ACCPndn: Adaptive Congestion Control Protocol in Named Data Networking by learning capacities using optimized Time-Lagged Feedforward Neural Network
Open accessIntegrity 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?
The TLFN trained with PSO-GA provides a lower prediction error than the TLFN with other training algorithms such as GA-PSO, GA, PSO only, and BP.
“Various tests on synthetic data collected from ns-2 simulations demonstrate that the TLFN trained with PSO-GA provides a lower prediction error than the TLFN with other training algorithms, such as GA-PSO, GA or PSO only, and BP.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [230]
ACCPndn's congestion control mechanism considerably outperforms other NDN congestion control protocols such as NACK and HoBHIS in terms of packet drop and link utilization.
“This congestion control mechanism considerably outperforms other NDN congestion control protocols, such as NACK [487] and HoBHIS [392], in terms of packet drop and link utilization.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [230]
Cites
Zadeh, L. A. (1965) · Information and Control · doi:10.1016/s0019-9958(65)90241-x · Cited by 67,855 · see citing works
Kennedy, J., & Eberhart, R. (2002) · Proceedings of ICNN'95 - International Conference on Neural Networks · doi:10.1109/icnn.1995.488968 · Cited by 58,192 · see citing works
Zadeh, L. A. (1996) · Cited by 46,960 · see citing works
Multilayer feedforward networks are universal approximators
Hornik, K., Stinchcombe, M., & White, H. (1989) · Neural Networks · doi:10.1016/0893-6080(89)90020-8 · Cited by 21,728 · see citing works
Multilayer feedforward networks are universal approximators
HornikK., StinchcombeM., & WhiteH. (1989) · Neural Networks · doi:10.5555/70405.70408 · Cited by 9,266 · see citing works
On the approximate realization of continuous mappings by neural networks
Funahashi, K.-I. (1989) · Neural Networks · doi:10.1016/0893-6080(89)90003-8 · Cited by 4,206 · see citing works
Jacobson, V. L., Smetters, D. K., Thornton, J. D., Plass, M. F., Briggs, N. H., & Braynard, R. L. (2009) · ACM International Conference on emerging Networking EXperiments and Technologies (CoNEXT) · doi:10.1145/1658939.1658941 · Cited by 4,081 · see citing works
Comparing inertia weights and constriction factors in particle swarm optimization
Eberhart, R. C., & Shi, Y. (2002) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2000.870279 · Cited by 2,969 · see citing works
Forecasting methods and applications
(1993) · Computers & Operations Research · doi:10.1016/0305-0548(93)90040-p · Cited by 2,393 · see citing works
A survey of information-centric networking
Ahlgren, B., Dannewitz, C., Imbrenda, C., Kutscher, D., & Ohlman, B. (2012) · IEEE Communications Magazine · doi:10.1109/mcom.2012.6231276 · Cited by 1,630 · see citing works
Moradi, M. H., & Abedini, M. (2011) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2011.08.023 · Cited by 1,076 · see citing works
Ren, C., An, N., Wang, J., Li, L., Hu, B., & Shang, D. (2013) · Knowledge-Based Systems · doi:10.1016/j.knosys.2013.11.015 · Cited by 532 · see citing works
A case for stateful forwarding plane
Yi, C., Afanasyev, A., Moiseenko, I., Wang, L., Zhang, B., & Zhang, L. (2013) · Computer Communications · doi:10.1016/j.comcom.2013.01.005 · Cited by 474 · see citing works
Hybrid neural network models for hydrologic time series forecasting
Jain, A., & Kumar, A. M. (2006) · Applied Soft Computing · doi:10.1016/j.asoc.2006.03.002 · Cited by 473 · see citing works
Analytic evaluation of RED performance
Bonald, T., May, M., & Bolot, J.-C. (2002) · Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064) · doi:10.1109/infcom.2000.832539 · Cited by 405 · see citing works
Time Series Prediction and Neural Networks
Frank, R., Davey, N., & Hunt, S. P. (2001) · Journal of Intelligent & Robotic Systems · doi:10.1023/a:1012074215150 · Cited by 325 · see citing works
Network of Information (NetInf) – An information-centric networking architecture
