Cooperative Content Caching Framework Using Cuckoo Search Optimization in Vehicular Edge Networks
Source abstract
Abstract
Vehicular edge networks (VENs) connect vehicles to share data and infotainment content collaboratively to improve network performance. Due to technological advancements, data growth is accelerating, making it difficult to always connect mobile devices and locations. For vehicle-to-vehicle (V2V) communication, vehicles are equipped with onboard units (OBU) and roadside units (RSU). Through back-haul, all user-uploaded data is cached in the cloud server’s main database. Caching stores and delivers database data on demand. Pre-caching the data on the upcoming predicted server, closest to the user, before receiving the request will improve the system’s performance. OBUs, RSUs, and base stations (BS) cache data in VENs to fulfill user requests rapidly. Pre-caching reduces data retrieval costs and times. Due to storage and computing expenses, complete data cannot be stored on a single device for vehicle caching. We reduce content delivery delays by using the cuckoo search optimization algorithm with cooperative content caching. Cooperation among end users in terms of data sharing with neighbors will positively affect delivery delays. The proposed model considers cooperative content caching based on popularity and accurate vehicle position prediction using K-means clustering. Performance is measured by caching cost, delivery cost, response time, and cache hit ratio. Regarding parameters, the new algorithm outperforms the alternative.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Caching and Content Delivery · IoT and Edge/Fog Computing · Vehicular Ad Hoc Networks (VANETs)
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Computer Science, COMSATS University Islamabad, Abbottabad Campus, Abbottabad 22060, Pakistan
Department of Computer Science, COMSATS University Islamabad, Lahore Campus, Lahore 54000, Pakistan
Software Engineering Department, Faculty of Science and Technology, ILMA University, Karachi 75190, Pakistan
Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Funding
Princess Nourah bint Abdulrahman University · Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
References (42)
Show source-provided references
1. Cooperative mobile edge computing system for VANET-based software-defined content delivery10.1016/j.compeleceng.2018.07.021 ↗
2. Proactive Cache-Based Location Privacy Preserving for Vehicle Networks10.1109/MWC.2017.1800127 ↗
3. Mobility Prediction-Assisted Over-the-Top Edge Prefetching for Hierarchical VANETs10.1109/JSAC.2018.2844681 ↗
4. Prediction Based Vehicular Caching: Where and What to Cache?10.1007/s11036-019-01300-z ↗
5. Yasir, M., Zaman, S.K.U., Maqsood, T., Rehman, F., and Mustafa, S. (2022). CoPUP: Content popularity and user preferences aware content caching framework in mobile edge computing. Clust. Comput., 1–15.10.1007/s10586-022-03624-0 ↗
6. Optimal Content Caching in Content-Centric Networks10.1155/2019/6373960 ↗
7. Deep Reinforcement Learning for Cooperative Content Caching in Vehicular Edge Computing and Networks10.1109/JIOT.2019.2945640 ↗
8. Zaman, S.K.U., Jehangiri, A.I., Maqsood, T., Umar, A.I., Khan, M.A., Jhanjhi, N.Z., Shorfuzzaman, M., and Masud, M. (2022). COME-UP: Computation Offloading in Mobile Edge Computing with LSTM Based User Direction Prediction. Appl. Sci., 12.10.3390/app12073312 ↗
9. Zhang, Y., Li, C., Luan, T.H., Fu, Y., and Zhu, L. (2018). Caching on Vehicles: A Lyapunov Based Online Algorithm. International Conference on Ad Hoc Networks, Springer.10.1007/978-3-030-05888-3_2 ↗
10. Caching mechanism for mobile edge computing in V2I networks10.1002/ett.3689 ↗
11. SLA-Aware Energy Efficient Resource Management for Cloud Environments10.1109/ACCESS.2018.2808320 ↗
12. Cooperative Edge Caching With Location-Based and Popular Contents for Vehicular Networks10.1109/TVT.2020.3004720 ↗
13. Zaman, S.K.U., Jehangiri, A.I., Maqsood, T., Haq, N.U., Umar, A.I., Shuja, J., Ahmad, Z., Ben Dhaou, I., and Alsharekh, M.F. (2022). LiMPO: Lightweight mobility prediction and offloading framework using machine learning for mobile edge computing. Clust. Comput., 1–19.10.1007/s10586-021-03518-7 ↗
14. Cooperative coding and caching scheduling via binary particle swarm optimization in software-defined vehicular networks10.1007/s00521-020-04978-5 ↗
