Survey of Multiple Destination Route Discovery Protocols

Authors

  • Reem Jafar Ismail Department of Computer Science, Cihan University-Erbil, Kurdistan Region, Iraq
  • Samar Jaafar Ismael Department of Electromechanical Engineering- University of Technology
  • Dr. Sara Raouf Muhamad Amin Department of Information System Engineering- Erbil Polytechnic University
  • Wassan Adnan Hashim Medical Instruments Techniques Department- AlQAlam University College
  • Israa Tahseen Ali Department of Computer Science- University of Technology

DOI:

https://doi.org/10.22399/ijcesen.385

Keywords:

Multiple destinations, Route discovery protocols , Artificial intelligence

Abstract

Route discovery protocols for multiple destination is one of the most interesting research topics since it is applied for real-world applications and needed in smart cities services such as: delivery services. The inclusion of artificial intelligence can improve the performance of multiple destination route discovery protocols. In this paper, we studied and analyzed multiple destination route discovery protocols based on different search strategies especially artificial intelligent methods. The survey compares between multiple destination route discovery protocols relate to its applications and implementation tools.

References

M. Burlacu, R. G. Boboc, and E. V. Butilă, “Smart Cities and Transportation: Reviewing the Scientific Character of the Theories,” Sustainability, vol. 14, no. 13, p. 8109, Jul. 2022, doi: 10.3390/su14138109. DOI: https://doi.org/10.3390/su14138109

J. Zhang, J. Fan and Z. Luo, “Generating Multi-destination Maps,” IEEE Transactions on Visualization and Computer Graphics, vol. 23, no. 8, pp. 1964-1976, 2017. DOI: https://doi.org/10.1109/TVCG.2016.2597827

H. Siam and M. B. Younes, “Multi-destinations Round Trip Planner Protocol,” 2018 5th International Symposium on Innovation in Information and Communication Technology (ISIICT), pp. 1-5, 2018. DOI: https://doi.org/10.1109/ISIICT.2018.8613719

Reem J. Ismail, Mohammed H. Shukur, and Samar J. Ismael, “Route Discovery Development for Multiple Destination Using Artificial Ant Colony: Google Map Case Study,” Cihan University-Erbil Scientific Journal (CUESJ), vol. 6, no. 2, pp. 41-48, 2022. DOI: https://doi.org/10.24086/cuesj.v6n2y2022.pp41-48

Timothy S., Tingjun L., Chaomin L., Gene Eu Jan and Junfeng M., “A Node Selection Algorithm to Graph-Based Multi-Waypoint Optimization Navigation and Mapping,” Intelligence & Robotics, 2 (4):333¬54, 2022. DOI: https://doi.org/10.20517/ir.2022.21

Ky Phuc, P.N.; Phuong Thao, N.L. Ant Colony Optimization for Multiple Pickup and Multiple Delivery Vehicle Routing

Problem with Time Window and Heterogeneous Fleets. Logistics 2021, 5, 28. https://doi.org/10.3390/ logistics5020028. DOI: https://doi.org/10.3390/logistics5020028

Y. Huang, J. C. Ying, P. S. Yu and V. Tseng, “Dynamic Graph Mining for Multi-Weight Multi-Destination Route Planning with Deadlines Constraints,” ACM Transactions on Knowledge Discovery from Data, vol. 15, no. 1, pp. 1-32, 2021. DOI: https://doi.org/10.1145/3412363

H. Zhuang, K. Dong, Y. Qi, N. Wang and L. Dong, “Multi-Destination Path Planning Method Research of Mobile Robots Based on Goal of Passing through the Fewest Obstacles,” Applied Science, vol. 11, p. 7378, 2021. DOI: https://doi.org/10.3390/app11167378

