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REVIEW 3 major objections 6 minor 191 references

Fog Computing Systems: State of the Art, Research Issues and Future Trends, with a Focus on Resilience

T0 review · 3 major / 6 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read This paper argues that resilient fog computing systems can be achieved by orchestrating game theory, SDN, NFV, and machine learning into a four-layer architecture that implements detect, absorb, recover, and adapt.

desk verdict A competent, broad survey of fog resilience with a thin, unsupported four-layer architecture; the survey is useful, the architecture claims need to be reframed as a research agenda. read the letter →

arxiv 1908.05077 v4 pith:FVC5RSA4 submitted 2019-08-14 cs.CR cs.NIcs.SYeess.SY

Jose Moura , David Hutchison This is my paper · ORCID
classification cs.CRcs.NIcs.SYeess.SY
keywords fogcomputingresiliencegametheorysoftware-definednetworkingnetworkfunctionvirtualizationmachinelearninginternetofthingscyber-physicalsystems
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This survey paper argues that fog computing systems—networks of IoT devices, edge servers, and wireless sensors supporting time-sensitive applications like power grid control, healthcare, and industrial automation—can be made resilient by orchestrating four technologies: game theory, software-defined networking (SDN), network function virtualization (NFV), and machine learning. The paper claims that no single technology suffices; resilience emerges from combining them in a four-layer hierarchical design that implements the detect, absorb, recover, and adapt cycle. If the authors are right, the orchestration approach would give fog systems a concrete blueprint for maintaining service under cyber-attacks and natural failures. This matters because the scenarios the paper surveys all face open resilience problems that current security-only surveys do not address.

What carries the argument

The central object is the four-layer hierarchical design of a resilient fog and IoT system (Table IV of the paper). Each layer maps to a phase of the detect-absorb-recover-adapt resilience model: layer 1 (sensors/actuators) detects and absorbs; layer 2 (switching) detects, absorbs, and recovers; layer 3 (control) adapts and recovers using SDN controllers; layer 4 (intelligent management) adapts via NFV, SDN, intent engines, and ML/AI. The mechanism that connects the layers is the SDN observation-action loop, where statistics are collected from the data plane, interpreted by ML/AI or game-theoretic models, and converted into new device rules pushed through the controller. The paper also proposes that game-theoretic models run in the backend as northbound SDN applications, participating only at moments when they potentially converge to a viable system configuration.

What would settle it

Run a representative game-theoretic management loop (e.g., Stackelberg anti-jamming or stochastic microgrid defense) on an emulated fog testbed with a hard control deadline, and measure whether equilibria are reached within the deadline during an active attack; missing the deadline during jamming or false-data injection would falsify the orchestration claim for that scenario.

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Extended reading notes

Core claim

The paper's central claim is that an effective way to meet the challenging requirements of managing resilient fog computing systems is to orchestrate diverse technologies—specifically game theory, SDN, NFV, and machine learning. The authors ground this in a four-layer hierarchical design: a sensor/actuator layer that detects and absorbs threats via interface chip programming; a switching layer that uses OpenFlow rules and queues to distribute resources and absorb/recover; a control layer with software-defined controllers for topology, traffic, and cyber-physical feedback; and a top intelligent management layer that adapts using NFV, SDN, an intent engine, and ML/AI. The detect-absorb-recover-adapt cycle, taken from resilience literature, is applied across fog scenarios such as mesh networks, network slicing, computation offloading, mobility support, data fusion, and threat management.

Load-bearing premise

The load-bearing premise is that the game-theoretic model can run in the backend of the fog system as an SDN northbound application and participate only at moments when it is likely to converge to a viable configuration, without proof that convergence is fast enough to meet the strict latency and synchronization requirements of time-sensitive applications.

Editorial extensions

If this is right

  • If the orchestration claim is right, a fog system can rely on SDN controllers to close the cyber-physical feedback loop while NFV reallocates resources elastically during a threat.
  • Game theory would provide a principled way to model attacker-defender and fault interactions, enabling automated protection mechanisms rather than static defenses.
  • Machine learning would let the system learn from past incidents and adjust management policies, turning resilience into a self-improving capability.
  • The four-layer design gives implementers a concrete checklist: detect at the edge, absorb at the switch, recover at the controller, and adapt at the management layer.
  • Network slicing and intent-based management become the practical vehicles for delivering per-flow quality guarantees while resilience mechanisms operate underneath.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The convergence-time caveat in Section 3.3 suggests a division of labor that the paper leaves implicit: fast local control at layers 2–3 should absorb threats in real time, while the slower game-theoretic optimization at layer 4 should only tune adaptation policies after the fact.
  • A testable extension would be to benchmark specific game models (e.g., Stackelberg anti-jamming or stochastic microgrid defense) against the control deadlines of the scenarios in Table V, producing concrete convergence-time budgets for the orchestration claim.
  • The same orchestration pattern could be applied beyond fog to any software-defined cyber-physical system, including vehicle platooning and remote surgery, where the detect-absorb-recover-adapt cycle maps naturally onto SDN/NFV control loops.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. This paper is a survey of resilience management for Fog Computing Systems (FCS), covering game theory (GT), SDN, NFV, and machine learning. It argues that orchestrating these technologies is an effective way to meet stringent latency and synchronization requirements in time-sensitive applications such as smart grids, healthcare, and Industry 4.0. The paper reviews FCS scenarios, compares modeling techniques (Table II), summarizes performance metrics (Table III), and proposes a four-layer hierarchical architecture (Table IV) with detect-absorb-recover-adapt activities. It also lists open issues and future trends.

Significance. If the orchestration claim were substantiated, the proposed architecture would be a useful organizing framework for resilient FCS design. The paper's strengths are its broad literature coverage, its useful comparison tables (I-III), and its explicit acknowledgment of limitations, including the convergence-time concern in §3.3. However, the central design is presented without implementation, simulation, or formal analysis, and the key timing assumption about game-theoretic convergence is neither bounded nor validated. As a survey, the paper is informative; as a proposal for a resilience architecture, it is currently unsupported.

major comments (3)
  1. [Section 3.3, Table II] The load-bearing premise that a game-theoretic model running as an SDN northbound application can operate without jeopardizing resilience is unsupported. The text itself states that 'theoretical game models may need a significant amount of time for discovering stable and optimum system configurations,' and Table II lists 'high convergence time' as a disadvantage of evolutionary models and notes that stochastic games are 'very challenging to timely discover equilibria.' No bound, convergence criterion, or detection mechanism is provided for the proposed backend model, so the claim that it participates 'only in those instants ... where the model potentially converges to a viable system configuration' cannot be evaluated. This matters because the motivating applications require strict latency at the absorb/recover stage; an unbounded GT convergence time could add delay exactly when the system must respond.
  2. [Section 4, Table IV] The four-layer hierarchical design is asserted without validation. Table IV maps detect/absorb/recover/adapt activities to layers, but the paper provides no implementation, simulation, formal analysis, or comparison to alternative architectures. Consequently, the central claim that this design improves resilience of FCSs enough to support time-sensitive applications remains a conjecture rather than a demonstrated result. The paper would need at least a prototype or a formal performance model to support the claimed benefits.
  3. [Section 5.1, Table V] The claim in §5.1 that the detect-absorb-recover-adapt model can be 'successfully applied' to the scenarios in Table V is not backed by quantitative evidence. For example, the 'Threat management' row proposes actions such as discarding malign packets or selecting alternative paths, but no detection accuracy, recovery-time, or availability results are reported. This makes the generalizability claim in the conclusion stronger than the evidence supports.
minor comments (6)
  1. [Section 5.1 heading] The heading 'Interesting Propects' contains a typo; it should read 'Interesting Prospects'.
  2. [Section 5.5 heading] The heading 'Efficent Data Dissemination' contains a typo; it should read 'Efficient Data Dissemination'.
  3. [Section 4, first paragraph] The phrase 'legacy Cypher-Physical Systems' should read 'legacy Cyber-Physical Systems'.
  4. [Table III, Reasoning row] The entry 'Level ok knowledge (labels) extracted from data features' contains a typo; it should read 'Level of knowledge (labels) extracted from data features'.
  5. [References] The reference list includes works dated 2020 (e.g., [88], [112], [177], [187], [190], [192]) although the arXiv submission is dated 2019; please clarify the version and update the metadata accordingly.
  6. [Figures 1 and 2] The captions for Figures 1 and 2 are present, but the figures themselves do not appear in the text, so the reader cannot assess the evolution diagram or the logical roadmap discussed in Section 1.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the paper is a survey proposing an orchestration architecture; no prediction or derivation reduces to its inputs.

full rationale

This is a survey/position paper without equations, fitted parameters, or simulated predictions. The central claim—that orchestrating GT, SDN, NFV, and ML can support resilient FCS management—is an argued synthesis of the surveyed literature, not a result derived from a self-referential definition. The authors' self-citations ([19] Moura & Hutchison on game theory for MEC; [20] Dobson, Hutchison et al. on self-organization and resilience; [184] Vales, Moura, & Marinheiro on fog storage) are used as background or related work, not as the justification of the paper's proposed four-layer design in Table IV. No 'uniqueness theorem' or imported ansatz is invoked to force the architecture. The acknowledged limitation in Section 3.3 that game-theoretic models 'may need a significant amount of time for discovering stable and optimum system configurations' is an admitted open problem, and the authors propose a qualitative mitigation (running GT as a northbound SDN application participating only when it 'potentially converges'), but this is an unsupported design assumption, not circularity. Because there is no derivation chain whose conclusion equals its premise, the appropriate finding is no significant circularity.

Assumptions & free parameters 0 free parameters · 5 assumptions · 0 invented entities

No free parameters or invented entities exist because the paper reports no measurements, simulations, or new physical or conceptual objects. The central proposal rests on the adopted resilience lifecycle, the paper's definition of fog computing, the assumption that GT, SDN, NFV, and ML can be orchestrated effectively, and the unverified latency assumption about backend game-theoretic models.

assumptions (5)
  • domain assumption Resilience is defined and operationalized as a detect, absorb, recover, and adapt lifecycle, taken from Connelly et al. [8].
    Sections 1 and 4 use this four-stage lifecycle as the organizing principle for the proposed architecture; it is adopted from prior work, not derived in this paper.
  • domain assumption Fog computing is defined as a bidirectional convergence between cloud and edge computational resources, as shown in Figure 1.
    Section 2 and Figure 1 define FCS in this way. Alternative definitions of fog and edge computing exist, so this choice shapes the entire survey.
  • ad hoc to paper Orchestrating game theory, SDN, NFV, and machine learning is an effective way to achieve resilient FCS management.
    This is the paper's central proposal, stated in Section 1 and Section 3.4, but it is asserted without implementation, simulation, or formal support.
  • ad hoc to paper A game-theoretic model running as an SDN northbound application can avoid adding latency because it acts only when it converges to a viable configuration.
    Section 3.3 makes this deployment assumption without any measurement or bound on convergence time, despite the paper's emphasis on low-latency fog applications.
  • domain assumption The cited literature, including two of the authors' own surveys, accurately supports the stated claims.
    The survey relies on published papers, including references [19] and [20], rather than independent replication. This is trust-based rather than circular, but it remains an unverified premise.

