Adaptive Opportunistic Routing for Wireless Ad Hoc Networks
Abstract
A distributed adaptive opportunistic routing scheme for multihop wireless ad hoc networks is proposed. The proposed scheme utilizes a reinforcement learning framework to opportunistically route the packets even in the absence of reliable knowledge about channel statistics and network model. This scheme is shown to be optimal with respect to an expected average per-packet reward criterion. The proposed routing scheme jointly addresses the issues of learning and routing in an opportunistic context, where the network structure is characterized by the transmission success probabilities. In particular, this learning framework leads to a stochastic routing scheme that optimally “explores” and “exploits” the opportunities in the network.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
After convergence, d-AdaptOR achieves ETX comparable to a topology-aware routing algorithm.
“After convergence (≈ 300sec) • ETX comparable to a topology-aware routing algorithm”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [59]
d-AdaptOR achieves more than 30% improvement over greedy-SR, greedy ExOR and SRCR with a single flow.
“> 30% improvement over greedy-SR, greedy ExOR and SRCR with a single flow”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [59]
d-AdaptOR's improvement decreases with the number of flows.
“Improvement decreases with # flows”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [59]
d-AdaptOR consistently outperforms existing adaptive routing algorithms in terms of number of retransmissions per packet.
“Simulations show that d-AdaptOR consistently outperforms existing adaptive routing algorithms, in terms of number of retransmissions per packet.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [59]
Routing in wireless ad hoc networks calls for decentralized RL where the learning capability is distributed among the routing nodes.
“routing in wireless ad hoc networks calls for decentralized RL [59,277] where the learning capability is distributed among the routing nodes.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [59]
Cites
Artificial intelligence: a modern approach
Kumar, D. A., Balasubramanian, S., Byeon, D. H., & Manerkar, P. G. V. (1995) · Choice Reviews Online · doi:10.5860/choice.33-1577 · Cited by 22,182 · see citing works
Markov decision processes: Discrete stochastic dynamic programming
Hazeghi, K., & Puterman, M. L. (1995) · Journal of the American Statistical Association · doi:10.2307/2291177 · Cited by 8,037 · see citing works
Puterman, M. L. (1994) · Wiley series in probability and statistics · doi:10.1002/9780470316887 · Cited by 6,057 · see citing works
Parallel and Distributed Computation: Numerical Methods
Bertsekas, D. P., & Tsitsiklis, J. N. (1989) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 5,398 · see citing works
A review of current routing protocols for ad hoc mobile wireless networks
Royer, E. M., & Toh, C. K. (1999) · IEEE Personal Communications · doi:10.1109/98.760423 · Cited by 3,772 · see citing works
(2012) · Springer eBooks · doi:10.1007/978-1-4419-1428-6_5086 · Cited by 3,601 · see citing works
Bertsekas, D. P. (2001) · Encyclopedia of Optimization · doi:10.1007/0-306-48332-7_333 · Cited by 1,486 · see citing works
Biswas, S., & Morris, R. (2005) · ACM SIGCOMM Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications (SIGCOMM) · doi:10.1145/1080091.1080108 · Cited by 1,357 · see citing works
Trading structure for randomness in wireless opportunistic routing
Chachulski, S., Jennings, M., Katti, S., & Katabi, D. (2007) · ACM SIGCOMM Conference (SIGCOMM) · doi:10.1145/1282380.1282400 · Cited by 1,076 · see citing works