Dannewitz, C., Kutscher, D., Ohlman, B., Farrell, S., Ahlgren, B., & Karl, H. (2013) · Computer Communications · doi:10.1016/j.comcom.2013.01.009 · Cited by 307 · see citing works
Neural network ensemble operators for time series forecasting
Kourentzes, N., Barrow, D. K., & Crone, S. F. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.12.011 · Cited by 294 · see citing works
Forecasting tourism demand to Catalonia: Neural networks vs. time series models
Clavería, O., & Torra, S. (2013) · Economic Modelling · doi:10.1016/j.econmod.2013.09.024 · Cited by 271 · see citing works
Interest flooding attack and countermeasures in Named Data Networking
Afanasyev, A., Mahadevan, P., Moiseenko, I., Uzun, E., & Zhang, L. (2013) · Cited by 246 · see citing works
EWMA Control Charts with Time-Varying Control Limits and Fast Initial Response
Steiner, S. H. (1999) · Journal of Quality Technology · doi:10.1080/00224065.1999.11979899 · Cited by 218 · see citing works
A new hybrid artificial neural networks and fuzzy regression model for time series forecasting
Khashei, M., Hejazi, S. R., & Bijari, M. (2007) · Fuzzy Sets and Systems · doi:10.1016/j.fss.2007.10.011 · Cited by 208 · see citing works
Multi‐scale Internet traffic forecasting using neural networks and time series methods
Cortez, P. C., Miguel Rio, C., Rocha, M., & Sousa, P. (2010) · Expert Systems · doi:10.1111/j.1468-0394.2010.00568.x · Cited by 198 · see citing works
Multi-Objective Machine Learning
Jin, Y. (2006) · Studies in computational intelligence · doi:10.1007/3-540-33019-4 · Cited by 196 · see citing works
Artificial Neural Network trained by Particle Swarm Optimization for non-linear channel equalization
Das, G., Pattnaik, P. K., & Padhy, S. K. (2013) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.10.053 · Cited by 191 · see citing works
Bandwidth and storage sharing performance in information centric networking
Muscariello, L., Carofiglio, G., & Gallo, M. (2011) · doi:10.1145/2018584.2018593 · Cited by 181 · see citing works
Integration of particle swarm optimization and genetic algorithm for dynamic clustering
Kuo, R. J., Syu, Y.-R., Chen, Z., & Tien, F.-C. (2012) · Information Sciences · doi:10.1016/j.ins.2012.01.021 · Cited by 156 · see citing works
A lightweight mechanism for detection of cache pollution attacks in Named Data Networking
Conti, M., Gasti, P., & Teoli, M. (2013) · Computer Networks · doi:10.1016/j.comnet.2013.07.034 · Cited by 156 · see citing works
A fuzzy anomaly detection system based on hybrid PSO-Kmeans algorithm in content-centric networks
Karami, A., & Zapata, M. G. (2014) · Neurocomputing · doi:10.1016/j.neucom.2014.08.070 · Cited by 149 · see citing works
Self-citationCCTCP: A scalable receiver-driven congestion control protocol for content centric networking
Saino, L., Cocora, C., & Pavlou, G. (2013) · International Conference on Communications · doi:10.1109/icc.2013.6655143 · Cited by 137 · see citing works
Optimal multipath congestion control and request forwarding in Information-Centric Networks
Carofiglio, G., Gallo, M., Muscariello, L., Papalini, M., & Wang, S. (2013) · IEEE International Conference on Network Protocols (ICNP) · doi:10.1109/icnp.2013.6733576 · Cited by 128 · see citing works
An effective hop-by-hop Interest shaping mechanism for CCN communications
Rozhnova, N., & Fdida, S. (2012) · 2012 Proceedings IEEE INFOCOM Workshops · doi:10.1109/infcomw.2012.6193514 · Cited by 125 · see citing works
Multiresolution FIR neural-network-based learning algorithm applied to network traffic prediction
Alarcon‐Aquino, V., & Barria, J. A. (2006) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · doi:10.1109/tsmcc.2004.843217 · Cited by 123 · see citing works
Internet Traffic Forecasting using Neural Networks
Cortez, P. C., Miguel Rio, C., Rocha, M., & Sousa, P. (2006) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2006.1716452 · Cited by 118 · see citing works
Internet Traffic Forecasting using Neural Networks
Cortez, P., Rio, M., Rocha, M., & Sousa, P. (2006) · The 2006 IEEE International Joint Conference on Neural Network Proceedings · doi:10.1109/ijcnn.2006.247142 · Cited by 118 · see citing works
