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Efficient and Secure Routing Protocol Based on Artificial Intelligence Algorithms With UAV-Assisted for Vehicular Ad Hoc Networks in Intelligent Transportation Systems

Published: 01 July 2021 Publication History

Abstract

Vehicular Ad hoc Networks (VANETs) that are considered as a subset of Mobile Ad hoc Networks (MANETs) can be applied in the field of transportation especially in Intelligent Transportation Systems (ITS). The routing process in these networks is a challenging task due to rapid topology changes, high vehicle mobility and frequent disconnection of links. Therefore, developing an efficient routing protocol that satisfies restriction of delay and minimum overhead is faced with many difficulties and limitations. Also, the detection of malicious vehicles is a significant task in VANETs. To address these issues, using Unmanned Aerial Vehicles (UAVs) can be helpful to cope with these limitations. In this paper, operation of UAVs in ad hoc mode and their cooperation with vehicles in VANETs are studied to help in the process of routing and detection of malicious vehicles. A routing protocol named VRU is proposed that includes two distinct ways of routing of data: (1) delivering packets of data between vehicles with the help of UAVs using a protocol named VRU_vu, and (2) routing packet of data between UAVs using a protocol named VRU_u. The NS-2.35 simulator under Linux Ubuntu 12.04 is utilized in order to appraise the performance of VRU routing components in an urban scenario. Also, VanetMobiSim generator of mobility and MobiSim are used to produce the motions of vehicles and to produce the motions of UAVs, respectively. The performance analysis displays that VRU protocol can improve the packet delivery ratio by 16% and detection ratio by 7% compared to other reviewed routing protocol. Also, VRU protocol decreases end-to-end delay by an average of 13% and overhead by 40%.

