Cognitive Networks: Towards Self‐Aware Networks
Abstract
Cognitive networks can dynamically adapt their operational parameters in response to user needs or changing environmental conditions. They can learn from these adaptations and exploit knowledge to make future decisions. Cognitive networks are the future, and they are needed simply because they enable users to focus on things other than configuring and managing networks. Without cognitive networks, the pervasive computing vision calls for every consumer to be a network technician. The applications of cognitive networks enable the vision of pervasive computing, seamless mobility, ad-hoc networks, and dynamic spectrum allocation, among others. In detail, the authors describe the main features of cognitive networks clearly indicating that cognitive network design can be applied to any type of network, being fixed or wireless. They explain why cognitive networks promise better protection against security attacks and network intruders and how such networks will benefit the service operator as well as the consumer. Cognitive Networks Explores the state-of-the-art in cognitive networks, compiling a roadmap to future research. Covers the topic of cognitive radio including semantic aspects. Presents hot topics such as biologically-inspired networking, autonomic networking, and adaptive networking. Introduces the applications of machine learning and distributed reasoning to cognitive networks. Addresses cross-layer design and optimization. Discusses security and intrusion detection in cognitive networks. Cognitive Networks is essential reading for advanced students, researchers, as well as practitioners interested in cognitive & wireless networks, pervasive computing, distributed learning, seamless mobility, and self-governed networks. With forewords by Joseph Mitola III as well as Sudhir Dixit.
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?
Network operations and management still remains cumbersome, and network faults are prevalent primarily due to human error.
“Despite these advances, network operations and management still remains cumbersome, and network faults are prevalent primarily due to human error [291].”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [291]
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
A Survey on Trust Management for Mobile Ad Hoc Networks
Cho, J.-H., Swami, A., & Chen, I.-R. (2010) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2011.092110.00088 · Cited by 672 · see citing works
Machine Learning for Cognitive Network Management
Ayoubi, S., Limam, N., Salahuddin, M. A., Shahriar, N., Boutaba, R., Estrada-Solano, F., & Caicedo, O. M. (2018) · IEEE Communications Magazine · doi:10.1109/mcom.2018.1700560 · Cited by 217 · see citing works
Trends in the development of communication networks: Cognitive networks
Fortuna, C., & Mohorčič, M. (2009) · Computer Networks · doi:10.1016/j.comnet.2009.01.002 · Cited by 178 · see citing works
Learning and Reasoning in Cognitive Radio Networks
Gavrilovska, L. M., Atanasovski, V. M., Irene Macaluso, B., & DaSilva, L. A. (2013) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2013.030713.00113 · Cited by 98 · see citing works
Spatial Spectrum Sharing for Satellite and Terrestrial Communication Networks
Zhang, C., Jiang, C., Kuang, L., Jin, J., He, Y., & Han, Z. (2019) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2018.2889585 · Cited by 88 · see citing works
Spectrum Sensing and Recognition in Satellite Systems
Zhang, C., Jiang, C., Jin, J., et al. (2019) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2019.2893388 · Cited by 70 · see citing works
Jamming mitigation in cognitive radio networks using a modified Q-learning algorithm
Slimeni, F., Scheers, B., Chtourou, Z., et al. (2015) · International Conference on Military Communications and Information Systems (ICMCIS) · doi:10.1109/icmcis.2015.7158697 · Cited by 69 · see citing works
