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Volume 8 Issue 9
Sep.  2021

IEEE/CAA Journal of Automatica Sinica

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Article Contents
Mohamed Amine Ferrag, Lei Shu and Kim-Kwang Raymond Choo, "Fighting COVID-19 and Future Pandemics With the Internet of Things: Security and Privacy Perspectives," IEEE/CAA J. Autom. Sinica, vol. 8, no. 9, pp. 1477-1499, Sept. 2021. doi: 10.1109/JAS.2021.1004087
Citation: Mohamed Amine Ferrag, Lei Shu and Kim-Kwang Raymond Choo, "Fighting COVID-19 and Future Pandemics With the Internet of Things: Security and Privacy Perspectives," IEEE/CAA J. Autom. Sinica, vol. 8, no. 9, pp. 1477-1499, Sept. 2021. doi: 10.1109/JAS.2021.1004087

Fighting COVID-19 and Future Pandemics With the Internet of Things: Security and Privacy Perspectives

doi: 10.1109/JAS.2021.1004087
Funds:  This work was supported in part by the Research Start-Up Fund for Talent Researcher of Nanjing Agricultural University (77H0603). The work of K.-K. R. Choo was supported only by the Cloud Technology Endowed Professorship
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  • The speed and pace of the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2; also referred to as novel Coronavirus 2019 and COVID-19) have resulted in a global pandemic, with significant health, financial, political, and other implications. There have been various attempts to manage COVID-19 and other pandemics using technologies such as Internet of Things (IoT) and 5G/6G communications. However, we also need to ensure that IoT devices used to facilitate COVID-19 monitoring and treatment (e.g., medical IoT devices) are secured, as the compromise of such devices can have significant consequences (e.g., life-threatening risks to COVID-19 patients). Hence, in this paper we comprehensively survey existing IoT-related solutions, potential security and privacy risks and their requirements. For example, we classify existing security and privacy solutions into five categories, namely: authentication and access control solutions, key management and cryptography solutions, blockchain-based solutions, intrusion detection systems, and privacy-preserving solutions. In each category, we identify the associated challenges. We also identify a number of recommendations to inform future research.

     

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  • [1]
    WHO, “Novel coronavirus (2019-nCoV): Situation report-10, ” World Health Organization, Jan. 2020.
    [2]
    Y. Y. Zheng, Y. T. Ma, J. Y. Zhang, and X. Xie, “COVID-19 and the cardiovascular system,” Nat. Rev. Cardiol., vol. 17, no. 5, pp. 259–260, Mar. 2020. doi: 10.1038/s41569-020-0360-5
    [3]
    D. S. W. Ting, L. Carin, V. Dzau, and T. Y. Wong, “Digital technology and COVID-19,” Nat. Med., vol. 26, no. 4, pp. 459–461, Mar. 2020. doi: 10.1038/s41591-020-0824-5
    [4]
    V. Chamola, V. Hassija, V. Gupta, and M. Guizani, “A comprehensive review of the COVID-19 pandemic and the role of IoT, drones, AI, blockchain, and 5G in managing its impact,” IEEE Access, vol. 8, pp. 90225–90265, May 2020. doi: 10.1109/ACCESS.2020.2992341
    [5]
    M. C. Chang and D. Park, “How can blockchain help people in the event of pandemics such as the COVID-19?” J. Med. Syst., vol. 44, no. 5, Article No. 102, Apr. 2020. doi: 10.1007/s10916-020-01577-8
    [6]
    V. Shubina, S. Holcer, M. Gould, and E. S. Lohan, “Survey of decentralized solutions with mobile devices for user location tracking, proximity detection, and contact tracing in the COVID-19 Era,” Data, vol. 5, no. 4, Article No. 87, Sept. 2020. doi: 10.3390/data5040087
    [7]
    L. Garg, E. Chukwu, N. Nasser, C. Chakraborty, and G. Garg, “Anonymity preserving IoT-based COVID-19 and other infectious disease contact tracing model,” IEEE Access, vol. 8, pp. 159402–159414, Aug. 2020. doi: 10.1109/ACCESS.2020.3020513
    [8]
    D. Vekaria, A. Kumari, S. Tanwar, and N. Kumar, “Boost: An AI-based data analytics scheme for COVID-19 prediction and economy boosting,” IEEE Internet Things J., DOI: 10.1109/JIOT.2020.3047539
    [9]
    Harvard College. Surveys, apps to track COVID-19. [Online]. Available: https://www.hsph.harvard.edu/coronavirus/covid-19-response-public-health-in-action/surveys-apps-to-track-covid-19/, Accessed on: Dec. 27, 2020.
    [10]
    Covid symptom study. [Online]. Available: https://covid.joinzoe.com/us-2, Accessed on: Dec. 27, 2020.