References

[1]
S. Al-Sultan, M. M. Al-Doori, A. H. Al-Bayatti, and H. Zedan, “A comprehensive survey on vehicular ad hoc network,” J. Netw. Comput. Appl., vol. 37, pp. 380–392, Jan. 2014.
[2]
H. Hasrouny, A. E. Samhat, C. Bassil, and A. Laouiti, “Misbehavior detection and efficient revocation within VANET,” J. Inf. Secur. Appl., vol. 46, pp. 193–209, Jun. 2019.
[3]
H. Fatemidokht and M. K. Rafsanjani, “F-ant: An effective routing protocol for ant colony optimization based on fuzzy logic in vehicular ad hoc networks,” Neural Comput. Appl., vol. 29, no. 11, pp. 1127–1137, Jun. 2018.
[4]
B. Mokhtar and M. Azab, “Survey on security issues in vehicular ad hoc networks,” Alexandria Eng. J., vol. 54, no. 4, pp. 1115–1126, Dec. 2015.
[5]
S.-F. Tzeng, S.-J. Horng, T. Li, X. Wang, P.-H. Huang, and M. K. Khan, “Enhancing security and privacy for identity-based batch verification scheme in VANETs,” IEEE Trans. Veh. Technol., vol. 66, no. 4, pp. 3235–3248, Apr. 2017.
[6]
X. Zhang and X. Zhang, “A binary artificial bee colony algorithm for constructing spanning trees in vehicular ad hoc networks,” Ad Hoc Netw., vol. 58, pp. 198–204, Apr. 2017.
[7]
B. T. Sharef, R. A. Alsaqour, and M. Ismail, “Vehicular communication ad hoc routing protocols: A survey,” J. Netw. Comput. Appl., vol. 40, pp. 363–396, Apr. 2014.
[8]
A. Tewari and B. B. Gupta, “Security, privacy and trust of different layers in Internet-of-Things (IoTs) framework,” Future Gener. Comput. Syst., vol. 108, pp. 909–920, Jul. 2020.
[9]
A. Al-Qerem, M. Alauthman, and A. Almomani, “IoT transaction processing through cooperative concurrency control on fog–cloud computing environment,” Soft Comput., vol. 24, no. 8, pp. 5695–5711, 2020.
[10]
I. Bekmezci, O. K. Sahingoz, and Ş. Temel, “Flying ad-hoc networks (FANETs): A survey,” Ad Hoc Netw., vol. 11, no. 3, pp. 1254–1270, May 2013.
[11]
A. V. Leonov, “Applying bio-inspired algorithms to routing problem solution in FANET,” Bull. SUSU, vol. 17, no. 2, pp. 5–23, 2017.
[12]
E. Yanmaz, M. Quaritsch, S. Yahyanejad, B. Rinner, H. Hellwagner, and C. Bettstetter, “Communication and coordination for drone networks,” in Proc. Ad Hoc Netw. Ottawa, ON, Canada: Springer, 2017, pp. 79–91.
[13]
K. Daniel, B. Dusza, A. Lewandowski, and C. Wietfeld, “Airshield: A system-of-systems MUAV remote sensing architecture for disaster response,” in Proc. 3rd Annu. IEEE Syst. Conf., Vancouver, BC, Canada, Mar. 2009, pp. 196–200.
[14]
S. A. Hadiwardoyo, E. Hernández-Orallo, C. T. Calafate, J. C. Cano, and P. Manzoni, “Experimental characterization of UAV-to-car communications,” Comput. Netw., vol. 136, pp. 105–118, May 2018.
[15]
F. Mirsadeghi, M. K. Rafsanjani, and B. B. Gupta, “A trust infrastructure based authentication method for clustered vehicular ad hoc networks,” Peer Peer Netw. Appl., 2020, pp. 1–17.
[16]
Z. A. Al-Sharif, M. I. Al-Saleh, L. M. Alawneh, Y. I. Jararweh, and B. Gupta, “Live forensics of software attacks on cyber–physical systems,” Future Gener. Comput. Syst., vol. 108, pp. 1217–1229, Jul. 2020.
[17]
M. Dorigo and G. D. Caro, “Ant colony optimization: A new meta-heuristic,” in Proc. Congr. Evol. Comput. (CEC), Washington, DC, USA, Jul. 1999, pp. 1470–1477.
[18]
A. Daeinabi, A. G. P. Rahbar, and A. Khademzadeh, “VWCA: An efficient clustering algorithm in vehicular ad hoc networks,” J. Netw. Comput. Appl., vol. 34, no. 1, pp. 207–222, Jan. 2011.
[19]
K. Bylykbashi, D. Elmazi, K. Matsuo, M. Ikeda, and L. Barolli, “Effect of security and trustworthiness for a fuzzy cluster management system in VANETs,” Cognit. Syst. Res., vol. 55, pp. 153–163, Jun. 2019.
[20]
M. R. Jabbarpouret al., “Ant-based vehicle congestion avoidance system using vehicular networks,” Eng. Appl. Artif. Intell., vol. 36, pp. 303–319, Nov. 2014.
[21]
B. R. Bellur, M. G. Lewis, and F. L. Templin, “An ad-hoc network for teams of autonomous vehicles,” in Proc. 1st Annu. Symp. Auton. Intell. Netw. Syst., 2002, pp. 1–6.
[22]
L. Lin, Q. Sun, J. Li, and F. Yang, “A novel geographic position mobility oriented routing strategy for UAVs,” J. Comput. Inf. Syst., vol. 8, no. 2, pp. 709–716, 2012.
[23]
K. Liu, J. Zhang, and T. Zhang, “The clustering algorithm of UAV networking in near-space,” in Proc. 8th Int. Symp. Antennas, Propag. EM Theory, 2008, pp. 1550–1553.
[24]
J. Nzouonta, N. Rajgure, G. Wang, and C. Borcea, “VANET routing on city roads using real-time vehicular traffic information,” IEEE Trans. Veh. Technol., vol. 58, no. 7, pp. 3609–3626, Sep. 2009.
[25]
H. Fatemidokht and M. K. Rafsanjani, “QMM-VANET: An efficient clustering algorithm based on QoS and monitoring of malicious vehicles in vehicular ad hoc networks,” J. Syst. Softw., vol. 165, Jul. 2020, Art. no.
[26]