Improving the Reliability of Wireless Networks Using Cognitive Radios
Azarfar, A., Frigon, J., & Sansò, B. (2011) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2011.021111.00064 · Cited by 61 · see citing works
Survey of cognitive radio architectures
Amanna, A., & Reed, J. H. (2010) · Proceedings of IEEE Southeastcon · doi:10.1109/secon.2010.5453869 · Cited by 44 · see citing works
A Survey on Traffic Prediction Techniques Using Artificial Intelligence for Communication Networks
Chen, A., Law, J., & Aibin, M. (2021) · Telecom · doi:10.3390/telecom2040029 · Cited by 47 · see citing works
The security in cognitive radio networks
Zhang, X., & Li, C. (2009) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1145/1582379.1582447 · Cited by 43 · see citing works
EDFA adaptive gain control effect analysis over an amplifier cascade in a DWDM optical system
de Moura, U. C., Oliveira, J. R. F., de Oliveira, J. C. R. F., et al. (2013) · doi:10.1109/imoc.2013.6646469 · Cited by 39 · see citing works
Building Programmable Wireless Networks: An Architectural SurveyPreprint
Qadir, J., Ahmed, N., & Ahad, N. (2016) · Cited by 36 · see citing works
Enhanced robust cooperative spectrum sensing in cognitive radio
Zhu, F., & Seo, S. (2009) · Journal of Communications and Networks · doi:10.1109/jcn.2009.6391387 · Cited by 35 · see citing works
A sensing and etiquette reputation-based trust management for centralized cognitive radio networks
Pei, Q., Yuan, B., Li, L., et al. (2012) · Neurocomputing · doi:10.1016/j.neucom.2012.08.005 · Cited by 34 · see citing works
Resource-Minimized Channel Assignment for Multi-Transceiver Cognitive Radio Networks
Irwin, R. E., MacKenzie, A. B., & DaSilva, L. A. (2013) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2013.130310 · Cited by 33 · see citing works
Self-adaptive erbium-doped fiber amplifiers using machine learning
Barboza, E. D. A., Bastos-Filho, C. J. A., Martins-Filho, J. F., de Moura, U. C., & Oliveira, J. R. F. (2013) · SBMO/IEEE MTT-S International Microwave and Optoelectronics Conference (IMOC) · doi:10.1109/imoc.2013.6646588 · Cited by 29 · see citing works
Li, S., & Kokar, M. M. (2012) · Springer eBooks · doi:10.1007/978-1-4614-0968-7_5 · Cited by 30 · see citing works
Building programmable wireless networks: an architectural survey
Qadir, J., Ahmed, N., & Ahad, N. (2014) · EURASIP Journal on Wireless Communications and Networking · doi:10.1186/1687-1499-2014-172 · Cited by 28 · see citing works
Primary user emulation attack in dynamic spectrum access networks: a game-theoretic approach
Tan, Y.-F., Sengupta, S., & Subbalakshmi, K. P. (2012) · IET Communications · doi:10.1049/iet-com.2010.0573 · Cited by 27 · see citing works
Saghiri, A. M. (2019) · Studies in big data · doi:10.1007/978-981-13-8775-3_8 · Cited by 25 · see citing works
Security Issues of Cognitive Radio Network
Sadkhan, S. B., & Reda, D. M. (2019) · International Conference on Engineering Technology and its Applications (IICETA) · doi:10.1109/iiceta47481.2019.9012977 · Cited by 23 · see citing works
Next Generation Flexible and Cognitive Heterogeneous Optical Networks
Tomkos, I., Angelou, M., Barroso, R. J. D., et al. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-30241-1_20 · Cited by 22 · see citing works
CLARA: Closed Loop-based Zero-touch Network Management Framework
de Sousa, N. F. S., & Rothenberg, C. E. (2021) · IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN) · doi:10.1109/nfv-sdn53031.2021.9665048 · Cited by 22 · see citing works
Cognitive Wireless Networks Based Spectrum Sensing Strategies: A Comparative Analysis
Haldorai, A., Sivaraj, J., Nagabushanam, M., et al. (2022) · Applied Computational Intelligence and Soft Computing · doi:10.1155/2022/6988847 · Cited by 22 · see citing works
Cognitive LF-Ant: A Novel Protocol for Healthcare Wireless Sensor Networks
Sousa, M. P., Lopes, W. T. A., Madeiro, F., & de Alencar, M. S. (2012) · Sensors · doi:10.3390/s120810463 · Cited by 21 · see citing works