Geographic random forwarding (geraf) for ad hoc and sensor networks: multihop performance
Zorzi, M., & Rao, R. R. (2003) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2003.1255648 · Cited by 885 · see citing works
Architecture and evaluation of an unplanned 802.11b mesh network
Bicket, J. C., Aguayo, D., Biswas, S., & Morris, R. (2005) · ACM Annual International Conference on Mobile Computing and Networking (MobiCom) · doi:10.1145/1080829.1080833 · Cited by 749 · see citing works
Packet Routing in Dynamically Changing Networks: A Reinforcement Learning Approach
Boyan, J. A., & Littman, M. L. (1994) · Cited by 694 · see citing works
Reinforcement Learning with Hierarchies of Machines
Parr, R., & Russell, S. (1997) · Cited by 618 · see citing works
Biswas, S., & Morris, R. (2005) · ACM SIGCOMM Computer Communication Review · doi:10.1145/1090191.1080108 · Cited by 525 · see citing works
Trading structure for randomness in wireless opportunistic routing
Chachulski, S., Jennings, M., Katti, S., & Katabi, D. (2007) · ACM SIGCOMM Computer Communication Review · doi:10.1145/1282427.1282400 · Cited by 485 · see citing works
Wireless communications and networks
(2017) · International Conference on Smart Systems and Technologies (SST) · doi:10.1109/sst.2017.8188678 · Cited by 459 · see citing works
Resnick, S. I. (1999) · Cited by 387 · see citing works
Breiman, L. (1992) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611971286 · Cited by 374 · see citing works
Selection diversity forwarding in a multihop packet radio network with fading channel and capture
Larsson, P. (2001) · ACM SIGMOBILE Mobile Computing and Communications Review · doi:10.1145/509506.509517 · Cited by 164 · see citing works
Wireless Communications and Networks
Chih‐Lin, I., Tsai, J.-A., & Wu, H. (2004) · Cited by 148 · see citing works
Predictive Q-Routing: A Memory-based Reinforcement Learning Approach to Adaptive Traffic Control
Choi, S. P. M., & Yeung, D. (1995) · doi:10.5555/2998828.2998961 · Cited by 140 · see citing works
Exploiting Path Diversity in the Link Layer in Wireless Ad Hoc Networks
Jain, S., & Das, S. R. (2005) · IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM) · doi:10.1109/wowmom.2005.42 · Cited by 136 · see citing works
Asynchronous stochastic approximation and Q-learning
Tsitsiklis, J. N. (2002) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc.1993.325119 · Cited by 113 · see citing works
Dual Reinforcement Q-Routing: An On-Line Adaptive Routing Algorithm
Miikkulainen, S. K. A. R. (1997) · Cited by 97 · see citing works
Performance analysis of the AntNet algorithm
Dhillon, S. S., & Mieghem, P. V. (2006) · Computer Networks · doi:10.1016/j.comnet.2006.11.002 · Cited by 88 · see citing works
Cooperative Opportunistic Routing Using Transmit Diversity in Wireless Mesh Networks
Kurth, M., Zubow, A., & Redlich, J.-P. (2008) · doi:10.1109/infocom.2008.188 · Cited by 78 · see citing works
Stochastic Routing in Ad-Hoc Networks
Lott, C., & Teneketzis, D. (2006) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2005.860280 · Cited by 74 · see citing works
Introduction to Radio Propagation for Fixed and Mobile Communications
Doble, J. E. (1996) · International Linear Collider · Cited by 68 · see citing works
Optimal Backpressure Routing for Wireless Networks with Multi-Receiver Diversity
Neely, M. J. (2006) · Conference on Information Sciences and Systems · doi:10.1109/ciss.2006.286424 · Cited by 53 · see citing works
Towards Throughput and Delay Optimal Routing for Wireless Ad-Hoc Networks
Gupta, P., & Javidi, T. (2007) · 2018 52nd Asilomar Conference on Signals, Systems, and Computers · doi:10.1109/acssc.2007.4487207 · Cited by 34 · see citing works
