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

Abstract

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.

Keywords

VANETs SDN fuzzy logic cloud computing fog computing edge computing FS-ANVPC trustworthiness

Get full access to this article

View all access options for this article.

References

Bonomi,

Milito,

Zhu and

Addepalli, Fog computing and its role in the Internet of things, in: Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing, ACM, 2012, pp. 13–16. doi:10.1145/2342509.2342513.

Ge,

Li and

Li, 5G software defined vehicular networks, IEEE Communications Magazine 55(7) (2017), 87–93. doi:10.1109/MCOM.2017.1601144.

Y.C.

Hu,

Patel,

Sabella,

Sprecher and

Young, Mobile edge computing – A key technology towards 5G, ETSI white paper 11(11) (2015), 1–16.

Hussain,

Son,

Eun,

Kim and

Oh, Rethinking vehicular communications: Merging VANET with cloud computing, in: 4th International Conference on Cloud Computing Technology and Science (CloudCom), IEEE, 2012, pp. 606–609.

Kandel, Fuzzy Expert Systems, CRC Press, Inc., Boca Raton, FL, USA, 1992. ISBN 0-8493-4297-X.

G.J.

Klir and

T.A.

Folger, Fuzzy Sets, Uncertainty, and Information, Prentice Hall, Upper Saddle River, NJ, USA, 1988.

Ku,

Lu,

Gerla,

R.L.

Gomes,

Ongaro and

Cerqueira, Towards software-defined VANET: Architecture and services, in: 13th Annual Mediterranean Ad Hoc Networking Workshop (MED-HOC-NET), 2014, pp. 103–110. doi:10.1109/MedHocNet.2014.6849111.

F.M.

McNeill and

Thro, Fuzzy Logic: A Practical Approach, Academic Press Professional, Inc., San Diego, CA, USA, 1994. ISBN 0-12-485965-8.

Munakata and

Jani, Fuzzy systems: An overview, Communications of the ACM 37(3) (1994), 69–77.

10.

Olariu,

Hristov and

Yan, The next paradigm shift: From vehicular networks to vehicular clouds, Mobile Ad Hoc Networking: Cutting Edge Directions 56(6) (2013), 645–700.

11.

Palmieri, Bayesian resource discovery in infrastructure-less networks, Information Sciences 376 (2017), 95–109. doi:10.1016/j.ins.2016.10.021.

12.

Palmieri, A reliability and latency-aware routing framework for 5G transport infrastructures, Computer Networks 179 (2020), 107365. doi:10.1016/j.comnet.2020.107365.

13.

Qafzezi,

Bylykbashi,

Ikeda,

Matsuo and

Barolli, Coordination and management of cloud, fog and edge resources in SDN-VANETs using fuzzy logic: A comparison study for two fuzzy-based systems, Internet of Things 11 (2020), 100169. doi:10.1016/j.iot.2020.100169.

14.

Stojmenovic,

Wen,

Huang and

Luan, An overview of fog computing and its security issues, Concurrency and Computation: Practice and Experience 28(10) (2016), 2991–3005. doi:10.1002/cpe.3485.

15.

N.B.

Truong,

G.M.

Lee and

Ghamri-Doudane, Software defined networking-based vehicular adhoc network with fog computing, in: 2015 IFIP/IEEE International Symposium on Integrated Network Management (IM), 2015, pp. 1202–1207.

16.

Xu,

Zhou,

Cheng,

Lyu,

Shi,

Chen and

Shen, Internet of vehicles in big data era, IEEE/CAA Journal of Automatica Sinica 5(1) (2018), 19–35. doi:10.1109/JAS.2017.7510736.

17.

Yuan,

Zhou,

Li,

Liu,

Yang and

X.S.

Shen, Toward efficient content delivery for automated driving services: An edge computing solution, IEEE Network 32(1) (2018), 80–86. doi:10.1109/MNET.2018.1700105.

18.

L.A.

Zadeh and

Kacprzyk, Fuzzy Logic for the Management of Uncertainty, John Wiley & Sons, Inc., New York, NY, USA, 1992.

19.

H.-J.

Zimmermann, Fuzzy control, in: Fuzzy Set Theory and Its Applications, Springer, 1996, pp. 203–240. doi:10.1007/978-94-015-8702-0_11.