@inproceedings{10.1145/3583120.3589571, author = {Feraudo, Angelo}, title = {PhD Forum Abstract: Vehicular-based Support to Cooperative Edge Computing based Applications in Next-gen Networks}, year = {2023}, isbn = {9798400701184}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3583120.3589571}, doi = {10.1145/3583120.3589571}, abstract = {Today’s advancements in IoT devices and edge computing platforms have given rise to new scenarios enabling context-aware applications in extremely interconnected environments. To promote the standardization of these platforms the European Telecommunications Standards Institute (ETSI) proposed the Multi-access Edge Computing (MEC) standard, enabling the execution of cloud-like services at the network edge. In this work, we propose the design of a novel MEC-compliant architecture that leverages underutilized far-edge resources to enlarge MEC edge node computational capacity and enhance service availability in highly mobile networks. Our approach allows far-edge devices to participate in a negotiation process embodying a rewarding system while addressing resource volatility as these devices join and leave the edge node resource infrastructure. Furthermore, we developed an original simulation framework to replicate the proposed architecture by using vehicle resources as far-edge devices. Its primary purpose is to demonstrate the viability and flexibility of our proposal, as well as to investigate novel application scenarios using real-world datasets. Our preliminary results show the feasibility and effectiveness of our proposal when using vehicular-based virtual resources in realistically simulated 5G networks.}, booktitle = {Proceedings of the 22nd International Conference on Information Processing in Sensor Networks}, pages = {352–353}, numpages = {2}, keywords = {5G, IoV, Multi-access Edge Computing, Vehicular Computing}, location = {, San Antonio, TX, USA, }, series = {IPSN '23} }