Cognitive Radio Jamming Mitigation using Markov Decision Process and Reinforcement Learning
Slimeni, F., Scheers, B., Chtourou, Z., et al. (2015) · Procedia Computer Science · doi:10.1016/j.procs.2015.12.013 · Cited by 20 · see citing works
Cognitive, Heterogeneous and Reconfigurable Optical Networks: The CHRON Project
Caballero, A., Borkowski, R., de Miguel, I., et al. (2014) · Journal of Lightwave Technology · doi:10.1109/jlt.2014.2318994 · Cited by 20 · see citing works
Kaneko, M., Nakano, T., Hayashi, K., et al. (2016) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2016.2555840 · Cited by 19 · see citing works
An AI-Empowered Framework for Cross-Layer Softwarized Infrastructure State Assessment
Diamanti, A., Vílchez, J. M. S., & Secci, S. (2022) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2022.3161872 · Cited by 16 · see citing works
Blind Spot of Spectrum Awareness Techniques in Non-geostationary Satellite Systems
Zhang, C., Jin, J., Kuang, L., et al. (2018) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2018.2826258 · Cited by 15 · see citing works
Cognitive radio architecture evolution: annals of telecommunications
Mitola, J. (2009) · Annals of Telecommunications · doi:10.1007/s12243-009-0101-6 · Cited by 15 · see citing works
Cross‐layer error recovery in wireless access networks: The ARQ proxy approach
Kliazovich, D., Redana, S., & Granelli, F. (2011) · International Journal of Communication Systems · doi:10.1002/dac.1271 · Cited by 14 · see citing works
A Review on Cross-Layer Design Approach in WSN by Different Techniques
Parween, S., & Hussain, S. Z. (2020) · Advances in Science Technology and Engineering Systems Journal · doi:10.25046/aj050488 · Cited by 14 · see citing works
Zhu, J., Wang, J., Luo, T., et al. (2009) · Telecommunication Systems · doi:10.1007/s11235-009-9174-9 · Cited by 13 · see citing works
On detecting termination in cognitive radio networks
Sharma, S., & Singh, A. K. (2014) · International Journal of Network Management · doi:10.1002/nem.1870 · Cited by 11 · see citing works
Distributed Protocol Stacks: A Framework for Balancing Interoperability and Optimization
Kliazovich, D., & Granelli, F. (2008) · doi:10.1109/iccw.2008.51 · Cited by 11 · see citing works
Oliveira, J. L. D., Siqueira, M., Curiel, G., et al. (2013) · doi:10.1109/imoc.2013.6646553 · Cited by 10 · see citing works
Collaborative Cross System AI: Toward 5G System and Beyond
Bagaa, M., Taleb, T., Riekki, J. P., & Song, J. (2021) · IEEE Network · doi:10.1109/mnet.011.2000607 · Cited by 11 · see citing works
Privacy and Security of Wireless Communication Networks
Sadkhan, S. B., & Abbas, N. A. (2015) · IGI Global eBooks · doi:10.4018/978-1-4666-8751-6.ch080 · Cited by 9 · see citing works
The Security of Physical Layer in Cognitive Radio Networks
Yu, Y., Hu, L., Li, H., et al. (2014) · Journal of Communications · doi:10.12720/jcm.9.12.916-922 · Cited by 9 · see citing works
Primary Exclusive Region and Throughput of Cognitive Dual-Polarized Networks
Dricot, J., Ferrari, G., Horlin, F., et al. (2010) · IEEE International Conference on Communications workshops · doi:10.1109/iccw.2010.5503881 · Cited by 9 · see citing works
Mihailović, A., Nguengang, G., Borgel, J., et al. (2009) · doi:10.1109/emerging.2009.10 · Cited by 9 · see citing works
Jellyfish attack in MANETs: A review
Kaur, S., Kaur, R., & Verma, A. K. (2015) · International Conference on Electrical, Computer and Communication Technologies (ICECCT) · doi:10.1109/icecct.2015.7226168 · Cited by 8 · see citing works
Two Decades of AI4NETS - AI/ML for Data Networks: Challenges & Research Directions
Casas, P. (2020) · IEEE/IFIP Network Operations and Management Symposium (NOMS) · doi:10.1109/noms47738.2020.9110266 · Cited by 8 · see citing works
Multicriteria reinforcement learning based on a Russian doll method for network routing
Pétrowski, A., Aissanou, F., Benyahia, I., et al. (2010) · IEEE International Conference on Intelligent Systems · doi:10.1109/is.2010.5548378 · Cited by 8 · see citing works
