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REVIEW 3 major objections 5 minor 1 cited by

AGI Enabled Solutions For IoX Layers Bottlenecks In Cyber-Physical-Social-Thinking Space

T0 review · 3 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read AGI techniques can be matched to each IoX bottleneck, says a 98-study survey.

desk verdict Useful layered taxonomy of AI-for-IoX work, but the central AGI claim is not supported by the paper's own inclusion criteria. read the letter →

arxiv 2506.22487 v1 pith:LU73L6GR submitted 2025-06-24 cs.NI cs.AI

Amar Khelloufi , Huansheng Ning , Sahraoui Dhelim , Jianguo Ding This is my paper
classification cs.NIcs.AI
keywords ArtificialGeneralIntelligence(AGI)InternetofEverything(IoX)Cyber-Physical-Social-Thinking(CPST)sensinglayerbottlenecksnetworkapplicationneuro-symbolicreasoningactiveinference
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 of 98 studies asks whether Artificial General Intelligence can relieve the bottlenecks that choke the Internet of Everything (IoX) in cyber-physical-social-thinking spaces. It argues that AGI-driven strategies map cleanly onto the three-layer IoX architecture: adaptive sensor fusion and edge preprocessing for sensing-layer data overload, semantic communications and causal reasoning for network-layer protocol and spectrum problems, and large-language-model semantic modeling for application-layer identity explosion. A sympathetic reader would care because the survey turns a diffuse “AGI will fix everything” intuition into a layer-by-layer technical agenda, naming concrete mechanisms that could carry the fix. The paper claims these AGI-enabled solutions effectively mitigate the bottlenecks while flagging that large-scale validation is still missing.

What carries the argument

The central organizing object is the three-layer AGI-enabled IoX architecture (sensing, network, application) set inside the cyber-physical-social-thinking (CPST) hyperspace. The carrying mechanism is the pairing of each bottleneck with a specific AGI technique: active inference for adaptive sensing and edge preprocessing, neuro-symbolic reasoning for robust sensor fusion, semantic communications for protocol heterogeneity, causal reasoning for dynamic spectrum management, and large-language-model semantic modeling plus knowledge graphs for application-layer identity explosion. Cross-layer integration strategies, including joint sensing-communication-AI frameworks, federated learning with blockchain, meta-learning, and active inference, are the paper's proposed unification mechanism.

What would settle it

Reproduce the reported active-inference edge sensing result [60] in a field test that compares it against a conventional hand-tuned preprocessing pipeline with matched engineering effort; if the conventional pipeline matches the 50 percent latency reduction without any general-reasoning component, the claim that AGI specifically resolves sensing-layer bottlenecks is falsified.

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Extended reading notes

Core claim

On its own terms, the paper's central claim is that AGI, defined as cross-domain reasoning and autonomous adaptation beyond narrow AI, is the right tool for the IoX bottleneck problem, and that the available literature already contains the building blocks. Each layer has a designated AGI mechanism: neuro-symbolic reasoning and active inference at the sensing layer, semantic communications and federated multi-agent learning at the network layer, and large language models plus knowledge representation at the application layer. The survey concludes that AGI-enabled solutions effectively mitigate sensing-layer challenges, improve reliability and bandwidth efficiency at the network layer, and drive semantic understanding and orchestration at the application layer, while acknowledging unresolved computational, scalability, and validation gaps.

Load-bearing premise

The central claim assumes that the surveyed advanced AI methods, including neuro-symbolic reasoning, active inference, causal reasoning, and foundation models, count as AGI; if that substitution is rejected, the evidence for AGI-specific bottleneck mitigation disappears.

Editorial extensions

If this is right

  • Sensing-layer data overload can be managed by adaptive sensor fusion, edge preprocessing, and selective attention, with reported latency reductions of up to 50 percent.
  • Network-layer protocol heterogeneity and spectrum scarcity can be addressed by semantic communications, causal reasoning, and multi-agent reinforcement learning with federated learning, improving throughput by about 25 percent while preserving privacy.
  • Application-layer identity explosion can be handled by large-language-model semantic modeling and dynamically built knowledge graphs.
  • Cross-layer integration strategies such as joint sensing-communication-AI frameworks, meta-learning, and active inference can yield substantial system-level gains, including roughly 51.5 percent latency reduction and 52.9 percent energy improvement in cited studies.
  • Future AGI-IoX systems will need large-scale field testing, standardized architectural interfaces, and ethical governance before the reported benefits can be treated as deployable.

Reading between the lines

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

  • Inference: If the mapping holds, the practical payoff does not require true general intelligence, because the surveyed techniques are deployable with today's narrow AI; the paper's real contribution is an architectural blueprint for matching AI toolkits to IoX bottlenecks.
  • Inference: The screening criterion that equates “advanced AI methodologies” with AGI suggests a direct test: re-run the taxonomy with “narrow AI” substituted for “AGI” and compare which bottleneck solutions survive the relabeling.
  • Inference: Some headline cross-layer gains come from studies that are not themselves AGI systems, so a fair evaluation would isolate whether AGI-specific components add anything beyond conventional cross-layer optimization.
  • Inference: A testable extension is a benchmark suite with standardized bottleneck metrics across the three layers, enabling head-to-head comparison of AGI-labeled versus conventional solutions in the same deployment environment.
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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 / 5 minor

Summary. This survey claims to provide a systematic review of AGI-enabled solutions for bottlenecks in the sensing, network, and application layers of Internet of Everything (IoX) systems within the Cyber-Physical-Social-Thinking (CPST) framework. The authors present a three-layer taxonomy, a PRISMA-style selection of 98 papers, and a qualitative synthesis suggesting that AGI-driven strategies such as adaptive sensor fusion, edge preprocessing, and semantic modeling address sensing-layer data overload, network-layer protocol heterogeneity, and application-layer identity explosion. The paper also discusses cross-layer integration, future research directions, and challenges including computational requirements and real-world validation.