    [11]
    Covid symptom study. [Online]. Available: https://www.webmd.com/lung/coronavirus-apps, Accessed on: Dec. 27, 2020.
    [12]
    A. H. M. Aman, W. H. Hassan, S. Sameen, Z. S. Attarbashi, M. Alizadeh, and L. A. Latiff, “IoMT amid COVID-19 pandemic: Application, architecture, technology, and security,” J. Netw. Comput. Appl., vol. 174, Article No. 102886, Jan. 2021. doi: 10.1016/j.jnca.2020.102886
    [13]
    M. Kolhar, F. Al-Turjman, A. Alameen, and M. M. Abualhaj, “A three layered decentralized IoT biometric architecture for city lockdown during COVID-19 outbreak,” IEEE Access, vol. 8, pp. 163608–163617, Sept. 2020. doi: 10.1109/ACCESS.2020.3021983
    [14]
    I. Ahmed, A. Ahmad, and G. Jeon, “An IoT based deep learning framework for early assessment of COVID-19,” IEEE Internet Things J., 2020. DOI: 10.1109/JIOT.2020.3034074
    [15]
    Z. Fadlullah, M. M. Fouda, A. S. K. Pathan, N. Nasser, A. Benslimane, and Y. D. Lin, “Smart IoT solutions for combating the COVID-19 pandemic,” IEEE Internet Things Mag., vol. 3, no. 3, pp. 10–11, Oct. 2020. doi: 10.1109/MIOT.2020.9241464
    [16]
    S. Misra, P. K. Deb, N. Koppala, A. Mukherjee, and S. W. Mao, “S-NAV: Safety-aware IoT navigation tool for avoiding COVID-19 hotspots,” IEEE Internet Things J., vol. 8, no. 8, pp. 6975–6982, Nov. 2020.
    [17]
    S. Munzert, P. Selb, A. Gohdes, L. F. Stoetzer, W. Lowe, “Tracking and promoting the usage of a COVID-19 contact tracing app,” Nature Human Behaviour, vol. 5, no. 2, pp. 247–255, 2021.
    [18]
    A. Roy, F. H. Kumbhar, H. S. Dhillon, N. Saxena, S. Y. Shin, and S. Singh, “Efficient monitoring and contact tracing for COVID-19: A smart IoT-based framework,” IEEE Internet Things Mag., vol. 3, no. 3, pp. 17–23, Oct. 2020. doi: 10.1109/IOTM.0001.2000145
    [19]
    M. Mukherjee, R. Matam, L. Shu, L. Maglaras, M. A. Ferrag, N. Choudhury, and V. Kumar, “Security and privacy in fog computing: Challenges,” IEEE Access, vol. 5, pp. 19293–19304, Sept. 2017. doi: 10.1109/ACCESS.2017.2749422
    [20]
    Y. Abdulsalam and M. S. Hossain, “COVID-19 networking demand: An auction-based mechanism for automated selection of edge computing services,” IEEE Trans. Netw. Sci. Eng., 2020. DOI: 10.1109/TNSE.2020.3026637
    [21]
    Y. Siriwardhana, C. De Alwis, G. Gur, M. Ylianttila, and M. Liyanage, “The fight against the COVID-19 pandemic with 5G technologies,” IEEE Eng. Manag. Rev., vol. 48, no. 3, pp. 72–84, Aug. 2020. doi: 10.1109/EMR.2020.3017451
    [22]
    M. Mukherjee, M. A. Ferrag, L. Maglaras, A. Derhab, and M. Aazam, “Security and privacy issues and solutions for fog,” Fog and Fogonomics: Challenges and Practices of Fog Computing, Communication, Networking, Strategy, and Economics, pp. 353–374, 2020.
    [23]
    I. F. Akyildiz, M. Ghovanloo, U. Guler, T. Ozkaya-Ahmadov, A. F. Sarioglu, and B. D. Unluturk, “PANACEA: An internet of bio-nanothings application for early detection and mitigation of infectious diseases,” IEEE Access, vol. 8, pp. 140512–140523, Jul. 2020. doi: 10.1109/ACCESS.2020.3012139
    [24]
    N. Ahmed, R. A. Michelin, W. L. Xue, S. Ruj, R. Malaney, S. S. Kanhere, A. Seneviratne, W. Hu, H. Janicke, and S. K. Jha, “A survey of COVID-19 contact tracing apps,” IEEE Access, vol. 8, pp. 134577–134601, Jul. 2020. doi: 10.1109/ACCESS.2020.3010226
    [25]
    M. Ndiaye, S. S. Oyewobi, A. M. Abu-Mahfouz, G. P. Hancke, A. M. Kurien, and K. Djouani, “IoT in the wake of COVID-19: A survey on contributions, challenges and evolution,” IEEE Access, vol. 8, pp. 186821–186839, Oct. 2020. doi: 10.1109/ACCESS.2020.3030090
    [26]
    M. Nasajpour, S. Pouriyeh, R. M. Parizi, M. Dorodchi, M. Valero, and H. R. Arabnia, “Internet of things for current COVID-19 and future pandemics: An exploratory study,” J. Healthc. Inform. Res., vol. 4, no. 4, pp. 325–364, Nov. 2020. doi: 10.1007/s41666-020-00080-6
    [27]
    A. Sufian, A. Ghosh, A. S. Sadiq, and F. Smarandache, “A survey on deep transfer learning to edge computing for mitigating the COVID-19 pandemic,” J. Syst. Arch., vol. 108, p. 101830, Sept. 2020.