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Cite this review

Pith. "Pith review of Fog Computing Systems: State of the Art, Research Issues and Future Trends, with a Focus on Resilience." pith.science (2026). https://pith.science/paper/FVC5RSA4

@misc{pith2026190805077,
  author       = {Pith},
  title        = {Pith review of: Fog Computing Systems: State of the Art, Research Issues and Future Trends, with a Focus on Resilience},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FVC5RSA4}},
  note         = {Machine review of arXiv:1908.05077}
}
read the original abstract

Many future innovative computing services will use Fog Computing Systems (FCS), integrated with Internet of Things (IoT) resources. These new services, built on the convergence of several distinct technologies, need to fulfil time-sensitive functions, provide variable levels of integration with their environment, and incorporate data storage, computation, communications, sensing, and control. There are, however, significant problems to be solved before such systems can be considered fit for purpose. The high heterogeneity, complexity, and dynamics of these resource-constrained systems bring new challenges to their robust and reliable operation, which implies the need for integral resilience management strategies. This paper surveys the state of the art in the relevant fields, and discusses the research issues and future trends that are emerging. We envisage future applications that have very stringent requirements, notably high-precision latency and synchronization between a large set of flows, where FCSs are key to supporting them. Thus, we hope to provide new insights into the design and management of resilient FCSs that are formed by IoT devices, edge computer servers and wireless sensor networks; these systems can be modelled using Game Theory, and flexibly programmed with the latest software and virtualization platforms.

Figures

Figures reproduced from arXiv: 1908.05077 by the authors.

Figure 1
Figure 1. Evolution of Networked Systems to Fog Computing Systems FCS is about a physical facility with embedded sensors and actuators that can be remotely monitored and controlled by computerized systems [7]. The monitoring and control are made by logical control loops over physical communication channels. These channels are established between the sensors/actuators and the computers that manage them. The channels transfer b… view at source ↗
Figure 3
Figure 3. Outline of a Software-Defined Resilient Fog Computing System 3.1. Resilience Definition and Novel Management Challenges The NIST1 definition of a Cyber-Physical System (CPS) is a commonly accepted one. According to that definition, CPSs are engineering-based systems offering a functionality strongly dependent on the interaction among computational and physical processes. This integration enables the deployment of em… view at source ↗

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Reference graph

Works this paper leans on

191 extracted references · 64 canonical work pages

  1. [88]

    Internet of Medical Things (IoMT) for orthopaedic in COVID-19 pandemic: Roles, challenges, and applications,

    R. Pratap Singh, M. Javaid, A. Haleem, R. Vaishya, and S. Ali, “Internet of Medical Things (IoMT) for orthopaedic in COVID-19 pandemic: Roles, challenges, and applications,” Journal of Clinical Orthopaedics and Trauma. Elsevier B.V., pp. 1–5, May 15, 2020, doi: 10.1016/j.jcot.2020.05.011

    work page doi:10.1016/j.jcot.2020.05.011 2020

  2. [177]

    5G network slicing using SDN and NFV: A survey of taxonomy, architectures and future challenges,

    A. A. Barakabitze, A. Ahmad, R. Mijumbi, and A. Hines, “5G network slicing using SDN and NFV: A survey of taxonomy, architectures and future challenges,” Comput. Networks , vol. 167, p. 106984, Feb. 2020, doi: 10.1016/j.comnet.2019.106984

    arXiv 2020

  3. [192]

    Conv ergence of Edge Computing and Deep Learning: A Comprehensive Survey,

    X. Wang, Y. Han, V. C. M. Leung, D. Niyato, X. Yan, and X. Chen, “Conv ergence of Edge Computing and Deep Learning: A Comprehensive Survey,” IEEE Communications Surveys and Tutorials, vol. 22, no. 2. Institute of Electrical and Electronics Engineers Inc., pp. 869–904, Apr. 01, 2020, doi: 10.1109/COMST.2020.2970550

    arXiv 2020

  4. [1]

    A Survey on Internet of Things: Architecture, Enabling 28 of 38 Technologies, Security and Privacy, and Applications,

    J. Lin, W. Yu, N. Zhang, X. Yang, H. Zhang, and W. Zhao, “A Survey on Internet of Things: Architecture, Enabling 28 of 38 Technologies, Security and Privacy, and Applications,” IEEE Internet Things J., vol. 4, no. 5, pp. 1125–1142, 2017, doi: 10.1109/JIOT.2017.2683200

    arXiv 2017

  5. [2]

    Cyber–physical systems: Extending pervasive sensing from control theory to the Internet of Things,

    B. Bordel, R. Alcarria, T. Robles, and D. Martín, “Cyber–physical systems: Extending pervasive sensing from control theory to the Internet of Things,” Pervasive Mob. Comput., vol. 40, pp. 156–184, 2017, doi: 10.1016/J.PMCJ.2017.06.011

    work page doi:10.1016/j.pmcj.2017.06.011 2017

  6. [3]

    Optimal Denial-of-Service Attack Scheduling With Energy Constraint Over Packet- Dropping Networks,

    J. Qin, M. Li, L. Shi, and X. Yu, “Optimal Denial-of-Service Attack Scheduling With Energy Constraint Over Packet- Dropping Networks,” IEEE Trans. Automat. Contr. , vol. 63, no. 6, pp. 1648 –1663, Jun. 2018, doi: 10.1109/TAC.2017.2756259

    arXiv 2018

  7. [4]

    Resilience and survivability in communication networks: Strategies, principles, and survey of disciplines,

    J. P. G. Sterbenz et al., “Resilience and survivability in communication networks: Strategies, principles, and survey of disciplines,” Comput. Networks, vol. 54, no. 8, pp. 1245–1265, Jun. 2010, doi: 10.1016/j.comnet.2010.03.005

    doi:10.1016/j.comnet.2010.03.005 2010

  8. [5]

    CERT Vulnerabili ty Notes Database

    Carnegie Mellon University, “CERT Vulnerabili ty Notes Database.” Accessed: Aug. 10, 2019. [Online]. Available: https://www.kb.cert.org/vuls/bypublished/desc/

    2019

Show all 191 references
  1. [6]

    Cisco Secure Boot Hardware Tampering Vulnerability

    Cisco, “Cisco Secure Boot Hardware Tampering Vulnerability.” Accessed: Aug. 10, 2019. [Online]. Available: https://tools.cisco.com/security/center/content/CiscoSecurityAdvisory/cisco-sa-20190513-secureboot

    2019

  2. [7]

    Big data for cyber physical systems in industry 4.0: a survey,

    L. Da Xu and L. Duan, “Big data for cyber physical systems in industry 4.0: a survey,” Enterp. Inf. Syst., vol. 13, no. 2, pp. 148–169, 2019, doi: 10.1080/17517575.2018.1442934

    2019

  3. [8]

    Features of resilience,

    E. B. Connelly, C. R. Allen, K. Hatfield, J. M. Palma -Oliveira, D. D. Woods, and I. Linkov, “Features of resilience,” Environ. Syst. Decis., vol. 37, no. 1, pp. 46–50, Mar. 2017, doi: 10.1007/s10669-017-9634-9

    2017 doi

  4. [9]

    Designing Resilien t Systems-of-Systems: A Survey of Metrics, Methods, and Challenges,

    P. Uday and K. Marais, “Designing Resilien t Systems-of-Systems: A Survey of Metrics, Methods, and Challenges,” Syst. Eng., vol. 18, no. 5, pp. 491–510, 2015, doi: 10.1002/sys.21325

    2015 doi

  5. [10]

    Physical Intrusion Games —Optimizing Surveillance by Simulation and Game Theory,

    S. Rass, A. Alshawish, M. A. Abid, S. Schauer, Q. Zhu, and H. De Meer, “Physical Intrusion Games —Optimizing Surveillance by Simulation and Game Theory,” IEEE Access , vol. 5, pp. 8394 –8407, 2017, doi: 10.1109/ACCESS.2017.2693425

    2017

  6. [11]

    A Survey on Advanced Persistent Threats: Techniques, Solutions, Challenges, an d Research Opportunities,

    A. Alshamrani, S. Myneni, A. Chowdhary, and D. Huang, “A Survey on Advanced Persistent Threats: Techniques, Solutions, Challenges, an d Research Opportunities,” IEEE Commun. Surv. Tutorials , p. 1, 2019, doi: 10.1109/COMST.2019.2891891

    2019

  7. [12]

    Safety and Security in Cyber -Physical Systems and Internet -of-Things Systems,

    M. Wolf and D. Serpanos, “Safety and Security in Cyber -Physical Systems and Internet -of-Things Systems,” Proc. IEEE, vol. 106, no. 1, pp. 9–20, Jan. 2018, doi: 10.1109/JPROC.2017.2781198

    2018

  8. [13]

    A Roadmap Toward the Resilient Internet of Things for Cyber-Physical Systems,

    D. Ratasich, F. Khalid, F. Geissler, R. Grosu, M. Shafique, and E. Bartocci, “A Roadmap Toward the Resilient Internet of Things for Cyber-Physical Systems,” IEEE Access, vol. 7, pp. 13260–13283, 2019, doi: 10.1109/ACCESS.2019.2891969

    2019

  9. [14]

    Towards the decentralised cloud: Survey on approaches and challenges for mobile, ad hoc, and edge computing,

    A. J. Ferrer, J. M. Marquès, and J. Jorba, “Towards the decentralised cloud: Survey on approaches and challenges for mobile, ad hoc, and edge computing,” ACM Comput. Surv., vol. 51, no. 6, pp. 1–36, Jan. 2019, doi: 10.1145/3243929

    2019 doi

  10. [15]

    Device -Enhanced MEC: Multi-Access Edge Computing (MEC) Aided by End Device Computation and Caching: A Survey,

    M. Mehrabi, D. You, V. Latzko, H. Salah, M. Reisslein, and F. H. P. Fitzek, “Device -Enhanced MEC: Multi-Access Edge Computing (MEC) Aided by End Device Computation and Caching: A Survey,” IEEE Access, vol. 7, pp. 166079– 166108, 2019, doi: 10.1109/ACCESS.2019.2953172

    2019

  11. [16]

    DDoS Attacks at the Application Layer: Challenges and Research Perspectives for Safeguarding Web Applications,

    A. Praseed and P. S. Thilagam, “DDoS Attacks at the Application Layer: Challenges and Research Perspectives for Safeguarding Web Applications,” IEEE Commun. Surv. Tutorials , vol. 21, no. 1, pp. 661 –685, 2019, doi: 10.1109/COMST.2018.2870658

    2019

  12. [17]

    A Detailed Investigation and Analysis of Using Machine Learning Techniques for Intrusion Detection,

