An Enhanced Bio-Inspired Routing Algorithm for Vehicular Ad Hoc Networks
DOI:
https://doi.org/10.48048/tis.2022.4188Keywords:
VANETs, Routing, Pheromone count, Optimal path, Reliable communicationAbstract
Vehicular ad hoc networks (VANETs) demands reliable communication mechanisms for time-critical communication between vehicles. In VANETs, communication links between vehicles are prone to frequent breaks due to high mobility and topology changes. In this context, this work presents an enhanced bio-inspired routing algorithm (EBIRA) to provide reliable communication. In EBIRA, enhanced ant colony optimization (EACO) finds the optimal long-life short-distance routes with the minimum hops based on distance, received signal strength metric, hop count, and evaporation rate. In EBIRA, the selected path has a short distance and a high level of connectivity at the link level with minimum hops. Choosing the shortest path through minimum hops with high connectivity level links improves route lifetime and reduces frequent link breaks between vehicles. Simulation results show that the performance of EBIRA is better than reliable route discovery by using ant colony optimization (RDACO) and road-aware geographic routing protocol (RAGR) in terms of packet delivery ratio, throughput, and latency. Furthermore, variations of the received signal strength based on vehicle density and speed are evaluated, and the EBIRA route discovery success ratio is estimated and shown based on vehicle density at speed.
HIGHLIGHTS
- Vehicular ad hoc networks (VANETs) demands reliable communication mechanisms for time-critical communication between vehicles. In VANETs, communication links between vehicles are prone to frequent breaks due to high mobility and topology changes
- VANET routing faces various challenges due to its unique characteristics such as high mobility, dynamic topology, unlimited network size, no infrastructure, and wireless communication
- Choosing the shortest path through minimum hops with high connectivity level links improves route lifetime and reduces frequent link breaks between vehicles
- The Less complexity, adaptability and self-organizing characteristics of ACO can cope with frequent topology changes, high mobility, absence of infrastructure, and wireless communication. The robustness feature of the ACO often helps to overcome network interruptions in the form of disconnections
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K Renuka, DS Roy and KHK Reddy. An SDN empowered location aware routing for energy efficient next generation vehicular networks. IET Intell. Transport syst. 2021; 15, 308-19.
S Al-Sultan, MM Al-Doori, AH Al-Bayatti and H Zedan. A comprehensive survey on vehicular Ad Hoc network. J. Netw. Comput. Appl. 2014; 37, 380-92.
H Bello-Salau, A Aibinu, Z Wang, A Onumanyi, E Onwuka and J Dukiya. An optimized routing algorithm for vehicle ad-hoc networks. Eng. Sci. Tech. Int. J. 2019; 22, 754-66.
Y Azzoug and A Boukra. Bio-inspired VANET routing optimization: An overview. Artif. Intell. Rev. 2021; 54, 1005-62.
J Sun, W Fang, X Wu, Z Xie and W Xu. QoS multicast routing using a quantum-behaved particle swarm optimization algorithm. Eng. Appl. Artif. Intell. 2011; 24, 123-31.
R Ramamoorthy and M Thangavelu. Group based dual mode key management scheme for secure
communication in vehicular ad hoc networks. Wireless Pers. Comm. 2021; 120, 949-73.
W Deng, J Xu and H Zhao. An improved ant colony optimization algorithm based on hybrid strategies for scheduling problem. IEEE Access 2019; 7, 20281-92.
G Li and L Boukhatem. An intersection-based delay sensitive routing for vanets using aco algorithm. In: Proceedings of the 23rd International Conference on Computer Communication and Networks, Shanghai, China. 2014, p. 750-7.
S Bitam, A Mellouk and S Zeadally. Bio-inspired routing algorithms survey for vehicular ad hoc networks. IEEE Comm. Surv. Tutorials 2015; 17, 843-67.
F Dressler and O Akan. Bio-inspired networking: From theory to practice. IEEE Comm. Mag. 2010; 48, 176-83.
Y Lin, J Zhang, HS Chung, WH Ip, Y Li and Shi. An ant colony optimization approach for maximizing the lifetime of heterogeneous wireless sensor networks. IEEE Trans. Syst. Man Cybern. C 2012; 42, 408-20.
R Ramamoorthy and M Thangavelu. An improved distance-based ant colony optimization routing for vehicular ad hoc networks. Int. J. Comm. Syst. 2020; 33, e4502.
B Kazemi, S Ahmadi and Talebi. Optimum and reliable routing in VANETs: An opposition based ant colony algorithm scheme. In: Proceedings of the 2013 International Conference on Connected Vehicles and Expo, Las Vegas NV, United States. 2013, p. 926-30.
D Sinwar, N Sharma, SK Maakar and S Kumar. Analysis and comparison of ant colony optimization algorithm with DSDV, AODV, and AOMDV based on shortest path in MANET. J. Inform. Optim. Sci. 2020; 41, 621-32.
H Rana, P Thulasiraman and RK Thulasiram. MAZACORNET: Mobility aware zone-based ant colony optimization routing for VANET. In: Proceedings of the 2013 IEEE Congress on Evolutionary Computation, Cancun, Mexico. 2013, p. 2948-55.
O Chakroun, S Cherkaoui and J Rezgui. MUDDS: Multi-metric unicast data dissemination scheme for 802.11p VANETs. In: Proceedings of the 8th International Wireless Communications and Mobile Computing Conference, Yogyakarta, Indonesia. 2012, p. 1074-9.
SM Basavaraj and RM Jyoti. Reliable traffic and mobility aware position based vehicular ad hoc network routing using ACO. Int. J. Adv. Sci. Tech. 2020; 29, 645-69.