Self-citationStochastic routing in ad hoc wireless networks
Lott, C., & Teneketzis, D. (2002) · Proceedings of the IEEE Conference on Decision & Control, including the Symposium on Adaptive Processes · doi:10.1109/cdc.2000.914141 · Cited by 28 · see citing works
Convergence results for ant routing algorithms via stochastic approximation and optimization
Purkayastha, P., & Baras, J. S. (2007) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc.2007.4434982 · Cited by 17 · see citing works
Sensitivity Analysis of an Optimal Routing Policy in an Ad Hoc Wireless Network
Javidi, T., & Teneketzis, D. (2004) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2004.832204 · Cited by 16 · see citing works
Self-citation
Flagged first, then by citations
1 of the 34 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Boutaba, R., Salahuddin, M. A., Limam, N., Ayoubi, S., Shahriar, N., Estrada-Solano, F., & Caicedo, O. M. (2018) · Journal of Internet Services and Applications · doi:10.1186/s13174-018-0087-2 · Cited by 1,036 · see citing works
Reinforcement Learning Based Routing in Networks: Review and Classification of Approaches
Mammeri, Z. (2019) · IEEE Access · doi:10.1109/access.2019.2913776 · Cited by 435 · see citing works
A Survey on Opportunistic Routing in Wireless Communication Networks
Chakchouk, N. (2015) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2411335 · Cited by 258 · see citing works
Cellular Network Traffic Scheduling With Deep Reinforcement Learning
Chinchali, S., Hu, P., Chu, T., Sharma, M., Bansal, M., Misra, R., Pavone, M., & Katti, S. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11339 · Cited by 218 · see citing works
“RL has also been applied to wireless networks, but mostly classical problems such as power control (Vengerov, Bambos, and Berenji 2005) or call admission control and routing (Bhorkar et al. 2012; Marbach, Mihatsch, and Tsitsiklis 1998).”
MethodDesign, Resource Management, and Evaluation of Fog Computing Systems: A Survey
Martinez, I., Hafid, A. S., & Jarray, A. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3022699 · Cited by 152 · see citing works
Application of reinforcement learning to routing in distributed wireless networks: a review
Al-Rawi, H. A. A., Ng, M. A., & Yau, K. A. (2013) · Artificial Intelligence Review · doi:10.1007/s10462-012-9383-6 · Cited by 103 · see citing works
In-Network Machine Learning Using Programmable Network Devices: A Survey
Zheng, C., Hong, X., Ding, D., Vargaftik, S., Ben-Itzhak, Y., & Zilberman, N. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3344351 · Cited by 90 · see citing works
Software-Defined Vehicular Networks With Trust Management: A Deep Reinforcement Learning Approach
Zhang, D., Yu, F. R., Yang, R., & Zhu, L. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3025684 · Cited by 76 · see citing works
Reconfigurable Wireless Networks
Mougy, A. E., Ibnkahla, M. A., Hattab, G., & Ejaz, W. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2014.2376812 · Cited by 63 · see citing works
Scalable Dynamic Routing Protocol for Cognitive Radio Sensor Networks
Spachos, P., & Hantzinakos, D. (2014) · IEEE Sensors Journal · doi:10.1109/jsen.2014.2309138 · Cited by 61 · see citing works
Saini, T. K., & Sharma, S. C. (2019) · Ad Hoc Networks · doi:10.1016/j.adhoc.2019.03.001 · Cited by 58 · see citing works
Intelligent IoT Connectivity: Deep Reinforcement Learning Approach
Kwon, M., Lee, J., & Park, H. (2019) · IEEE Sensors Journal · doi:10.1109/jsen.2019.2949997 · Cited by 55 · see citing works
Cross-Layer Aided Energy-Efficient Opportunistic Routing in Ad Hoc Networks