    P. Mishra, V. Varadharajan, U. Tupakula, and E. S. Pilli, “A Detailed Investigation and Analysis of Using Machine Learning Techniques for Intrusion Detection,” IEEE Commun. Surv. Tutorials , vol. 21, no. 1, pp. 686 –728, 2019, doi: 10.1109/COMST.2018.2847722

    2019

  13. [18]

    Cyber -physical systems and their security issues,

    R. Alguliyev, Y. Imamverdiyev, and L. Sukhostat, “Cyber -physical systems and their security issues,” Comput. Ind., vol. 100, pp. 212–223, 2018, doi: 10.1016/J.COMPIND.2018.04.017

    2018 doi

  14. [19]

    Game Theory for Multi -Access Edge Computing: Survey, Use Cases, and Future 29 of 38 Trends,

    J. Moura and D. Hutchison, “Game Theory for Multi -Access Edge Computing: Survey, Use Cases, and Future 29 of 38 Trends,” IEEE Commun. Surv. Tutorials, vol. 21, no. 1, pp. 260–288, 2019, doi: 10.1109/COMST.2018.2863030

    2019

  15. [20]

    Self -Organization and Resilience for Networked Systems: Design Principles and Open Research Issues,

    S. Dobson, D. Hutchison, A. Mauthe, A. Schaeffer -Filho, P. Smith, and J. P. G. Sterbenz, “Self -Organization and Resilience for Networked Systems: Design Principles and Open Research Issues,” Proc. IEEE, vol. 107, no. 4, pp. 819– 834, 2019, doi: 10.1109/JPROC.2019.2894512

    2019

  16. [21]

    Smart energy systems for sustainable smart cities: Current developments, trends and future directions,

    E. O’Dwyer, I. Pan, S. Acha, and N. Shah, “Smart energy systems for sustainable smart cities: Current developments, trends and future directions,” Appl. Energy, vol. 237, pp. 581–597, Mar. 2019, doi: 10.1016/J.APENERGY.2019.01.024

    2019 doi

  17. [22]

    A Survey of Machine Learning Techniques Applied to Software Defined Networking (SDN): Research Issues and Challenges,

    J. Xie et al., “A Survey of Machine Learning Techniques Applied to Software Defined Networking (SDN): Research Issues and Challenges,” IEEE Commun. Surv. Tutorials , vol. 21, no. 1, pp. 393 –430, 2019, doi: 10.1109/COMST.2018.2866942

    2019

  18. [23]

    Resource Allocation for Ultra -Dense Networks: A Survey, Some Research Issues and Challenges,

    Y. Teng, M. Liu, F. R. Yu, V. C. M. Leung, M. Song, and Y. Zhang, “Resource Allocation for Ultra -Dense Networks: A Survey, Some Research Issues and Challenges,” IEEE Commun. Surv. Tutorials , p. 1, 2018, doi: 10.1109/COMST.2018.2867268

    2018

  19. [24]

    Small Cells in the Forthcoming 5G/IoT: Traffic Modelling and Deployment Overview,

    F. Al -Turjman, E. Ever, and H. Zahmatkesh, “Small Cells in the Forthcoming 5G/IoT: Traffic Modelling and Deployment Overview,” IEEE Commun. Surv. Tutorials , vol. 21, no. 1, pp. 28 –65, 2019, doi: 10.1109/COMST.2018.2864779

    2019

  20. [25]

    A Survey of Deployment Solutions and Optimization Strategies for Hybrid SDN Networks,

    X. Huang, S. Cheng, K. Cao, P. Cong, T. Wei, and S. Hu, “A Survey of Deployment Solutions and Optimization Strategies for Hybrid SDN Networks,” IEEE Commun. Surv. Tutorials, p. 1, 2018, doi: 10.1109/COMST.2018.2871061

    2018

  21. [26]

    Fault Management in Software -Defined Networking: A Survey,

    Y. Yu et al., “Fault Management in Software -Defined Networking: A Survey,” IEEE Commun. Surv. Tutorials , p. 1, 2018, doi: 10.1109/COMST.2018.2868922

    2018

  22. [27]

    Auto nomous Cars: Research Results, Issues and Future Challenges,

    R. Hussain and S. Zeadally, “Auto nomous Cars: Research Results, Issues and Future Challenges,” IEEE Commun. Surv. Tutorials, p. 1, 2018, doi: 10.1109/COMST.2018.2869360

    2018

  23. [28]

    The sensable city: A survey on the deployment and management for smart city monitoring,

    R. Du, P. Santi, M. Xiao, A. V Vasilakos, and C. Fischione, “The sensable city: A survey on the deployment and management for smart city monitoring,” IEEE Commun. Surv. Tutorials, p. 1, 2018, doi: 10.1109/COMST.2018.2881008

    2018

  24. [29]

    Internet of Mobile Things: Overview of LoRaWAN, DASH7, and NB-IoT in LPWANs standards and Supported Mobility,

    W. Ayoub, A. E. Samhat, F. Nouvel, M. Mroue, and J. Prevotet, “Internet of Mobile Things: Overview of LoRaWAN, DASH7, and NB-IoT in LPWANs standards and Supported Mobility,” IEEE Commun. Surv. Tutorials, p. 1, 2018, doi: 10.1109/COMST.2018.2877382

    2018

  25. [30]

    Applications of Economic and Pricing Models for Resource Management in 5G Wireless Networks: A Survey,

    N. C. Luong, P. Wang, D. Niy ato, Y.-C. Liang, Z. Han, and F. Hou, “Applications of Economic and Pricing Models for Resource Management in 5G Wireless Networks: A Survey,” IEEE Commun. Surv. Tutorials , p. 1, 2018, doi: 10.1109/COMST.2018.2870996

    2018

  26. [31]

    Data -driven cybersecurity incident prediction: A survey,

    N. Sun, J. Zhang, P. Rimba, S. Ga o, Y. Xiang, and L. Y. Zhang, “Data -driven cybersecurity incident prediction: A survey,” IEEE Commun. Surv. Tutorials, p. 1, 2018, doi: 10.1109/COMST.2018.2885561

    2018

  27. [32]

    Robust Cyber–Physical Systems: Concept, models, and implementation,

    F. Hu et al., “Robust Cyber–Physical Systems: Concept, models, and implementation,” Futur. Gener. Comput. Syst. , vol. 56, pp. 449–475, Mar. 2016, doi: 10.1016/J.FUTURE.2015.06.006

    2016 doi

  28. [33]

    Survey Paper Security , privacy and trust in Internet of Things: The road ahead,

    S. Sicari, A. Rizzardi, L. A. Grieco, and A. Coen -Porisini, “Survey Paper Security , privacy and trust in Internet of Things: The road ahead,” Elsevier, 2015, doi: 10.1016/j.comnet.2014.11.008

    2015 doi

  29. [34]

    A pairwise key predistribution scheme for wireless sensor networks,

    W. Du, J. Deng, Y. S. Han, P. K. Varshney, J. Katz, and A. Khalili, “A pairwise key predistribution scheme for wireless sensor networks,” ACM Transactions on Information and System Security , vol. 8, no. 2. pp. 228 –258, May 2005, doi: 10.1145/1065545.1065548

    2005

  30. [35]

    Establishing Pairwise Keys in Distributed Sensor Networks,

    D. Liu, P. Ning, and R. Li, “Establishing Pairwise Keys in Distributed Sensor Networks,” ACM Trans. Inf. Syst. Secur., vol. 8, no. 1, pp. 41–77, 2005, doi: 10.1145/1053283.1053287

    2005

  31. [36]

    Applications of Blockchains in the Internet of Things: A Comprehensive Survey,

    M. S. Ali, M. Vecchio, M. Pincheira, K. Dolui, F. Antonelli, and M. H. Rehmani, “Applications of Blockchains in the Internet of Things: A Comprehensive Survey,” IEEE Communications Surveys and Tutorials, vol. 21, no. 2. Institute of Electrical and Electronics Engineers Inc., p...

    2019

  32. [37]

    Cyber physical systems security: Analysis, challenges and solutions,

    Y. Ashibani and Q. H. Mahmoud, “Cyber physical systems security: Analysis, challenges and solutions,” Comput. Secur., vol. 68, pp. 81–97, Jul. 2017, doi: 10.1016/J.COSE.2017.04.005

    2017 doi

  33. [38]

    Resilience Enhancement of Cyber-Physical Systems: A Review,

    S. F. Mihalache, E. Pricop, and J. Fattahi, “Resilience Enhancement of Cyber-Physical Systems: A Review,” Springer, Cham, 2019, pp. 269–287

    2019

  34. [39]

    New Services and Capabilities for Network 2030: Description, Technical Gap and Performance Target Analysis,

    Focus Group on Technologies for Netwo rk 2030, “New Services and Capabilities for Network 2030: Description, Technical Gap and Performance Target Analysis,” ITU-T, https://www.itu.int/en/ITU - T/focusgroups/net2030/Documents/Deliverable_NET2030.pdf, pp. 1 –45, 2018, [Online]. ...

    2018

  35. [40]

    A Comprehensive Survey on Fog Computing: State-of-the-Art and Research Challenges,

    C. Mouradian, D. Naboulsi, S. Yangui, R. H. Glitho, M. J. Morrow, and P. A. Polakos, “A Comprehensive Survey on Fog Computing: State-of-the-Art and Research Challenges,” IEEE Communications Surveys and Tutorials, vol. 20, no. 1. Institute of Electrical and Electronics Engineer...