15. Khelifi, H., Luo, S., Nour, B., Sellami, A., Moungla, H., and Nait-Abdesselam, F. (2018, January 9–13). An Optimized Proactive Caching Scheme Based on Mobility Prediction for Vehicular Networks. Proceedings of the 2018 IEEE Global Communications Conference (GLOBECOM), Abu Dhabi, United Arab Emirates.10.1109/GLOCOM.2018.8647898 ↗
16. Yang, X.-S. (2020). Nature-Inspired Optimization Algorithms, Academic Press.10.1016/B978-0-12-821986-7.00018-4 ↗
17. A Comparative Study of Cuckoo Algorithm and Ant Colony Algorithm in Optimal Path Problems10.1051/matecconf/201823203003 ↗
18. A zone-based content pre-caching strategy in vehicular edge networks10.1016/j.future.2019.12.050 ↗
19. An Edge Caching Scheme to Distribute Content in Vehicular Networks10.1109/TVT.2018.2824345 ↗
20. Peer-to-Peer Cooperative Caching for Data Dissemination in Urban Vehicular Communications10.1109/JSYST.2013.2285611 ↗
21. Mobility-aware computational offloading in mobile edge networks: A survey10.1007/s10586-021-03268-6 ↗
22. Ma, J., Wang, J., Liu, G., and Fan, P. (2017, January 4–8). Low Latency Caching Placement Policy for Cloud-Based VANET with Both Vehicle Caches and RSU Caches. Proceedings of the 2017 IEEE Globecom Workshops (GC Wkshps), Singapore.10.1109/GLOCOMW.2017.8269203 ↗
23. SLA-Aware Best Fit Decreasing Techniques for Workload Consolidation in Clouds10.1109/ACCESS.2019.2941145 ↗
24. Ding, R., Wang, T., Song, L., Han, Z., and Wu, J. (2015, January 9–12). Roadside-unit caching in vehicular ad hoc networks for efficient popular content delivery. Proceedings of the 2015 IEEE Wireless Communications and Networking Conference (WCNC), New Orleans, LA, USA.
25. Husnain, G., and Anwar, S. (2021). An intelligent cluster optimization algorithm based on Whale Optimization Algorithm for VANETs (WOACNET). PLoS ONE, 16.10.1371/journal.pone.0250271 ↗
26. Formal verification and performance evaluation of task scheduling heuristics for makespan optimization and workflow distribution in large-scale computing systems
27. An Improved Harmony Search Algorithm for Proactive Routing Protocol in VANET
28. Glass, S.C. (2017). Improving Privacy with Intelligent Cooperative Caching in Vehicular Ad Hoc Networks. [Ph.D. Thesis, Florida Atlantic University].
29. A Load Balanced Task Scheduling Heuristic for Large-Scale Computing Systems10.32604/csse.2019.34.079 ↗
30. Li, L., Chan, C.A., Erfani, S., and Leckie, C. (2019, January 14–19). Adaptive Edge Caching based on Popularity and Prediction for Mobile Networks. Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary.10.1109/IJCNN.2019.8851794 ↗
31. Performance comparison of simulated annealing, GA and ACO applied to TSP
32. Ab Wahab, M.N., Nefti-Meziani, S., and Atyabi, A. (2015). A Comprehensive Review of Swarm Optimization Algorithms. PLoS ONE, 10.10.1371/journal.pone.0122827 ↗
33. Safi, A., Ahmad, Z., Jehangiri, A.I., Latip, R., Zaman, S.K.U., Khan, M.A., and Ghoniem, R.M. (2022). A Fault Tolerant Surveillance System for Fire Detection and Prevention Using LoRaWAN in Smart Buildings. Sensors, 22.10.3390/s22218411 ↗
34. New discrete cuckoo search optimization algorithms for effective route discovery in IoT-based vehicular ad-hoc networks10.1109/ACCESS.2020.3014736 ↗
35. Implementing Discrete Cuckoo Search Algorithm for TSP using MPI and Beowulf Cluster
36. A novel cuckoo search optimization algorithm based on Gauss distribution
37. Hajlaoui, R., Alaya, B., and Mchergui, A. (June, January 30). Optimized VANET Routing Protocol Using Cuckoo Search Algorithm. Proceedings of the 2022 International Wireless Communications and Mobile Computing (IWCMC), Dubrovnik, Croatia.10.1109/IWCMC55113.2022.9824998 ↗
38. Marwah, G.P.K., Jain, A., Malik, P.K., Singh, M., Tanwar, S., Safirescu, C.O., Mihaltan, T.C., Sharma, R., and Alkhayyat, A. (2022). An Improved Machine Learning Model with Hybrid Technique in VANET for Robust Communication. Mathematics, 10.10.3390/math10214030 ↗
39. Giraffe kicking optimization algorithm provides efficient routing mechanism in the field of vehicular ad hoc networks10.1007/s12652-021-03519-9 ↗
40. Lv, B., Wang, Z., Huang, T., Chen, J., and Liu, Y. (2010, January 23–25). A hierarchical virtual resource management architecture for network virtualization. Proceedings of the 2010 6th International Conference on Wireless Communications Networking and Mobile Computing (WiCOM), Chengdu, China.10.1109/WICOM.2010.5601454 ↗
41. The movielens datasets: History and context
42. User Preference Learning-Based Edge Caching for Fog Radio Access Network10.1109/TCOMM.2018.2880482 ↗