Taxonomy of security attacks and threats in cognitive radio networks
Soliman, J. N., Mageed, T. A., & El‐Hennawy, H. (2017) · doi:10.1109/jec-ecc.2017.8305794 · Cited by 8 · see citing works
A Survey on Challenges in Designing Cognitive Engines
Saghiri, A. M. (2020) · International Conference on Web Research (ICWR) · doi:10.1109/icwr49608.2020.9122273 · Cited by 9 · see citing works
Stability and Delay Analysis of an Adaptive Channel-Aware Random Access Wireless Network
Dimitriou, I., & Παππάς, Ν. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-61428-1_5 · Cited by 7 · see citing works
Security threats in military cognitive radio networks
Slimeni, F., Scheers, B., & Chtourou, Z. (2015) · International Conference on Military Communications and Information Systems (ICMCIS) · doi:10.1109/icmcis.2015.7158714 · Cited by 7 · see citing works
Efficient Traffic Aware Multipath Routing Algorithm in Cognitive Networks
Li, J., Wang, X., Feng, L., et al. (2011) · International Conference on Genetic and Evolutionary Computing · doi:10.1109/icgec.2011.75 · Cited by 7 · see citing works
Link State Protocol Data Mining for Shared Risk Link Group Detection
Das, G., Papadimitriou, D. B., Tavernier, W., et al. (2010) · Proceedings/Proceedings - International Conference on Computer Communications and Networks · doi:10.1109/icccn.2010.5560151 · Cited by 7 · see citing works
Privacy and Security of Wireless Communication Networks
Sadkhan, S. B., & Abbas, N. A. (2013) · Advances in wireless technologies and telecommunication book series · doi:10.4018/978-1-4666-4781-7.ch004 · Cited by 7 · see citing works
Constructing secured cognitive wireless networks: experiences and challenges
Zhang, X., & Li, C. (2009) · Wireless Communications and Mobile Computing · doi:10.1002/wcm.878 · Cited by 6 · see citing works
Hybrid SDN Deployment Using Machine Learning
Wei, S. H., Tan, S. C., & Lee, C. K. (2021) · Lecture notes in electrical engineering · doi:10.1007/978-981-33-4069-5_19 · Cited by 5 · see citing works
Doubly Cognitive Architecture Based Cognitive Wireless Sensor Networks
Kumar, S., Singhal, D., & Murthy, G. R. (2011) · International Journal of Wireless Networks and Broadband Technologies · doi:10.4018/ijwnbt.2011040103 · Cited by 5 · see citing works
Li, J., Wang, X., Jia, J., et al. (2014) · Mathematical Problems in Engineering · doi:10.1155/2014/453564 · Cited by 5 · see citing works
Oliveira, E. M., Reale, R. F., & Martins, J. S. B. (2018) · Proceedings - IEEE Symposium on Computers and Communications/IEEE Symposium on Computers and Communications · doi:10.1109/iscc.2018.8538667 · Cited by 5 · see citing works
A heterogeneous wireless network interconnection strategy based on IP switching
Yuan, L. (2011) · IEEE International Conference on Computer Science and Automation Engineering · doi:10.1109/csae.2011.5952892 · Cited by 5 · see citing works
SRLG identification from time series analysis of link state data
Das, G., Papadimitriou, D. B., Puype, B., et al. (2011) · doi:10.1109/comsnets.2011.5716485 · Cited by 5 · see citing works
Collaborative security for the internet of things
Saied, Y. B. (2013) · doi:10.70675/7e082ceezbce4z4c56zb3e7z2a808ed80302 · Cited by 8 · see citing works
Secure Access Control Guarding Against Internal Attacks in Distributed Networks
Guo, J., Zhou, X., Yuan, J., et al. (2012) · Wireless Personal Communications · doi:10.1007/s11277-012-0541-6 · Cited by 4 · see citing works
Cognitive DISH: Virtual Spectrum Sensing Meets Cooperation
Luo, T., & Motani, M. (2009) · Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON) · doi:10.1109/sahcn.2009.5168917 · Cited by 4 · see citing works
A Electric Network Reconfiguration Strategy with Case-Based Reasoning for the Smart Grid
Calhau, F. G., & Martins, J. S. B. (2019) · Brazilian Conference on Intelligent Systems · doi:10.1109/bracis.2019.00116 · Cited by 4 · see citing works
