Traffic Measurement Optimization for Cross-Domain Ad Hoc Networks Based on Meta-Learning and Reinforcement Learning

SONG Jian; NIE Laisen; TAO Zui; YUAN Qiendong

doi:10.11993/j.issn.2096-3920.2024-0094

Volume 32 Issue 4

Aug 2024

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Article Navigation > Journal of Unmanned Undersea Systems > 2024 > 32(4): 668-677

SONG Jian, NIE Laisen, TAO Zui, YUAN Qiendong. Traffic Measurement Optimization for Cross-Domain Ad Hoc Networks Based on Meta-Learning and Reinforcement Learning[J]. Journal of Unmanned Undersea Systems, 2024, 32(4): 668-677. doi: 10.11993/j.issn.2096-3920.2024-0094

Citation:

SONG Jian, NIE Laisen, TAO Zui, YUAN Qiendong. Traffic Measurement Optimization for Cross-Domain Ad Hoc Networks Based on Meta-Learning and Reinforcement Learning[J]. Journal of Unmanned Undersea Systems, 2024, 32(4): 668-677. doi: 10.11993/j.issn.2096-3920.2024-0094

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Traffic Measurement Optimization for Cross-Domain Ad Hoc Networks Based on Meta-Learning and Reinforcement Learning

doi: 10.11993/j.issn.2096-3920.2024-0094

School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, China

Received Date: 2024-05-28
Accepted Date: 2024-07-15
Rev Recd Date: 2024-07-05

Available Online: 2024-07-16

Abstract

Abstract

Cross-domain Ad Hoc network is a network that self-organizes nodes on different media and adapts to network topology. In cross-domain communication networks, direct measurement technology helps obtain accurate end-to-end network traffic information. However, the low computational power and low storage characteristics of some nodes in the cross-domain network hinder all nodes from running the network traffic measurement process. To address this issue, a network traffic measurement optimization method based on meta-learning and proximal policy optimization(PPO) was proposed. This method determined the set of nodes that performed network traffic measurement in the next time slot according to the network operating environment of the previous time slot, so as to perform the measurement process on as few nodes as possible to obtain as much network traffic information as possible. Three network datasets were used to verify the proposed method. The experimental results show that the traffic measurement optimization algorithm for cross-domain Ad Hoc networks based on meta-learning and reinforcement learning can effectively select the nodes with large traffic flow, with faster convergence speed and higher measurement efficiency.
- cross-domain Ad Hoc network,
- network traffic measurement,
- meta-learning,
- proximal policy optimization,
- reinforcement learning

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References(24)

