Role of Game-Theoretic Approaches in Ad-hoc Network Communications: A Comprehensive Review
Keywords:
Ad-hoc networks, Cross-layer optimization, Distributed algorithms, Game theory, Power controlAbstract
Game-theoretic models provide a solid foundation for creating reliable and efficient communication in wireless ad-hoc networks without central control. This work combines methods from the physical, MAC, network, and application layers to reflect the strategic relationships that arise from interference, competition, and forwarding incentives. At the physical layer, non-cooperative and potential game models support distributed power control and waveform adjustment. At the MAC layer, competition games assist with adaptive backoff, collision avoidance, and fairness in settings similar to 802.11. At the network layer, forwarding and routing are viewed as social dilemmas. Repeated and evolutionary dynamics, along with incentive systems like credits, reputation, and auctions, help maintain cooperation despite mobility and energy limitations. Bayesian games address incomplete information and noisy monitoring, while mechanism design ensures local benefits align with broader goals. Security is handled through attacker-defender games to counter threats such as jamming, misrouting, and collusion, along with trust management. Extensions for delay-tolerant, IoT/WSN, and vehicular networks include spectrum sharing, relay selection, and resource auctions. The discussion compares equilibrium concepts, efficiency losses, convergence properties, and learning dynamics. It also highlights ongoing challenges in multi-agent learning, integration with AI protocols, privacy-friendly incentives, and reliable multi-hop coordination for practical protocol development.
References
H. Messabih, C. A. Kerrache, Y. Cheriguene, C. T. Calafate, and F. Z. Bousbaa, “An Overview of Game Theory Approaches for Mobile Ad-Hoc Networks’ Security,” IEEE Access, vol. 11, pp. 107581–107604, 2023, doi: https://doi.org/10.1109/access.2023.3321082.
T. N. Tran, T. V. Nguyen, K. Shim, “A Game Theory Based Clustering Protocol to Support Multicast Routing in Cognitive Radio Mobile Ad Hoc Networks,” IEEE Access, vol. 8, pp. 141310–141330, Jan. 2020, doi: https://doi.org/10.1109/access.2020.3013644.
A. Seddighi, S. Asghari, M. Romoozi, and H. Babaei, “A Review of Game Theory-Based Trust Modeling Approaches in Infrastructure-Free Social Networks,” Management Strategies and Engineering Sciences, vol. 7, no. 4, pp. 81–95, 2025, doi: https://doi.org/10.61838/msesj.7.4.9.
U. Islam, F. Anwar, R. F. Olanrewaju, B. R. Pampori, and R. N. Mir, “A Game Theory-Based Strategic Approach to Ensure Reliable Data Transmission with Optimized Network Operations in Futuristic Mobile Adhoc Networks,” IEEE Access, vol. 8, pp. 124097–124109, Jan. 2020, doi: https://doi.org/10.1109/access.2020.3006043.
S. Sinha and S. Saha, “An improved communication strategy in vehicular ad hoc networks: adaptive game theoretic modelling approach,” International Journal of Mobile Communications, vol. 21, no. 2, pp. 273–273, Jan. 2023, doi: https://doi.org/10.1504/ijmc.2023.128824.
M. B. Bezziane, “Game Theory-Based UAV-Cloud for Service Selection Architecture in Flying Ad Hoc Networks,” IEEE Open Journal of Vehicular Technology, vol. 5, pp. 1692–1711, 2024, doi: https://doi.org/10.1109/ojvt.2024.3430818.
U. Singh, A. Dua, N. Kumar, and M. Guizani, “QoS Aware Uplink Scheduling for M2M Communication in LTE/LTE-A Network: A Game Theoretic Approach,” IEEE Transactions on Vehicular Technology, vol. 71, no. 4, pp. 4156–4170, Apr. 2022, doi: https://doi.org/10.1109/tvt.2021.3132535.
F. Zahedi and N. Farzaneh, “An evolutionary game theory-based security model in vehicular ad hoc networks,” International Journal of Communication Systems, vol. 33, no. 6, p. e4290, Jan. 2020, doi: https://doi.org/10.1002/dac.4290.
U. Ravale and G. M. Borkar, “Secure and efficient trust enabled routing in mobile ad hoc network using game theory and grey wolf optimisation techniques,” IET Wireless Sensor Systems, Oct. 2024, doi: https://doi.org/10.1049/wss2.12095.
A. Alqahtani, S. Alsubai, M. Sha, and M. Bhatia, “Game Theoretic Systematic Approach for Transportation Quality Assessment,” Mobile Networks and Applications, Aug. 2023, doi: https://doi.org/10.1007/s11036-023-02233-4.
T. Cao, C. Xu, J. Du, Y. Li, “Reliable and Efficient Multimedia Service Optimization for Edge Computing-Based 5G Networks: Game Theoretic Approaches,” IEEE Transactions on Network and Service Management/IEEE eTransactions on Network and Service Management, vol. 17, no. 3, pp. 1610–1625, Sep. 2020, doi: https://doi.org/10.1109/tnsm.2020.2993886.
D. A. Hammood, H. A. Rahim, A. Alkhayyat, and R. B. Ahmad, “Duty Cycle Optimization Using Game Theory Two Master Nodes Cooperative Protocol in WBAN,” Wireless Personal Communications, vol. 122, no. 3, pp. 2479–2504, Sep. 2021, doi: https://doi.org/10.1007/s11277-021-09003-z.
T. Li, C. Li, C. Yang, J. Shao, “A mean field game-theoretic cross-layer optimization for multi-hop swarm UAV communications,” Journal of Communications and Networks, vol. 24, no. 1, pp. 68–82, Feb. 2022, doi: https://doi.org/10.23919/jcn.2021.000035.
J. Xie and T. Murase, “Multiple User Cooperative Mobility in Mobile Ad Hoc Networks: An Interaction Position Game,” IEEE Access, vol. 8, pp. 126297–126314, 2020, doi: https://doi.org/10.1109/access.2020.3007931.
S. M. Tiwari and A. S. Baghel, “Improving Dynamic Behavior of Vehicular Ad Hoc Networks by Integrating Game Theory Technique,” Communications in Computer and Information Science, pp. 173–183, Jan. 2024, doi: https://doi.org/10.1007/978-3-031-58956-0_13.
N. Phull, P. Singh, M. Shabaz, and F. Sammy, “Enhancing Vehicular Ad Hoc Networks’ Dynamic Behavior by Integrating Game Theory and Machine Learning Techniques for Reliable and Stable Routing,” Security and Communication Networks, vol. 2022, pp. 1–11, May 2022, doi: https://doi.org/10.1155/2022/4108231.
L. Jia, N. Qi, Z. Su, F. Chu, “Game Theory and Reinforcement Learning for Anti-jamming Defence in Wireless Communications: Current Research, Challenges, and Solutions,” IEEE Communications Surveys & Tutorials, pp. 1–1, 2024, doi: https://doi.org/10.1109/comst.2024.3482973.
Z. Sun, Y. Liu, J. Wang, C. Anil, and D. Cao, “Game Theoretic Approaches in Vehicular Networks: A Survey,” Arxiv (Cornell University), Jan. 2020, doi: https://doi.org/10.48550/arxiv.2006.00992.
J. Aguila‐Leon, C. Chiñas‐Palacios, and C. Vargas‐Salgado, “Amultimicrogridenergy management model implementing an evolutionary game‐theoretic approach,” International Transactions on Electrical Energy Systems, vol. 30, no. 11, Sep. 2020, doi: https://doi.org/10.1002/2050-7038.12617.