O. S. Oubbati, A. Lakas, N. Lagraa, and M. B. Yagoubi, “ETAR: Efficient traffic light aware routing protocol for vehicular networks,” in Proc. Int. Wireless Commun. Mobile Comput. Conf. (IWCMC), Aug. 2015, pp. 297–301.
[27]
G. S. Khekare and A. V. Sakhare, “A smart city framework for intelligent traffic system using VANET,” in Proc. Int. Mutli-Conf. Autom., Comput., Commun., Control Compressed Sens. (iMac4s), Kottayam, India, Mar. 2013, pp. 302–305.
[28]
C. Perkins, E. Belding-Royer, and S. Das, “Ad hoc on-demand distance vector (AODV) routing,” RFC Editor, USA, Tech. Rep. RFC3561, 2003.
[29]
S. E. Bibri, “The IoT for smart sustainable cities of the future: An analytical framework for sensor-based big data applications for environmental sustainability,” Sustain. Cities Soc., vol. 38, pp. 230–253, Apr. 2018.
[30]
A. Carieet al., “An Internet of software defined cognitive radio ad-hoc networks based on directional antenna for smart environments,” Sustain. Cities Soc., vol. 39, pp. 527–536, May 2018.
[31]
O. S. Oubbati, A. Lakas, N. Lagraa, and M. B. Yagoubi, “CRUV: Connectivity-based traffic density aware routing using UAVs for VANets,” in Proc. Int. Conf. Connected Vehicles Expo (ICCVE), Oct. 2015, pp. 68–73.
[32]
O. S. Oubbati, A. Lakas, F. Zhou, M. Günes, N. Lagraa, and M. B. Yagoubi, “Intelligent UAV-assisted routing protocol for urban VANETs,” Comput. Commun., vol. 107, pp. 93–111, Jul. 2017.
[33]
R. Shirani, M. St-Hilaire, T. Kunz, Y. Zhou, J. Li, and L. Lamont, “On the delay of reactive-greedy-reactive routing in unmanned aeronautical ad-hoc networks,” Proc. Comput. Sci., vol. 10, pp. 535–542, Aug. 2012.
[34]
P. Golle, D. Greene, and J. Staddon, “Detecting and correcting malicious data in VANETs,” in Proc. 1st ACM Workshop Veh. Ad Hoc Netw. (VANET), Philadelphia, PA, USA, 2004, pp. 29–37.
[35]
S. Gurung, D. Lin, A. Squicciarini, and E. Bertino, “Information-oriented trustworthiness evaluation in vehicular ad-hoc networks,” in Proc. Int. Conf. Netw. Syst. Secur. Sapporo, Japan: Springer, 2013, pp. 94–108.
[36]
C. A. Kerrache, N. Lagraa, C. T. Calafate, and A. Lakas, “TFDD: A trust-based framework for reliable data delivery and DoS defense in VANETs,” Veh. Commun., vol. 9, pp. 254–267, Jul. 2017.
[37]
C. A. Kerrache, A. Lakas, N. Lagraa, and E. Barka, “UAV-assisted technique for the detection of malicious and selfish nodes in VANETs,” Veh. Commun., vol. 11, pp. 1–11, Jan. 2018.
[38]
K. Singh and A. K. Verma, “FCTM: A novel fuzzy classification trust model for enhancing reliability in flying ad hoc networks (FANETs).” Ad Hoc Sensor Wireless Netw., vol. 40, nos. 1–2, pp. 23–47, 2018.
[39]
Y. Yu, L. Ru, W. Chi, Y. Liu, Q. Yu, and K. Fang, “Ant colony optimization based polymorphism-aware routing algorithm for ad hoc UAV network,” Multimedia Tools Appl., vol. 75, no. 22, pp. 14451–14476, Nov. 2016.
[40]
M. Chahal and S. Harit, “Network selection and data dissemination in heterogeneous software-defined vehicular network,” Comput. Netw., vol. 161, pp. 32–44, Oct. 2019.
[41]
A. Bensalem and D. E. Boubiche, “EBEESU: ElectriBio-inspired energy-efficient self-organization model for unmanned aerial ad-hoc network,” Ad Hoc Netw., vol. 107, Oct. 2020, Art. no.
[42]
W. Fisher, “Development of DSRC/wave standards,” in Proc. IEEE Annapolis, Jun. 2007.
[43]
J. Shawe-Taylor and N. Cristianini, Kernel Methods for Pattern Analysis. Cambridge, U.K.: Cambridge Univ. Press, 2004.
[44]
E. Barka, C. A. Kerrache, R. Hussain, N. Lagraa, A. Lakas, and S. H. Bouk, “A trusted lightweight communication strategy for flying named data networking,” Sensors, vol. 18, no. 8, pp. 2683–2700, 2018.
[45]
K. Fall and K. Varadhan. (2007). The Network Simulator (NS-2). Accessed: Sep.2019. [Online]. Available: http://www.isi.edu/nsnam/ns
[46]
J. Härri, F. Filali, C. Bonnet, and M. Fiore, “VanetMobiSim: Generating realistic mobility patterns for VANETs,” in Proc. 3rd Int. Workshop Veh. Ad Hoc Netw. (VANET), 2006, pp. 96–97.
[47]
S. M. Mousavi, H. R. Rabiee, M. Moshref, and A. Dabirmoghaddam, “Mobisim: A framework for simulation of mobility models in mobile ad-hoc networks,” in Proc. 3rd IEEE Int. Conf. Wireless Mobile Comput., Netw. Commun. (WiMOB), White Plains, NY, USA, Oct. 2007, p. 82.
[48]
H. Sedjelmaci and S. M. Senouci, “An accurate and efficient collaborative intrusion detection framework to secure vehicular networks,” Comput. Electr. Eng., vol. 43, pp. 33–47, Apr. 2015.

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          cover image IEEE Transactions on Intelligent Transportation Systems
          IEEE Transactions on Intelligent Transportation Systems  Volume 22, Issue 7
          July 2021
          867 pages

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