SeMoM, a semantic middleware for IoT healthcare applications
Zgheib, R. (2017) · theses.fr (ABES) · doi:10.70675/80992e30ze695z46cfzaa20z3651343aa3f2 · Cited by 4 · see citing works
Barroso, R. J. D., Fernández, N., Siracusa, D., et al. (2015) · Photonic Network Communications · doi:10.1007/s11107-015-0562-7 · Cited by 3 · see citing works
The SYRROCA AI-empowered network automation platform
Diamanti, A., Vílchez, J. M. S., & Secci, S. (2021) · Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN) · doi:10.1109/icin51074.2021.9385535 · Cited by 3 · see citing works
Sum-Rate Maximization Schemes for $K$ -User MISO Interference Channels With a Cognitive Relay
Shin, H. M., Lee, S.-R., Kong, H.-B., et al. (2015) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2015.2454496 · Cited by 3 · see citing works
Building the Knowledge Base through Bayesian Network for Cognitive Wireless Networks
Du, N., Bai, Y., Luo, L., et al. (2011) · IEEE International Conference on Parallel and Distributed Systems (ICPADS) · doi:10.1109/icpads.2011.38 · Cited by 3 · see citing works
Application-aware dynamic spectrum access
Kumar, A., & Shin, K. G. (2011) · Wireless Networks · doi:10.1007/s11276-011-0398-8 · Cited by 3 · see citing works
Secure Distributed Routing Algorithm with Optimizing Energy Consumption for Cognitive Radio Networks
Guo, J., & Zhou, X. (2013) · Wireless Personal Communications · doi:10.1007/s11277-013-1164-2 · Cited by 3 · see citing works
Study on Heterogeneous Networks Integration Based on Cognitive Wireless Network
Tang, L., Chi, H., & Wu, J. (2010) · doi:10.1109/cise.2010.5677267 · Cited by 3 · see citing works
Hosseini, E., & Falahati, A. (2013) · IETE Journal of Research · doi:10.4103/0377-2063.123747 · Cited by 3 · see citing works
Doubly Cognitive Architecture Based Cognitive Wireless Sensor Networks
Kumar, S., Singhal, D., & Murthy, G. R. (2013) · IGI Global eBooks · doi:10.4018/978-1-4666-3902-7.ch009 · Cited by 3 · see citing works
Cognitive RBAC in Small Heterogeneous Networks
Chen, H., & Violetta, M. A. (2012) · International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS) · doi:10.1109/imis.2012.145 · Cited by 2 · see citing works
Sunilkumar, G., Thriveni, J., Venugopal, K. R., et al. (2015) · Procedia Computer Science · doi:10.1016/j.procs.2015.01.010 · Cited by 2 · see citing works
Wiśniewska, A., Shattal, M. A., Khan, B. M., et al. (2017) · Mobile Networks and Applications · doi:10.1007/s11036-017-0950-6 · Cited by 2 · see citing works
Dynamic Channel Allocation Mechanism Based on Characteristic Matching in Cognitive Wireless Network
Miao, D., Tian, F., Yao, Y., et al. (2010) · doi:10.1109/wicom.2010.5600889 · Cited by 2 · see citing works
Modem link-property advertisements
Ivancic, W. D., Floreani, D., Wood, L., et al. (2012) · Proceedings - IEEE Aerospace Conference · doi:10.1109/aero.2012.6187146 · Cited by 2 · see citing works
Game Based Self-Coexistence Schemes in Cognitive Radio Networks
Das, S. K., Gardellin, V., & Lenzini, L. (2011) · Springer eBooks · doi:10.1007/978-1-4419-6172-3_17 · Cited by 2 · see citing works
Li, S., & Kokar, M. M. (2012) · Springer eBooks · doi:10.1007/978-1-4614-0968-7_7 · Cited by 2 · see citing works
Li, S., & Kokar, M. M. (2012) · Springer eBooks · doi:10.1007/978-1-4614-0968-7_2 · Cited by 2 · see citing works
Lightweight Mechanisms for Self-configuring Protocols
Patouni, E., & Alonistioti, N. (2010) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-642-16644-0_11 · Cited by 1 · see citing works
Toward Self-Organizing Search Systems
Bartoň, S., Dohnal, V., Sedmidubský, J., et al. (2009) · Computer communications and networks · doi:10.1007/978-1-84882-229-0_3 · Cited by 1 · see citing works
Developing the building blocks for cognitive communications: Adaptive rates & intelligent networking
Horne, W. D., Suaris, T., Gilstrap, R., et al. (2011) · doi:10.1109/aero.2011.5747368 · Cited by 1 · see citing works
