International Journal of Wireless and Microwave Technologies (IJWMT)
IJWMT Vol. 16, No. 5, 8 Oct. 2026
Cover page and Table of Contents: PDF (size: 786KB)
Semantic-Aware Partial Replication with Reinforcement Learning for Real-Time Heterogeneous Data Management in Mobile Computing Networks
PDF (786KB), PP.130-144
Views: 0 Downloads: 0
Author(s)
Chandrani Chakravroty
1,*
Usha J.
1
Index Terms
Semantic data management, partial replication, real-time systems, RDF, SPARQL, IoT, VANET
Abstract
Real-time mobile computing environments, such as smart cities and disaster-response networks, generate heterogeneous and high-velocity data streams in highly dynamic settings. Traditional full replication incurs prohibitive bandwidth overhead, while naive partial replication risks missing life-critical alerts. This paper presents Semantic-Aware Partial Replication (SAPR), a unified and lightweight framework that resolves this critical bandwidth-reliability trade-off by intelligently selecting the most valuable information for dissemination. SAPR is an integrated solution to combine RDF/SPARQL-based semantic ranking, real-time priority handling, and efficiency-per-byte optimization with Deep Q-Network (DQN) adaptive control. The framework computes a semantic priority score based on data type, urgency, and spatial context, and then replicates the most utility-efficient triples per byte. Crucially, a DQN agent dynamically adapts the replication factor (k) by observing network conditions like mobility dynamics, neighbor count, and alert frequency, learning to prioritize bandwidth conservation in congested environments. Using a 26-node mobile network simulation validated with real CRAWDAD taxi traces and a 10,000-triple RDF dataset, SAPR achieves an exceptional balance. The proposed RL-SAPR scheme demonstrates 82% bandwidth reduction while successfully maintaining 99% alert delivery accuracy, significantly outperforming both semantic and priority-only baselines. This work confirms the feasibility of combining semantic intelligence with reinforcement learning to enable reliable, bandwidth-efficient knowledge sharing in mission-critical, bandwidth-constrained mobile networks.
Cite This Paper
Chandrani Chakravroty, Usha J., "Semantic-Aware Partial Replication with Reinforcement Learning for Real-Time Heterogeneous Data Management in Mobile Computing Networks", International Journal of Wireless and Microwave Technologies(IJWMT), Vol.16, No.5, pp. 130-144, 2026. DOI:10.5815/ijwmt.2026.05.08
Reference
[1]G. F. Riley and T. R. Henderson, "The ns-3 network simulator," in Modeling and Tools for Network Simulation, Springer, 2010, pp. 15–34. DOI: 10.1007/978-3-642-12331-3_2
[2]H. Shahwani, S. A. Shah, M. Ashraf, M. Akram, J. (Paul) Jeong, J. Shin, "A comprehensive survey on data dissemination in Vehicular Ad Hoc Networks," Vehicular Communications, vol. 34, Art. no. 100420, Apr. 2022, doi: 10.1016/j.vehcom.2021.100420.
[3]S. A. Alghamdi, "Novel path similarity aware clustering and safety message dissemination via mobile gateway selection in cellular 5G-based V2X and D2D communication for urban environment," Ad Hoc Networks, vol. 103, Art. no. 102150, Jun. 2020, doi: 10.1016/j.adhoc.2020.102150.
[4]Y. A. Debalki, J. Hou, H. Ullah, B. Y. Adane, "Multi-hop data dissemination using a multi-metric contention-based broadcast suppression strategy in VANETs," Ad Hoc Networks, vol. 140, Art. no. 103070, Mar. 2023, doi: 10.1016/j.adhoc.2022.103070.
[5]S. Zander and B. Schandl, "Context-driven RDF Data Replication on Mobile Devices," Semantic Web, vol. 3, no. 2, pp. 131–155, 2012, doi: 10.3233/SW-2011-0043.