Multipath congestion control in content-centric networks
Carofiglio, G., Gallo, M., Muscariello, L., & Papali, M. (2013) · IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) · doi:10.1109/infcomw.2013.6970718 · Cited by 114 · see citing works
Adaptive neural network model for time-series forecasting
Wong, W. K., Xia, M., & Chu, W. C. (2010) · European Journal of Operational Research · doi:10.1016/j.ejor.2010.05.022 · Cited by 103 · see citing works
Kuo, R. J., & Lin, L. M. (2010) · Decision Support Systems · doi:10.1016/j.dss.2010.05.006 · Cited by 102 · see citing works
Karami, A., & Zapata, M. G. (2015) · Computer Networks · doi:10.1016/j.comnet.2015.01.020 · Cited by 88 · see citing works
Self-citationEvaluating CCN multi-path interest forwarding strategies
Rossini, G., & Rossi, D. (2013) · Computer Communications · doi:10.1016/j.comcom.2013.01.008 · Cited by 85 · see citing works
Tuning of neural networks using particle swarm optimization to model MIG welding process
Malviya, R. K., & Pratihar, D. K. (2011) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2011.07.001 · Cited by 83 · see citing works
Karami, A., & Johansson, R. (2014) · UEL Research Repository (University of East London) · doi:10.6688/jise.2014.30.2.14 · Cited by 83 · see citing works
Self-citationRuan, G., & Tan, Y. (2009) · Soft Computing · doi:10.1007/s00500-009-0440-2 · Cited by 68 · see citing works
A hybrid multiobjective RBF-PSO method for mitigating DoS attacks in Named Data Networking
Karami, A., & Zapata, M. G. (2014) · Neurocomputing · doi:10.1016/j.neucom.2014.11.003 · Cited by 61 · see citing works
Self-citationHTTP Botnet Detection Using Adaptive Learning Rate Multilayer Feed-Forward Neural Network
Venkatesh, G., & Nadarajan, R. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-30955-7_5 · Cited by 57 · see citing works
Barabas, M., Boanea, G., Rus, A. B., Dobrotă, V., & Domingo‐Pascual, J. (2011) · IEEE International Conference on Intelligent Computer Communication and Processing (ICCP) · doi:10.1109/iccp.2011.6047849 · Cited by 50 · see citing works
On the performance of bandwidth and storage sharing in information-centric networks
Carofiglio, G., Gallo, M., & Muscariello, L. (2013) · Computer Networks · doi:10.1016/j.comnet.2013.08.018 · Cited by 45 · see citing works
On Performance of Cache Policies in Named Data Networking
Ran, J. H., Lv, N., Zhang, D., yuan, Y., & Xie, Z. Y. (2013) · International Conference on Advanced Computer Science and Electronics Information (ICACSEI) · doi:10.2991/icacsei.2013.160 · Cited by 43 · see citing works
Gomes, G. S. D. S., & Ludermir, T. B. (2013) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.05.053 · Cited by 42 · see citing works
Fuzzy explicit marking: A unified congestion controller for Best-Effort and Diff-Serv networks
Chrysostomou, C., Pitsillides, A., & Şekercioğlu, Y. A. (2008) · Computer Networks · doi:10.1016/j.comnet.2008.11.002 · Cited by 42 · see citing works
Chamkalani, A., Mae'soumi, A., & Sameni, A. (2013) · Journal of Natural Gas Science and Engineering · doi:10.1016/j.jngse.2013.06.002 · Cited by 41 · see citing works
Lee, C., & Lee, Y.-C. (2011) · Information Sciences · doi:10.1016/j.ins.2011.09.036 · Cited by 41 · see citing works
A Novel BP Neural Network Model for Traffic Prediction of Next Generation Network
Zhu, L., Lei, Q., Kouying, X., & Zhang, X. (2009) · International Conference on Natural Computation (ICNC) · doi:10.1109/icnc.2009.673 · Cited by 39 · see citing works
Li, F., Sun, J., Zukerman, M., Liu, Z., Xu, Q., Chan, S., Chen, G., & Ko, K.-T. (2014) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2014.01.005 · Cited by 33 · see citing works
Kuo, R. J., Hung, S.-H., & Cheng, W. C. (2013) · Information Sciences · doi:10.1016/j.ins.2013.10.035 · Cited by 32 · see citing works
Yogi, S., Subhashini, K. R., & Satapathy, J. K. (2010) · doi:10.1109/iciinfs.2010.5578726 · Cited by 31 · see citing works
A simple active queue management based on the prediction of the packet arrival rate
Xu, Q., & Sun, J. (2014) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2014.03.006 · Cited by 30 · see citing works
