Deep Reinforcement Learning (DRL)-based Resource Management in Software-Defined and Virtualized Vehicular Ad Hoc Networks

2017 ◽  
Author(s):  
Ying He ◽  
F. Richard Yu ◽  
Nan Zhao ◽  
Hongxi Yin ◽  
Azzedine Boukerche
Keyword(s):  
Reinforcement Learning ◽  
Resource Management ◽  
Ad Hoc Networks ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Hoc Networks

A fuzzy-based approach for resource management in SDN-VANETs: Effect of trustworthiness on assessment of available edge computing resources

2021 ◽  
pp. 1-12
Author(s):  
Ermioni Qafzezi ◽  
Kevin Bylykbashi ◽  
Phudit Ampririt ◽  
Makoto Ikeda ◽  
Keita Matsuo ◽  
...  
Keyword(s):  
Resource Management ◽  
Ad Hoc Networks ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Distributed Processing ◽  
Transportation Systems ◽  
Edge Computing ◽  
Processing Capability ◽  
Hoc Networks ◽  
Available Resources

Vehicular Ad hoc Networks (VANETs) aim to improve the efficiency and safety of transportation systems by enabling communication between vehicles and roadside units, without relying on a central infrastructure. However, since there is a tremendous amount of data and significant number of resources to be dealt with, data and resource management become their major issues. Cloud, Fog and Edge computing, together with Software Defined Networking (SDN) are anticipated to provide flexibility, scalability and intelligence in VANETs while leveraging distributed processing environment. In this paper, we consider this architecture and implement and compare two Fuzzy-based Systems for Assessment of Neighboring Vehicles Processing Capability (FS-ANVPC1 and FS-ANVPC2) to determine the processing capability of neighboring vehicles in Software Defined Vehicular Ad hoc Networks (SDN-VANETs). The computational, networking and storage resources of vehicles comprise the Edge Computing resources in a layered Cloud-Fog-Edge architecture. A vehicle which needs additional resources to complete certain tasks and process various data can use the resources of the neighboring vehicles if the requirements to realize such operations are fulfilled. The proposed systems are used to assess the processing capability of each neighboring vehicle and based on the final value, it can be determined whether the edge layer can be used by the vehicles in need. FS-ANVPC1 takes into consideration the available resources of the neighboring vehicles and the predicted contact duration between them and the present vehicle, while FS-ANVPC2 includes in addition the vehicles trustworthiness value. Our systems take also into account the neighboring vehicles’ willingness to share their resources and determine the processing capability for each neighbor. We evaluate the proposed systems by computer simulations. The evaluation results show that FS-ANVPC1 decides that helpful neighboring vehicles are the ones that are predicted to be within the vehicle communication range for a while and have medium/large amount of available resources. FS-ANVPC2 considers the same neighboring vehicles as helpful neighbors only if they have at least a moderate trustworthiness value ( VT = 0.5). When VT is higher, FS-ANVPC2 takes into consideration also neighbors with less available resources.

Hierarchical Routing for Vehicular Ad Hoc Networks via Reinforcement Learning

2019 ◽  
Vol 68 (2) ◽  
pp. 1852-1865 ◽  
Author(s):  
Fan Li ◽  
Xiaoyu Song ◽  
Huijie Chen ◽  
Xin Li ◽  
Yu Wang
Keyword(s):  
Reinforcement Learning ◽  
Ad Hoc Networks ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Hierarchical Routing ◽  
Hoc Networks
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