Zuo, J., Dong, C., Nguyen, H. V., Ng, S. X., Yang, L., & Hanzo, L. (2014) · IEEE Transactions on Communications · doi:10.1109/tcomm.2013.121413.120767 · Cited by 54 · see citing works
Artificial intelligence based cognitive routing for cognitive radio networks
Qadir, J. (2015) · Artificial Intelligence Review · doi:10.1007/s10462-015-9438-6 · Cited by 52 · see citing works
AI Routers & Network Mind: A Hybrid Machine Learning Paradigm for Packet Routing
Yao, H., Mai, T., Jiang, C., Kuang, L., & Guo, S. (2019) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2019.2937609 · Cited by 48 · see citing works
Ryu, J., & Kim, S. (2023) · IEEE Access · doi:10.1109/access.2023.3242608 · Cited by 47 · see citing works
DA-DRLS: Drift adaptive deep reinforcement learning based scheduling for IoT resource management
Chowdhury, A., Raut, S., & Narman, H. S. (2019) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2019.04.010 · Cited by 47 · see citing works
Cross-Layer Aided Energy-Efficient Routing Design for Ad Hoc Networks
Zuo, J., Dong, C., Ng, S. X., Yang, L., & Hanzo, L. (2015) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2395378 · Cited by 42 · see citing works
Bhavanasi, S. S., Pappone, L., & Esposito, F. (2023) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2023.3287936 · Cited by 35 · see citing works
Proliferation of Opportunistic Routing: A Systematic Review
Dalal, R., Khari, M., Anzola, J., & García‐Díaz, V. (2021) · IEEE Access · doi:10.1109/access.2021.3136927 · Cited by 33 · see citing works
QMOR: QoS-Aware Multi-sink Opportunistic Routing for Wireless Multimedia Sensor Networks
Shen, H., Bai, G., Tang, Z., & Zhao, L. (2013) · Wireless Personal Communications · doi:10.1007/s11277-013-1425-0 · Cited by 30 · see citing works
Liu, L., & Liu, Y. (2014) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2014.2368571 · Cited by 29 · see citing works
Toward Optimal Adaptive Online Shortest Path Routing With Acceleration Under Jamming Attack
Zhou, P., Xu, J., Wang, W., Hu, Y., Wu, D. O., & Ji, S. (2019) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2019.2930464 · Cited by 26 · see citing works
Five Decades of Hierarchical Modulation and Its Benefits in Relay-Aided Networking
Sun, H. F., Dong, C., Ng, S. X., & Hanzo, L. (2015) · IEEE Access · doi:10.1109/access.2015.2510702 · Cited by 25 · see citing works
Endhost-based shortest path routing in dynamic networks: An online learning approach
He, T., Goeckel, D., Raghavendra, R., & Towsley, D. (2013) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infcom.2013.6567023 · Cited by 20 · see citing works
On the throughput optimization for message dissemination in opportunistic underwater sensor networks
Liu, L., Wang, R., Xiao, G., & Guo, D. (2020) · Computer Networks · doi:10.1016/j.comnet.2020.107097 · Cited by 20 · see citing works
Barve, S., & Kulkarni, P. A. (2014) · Mobile Networks and Applications · doi:10.1007/s11036-014-0551-6 · Cited by 19 · see citing works
Wang, X., Li, D., Zhang, X., & Cao, Y. (2018) · Applied Sciences · doi:10.3390/app8071165 · Cited by 19 · see citing works
Menon, V. G., & Prathap, P. M. J. (2016) · International Journal of Engineering and Technology · doi:10.21817/ijet/2016/v8i5/160805409 · Cited by 18 · see citing works
Yuan, H., Guo, W., Jin, Y., Wang, S., & Ni, M. (2017) · IET Communications · doi:10.1049/iet-com.2016.0640 · Cited by 18 · see citing works
COF: Exploiting Concurrency for Low Power Opportunistic Forwarding
Liu, D., Hou, M., Cao, Z., He, Y., Ji, X., & Zheng, X. (2015) · IEEE International Conference on Network Protocols (ICNP) · doi:10.1109/icnp.2015.13 · Cited by 17 · see citing works
