A comprehensive study on IoT privacy and security challenges with focus on spectrum sharing in Next-Generation networks (5G/6G/beyond)

Abstract

The emergence of the Internet of Things (IoT) has triggered a massive digital transformation across numerous sectors. This transformation requires efficient wireless communication and connectivity, which depend on the optimal utilization of the available spectrum resource. Given the limited availability of spectrum resources, spectrum sharing has emerged as a favored solution to empower IoT deployment and connectivity, so adequate planning of the spectrum resource utilization is thus essential to pave the way for the next generation of IoT applications, including 5G and beyond. This article presents a comprehensive study of prevalent wireless technologies employed in the field of the spectrum, with a primary focus on spectrum-sharing solutions, including shared spectrum. It highlights the associated security and privacy concerns when the IoT devices access the shared spectrum. This survey examines the benefits and drawbacks of various spectrum-sharing technologies and their solutions for various IoT applications. Lastly, it identifies future IoT obstacles and suggests potential research directions to address them.

Keywords

Spectrum sharing / IoT devices / Security / Privacy

Declaration of competing interest

The authors declare that they have no known competing finan-cial interests or personal relationships that could have appeared to influence the work reported in this paper.

References

[1] A.A. Salih, S. Zeebaree, A.S. Abdulraheem, R.R. Zebari, M. Sadeeq, O.M. Ahmed, Evolution of mobile wireless communication to 5G revolution, Technol. Rep. Kansai Univ. 62 (5) (2020) 2139-2151.

[2] M. Shi, Technology Base of Mobile Cellular Operators in Germany and China: A Comparative Study from the Perspective of the Resource Based View, Univ.-Verlag der TU, Univ.-Bibliothek, 2007, URL https://books.google.com/books?id=8ZRo-lxUDwkC.

[3] P. Taylor, Forecast number of mobile 5G subscriptions worldwide from 2019 to 2026(in billions), 2023, URL https://www.statista.com/statistics/760275/5g-mobile-subscriptions-worldwide/.

[4] N. Al-Falahy, O.Y. Alani, Technologies for 5G networks: Challenges and opportunities, It Prof. 19 (1) (2017) 12-20.

[5] F. Tang, Z.M. Fadlullah, N. Kato, F. Ono, R. Miura, AC-POCA: Anticoordination game based partially overlapping channels assignment in combined UAV and D2D-based networks, IEEE Trans. Veh. Technol. 67 (2) (2017) 1672-1683.

[6] D. Takaishi, Y. Kawamoto, H. Nishiyama, N. Kato, F. Ono, R. Miura, Virtual cell based resource allocation for efficient frequency utilization in unmanned aircraft systems, IEEE Trans. Veh. Technol. 67 (4) (2017) 3495-3504.

[7] J.H. Reed, J.T. Bernhard, J.-M. Park, Spectrum access technologies: The past, the present, and the future, Proc. IEEE 100 (Special Centennial Issue) (2012) 1676-1684, http://dx.doi.org/10.1109/JPROC.2012.2187140.

[8] S. Kumar, P. Tiwari, M. Zymbler, Internet of Things is a revolutionary approach for future technology enhancement: a review, J. Big Data 6 (1) (2019) 1-21.

[9] S. Madakam, V. Lake, V. Lake, V. Lake, et al., Internet of Things (IoT): A literature review, J. Comput. Commun. 3 (05) (2015) 164.

[10] L.S. Vailshery, Number of Internet of Things (IoT) connected devices worldwide from 2019 to 2021, with forecasts from 2022 to 2030 (in billions) by www.statista.com, 2022, URL https://www.statista.com/statistics/1183457/iot-connected-devices-worldwide/.

[11] J. Camhi, Former Cisco CEO John Chambers predicts 500 billion connected devices by 2025, 2015, URL https://www.businessinsider.com/former-cisco-ceo-500-billion-connected-devices-by-2025-2015-11

[12] M. Chui, M. Loffler, R. Roberts, The Internet of Things, Tech. Rep., McKinsey Global Institute, 2010.

[13] J. Gubbi, R. Buyya, S. Marusic, M. Palaniswami, Internet of Things (IoT): A vision, architectural elements, and future directions, Future Gener. Comput. Syst. 29 (7) (2013) 1645-1660, http://dx.doi.org/10.1016/j.future.2013.01.010, Including Special sections: Cyber-enabled Distributed Computing for Ubiquitous Cloud and Network Services & Cloud Computing and Scientific Applications — Big Data, Scalable Analytics, and Beyond, URL https://www.sciencedirect.com/science/article/pii/S0167739X13000241.

[14] H. Suo, J. Wan, C. Zou, J. Liu, Security in the internet of things: a review, in: 2012 International Conference on Computer Science and Electronics Engineering, Vol. 3, IEEE, 2012, pp. 648-651.

[15] L. Zhang, Y.-C. Liang, M. Xiao, Spectrum sharing for Internet of Things: A survey, IEEE Wirel. Commun. 26 (3) (2019) 132-139, http://dx.doi.org/10.1109/MWC.2018.1800259.

[16] K. Zhao, L. Ge, A survey on the Internet of Things security, in: 2013 Ninth International Conference on Computational Intelligence and Security, 2013, pp. 663-667, http://dx.doi.org/10.1109/CIS.2013.145.

[17] P. Chanal, M. Kakkasageri, Security and privacy in IoT: A survey, Wirel. Pers. Commun. 115 (2020) http://dx.doi.org/10.1007/s11277-020-07649-9.