    [28]
    A. A. Hussain, O. Bouachir, F. Al-Turjman, and M. Aloqaily, “AI techniques for COVID-19,” IEEE Access, vol. 8, pp. 128776–128795, Jul. 2020. doi: 10.1109/ACCESS.2020.3007939
    [29]
    O. S. Albahri, A. A. Zaidan, A. S. Albahri, B. B. Zaidan, K. H. Abdulkareem, Z. T. Al-Qaysi, A. H. Alamoodi, A. M. Aleesa, M. A. Chyad, R. M. Alesa, L. C. Kem, M. M. Lakulu, A. B. Ibrahim, and N. A. Rashid, “Systematic review of artificial intelligence techniques in the detection and classification of COVID-19 medical images in terms of evaluation and benchmarking: Taxonomy analysis, challenges, future solutions and methodological aspects,” J. Infection Public Health, vol. 13, no. 10, pp. 1381–1396, Oct. 2020. doi: 10.1016/j.jiph.2020.06.028
    [30]
    F. Shi, J. Wang, J. Shi, Z. Y. Wu, Q. Wang, Z. Y. Tang, K. L. He, Y. H. Shi, and D. G. Shen, “Review of artificial intelligence techniques in imaging data acquisition, segmentation, and diagnosis for COVID-19,” IEEE Rev. Biomed. Eng., vol. 14, pp. 4–15, Apr. 2020.
    [31]
    D. Marbouh, T. Abbasi, F. Maasmi, I. A. Omar, M. S. Debe, K. Salah, R. Jayaraman, and S. Ellahham, “Blockchain for COVID-19: Review, opportunities, and a trusted tracking system,” Arab. J. Sci. Eng., vol. 45, no. 12, pp. 9895–9911, Oct. 2020. doi: 10.1007/s13369-020-04950-4
    [32]
    A. Kalla, T. Hewa, R. A. Mishra, M. Ylianttila, and M. Liyanage, “The role of blockchain to fight against COVID-19,” IEEE Eng. Manag. Rev., vol. 48, no. 3, pp. 85–96, Aug. 2020. doi: 10.1109/EMR.2020.3014052
    [33]
    V. Jahmunah, V. K. Sudarshan, S. L. Oh, R. Gururajan, R. Gururajan, X. J. Zhou, X. H. Tao, O. Faust, E. J. Ciaccio, K. H. Ng, and U. R. Acharya, “Future IoT tools for COVID-19 contact tracing and prediction: A review of the state-of-the-science,” Int. J. Imaging Syst. Technol., vol. 31, no. 2, pp. 455–471, Jun. 2021. doi: 10.1002/ima.22552
    [34]
    M. S. Nawaz, P. Fournier-Viger, A. Shojaee, and H. Fujita, “Using artificial intelligence techniques for COVID-19 genome analysis,” Appl. Intell., vol. 51, no. 5, pp. 3086–3103, Feb. 2021. doi: 10.1007/s10489-021-02193-w
    [35]
    H. Snyder, “Literature review as a research methodology: An overview and guidelines,” J. Bus. Res., vol. 104, pp. 333–339, Nov. 2019. doi: 10.1016/j.jbusres.2019.07.039
    [36]
    H. Lin, S. Garg, J. Hu, X. D. Wang, M. J. Piran, and M. S. Hossain, “Privacy-enhanced data fusion for COVID-19 applications in intelligent internet of medical things,” IEEE Internet Things J., 2020. DOI: 10.1109/JIOT.2020.3033129
    [37]
    M. A. Ferrag, L. Maglaras, and A. Derhab, “Authentication and authorization for mobile IoT devices using biofeatures: Recent advances and future trends,” Secur. Commun. Netw., vol. 2019, p. 5452870, May 2019.