Significance. If the central claims were supported, the survey would be a valuable map of where general-intelligence capabilities can be deployed across IoX layers. The paper's strengths include a transparent search protocol, a PRISMA flow diagram, an explicit taxonomy organized by layer and technique, and a broad compilation of recent literature. However, the evidence base does not actually demonstrate AGI as the paper defines it: most included studies are conceptual or use narrow, task-specific AI methods. The survey is best read as a scoping review of advanced AI techniques applied to IoX bottlenecks, not as evidence that AGI specifically is effective. This distinction materially changes the paper's contribution and is the main reason the manuscript needs revision.

major comments (3)
  1. [Section III-A vs. Section I] The inclusion criterion called 'AGI Focus' equates AGI with 'advanced AI methodologies applicable to IoX or IoT systems,' explicitly including neuro-symbolic reasoning, active inference, causal reasoning, and foundation models. This contradicts Section I's definition of AGI as the ability to 'perform a wide range of cognitive tasks at a human-like level.' Several included studies are narrow by that definition, for example active sensing on edge devices [60], MAPPO with federated learning [66], and an LLM-based multi-agent system for urban IoT [33]. Because the corpus was selected using this broad criterion, the conclusion in Section IX that 'AGI-enabled solutions effectively mitigate sensing-layer challenges' is not supported by the reviewed evidence. The authors should either (a) apply a definition-consistent AGI criterion and separate evidence for narrow AI from evidence for general intelligence, or (b) reframe the survey's scope as 'advanced AI' and revise the title, abstract, and conclusions accordingly.
  2. [Section V and Table II] The 'critical analysis' in Section V and the summary in Table II mix conceptual and experimental studies without quality assessment or quantitative synthesis. Many entries are explicitly conceptual (e.g., [69], [75], [32], [94]), and the experimental numbers that appear in Section VI (e.g., 61% reliability improvement in [37], 25% throughput improvement in [66], 50% perception latency reduction in [60], 95% memory reduction in [57]) are drawn from heterogeneous cited works with different baselines, tasks, and evaluation protocols. Therefore, the claim in Section III-C that the review synthesizes 'performance impacts' and the conclusion that AGI 'effectively mitigates' bottlenecks are not supported by a reproducible evidence aggregation. A systematic review should include an explicit assessment of study quality and a transparent account of which findings are replicated across multiple independent studies.
  3. [Section IX] The conclusion self-acknowledges 'the lack of large-scale, real-world validation for many proposed architectures' and lists computational requirements, scalability, and robustness as open problems. This limitation is in direct tension with the abstract's 'Key findings suggest that AGI-driven strategies... offer novel solutions' and with the conclusion's assertion that these solutions 'effectively mitigate sensing-layer challenges.' The strength of the claims should be calibrated to the evidence: if the surveyed works are mostly conceptual or narrowly validated, the paper should present them as promising research directions rather than demonstrated solutions.
minor comments (5)
  1. [Abstract] The abstract contains grammatical errors and a sentence fragment: 'while resolving network-layer issues such as protocol heterogeneity and dynamic spectrum management, neuro-symbolic reasoning, active inference, and causal reasoning,' is not a complete clause, and 'we believe AGI-enhanced IoX is emerging' should be capitalized and integrated. A careful proofread is needed.
  2. [Throughout] Terminology for the 'Internet of Thinking' is inconsistently rendered as 'IoTk' in Section I and 'IoK' in Sections II and V and Figure 5. Please unify the notation.
  3. [Table II] Table II labels many entries as 'Key Findings' even when the study is explicitly conceptual. Adding an 'Evidence Type' column or using phrases like 'Proposed approach' for conceptual works would make the table more informative and prevent readers from mistaking proposals for validated results.
  4. [Section VII-A] The paragraph on cross-layer coordination contains a comma splice and an incomplete 'for example, higher sensing fidelity increasing data transmission demands beyond network capacity' construction. Recast this paragraph to improve readability.
  5. [Reference citations] The text alternates between different citing styles, e.g., 'Authors in [46] identify,' 'the study in [47] highlight,' and '[45] emphasize.' Use a consistent citation style throughout.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the survey's conclusions are inductive syntheses of externally reported results, not derivations from the paper's own definitions.