Adaptive congestion protocol: A congestion control protocol with learning capability
Lestas, M., Pitsillides, A., Ioannou, P., & Hadjipollas, G. (2007) · Computer Networks · doi:10.1016/j.comnet.2007.04.002 · Cited by 30 · see citing works
A greedy ant colony forwarding algorithm for Named Data Networking
Li, C., Liu, W., & Okamura, K. (2012) · Proceedings of the Asia-Pacific Advanced Network · doi:10.7125/apan.34.3 · Cited by 30 · see citing works
User-assisted in-network caching in information-centric networking
Lee, H., & Nakao, A. (2013) · Computer Networks · doi:10.1016/j.comnet.2013.07.008 · Cited by 29 · see citing works
Ranking cloud services using fuzzy multi-attribute decision making
Shivakumar, U., Ravi, V., & Gangadharan, G. R. (2013) · Proceedings of ... IEEE International Conference on Fuzzy Systems · doi:10.1109/fuzz-ieee.2013.6622319 · Cited by 19 · see citing works
Saucez, D., Grieco, L. A., & Barakat, C. (2012) · doi:10.1145/2413219.2413226 · Cited by 19 · see citing works
Heckmann, O. (2006) · Medical Entomology and Zoology · Cited by 18 · see citing works
An Effective Congestion Control Scheme in Content-Centric Networking
Fu, T., Li, Y., Lin, T., Tan, H., Tang, H. N., & Ci, S. (2012) · International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) · doi:10.1109/pdcat.2012.43 · Cited by 18 · see citing works
An NN approach for MPEG video traffic prediction
Dharmadhikari, V. B., & Gavade, J. D. (2010) · International Conference on Software Technology and Engineering · doi:10.1109/icste.2010.5608912 · Cited by 18 · see citing works
Dynamic Particle Swarm Optimization to Solve Multi-objective Optimization Problem
Urade, H. S., & Patel, R. (2012) · Procedia Technology · doi:10.1016/j.protcy.2012.10.034 · Cited by 17 · see citing works
Adaptive neural queue management for TCP networks
Cho, H. C., Fadali, S. M., & Lee, H. (2008) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2008.02.002 · Cited by 16 · see citing works
The Competitive Internet Service Provider
Heckmann, O. (2006) · Wiley eBooks · doi:10.1002/9780470017906 · Cited by 16 · see citing works
Baig, Z. A., & Khan, S. A. (2010) · International Conference on Future Networks · doi:10.1109/icfn.2010.73 · Cited by 15 · see citing works
Multipath Routing Management using Neural Networks-Based Traffic Prediction
Barabas, M., Boanea, G., & Dobrotă, V. (2011) · Cited by 15 · see citing works
In-cluster vector evaluated particle swarm optimization for distributed regression in WSNs
Shakibian, H., & Charkari, N. M. (2014) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2014.02.011 · Cited by 12 · see citing works
Network Intrusion Detection Using Genetic Algorithm and Neural Network
Gomathy, A., & Lakshmipathi, B. (2011) · Communications in computer and information science · doi:10.1007/978-3-642-22555-0_41 · Cited by 10 · see citing works
Karami, A., & Zapata, M. G. (2014) · UPCommons institutional repository (Universitat Politècnica de Catalunya) · Cited by 8 · see citing works
Self-citationA loss-based TCP design in ICN
Xia, C., Xu, M., & Wang, Y. (2013) · Wireless and Optical Communication Conference (WOCC) · doi:10.1109/wocc.2013.6676409 · Cited by 8 · see citing works
Response of the parameters of a neural network to pseudoperiodic time series
Zhao, Y., Weng, T., & SMALL, M. P. (2013) · Physica D Nonlinear Phenomena · doi:10.1016/j.physd.2013.11.002 · Cited by 7 · see citing works
Network congestion avoidance strategy with particle filter
Qian, X., Jing, Y., & Tian, J. (2007) · Computer Communications · doi:10.1016/j.comcom.2007.11.007 · Cited by 6 · see citing works
Web traffic prediction with artificial neural networks
Gluszek, A., Kekez, M., & Rudziński, F. (2005) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.610751 · Cited by 4 · see citing works
Evolving time-lagged feedforward neural networks for time series forecasting
Donate, J. P., Cortez, P. C., Sanchis, A., & Gutiérrez, G. (2011) · Genetic and Evolutionary Computation Conference Companion (GECCO Companion) · doi:10.1145/2001858.2001950 · Cited by 3 · see citing works