    [38]
    M. A. Ferrag, L. Maglaras, A. Derhab, and H. Janicke, “Authentication schemes for smart mobile devices: Threat models, countermeasures, and open research issues,” Telecommun. Syst., vol. 73, no. 2, pp. 317–348, Feb. 2020. doi: 10.1007/s11235-019-00612-5
    [39]
    M. A. Ferrag, L. Maglaras, A. Argyriou, D. Kosmanos, and H. Janicke, “Security for 4G and 5G cellular networks: A survey of existing authentication and privacy-preserving schemes,” J. Netw. Comput. Appl., vol. 101, pp. 55–82, Jan. 2018. doi: 10.1016/j.jnca.2017.10.017
    [40]
    M. A. Ferrag, L. A. Maglaras, H. Janicke, J. M. Jiang, and L. Shu, “Authentication protocols for internet of things: A comprehensive survey,” Secur. Commun. Netw., vol. 2017, p. 6562953, Nov. 2017.
    [41]
    D. B. He, N. Kumar, H. Q. Wang, L. N. Wang, K. K. R. Choo, and A. Vinel, “A provably-secure cross-domain handshake scheme with symptoms- matching for mobile healthcare social network,” IEEE Trans. Dependable Secure Comput., vol. 15, no. 4, pp. 633–645, Jul. 2016.
    [42]
    D. Dolev and A. Yao, “On the security of public key protocols,” IEEE Trans. Inf. Theory, vol. 29, no. 2, pp. 198–208, Mar. 1983. doi: 10.1109/TIT.1983.1056650
    [43]
    H. W. Tan, P. Kim, and I. Chung, “Practical homomorphic authentication in cloud-assisted VANETs with Blockchain-based healthcare monitoring for pandemic control,” Electronics, vol. 9, no. 10, p. 1683, Oct. 2020.
    [44]
    T. Alladi, V. Chamola, and Naren, “HARCI: A two-way authentication protocol for three entity healthcare IoT networks,” IEEE J. Sel. Areas Commun., vol. 39, no. 2, pp. 361–369, Feb. 2021. doi: 10.1109/JSAC.2020.3020605
    [45]
    S. Challa, A. K. Das, V. Odelu, N. Kumar, S. Kumari, M. K. Khan, and A. V. Vasilakos, “An efficient ECC-based provably secure three-factor user authentication and key agreement protocol for wireless healthcare sensor networks,” Comput. Electr. Eng., vol. 69, pp. 534–554, Jul. 2018. doi: 10.1016/j.compeleceng.2017.08.003
    [46]
    J. Srinivas, A. K. Das, N. Kumar, and J. J. P. C. Rodrigues, “Cloud centric authentication for wearable healthcare monitoring system,” IEEE Trans. Dependable Secure Comput., vol. 17, no. 5, pp. 942–956, Sep.–Oct. 2020. doi: 10.1109/TDSC.2018.2828306
    [47]
    M. A. Ferrag, M. Derdour, M. Mukherjee, A. Derhab, L. Maglaras, and H. Janicke, “Blockchain technologies for the internet of things: Research issues and challenges,” IEEE Internet Things J., vol. 6, no. 2, pp. 2188–2204, Apr. 2019. doi: 10.1109/JIOT.2018.2882794
    [48]
    H. R. Hasan, K. Salah, R. Jayaraman, J. Arshad, I. Yaqoob, M. Omar, and S. Ellahham, “Blockchain-based solution for COVID-19 digital medical passports and immunity certificates,” IEEE Access, vol. 8, pp. 222093–222108, Dec. 2020. doi: 10.1109/ACCESS.2020.3043350
    [49]
    A. Shukla, N. Patel, S. Tanwar, B. Sadoun, and M. S. Obaidat, “BDoTs: Blockchain-based evaluation scheme for online teaching under COVID-19 environment,” in Proc. Int. Conf. Computer, Information and Telecommunication Systems, Hangzhou, China, 2020, pp. 1–5.