full rationale

This paper is a literature survey rather than a derivation chain. Its central claims, that AGI-driven strategies mitigate sensing, network, and application bottlenecks, are inductive summaries of results reported in 98 externally selected studies (e.g., [60], [66], [77]), each with its own experiments or conceptual arguments. No equation is fitted and no prediction is generated from the survey's own framework. The heavy use of the authors' prior work ([12], [15], [23], [65], [97]) defines the IoX/CPST vocabulary and bottleneck inventory, but the survey does not invoke those citations as proof that AGI solutions work; the supporting evidence is the reviewed literature itself. The broad 'AGI Focus' screening criterion in Section III-A, which admits 'advanced AI methodologies' such as neuro-symbolic reasoning and active inference, creates a construct-validity concern about whether the reviewed corpus truly tests AGI as defined in Section I; however, this is a scope or conflation issue, not a circular derivation, because the conclusion's effectiveness claims are taken from the studies' reported outcomes rather than from the inclusion rule. The paper also explicitly acknowledges the lack of large-scale real-world validation (Section IX), further indicating that its conclusions are offered as a research synthesis rather than as a closed derivational loop. No load-bearing step reduces to the paper's own inputs by definition or by self-citation.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The survey contains no fitted free parameters. Its load-bearing assumptions are definitional: AGI is treated as including advanced narrow-AI methods, the CPST/IoX layered framework is taken as given, and benefits measured in isolated narrow-AI systems are assumed to transfer to AGI-scale deployments. No new entities are introduced; terms such as Telecom Brain and GenAINet are quoted from cited works.

assumptions (4)
  • ad hoc to paper AGI includes neuro-symbolic reasoning, active inference, causal reasoning, and foundation models; papers using these methods qualify as AGI evidence.
    Section III-A, the AGI Focus criterion, explicitly extends AGI to advanced AI methodologies and to approaches that demonstrate general intelligence capabilities beyond narrow AI. This is an operational definition chosen for the survey, not a standard consensus, and it lets narrow-AI results such as RL, federated learning, and LLMs carry the AGI conclusion.
  • domain assumption The Cyber-Physical-Social-Thinking (CPST) hyperspace and the sensing, network, and application layer split are the correct organizing framework for IoX bottlenecks.
    Section I and Figure 2 adopt CPST and the three-layer architecture from the authors' prior line of work, references [12], [15], [16], and [23], without comparing this taxonomy to competing IoT or edge-computing taxonomies.
  • domain assumption Performance gains reported in cited narrow-AI and conceptual systems transfer to AGI-enhanced IoX systems at scale.
    Section VI and Table III translate cited experimental gains, such as 50% latency reduction in [60] and 25% throughput improvement in [66], into AGI-layer optimizations. The survey itself notes in Section VIII that large-scale real-world validation is still missing.
  • domain assumption AGI with cross-domain reasoning and autonomous adaptation is compatible with resource-constrained IoX edge devices.
    The AGI-driven layer enhancements section assumes deployability, while Table III entries list implementation challenges such as computational complexity and synchronization, and Section VIII calls for lightweight inference engines and hardware accelerators.

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Cite this review

Pith. "Pith review of AGI Enabled Solutions For IoX Layers Bottlenecks In Cyber-Physical-Social-Thinking Space." pith.science (2026). https://pith.science/paper/LU73L6GR

@misc{pith2026250622487,
  author       = {Pith},
  title        = {Pith review of: AGI Enabled Solutions For IoX Layers Bottlenecks In Cyber-Physical-Social-Thinking Space},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LU73L6GR}},
  note         = {Machine review of arXiv:2506.22487}
}
read the original abstract

The integration of the Internet of Everything (IoX) and Artificial General Intelligence (AGI) has given rise to a transformative paradigm aimed at addressing critical bottlenecks across sensing, network, and application layers in Cyber-Physical-Social Thinking (CPST) ecosystems. In this survey, we provide a systematic and comprehensive review of AGI-enhanced IoX research, focusing on three key components: sensing-layer data management, network-layer protocol optimization, and application-layer decision-making frameworks. Specifically, this survey explores how AGI can mitigate IoX bottlenecks challenges by leveraging adaptive sensor fusion, edge preprocessing, and selective attention mechanisms at the sensing layer, while resolving network-layer issues such as protocol heterogeneity and dynamic spectrum management, neuro-symbolic reasoning, active inference, and causal reasoning, Furthermore, the survey examines AGI-enabled frameworks for managing identity and relationship explosion. Key findings suggest that AGI-driven strategies, such as adaptive sensor fusion, edge preprocessing, and semantic modeling, offer novel solutions to sensing-layer data overload, network-layer protocol heterogeneity, and application-layer identity explosion. The survey underscores the importance of cross-layer integration, quantum-enabled communication, and ethical governance frameworks for future AGI-enabled IoX systems. Finally, the survey identifies unresolved challenges, such as computational requirements, scalability, and real-world validation, calling for further research to fully realize AGI's potential in addressing IoX bottlenecks. we believe AGI-enhanced IoX is emerging as a critical research field at the intersection of interconnected systems and advanced AI.

Figures

Figures reproduced from arXiv: 2506.22487 by the authors.

Figure 1
Figure 1. The Hierarchical Relationship of AI, AGI, and ASI [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Bottleneck issues rises in IoX [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Three-Layer AGI-Enabled IoX Architecture [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: PRISMA flow diagram illustrating the process of study selection for the systematic review [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: Taxonomy of AGI-enabled solutions for IoX layers (IoT, IoP, IoK) in cyber-physical-social-thinking space. [PITH_FULL_IMAGE:figures/full_fig_p013_5.png]

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Toward Edge General Intelligence with Agentic AI and Agentification: Concepts, Technologies, and Future Directions

    cs.NI 2025-08 conditional novelty 4.0 of 10

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Reference graph

Works this paper leans on

109 extracted references · 67 canonical work pages · cited by 1 Pith paper

  1. [60]

    Towards smart and adaptive agents for active sensing on edge devices,

    V . Devendra, d. P. Miguel, and V . Tim, “Towards smart and adaptive agents for active sensing on edge devices,” 2025

    work page 2025

  2. [66]