[6]A. Le-Tuan, C. Hayes, M. Hauswirth, and D. Le-Phuoc, "Pushing the Scalability of RDF Engines on IoT Edge Devices," Sensors, vol. 20, no. 10, p. 2788, 2020. DOI: 10.3390/s20102788
[7]P. Sowiński, K. Wasielewska-Michniewska, M. Ganzha, W. Pawłowski, P. Szmeja, and M. Paprzycki, "Efficient RDF Streaming for the Edge-Cloud Continuum," in Proceedings of the 2022 IEEE 8th World Forum on Internet of Things (WF-IoT), Yokohama, Japan, Oct.–Nov. 2022, pp. 1–8. doi: 10.1109/WF-IoT54382.2022.10152225.
[8]B. Schandl and S. Zander, "Adaptive RDF Graph Replication for Mobile Semantic Web Applications," Ubiquitous Computing and Communication Journal, vol. 4, pp. 738–745, 2009. Available: https://eprints.cs.univie.ac.at/151/1/schandl.pdf
[9]H. H. Duong and I. Demeure, "Proactive Data Replication Using Semantic Information within Mobility Groups in MANET," in Information Networking. Towards Ubiquitous Networking Computing, Springer, 2009, pp. 129–143. DOI: 10.1007/978-3-642-01802-2_10
[10]C.-A. La, P. Michiardi, C. Casetti, C. F. Chiasserini, and M. Fiore, "Content Replication in Mobile Networks," IEEE Journal on Selected Areas in Communications, vol. 30, no. 9, pp. 1762–1770, Oct. 2012. DOI: 10.1109/JSAC.2012.121021
[11]G. Fersi, W. Louati, and M. Ben Jemaa, "Distributed Hash Table-Based Routing and Data Management in Wireless Sensor Networks: A Survey," Wireless Networks, vol. 19, no. 2, pp. 219–236, Feb. 2013, doi: 10.1007/s11276-012-0461-0.
[12]V. Natarajan, S. Zhu, M. Srivatsa, and J. Opper, "Semantics-Aware Storage and Replication of Trust Metadata in Mobile Ad-hoc Networks," in Proceedings of the 26th IEEE International Conference on Advanced Information Networking and Applications (AINA), Fukuoka, Japan, Mar. 2012, pp. 376–383, doi: 10.1109/AINA.2012.89
[13]J. Zhu, C. Huang, X. Fan, S. Guo, and B. Fu, "EDDA: An Efficient Distributed Data Replication Algorithm in VANETs," Sensors, vol. 18, no. 2, Art. no. 547, Feb. 2018, doi: 10.3390/s18020547.
[14]A. Hazra, V. M. R. Tummala, N. Mazumdar, D. K. Sah, and M. Adhikari, "Deep Reinforcement Learning in Edge Networks: Challenges and Future Directions," Physical Communication, vol. 66, Art. no. 102460, Oct. 2024, doi: 10.1016/j.phycom.2024.102460.
[15]Y. Hao, M. Chen, H. Gharavi, Y. Zhang, and K. Hwang, "Deep Reinforcement Learning for Edge Service Placement in Softwarized Industrial Cyber-Physical System," IEEE Transactions on Industrial Informatics, vol. 17, no. 8, pp. 5552–5561, Aug. 2021, doi: 10.1109/TII.2020.3041713
[16]A. A. Ismail, N. E. Khalifa, R. A. El-Khoribi, "A survey on resource scheduling approaches in multi-access edge computing environment: a deep reinforcement learning study," Cluster Computing, vol. 28, no. 3, Art. no. 184, Jan. 2025, doi: 10.1007/s10586-024-04893-7.
[17]V. Kumar, K. S. Vaisla, and S. D. Sudarsan, "Priority Based Data Scheduling in VANETs," in Proceedings of the 2016 International Conference on Advances in Computing and Communication Engineering (ICACCE), 2016, doi: 10.1109/ICACCE.2016.8073717
[18]E. Felemban, A. Naseer, and A. Amjad, "Priority-based Routing Framework for VANETs," EAI Endorsed Transactions on Energy Web, vol. 8, no. 31, Art. no. e6, Jul. 2020, doi: 10.4108/eai.13-7-2018.165511.
[19]L. Bracciale, M. Bonola, P. Loreti, G. Bianchi, R. Amici, and A. Rabuffi, "CRAWDAD Dataset Roma/Taxi (v. 2014-07-17)," CRAWDAD Wireless Data Repository, Aug. 2014. Available: https://crawdad.org/roma/taxi/20140717