Flagged first, then by citations
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
Survey on 6G Frontiers: Trends, Applications, Requirements, Technologies and Future Research
de Alwis, C., Kalla, A., Pham, Q., Kumar, P., Dev, K., Hwang, W., & Liyanage, M. (2021) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2021.3071496 · Cited by 809 · see citing works
Qayyum, A., Usama, M., Qadir, J., & Al-Fuqaha, A. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2975048 · Cited by 257 · see citing works
Bian, K., & Priyadarshi, R. (2024) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-024-10110-w · Cited by 139 · see citing works
Congestion control in named data networking – A survey
Ren, Y., Li, J., Shi, S., Li, L., Wang, G., & Zhang, B. (2016) · Computer Communications · doi:10.1016/j.comcom.2016.04.017 · Cited by 122 · see citing works
Baldomero‐Naranjo, M., Martínez-Merino, L. I., & Rodríguez-Chía, A. M. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115017 · Cited by 79 · see citing works
Karami, A. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.04.038 · Cited by 66 · see citing works
Self-citationACCP: adaptive congestion control protocol in named data networking based on deep learning
Liu, T., Zhang, M., Zhu, J., Zheng, R., Liu, R., & Wu, Q. (2018) · Neural Computing and Applications · doi:10.1007/s00521-018-3408-2 · Cited by 34 · see citing works
Alubady, R., Salman, M., & Mohamed, A. S. I. (2023) · Telecommunication Systems · doi:10.1007/s11235-023-01015-3 · Cited by 29 · see citing works
Wang, M., Xu, C., Chen, X., Hao, H., Zhong, L., & Wu, D. O. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2929263 · Cited by 29 · see citing works
Lee, R.-T., Leau, Y., Park, Y. J., & Anbar, M. (2021) · IETE Technical Review · doi:10.1080/02564602.2021.1957029 · Cited by 25 · see citing works
Novel Cooperative and Fully-Distributed Congestion Control Mechanism for Content Centric Networking
Ndikumana, A., Ullah, S., Thar, K., Tran, N. H. K., Park, B. J., & Hong, C. S. (2017) · IEEE Access · doi:10.1109/access.2017.2778339 · Cited by 23 · see citing works
Hop-by-hop interest rate notification and adjustment in named data networks
Mejri, S., Touati, H., & Kamoun, F. (2018) · IEEE Wireless Communications and Networking Conference · doi:10.1109/wcnc.2018.8377374 · Cited by 22 · see citing works
Metaheuristic Routing: A Taxonomy and Energy-Efficient Framework for Internet of Things
Rana, B., Singh, Y. P., & Singh, H. (2021) · IEEE Access · doi:10.1109/access.2021.3128814 · Cited by 20 · see citing works
Hop-by-Hop Congestion Control for Named Data Networks
Mejri, S., Touati, H., Malouch, N., & Kamoun, F. (2017) · IEEE/ACS International Conference on Computer Systems and Applications (AICCSA) · doi:10.1109/aiccsa.2017.36 · Cited by 20 · see citing works
AFSndn: A novel adaptive forwarding strategy in named data networking based on Q-learning
Zhang, M., Wang, X., Liu, T., Zhu, J., & Wu, Q. (2020) · Peer-to-Peer Networking and Applications · doi:10.1007/s12083-019-00845-w · Cited by 19 · see citing works
Wang, M., Xu, C., Chen, X., Zhong, L., & Muntean, G. (2020) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2020.102574 · Cited by 10 · see citing works
A Secure Smart Home using Internet-of-Things
Ibrahim, J. M., Karami, A., & Jafari, F. (2017) · International Conference on Information Management and Engineering (ICIME) · doi:10.1145/3149572.3149577 · Cited by 9 · see citing works
Self-citationFlooding Control in Named Data Networking
Dash, S., Sahu, B. J. R., Saxena, N., & Roy, A. (2017) · IETE Technical Review · doi:10.1080/02564602.2017.1281173 · Cited by 9 · see citing works
Zeng, L., Ni, H., & Han, R. (2021) · Electronics · doi:10.3390/electronics10070806 · Cited by 6 · see citing works
Q‐learning‐based congestion control strategy for information‐centric networking
Wei, M., & Zhang, L. (2020) · Internet Technology Letters · doi:10.1002/itl2.195 · Cited by 6 · see citing works
Are NDN Congestion Control Solutions Compatible with Big Data Traffic?