References

[1]	RAMPHULL D, MUNGUR A, ARMOOGUM S, et al. A review of mobile Ad Hoc network(MANET) protocols and their applications[C]//2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS). Chongqing, China: IEEE, 2021: 204-211.
[2]	CHO J H, SWAMI A, CHEN R. A survey on trust management for mobile Ad Hoc networks[J]. IEEE Communications Surveys & Tutorials, 2010, 13(4): 562-583.
[3]	CONTI M, GIORDANO S. Mobile Ad Hoc networking: Milestones, challenges, and new research directions[J]. IEEE Communications Magazine, 2014, 52(1): 85-96. doi: 10.1109/MCOM.2014.6710069
[4]	HAMDI M M, AUDAH L, RASHID S A, et al. A review of applications, characteristics and challenges in vehicular Ad Hoc networks(VANETs)[C]//2020 International Congress on Human-Computer Interaction, Optimization and Robotic Applications(HORA). Ankara, Turkey: IEEE, 2020: 1-7.
[5]	JHAVERI R H, PATEL S J, JINWALA D C. DoS attacks in mobile Ad Hoc networks: A survey[C]//2012 Second International Conference on Advanced Computing & Communication Technologies. Rohtak, India: IEEE, 2012: 535-541.
[6]	SAHINGOZ O K. Networking models in flying Ad Hoc networks(FANETs): Concepts and challenges[J]. Journal of Intelligent & Robotic Systems, 2014, 74: 513-527.
[7]	CLEMM A, ZHANI M F, BOUTABA R. Network management 2030: Operations and control of network 2030 services[J]. Journal of Network and Systems Management, 2020, 28(4): 721-750. doi: 10.1007/s10922-020-09517-0
[8]	FATIMA M, KHURSHEED A. Heterogeneous Ad Hoc network management: An overview[M]//SANJOY D. Cloud computing enabled big-data analytics in 2ireless Ad-Hoc networks. Boca Raton: CRC Press, 2022: 103-123.
[9]	GHODE S D, BHOYAR K K. NEMA: Node energy monitoring algorithm for zone head selection in mobile Ad Hoc network using residual battery power of node[C]//2016 International Conference on Wireless Communications, Signal Processing and Networking(WiSPNET). Chennai, India: IEEE, 2016: 1999-2004.
[10]	SALSANO S, PATRIARCA F, PRESTI F L, et al. Accurate and efficient measurements of IP level performance to drive interface selection in heterogeneous wireless networks[J]. IEEE Transactions on Mobile Computing, 2018, 17(10): 2223-2235. doi: 10.1109/TMC.2018.2807842
[11]	YU Y, NING Z, SONG Q, et al. A dynamic cooperative monitor node selection algorithm in wireless mesh networks[C]//2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium on Cyberspace Safety and Security, and 2015 IEEE 12th International Conference on Embedded Software and Systems. New York, NY, USA: IEEE, 2015: 1800-1805.
[12]	YU Y, GUO L, HUANG J, et al. A cross-layer security monitoring selection algorithm based on traffic prediction[J]. IEEE Access, 2018, 6: 35382-35391. doi: 10.1109/ACCESS.2018.2851993
[13]	PHAN X T, MARTINEZ-CASANUEVA I D, FUKUDA K. Adaptive and distributed monitoring mechanism in software-defined networks[C]//2017 13th International Conference on Network and Service Management(CNSM). Tokyo, Japan: IEEE, 2017: 1-5.
[14]	SHIN D H, BAGCHI S, WANG C C. Toward optimal distributed monitoring of multi-channel wireless networks[J]. IEEE Transactions on Mobile Computing, 2015, 15(7): 1826-1838.
[15]	BOUHTOU M, KLOPFENSTEIN O. Robust optimization for selecting netflow points of measurement in an IP network[C]//IEEE GLOBECOM 2007-IEEE Global Telecommunications Conference. Washington, D.C., USA: IEEE, 2007: 2581-2585.
[16]	NIE L, WANG H, JIANG X, et al. Traffic measurement optimization based on reinforcement learning in large-scale its-oriented backbone networks[J]. IEEE Access, 2020, 8: 36988-36996. doi: 10.1109/ACCESS.2020.2975238
[17]	ABURUMMAN A, SEO W J, ISLAM R, et al. A secure cross-domain sip solution for mobile Ad Hoc network using dynamic clustering[C]//Security and Privacy in Communication Networks: 11th EAI International Conference, SecureComm 2015. Dallas, TX, USA: Springer International Publishing, 2015: 649-664.
[18]	YANG Y, WU J, LONG C, et al. A blockchain-based cross-domain authentication for conditional privacy preserving in vehicular ad-hoc network[C]//2021 The 3rd International Conference on Blockchain Technology. Shanghai, China: Association for Computing Machinery, 2021: 183-188.
[19]	Li S E. Deep reinforcement learning[M]//Reinforcement Learning for Sequential Decision and Optimal Control. Singapore: Springer Nature Singapore, 2023: 365-402.
[20]	OROOJLOOY A, HAJINEZHAD D. A review of cooperative multi-agent deep reinforcement learning[J]. Applied Intelligence, 2023, 53(11): 13677-13722. doi: 10.1007/s10489-022-04105-y
[21]	YU C, VELU A, VINITSKY E, et al. The surprising effectiveness of PPO in cooperative multi-agent games[J]. Advances in Neural Information Processing Systems, 2022, 35: 24611-24624.
[22]	HOSPEDALES T, ANTONIOU A, MICAELLI P, et al. Meta-Learning in neural networks: A survey[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 44(9): 5149-5169.
[23]	WANG J, HU J, MIN G, et al. Fast adaptive task offloading in edge computing based on meta reinforcement learning[J]. IEEE Transactions on Parallel and Distributed Systems, 2020, 32(1): 242-253.
[24]	JEONG T, KIM H. OOD-MAML: Meta-learning for few-shot out-of-distribution detection and classification[J]. Advances in Neural Information Processing Systems, 2020, 33: 3907-3916.