[18] W. Shang, Q. Ding, A. Marianantoni, J. Burke, L. Zhang, Securing building management systems using named data networking, IEEE Netw. 28 (3) (2014) 50-56.

[19] J. Liu, Y. Xiao, C.P. Chen, Authentication and access control in the internet of things, in: 2012 32nd International Conference on Distributed Computing Systems Workshops, IEEE, 2012, pp. 588-592.

[20] D. Miorandi, S. Sicari, F. De Pellegrini, I. Chlamtac, Internet of things: Vision, applications and research challenges, Ad hoc Netw. 10 (7) (2012) 1497-1516.

[21] M. Usman, I. Ahmed, M.I. Aslam, S. Khan, U.A. Shah, SIT: a lightweight encryption algorithm for secure internet of things, 2017, arXiv preprint arXiv:1704.08688.

[22] G. Zhang, L. Kou, L. Zhang, C. Liu, Q. Da, J. Sun, A new digital watermarking method for data integrity protection in the perception layer of IoT, Secur. Commun. Netw. 2017 (2017).

[23] D.M. Mendez, I. Papapanagiotou, B. Yang, Internet of things: Survey on security and privacy, 2017, arXiv preprint arXiv:1707.01879.

[24] R. Vignesh, A. Samydurai, Security on internet of things (IoT) with challenges and countermeasures, Int. J. Eng. Dev. Res. IJEDR 5 (1) (2017) 417-423.

[25] A.A. Abi Sen, F.A. Eassa, K. Jambi, M. Yamin, Preserving privacy in internet of things: a survey, Int. J. Inf. Technol. 10 (2018) 189-200.

[26] N. Miloslavskaya, A. Tolstoy, Internet of Things: information security challenges and solutions, Cluster Comput. 22 (2019) 103-119.

[27] M. Parvini, A.H. Zarif, A. Nouruzi, N. Mokari, M.R. Javan, B. Abbasi, A. Ghasemi, H. Yanikomeroglu, Spectrum sharing schemes from 4G to 5G and beyond: Protocol flow, regulation, ecosystem, economic, IEEE Open J. Commun. Soc. 4 (2023) 464-517.

[28] A. Ali, W. Hamouda, Advances on spectrum sensing for cognitive radio networks: Theory and applications, IEEE Commun. Surv. Tutor. 19 (2) (2016) 1277-1304.

[29] S.K. Sharma, E. Lagunas, S. Chatzinotas, B. Ottersten, Application of compressive sensing in cognitive radio communications: A survey, IEEE Commun. Surv. Tutor. 18 (3) (2016) 1838-1860.

[30] K. Cichoń, A. Kliks, H. Bogucka, Energy-efficient cooperative spectrum sensing: A survey, IEEE Commun. Surv. Tutor. 18 (3) (2016) 1861-1886.

[31] M. El Tanab, W. Hamouda, Resource allocation for underlay cognitive radio networks: A survey, IEEE Commun. Surv. Tutor. 19 (2) (2016) 1249-1276.

[32] A. Ahmad, S. Ahmad, M.H. Rehmani, N.U. Hassan, A survey on radio resource allocation in cognitive radio sensor networks, IEEE Commun. Surv. Tutor. 17 (2) (2015) 888-917.

[33] G.I. Tsiropoulos, O.A. Dobre, M.H. Ahmed, K.E. Baddour, Radio resource allocation techniques for efficient spectrum access in cognitive radio networks, IEEE Commun. Surv. Tutor. 18 (1) (2014) 824-847.

[34] W. Liang, S.X. Ng, L. Hanzo, Cooperative overlay spectrum access in cognitive radio networks, IEEE Commun. Surv. Tutor. 19 (3) (2017) 1924-1944.

[35] R.K. Sharma, D.B. Rawat, Advances on security threats and countermeasures for cognitive radio networks: A survey, IEEE Commun. Surv. Tutor. 17 (2) (2014) 1023-1043.

[36] M. Bkassiny, Y. Li, S.K. Jayaweera, A survey on machine-learning techniques in cognitive radios, IEEE Commun. Surv. Tutor. 15 (3) (2012) 1136-1159.

[37] L. Gavrilovska, V. Atanasovski, I. Macaluso, L.A. DaSilva, Learning and reasoning in cognitive radio networks, IEEE Commun. Surv. Tutor. 15 (4) (2013) 1761-1777.

[38] R.H. Tehrani, S. Vahid, D. Triantafyllopoulou, H. Lee, K. Moessner, Licensed spectrum sharing schemes for mobile operators: A survey and outlook, IEEE Commun. Surv. Tutor. 18 (4) (2016) 2591-2623.

[39] S.K. Sharma, T.E. Bogale, L.B. Le, S. Chatzinotas, X. Wang, B. Ottersten, Dynamic spectrum sharing in 5G wireless networks with full-duplex technology: Recent advances and research challenges, IEEE Commun. Surv. Tutor. 20 (1) (2017) 674-707.

[40] A.M. Voicu, L. Simić, M. Petrova, Survey of spectrum sharing for inter-technology coexistence, IEEE Commun. Surv. Tutor. 21 (2) (2018) 1112-1144.

[41] F.A. De Figueiredo, X. Jiao, W. Liu, R. Mennes, I. Jabandzić, I. Moerman, A spectrum sharing framework for intelligent next generation wireless networks, IEEE Access 6 (2018) 60704-60735.