Mejri, S., Touati, H., & Kamoun, F. (2018) · International Conference on High Performance Computing & Simulation (HPCS) · doi:10.1109/hpcs.2018.00154 · Cited by 4 · see citing works
Buvanesvari, R., & Suresh Joseph, K. (2022) · Journal of Experimental & Theoretical Artificial Intelligence · doi:10.1080/0952813x.2022.2115141 · Cited by 2 · see citing works
Fuzzy Logic‐based Efficient Interest Forwarding (FLEIF) in Named Data Networking
Qureshi, Z., Shah, P. A., Hasan, N. U., Yasmin, S., Aadil, F., Khan, M. F., Nam, Y., & Farooq, S. S. (2019) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.3577 · Cited by 1 · see citing works
Securing Smart Homes: A Cloud-Based IoT Approach to Intelligent Home Security
Alkhzaimi, H. A., Thabit, F., Can, Ö., Abougarair, A. J., Abdaljlil, S., & Alhomdy, S. A. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-71426-9_20 · Cited by 1 · see citing works
Bassfar, Z. (2017) · American Journal of Applied Sciences · doi:10.3844/ajassp.2017.772.778
A Game Theoretic Framework for Congestion Control in Named Data Networking
Yao, J., Yin, B., Tan, X., & Bao, Y. (2017) · Information Technology And Control · doi:10.5755/j01.itc.46.4.15680
Glisic, S., & Lorenzo, B. (2022) · Wiley eBooks · doi:10.1002/9781119790327.ch7
Congestion control in mobile named data networking: A dynamic node suppression approach
Madureira, A. L. R., & Sampaio, L. N. (2026) · Computer Networks · doi:10.1016/j.comnet.2026.112344
- Authors
- Karami, A.
- Affiliations
- Universitat Politècnica de Catalunya (ES)
- Corresponding author
- Karami, Amin
- ORCID
- Karami, Amin
- Venue
- Journal of Network and Computer Applications
- Publisher
- Elsevier BV
- Published
- 2015-10
- Volume
- 56
- Pages
- 1-18
- ISSN
- 1084-8045 (ISSN-L)
- OpenAlex
- W850683210
- Cited by
- 29
- References
- 79
- Citations by year
- 2026: 1 · 2024: 2 · 2023: 1 · 2022: 2 · 2021: 5 · 2020: 4 · 2019: 2 · 2018: 5 · 2017: 6 · 2016: 1
- FWCI
- 4.23
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- Journal of Network and Computer Applications (publishedVersion)
- UPCommons institutional repository (Universitat Politècnica de Catalunya) (publishedVersion)
- UEL Research Repository (University of East London) (acceptedVersion)
- RECERCAT (Consorci de Serveis Universitaris de Catalunya) (submittedVersion, public-domain)
- UEL Research Repository (University of East London) (submittedVersion, cc-by-nc-nd)
- Funders
- projects (TIN2013-47272-C2-2, SGR-2014-881)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Traffic and Congestion Control
- Physical Sciences › Computer Science › Computer Networks and Communications › Caching and Content Delivery
- Physical Sciences › Computer Science › Computer Networks and Communications › Software-Defined Networks and 5G
- Keywords
- Named Data Networking, congestion control, particle swarm optimization, genetic algorithm, hybrid metaheuristic, fuzzy logic control
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