    [50]
    P. Huang, L. K. Guo, M. Li, and Y. G. Fang, “Practical privacy-preserving ECG-based authentication for IoT-based healthcare,” IEEE Internet Things J., vol. 6, no. 5, pp. 9200–9210, Jul. 2019. doi: 10.1109/JIOT.2019.2929087
    [51]
    Y. Zhang, R. Gravina, H. M. Lu, M. Villari, and G. Fortino, “PEA: Parallel electrocardiogram-based authentication for smart healthcare systems,” J. Netw. Comput. Appl., vol. 117, pp. 10–16, Sept. 2018. doi: 10.1016/j.jnca.2018.05.007
    [52]
    S. Roy, A. K. Das, S. Chatterjee, N. Kumar, S. Chattopadhyay, and J. J. P. C. Rodrigues, “Provably secure fine-grained data access control over multiple cloud servers in mobile cloud computing based healthcare applications,” IEEE Trans. Ind. Inform., vol. 15, no. 1, pp. 457–468, Jan. 2019. doi: 10.1109/TII.2018.2824815
    [53]
    F. Wu, L. L. Xu, S. Kumari, X. Li, A. K. Das, and J. Shen, “A lightweight and anonymous RFID tag authentication protocol with cloud assistance for e-healthcare applications,” J. Ambient Intell. Human. Comput., vol. 9, no. 4, pp. 919–930, Aug. 2018. doi: 10.1007/s12652-017-0485-5
    [54]
    R. Chaudhary, A. Jindal, G. S. Aujla, N. Kumar, A. K. Das, and N. Saxena, “LSCSH: Lattice-based secure cryptosystem for smart healthcare in smart cities environment,” IEEE Commun. Mag., vol. 56, no. 4, pp. 24–32, Apr. 2018. doi: 10.1109/MCOM.2018.1700787
    [55]
    A. K. Das, A. K. Sutrala, V. Odelu, and A. Goswami, “A secure smartcard-based anonymous user authentication scheme for healthcare applications using wireless medical sensor networks,” Wireless Pers. Commun., vol. 94, no. 3, pp. 1899–1933, Jun. 2017. doi: 10.1007/s11277-016-3718-6
    [56]
    L. P. Zhang, Y. X. Zhang, S. Y. Tang, and H. Luo, “Privacy protection for e-health systems by means of dynamic authentication and three-factor key agreement,” IEEE Trans. Ind. Electron., vol. 65, no. 3, pp. 2795–2805, Mar. 2018. doi: 10.1109/TIE.2017.2739683
    [57]
    M. Wazid, A. K. Das, N. Kumar, M. Conti, and A. V. Vasilakos, “A novel authentication and key agreement scheme for implantable medical devices deployment,” IEEE J. Biomed. Health Inform., vol. 22, no. 4, pp. 1299–1309, Jul. 2018. doi: 10.1109/JBHI.2017.2721545
    [58]
    J. Zhou, Z. F. Cao, X. L. Dong, and X. D. Lin, “PPDM: A privacy-preserving protocol for cloud-assisted e-healthcare systems,” IEEE J. Sel. Top. Signal Process., vol. 9, no. 7, pp. 1332–1344, Oct. 2015. doi: 10.1109/JSTSP.2015.2427113
    [59]
    J. Zhou, Z. F. Cao, X. L. Dong, N. X. Xiong, and A. V. Vasilakos, “4S: A secure and privacy-preserving key management scheme for cloud- assisted wireless body area network in m-healthcare social networks,” Inf. Sci., vol. 314, pp. 255–276, Sept. 2015. doi: 10.1016/j.ins.2014.09.003
    [60]
    M. Masud, G. S. Gaba, S. Alqahtani, G. Muhammad, B. B. Gupta, P. Kumar, and A. Ghoneim, “A lightweight and robust secure key establishment protocol for internet of medical things in COVID-19 patients care,” IEEE Internet Things J., 2020. DOI: 10.1109/JIOT.2020.3047662
    [61]
    M. Wazid, B. Bera, A. Mitra, A. K. Das, and R. Ali, “Private blockchain-envisioned security framework for AI-enabled IoT-based drone-aided healthcare services,” in Proc. 2nd ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond, London, UK, 2020, pp. 37–42.
    [62]
    S. Saha, A. K. Sutrala, A. K. Das, N. Kumar, and J. J. P. C. Rodrigues, “On the design of blockchain-based access control protocol for IoT-enabled healthcare applications,” in Proc. IEEE Int. Conf. Communications, Dublin, Ireland, 2020, pp. 1–6.
    [63]
    G. S. Aujla and A. Jindal, “A decoupled Blockchain approach for edge- envisioned IoT-based healthcare monitoring,” IEEE J. Sel. Areas Commun., vol. 39, no. 2, Feb. 2021.