[42] F. Hu, B. Chen, K. Zhu, Full spectrum sharing in cognitive radio networks toward 5G: A survey, IEEE Access 6 (2018) 15754-15776.

[43] B. Chen, J. Chen, Y. Gao, J. Zhang, Coexistence of LTE-LAA and Wi-Fi on 5 GHz with corresponding deployment scenarios: A survey, IEEE Commun. Surv. Tutor. 19 (1) (2016) 7-32.

[44] G. Naik, J. Liu, J.-M.J. Park, Coexistence of wireless technologies in the 5 GHz bands: A survey of existing solutions and a roadmap for future research, IEEE Commun. Surv. Tutor. 20 (3) (2018) 1777-1798.

[45] S. Lagen, L. Giupponi, S. Goyal, N. Patriciello, B. Bojović, A. Demir, M. Beluri, New radio beam-based access to unlicensed spectrum: Design challenges and solutions, IEEE Commun. Surv. Tutor. 22 (1) (2019) 8-37.

[46] A. Goldsmith, Wireless Communications, Cambridge University Press, 2005.

[47] C.-F. Chiasserini, R.R. Rao, Coexistence mechanisms for interference mitigation in the 2.4-GHz ISM band, IEEE Trans. Wireless Commun. 2 (5) (2003) 964-975.

[48] A. Ahmed, Y.D. Zhang, A. Hassanien, B. Himed, OFDM-based joint radar-communication system: Optimal sub-carrier allocation and power distribution by exploiting mutual information, in: 2019 53rd Asilomar Conference on Signals, Systems, and Computers, IEEE, 2019, pp. 559-563.

[49] S. Zhang, Y. Gu, B. Wang, Y.D. Zhang, Robust astronomical imaging under coexistence with wireless communications, in: 2017 51st Asilomar Conference on Signals, Systems, and Computers, IEEE, 2017, pp. 1301-1305.

[50] A. Ahmed, Y.D. Zhang, B. Himed, Multi-user dual-function radar-communications exploiting sidelobe control and waveform diversity, in: 2018 IEEE Radar Conference, RadarConf18, IEEE, 2018, pp. 0698-0702.

[51] A. Ahmed, Y. Gu, D. Silage, Y.D. Zhang, Power-efficient multi-user dual-function radar-communications, in: 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC, IEEE, 2018, pp. 1-5.

[52] A. Ahmed, Y.D. Zhang, Y. Gu, Dual-function radar-communications using QAM-based sidelobe modulation, Digit. Signal Process. 82 (2018) 166-174.

[53] A. Ahmed, Y.D. Zhang, B. Himed, Distributed dual-function radar-communication MIMO system with optimized resource allocation, in: 2019 IEEE Radar Conference, RadarConf, IEEE, 2019, pp. 1-5.

[54] N. Bradai, L.C. Fourati, L. Kamoun, WBAN data scheduling and aggregation under WBAN/WLAN healthcare network, Ad Hoc Netw. 25 (2015) 251-262.

[55] Y. Zhang, X. Li, M.G. Amin, Mobile ad hoc networks exploiting multi-beam antennas, in: Handbook on Advancements in Smart Antenna Technologies for Wireless Networks, IGI Global, 2009, pp. 398-424.

[56] X. Li, Y. Zhang, M.G. Amin, Priority-based access schemes and throughput performance in wireless networks exploiting multibeam antennas, IEEE Trans. Veh. Technol. 58 (7) (2009) 3569-3578.

[57] A. Ahmed, S. Zhang, V.S. Amin, Y.D. Zhang, Spectrum sharing strategy for radio frequency-based medical services, in: 2019 IEEE Signal Processing in Medicine and Biology Symposium, SPMB, IEEE, 2019, pp. 1-4.

[58] Z. Yi, Z. Shan, L. Hong-xin, C. Cheng, W. Zhu-juan, Structural healthcare services for multi-users based on spectrum sharing strategy, in: 2017 6th International Conference on Computer Science and Network Technology, ICCSNT, IEEE, 2017, pp. 352-355.

[59] H. Zhou, W. Xu, Y. Bi, J. Chen, Q. Yu, X.S. Shen, Toward 5G spectrum sharing for immersive-experience-driven vehicular communications, IEEE Wirel. Commun. 24 (6) (2017) 30-37.

[60] J. Hu, S.K. Moorthy, A. Harindranath, Z. Guan, N. Mastronarde, E.S. Bentley, S. Pudlewski, Swarmshare: Mobility-resilient spectrum sharing for swarm uav networking in the 6 ghz band, in: 2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking, SECON, IEEE, 2021, pp. 1-9.

[61] B. Yang, T. Taleb, Z. Wu, L. Ma, Spectrum sharing for secrecy performance enhancement in D2D-enabled UAV networks, IEEE Netw. 34 (6) (2020) 156-163, http://dx.doi.org/10.1109/MNET.011.2000093.

[62] A. Shamsoshoara, M. Khaledi, F. Afghah, A. Razi, J. Ashdown, Distributed cooperative spectrum sharing in UAV networks using multi-agent reinforcement learning, in: 2019 16th IEEE Annual Consumer Commu-nications & Networking Conference, CCNC, 2019, pp. 1-6, http://dx.doi.org/10.1109/CCNC.2019.8651796

[63] Z. He, H. Shan, Y. Bi, Z. Xiang, Z. Su, T.H. Luan, Spectrum sharing for vehicular communications in a multi-operator scenario, in: 2019 11th International Conference on Wireless Communications and Signal Processing, WCSP, 2019, pp. 1-6, http://dx.doi.org/10.1109/WCSP.2019.8928130.