T Jeyaprakash and R Mukesh. An optimized node selection routing protocol for vehicular ad-hoc networks a hybrid model. J. Comm. Software Syst. 2015; 11, 80-5.
N Sallom Kadhim, MN Mohammed, M Abdul Majid, SQ Mohamd and H Tao. An efficient route selection based on AODV algorithm for VANET. Indian J. Sci. Tech. 2016; 9, 1-6.
S Zhang, X Liu and M Wang. A novel ant colony optimization algorithm for the shortest-path problem in traffic networks. Filomat 2018; 32, 1619-28.
N Melaouene and R Romadi. An enhanced routing algorithm using ant colony optimization and VANET infrastructure. In: Proceedings of the 6th International Conference on Traffic and Logistic Engineering, Bangkok, Thailand. 2019, p. 1-5.
SLO Correia, J Celestino and O Cherkaoui. Mobility-aware ant colony optimization routing for vehicular ad hoc networks. In: Proceedings of the 2011 IEEE wireless communications and networking conference, Cancun, Mexico. 2011, p. 1125-30.
A Mohammadnia, R Rahmani, S Mohammadnia and M Bekravi. A load balancing routing mechanism based on ant colony optimization algorithm for vehicular adhoc network. Int. J. Netw. Comput. Eng. 2016; 7, 1-10.
R Kumar and SK Routray. Ant colony based dynamic source routing for VANET. In: Proceedings of the 2nd International Conference on Applied and Theoretical Computing and Communication Technology, Bangalore, India. 2016, p. 279-82.
R Silva, HS Lopes and W Godoy. A heuristic algorithm based on ant colony optimization for multi-objective routing in vehicle ad hoc networks. In: Proceedings of the 2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence, Ipojuca, Brazil. 2013, p. 435-40.
P Vijayalakshmi, SAJ Francis and JA Dinakaran. A robust energy efficient ant colony optimization routing algorithm for multi-hop ad hoc networks in MANETs. Wireless Netw. 2016; 22, 2081-100.
H Dong, X Zhao, L Qu, X Chi and X Cui. Multi-hop routing optimization method based on improved ant algorithm for vehicle to roadside network. J. Bionic Eng. 2014; 11, 490-6.
E Khoza, C Tu and PA Owolawi. Decreasing traffic congestion in VANETs using an improved hybrid ant colony optimization algorithm. J. Comm. 2020; 15, 676-86.
A Datta, C Chowdhury and S Neogy. Ant-AODV-VANET: A bio-inspired routing protocol for VANET. In: Proceedings of the International Conference on Emerging Research in Computing, Information, Communication and Applications Springer, Singapore. 2016, p. 251-61.
AM Oranj, RM Alguliev, F Yusifov and S Jamali. Routing algorithm for vehicular ad hoc network based on dynamic ant colony optimization. Int. J. Electron. Electr. Eng. 2016; 4, 79-83.
R Ramamoorthy and M Thangavelu. An enhanced hybrid ant colony optimization routing protocol for vehicular ad-hoc networks. J. Ambient Intell. Hum. Comput. 2021, https://doi.org/10.1007/s12652-021-03176-y.
KN Qureshi, F Bashir and AH Abdullah. Distance and signal quality aware next hop selection routing protocol for vehicular ad hoc networks. Neural Comput. Appl. 2020; 32, 2351-64.
WH Kuo, S Dong and JS Huang. Pheromone-based V2V unicast routing scheme in VANETs. In: Proceedings of the 2013 International Conference on Connected Vehicles and Expo, Las Vegas NV, United States. 2013, p. 783-4.
Z Ding, P Ren and Q Du. Ant colony optimization based delay-sensitive routing protocol in vehicular ad hoc networks. B Li, M Yang, H Yuan and Z Yan (Eds.). IoTaaS 2018: IoT as a service. Springer, Cham, Switzerland, 2018, p. 138-48.
JC Dias, P Machado, DC Silva and PH Abreu. An inverted ant colony optimization approach to traffic. Eng. Appl. Artif. Intell. 2014; 36,122-33.
R Raghu, Prabhushankar R, Rajaram J, Vaiyapuri M. Efficient dead reckoning approach for localization prediction in VANETs. J Appl Sci Comput. 2019; 6, 2093-2099.
R Raghu, M. Hema, and J. Jency Priya. Efficient Cluster Based Algorithm for Structured Wireless Sensor Networks. CIKITUSI Journal for Multidisciplinary Research. 2019; 6, 282-288.
SR Yahiabadi, B Barekatain and K Raahemifar. TIHOO: An enhanced hybrid routing protocol in vehicular ad-hoc networks. J. Wireless Comm. Netw. 2019; 1, 1-19.
S Chatterjee and S Das. Ant colony optimization based enhanced dynamic source routing algorithm for mobile Ad-hoc network. Inform. Sci. 2015; 295, 67-90.
R Raghu and T Menakadevi. A survey on anonymous secure on-demand routing protocols in MANETs. Middle East J. Sci. Res. 2016; 24, 3869-80.
A Srivastava, A Prakash and R Tripathi. Quality-of-service based reliable route discovery using ant colony optimization for VANET. In: Proceedings of the IEEE Conference on Information and Communication Technology, Allahabad, India. 2019, p. 1-6.
KN Qureshi, AH Abdullah and A Altameem. Road aware geographical routing protocol coupled with distance, direction and traffic density metrics for urban vehicular ad hoc networks. Wireless Pers. Comm. 2017; 92, 1251-70.
R Ramamoorthy and Thangavelu. An enhanced distance and residual energy-based congestion aware ant colony optimization routing for vehicular ad hoc networks. Int J Commun Syst. 2022, e5179, https://doi:10.1002/dac.5179.
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