    [64]
    G. Thamilarasu, A. Odesile, and A. Hoang, “An intrusion detection system for internet of medical things,” IEEE Access, vol. 8, pp. 181560–181576, Sept. 2020. doi: 10.1109/ACCESS.2020.3026260
    [65]
    K. P. Yu, L. Tan, X. L. Shang, J. J. Huang, G. Srivastava, and P. Chatterjee, “Efficient and privacy-preserving medical research support platform against COVID-19: A blockchain-based approach,” IEEE Consum. Electron. Mag., vol. 10, no. 2, pp. 111–120, Mar. 2021. doi: 10.1109/MCE.2020.3035520
    [66]
    P. Kumar, G. P. Gupta, and R. Tripathi, “An ensemble learning and fog- cloud architecture-driven cyber-attack detection framework for IoMT networks,” Comput. Commun., vol. 166, pp. 110–124, Jan. 2021. doi: 10.1016/j.comcom.2020.12.003
    [67]
    W. J. Li, S. Tug, W. Z. Meng, and Y. Wang, “Designing collaborative blockchained signature-based intrusion detection in IoT environments,” Future Gener. Comput. Syst., vol. 96, pp. 481–489, Jul. 2019. doi: 10.1016/j.future.2019.02.064
    [68]
    D. J. He, Q. Qiao, Y. Gao, J. J. Zheng, S. Chan, J. X. Li, and N. Guizani, “Intrusion detection based on stacked Autoencoder for connected healthcare systems,” IEEE Netw., vol. 33, no. 6, pp. 64–69, Nov.-Dec. 2019. doi: 10.1109/MNET.001.1900105
    [69]
    F. W. Wang, H. Zhu, X. M. Liu, R. X. Lu, J. F. Hua, H. Li, and H. Li, “Privacy- preserving collaborative model learning scheme for e-healthcare,” IEEE Access, vol. 7, pp. 166054–166065, Nov. 2019. doi: 10.1109/ACCESS.2019.2953495
    [70]
    G. M. Wang, R. X. Lu, C. Huang, and Y. L. Guan, “An efficient and privacy- preserving pre-clinical guide scheme for mobile e-healthcare,” J. Inf. Secur. Appl., vol. 46, pp. 271–280, 2019.
    [71]
    Y. D. Zheng, R. X. Lu, and J. Shao, “Achieving efficient and privacy- preserving k-NN query for outsourced ehealthcare data,” J. Med. Syst., vol. 43, no. 5, Article No. 123, Mar. 2019. doi: 10.1007/s10916-019-1229-1
    [72]
    X. Yang, R. X. Lu, J. Shao, X. H. Tang, and H. M. Yang, “An efficient and privacy-preserving disease risk prediction scheme for e-healthcare,” IEEE Internet Things J., vol. 6, no. 2, pp. 3284–3297, Apr. 2019. doi: 10.1109/JIOT.2018.2882224
    [73]
    C. Zhang, L. H. Zhu, C. Xu, and R. X. Lu, “PPDP: An efficient and privacy- preserving disease prediction scheme in cloud-based e-healthcare system,” Future Gener. Comput. Syst., vol. 79, pp. 16–25, Feb. 2018. doi: 10.1016/j.future.2017.09.002
    [74]
    H. Zhu, X. X. Liu, R. X. Lu, and H. Li, “Efficient and privacy-preserving online medical prediagnosis framework using nonlinear SVM,” IEEE J. Biomed. Health Inform., vol. 21, no. 3, pp. 838–850, May 2017. doi: 10.1109/JBHI.2016.2548248
    [75]
    X. M. Liu, R. X. Lu, J. F. Ma, L. Chen, and B. D. Qin, “Privacy-preserving patient-centric clinical decision support system on naive Bayesian classification,” IEEE J. Biomed. Health Inform., vol. 20, no. 2, pp. 655–668, Mar. 2016. doi: 10.1109/JBHI.2015.2407157
    [76]
    M. A. Ferrag and L. Shu, “The performance evaluation of blockchain-based security and privacy systems for the internet of things: A tutorial,” IEEE Internet Things J., 2021. DOI: 10.1109/JIOT.2021.3078072
    [77]
    L. Maglaras, T. Cruz, M. A. Ferrag, and H. Janicke, “Teaching the process of building an intrusion detection system using data from a small-scale SCADA testbed,” Internet Technol. Lett., vol. 3, no. 1, Article No. e132, Feb. 2020. doi: 10.1002/itl2.132
    [78]
    R. Mitchell and I. R. Chen, “Behavior rule specification-based intrusion detection for safety critical medical cyber physical systems,” IEEE Trans. Dependable Secure Comput., vol. 12, no. 1, pp. 16–30, Jan.-Feb. 2015. doi: 10.1109/TDSC.2014.2312327
    [79]
    IBM. Cloud. [Online]. Available: https://www.ibm.com/cloud, Accessed on: Mar. 04, 2021.
    [80]
    Google cloud platform. [Online]. Available: https://www.bbsmax.com/A/kPzO8jL7Jx/, Accessed on: Mar. 04, 2021.
    [81]
    Microsoft azure. [Online]. Available: https://azure.microsoft.com/en-us/, Accessed on: Mar. 04, 2021.
    [82]
    Amazon Web Services. [Online]. Available: https://aws.amazon.com/, Accessed on: Mar. 04, 2021.
    [83]
    Dell EMC. [Online]. Available: https://www.delltechnologies.com/en-in/service-providers/edge-computing.htm, Accessed on: Mar. 04, 2021.
