Call For Paper - Upcoming Conferences
Research Article | Open Access | Download PDF
Volume 13 | Issue 4 | Year 2026 | Article Id. IJEEE-V13I4P104 | DOI : https://doi.org/10.14445/23488379/IJEEE-V13I4P104

A²LFU: An Adaptive Ant Colony Optimization-BASED Hybrid Strategy for Efficient Caching in Content-Centric Networks

A R Charulatha, Dr. C. Victoria Priscilla

Received Revised Accepted Published
08 Jan 2026 16 Feb 2026 17 Mar 2026 30 Apr 2026

Citation :

A R Charulatha, Dr. C. Victoria Priscilla, "A²LFU: An Adaptive Ant Colony Optimization-BASED Hybrid Strategy for Efficient Caching in Content-Centric Networks," International Journal of Electrical and Electronics Engineering, vol. 13, no. 4, pp. 56-66, 2026. Crossref, https://doi.org/10.14445/23488379/IJEEE-V13I4P104

Abstract

The advancement of content-focused communication applications has made the Content-Centric Networking (CCN) appear, where network caching can play a key role to improve the efficiency and reduce redundancy in retrieving data. Classic approaches, such as LRU (Least Recently Used), LFU (Least Frequently Used), and ProbCache at the level of caching, have already been used. Of these, LFU is well known for its effectiveness, but it fails to be adaptive towards changes in content popularity as well as network topology under dynamic and real-time scenarios. In order to mitigate these issues, in this paper, a new caching method named "Fully Tuned Adaptive Ant Colony Optimization-based LFU Replacement" (A²LFU Hybrid) is proposed. The strategy proposed considers that both LFU and Ant Colony Optimization (ACO) are used jointly in order to make the policy adaptation more flexible for real-time network conditions, such as variation of content popularity. Performance analyses with traces from real traffic loads show that A²LFU Hybrid can achieve up to % higher average hit ratio and overall system performance than existing conventional methods, such as LFU, LFUDA, LRFU, etc., and recently proposed adaptive solutions. It also outperforms the other recent algorithms, such as Adaptive Ant Colony Optimization (ACO) and Adaptive Cuckoo Search.

Keywords

Content-Centric Networking (CCN), Ant Colony Optimization (ACO), Least Frequently Used, Adaptive Caching, Hybrid Strategy, Network Performance.

References

  1. Bomin Mao et al., “On a Hierarchical Content Caching and Asynchronous Updating Scheme for Non-Terrestrial Network-Assisted Connected Automated Vehicles,” IEEE Journal on Selected Areas in Communications, vol. 43, no. 1, pp. 64-74, 2025.
    [    CrossRef] [Google Scholar] [Publisher Link]
  2. Yasar Khan et al., “A Latency-Aware and Resource-Efficient Content Caching Scheme for Content-Centric Networks,” IEEE Access, vol. 13, pp. 139650-139664, 2025.
    [    CrossRef] [Google Scholar] [Publisher Link]
  3. Suwimol Jungjit et al., “Ant Colony Optimization for Multi-Label Correlation-based Feature Selection Method,” 2025 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT & NCON), pp. 797-802, Nan, Thailand, pp. 797-802, 2025.
    [    CrossRef] [Google Scholar] [Publisher Link]
  4. Zhicheng Zhao, and Weihua Cao, “Extensive-Form Game Theory-Based Content Caching in Edge Networks,” IEEE MultiMedia, vol. 32, no. 2, pp. 127-137, 2025.
    [    CrossRef] [Google Scholar] [Publisher Link]
  5. Mohammed A. Awadallah et al., “Multi-objective Ant Colony Optimization: Review,” Archives of Computational Methods in Engineering, vol. 32, pp. 995-1037, 2025.
    [    CrossRef] [Google Scholar] [Publisher Link]
  6. Shuling Yin, Jiahai Tu, and Xiaoyan Chen, “A New Tree-based Data Aggregation Method in the Wireless Sensor Networks using Ant Colony Optimization and Cuckoo Search Algorithms,” Journal of Engineering and Applied Science, vol. 72, pp. 1-29, 2025.
    [    CrossRef] [Google Scholar] [Publisher Link]
  7. Lei Chen, “Ant Colony Optimization based Information-Centric Networking Delivery Strategy via Flow Analysis and Scheduling,” Internet Technology Letters, vol. 4, no. 5, 2021.
    [    CrossRef] [Google Scholar] [Publisher Link]
  8. Hamid Asmat et al., “Energy-Efficient Centrally Controlled Caching Contents for Information-Centric Internet of Things,” IEEE Access, vol. 8, pp. 126358-126369, 2020.
    [    CrossRef] [Google Scholar] [Publisher Link]
  9. Xiaohan Qiu et al., “Integrated Host- and Content-Centric Routing for Efficient and Scalable Networking of UAV Swarm,” IEEE Transactions on Mobile Computing, vol. 23, no. 4, pp. 2927-2942, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  10. Xiaonan Wang, and Xilan Chen, “Social Attributes-Based Content Delivery for Sparse Vehicular Content-Centric Network,” IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 12, pp. 14406-14414, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  11. Bahman Abolhassani, John Tadrous, and Atilla Eryilmaz, “Optimal Load-Splitting and Distributed-Caching for Dynamic Content Over the Wireless Edge,” IEEE/ACM Transactions on Networking, vol. 31, no. 5, pp. 2178-2190, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  12. Dongyang Li et al., “Deep Learning-Enabled Joint Edge Content Caching and Power Allocation Strategy in Wireless Networks,” IEEE Transactions on Vehicular Technology, vol. 73, no. 3, pp. 3639-3651, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  13. Arooj Masood et al., “A Review on AI-Enabled Congestion Control Schemes for Content Centric Networks,” 2023 14th International Conference on Information and Communication Technology Convergence (ICTC), Jeju Island, Korea, Republic of, pp. 659-662, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  14. Hanwen Li et al., “A Survey of Edge Caching: Key Issues and Challenge,” Tsinghua Science and Technology, vol. 29, no. 3, pp. 818-842, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  15. Sumit Kumar et al., “Minimizing Delay in Content-centric Networks using Heuristics-based In-network Caching,” Cluster Computing, vol. 25, pp. 417-431, 2022.
    [    CrossRef] [Google Scholar] [Publisher Link]
  16. Yannis Thomas et al., “Object-Oriented Packet Caching for ICN,” Proceedings of the 2nd International Conference on Information-Centric Networking, San Francisco California USA, pp. 89-98, 2015.
    [CrossRef] [Google Scholar] [Publisher Link]
  17. Ying Li, “Application of Ant Colony Algorithm in Network Routing Optimization,” 2025 3rd International Conference on Data Science and Information System (ICDSIS), Hassan, India, pp. 1-5, 2025.
    [    CrossRef] [Google Scholar] [Publisher Link]
  18. Joonyoung Lim, Dongju Kim, and Younghwan Yoo, “Joint Cache Allocation and Replacement for Content-Centric Network-Based Private 5G Networks: Deep Reinforcement Learning Approach,” IEEE Access, vol. 12, pp. 56214-56225, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  19. Subodh Mishra et al., “An Efficient Content Replacement Policy to Retain Essential Content in Information-Centric Networking based Internet of Things Network,” Ad Hoc Networks, vol. 155, pp. 1-12, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]