Totoro: A Scalable Federated Learning Engine for the Edge
Ching, C.-W., Chen, X., Kim, T., Ji, B., Wang, Q., Silva, D. D., & Hu, L. (2024) · European Conference on Computer Systems · doi:10.1145/3627703.3629575 · Cited by 15 · see citing works
AgileDART: An Agile and Scalable Edge Stream Processing Engine
Ching, C.-W., Chen, X., Kim, C., Wang, T., Chen, D., Silva, D. D., & Hu, L. (2025) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2025.3526143 · Cited by 15 · see citing works
Wireless multihop backhauls for rural areas: A preliminary study
Zaidi, Z., & Lan, K.-C. (2017) · PLoS ONE · doi:10.1371/journal.pone.0175358 · Cited by 15 · see citing works
Jafarzadeh, S. Z., & Yaghmaee, M. H. (2014) · International Conference on Computer and Knowledge Engineering · doi:10.1109/iccke.2014.6993408 · Cited by 15 · see citing works
Propagation control of data forwarding in opportunistic underwater sensor networks
Liu, L., Wang, P., & Wang, R. (2017) · Computer Networks · doi:10.1016/j.comnet.2017.01.009 · Cited by 15 · see citing works
TSOR: Thompson Sampling-Based Opportunistic Routing
Huang, Z., Xu, Y., & Pan, J. (2021) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2021.3082080 · Cited by 14 · see citing works
Delay Analytical Models for Opportunistic Routing in Wireless Ad Hoc Networks
Chung, K.-C., Kuo, W., & Liao, W. (2016) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2016.2617891 · Cited by 11 · see citing works
Route selection over clustered cognitive radio networks: An experimental evaluation
Musavi, M., Yau, K. A., Syed, A. R., Mohamad, H., & Ramli, N. (2018) · Computer Communications · doi:10.1016/j.comcom.2018.07.035 · Cited by 11 · see citing works
Design of energy-aware QoS routing protocol in wireless sensor networks using reinforcement learning
Jafarzadeh, S. Z., & Yaghmaee, M. H. (2014) · Conference proceedings - Canadian Conference on Electrical and Computer Engineering · doi:10.1109/ccece.2014.6900988 · Cited by 11 · see citing works
Delay-Constrained Routing Based on Stochastic Model for Flying Ad Hoc Networks
Wen, S., & Huang, C. (2018) · Mobile Information Systems · doi:10.1155/2018/6056419 · Cited by 11 · see citing works
Routing with Graph Convolutional Networks and Multi-Agent Deep Reinforcement Learning
Bhavanasi, S. S., Pappone, L., & Esposito, F. (2022) · IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN) · doi:10.1109/nfv-sdn56302.2022.9974607 · Cited by 10 · see citing works
Al-Rawi, H. A. A., & Yau, K. A. (2013) · IEEE Symposium on Computational Intelligence for Communication Systems and Networks (CIComms) · doi:10.1109/cicomms.2013.6582850 · Cited by 10 · see citing works
Menon, V. G., & Prathap, P. M. J. (2018) · Advances in computer and electrical engineering book series · doi:10.4018/978-1-5225-5693-0.ch001 · Cited by 10 · see citing works
Distributed online learning of the shortest path under unknown random edge weights
Tehrani, P., & Zhao, Q. Q. (2013) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2013.6638236 · Cited by 10 · see citing works
Vidyadhar, V., Nagaraj, R., & V, D. (2021) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2021.0120423 · Cited by 10 · see citing works
Reinforcement learning models for scheduling in wireless networks
Yau, K. A., Kwong, K. H., & Shen, C. (2013) · Frontiers of Computer Science · doi:10.1007/s11704-013-2291-3 · Cited by 10 · see citing works
A Survey of Applying Reinforcement Learning Techniques to Multicast Routing
Ashour, O., St‐Hilaire, M., Kunz, T., & Wang, M. (2019) · IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) · doi:10.1109/uemcon47517.2019.8993014 · Cited by 9 · see citing works