M. Asaduzzaman, T. K. Geok, F. Hossain, S. Sayeed, A. Abdaziz, H. Y. Wong, C. P. Tso, S. Ahmed and B. A. Bari, “An Efficient Shortest Path Algorithm: Multi-Destinations in an Indoor Environment,” Symmetry, vol. 13, p. 421, 2021. DOI: https://doi.org/10.3390/sym13030421

A. Hakeem, N. Gehani, X. Ding, R. Curtmola and C. Borcea. Multi-Destination Vehicular Route Planning with Parking and Traffic Constraints,” In: MobiQuitous 19: Proceedings of the 16th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services. pp. 298-307, 2019. DOI: https://doi.org/10.1145/3360774.3360824

Shushang C. ,Pengying D. and Jingjing H., “Research on Multi-Destination Delivery Route Optimization of Unmanned Express Vehicles,” 6th International Conference on Systems and Informatics (ICSAI), 2019.

Yu Huang, Vincent S. Tseng and Bo-Hau Lin. Efficient Multi-Destinations Route Planning with Deadlines and Cost Constraints. IEEE 18th International Conference on Mobile Data Management, 2017. DOI: https://doi.org/10.1109/MDM.2017.38

E. H. C. Lu, W. Lee and V. Tseng, “Mining Fastest Path from Trajectories with Multiple Destinations in Road Networks,” Knowledge and Information Systems, vol. 29, no. 1. pp. 25-53, 2011. DOI: https://doi.org/10.1007/s10115-010-0333-9

K. Koritsoglou, P. Laskas, V. Patras, A. D. Boursianis, K. Grigoriadis and I. Fudos, "A bi-directional shortest path calculation speed up technique for RDBMS," 2022 7th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM), Ioannina, Greece, 2022, pp. 1-4, doi: 10.1109/SEEDA-CECNSM57760.2022.9932973. DOI: https://doi.org/10.1109/SEEDA-CECNSM57760.2022.9932973

H. Xu, "The Dimensional Analysis of Tree Network," 2022 IEEE 2nd International Conference on Power, Electronics and Computer Applications (ICPECA), Shenyang, China, 2022, pp. 685-689, doi: 10.1109/ICPECA53709.2022.9719001. DOI: https://doi.org/10.1109/ICPECA53709.2022.9719001

Stuart Russell and Peter Norvig. Artificial Intelligence: A Modern Approach, 4th US edition, 2022.

M. Dorigo, M. Birattari and T. Stutzle, "Ant colony optimization," in IEEE Computational Intelligence Magazine, vol. 1, no. 4, pp. 28-39, Nov. 2006. DOI: https://doi.org/10.1109/CI-M.2006.248054

S. Kumar, V. Kumar-Solanki, S. K. Choudhary, A. Selamat and R. González-Crespo. Comparative study on ant colony optimization (ACO) and K-means clustering approaches for jobs scheduling and energy optimization model in internet of things (IoT). International Journal of Interactive Multimedia and Artificial Intelligence, vol. 6, no. 1, 2020. DOI: https://doi.org/10.9781/ijimai.2020.01.003

Luo C, Xiao Y, Yang SX, Jan GE. Improving vehicle navigation by a heading¬ enabled ACO approach. World Automation Congress. Rio Grande, USA: IEEE; 2016. pp. 1–6. DOI: https://doi.org/10.1109/WAC.2016.7583012

Lei T, Luo C, Jan GE, Fung K. Variable speed robot navigation by an ACO approach. International Conference on Swarm Intelligence. Cham: Springer; 2019. pp. 232–42. DOI: https://doi.org/10.1007/978-3-030-26369-0_22

Lazreg M, Benamrane N. Hybrid system for optimizing the robot mobile navigation using ANFIS and PSO. Rob Auton Syst 2022;153:104114. DOI: https://doi.org/10.1016/j.robot.2022.104114

Abdulrazak A. Mohammed and Ghassan A. QasMarrogy. Thermal effect on the optical signal of fiber optics networks, International Review of Applied Sciences and Engineering, 2022. DOI: https://doi.org/10.1556/1848.2021.00328

L. Parungao, F. Hein and W. Lim. “Dijkstra algorithm based intelligent path planning with topological map and wireless communication”. ARPN Journal of Engineering and Applied Sciences, vol. 13, no. 8, pp. 2753-2763, 2018.