    Design of an iterative method for dynamic resource management in 5g networks with iot integration operations,

    A. Venkata Mahesh, “Design of an iterative method for dynamic resource management in 5g networks with iot integration operations,”Journal of Electrical Systems, vol. 20, no. 5s, pp. 2551–2569, 9 2024. [Online]. Available: http://dx.doi.org/10.52783/cana.v32.1721

    work page doi:10.52783/cana.v32.1721 2024

  3. [33]

    Citygpt: Towards urban iot learning, analysis and interaction with multi-agent system,

    Q. Guan, J. Ouyang, D. Wu, and W. Yu, “Citygpt: Towards urban iot learning, analysis and interaction with multi-agent system,”arXiv.org, 2024. [Online]. Available: https://arxiv.org/abs/2405.14691

    arXiv 2024

  4. [69]

    Distributed machine learning for terrestrial and non-terrestrial internet of things networks,

    T. N. Do and G. Kaddoum, “Distributed machine learning for terrestrial and non-terrestrial internet of things networks,”IEEE Internet of Things Magazine, vol. 6, no. 4, pp. 54–61, 2023. [Online]. Available: http://dx.doi.org/10.1109/IOTM.001.2300063

    work page doi:10.1109/iotm.001.2300063 2023

  5. [75]

    Di Maio,System Level Knowledge Representation for Edge Intelligence

    P. Di Maio,System Level Knowledge Representation for Edge Intelligence. Springer International Publishing, 2022, pp. 255–275. [Online]. Available: http://dx.doi. org/10.1007/978-3-030-80821-1 12

    work page doi:10.1007/978-3-030-80821-1 2022

  6. [32]

    Designing ecosystems of intelligence from first principles,

    K. J. Friston, M. J. Ramstead, A. B. Kiefer, A. Tschantz, C. L. Buckley, M. Albarracin, R. J. Pitliya, C. Heins, B. Klein, B. Millidge, D. A. Sakthivadivel, T. St Clere Smithe, M. Koudahl, S. E. Tremblay, C. Petersen, K. Fung, J. G. Fox, S. Swanson, D. Mapes, and G. Ren ´e, “Designing ecosystems of intelligence from first principles,”Collective Intelligen...

  7. [94]

    Decentralized, secure and cognitive architecture for au- tomotive cyberphysical system of systems,

    A. P. Fournaris, A. Lalos, P. Kapsalas, and C. Koulamas, “Decentralized, secure and cognitive architecture for au- tomotive cyberphysical system of systems,” in2020 9th Mediterranean Conference on Embedded Computing, MECO 2020. IEEE, 2020, pp. 1–5. [Online]. Available: http://dx.doi.org/10.1109/MECO49872.2020.9134336

    arXiv 2020

  8. [37]

    Joint sensing, communication, and ai: A trifecta for resilient thz user experiences,

    C. Chaccour, W. Saad, M. Debbah, and H. V . Poor, “Joint sensing, communication, and ai: A trifecta for resilient thz user experiences,”IEEE Transactions on Wireless Communications, vol. 23, no. 9, pp. 11 444–11 460, 2024. [Online]. Available: http://dx.doi.org/10.1109/TWC.2024.3382192

    arXiv 2024

  9. [57]

    Intelligent iot systems for civil infrastructure monitoring: A research roadmap,

    E. Bertino, M. Jahanshahi, A. Singla, and R.-T. Wu, “Intelligent iot systems for civil infrastructure monitoring: A research roadmap,”Discover Internet of Things, 12 2020. [Online]. Available: http://dx.doi.org/ 10.21203/rs.3.rs-119076/v1

    work page doi:10.21203/rs.3.rs-119076/v1 2020

Show all 109 references
  1. [1]

    Internet of every- thing,

    I. Chatzigiannakis and C. Tselios, “Internet of every- thing,” inIntelligent Computing for Interactive System Design: Statistics, Digital Signal Processing, and Ma- chine Learning in Practice, 2021, pp. 21–56

    2021

  2. [2]

    A tutorial of cyber- syndrome viewed from cyber-physical-social-thinking space and maslow’s hierarchy of needs,

    F. Shi, H. Ning, and S. Dhelim, “A tutorial of cyber- syndrome viewed from cyber-physical-social-thinking space and maslow’s hierarchy of needs,”arXiv preprint arXiv:2111.02775, 2021

    2021 arXiv

  3. [3]

    Ning,Unit and ubiquitous internet of things

    H. Ning,Unit and ubiquitous internet of things. CRC press, 2013

    2013

  4. [4]

    Socially intelligent robots: dimensions of human–robot interaction,

    K. Dautenhahn, “Socially intelligent robots: dimensions of human–robot interaction,”Philosophical transactions of the royal society B: Biological sciences, vol. 362, no. 1480, pp. 679–704, 2007

    2007

  5. [5]

    Compath: User interest mining in heterogeneous signed social networks for internet of people,

    S. Dhelim, H. Ning, and N. Aung, “Compath: User interest mining in heterogeneous signed social networks for internet of people,”IEEE Internet of Things Journal, 2020. [Online]. Available: https: //ieeexplore.ieee.org/abstract/document/9253614/

    2020

  6. [6]