Opportunistic Networks: An Empirical Research of Routing Protocols and Mobility Models
Ahmad, K., Fathima, M., Hossen, M. S., Ahamed, J., & Ahmad, K. A. B. (2023) · SN Computer Science · doi:10.1007/s42979-023-02054-y · Cited by 9 · see citing works
Coordinated opportunistic routing protocol for wireless mesh networks
Ajmal, M. M., Madani, S. A., Maqsood, T., Bilal, K., Nazir, B., & Khizar Hayat, A. (2013) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2013.06.012 · Cited by 9 · see citing works
EESOR: Energy Efficient Selective Opportunistic Routing in Wireless Sensor Networks
Devi, C. R. Y., Shivaraj, B., Manjula, S. H., Venugopal, K. R., & Patnaik, L. M. (2014) · Communications in computer and information science · doi:10.1007/978-3-642-54525-2_2 · Cited by 9 · see citing works
Energy efficient data correlation aware opportunistic routing protocol for wireless sensor networks
Qin, X., Huang, G., Zhang, B., & Li, C. (2021) · Peer-to-Peer Networking and Applications · doi:10.1007/s12083-021-01124-3 · Cited by 9 · see citing works
Anycast Q-routing in wireless sensor networks for healthcare monitoring
Khianjoom, S., & Usaha, W. (2014) · International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON) · doi:10.1109/ecticon.2014.6839897 · Cited by 9 · see citing works
Achieving Congestion Diversity in Multi-Hop Wireless Mesh Networks
Bhorkar, A., Javidi, T., & Snoeren, A. C. (2014) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2014.2323076 · Cited by 8 · see citing works
Self-citationLiu, L., Wang, R., & Wu, J. (2018) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2018.08.004 · Cited by 7 · see citing works
Opportunistic routing under unknown stochastic models
Tehrani, P., Zhao, Q. Q., & Javidi, T. (2013) · IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing · doi:10.1109/camsap.2013.6714028 · Cited by 7 · see citing works
Self-citationShortest Path Routing in Unknown Environments: Is the Adaptive Optimal Strategy Available?
Zhou, P., Cheng, L., & Wu, D. O. (2016) · IEEE International Conference on Sensing, Communication, and Networking (SECON) · doi:10.1109/sahcn.2016.7733015 · Cited by 6 · see citing works
Menon, V. G. (2019) · Preprints.org · doi:10.20944/preprints201907.0239.v1 · Cited by 6 · see citing works
Huang, F., Cui, J., Chang, Y., Wang, T. Y., Wang, M., & Yang, Y. (2024) · International Journal of Distributed Sensor Networks · doi:10.1155/2024/7471329 · Cited by 4 · see citing works
Energy efficiency optimization routing decision scheme in three‐dimensional wireless ad hoc network
Liu, D., Chen, G., & Wang, Y. (2023) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.7624 · Cited by 4 · see citing works
Evaluating the effect of path diversity over QoS and QoE in a high speed indoor mesh backbone
Chakraborty, S., & Nandi, S. (2014) · International Conference on Communication Systems & Networks (COMSNETS) · doi:10.1109/comsnets.2014.6734896 · Cited by 3 · see citing works
Reinforcement Learning Based Spectrum Sensing and Resource Allocation in WSN-IoT Smart Applications
Deepthi, J. V. N. R., Khan, A. K., & Acharjee, T. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-6690-5_8 · Cited by 3 · see citing works
Vo, N., Nguyen, M.-H., Ha, D., & Huynh, D. (2013) · International Conference on Computing, Management and Telecommunications · doi:10.1109/commantel.2013.6482396 · Cited by 3 · see citing works
Menon, V. G. (2019) · Preprints.org · doi:10.20944/preprints201903.0118.v1 · Cited by 3 · see citing works
Reinforcement Learning Based Energy-Efficient Routing with Latency Constraints for FANETs
Qi, X., Li, J., Lv, Z., & Xiao, L. (2023) · Globecom · doi:10.1109/globecom54140.2023.10437037 · Cited by 2 · see citing works