    2018

  36. [41]

    A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications,

    S. Wang, X. Zhang, Y. Zhang, L. Wang, J. YANG, and W. Wang, “A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications,” IEEE Access , vol. 5, pp. 6757 –6779, 2017, doi: 10.1109/ACCESS.2017.2685434

    2017

  37. [42]

    All one needs to know about fog computing and related edge computing paradigms: A complete survey,

    A. Yousefpour et al. , “All one needs to know about fog computing and related edge computing paradigms: A complete survey,” Journal of Systems Architecture. 2019, doi: 10.1016/j.sysarc.2019.02.009

    2019 doi

  38. [43]

    Edge computing: A survey,

    W. Z. Khan, E. Ahmed, S. Hakak, I. Yaqoob, and A. Ahmed, “Edge computing: A survey,” Futur. Gener. Comput. Syst., vol. 97, pp. 219–235, 2019, doi: 10.1016/J.FUTURE.2019.02.050

    2019 doi

  39. [44]

    A Survey on Mobile Edge Computing: The Communication Perspective,

    Y. Mao, C. You, J. Zhang, K. Huang, and K. B. Letaief, “A Survey on Mobile Edge Computing: The Communication Perspective,” IEEE Commun. Surv. Tutorials, vol. 19, no. 4, pp. 2322–2358, 2017, doi: 10.1109/COMST.2017.2745201

    2017

  40. [45]

    On Multi-Access Edge Computing: A Survey of the Emerging 5G Network Edge Architecture & Orchestration,

    T. Taleb, K. Samdanis, B. Mada, H. Flinck, S. Dutta, and D. Sabella, “On Multi-Access Edge Computing: A Survey of the Emerging 5G Network Edge Architecture & Orchestration,” IEEE Commun. Surv. Tutorials, vol. PP, no. 99, p. 1, 2017, doi: 10.1109/COMST.2017.2705720

    2017

  41. [46]

    A Survey on the Edge Computing for the Internet of Things,

    W. Yu et al., “A Survey on the Edge Computing for the Internet of Things,” IEEE Access, vol. 6. Institute of Electrical and Electronics Engineers Inc., pp. 6900–6919, Nov. 2017, doi: 10.1109/ACCESS.2017.2778504

    2017

  42. [47]

    Edg e computing technologies for Internet of Things: a primer,

    Y. Ai, M. Peng, and K. Zhang, “Edg e computing technologies for Internet of Things: a primer,” Digit. Commun. Networks, vol. 4, no. 2, pp. 77–86, 2018, doi: 10.1016/J.DCAN.2017.07.001

    2018 doi

  43. [48]

    Internet of things forensics: Recent advances, taxonomy, requirements, and open challenges,

    I. Yaqoob, I. A. T. Hashem, A. Ahmed, S. M. A. Kazmi, and C. S. Hong, “Internet of things forensics: Recent advances, taxonomy, requirements, and open challenges,” Futur. Gener. Comput. Syst. , vol. 92, pp. 265 –275, Mar. 2019, doi: 10.1016/J.FUTURE.2018.09.058

    2019 doi

  44. [49]

    Integrated Blockchain and Edge Computing Systems: A Survey, Some Research Issues and Challenges,

    R. Yang, F. R. Yu, P. Si, Z. Yang, and Y. Zhang, “Integrated Blockchain and Edge Computing Systems: A Survey, Some Research Issues and Challenges,” IEEE Communications Surveys and Tutorials, vol. 21, no. 2. Institute of Electrical and Electronics Engineers Inc., pp. 1508–1532,...

    2019

  45. [50]

    Mobile Edge Computing: A Survey on Architecture and Computation Offloading,

    P. Mach and Z. Becvar, “Mobile Edge Computing: A Survey on Architecture and Computation Offloading,” IEEE Commun. Surv. Tutorials, vol. 19, no. 3, pp. 1628–1656, 2017, doi: 10.1109/COMST.2017.2682318

    2017

  46. [51]

    Cyber security of a power grid: State-of-the-art,

    C. C. Sun, A. Hahn, and C. C. Liu, “Cyber security of a power grid: State-of-the-art,” International Journal of Electrical Power and Energy Systems. 2018, doi: 10.1016/j.ijepes.2017.12.020

    2018 doi

  47. [52]

    The role of smart grids in the building sector,

    D. Kolokotsa, “The role of smart grids in the building sector,” Energy Build., vol. 116, pp. 703 –708, Mar. 2016 , doi: 10.1016/J.ENBUILD.2015.12.033

    2016 doi

  48. [53]

    Next Generation 5G Wireless Networks: A Comprehensive Survey,

    M. Agiwal, A. Roy, and N. Saxena, “Next Generation 5G Wireless Networks: A Comprehensive Survey,” IEEE Commun. Surv. Tutorials, vol. 18, no. 3, pp. 1617–1655, 2016, doi: 10.1109/COMST.2016.2532458

    2016

  49. [54]

    Ubiquitous Data Accessing Method in IoT-Based Information System for Emergency Medical Services,

    Boyi Xu, Li Da Xu, Hongming Cai, Cheng Xie, Jingyuan Hu, and Fenglin Bu, “Ubiquitous Data Accessing Method in IoT-Based Information System for Emergency Medical Services,” IEEE Trans. Ind. Informatics, vol. 10, no. 2, pp. 1578– 31 of 38 1586, 2014, doi: 10.1109/TII.2014.2306382

    2014

  50. [55]

    A survey on Information and Communication Technologies for Industry 4.0: state of the art, taxonomies, perspectives, and challenges,

    G. Aceto, V. Persico, and A. Pescape, “A survey on Information and Communication Technologies for Industry 4.0: state of the art, taxonomies, perspectives, and challenges,” IEEE Commun. Surv. Tutorials, vol. 21, no. 4, pp. 3467–3501, 2019, doi: 10.1109/comst.2019.2938259

    2019

  51. [56]

    Resilience and Security: A Qualitative Survey of Urban Smart Grid Architectures,

    P. Eder -Neuhauser, T. Zseby, and J. Fabini, “Resilience and Security: A Qualitative Survey of Urban Smart Grid Architectures,” IEEE Access, vol. 4, pp. 839–848, 2016, doi: 10.1109/ACCESS.2016.2531279

    2016

  52. [57]

    Randomi zed PHEV Charging Under Distribution Grid Constraints,

    K. Zhou and L. Cai, “Randomi zed PHEV Charging Under Distribution Grid Constraints,” IEEE Trans. Smart Grid , vol. 5, no. 2, pp. 879–887, Mar. 2014, doi: 10.1109/TSG.2013.2293733

    2014

  53. [58]

    Wireless Neighborhood Area Networks With QoS Support for Demand Response in Smart Grid,

    P.-Y. Kong, “Wireless Neighborhood Area Networks With QoS Support for Demand Response in Smart Grid,” IEEE Trans. Smart Grid, vol. 7, no. 4, pp. 1913–1923, Jul. 2016, doi: 10.1109/TSG.2015.2421991

    1913

  54. [59]

    A Survey of IoT-Enabled Cyberattacks: Assessing Attack Paths to Critical Infrastruc tures and Services,

    I. Stellios, P. Kotzanikolaou, M. Psarakis, C. Alcaraz, and J. Lopez, “A Survey of IoT-Enabled Cyberattacks: Assessing Attack Paths to Critical Infrastruc tures and Services,” IEEE Commun. Surv. Tutorials , vol. 20, no. 4, pp. 3453 –3495, 2018, doi: 10.1109/COMST.2018.2855563

    2018

  55. [60]

    A Critical Review of Practices and Challenges in Intrusion Detection Systems for IoT: To ward Universal and Resilient Systems,

    E. Benkhelifa, T. Welsh, and W. Hamouda, “A Critical Review of Practices and Challenges in Intrusion Detection Systems for IoT: To ward Universal and Resilient Systems,” IEEE Commun. Surv. Tutorials , vol. 20, no. 4, pp. 3496 – 3509, 2018, doi: 10.1109/COMST.2018.2844742

    2018

  56. [61]

    Reliable Wireless Communication Networks for Demand Response Control,

    L. Zheng, N. Lu, and L. Cai, “Reliable Wireless Communication Networks for Demand Response Control,” IEEE Trans. Smart Grid, vol. 4, no. 1, pp. 133–140, Mar. 2013, doi: 10.1109/TSG.2012.2224892

    2013

  57. [62]

    A Wireless Communication Architecture for Smart Grid Distribution Networks,

    A. Abdrabou, “A Wireless Communication Architecture for Smart Grid Distribution Networks,” IEEE Syst. J., vol. 10, no. 1, pp. 251–261, Mar. 2016, doi: 10.1109/JSYST.2014.2304291

    2016

  58. [63]

    A Game Theoretic Model for Smart Grids Demand Management,

    S. Belhaiza and U. Baroudi, “A Game Theoretic Model for Smart Grids Demand Management,” IEEE Trans. Smart Grid, vol. 6, no. 3, pp. 1386–1393, 2015, doi: 10.1109/TSG.2014.2376632

    2015

  59. [64]

    Incentive-based demand response for smart grid with reinforcement learning and deep neural network,

    R. Lu and S. H. Hong, “Incentive-based demand response for smart grid with reinforcement learning and deep neural network,” Appl. Energy, vol. 236, pp. 937–949, 2019, doi: 10.1016/J.APENERGY.2018.12.061

    2019 doi

  60. [65]

    Resilient Design of Networked Control Systems Under Time Delay Switch Attacks, Application in Smart Grid,

    A. Sargolzaei, K. K. Yen, M. N. Abdelghani, S. Sargolzaei, and B. Carbunar, “Resilient Design of Networked Control Systems Under Time Delay Switch Attacks, Application in Smart Grid,” IEEE Access, vol. 5, pp. 15901 –15912, 2017, doi: 10.1109/ACCESS.2017.2731780

    2017

  61. [66]

    A Review of False Data Injection A ttacks Against Modern Power Systems,

    G. Liang, J. Zhao, F. Luo, S. R. Weller, and Z. Y. Dong, “A Review of False Data Injection A ttacks Against Modern Power Systems,” IEEE Trans. Smart Grid, vol. 8, no. 4, pp. 1630–1638, Jul. 2017, doi: 10.1109/TSG.2015.2495133

    2017

  62. [67]

    A two -layer game theoretical attack -defense model for a false data injection attack against power systems,

    Q. Wang, W. Tai, Y. Tang, M. Ni, and S. You, “A two -layer game theoretical attack -defense model for a false data injection attack against power systems,” Int. J. Electr. Power Energy Syst. , vol. 104, pp. 169 –177, Jan. 2019, doi: 10.1016/J.IJEPES.2018.07.007

    2019 doi

  63. [68]

    Toward a Consensus on the Definition and Taxonomy of Power System Resilience,

    A. Gholami, T. Shekari, M. H. Amirioun, F. Aminifar, M. H. Amini, and A. Sargolzaei, “Toward a Consensus on the Definition and Taxonomy of Power System Resilience,” IEEE Access , vol. 6, pp. 32035 –32053, 2018, doi: 10.1109/ACCESS.2018.2845378

    2018

  64. [69]

    Sensing Technologies for Monitoring Intelligent Buildings: A Review,

    A. Kumar , A. Singh, A. Kumar, M. K. Singh, P. Mahanta, and S. C. Mukhopadhyay, “Sensing Technologies for Monitoring Intelligent Buildings: A Review,” IEEE Sens. J. , vol. 18, no. 12, pp. 4847 –4860, Jun. 2018, doi: 10.1109/JSEN.2018.2829268

    2018

  65. [70]

    Energy Efficient Management of Pipelines in Buildings Using Linear Wireless Sensor Networks,

    S. Varshney et al., “Energy Efficient Management of Pipelines in Buildings Using Linear Wireless Sensor Networks,” Sensors, vol. 18, no. 8, 2018, doi: 10.3390/s18082618

    2018 doi

  66. [71]

    ReViCEE: A recommendation based approach for personalized control, visual comfort & energy efficiency in buildings,

    P. Kar, A. Shareef, A. Kumar, K. T. Harn, B. Kalluri, and S. K. Panda, “ReViCEE: A recommendation based approach for personalized control, visual comfort & energy efficiency in buildings,” Build. Environ., vol. 152, pp. 135–144, 2019, doi: 10.1016/J.BUILDENV.2019.01.035. 32 of 38

    2019 doi

  67. [72]

    A deep learning and gamification approach to improving human-building interaction and energy efficiency in smart infrastructure,

    I. C. Konstantakopoulos, A. R. Barkan, S. He, T. Veeravalli, H. Liu, and C. Spanos, “A deep learning and gamification approach to improving human-building interaction and energy efficiency in smart infrastructure,” Appl. Energy, vol. 237, pp. 810–821, Mar. 2019, doi: 10.1016/J...

