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Mode selection, caching and physical layer security for fog networks

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Języki publikacji
EN
Abstrakty
EN
Fog networks facilitate ultra-low latency through the use of data availability near the network edge in fog servers. Existing work in fog networks considers the objective of energy efficiency and low latency for internet-of-things (IoT) for resource allocation. These works provide solutions to energy efficiency and low latency resource allocation problem without consideration of secure communication. This article investigates the benefits of fog architecture from the perspective of three promising technologies namely device-to-device (D2D) communication, caching, and physical layer security. We propose security provisioning followed by mode selection for D2D-assisted fog networks. The secrecy rate maximization problem is formulated first, which belongs to mixed-integer nonlinear programming (MINLP) problem. It is NP-hard, that is why an exhaustive search for finding the solution is complex. Keeping in view the complexity, a nonlinear technique namely outer approximation algorithm (OAA) is applied. OAA is a traditional algorithm, whose results are compared with the proposed heuristic algorithm, namely the security heuristic algorithm (SHA). Performance of the network is observed for the different numbers of eavesdroppers, IoT nodes, and fog nodes.
Rocznik
Strony
art. no. e142652
Opis fizyczny
Bibliogr. 34 poz., rys., tab.
Twórcy
autor
  • School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology, Islamabad, Pakistan
  • Military College of Signals, National University of Sciences and Technology, Rawalpindi, Pakistan
autor
  • Military College of Signals, National University of Sciences and Technology, Rawalpindi, Pakistan
  • Telecommunication Engineering Department, University of Engineering and Technology, Taxila
autor
  • School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology, Islamabad, Pakistan
  • Department of Electrical and Electronic Engineering, University of Jeddah, Jeddah, Saudi Arabia
autor
  • Department of Electrical and Electronic Engineering, University of Jeddah, Jeddah, Saudi Arabia
Bibliografia
  • [1] S. Parveen, P. Singh, and D. Arora, “Fog computing research opportunities and challenges: A comprehensive survey,” in Proceedings of First International Conference on Computing, Communications, and Cyber-Security (IC4S 2019). Springer, 2020, pp. 171–181.
  • [2] C. Zhang, “Design and application of fog computing and internet of things service platform for smart city,” Future Generation Comput. Syst., vol. 112, pp. 630–640, 2020.
  • [3] R. Basir, S. Qaisar, M. Ali, M. Aldwairi, M. I. Ashraf, A. Mahmood, and M. Gidlund, “Fog computing enabling industrial internet of things: State-of-the-art and research challenges,” Sensors, vol. 19, no. 21, p. 4807, 2019.
  • [4] L. Mora, R. Bolici, and M. Deakin, “The first two decades of smart-city research: A bibliometric analysis,” J. Urban Technol., vol. 24, no. 1, pp. 3–27, 2017.
  • [5] M. Luthra, B. Koldehofe, and R. Steinmetz, “Transitions for increased flexibility in fog computing: A case study on complex event processing,” Informatik Spektrum, vol. 42, no. 4, pp. 244–255, 2019.
  • [6] N.K. Giang, R. Lea, and V.C. Leung, “Developing applications in large scale, dynamic fog computing: A case study,” Software, Pract. Experience, vol. 50, no. 5, pp. 519–532, 2020.
  • [7] B. Tang, Z. Chen, G. Hefferman, T.Wei, H. He, and Q. Yang, “A hierarchical distributed fog computing architecture for big data analysis in smart cities,” in Proceedings of the ASE BigData & Social Informatics 2015, 2015, pp. 1–6.
  • [8] C. Dsouza, G.-J. Ahn, and M. Taguinod, “Policy-driven security management for fog computing: Preliminary framework and a case study,” in Proceedings of the 2014 IEEE 15th international conference on information reuse and integration (IEEE IRI 2014). IEEE, 2014, pp. 16–23.
  • [9] M.S. ElBamby, M. Bennis, W. Saad, and M. Latva-Aho, “Content-aware user clustering and caching in wireless small cell networks,” in 2014 11th International Symposium on Wireless Communications Systems (ISWCS). IEEE, 2014, pp. 945–949.
  • [10] B. Ma, W. Guo, and J. Zhang, “A survey of online data-driven proactive 5g network optimisation using machine learning,” IEEE Access, vol. 8, pp. 35 606–35 637, 2020.
  • [11] M.C. Gursoy, C. Zhong, and S. Velipasalar, Deep Multi-Agent Reinforcement Learning for Cooperative Edge Caching. John Wiley & Sons, Ltd, 2020, ch. 21, pp. 439–457.
  • [12] M. Sun, X. Xu, X. Tao, and P. Zhang, “Large-scale user-assisted multi-task online offloading for latency reduction in d2d-enabled heterogeneous networks,” IEEE Trans. Network Sci. Eng., vol. 7, no. 4, pp. 2456–2467, 2020.
  • [13] O. Khalid, I.A. Khan, R.N.B. Rais, and A.W. Malik, “An insight into 5g networks with fog computing,” Fog Comput., Theory Pract., pp. 505–527, 2020.
  • [14] Y. Zhu, L. Wang, K.-K. Wong, and R.W. Heath, “Secure communications in millimeter wave ad hoc networks,” IEEE Trans. Wireless Commun., vol. 16, no. 5, pp. 3205–3217, 2017.