    [84]
    FUJITSU IoT solution INTELLIEDGE. [Online]. Available: https://www.fujitsu.com/global/products/computing/pc/edge-computing/, Accessed on: Mar. 04, 2021.
    [85]
    Google’s edge TPU. [Online]. Available: https://cloud.google.com/edge-tpu, Accessed on: Mar. 04, 2021.
    [86]
    Microsoft’s vision AI toolkit. [Online]. Available: https://azure.github.io/Vision-AI-DevKit-Pages/, Accessed on: Mar. 04, 2021.
    [87]
    Lighty. [Online]. Available: https://lighty.io/, Accessed on: Mar. 04, 2021.
    [88]
    Cherry. [Online]. Available: https://github.com/superkkt/cherry/, Accessed on: Mar. 04, 2021.
    [89]
    OpenBaton. [Online]. Available: https://openbaton.github.io/, Accessed on: Mar. 04, 2021.
    [90]
    P. Berde, M. Gerola, J. Hart, Y. Higuchi, M. Kobayashi, T. Koide, B. Lantz, B. O’Connor, P. Radoslavov, W. Snow, and Parulkar G, “ONOS: Towards an open, distributed SDN OS,” in Proc. 3rd Workshop on Hot Topics in Software Defined Networking, Chicago, USA, 2014, pp. 1–6.
    [91]
    OPENFV. [Online]. Available: https://www.opnfv.org/, Accessed on: Mar. 04, 2021.
    [92]
    Z. K. Khattak, M. Awais, and A. Iqbal, “Performance evaluation of OpenDaylight SDN controller,” in Proc. 20th IEEE Int. Conf. Parallel and Distributed Systems, Hsinchu, Taiwan, China, 2014, pp. 671–676.
    [93]
    M. A. Ferrag, L. Maglaras, H. Janicke, and R. Smith, “Deep learning techniques for cyber security intrusion detection: A detailed analysis,” in Proc. 6th Int. Symp. for ICS & SCADA Cyber Security Research, 2019, pp. 126–136.
    [94]
    A. M. Ismael and A. Sengur, “Deep learning approaches for COVID-19 detection based on chest X-ray images,” Exp. Syst. Appl., vol. 164, Article No. 114054, Feb. 2021. doi: 10.1016/j.eswa.2020.114054
    [95]
    M. M. Islam, F. Karray, R. Alhajj, and J. Zeng, “A review on deep learning techniques for the diagnosis of novel coronavirus (COVID-19),” IEEE Access, vol. 9, pp. 30551–30572, Feb. 2021. doi: 10.1109/ACCESS.2021.3058537
    [96]
    Boeing. [Online]. Available: https://www.boeing.com/defense/autonomous-systems/index.page, Accessed on: Mar. 04, 2021.
    [97]
    DHL parcelcopter. [Online]. Available: https://discover.dhl.com/business/business-ethics/parcelcopter-drone-technology, Accessed on: Mar. 04, 2021.
    [98]
    Zipline. [Online]. Available: https://flyzipline.com/, Accessed on: Mar. 04, 2021.
    [99]
    Wingcopter. [Online]. Available: https://wingcopter.com/, Accessed on: Mar. 04, 2021.
    [100]
    Flytrex. [Online]. Available: https://flytrex.com/, Accessed on: Mar. 04, 2021.
    [101]
    UPS. UPS flight forwardTM drone delivery. [Online]. Available: https://www.ups.com/us/en/services/shipping-services/flight-forward-drones.page, Accessed on: Mar. 04, 2021.
    [102]
    Amazon prime air. [Online]. Available: https://www.amazon.com/Amazon-Prime-Air/b?ie=UTF8&node=8037720011, Accessed on: Mar. 04, 2021.
    [103]
    Wing. [Online]. Available: https://wing.com/, Accessed on: Mar. 04, 2021.
    [104]
    'T. M. Fernandez-Carames and P. Fraga-Lamas, “Towards post-quantum blockchain: A review on blockchain cryptography resistant to quantum computing attacks,” IEEE Access, vol. 8, pp. 21091–21116, Jan. 2020. doi: 10.1109/ACCESS.2020.2968985
    [105]
    M. S. Hossain, G. Muhammad, and N. Guizani, “Explainable AI and mass surveillance system-based healthcare framework to combat COVID-19 like pandemics,” IEEE Netw., vol. 34, no. 4, pp. 126–132, Jul.–Aug. 2020. doi: 10.1109/MNET.011.2000458
    [106]
    “AI puts Moderna within striking distance of beating COVID-19,” https://digital.hbs.edu/artificial-intelligence-machine-learning/ai-puts-moderna-within-striking-distance-of-beating-COVID-19/, Accessed on: Dec. 27, 2020.