Dian Rachmawati and Lysander Gustin. “Analysis of Dijkstra's Algorithm and A* Algorithm in Shortest Path Problem”. Journal of Physics: Conference Series, Volume 1566, 4th International Conference on Computing and Applied Informatics 2019 (ICCAI 2019) 26-27 November 2019, Medan, Indonesia.

T. Guilmeau, E. Chouzenoux and V. Elvira, "Simulated Annealing: a Review and a New Scheme," 2021 IEEE Statistical Signal Processing Workshop (SSP), Rio de Janeiro, Brazil, 2021, pp. 101-105, doi: 10.1109/SSP49050.2021.9513782. DOI: https://doi.org/10.1109/SSP49050.2021.9513782

Young-Jae Jeon, Jae-Chul Kim, Jin-O Kim, Joong-Rin Shin and K. Y. Lee, "An efficient simulated annealing algorithm for network reconfiguration in large-scale distribution systems," in IEEE Transactions on Power Delivery, vol. 17, no. 4, pp. 1070-1078, Oct. 2002, doi: 10.1109/TPWRD.2002.803823. DOI: https://doi.org/10.1109/TPWRD.2002.803823

W. A. Hashim, J. H. Hameed, R. J. Ismail and M. Q. Al-Obaidi, "Optimizing Power Controller Performance using Genetic Algorithm: A Step-by-Step Approach for Finding Optimal Values and Improving Control Performance," 2023 16th International Conference on Developments in eSystems Engineering (DeSE), Istanbul, Turkiye, 2023, pp. 87-93, doi: 10.1109/DeSE60595.2023.10469612. DOI: https://doi.org/10.1109/DeSE60595.2023.10469612

Hasan, Hasan Fahmi, Alyaa Asaad Mahdi, and Vanye Zira. Cost and Energy Efficient Hybrid Wireless Optical Networks: FiWi Access Network. LAP LAMBERT Academic Publishing, 2019.

Ismail, R., Jasim, K., Ismael, S., & Solaimanzadeh, S. (2021). Enhancing Wireless Sensor Networks Routing Protocols based on Cross Layer Interaction. Cihan University-Erbil Scientific Journal, 5(2), 52-55. https://doi.org/10.24086/cuesj.v5n2y2021.pp52-55 DOI: https://doi.org/10.24086/cuesj.v5n2y2021.pp52-55

Atheem, Hussein K. Abdul; Ali, Israa T.; and Al Alawy, Faiz A. (2024) "A Comprehensive Analysis of Deep Learning and Swarm Intelligence Techniques to Enhance Vehicular Ad-hoc NETwork Performance," Journal of Soft Computing and Computer Applications: Vol. 1: Iss. 1, Article 1004.

Available at: https://jscca.uotechnology.edu.iq/jscca/vol1/iss1/5

Ismail, R., Ismae, S., & Ali, W. (2019). Energy Management in Wireless Sensor Networks for Internet of Things Applications. Cihan University-Erbil Scientific Journal, 3(2), 48-52. https://doi.org/10.24086/cuesj.v3n2y2019.pp48-52 DOI: https://doi.org/10.24086/cuesj.v3n2y2019.pp48-52

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Published

2024-08-31

How to Cite

Jafar Ismail, R., Samar Jaafar Ismael, Dr. Sara Raouf Muhamad Amin, Wassan Adnan Hashim, & Israa Tahseen Ali. (2024). Survey of Multiple Destination Route Discovery Protocols. International Journal of Computational and Experimental Science and Engineering, 10(3). https://doi.org/10.22399/ijcesen.385

Issue

Vol. 10 No. 3 (2024): IJCESEN

Section

Review Article

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