[64] K. Huang, Z. Luo, L. Liang, S. Jin, Fast spectrum sharing in vehicular networks: A meta reinforcement learning approach, in: 2022 IEEE 96th Vehicular Technology Conference, VTC2022-Fall, IEEE, 2022, pp. 1-5.

[65] C. Chen, J. Huang, H. Cui, Spectrum resource management and access mode selection in D2D-enabled vehicular networks, in: 2021 Computing, Communications and IoT Applications, ComComAp, IEEE, 2021, pp. 156-161.

[66] L. Liang, H. Ye, G.Y. Li, Spectrum sharing in vehicular networks based on multi-agent reinforcement learning, IEEE J. Sel. Areas Commun. 37 (10) (2019) 2282-2292.

[67] S. Lee, J. Youn, B.C. Jung, Hybrid AF/DF cooperative relaying technique with phase steering for industrial IoT networks, Energies 14 (4) (2021) 937.

[68] F. Lin, C. Chen, N. Zhang, X. Guan, X. Shen, Autonomous channel switching: Towards efficient spectrum sharing for industrial wireless sensor networks, IEEE Internet Things J. 3 (2) (2015) 231-243.

[69] Z. Shi, X. Xie, H. Lu, H. Yang, M. Kadoch, M. Cheriet, Deep-reinforcement-learning-based spectrum resource management for industrial Internet of Things, IEEE Internet Things J. 8 (5) (2020) 3476-3489.

[70] P. Ojanen, S. Yrjölä, M. Matinmikko-Blue, Assessing the feasibility of the spectrum sharing concepts for private industrial networks operating above 5 GHz, in: 2020 14th European Conference on Antennas and Propagation, EuCAP, IEEE, 2020, pp. 1-5.

[71] M. Zhang, J. Chen, S. He, L. Yang, X. Gong, J. Zhang, Privacy-preserving database assisted spectrum access for industrial internet of things: a distributed learning approach, IEEE Trans. Ind. Electron. 67 (8) (2019) 7094-7103.

[72] S.K.Y. Donzia, H.-K. Kim, H.J. Hwang, A software model for precision agriculture framework based on smart farming system and application of IoT gateway, in:International Conference on Computational Science & Intelligence and Applied Informatics, 2018.

[73] FederatedWireless, Intel, blue white robotics, and federated wireless create autonomous agriculture with robotics & private wireless, 2022, URL https://www.federatedwireless.com/news/blue-white-robotics-and-federated-wireless-collaborate-with-intel-to-chart-a-new-path-to-autonomous-agriculture-with-flexible-robotics-and-private-wireless/.

[74] Martha DeGrasse-fiercewireless, Farmers use private LTE in CBRS spectrum to increase yields, 2020, URL https://www.fiercewireless.com/private-wireless/farmers-use-private-lte-cbrs-spectrum-to-increase-yields.

[75] Joan Engebretson-telecompetitor, Smart farm leverages private LTE network, robotics, 2022, URL https://www.telecompetitor.com/smart-farm-leverages-private-lte-network-robotics/.

[76] Q. Li, Z. Feng, W. Li, T.A. Gulliver, P. Zhang, Joint spatial and temporal spectrum sharing for demand response management in cognitive radio enabled smart grid, IEEE Trans. Smart Grid 5 (4) (2014) 1993-2001.

[77] M. You, Q. Liu, H. Sun, New communication strategy for spectrum sharing enabled smart grid cyber-physical system, IET Cyber-Phys. Syst. Theory Appl. 2 (3) (2017) 136-142.

[78] E.F. Orumwense, K.M. Abo-Al-Ez, An energy efficient network architecture and spectrum sharing technique for cognitive radio based smart grid communications, Int. J. Commun. Antenna Propag. (2020).

[79] V.C. Gungor, D. Sahin, Cognitive radio networks for smart grid applications: A promising technology to overcome spectrum inefficiency, IEEE Veh. Technol. Mag. 7 (2) (2012) 41-46, http://dx.doi.org/10.1109/MVT.2012.2190183.

[80] I. Parvez, A.I. Sarwat, A spectrum sharing based metering infrastructure for smart grid utilizing LTE and WiFi, 2019.

[81] T.C. Clancy, N. Goergen, Security in cognitive radio networks: Threats and mitigation, in: 2008 3rd International Conference on Cognitive Radio Oriented Wireless Networks and Communications, CrownCom 2008, IEEE, 2008, pp. 1-8.

[82] S. Parvin, F.K. Hussain, O.K. Hussain, S. Han, B. Tian, E. Chang, Cognitive radio network security: A survey, J. Netw. Comput. Appl. 35 (6) (2012) 1691-1708.

[83] R. Ma, H.-H. Chen, W. Meng, Dynamic spectrum sharing for the coexistence of smart utility networks and WLANs in smart grid communications, IEEE Netw. 31 (1) (2016) 88-96.

[84] O.E. energy, The smart grid: An introduction, URL http://www.oe.energy.gov/SmartGridIntroduction.htm.

[85] Simmons,Mobile tips and tricks, URL http://www.mobiletipstricks.com/home-control-centre/.

