Review of Routing Protocols for Mobile Ad Hoc Networks
DOI:
https://doi.org/10.62051/ijcsit.v3n1.04Keywords:
Ad Hoc network, Routing policy, Proactive routing, Reactive routing, Clustering routingAbstract
Research on Mobile Ad Hoc Networks is of great significance in various fields including military, emergency response, Internet of Things (IoT), vehicular networking, aviation, and scientific research. This research contributes to the advancement and application of mobile communication technologies. Both domestic and international researchers have conducted in-depth studies on routing protocol design, which encompasses various aspects such as protocol design, topology management, link quality estimation, security, and energy efficiency. These areas represent current research hotspots and challenges. With the continuous development of wireless communication technologies and increasing demands for application, research on routing in Mobile Ad Hoc Networks remains highly significant with broad prospects for development. Therefore, research on routing strategies for Mobile Ad Hoc Networks has garnered widespread attention in academic circles worldwide, leading to numerous valuable research outcomes. This paper comprehensively analyzes the requirements related to the design of routing protocols for mobile self-organizing networks and comprehensively investigates the existing routing protocols, and classifies and investigates the routing protocols for mobile self-organizing networks in order to promote the research of next-generation mobile self-organizing network routing in China, which provides a reference.
Downloads
References
Zhang Y, Shen Y, Jiang X, et al. Secure Millimeter-Wave Ad Hoc Communications Using Physical Layer Security [J]. IEEE Transactions on Information Forensics and Security, 2021, 17: 99-114.
Avellino I, Nozari S, Canlorbe G, et al. Surgical Video Summarization: Multifarious Yses, Summarization Process and Ad-hoc Coordination [C]. Proceedings of the 16th ACM on Human-Computer Interaction, 2021, 5: 1-23.
Hu F, Sharma N K. Security Considerations in Ad Hoc Sensor Networks [J]. Ad Hoc Networks, 2005, 3(1): 69-89.
Lwin M T, Yim J, Ko Y B. Blockchain-based Lightweight Trust Management in Mobile Ad-hoc Networks [J]. Sensors, 2020, 20(3): 698.
Najm I, Lal D, Alonso Vanegas M, et al. The ILAE Consensus Classification of Focal Cortical Dysplasia: an Update Proposed by an Ad Hoc Task Force of the ILAE Diagnostic Methods Commission [J]. Epilepsia, 2022, 63(8): 1899-1919.
akew D S, Sa’ad U, Dao N N, et al. Routing in Flying Ad Hoc Networks: A Comprehensive Survey [J]. IEEE Communications Surveys and Tutorials, 2020, 22(2): 1071-1120.
Gupta N, Jain A, Vaisla K S, et al. Performance Analysis of DSDV and OLSR Wireless Sensor Network Routing Protocols Using FPGA Hardware and Machine Learning [J]. Multimedia Tools and Applications, 2021, 80: 22301-22319.
Malhi A K, Batra S, Pannu H S. Security of Vehicular Ad-hoc Networks: A Comprehensive Survey [J]. Computers & Security, 2020, 89: 101664.
Elhoseny M, Shankar K. Reliable Data Transmission Model for Mobile Ad Hoc Network Us-ing Signcryption Technique [J]. IEEE Transactions on Reliability, 2019, 69(3): 1077-1086.
Xing N, Zong Q, Dou L, et al. A Game Theoretic Approach for Mobility Prediction Clustering in Unmanned Aerial Vehicle Networks [J]. IEEE Transactions on Vehicular Technology, 2019, 68(10): 9963-9973.
Fang K, Ru L, Yu Y, et al. An Energy Balance and Mobility Prediction Clustering Algrithm for Large-scale UAV Ad Hoc Networks [J]. Engineering Review, 2019, 39(1): 1-10.
Ni M, Zhong Z, Zhao D. MPBC: A Mobility Prediction-based Clustering Scheme for Ad Hoc Networks [J]. IEEE Transactions on Vehicular Technology, 2011, 60(9): 4549-4559.
Sang Q, Wu H, Xing L, et al. An Energy-Efficient Opportunistic Routing Protocol Based on Trajectory Prediction for FANETs [J]. IEEE Access, 2020, 8: 192009-192020.
Bilen T, Aydemir P J, Konu A E, et al. Customized K-Means Based Topology Clustering for Aeronautical Ad-hoc Networks [C]. Proceedings of the 26th International Workshop on Com-puter Aided Modeling and Design of Communication Links and Networks, 2021: 1-5.
Neelakandan S ,Prakash M ,Youseef A , et al.An Efficient Metaheuristic-Based Clustering with Routing Protocol for Underwater Wireless Sensor Networks [J]. Sensors, 2022,22(2): 415-415.
Xiao X, Guo H, Mao S. The Modeling Mechanism, Extension and Optimization of Grey GM (1, 1) Model [J]. Applied Mathematical Modelling, 2014, 38(5-6): 1896-1910.
Liu S, Zeng B, Liu J, et al. Four Basic Models of GM (1, 1) and Their Suitable Sequences [J]. Grey Systems: Theory and Application, 2015, 5(2): 141-156.
Yan M, Li S, Chan C A, et al. Mobility Prediction Using a Weighted Markov Model Based on Mobile User Classification [J]. Sensors, 2021, 21(5): 1740.
Désir A, Goyal V, Segev D, et al. Constrained Assortment Optimization Under the Markov Chain–based Choice Model [J]. Management Science, 2020, 66(2): 698-721.
Albergo M S, Kanwar G, Shanahan P E. Flow-based Generative Models for Markov Chain Monte Carlo in Lattice Field Theory [J]. Physical Review, 2019, 100(3): 034515.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