Information and Knowledge in Telecommunications: A Service-Dominant View
Räisänen, V. (2012) · Annual SRII Global Conference · doi:10.1109/srii.2012.32 · Cited by 1 · see citing works
Primary exclusive region and optimality of the link-level throughput of cognitive terminals
Dricot, J., Ferrari, G., Quitin, F., et al. (2010) · doi:10.1109/pimrcw.2010.5670517 · Cited by 1 · see citing works
Reputation-Based Trust Management for Distributed Spectrum Sensing
Gonigle, S. M., Wang, Q., Wang, M., et al. (2014) · Lecture notes in electrical engineering · doi:10.1007/978-3-319-03692-2_20 · Cited by 1 · see citing works
Anonymous and Secure Communication Protocol for Cognitive Radio Ad Hoc Networks
Rahman, S. M. M., Kamruzzaman, S. M., Almogren, A., et al. (2014) · IEEE International Symposium on Multimedia (ISM) · doi:10.1109/ism.2014.85 · Cited by 1 · see citing works
Public-Keys-Based Entity Authentication Protocol with Digital Signature for Cognitive Radio Networks
Wang, C., & Guo, J. (2012) · Lecture notes in electrical engineering · doi:10.1007/978-3-642-27296-7_53 · Cited by 1 · see citing works
Intrusion Detection System Platform Based on Light-Weighted Hybrid Artificial Immune Algorithms
Chen, J., & Dongyong, Y. (2009) · International Conference on Natural Computation (ICNC) · doi:10.1109/icnc.2009.509 · Cited by 1 · see citing works
Improving situation awareness in cognitive networks using the Self-Organizing Map
Kärkkäinen, A. P. (2011) · IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support · doi:10.1109/cogsima.2011.5753752 · Cited by 1 · see citing works
Fernández, P., Barroso, R. J. D., Siracusa, D., et al. (2014) · International Conference on Optical Communications and Networks (ICOCN) · doi:10.1109/icocn.2014.6987070 · Cited by 1 · see citing works
A Cognitive Self-Organising Clustering Algorithm for Urban Scenarios
Sucasas, V., Saghezchi, F. B., Radwan, A., et al. (2016) · Wireless Personal Communications · doi:10.1007/s11277-016-3423-5 · Cited by 1 · see citing works
Cognitive Internet of Things: Challenges and Solutions
Saghiri, A. M. (2022) · Internet of things · doi:10.1007/978-3-030-87059-1_13 · Cited by 1 · see citing works
Leong, C. C., Chang, K. F., Choi, W., et al. (2012) · IEEE Asia-Pacific Conference on Antennas and Propagation · doi:10.1109/apcap.2012.6333205 · Cited by 1 · see citing works
Dynamic Self-configuration of User QoS for Next Generation Network
Feng, G., Wang, H., Li, B., et al. (2009) · doi:10.1109/npc.2009.12 · Cited by 1 · see citing works
Soliman, J. N., Mageed, T. A., & El‐Hennawy, H. (2017) · International Conference on Intelligent Computing and Information Systems (ICICIS) · doi:10.1109/intelcis.2017.8260019 · Cited by 1 · see citing works
Emergence of pecking order in social Cognitive Radio societies
Wiśniewska, A., Shattal, M. A., Khan, B., et al. (2018) · IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) · doi:10.1109/infcomw.2018.8407008 · Cited by 1 · see citing works
- Venue
- Wiley eBooks
- Publisher
- Wiley
- Published
- 2007-07-25
- OpenAlex
- W4251270412
- Cited by
- 132
- References
- 0
- Citations by year
- 2024: 1 · 2023: 1 · 2022: 4 · 2021: 6 · 2020: 4 · 2019: 6 · 2018: 6 · 2017: 8 · 2016: 4 · 2015: 8 · 2014: 9 · 2013: 12 · 2012: 22
- FWCI
- 6.77
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Wiley eBooks (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Cognitive Radio Networks and Spectrum Sensing
- Physical Sciences › Computer Science › Computer Networks and Communications › Software-Defined Networks and 5G
- Physical Sciences › Computer Science › Computer Networks and Communications › Wireless Networks and Protocols
- Keywords
- cognitive networks, cognitive radio, ubiquitous computing, seamless mobility, ad hoc networks, dynamic spectrum allocation, network security, intrusion detection, autonomic networking, machine learning, distributed reasoning, cross-layer design, wireless networks
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