[86] T. Li, J. Ren, X. Tang, Secure wireless monitoring and control systems for smart grid and smart home, IEEE Wirel. Commun. 19 (3) (2012) 66-73.

[87] B.K. Dash, Spectrum Sharing and Interference in Smart Homes ( Ph.D. thesis), The University of Texas Rio Grande Valley, 2021.

[88] H. Kour, R.K. Jha, S. Jain, A comprehensive survey on spectrum sharing: Architecture, energy efficiency and security issues, J. Netw. Comput. Appl. 103 (2018) 29-57, http://dx.doi.org/10.1016/j.jnca.2017.11.010, URL https://www.sciencedirect.com/science/article/pii/S1084804517303879.

[89] C. Zina, M. Hasna, R. Hamila, N. Hamdi, Location privacy preservation in secure crowdsourcing-based cooperative spectrum sensing, EURASIP J. Wireless Commun. Networking 2016 (1) (2016) 1-11.

[90] Y. Zeng, L. Xu, X. Yang, X. Yi, I. Khalil, Lightweight privacy preservation for secondary users in cognitive radio networks, J. Netw. Comput. Appl. 162 (2020) 102652, http://dx.doi.org/10.1016/j.jnca.2020.102652, URL https://www.sciencedirect.com/science/article/pii/S1084804520301260.

[91] X. Wang, M. Umehira, B. Han, H. Zhou, P. Li, C. Wu, An efficient privacy preserving spectrum sharing framework for Internet of Things, IEEE Access 8 (2020) 34675-34685, http://dx.doi.org/10.1109/ACCESS.2020.2974227.

[92] M. Grissa, A.A. Yavuz, B. Hamdaoui, Location privacy in cognitive radios with multi-server private information retrieval, IEEE Trans. Cogn. Commun. Netw. 5 (4) (2019) 949-962.

[93] M. Zhang, J. Chen, S. He, L. Yang, X. Gong, J. Zhang, Privacy-preserving database assisted spectrum access for industrial Internet of Things: A distributed learning approach, IEEE Trans. Ind. Electron. 67 (8) (2020) 7094-7103, http://dx.doi.org/10.1109/TIE.2019.2938491.

[94] A.M. Salama, M. Li, L. Lazos, Y. Xiao, M. Krunz, On the privacy and utility tradeoff in database-assisted dynamic spectrum access, in: 2018 IEEE International Symposium on Dynamic Spectrum Access Networks, DySPAN, 2018, pp. 1-10, http://dx.doi.org/10.1109/DySPAN.2018.8610485.

[95] P.R. Vaka, S. Bhattarai, J.-M.J. Park, Location privacy of non-stationary incumbent systems in spectrum sharing, in: 2016 IEEE Global Communications Conference, GLOBECOM, 2016, pp. 1-6, http://dx.doi.org/10.1109/GLOCOM.2016.7841962.

[96] M. Massaro, F. Beltrán, Will 5G lead to more spectrum sharing? Discussing recent developments of the LSA and the CBRS spectrum sharing frameworks, Telecommun. Policy 44 (7) (2020) 101973, http://dx.doi.org/10.1016/j.telpol.2020.101973, URL https://www.sciencedirect.com/science/article/pii/S0308596120300653.

[97] P.C. Pappa, A. Sarbhai, A. Baset, S. Kasera, M. Buddhikot, Spectrum sharing in CBRS using blockchain, in: 2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems, MASS, 2020, pp. 631-639, http://dx.doi.org/10.1109/MASS50613.2020.00082.

[98] J. Jeon, R.D. Ford, V.V. Ratnam, J. Cho, J. Zhang, Coordinated dynamic spectrum sharing for 5G and beyond cellular networks, IEEE Access 7 (2019) 111592-111604, http://dx.doi.org/10.1109/ACCESS.2019.2934385.

[99] Y. Ye, D. Wu, Z. Shu, Y. Qian, Overview of LTE spectrum sharing technologies, IEEE Access 4 (2016) 8105-8115, http://dx.doi.org/10.1109/ACCESS.2016.2626719.

[100] M. Labib, A.F. Martone, V. Marojevic, J.H. Reed, A.I. Zaghloul, A stochastic optimization approach for spectrum sharing of radar and LTE systems, IEEE Access 7 (2019) 60814-60826, http://dx.doi.org/10.1109/ACCESS.2019.2913888.

[101] U. Challita, D. Sandberg, Deep reinforcement learning for dynamic spectrum sharing of LTE and NR, in: ICC 2021 - IEEE International Conference on Communications, 2021, pp. 1-6, http://dx.doi.org/10.1109/ICC42927.2021.9500325.

[102] J.G. Andrews, S. Buzzi, W. Choi, S.V. Hanly, A. Lozano, A.C. Soong, J.C. Zhang, What will 5G be? IEEE J. Sel. Areas Commun. 32 (6) (2014) 1065-1082.

[103] C.-H. Ko, D.H. Huang, S.-H. Wu, Cooperative spectrum sensing in TV white spaces: when cognitive radio meets cloud, in: 2011 IEEE Conference on Computer Communications Workshops, IEEE, 2011, pp. 672-677, INFOCOM WKSHPS.

[104] M. Pan, P. Li, Y. Song, Y. Fang, P. Lin, S. Glisic, When spectrum meets clouds: Optimal session based spectrum trading under spectrum uncertainty, IEEE J. Sel. Areas Commun. 32 (3) (2013) 615-627.