    From the internet of things to the internet of people,

    J. Miranda, N. M ¨akitalo, J. Garcia-Alonso, J. Berrocal, T. Mikkonen, C. Canal, and J. M. Murillo, “From the internet of things to the internet of people,”IEEE Internet Computing, vol. 19, no. 2, pp. 40–47, 2015

    2015

  7. [7]

    Development of multimodal user interfaces to internet for common people,

    D. Samanta, S. Ghosh, S. Dey, S. Sarcar, M. K. Sharma, P. K. Saha, and S. Maiti, “Development of multimodal user interfaces to internet for common people,” in2012 4th International Conference on Intelligent Human Computer Interaction (IHCI), 2012, pp. 1–8. PREPRINT ARXIV 27

    2012

  8. [8]

    The internet of people: A human and data-centric paradigm for the next generation internet,

    M. Conti and A. Passarella, “The internet of people: A human and data-centric paradigm for the next generation internet,”Computer Communications, vol. 131, pp. 51–65, 2018, cOMCOM 40 years. [Online]. Available: https://www.sciencedirect.com/ science/article/pii/S0140366418305127

    2018

  9. [9]

    Internet of brain, thought, thinking, and creation,

    Z. Zhang, R. Yin, and H. Ning, “Internet of brain, thought, thinking, and creation,”Chinese journal of electronics, vol. 31, no. 6, pp. 1025–1042, 2022

    2022

  10. [10]

    Cyber- physical-social systems: Taxonomy, challenges, and op- portunities,

    S. Pasandideh, P. Pereira, and L. Gomes, “Cyber- physical-social systems: Taxonomy, challenges, and op- portunities,”IEEE Access, vol. 10, pp. 42 404–42 419, 2022

    2022

  11. [11]

    Cyber-physical-social based security architecture for future internet of things,

    H. Ning, H. Liuet al., “Cyber-physical-social based security architecture for future internet of things,”Ad- vances in Internet of Things, vol. 2, no. 01, p. 1, 2012

    2012

  12. [12]

    Cyber-physical-social-thinking space based science and technology framework for the internet of things,

    H. Ning and H. Liu, “Cyber-physical-social-thinking space based science and technology framework for the internet of things,”Science China. Information Sci- ences, vol. 58, no. 3, pp. 1–19, 2015

    2015

  13. [13]

    Intelligence (agi),

    O. Arshi and A. Chaudhary, “Intelligence (agi),”Artifi- cial General Intelligence (AGI) Security: Smart Appli- cations and Sustainable Technologies, p. 1, 2024

    2024

  14. [14]

    General cyberspace: Cyberspace and cyber-enabled spaces,

    H. Ning, X. Ye, M. A. Bouras, D. Wei, and M. Daneshmand, “General cyberspace: Cyberspace and cyber-enabled spaces,”. . . Internet of Things . . ., 2018. [Online]. Available: https://ieeexplore.ieee.org/abstract/ document/8315126/

    2018

  15. [15]

    From iot to future cyber-enabled internet of x and its fundamental issues,

    H. Ning, F. Shi, S. Cui, and M. Daneshmand, “From iot to future cyber-enabled internet of x and its fundamental issues,”IEEE Internet of Things . . .,

  16. [16]

    A review of the smart world,

    H. Liu, H. Ning, Q. Mu, Y . Zheng, J. Zeng, L. T. Yang, R. Huang, and J. Ma, “A review of the smart world,”Future generation . . ., 2019. [Online]. Available: https://www.sciencedirect.com/ science/article/pii/S0167739X17319532

    2019

  17. [17]

    The intelligence spectrum: Unraveling the path from ani to asi,

    S. Iqbal, “The intelligence spectrum: Unraveling the path from ani to asi,”Journal of Computing & Biomed- ical Informatics, vol. 7, no. 02, 2024

    2024

  18. [18]

    A study on narrow artificial intelligence—an overview,

    M. V . S. Babu and K. Banana, “A study on narrow artificial intelligence—an overview,”Int. J. Eng. Sci. Adv. Technol, vol. 24, pp. 210–219, 2024

    2024

  19. [19]

    Levels of agi for operationalizing progress on the path to agi,

    M. R. Morris, J. Sohl-Dickstein, N. Fiedel, T. Warkentin, A. Dafoe, A. Faust, C. Farabet, and S. Legg, “Levels of agi for operationalizing progress on the path to agi,”arXiv preprint arXiv:2311.02462, 2023

    2023

  20. [20]

    Evolution and control of artificial superintelligence (asi): A management perspective,

    K. Narain, A. Swami, A. Srivastava, and S. Swami, “Evolution and control of artificial superintelligence (asi): A management perspective,”Journal of Advances in Management Research, vol. 16, no. 5, pp. 698–714, 2019

    2019

  21. [21]

    Security and privacy issues of physical ob- jects in the iot: Challenges and opportunities,

    X. Yao, F. Farha, R. Li, I. Psychoula, L. Chen, and H. Ning, “Security and privacy issues of physical ob- jects in the iot: Challenges and opportunities,”Digital Communications and Networks, vol. 7, no. 3, pp. 373– 384, 2021

    2021

  22. [22]

    A survey: Cyber-physical-social systems and their system- level design methodology,

    J. Zeng, L. T. Yang, M. Lin, H. Ning, and J. Ma, “A survey: Cyber-physical-social systems and their system- level design methodology,”Future Generation Com- puter Systems, vol. 105, pp. 1028–1042, 2020

    2020

  23. [23]

    A survey on the bottleneck between applications exploding and user requirements in iot,