    2019 doi

  68. [73]

    Design Automation for Smart Building Systems,

    R. Jia et al., “Design Automation for Smart Building Systems,” Proc. IEEE, vol. 106, no. 9, pp. 1680 –1699, 2018, doi: 10.1109/JPROC.2018.2856932

    2018

  69. [74]

    Application-level Programmable Internetwork Environment,

    I. W. Marshall et al., “Application-level Programmable Internetwork Environment,” BT Technol. J., vol. 17, no. 2, pp. 82–94, 1999, doi: 10.1023/A:1009689900903

    1999 doi

  70. [75]

    QoS filters: Addressing the heterogeneity gap,

    N. Yeadon, A. Mauthe, F. García, and D. Hutchison, “QoS filters: Addressing the heterogeneity gap,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioin formatics), 1996, vol. 1045, pp. 227–243, doi: 10.1...

    1996 doi

  71. [76]

    Software -defined networking in cellular radio access networks: potential and challenges,

    M. Arslan, K. Sundaresan, and S. Rangarajan, “Software -defined networking in cellular radio access networks: potential and challenges,” IEEE Commun. Mag. , vol. 53, no. 1, pp. 150 –156, Jan. 2015, doi: 10.1109/MCOM.2015.7010528

    2015

  72. [77]

    QoE Driven Decentralized Spectrum Sharing in 5G Networks: Potential Game Approach,

    N. Zhang, S. Zhang, J. Zheng, X. Fang, J. W. Mark, and X. Shen, “QoE Driven Decentralized Spectrum Sharing in 5G Networks: Potential Game Approach,” IEEE Trans. Veh. Technol. , vol. 66, no. 9, pp. 7797 –7808, 2017, doi: 10.1109/TVT.2017.2682236

    2017

  73. [78]

    Mobile Cyber Physical Systems: Current Challenges and Future Networking Applications,

    Y. Guo, X. Hu, B. Hu, J. Cheng, M. Zhou, and R. Y. K. Kwok, “Mobile Cyber Physical Systems: Current Challenges and Future Networking Applications,” IEEE Access, vol. 6, pp. 12360–12368, 2018, doi: 10.1109/ACCESS.2017.2782881

    2018

  74. [79]

    A Survey on Platoon-Based Vehicular Cyber-Physical Systems,

    D. Jia, K. Lu, J. Wang, X. Zhang, and X. Shen, “A Survey on Platoon-Based Vehicular Cyber-Physical Systems,” IEEE Commun. Surv. Tutorials, vol. 18, no. 1, pp. 263–284, 2016, doi: 10.1109/COMST.2015.2410831

    2016

  75. [80]

    Enabling technologies for fog computing in healthcare IoT systems,

    A. A. Mutlag, M. K. Ab d Ghani, N. Arunkumar, M. A. Mohammed, and O. Mohd, “Enabling technologies for fog computing in healthcare IoT systems,” Futur. Gener. Comput. Syst. , vol. 90, pp. 62 –78, Jan. 2019, doi: 10.1016/J.FUTURE.2018.07.049

    2019 doi

  76. [81]

    Keynote Paper: From EDA to IoT eHealth: Promises, Challenges, and Solutions,

    F. Firouzi, B. Farahani, M. Ibrahim , and K. Chakrabarty, “Keynote Paper: From EDA to IoT eHealth: Promises, Challenges, and Solutions,” IEEE Trans. Comput. Des. Integr. Circuits Syst. , vol. 37, no. 12, pp. 2965 –2978, 2018, doi: 10.1109/TCAD.2018.2801227

    2018

  77. [82]

    The internet of things in healthcare: An overview,

    Y. YIN, Y. Zeng, X. Chen, and Y. Fan, “The internet of things in healthcare: An overview,” J. Ind. Inf. Integr., vol. 1, pp. 3–13, Mar. 2016, doi: 10.1016/J.JII.2016.03.004

    2016 doi

  78. [83]

    Health-CPS: Healthcare Cyber-Physical System Assisted by Cloud and Big Data,

    Y. Zhang, M. Qiu, C.-W. Tsai, M. M. Hassan, and A. Alamri, “Health-CPS: Healthcare Cyber-Physical System Assisted by Cloud and Big Data,” IEEE Syst. J., vol. 11, no. 1, pp. 88–95, Mar. 2017, doi: 10.1109/JSYST.2015.2460747

    2017

  79. [84]

    Security Tradeoffs in Cyber Physical Systems: A Case Study Survey on Impla ntable Medical Devices,

    R. Altawy and A. M. Youssef, “Security Tradeoffs in Cyber Physical Systems: A Case Study Survey on Impla ntable Medical Devices,” IEEE Access, vol. 4, pp. 959–979, 2016, doi: 10.1109/ACCESS.2016.2521727

    2016

  80. [85]

    Fog Computing in Healthcare –A Review and Discussion,

    F. A. Kraemer, A. E. Braten, N. Tamkittikhun, and D. Palma, “Fog Computing in Healthcare –A Review and Discussion,” IEEE Access, vol. 5, pp. 9206–9222, 2017, doi: 10.1109/ACCESS.2017.2704100

    2017

  81. [86]

    Exploring interoperability assessment for Future Internet Architectures roll out,

    C. Guimarães, J. Quevedo, R. Ferreira, D. Corujo, and R. L. Aguiar, “Exploring interoperability assessment for Future Internet Architectures roll out,” J. Netw. Comput. Appl., vol. 136, pp. 38–56, Jun. 2019, doi: 10.1016/J.JNCA.2019.04.008

    2019 doi

  82. [87]

    Internet of Things for Smart Healthcare: Technologies, Challenges, and Opportunities,

    S. B. Baker, W. Xiang, and I. Atkinson, “Internet of Things for Smart Healthcare: Technologies, Challenges, and Opportunities,” IEEE Access, vol. 5, pp. 26521–26544, 2017, doi: 10.1109/ACCESS.2017.2775180

    2017

  83. [89]

    Review of Cyber -Physical System in Healthcare,

    S. A. Haque, S. M. Aziz, and M. Rahman, “Review of Cyber -Physical System in Healthcare,” Int. J. Distrib. Sens. Networks, vol. 10, no. 4, pp. 1–20, 2014, doi: 10.1155/2014/217415. 33 of 38

    2014 doi

  84. [90]

    How Virtualization, Decentralization and Network Building Change the Manufacturing Landscape: An Industry 4.0 Perspective,

    M. Brettel, N. Friederichsen, M. Keller, and M. Rosenberg, “How Virtualization, Decentralization and Network Building Change the Manufacturing Landscape: An Industry 4.0 Perspective,” Int. J. Mech. aerospace, Ind. mechatronics Eng., vol. 8, no. 1, pp. 37–44, 2014, [Online]. Av...

    2014

  85. [91]

    Orchestrating 5G Network Slices to Support Industrial Internet and to Shape Next-Generation Smart Factories,

    T. Taleb, I. Afolabi, and M. Bagaa, “Orchestrating 5G Network Slices to Support Industrial Internet and to Shape Next-Generation Smart Factories,” IEEE Netw., vol. 33, no. 4, pp. 146–154, Jul. 2019, doi: 10.1109/MNET.2018.1800129

    2019

  86. [92]

    Data -driven intelligent future network: Architecture, use cases, and challenges,

    C. Fang, S. Guo, Z. Wang, H. Huang, H. Yao, and Y. Liu, “Data -driven intelligent future network: Architecture, use cases, and challenges,” IEEE Commun. Mag., vol. 57, no. 7, pp. 34–40, Jul. 2019, doi: 10.1109/MCOM.2019.1800769

    2019

  87. [93]

    Adaptable and Data-Driven Softwarized Networks: Review, Opportunities, and Challenges,

    W. Kellerer, P. Kalmbach, A. Blenk, A. Basta, M. Reisslein, and S. Schmid, “Adaptable and Data-Driven Softwarized Networks: Review, Opportunities, and Challenges,” Proc. IEEE , vol. 107, no. 4, pp. 711 –731, 2019, doi: 10.1109/JPROC.2019.2895553

    2019

  88. [94]

    Artificial Intelligence Defined 5G Radio Access Networks,

    M. Yao, M. Sohul, V. Marojevic, and J. H. Reed, “Artificial Intelligence Defined 5G Radio Access Networks,” IEEE Commun. Mag., vol. 57, no. 3, pp. 14–20, Mar. 2019, doi: 10.1109/MCOM.2019.1800629

    2019

  89. [95]

    The wireless control plane: An overview and directions for future research,

    E. A. Mazied et al., “The wireless control plane: An overview and directions for future research,” J. Netw. Comput. Appl., vol. 126, pp. 104–122, Jan. 2019, doi: 10.1016/J.JNCA.2018.09.017

    2019 doi

  90. [96]

    Enabling Virtual Reality for the Tactile Internet: Hurdles and Opportunities,

    M. T. Vega, T. Mehmli, J. Van Der Hooft, T. Wauters, and F. De Turck, “Enabling Virtual Reality for the Tactile Internet: Hurdles and Opportunities,” in 14th International Conference on Network and Service Management, CNSM 2018 and Workshops, 1st International Workshop on High...

    2018

  91. [97]

    Data Offloading in 5G -Enabled Software- Defined Vehicular Networks: A Stackelberg-Game-Based Approach,

    G. S. Aujla, R. Chaudhary, N. Kumar, J. J. P. C. Rodrigues, and A. Vinel, “Data Offloading in 5G -Enabled Software- Defined Vehicular Networks: A Stackelberg-Game-Based Approach,” IEEE Commun. Mag., vol. 55, no. 8, pp. 100–108, 2017, doi: 10.1109/MCOM.2017.1601224

    2017

  92. [98]

    TARCO: Two-Stage Auction for D2D Relay Aided Computation Resource Allocation in HetNet,

    L. Chen, J. Wu, X.-X. Zhang, and G. Zhou, “TARCO: Two-Stage Auction for D2D Relay Aided Computation Resource Allocation in HetNet,” IEEE Trans. Serv. Comput., p. 1, 2018, doi: 10.1109/TSC.2018.2792024

    2018

  93. [99]

    Smart building energy efficiency via social game: a robust utility learning framework for closing–the–loop,

    I. C. Konstantakopoulos, L. J. Ratliff, M. Jin, C. Spanos, and S. S. Sastry, “ Smart building energy efficiency via social game: a robust utility learning framework for closing–the–loop,” in 2016 1st International Workshop on Science of Smart City Operations and Platforms Engi...