[105] J. Mitola, J. Guerci, J. Reed, Y.-D. Yao, Y. Chen, T.C. Clancy, J. Dwyer, H. Li, H. Man, R. McGwier, et al., Accelerating 5G QoE via public-private spectrum sharing, IEEE Commun. Mag. 52 (5) (2014) 77-85.

[106] S. Dogan-Tusha, M.I. Rochman, A. Tusha, H. Nasiri, J. Helzerman, M. Ghosh, Evaluating the interference potential in 6 GHz: An extensive measurement campaign of a dense indoor wi-fi 6E network,in:Proceedings of the 17th ACM Workshop on Wireless Network Testbeds, Experimental Evaluation & Characterization, 2023, pp. 56-63.

[107] S. Dogan-Tusha, M.I. Rochman, A. Tusha, H. Nasiri, J. Helzerman, M. Ghosh, Evaluating the interference potential in 6 ghz: an extensive measurement campaign of a dense indoor wi-fi 6e network,in:Proceedings of the 17th ACM Workshop on Wireless Network Testbeds, Experimental Evaluation & Characterization, 2023, pp. 56-63.

[108] R. Saleem, S.A. Alvi, S. Durrani, Performance-fairness trade-off for Wi-Fi and LTE-LAA coexistence, IEEE Access 9 (2021) 62446-62459.

[109] N. Wang, L. Jiao, P. Wang, M. Dabaghchian, K. Zeng, Efficient identity spoofing attack detection for IoT in mm-wave and massive MIMO 5G communication, in: 2018 IEEE Global Communications Conference, GLOBECOM, IEEE, 2018, pp. 1-6.

[110] C. Hu, Y. Liu, Z. Lu, S. Zhao, X. Han, J. Xiong, Smartphone location spoofing attack in wireless networks, in: Security and Privacy in Communication Networks: 17th EAI International Conference, SecureComm 2021, Virtual Event, September 6-9, 2021, Proceedings, Part II 17, Springer, 2021, pp. 295-313.

[111] Y. Shi, K. Davaslioglu, Y.E. Sagduyu, Generative adversarial network in the air: Deep adversarial learning for wireless signal spoofing, IEEE Trans. Cogn. Commun. Netw. 7 (1) (2020) 294-303.

[112] L. Xiao, Y. Li, G. Liu, Q. Li, W. Zhuang, Spoofing detection with reinforcement learning in wireless networks, in: 2015 IEEE Global Communications Conference, GLOBECOM, IEEE, 2015, pp. 1-5.

[113] A. Alagil, M. Alotaibi, Y. Liu, Randomized positioning DSSS for antijamming wireless communications, in: 2016 International Conference on Computing, Networking and Communications, ICNC, IEEE, 2016, pp. 1-6.

[114] G. Han, L. Xiao, H.V. Poor, Two-dimensional anti-jamming communication based on deep reinforcement learning, in: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP, IEEE, 2017, pp. 2087-2091.

[115] Y. Wu, B. Wang, K.R. Liu, T.C. Clancy, Anti-jamming games in multichannel cognitive radio networks, IEEE J. Sel. Areas Commun. 30 (1) (2011) 4-15.

[116] M. Labib, S. Ha, W. Saad, J.H. Reed, A colonel blotto game for antijamming in the internet of things, in: 2015 IEEE Global Communications Conference, GLOBECOM, IEEE, 2015, pp. 1-6.

[117] L. Xiao, J. Liu, Q. Li, N.B. Mandayam, H.V. Poor, User-centric view of jamming games in cognitive radio networks, IEEE Trans. Inf. Forensics Secur. 10 (12) (2015) 2578-2590.

[118] T. Erpek, Y.E. Sagduyu, Y. Shi, Deep learning for launching and mitigating wireless jamming attacks, IEEE Trans. Cogn. Commun. Netw. 5 (1) (2018) 2-14.

[119] Y. Gwon, S. Dastangoo, C. Fossa, H. Kung, Competing mobile network game: Embracing antijamming and jamming strategies with reinforcement learning, in: 2013 IEEE Conference on Communications and Network Security, CNS, IEEE, 2013, pp. 28-36.

[120] M.A. Aref, S.K. Jayaweera, S. Machuzak, Multi-agent reinforcement learning based cognitive anti-jamming, in: 2017 IEEE Wireless Communications and Networking Conference, WCNC, IEEE, 2017, pp. 1-6.

[121] M. Letafati, H. Behroozi, B.H. Khalaj, E.A. Jorswieck, Deep learning for hardware-impaired wireless secret key generation with man-in-the-middle attacks, in: 2021 IEEE Global Communications Conference, GLOBECOM, IEEE, 2021, pp. 1-6.

[122] S. Fang, Y. Liu, P. Ning, Mimicry attacks against wireless link signature and new defense using time-synched link signature, IEEE Trans. Inf. Forensics Secur. 11 (7) (2016) 1515-1527.

[123] P. Jiang, H. Wu, C. Wang, C. Xin, Virtual MAC spoofing detection through deep learning, in: 2018 IEEE International Conference on Communications, ICC, IEEE, 2018, pp. 1-6.

[124] M. Agarwal, S. Biswas, S. Nandi, Detection of de-authentication dos attacks in Wi-Fi networks: A machine learning approach, in: 2015 IEEE International Conference on Systems, Man, and Cybernetics, IEEE, 2015, pp. 246-251.