A Minimum Interference Cross-Layer Routing Protocol for Mobile Ad Hoc Networks
Gu, C., & Zhu, Q. (2013) · Wireless Personal Communications · doi:10.1007/s11277-013-1178-9 · Cited by 2 · see citing works
Markov Decision Process Based Wireless Multicast Opportunistic Routing
Ma, D., Tan, X., Zhou, Z., & Yu, S. (2014) · Chinese Control Conference (CCC) · doi:10.1109/chicc.2014.6895881 · Cited by 2 · see citing works
Maximizing Airtime Efficiency for Reliable Broadcast Streams in WMNs with Multi-Armed Bandits
Perin, G., Nophut, D., Badia, L., & Fitzek, F. H. P. (2020) · IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) · doi:10.1109/uemcon51285.2020.9298050 · Cited by 2 · see citing works
Galarza, C. E., Palma, J. M., Morais, C. F., Utria, J., Carvalho, L. D. P., Bustos, D., & Oliveira, R. C. L. F. (2021) · Sensors · doi:10.3390/s21238058 · Cited by 2 · see citing works
Reliable Multicast Routing Protocol Based on Reinforcement Learning
Ashour, O., Kunz, T., & St‐Hilaire, M. (2023) · IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) · doi:10.1109/pimrc56721.2023.10293864 · Cited by 2 · see citing works
Performance Evaluation of Route Selection Schemes over a Clustered Cognitive Radio Network
Musavi, M., Yau, K. A., Mohamad, H., & Ramli, N. (2019) · IEEE VTS Asia Pacific Wireless Communications Symposium (APWCS) · doi:10.1109/vts-apwcs.2019.8851652 · Cited by 1 · see citing works
A Multi-agent Reinforcement Learning Routing Protocol in Mobile Robot Network
Sun, M., Wang, X., Liu, X., Wu, S., Zhou, X., & Ouyang, C. (2021) · International Conference on Information Communication and Signal Processing (ICICSP) · doi:10.1109/icicsp54369.2021.9611881 · Cited by 1 · see citing works
Nourzad, N., & Krishnamachari, B. (2024) · IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN) · doi:10.1109/icmlcn59089.2024.10624789 · Cited by 1 · see citing works
Enhance QoS by Learning Data flow rates in Wireless Networks Using Hierarchical Docition
Sruthi, S., & Varghese, A. M. (2015) · Procedia Computer Science · doi:10.1016/j.procs.2015.10.108 · Cited by 1 · see citing works
QoS-Oriented Adaptive Expedient Broadcast Routing for Hybrid Wireless Network
Satre, S. M., & Jadhav, V. P. (2016) · International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics · doi:10.1109/aeeicb.2016.7538279 · Cited by 1 · see citing works
Research on Industrial Cyber Range Based on Multi-agent Cooperative Optimization
Miao, S., Li, Y., & Pan, Q. (2022) · Lecture notes in electrical engineering · doi:10.1007/978-981-16-9492-9_83 · Cited by 1 · see citing works
Near Optimal Adaptive Shortest Path Routing with Stochastic Links States under Adversarial AttackPreprint
Zhou, P., Cheng, L., & Wu, D. O. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.03348 · Cited by 1 · see citing works
Previous hop routing: exploiting opportunism in VANETs
Ali, A. K. (2018) · Loughborough University Institutional Repository (Loughborough University) · Cited by 1 · see citing works
Reinforcement Learning Framework for Opportunistic Routing in WSNsPreprint
Rao, G. S., & Ramana, A. V. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1310.8467
Topology refresh data forwarding protocol for opportunistic networks
Lau, P. (2015) · Wireless Telecommunications Symposium · doi:10.1109/wts.2015.7117244
Adaptive Energy-Based Updating Algorithm in Opportunistic Routing
Chu, J., Wu, M., Cao, S., & Wang, D. (2014) · IEEE Vehicular Technology Conference · doi:10.1109/vtcfall.2014.6965853
Modelling energy efficiency of OR protocols in wireless networks
Mazumdar, A. P., Waghmare, A., & Sairam, A. S. (2014) · International Journal of Ad Hoc and Ubiquitous Computing · doi:10.1504/ijahuc.2014.062490