    2016

  94. [100]

    Game Theory for Cyber Security and Privacy,

    C. T. Do et al., “Game Theory for Cyber Security and Privacy,” ACM Comput. Surv., vol. 50, no. 2, pp. 1–37, 2017, doi: 10.1145/3057268

    2017 doi

  95. [101]

    A hierarchical learning approach to anti-jamming channel selection strategies,

    F. Yao, L. Jia, Y. Sun, Y. Xu, S. Feng, and Y. Zhu, “A hierarchical learning approach to anti-jamming channel selection strategies,” Wirel. Networks, vol. 25, no. 1, pp. 201–213, Jan. 2019, doi: 10.1007/s11276-017-1551-9

    2019 doi

  96. [102]

    A game theoretic model for resource allocation among countermeasures with multiple attributes,

    E. C. Paulson, I. Linkov, and J. M. Keisler, “A game theoretic model for resource allocation among countermeasures with multiple attributes,” Eur. J. Oper. Res., vol. 252, no. 2, pp. 610–622, Jul. 2016, doi: 10.1016/J.EJOR.2016.01.026

    2016 doi

  97. [103]

    Bandwidth Slicing in Software -Defined 5G: A Stackelberg Game Approach,

    Z. Zhou, L. Ta n, B. Gu, Y. Zhang, and J. Wu, “Bandwidth Slicing in Software -Defined 5G: A Stackelberg Game Approach,” IEEE Veh. Technol. Mag., vol. 13, no. 2, pp. 102–109, Jun. 2018, doi: 10.1109/MVT.2018.2814022

    2018

  98. [104]

    Task-Driven Resource Assignment in Mobile Edge Computing Exploiting Evolutionary Computation,

    L. Wan, L. Sun, X. Kong, Y. Yuan, K. Sun, and F. Xia, “Task-Driven Resource Assignment in Mobile Edge Computing Exploiting Evolutionary Computation,” IEEE Wirel. Commun. , vol. 26, no. 6, pp. 94 –101, 2019, doi: 10.1109/MWC.001.1800582

    2019 doi

  99. [105]

    Cooperative and Distributed Computation Offloading for Blockchain-Empowered Industrial Internet of Things,

    W. Chen et al., “Cooperative and Distributed Computation Offloading for Blockchain-Empowered Industrial Internet of Things,” IEEE Internet Things J., p. 1, 2019, doi: 10.1109/JIOT.2019.2918296

    2019

  100. [106]

    Mitigating Jamming Attacks Using Energy Harvesting,

    G. Rezgui, E. V. Belmega, and A. Chorti, “Mitigating Jamming Attacks Using Energy Harvesting,” IEEE Wirel. Commun. Lett., p. 1, 2018, doi: 10.1109/LWC.2018.2871152

    2018

  101. [107]

    Zhu and T

    Q. Zhu and T. Basar, “Game -Theoretic Methods for Robustness, Security, and Resilience of Cyberphysical Control 34 of 38 Systems: Games-in-Games Principle for Optimal Cross-Layer Resilient Control Systems,” IEEE Control Syst., vol. 35, no. 1, pp. 46–65, 2015, doi: 10.1109/MCS....

    2015

  102. [108]

    Stochastic Games for Power Grid Protection Against Coordinated Cyber-Physical Attacks,

    L. Wei, A. I. Sarwat, W. Saad, and S. Biswas, “Stochastic Games for Power Grid Protection Against Coordinated Cyber-Physical Attacks,” IEEE Trans. Smart Grid, vol. 9, no. 2, pp. 684–694, Mar. 2018, doi: 10.1109/TSG.2016.2561266

    2018

  103. [109]

    Deceptive Attack and Defense Game in Honeypot -Enabled Networks for the Internet of Things,

    Q. D. La, T. Q. S. Quek, J. Lee, S. Jin, and H. Zhu, “Deceptive Attack and Defense Game in Honeypot -Enabled Networks for the Internet of Things,” IEEE Internet Things J. , vol. 3, no. 6, pp. 1025 –1035, 2016, doi: 10.1109/JIOT.2016.2547994

    2016

  104. [110]

    Game theoretical modelling of network/cybersecurity,

    A. Iqbal, L. J. Gunn, M. Guo, M. Ali Babar, and D. Abbott, “Game theoretical modelling of network/cybersecurity,” IEEE Access, vol. 7, pp. 154167–154179, 2019, doi: 10.1109/ACCESS.2019.2948356

    2019

  105. [111]

    Interdependent Strategic Security Risk Management with Bounded Rationality in the Internet of Things,

    J. Chen and Q. Zhu, “Interdependent Strategic Security Risk Management with Bounded Rationality in the Internet of Things,” IEEE Trans. Inf. Forensics Secur., vol. 14, no. 11, pp. 2958–2971, Nov. 2019, doi: 10.1109/TIFS.2019.2911112

    2019

  106. [112]

    Learning -Based Energy-Efficient Channel Selection for Edge Computing- Empowered Cognitive Machine -to-Machine Communications,

    H. Liao, Z. Zhou, B. Ai, and M. Guizani, “Learning -Based Energy-Efficient Channel Selection for Edge Computing- Empowered Cognitive Machine -to-Machine Communications,” in 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring), May 2020, pp. 1–6, doi: 10.1109/VTC2020...

    2020

  107. [113]

    Network service orchestration standardization: A technology survey,

    C. Rotsos et al., “Network service orchestration standardization: A technology survey,” Comput. Stand. Interfaces, vol. 54, pp. 203–215, Nov. 2017, doi: 10.1016/J.CSI.2016.12.006

    2017 doi

  108. [114]

    Management patterns: SDN-enabled network resilience management,

    P. Smith, A. Schaeffer-Filho, D. Hutchison, and A. Mauthe, “Management patterns: SDN-enabled network resilience management,” in IEEE/IFIP NOMS 2014 - IEEE/IFIP Network Operations and Management Symposium: Management in a Software Defined World, 2014, pp. 1–9, doi: 10.1109/NOMS...

    2014

  109. [115]

    Software-defined networking in cyber-physical systems: A survey,

    E. Molina and E. Jacob, “Software-defined networking in cyber-physical systems: A survey,” Comput. Electr. Eng., vol. 66, pp. 407–419, 2018, doi: 10.1016/J.COMPELECENG.2017.05.013

    2018 doi

  110. [116]

    SDN-based solutions for Moving Target Defense network protection,

    P. Kampanakis, H. Perros, and T. Beyene, “SDN-based solutions for Moving Target Defense network protection,” in Proceeding of IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks 2014 , Jun. 2014, pp. 1–6, doi: 10.1109/WoWMoM.2014.6918979

    2014

  111. [117]

    A decoy chain deployment method based on SDN and NFV against penetration attack.,

    Q. Zhao, C. Zhang, and Z. Zhao, “A decoy chain deployment method based on SDN and NFV against penetration attack.,” PLoS One, vol. 12, no. 12, pp. 1–23, 2017, doi: 10.1371/journal.pone.0189095

    2017 doi

  112. [118]

    Survey on SDN based network intrusion detection system using machine learning approaches,

    N. Sultana, N. Chilamkurti, W. Peng, and R. Alhadad, “Survey on SDN based network intrusion detection system using machine learning approaches,” Peer-to-Peer Netw. Appl., pp. 1–9, Jan. 2018, doi: 10.1007/s12083-017-0630-0

    2018 doi

  113. [119]

    An improved network security situation assessment approach in software defined networks,

    Z. Fan, Y. Xiao, A. Nayak, and C. Tan, “An improved network security situation assessment approach in software defined networks,” Peer-to-Peer Netw. Appl., pp. 1–15, 2017, doi: 10.1007/s12083-017-0604-2

    2017 doi

  114. [120]

    Identification and predication of network attack patterns in software-defined networking,

    X. Xu, S. Wang, and Y. Li, “Identification and predication of network attack patterns in software-defined networking,” Peer-to-Peer Netw. Appl., pp. 1–11, Jan. 2018, doi: 10.1007/s12083-017-0629-6

    2018 doi

  115. [121]

    A hierarchical mobility management scheme based on software defined networking,

    X. Yin, L. Wang, and S. Jiang, “A hierarchical mobility management scheme based on software defined networking,” Peer-to-Peer Netw. Appl., pp. 1–16, Jan. 2018, doi: 10.1007/s12083-017-0615-z

    2018 doi

  116. [122]

    The strategy of path determination and traffic scheduling in private campus networks based on SDN,

    J. Xiao, S. Chen, and M. Sui, “The strategy of path determination and traffic scheduling in private campus networks based on SDN,” Peer-to-Peer Netw. Appl., pp. 1–10, 2017, doi: 10.1007/s12083-017-0623-z

    2017 doi

  117. [123]

    Behavior Reconstruction Models for Large-scale Network Service Systems,

    Z. Zhang, L. Ge, P. Wang, and X. Zhou, “Behavior Reconstruction Models for Large-scale Network Service Systems,” Peer-to-Peer Netw. Appl., pp. 1–12, Jan. 2018, doi: 10.1007/s12083-017-0625-x

    2018 doi

  118. [124]

    Energy efficient topology management scheme based on clustering technique for software defined wireless sensor network,

    S. Din, A. Paul, A. Ahmad, and J. H. Kim, “Energy efficient topology management scheme based on clustering technique for software defined wireless sensor network,” Peer-to-Peer Netw. Appl., vol. 12, no. 2, pp. 348 –356, Mar. 2019, doi: 10.1007/s12083-017-0607-z

    2019 doi

  119. [125]

    An optimized protocol for QoS and energy efficiency on wireless body area networks,

    T. Bai et al., “An optimized protocol for QoS and energy efficiency on wireless body area networks,” Peer-to-Peer Netw. Appl., pp. 1–11, 2017, doi: 10.1007/s12083-017-0602-4. 35 of 38

    2017 doi

  120. [126]

    A Component Architecture for the Internet of Th ings,

    C. Brooks et al., “A Component Architecture for the Internet of Th ings,” Proc. IEEE, vol. 106, no. 9, pp. 1527 –1542, 2018, doi: 10.1109/JPROC.2018.2812598

    2018

  121. [127]

    Self-Organization and Artificial Life: A Review,

    C. Gershenson, V. Trianni, J. Werfel, and H. Sayama, “Self-Organization and Artificial Life: A Review,” 2018, [Online]. Available: http://arxiv.org/abs/1804.01144

    2018 arXiv

  122. [128]

    The Past, Present, and Future of Artificial Life,

    W. Aguilar, G. Santamaría -Bonfil, T. Froese, and C. Gershenson, “The Past, Present, and Future of Artificial Life,” Front. Robot. AI, vol. 1, p. 8, 2014, doi: 10.3389/frobt.2014.00008

    2014

  123. [129]

    Open Problems in Artificial Life,

    M. A. Bedau et al. , “Open Problems in Artificial Life,” Artif. Life , vol. 6, no. 4, pp. 363 –376, 2000, doi: 10.1162/106454600300103683

    2000 doi

  124. [130]

    State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems,

    Z. M. Fadlullah et al., “State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems,” IEEE Commun. Surv. Tutorials, vol. 19, no. 4, pp. 2432 –2455, 2017, doi: 10.1109/COMST.2017.2707140