    [107]
    A. Ulhaq, J. Born, A. Khan, D. P. S. Gomes, S. Chakraborty, and M. Paul, “COVID-19 control by computer vision approaches: A survey,” IEEE Access, vol. 8, pp. 179437–179456, Sept. 2020. doi: 10.1109/ACCESS.2020.3027685
    [108]
    E. Quiring, D. Klein, D. Arp, M. Johns, and K. Rieck, “Adversarial preprocessing: Understanding and preventing image-scaling attacks in machine learning,” in Proc. 29th USENIX Security Symp., 2020.
    [109]
    A. Rahman, M. S. Hossain, N. A. Alrajeh, and F. Alsolami, “Adversarial examples–security threats to COVID-19 deep learning systems in medical IoT devices,” IEEE Internet Things J., vol. 8, no. 12, pp. 9603–9610, 2021.
    [110]
    H. Ledford, D. Cyranoski, and R. Van Noorden, “The UK has approved a COVID vaccine-here’s what scientists now want to know,” Nature, vol. 588, no. 7837, pp. 205–206, Dec. 2020. doi: 10.1038/d41586-020-03441-8
    [111]
    ZDNET. IoT solutions power safe, speedy and cold COVID-19 vaccine delivery. [online]. Available: https://www.zdnet.com/article/iot-solutions-power-safe-speedy-and-cold-COVID-19-vaccine-delivery/, Accessed on: Dec. 27, 2020.
    [112]
    B. T. Chen, J. F. Wan, L. Shu, P. Li, M. Mukherjee, and B. X. Yin, “Smart factory of industry 4.0: Key technologies, application case, and challenges,” IEEE Access, vol. 6, pp. 6505–6519, Dec. 2017.
    [113]
    I. F. Akyildiz, M. Pierobon, S. Balasubramaniam, and Y. Koucheryavy, “The internet of bio-nano things,” IEEE Commun. Mag., vol. 53, no. 3, pp. 32–40, Mar. 2015. doi: 10.1109/MCOM.2015.7060516
    [114]
    N. Saeed, M. H. Loukil, H. Sarieddeen, T. Y. Al-Naffouri, and M. S. Alouini, “Body-centric terahertz networks: Prospects and challenges,” Pre-print, 2020. [Online]. Available: http://hdl.handle.net/10754/664913.
    [115]
    CNBC. Use of surveillance to fight coronavirus raises concerns about government power after pandemic ends. [Online]. Available: https://www.cnbc.com/2020/03/27/coronavirus-surveillance-used-by-governments-to-fight-pandemic-privacy-concerns.html, Accessed on: Dec. 27, 2020.
    [116]
    P. Mishra, A. Biswal, S. Garg, R. X. Lu, M. Tiwary, and D. Puthal, “Software defined internet of things security: Properties, state of the art, and future research,” IEEE Wirel. Commun., vol. 27, no. 3, pp. 10–16, Jun. 2020. doi: 10.1109/MWC.001.1900318
    [117]
    H. Xu, L. Zhang, O. Onireti, Y. Fang, W. J. Buchanan, and M. A. Imran, “BeepTrace: Blockchain-enabled privacy-preserving contact tracing for COVID-19 pandemic and beyond,” IEEE Internet Things J., vol. 8, no. 5, pp. 3915–3929, Mar. 2021. doi: 10.1109/JIOT.2020.3025953
    [118]
    P. V. Klaine, L. Zhang, B. P. Zhou, Y. Sun, H. Xu, and M. Imran, “Privacy- preserving contact tracing and public risk assessment using Blockchain for COVID-19 pandemic,” IEEE Internet Things Mag., vol. 3, no. 3, pp. 58–63, Sept. 2020. doi: 10.1109/IOTM.0001.2000078
    [119]
    P. F. Wang, C. Lin, M. S. Obaidat, Z. Yu, Z. Q. Wei, and Q. Zhang, “Contact tracing incentive for COVID-19 and other pandemic diseases from a crowdsourcing perspective,” IEEE Internet Things J., 2021. DOI: 10.1109/JIOT.2020.3049024
    [120]
    N. Koroniotis, N. Moustafa, E. Sitnikova, and B. Turnbull, “Towards the development of realistic botnet dataset in the internet of things for network forensic analytics: Bot-IoT dataset,” Future Gener. Comput. Syst., vol. 100, pp. 779–796, Nov. 2019. doi: 10.1016/j.future.2019.05.041
    [121]
    M. A. Ferrag, L. Maglaras, S. Moschoyiannis, and H. Janicke, “Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study,” J. Inf. Secur. Appl., vol. 50, p. 102419, Feb. 2020.

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