Spatial Qualification Tests for Highly Selective Compact Micromachined Band Pass Planar Filters
Hajj, R., Chatras, M., Blondy, P., Vendier, O., Tschanum, W., & Courtade, F. (2012) · International Journal of Microwave Science and Technology · doi:10.1155/2012/650754
Research on Dynamic Replication Routing Protocols for Opportunistic Networks
Tian, J., & Sun, Y. (2016) · Advances in computer science research · doi:10.2991/iccsae-15.2016.44
A New Routing Protocol for WMNs
Aminimanesh, Z. (2015) · International Journal on Recent and Innovation Trends in Computing and Communication · doi:10.17762/ijritcc2321-8169.150260
Online Opportunistic Routing in Cognitive Radio Ad-Hoc Network and Spectrum Management
Solao, H., Goudar, R. M., & Barve, S. (2015)
Inference in Networking Systems with Designed Measurements
Liu, C. (2021) · ScholarWorks@UMassAmherst (University of Massachusetts Amherst) · doi:10.7275/9474430.0
Routing in Reinforcement Learning Based Cognitive Radio Networks
Patel, J. R. (2017) · IEEE International Conference on Computational Intelligence and Computing Research (ICCIC) · doi:10.1109/iccic.2017.8524409
Device-to-device communication in cellular networks : multi-hop path selection and performance.
Yuan, H. (2026) · Warwick Research Archive Portal (University of Warwick) · doi:10.82444/warw.33742813
Overcoming The Limitations Of Classical Routing Algorithms By D-Adaptor Algorithm
Rakesh, B. (2018) · i-manager’s Journal on Wireless Communication Networks · doi:10.26634/jwcn.5.3.10345
Adaptive Packet Routing on Communication Networks Based on Reinforcement Learning
Deeka, T., Deeka, B., & On-rit, S. (2019) · Lecture notes in networks and systems · doi:10.1007/978-3-030-12385-7_12
Thompson sampling-based online decision making in network routing
Huang, Z. (2020) · UVic’s Research and Learning Repository (University of Victoria)
Mobile Energy Efficient Selective Opportunistic Routing for Mobile Wireless Sensor Networks
Chandravathana, I., Asaithambi, M., & Sangamithraa, P. B. (2017) · International Journal of Scientific Research in Computer Science Engineering and Information Technology · doi:10.32628/cseit17219
Routing simulation of brain network topology.
Jason, V. (2021) · doi:10.32920/ryerson.14653833
Liu, L., Wang, R., & Wu, J. (2020) · Springer eBooks · doi:10.1007/978-981-33-4381-8_2
CRDP: Community-Aware Routing Algorithm Based on DNN and PPO
Ma, H., You, J., Wu, H., Xing, L., & Zhang, X. (2025) · Wireless Personal Communications · doi:10.1007/s11277-025-11740-4
- Authors
- Bhorkar, A. A., Naghshvar, M., Javidi, T., & Rao, B. D.
- Affiliations
- University of California San Diego (US)
- Venue
- IEEE/ACM Transactions on Networking
- Published
- 2012-02
- Volume
- 20
- Issue
- 1
- Pages
- 243-256
- ISSN
- 1063-6692 (ISSN-L)
- 1558-2566
- OpenAlex
- W2070941899
- Cited by
- 102
- References
- 34
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 4 · 2023: 8 · 2022: 2 · 2021: 9 · 2020: 7 · 2019: 12 · 2018: 11 · 2017: 5 · 2016: 6 · 2015: 10 · 2014: 13 · 2013: 11 · 2012: 1
- FWCI
- 10.48
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE/ACM Transactions on Networking (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Mobile Ad Hoc Networks
- Physical Sciences › Computer Science › Computer Networks and Communications › Wireless Networks and Protocols
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Wireless Network Optimization
- Keywords
- wireless ad hoc networks, multi-hop routing, reinforcement learning, distributed routing, successful transmission probability, exploration-exploitation balance, stochastic routing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