    2017

  125. [131]

    Deep Learning in Mobile and Wireless Networking: A Survey,

    C. Zhang, P. Patras, and H. Haddadi, “Deep Learning in Mobile and Wireless Networking: A Survey,” IEEE Commun. Surv. Tutorials, vol. 21, no. 3, pp. 2224–2287, 2019, doi: 10.1109/COMST.2019.2904897

    2019

  126. [132]

    Towards a cognitive routing engine for software defined networks,

    F. Francois and E. Gelenbe, “Towards a cognitive routing engine for software defined networks,” in 2016 IEEE International Conference on Communications (ICC), 2016, pp. 1–6, doi: 10.1109/ICC.2016.7511138

    2016

  127. [134]

    Brain Intelligence: Go beyond Artificial Intelligence,

    H. Lu, Y. Li, M. Chen, H. Kim, and S. Serikawa, “Brain Intelligence: Go beyond Artificial Intelligence,” Mob. Networks Appl., vol. 23, no. 2, pp. 368–375, 2018, doi: 10.1007/s11036-017-0932-8

    2018 doi

  128. [135]

    Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues,

    Y. Sun, M. Peng, Y. Zhou, Y. Huang, and S. Mao, “Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues,” IEEE Commun. Surv. Tutorials , vol. 21, no. 4, pp. 3072 –3108, 2019, doi: 10.1109/COMST.2019.2924243

    2019

  129. [136]

    Artificial Neural Networks -Based Machine Learning for Wireless Networks: A Tutorial,

    M. Chen, U. Challita, W. Saad, C. Yin, and M. Debbah, “Artificial Neural Networks -Based Machine Learning for Wireless Networks: A Tutorial,” IEEE Commun. Surv. Tutorials , vol. 21, no. 4, pp. 3039 –3071, 2019, doi: 10.1109/COMST.2019.2926625

    2019

  130. [137]

    Learning IoT in Edge: Deep Learning for the Internet of Things with Edge Computing,

    H. Li, K. Ota, and M. Dong, “Learning IoT in Edge: Deep Learning for the Internet of Things with Edge Computing,” IEEE Netw., vol. 32, no. 1, pp. 96–101, Jan. 2018, doi: 10.1109/MNET.2018.1700202

    2018

  131. [138]

    Computational Intelligence in the Time of Cyber-Physical Systems and the Internet of Things,

    S. Ozawa, “Computational Intelligence in the Time of Cyber-Physical Systems and the Internet of Things,” in Artificial Intelligence in the Age of Neural Networks and Brain Computing, Academic Press, 2019, pp. 245–263

    2019

  132. [139]

    Meta -Learning for Realizing Self -x Management of Future Networks,

    M. A. Khan and H. Tembine, “Meta -Learning for Realizing Self -x Management of Future Networks,” IEEE Access, vol. 5, pp. 19072–19083, 2017, doi: 10.1109/ACCESS.2017.2745999

    2017

  133. [140]

    Fundamental Concepts of Cyber Resilience: Introduction and Overview,

    I. Linkov and A. Kott, “Fundamental Concepts of Cyber Resilience: Introduction and Overview,” in Cyber Resilience of Systems and Networks, Cham: Springer International Publishing, 2019, pp. 1–25

    2019

  134. [141]

    Analysis of Dependencies,

    N. Evans and W. Horsthemke, “Analysis of Dependencies,” in Cyber Resilience of Systems and Networks , Cham: Springer International Publishing, 2019, pp. 93–106

    2019

  135. [142]

    Regional Critical Infrastructu re,

    N. Evans and W. Horsthemke, “Regional Critical Infrastructu re,” in Cyber Resilience of Systems and Networks, Cham: Springer International Publishing, 2019, pp. 355–380

    2019

  136. [143]

    Applying Percolation Theory,

    T. J. Moore and J.-H. Cho, “Applying Percolation Theory,” in Cyber Resilience of Systems and Networks, Cham: Springer International Publishing, 2019, pp. 107–133

    2019

  137. [144]

    Systems Engineering Approaches,

    D. J. Bodeau and R. D. Graubart, “Systems Engineering Approaches,” in Cyber Resilience of Systems and Networks , Cham: Springer International Publishing, 2019, pp. 197–220

    2019

  138. [145]

    Comparing Fog Solutions for Energy Efficiency in Wireless Networks: Challenges and Opportunities,

    I. Al Ridhawi, M. Aloqaily, and A. Boukerche, “Comparing Fog Solutions for Energy Efficiency in Wireless Networks: Challenges and Opportunities,” IEEE Wirel. Commun., vol. 26, no. 6, pp. 80–86, 2019, doi: 10.1109/MWC.001.1900077. 36 of 38

    2019 doi

  139. [146]

    SDN-Based Internet of Autonomous Vehicles: An Energy- Efficient Approach for Controller Placement,

    K. Kaur, S. Garg, G. Kaddoum, N. Kumar, and F. Gagnon, “SDN-Based Internet of Autonomous Vehicles: An Energy- Efficient Approach for Controller Placement,” IEEE Wirel. Commun. , vol. 26, no. 6, pp. 72 –79, 2019, doi: 10.1109/MWC.001.1900112

    2019 doi

  140. [147]

    Fault-tolerance, fast and slow: exploiting failure asynchrony in distributed systems,

    A. Ramnatthan, A. Ganesan, J. Liu, A. C. Arpaci-Dusseau, and R. H. Arpaci-Dusseau, “Fault-tolerance, fast and slow: exploiting failure asynchrony in distributed systems,” in Proceedings of the 12th USENIX conference on Operating Systems Design and Implementation, 2018, pp. 391...

    2018

  141. [148]

    Managing Human Factors,

    G. Giacomello and G. Pescaroli, “Managing Human Factors,” in Cyber Resilience of Systems and Networks , Cham: Springer International Publishing, 2019, pp. 247–263

    2019

  142. [149]

    Assessing the impact of intra-cloud live migration on anomaly detection,

    N. U. H. Shirazi, S. Simpson, A. K. Marnerides, M. Watson, A. Mauthe, and D. Hutchison, “Assessing the impact of intra-cloud live migration on anomaly detection,” in 2014 IEEE 3rd International Conference on Cloud Networking, CloudNet 2014, Nov. 2014, pp. 52–57, doi: 10.1109/C...

    2014

  143. [150]

    Toward Reliable Data Delivery for Highly Dynamic Mobile Ad Hoc Networks,

    Shengbo Yang, Chai K iat Yeo, and Bu -Sung Lee, “Toward Reliable Data Delivery for Highly Dynamic Mobile Ad Hoc Networks,” IEEE Trans. Mob. Comput., vol. 11, no. 1, pp. 111–124, Jan. 2012, doi: 10.1109/TMC.2011.55

    2012 doi

  144. [151]

    Cooperative Caching for Efficient Data Access in Disruption Tolerant Networks,

    W. Gao, G. Cao, A. Iyengar, and M. Srivatsa, “Cooperative Caching for Efficient Data Access in Disruption Tolerant Networks,” IEEE Trans. Mob. Comput., vol. 13, no. 3, pp. 611–625, Mar. 2014, doi: 10.1109/TMC.2013.33

    2014 doi

  145. [152]

    Biologically Inspired Artificial Intelligence Techniques,

    N. Tandiya, E. J. M. Colbert, V. Marojevic, and J. H. Reed, “Biologically Inspired Artificial Intelligence Techniques,” in Cyber Resilience of Systems and Networks, Cham: Springer International Publishing, 2019, pp. 287–313

    2019

  146. [153]

    Internet of Things,

    M. Curado et al. , “Internet of Things,” in Cyber Resilience of Systems and Networks , Cham: Springer International Publishing, 2019, pp. 381–401

    2019

  147. [154]

    A Review of the Principles of Designing Smart Cyber -Physical Systems for Run -Time Adaptation: Learned Lessons and Open Issues,

    J. Tavcar and I. Horvath, “A Review of the Principles of Designing Smart Cyber -Physical Systems for Run -Time Adaptation: Learned Lessons and Open Issues,” IEEE Trans. Syst. Man, Cybern. Syst., vol. 49, no. 1, pp. 145–158, Jan. 2019, doi: 10.1109/TSMC.2018.2814539

    2019

  148. [155]

    The game -theoretic model and experimental investigation of cyber wargaming,

    E. J. M. Colbert, A. Kott, and L. P. Knachel, “The game -theoretic model and experimental investigation of cyber wargaming,” J. Def. Model. Simul. Appl. Methodol. Technol., pp. 1–18, 2018, doi: 10.1177/1548512918795061

    2018 doi

  149. [156]

    Modeling the Impact of Cyber Attacks,

    I. Kotenko, I. Saenko, and O. Lauta, “Modeling the Impact of Cyber Attacks,” in Cyber Resilience of Systems and Networks, Cham: Springer International Publishing, 2019, pp. 135–169

    2019

  150. [157]

    Metrics Based on the Mission Risk Perspective,

    S. Musman, S. Agbolosu -Amison, an d K. Crowther, “Metrics Based on the Mission Risk Perspective,” in Cyber Resilience of Systems and Networks, Cham: Springer International Publishing, 2019, pp. 41–65

    2019

  151. [158]

    Software -Defined Perimeter (SDP): State of t he Art Secure Solution for Modern Networks,

    A. Moubayed, A. Refaey, and A. Shami, “Software -Defined Perimeter (SDP): State of t he Art Secure Solution for Modern Networks,” IEEE Netw., vol. 33, no. 5, pp. 226–233, 2019, doi: 10.1109/MNET.2019.1800324

    2019

  152. [159]

    Middleware Architectures for the Smart Grid: A Survey on the State -of- the-Art, Taxonomy and Main Open Issues,

    J. Rodriguez-Molina and D. M. Kammen, “Middleware Architectures for the Smart Grid: A Survey on the State -of- the-Art, Taxonomy and Main Open Issues,” IEEE Commun. Surv. Tutorials , vol. 20, no. 4, pp. 2992 –3033, 2018, doi: 10.1109/COMST.2018.2846284

    2018

  153. [160]

    Middleware technologies for cloud of things: a survey,

    A. Farahzadi, P. Shams, J. Rezazadeh, and R. Farahbakhsh, “Middleware technologies for cloud of things: a survey,” Digit. Commun. Networks, vol. 4, no. 3, pp. 176–188, 2018, doi: 10.1016/J.DCAN.2017.04.005

    2018 doi

  154. [161]

    Enabling Collaborative Edge Computing for Software Defined Vehicular Networks,

    K. Wang, H. Yin, W. Quan, and G. Min, “Enabling Collaborative Edge Computing for Software Defined Vehicular Networks,” IEEE Netw., vol. 32, no. 5, pp. 112–117, 2018, doi: 10.1109/MNET.2018.1700364

    2018

  155. [162]

    A Blockchain -Enabled Trustless Crowd -Intelligence Ecosystem on Mobile Edge Computing,