    S. Cui, F. Farha, H. Ning, Z. Zhou, F. Shi, and M. Daneshmand, “A survey on the bottleneck between applications exploding and user requirements in iot,” IEEE Internet of Things Journal, vol. 9, no. 1, pp. 261– 273, 2021

    2021

  24. [24]

    Sensor search techniques for sensing as a service architecture for the internet of things,

    C. Perera, A. Zaslavsky, C. H. Liu, M. Compton, P. Christen, and D. Georgakopoulos, “Sensor search techniques for sensing as a service architecture for the internet of things,”IEEE Sensors Journal, vol. 14, no. 2, pp. 406–420, 2013

    2013

  25. [25]

    Learning how to communicate in the internet of things: Finite resources and heterogeneity,

    T. Park, N. Abuzainab, and W. Saad, “Learning how to communicate in the internet of things: Finite resources and heterogeneity,”IEEE Access, vol. 4, pp. 7063–7073, 2016

    2016

  26. [26]

    Improving latency in internet-of-things and cloud computing for real-time data transmission: a systematic literature review (slr),

    S. Shukla, M. F. Hassan, D. C. Tran, R. Akbar, I. V . Paputungan, and M. K. Khan, “Improving latency in internet-of-things and cloud computing for real-time data transmission: a systematic literature review (slr),” Cluster Computing, pp. 1–24, 2023

    2023

  27. [27]

    The internet of things and the explo- sion of interconnectivity,

    S. Ornes, “The internet of things and the explo- sion of interconnectivity,”Proceedings of the National Academy of Sciences, vol. 113, no. 40, pp. 11 059– 11 060, 2016

    2016

  28. [28]

    Task allocation among connected devices: Requirements, approaches, and challenges,

    V . Pilloni, H. Ning, and L. Atzori, “Task allocation among connected devices: Requirements, approaches, and challenges,”IEEE Internet of Things Journal,

  29. [29]

    A generic preprocessing architecture for multi-modal iot sensor data in artificial general intelligence †,

    N. Dmytryk and A. Leivadeas, “A generic preprocessing architecture for multi-modal iot sensor data in artificial general intelligence †,”Electronics (Switzerland), vol. 11, no. 22, p. 3816, 11 2022. [Online]. Available: http://dx.doi.org/10.3390/electronics11223816

    2022 doi

  30. [30]

    Meta-learning approaches for context-aware decision support in smart agriculture and autonomous systems,

    Z. B. Hassan and H. M. Yusof, “Meta-learning approaches for context-aware decision support in smart agriculture and autonomous systems,”PatternIQ Mining, vol. 1, no. 4, 11 2024. [Online]. Available: http://dx.doi.org/10.70023/sahd/241106

    2024 doi

  31. [31]

    Context- aware collaborative neuro-symbolic inference in iobts,

    T. Abdelzaher, N. D. Bastian, S. Jha, L. Kaplan, M. Srivastava, and V . V . Veeravalli, “Context- aware collaborative neuro-symbolic inference in iobts,” inProceedings - IEEE Military Communications Conference MILCOM, vol. 2022-November. IEEE, 11 2022, pp. 1053–1058. [Online]....

    2022

  32. [34]

    When iot meet llms: Applications and challenges,

    I. Kok, O. Demirci, and S. Ozdemir, “When iot meet llms: Applications and challenges,” in2024 IEEE International Conference on Big Data (BigData). IEEE, 12 2024, pp. 7075–7084. [Online]. Available: http://arxiv.org/abs/2411.17722

    2024 arXiv

  33. [35]

    The internet of senses: Building on semantic communications and edge intelligence,

    R. Joda, M. Elsayed, H. Abou-Zeid, R. Atawia, A. B. Sediq, G. Boudreau, M. Erol-Kantarci, and L. Hanzo, “The internet of senses: Building on semantic communications and edge intelligence,”IEEE Network, vol. 37, no. 3, pp. 68–75, 2023. [Online]. Available: http://dx.doi.org/10....

    2023 doi

  34. [36]

    Smart mobility with agent-based foundation models: Towards interactive and collaborative intelligent vehicles,

    B. Xia, P. Xie, and J. Wang, “Smart mobility with agent-based foundation models: Towards interactive and collaborative intelligent vehicles,”IEEE Transactions on Intelligent Vehicles, vol. 9, no. 7, pp. 1–4, 2024. [Online]. Available: http://dx.doi.org/10.1109/TIV .2024. 3457759

    2024 doi

  35. [38]

    Driverp: Rag and prompt engineering embodied parallel driving in cyber- physical-social spaces,

    J. Huang, H. Ma, T. Zhang, F. Lin, S. Ma, X. Wang, and F. Y . Wang, “Driverp: Rag and prompt engineering embodied parallel driving in cyber- physical-social spaces,” inProceedings - 2024 IEEE 4th International Conference on Digital Twins and Parallel Intelligence, DTPI 2024. I...