[125] Z. Luo, S. Zhao, Z. Lu, Y.E. Sagduyu, J. Xu, Adversarial machine learning based partial-model attack in IoT, in:Proceedings of the 2nd ACM Workshop on Wireless Security and Machine Learning, 2020, pp. 13-18.

[126] Z. Luo, S. Zhao, Z. Lu, J. Xu, Y.E. Sagduyu, When attackers meet AI: Learning-empowered attacks in cooperative spectrum sensing, IEEE Trans. Mob. Comput. 21 (5) (2020) 1892-1908.

[127] Z. Luo, S. Zhao, R. Duan, Z. Lu, Y.E. Sagduyu, J. Xu, Low-cost influence-limiting defense against adversarial machine learning attacks in cooperative spectrum sensing, in:Proceedings of the 3rd ACM Workshop on Wireless Security and Machine Learning, 2021, pp. 55-60.

[128] A. Kavousi-Fard, W. Su, T. Jin, A machine-learning-based cyber attack detection model for wireless sensor networks in microgrids, IEEE Trans. Ind. Inform. 17 (1) (2020) 650-658.

[129] G. Han, J. Tu, L. Liu, M. Martínez-García, Y. Peng, Anomaly detection based on multidimensional data processing for protecting vital devices in 6G-enabled massive IIoT, IEEE Internet Things J. 8 (7) (2021) 5219-5229.

[130] P. Satam, S. Hariri, WIDS: An anomaly based intrusion detection system for Wi-Fi (IEEE 802.11) protocol, IEEE Trans. Netw. Serv. Manag. 18 (1) (2020) 1077-1091.

[131] Y. Meng, J. Li, H. Zhu, X. Liang, Y. Liu, N. Ruan, Revealing your mobile password via WiFi signals: Attacks and countermeasures, IEEE Trans. Mob. Comput. 19 (2) (2019) 432-449.

[132] S. Fang, I. Markwood, Y. Liu, S. Zhao, Z. Lu, H. Zhu, No training hurdles: Fast training-agnostic attacks to infer your typing,in:Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, 2018, pp. 1747-1760.

[133] D. He, C. Liu, H. Wang, T.Q. Quek, Learning-based wireless powered secure transmission, IEEE Wirel. Commun. Lett. 8 (2) (2018) 600-603.

[134] A. Vashist, A. Keats, S.M.P. Dinakarrao, A. Ganguly, Securing a wireless network-on-chip against jamming-based denial-of-service and eavesdropping attacks, IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 27 (12) (2019) 2781-2791.

[135] L. Zhang, G. Ding, Q. Wu, Y. Zou, Z. Han, J. Wang, Byzantine attack and defense in cognitive radio networks: A survey, IEEE Commun. Surv. Tutor. 17 (3) (2015) 1342-1363.

[136] R. Chen, J.-M. Park, Ensuring trustworthy spectrum sensing in cognitive radio networks, in: 2006 1st IEEE Workshop on Networking Technologies for Software Defined Radio Networks, IEEE, 2006, pp. 110-119.

[137] I.F. Akyildiz, B.F. Lo, R. Balakrishnan, Cooperative spectrum sensing in cognitive radio networks: A survey, Phys. Commun. 4 (1) (2011) 40-62.

[138] A. Attar, H. Tang, A.V. Vasilakos, F.R. Yu, V.C. Leung, A survey of security challenges in cognitive radio networks: Solutions and future research directions, Proc. IEEE 100 (12) (2012) 3172-3186.

[139] Z. Gao, H. Zhu, S. Li, S. Du, X. Li, Security and privacy of collaborative spectrum sensing in cognitive radio networks, IEEE Wirel. Commun. 19 (6) (2012) 106-112.

[140] J.-M. Park, J.H. Reed, A. Beex, T.C. Clancy, V. Kumar, B. Bahrak, Security and enforcement in spectrum sharing, Proc. IEEE 102 (3) (2014) 270-281.

[141] F. Li, K.-Y. Lam, X. Li, Z. Sheng, J. Hua, L. Wang, Advances and emerging challenges in cognitive internet-of-things, IEEE Trans. Ind. Inform. 16 (8) (2019) 5489-5496.

[142] S. Benazzouza, M. Ridouani, F. Salahdine, A. Hayar, A survey on compressive spectrum sensing for cognitive radio networks, in: 2019 IEEE International Smart Cities Conference, ISC2, IEEE, 2019, pp. 535-541.

[143] L. Miao, X. Di, Z.-M. Huo, Z.-X. Sun, Research on spectrum sensing data falsification attack detection algorithm in cognitive [internet of things], Telecommun. Syst. 80 (2) (2022) 227-238.

[144] S.H. Kamel, M.B. Abd-el Malek, S.E. El-Khamy, Compressive spectrum sensing using chaotic matrices for cognitive radio networks, Int. J. Commun. Syst. 32 (6) (2019) e3899.

[145] W. Alhakami, A. Mansour, G.A. Safdar, Spectrum Sharing Security and Attacks in CRNs: A Review, SAI Organization, 2014.

[146] J. Marinho, J. Granjal, E. Monteiro, A survey on security attacks and countermeasures with primary user detection in cognitive radio networks, EURASIP J. Inf. Secur. 2015 (1) (2015) 1-14.

[147] V. Ramani, S.K. Sharma, Cognitive radios: a survey on spectrum sensing, security and spectrum handoff, China Commun. 14 (11) (2017) 185-208.

