with the most suitable service endpoint, minimizing end-to-end latency and improving load distribution is proposed.
SOVANET+ is presented, an extended scheduling technique that jointly accounts for service criticality, network load, and wireless link quality to allocate resources adaptively across coexisting Vehicle-to-Everything (V2X) services, supporting its viability for next-generation intelligent transportation systems.
Athanasios Kanavos, Gerasimos Papanikolaou-Ntais, A. Kaloxylos· Electronics· 0 citations
With the rapid development of Vehicle-to-Everything (V2X) and Vehicular Edge Computing (VEC), the massive computation-intensive tasks generated by intelligent vehicles during driving, including environmental perception, path planning, and autonomous driving decision-making, impose extremely high requirements on real-ti...
Jun Wang, Lin Chai, Yu-Mei Yang et al.· Journal of networking and ne...· 0 citations
This paper investigates a phased sensing-assisted mobile edge computing system composed of multiple unmanned aerial vehicles (UAVs). A framework is proposed to operate in three sequential phases: local user sensing, global state aggregation, and centralized decision making for distributed offloading. To achieve efficie...
In the sixth-generation (6G) era, wireless networks need to support a large number of ultra-low latency and high-reliability applications. However, conventional bit-level communication paradigms fail to capture the intrinsic meaning of multi-modal data, leading to inefficiencies in both communication and computation fo...
Fang-Fang Yin, Yue-Xin Liu, Wanli Ni et al.· IEEE Transactions on Communi...· 0 citations
Edge-assisted autonomous driving increasingly depends on delivering task-specific deep neural network (DNN) models from the infrastructure to vehicles, so that the environment perception models can be updated to adapt to changing traffic environments, service requirements, and heterogeneous onboard platforms. However,...
LUCID is presented, an LLM-agent--orchestrated, uplink-aware cloud-robotics pipeline that moves TP--RRM from solving a fixed formulation to dynamically orchestrating optimization problem schemas within a DITL environment and robustly adapts to changing intents, active-robot counts, and scenes.
Hyeonsu Lyu, Minwoo Kim, Sehyun Ryu et al.· 0 citations
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