    2024

  36. [39]

    Toward performance optimization in iot-based next-gen wireless sensor networks,

    M. Behzad, M. Abdullah, M. T. Hassan, Y . Ge, and M. A. Khan, “Toward performance optimization in iot-based next-gen wireless sensor networks,”arXiv preprint arXiv:1806.09980, 2018

    2018 arXiv

  37. [40]

    Ris-enabled integrated sensing, computing and communication for internet of robotic things,

    J. Shu, K. Ota, and M. Dong, “Ris-enabled integrated sensing, computing and communication for internet of robotic things,”IEEE Internet of Things Journal, 2024

    2024

  38. [41]

    Firedex: a prioritized iot data exchange middle- ware for emergency response,

    K. E. Benson, G. Bouloukakis, C. Grant, V . Issarny, S. Mehrotra, I. Moscholios, and N. Venkatasubrama- nian, “Firedex: a prioritized iot data exchange middle- ware for emergency response,” inProceedings of the 19th International Middleware Conference, 2018, pp. 279–292

    2018

  39. [42]

    Towards an interoperable internet of things through a web of virtual things at the fog layer,

    B. Negash, T. Westerlund, and H. Tenhunen, “Towards an interoperable internet of things through a web of virtual things at the fog layer,”Future Generation Computer Systems, vol. 91, pp. 96–107, 2019

    2019

  40. [43]

    Vers le futur internet d’objets au travers d’une optimisation inter-couches des protocoles stan- dardis´es,

    B. Pavkovic, “Vers le futur internet d’objets au travers d’une optimisation inter-couches des protocoles stan- dardis´es,” Ph.D. dissertation, Universit ´e de Grenoble, 2012

    2012

  41. [44]

    En- abling seamless connectivity: Networking innovations in wireless sensor networks for industrial application,

    S. Duobiene, R. Simni ˇskis, and G. Ra ˇciukaitis, “En- abling seamless connectivity: Networking innovations in wireless sensor networks for industrial application,” Sensors (Basel, Switzerland), vol. 24, no. 15, p. 4881, 2024

    2024

  42. [45]

    Low-latency in-band integration of multiple low-power wide-area networks,

    V . P. Modekurthy, D. Ismail, M. Rahman, and A. Sai- fullah, “Low-latency in-band integration of multiple low-power wide-area networks,” in2021 IEEE 27th Real-Time and Embedded Technology and Applications Symposium (RTAS). IEEE, 2021, pp. 333–346

    2021

  43. [46]

    Throughput optimization of interference limited cog- nitive radio-based internet of things (cr-iot) network,

    I. Bala, A. Sharma, A. Tselykh, and B.-G. Kim, “Throughput optimization of interference limited cog- nitive radio-based internet of things (cr-iot) network,” Journal of King Saud University-Computer and Infor- mation Sciences, vol. 34, no. 7, pp. 4233–4243, 2022

    2022

  44. [47]

    Architecture for an efficient integration of wireless sensor networks to the internet through internet of things gateways,

    P. O. Kamgueu, E. Nataf, and T. Djotio, “Architecture for an efficient integration of wireless sensor networks to the internet through internet of things gateways,” International Journal of Distributed Sensor Networks, vol. 13, no. 11, p. 1550147717744735, 2017

    2017

  45. [48]

    Priodex: a data exchange mid- dleware for efficient event prioritization in sdn-based iot systems,

    G. Bouloukakis, K. Benson, L. Scalzotto, P. Bellavista, C. Grant, V . Issarny, S. Mehrotra, I. Moscholios, and N. Venkatasubramanian, “Priodex: a data exchange mid- dleware for efficient event prioritization in sdn-based iot systems,”ACM Transactions on Internet of Things, vol...

    2021

  46. [49]

    Adaptive sensing for internet of robotic things platforms with integrated sensing, computing and communication ca- pabilities,

    X. Wang, L. Nkenyereye, S. Rani, and J. Lyu, “Adaptive sensing for internet of robotic things platforms with integrated sensing, computing and communication ca- pabilities,”IEEE Internet of Things Journal, 2024

    2024

  47. [50]

    Architecting and deploying iot smart applications: A performance–oriented approach,

    I. Zyrianoff, A. Heideker, D. Silva, J. Kleinschmidt, J.-P. Soininen, T. Salmon Cinotti, and C. Kamien- ski, “Architecting and deploying iot smart applications: A performance–oriented approach,”Sensors, vol. 20, no. 1, p. 84, 2019

    2019

  48. [51]

    Enhancing quality-of-service conditions using a cross-layer paradigm for ad-hoc vehicular com- munication,

    S. ur Rehman, M. A. Khan, M. Imran, T. A. Zia, and M. Iftikhar, “Enhancing quality-of-service conditions using a cross-layer paradigm for ad-hoc vehicular com- munication,”IEEE Access, vol. 5, pp. 12 404–12 416, 2017

    2017

  49. [52]

    The internet of cooperative agents architecture (x-ioca) for robots, hybrid sensor networks, and mec centers in complex environments: A search and rescue case study,

    J. Bravo-Arrabal, M. Toscano-Moreno, J. J. Fern ´andez- Lozano, A. Mandow, J. A. Gomez-Ruiz, and A. Garc´ıa- Cerezo, “The internet of cooperative agents architecture (x-ioca) for robots, hybrid sensor networks, and mec centers in complex environments: A search and rescue case ...