  • [15] K. Xiao, S. Zhang, and Y. He, “On the secrecy capacity of 5g new radio networks,” Wireless Commun. Mobile Comput., vol. 2018, 2018.
  • [16] J. Chen, L. Yang, and M.-S. Alouini, “Physical layer security for cooperative noma systems,” IEEE Trans. Veh. Technol., vol. 67, no. 5, pp. 4645–4649, 2018.
  • [17] Z. Lin, M. Lin, J.-B. Wang, Y. Huang, and W.-P. Zhu, “Robust secure beamforming for 5g cellular networks coexisting with satellite networks,” IEEE J. Sel. Areas Commun., vol. 36, no. 4, pp. 932–945, 2018.
  • [18] R. Rai, H. Zhu, and J. Wang, “Performance analysis of noma enabled fog radio access networks,” IEEE Trans. Commun., vol. 69, no. 1, pp. 382–397, 2020.
  • [19] Y. Wu, L.P. Qian, H. Mao, X. Yang, H. Zhou, X. Tan, and D.H. Tsang, “Secrecy-driven resource management for vehicular computation offloading networks,” IEEE Network, vol. 32, no. 3, pp. 84–91, 2018.
  • [20] L. Wang, K.-K. Wong, S. Jin, G. Zheng, and R.W. Heath, “A new look at physical layer security, caching, and wireless energy harvesting for heterogeneous ultra-dense networks,” IEEE Commun. Mag., vol. 56, no. 6, pp. 49–55, 2018.
  • [21] J. Tang, H. Tang, X. Zhang, K. Cumanan, G. Chen, K.-K.Wong, and J.A. Chambers, “Energy minimization in d2d-assisted cache-enabled internet of things: A deep reinforcement learning approach,” IEEE Trans. Ind. Inf., vol. 16, no. 8, pp. 5412–5423, 2019.
  • [22] S. Tu, M. Waqas, S.U. Rehman, M. Aamir, O.U. Rehman, Z. Jianbiao, and C.-C. Chang, “Security in fog computing: A novel technique to tackle an impersonation attack,” IEEE Access, vol. 6, pp. 74 993–75 001, 2018.
  • [23] R. Karasik, O. Simeone, and S.S. Shitz, “How much can d2d communication reduce content delivery latency in fog networks with edge caching?” IEEE Trans. Commun., vol. 68, no. 4, pp. 2308–2323, 2019.
  • [24] J. Wang, Y. Huang, S. Jin, R. Schober, X. You, and C. Zhao, “Resource management for device-to-device communication: A physical layer security perspective,” IEEE J. Sel. Areas Commun., vol. 36, no. 4, pp. 946–960, 2018.
  • [25] W. Aman, G.A.S. Sidhu, H.M. Furqan, and Z. Ali, “Enhancing physical layer security in af relay–assisted multicarrier wireless transmission,” Trans. Emerging Telecommun. Technol., vol. 29, no. 6, p. e3289, 2018.
  • [26] A.A.-N. Patwary, A. Fu, S.K. Battula, R.K. Naha, S. Garg, and A. Mahanti, “Fogauthchain: A secure location-based authentication scheme in fog computing environments using blockchain,” Comput. Commun., vol. 162, pp. 212–224, 2020.
  • [27] F. Mehdipour, B. Javadi, A. Mahanti, G. Ramirez-Prado, and E. Principles, “Fog computing realization for big data analytics,” Fog Edge Comput., Principles Paradigms, vol. 1, pp. 259–290, 2019.
  • [28] A.A.-N. Patwary et al., “Towards secure fog computing: A survey on trust management, privacy, authentication, threats and access control,” Electronics, vol. 10, no. 10, p. 1171, 2021.
  • [29] F. Irrum, M. Ali, M. Naeem, A. Anpalagan, S. Qaisar, and F. Qamar, “D2d-enabled resource management in secrecy-ensured 5g and beyond heterogeneous networks,” Phys. Commun., vol. 45, p. 101275, 2021.
  • [30] M.A. Duran and I.E. Grossmann, “An outer-approximation algorithm for a class of mixed-integer nonlinear programs,” Math. Program., vol. 36, no. 3, pp. 307–339, 1986.
  • [31] G. Golub and C. Van Loan, Matrix computations, 3rd ed. Baltimore: JHU Press, 2012.
  • [32] P. Bonami, “Basic Open-Source Nonlinear Mixed Integer Programming,” [Online] http://www.coin-or.org/Bonmin/, Accessed on Nov. 18, 2020.
  • [33] M.S. Elbamby, M. Bennis, W. Saad, M. Latva-Aho, and C.S. Hong, “Proactive edge computing in fog networks with latency and reliability guarantees,” EURASIP J Wireless Commun. Netw., vol. 2018, no. 1, pp. 1–13, 2018.
  • [34] R. Basir, S. Qaisar, M. Ali, and M. Naeem, “Cloudlet selection in cache-enabled fog networks for latency sensitive iot applications,” IEEE Access, vol. 9, pp. 93 224–93 236, 2021.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-8f4670f7-5b45-4fe9-ae0e-540dba7b01f0
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