    J. Xu, S. Wang, B. K. Bhargava, and F. Yang, “A Blockchain -Enabled Trustless Crowd -Intelligence Ecosystem on Mobile Edge Computing,” IEEE Trans. Ind. Informatics , vol. 15, no. 6, pp. 3538 –3547, Jun. 2019, doi: 10.1109/TII.2019.2896965

    2019

  156. [163]

    SecCL: Securing Collaborative Learning Systems via Trusted Bulletin Boards,

    Z. Zhang et al., “SecCL: Securing Collaborative Learning Systems via Trusted Bulletin Boards,” IEEE Commun. Mag., vol. 58, no. 1, pp. 47–53, Jan. 2020, doi: 10.1109/MCOM.001.1900130

    2020 doi

  157. [164]

    A Survey on Blockchain: A Game Theoretical Perspective,

    Z. Liu et al., “A Survey on Blockchain: A Game Theoretical Perspective,” IEEE Access, vol. 7, pp. 47615–47643, 2019, 37 of 38 doi: 10.1109/ACCESS.2019.2909924

    2019

  158. [165]

    RFC 8309: Service Models Explained,

    Q. Wu, W. Liu, and A. Farrel, “RFC 8309: Service Models Explained,” IETF, https://tools.ietf.org/html/rfc8309, pp. 1–23, 2018, [Online]. Available: https://tools.ietf.org/html/rfc8309

    2018

  159. [166]

    Intent NBI – Definition and Principles,

    ONF_TR-523, “Intent NBI – Definition and Principles,” Open Netw. Found. https//www.opennetworking.org/wp - content/uploads/2014/10/TR-523_Intent_Definition_Principles.pdf, pp. 1 –20, 2016, [Online]. Available: https://www.opennetworking.org/wp-content/uploads/2014/10/TR-523_In...

    2014

  160. [167]

    Intent -Based Networking - Concepts and Overview,

    A. Clemm, L. Ciavaglia, L. Granville, and J. Tantsura, “Intent -Based Networking - Concepts and Overview,” IETF, https://tools.ietf.org/id/draft-clemm-nmrg-dist-intent-03.html#TR523, pp. 1–21, 2019

    2019

  161. [168]

    Machine Learning for Resource Management in Cellular and IoT Networks: Potentials, Current Solutions, and Open Challenges,

    F. Hussain, S. A. Hassan, R. Hussain, and E. Hossain, “Machine Learning for Resource Management in Cellular and IoT Networks: Potentials, Current Solutions, and Open Challenges,” IEEE Commun. Surv. Tutorials, pp. 1–1, Jan. 2020, doi: 10.1109/comst.2020.2964534

    2020

  162. [169]

    Why (and How) Networks Should Run Themselves,

    N. Feamster and J. Rexford, “Why (and How) Networks Should Run Themselves,” arXiv:1710.11583, pp. 1–7, 2017

    2017 arXiv

  163. [170]

    A control theoretic analysis of RED,

    C. V. Hollot, V. Misra, D. Towsley, and W. B. Gong, “A control theoretic analysis of RED,” in Proceedings - IEEE INFOCOM, 2001, vol. 3, pp. 1510–1519, doi: 10.1109/infcom.2001.916647

    2001

  164. [171]

    Slicing Home Networks,

    Y. Yiakoumis, K.-K. Yap, S. Katti, G. Parulkar, and N. McKeown, “Slicing Home Networks,” in Proceedings of the 2Nd ACM SIGCOMM Workshop on Home Networks, 2011, pp. 1–6, doi: 10.1145/2018567.2018569

    2011

  165. [172]

    Network Slicing Based 5G and Future Mobile Networks: Mobility, Resource Management, and Challenges,

    H. Zhang, N. Liu, X. Chu, K. Long, A. -H. Aghvami, a nd V. C. M. Leung, “Network Slicing Based 5G and Future Mobile Networks: Mobility, Resource Management, and Challenges,” IEEE Commun. Mag., vol. 55, no. 8, pp. 138–145, 2017, doi: 10.1109/MCOM.2017.1600940

    2017

  166. [173]

    A survey on data fusion in internet of things: Towards secure and privacy- preserving fusion,

    W. Ding, X. Jing, Z. Yan, and L. T. Yang, “A survey on data fusion in internet of things: Towards secure and privacy- preserving fusion,” Inf. Fusion, vol. 51, pp. 129–144, 2019, doi: 10.1016/j.inffus.2018.12.001

    2019 doi

  167. [174]

    Big data caching for networking: moving from cloud to edge,

    E. Zeydan et al., “Big data caching for networking: moving from cloud to edge,” IEEE Commun. Mag., vol. 54, no. 9, pp. 36–42, 2016, doi: 10.1109/MCOM.2016.7565185

    2016

  168. [175]

    Distributed Continuous -Time Fault Estimation Control for Multiple Devices in IoT Networks,

    R. Casado-Vara, P. Novais, A. B. Gil, J. Prieto, and J. M. Corchado, “Distributed Continuous -Time Fault Estimation Control for Multiple Devices in IoT Networks,” IEEE Access, vol. 7, pp. 11972 –11984, 2019, doi: 10.1109/ACCESS.2019.2892905

    2019

  169. [176]

    An online optimization framework for distributed fog network formation with minimal latency,

    G. Lee, W. Saad, and M. Bennis, “An online optimization framework for distributed fog network formation with minimal latency,” IEEE Trans. Wirel. Commun. , vol. 18, no. 4, pp. 2 244–2258, Apr. 2019, doi: 10.1109/TWC.2019.2901850

    2019

  170. [178]

    Future directions of networked control systems: A combination of cloud control and fog control approach,

    Y. Zhan, Y. Xia, and A. V Vasilakos, “Future directions of networked control systems: A combination of cloud control and fog control approach,” Comput. Networks, vol. 161, pp. 235–248, 2019, doi: 10.1016/J.COMNET.2019.07.004

    2019 doi

  171. [179]

    Resource Slicing in Virtual Wireless Networks: A Survey,

    M. Richart, J. Baliosian, J. Serrat, and J.-L. Gorricho, “Resource Slicing in Virtual Wireless Networks: A Survey,” IEEE Trans. Netw. Serv. Manag., vol. 13, no. 3, pp. 462–476, 2016, doi: 10.1109/TNSM.2016.2597295

    2016

  172. [180]

    Segment Routing: a Comprehensive Survey of Research Activities, Standardization Efforts and Implementation Results,

    P. L. Ventre et al., “Segment Routing: a Comprehensive Survey of Research Activities, Standardization Efforts and Implementation Results,” arXiv: cs.NI/1904.03471, pp. 1–38, 2020

    1904 arXiv

  173. [181]

    Data fusion in cyber -physical-social systems: State -of-the-art and perspectives,

    P. Wang, L. T. Yang, J. Li, J. Chen, and S. Hu, “Data fusion in cyber -physical-social systems: State -of-the-art and perspectives,” Inf. Fusion, vol. 51, pp. 42–57, 2019, doi: 10.1016/j.inffus.2018.11.002

    2019 doi

  174. [182]

    A game theory approach for cooperative control to improve data quality and false data detection in WSN,

    R. Casado-Vara, F. Prieto-Castrillo, and J. M. Corchado, “A game theory approach for cooperative control to improve data quality and false data detection in WSN,” Int. J. Robust Nonlinear Control , vol. 28, no. 16, pp. 5087 –5102, Nov. 2018, doi: 10.1002/rnc.4306. 38 of 38

    2018 doi

  175. [183]

    Software Defined IoT Systems: Properties, State of the Art, and Future Research,

    P. Mishra, D. Puthal, M. Tiwary, and S. P. Mohanty, “Software Defined IoT Systems: Properties, State of the Art, and Future Research,” IEEE Wirel. Commun., vol. 26, no. 6, pp. 64–71, 2019, doi: 10.1109/MWC.001.1900083

    2019 doi

  176. [184]

    Energy -aware and adaptive fog storage mechanism with data replication ruled by spatio -temporal content po pularity,

    R. Vales, J. Moura, and R. Marinheiro, “Energy -aware and adaptive fog storage mechanism with data replication ruled by spatio -temporal content po pularity,” J. Netw. Comput. Appl. , vol. 135, pp. 84 –96, 2019, doi: 10.1016/j.jnca.2019.03.001

    2019 doi

  177. [185]

    What’s the Right Network Buffer Size?,

    Geoff Huston, “What’s the Right Network Buffer Size?,” The Internet Protocol Journal, vol. 23, no. 1, pp. 2–20, 2020

    2020

  178. [186]

    When mobile blockchain meets edge computing,

    Z. Xiong, Y. Zhang, D. Niyato, P. Wang, and Z. Han, “When mobile blockchain meets edge computing,” IEEE Commun. Mag., vol. 56, no. 8, pp. 33–39, Aug. 2018, doi: 10.1109/MCOM.2018.1701095

    2018

  179. [187]

    Microgrid transactive energy: Review, architectures, distributed ledger technologies, and market analysis,

    M. F. Zia, M. Benbouzid, E. Elbouchikhi, S. M. Muyeen, K. Techato, and J. M. Guerrero, “Microgrid transactive energy: Review, architectures, distributed ledger technologies, and market analysis,” IEEE Access, vol. 8. Institute of Electrical and Electronics Engineers Inc., pp. ...

    2020

  180. [188]

    Auction Mechanisms in Cloud/Fog Computing Resource Allocation for Public Blockchain Networks,

    Y. Jiao, P. Wang, D. Niyato, and K. Suankaewmanee, “Auction Mechanisms in Cloud/Fog Computing Resource Allocation for Public Blockchain Networks,” IEEE Trans. Parallel Distrib. Syst., vol. 30, no. 9, pp. 1975–1989, Mar. 2019, doi: 10.1109/tpds.2019.2900238

    1975

  181. [189]

    Cloud/fog computing resource management and pricing for blockchain networks,

    Z. Xiong, S. Feng, W. Wang, D. Niyato, P. Wang, and Z. Han, “Cloud/fog computing resource management and pricing for blockchain networks,” IEEE Internet Things J. , vol. 6, no. 3, pp. 4585 –4600, Jun. 2019, doi: 10.1109/JIOT.2018.2871706

    2019

  182. [190]

    Blockchain and Machine Learning for Communications and Networking Systems,

    Y. Liu, F. R. Yu, X. Li, H. Ji, and V. C. M. Leung, “Blockchain and Machine Learning for Communications and Networking Systems,” IEEE Commun. Surv. Tutorials , vol. 22, no. 2, pp. 1392 –1431, Apr. 2020, doi: 10.1109/COMST.2020.2975911

    2020

  183. [191]

    Internet of Things and Big Data Analytics for Smart and Connected Communities,

    Y. Sun, H. Song, A. J. Jara, and R. Bie, “Internet of Things and Big Data Analytics for Smart and Connected Communities,” IEEE Access, vol. 4, pp. 766–773, 2016, doi: 10.1109/ACCESS.2016.2529723

    2016

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.