[148] F.R. Yu, H. Tang, M. Huang, Z. Li, P.C. Mason, Defense against spectrum sensing data falsification attacks in mobile ad hoc networks with cognitive radios, in: MILCOM 2009-2009 IEEE Military Communications Conference, IEEE, 2009, pp. 1-7.

[149] A.G. Fragkiadakis, E.Z. Tragos, I.G. Askoxylakis, A survey on security threats and detection techniques in cognitive radio networks, IEEE Commun. Surv. Tutor. 15 (1) (2012) 428-445.

[150] I.F. Akyildiz, W.-Y. Lee, M.C. Vuran, S. Mohanty, A survey on spectrum management in cognitive radio networks, IEEE Commun. Mag. 46 (4) (2008) 40-48.

[151] M. Zaeri-Amirani, F. Afghah, J.D. Ashdown, Optimal relaying beamforming in multiple access broadcast channel (MABC) bidirectional cognitive radio networks in presence of interferers, Int. J. Wirel. Inf. Netw. 25 (2018) 383-398.

[152] B. Zhang, K. Chen, Selective spectrum leasing in internet of things via nash bargaining solutions, in: 2012 IEEE 2nd International Conference on Cloud Computing and Intelligence Systems, Vol. 2, IEEE, 2012, pp. 841-845.

[153] O. Simeone, I. Stanojev, S. Savazzi, Y. Bar-Ness, U. Spagnolini, R. Pickholtz, Spectrum leasing to cooperating secondary ad hoc networks, IEEE J. Sel. Areas Commun. 26 (1) (2008) 203-213.

[154] A.R. Korenda, M. Zaeri-Amirani, F. Afghah, A hierarchical stackelberg-coalition formation game theoretic framework for cooperative spectrum leasing, in: 2017 51st Annual Conference on Information Sciences and Systems, CISS, IEEE, 2017, pp. 1-6.

[155] N. Namvar, F. Afghah, Spectrum sharing in cooperative cognitive radio networks: A matching game framework, in: 2015 49th Annual Conference on Information Sciences and Systems, CISS, IEEE, 2015, pp. 1-5.

[156] F. Afghah, M. Costa, A. Razi, A. Abedi, A. Ephremides, A reputation-based stackelberg game approach for spectrum sharing with cognitive cooperation, in: 52nd IEEE Conference on Decision and Control, IEEE, 2013, pp. 3287-3292.

[157] F. Afghah, A. Shamsoshoara, L.L. Njilla, C.A. Kamhoua, Cooperative spectrum sharing and trust management in IoT networks, in: Modeling and Design of Secure Internet of Things, Wiley Online Library, 2020, pp. 79-109.

[158] M.W. Baidas, M.M. Afghah, F. Afghah, Distributed simultaneous wireless information and power transfer in multiuser amplify-and-forward ad hoc wireless networks, Int. J. Commun. Syst. 31 (1) (2018) e3411.

[159] G. Jakimoski, K. Subbalakshmi, Denial-of-service attacks on dynamic spectrum access networks, in: ICC Workshops- 2008 IEEE International Conference on Communications Workshops, IEEE, 2008, pp. 524-528.

[160] T. Weiss, J. Hillenbrand, A. Krohn, F.K. Jondral, Efficient signaling of spectral resources in spectrum pooling systems, in:Proc. 10th Symposium on Communications and Vehicular Technology, SCVT, Citeseer, 2003.

[161] T.A. Weiss, F.K. Jondral, Spectrum pooling: an innovative strategy for the enhancement of spectrum efficiency, IEEE Commun. Mag. 42 (3) (2004) S8-14.

[162] Q. Mahmoud, Cognitive Networks: Towards Self-Aware Networks, John Wiley & Sons, 2007.

[163] M. Grissa, A.A. Yavuz, B. Hamdaoui, Trustsas: A trustworthy spectrum access system for the 3.5 GHz CBRS band, in: IEEE INFOCOM 2019-IEEE Conference on Computer Communications, IEEE, 2019, pp. 1495-1503.

[164] K. Zhu, L. Huang, J. Nie, Y. Zhang, Z. Xiong, H.-N. Dai, J. Jin, Privacyaware double auction with time-dependent valuation for blockchain-based dynamic spectrum sharing in IoT systems, IEEE Internet Things J. (2022).

[165] C. Ji, Q. Zhu, Smart contract-based secure cooperative spectrum sensing algorithm, Int. J. Distrib. Sens. Netw. 17 (12) (2021) 15501477211058673.

[166] H. Du, L. Chen, et al., A collaborative spectrum sensing algorithm based on reputation update against malicious user attacks, Secur. Commun. Netw. 2023 (2023).

[167] M.K. Giri, S. Majumder, Extreme learning machine based identification of malicious users for secure cooperative spectrum sensing in cognitive radio networks, Wirel. Pers. Commun. 130 (3) (2023) 1993-2012.

[168] Z. Zhixuan, W. Jun, G. Jipeng, Z. Chen, S. Jin, Support vector machine process against probabilistic Byzantine attack for cooperative spectrum sensing in CRNs, in:Proceedings of the 2023 8th International Conference on Machine Learning Technologies, 2023, pp. 269-276.

[169] S. Li, H. Zhu, Z. Gao, X. Guan, K. Xing, X. Shen, Location privacy preservation in collaborative spectrum sensing, in: 2012 Proceedings IEEE INFOCOM, IEEE, 2012, pp. 729-737.