    2021

  50. [53]

    Emerging trends in mobile robots,

    N. B. Kwe and R. Priyadarshini, “Emerging trends in mobile robots,”Robotics and Smart Autonomous Systems, pp. 77–117, 2024

    2024

  51. [54]

    Sustainable iot sensing applications development through graphql-based ab- straction layer,

    R. Khan and A. Noor Mian, “Sustainable iot sensing applications development through graphql-based ab- straction layer,”Electronics, vol. 9, no. 4, p. 564, 2020

    2020

  52. [55]

    Dataflow management in the internet of things: Sensing, control, and security,

    D. Wei, H. Ning, F. Shi, Y . Wan, J. Xu, S. Yang, and L. Zhu, “Dataflow management in the internet of things: Sensing, control, and security,”Tsinghua Science . . .,

  53. [56]

    Cmxsafe: A proxy layer for securing internet-of-things PREPRINT ARXIV 29 communications,

    J. D. de Hoz Diego, T. Madi, and C. Konstantinou, “Cmxsafe: A proxy layer for securing internet-of-things PREPRINT ARXIV 29 communications,”IEEE Transactions on Information Forensics and Security, 2024

    2024

  54. [58]

    Recent progress on the convergence of the internet of things and artificial intelligence,

    F. Shi, H. Ning, W. Huangfu, F. Zhang, D. Wei, T. Hong, and M. Daneshmand, “Recent progress on the convergence of the internet of things and artificial intelligence,”Ieee . . ., 2020. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9199785/

    2020

  55. [59]

    Available: https://ieeexplore.ieee.org/ abstract/document/9449336/

    [Online]. Available: https://ieeexplore.ieee.org/ abstract/document/9449336/

  56. [61]

    Synergy between communication, computing, and caching for smart sens- ing in internet of things,

    M. Bouras, A. Ullah, and H. Ning, “Synergy between communication, computing, and caching for smart sens- ing in internet of things,”Procedia computer science,

  57. [62]

    Reconfigurable intelligent surface for internet of robotic things,

    N. Wanli, L. Ruyu, Z. Xinran, W. Peng, W. Wen, and T. Hui, “Reconfigurable intelligent surface for internet of robotic things,”IEEE Internet of Things Magazine, 2024

    2024

  58. [63]

    Air–ground integrated artificial intelligence of things with cognition-enhanced interference management,

    C. Ren, J. Song, M. Qiu, Y . Li, and X. Wang, “Air–ground integrated artificial intelligence of things with cognition-enhanced interference management,” Eurasip Journal on Advances in Signal Processing, vol. 2024, no. 1, 1 2024. [Online]. Available: http://dx.doi.org/10.1186/s...

    2024 doi

  59. [64]

    Autonomous crowdsensing: Operating and organizing crowdsensing for sensing automation,

    W. Wu, W. Yang, J. Li, Y . Zhao, Z. Zhu, B. Chen, S. Qiu, Y . Peng, and F. Y . Wang, “Autonomous crowdsensing: Operating and organizing crowdsensing for sensing automation,” IEEE Transactions on Intelligent Vehicles, vol. 9, no. 3, pp. 4254–4258, 2024. [Online]. Available: htt...

    2024

  60. [65]

    A survey and tutorial on “connection exploding meets efficient communication

    H. Ning, F. Farha, Z. N. Mohammad, and M. Daneshmand, “A survey and tutorial on “connection exploding meets efficient communication” in the internet of things,”. . . Internet of Things . . ., 2020. [Online]. Available: https: //ieeexplore.ieee.org/abstract/document/9098906/

    2020

  61. [67]

    Virtualizing ai at the distributed edge towards intelligent iot applications,

    C. Campolo, G. Genovese, A. Iera, and A. Molinaro, “Virtualizing ai at the distributed edge towards intelligent iot applications,”Journal of Sensor and Actuator Networks, vol. 10, no. 1, p. 13, 2 2021. [Online]. Available: http://dx.doi.org/10.3390/JSAN10010013

    2021 doi

  62. [68]

    Secure spectrum access, routing, and hybrid beamforming in an edge-enabled mmwave massive mimo crn-based internet of connected vehicle (iocv) environments,

    D. Pari and J. Natarajan, “Secure spectrum access, routing, and hybrid beamforming in an edge-enabled mmwave massive mimo crn-based internet of connected vehicle (iocv) environments,”Sensors, vol. 22, no. 15, p. 5647, 7 2022. [Online]. Available: http://dx.doi.org/ 10.3390/s22155647

    2022 doi

  63. [70]

    Fapl-dm-bc: A secure and scalable fl framework with adaptive privacy and dynamic masking, blockchain, and xai for the iovs,

    N. Sathwik, V . S. Amballa, M. Sujith, S. Msvpj, and P. M. Sai, “Fapl-dm-bc: A secure and scalable fl framework with adaptive privacy and dynamic masking, blockchain, and xai for the iovs,” 2025

    2025

  64. [71]

    Secure, intelligent, programmable space-air-ground integrated networks,

    S. Scott-Hayward, “Secure, intelligent, programmable space-air-ground integrated networks,” inProceedings of the 2023 Workshop on Recent Advances in Resilient and Trustworthy ML Systems in Autonomous Networks. ACM, 11 2023, pp. 1–1. [Online]. Available: http://dx.doi.org/10.11...

    2023

  65. [72]

    Gai-iov: Bridging generative ai and vehicular networks for ubiquitous edge intelligence,

    G. Xie, Z. Xiong, X. Zhang, R. Xie, S. Guo, M. Guizani, and H. V . Poor, “Gai-iov: Bridging generative ai and vehicular networks for ubiquitous edge intelligence,”IEEE Transactions on Wireless Communications, vol. 23, no. 10, pp. 12 799–12 814,

  66. [73]

    Convergence of information-centric networks and edge intelligence for iov: Challenges and future directions,

    S. S. Musa, M. Zennaro, M. Libsie, and E. Pietrosemoli, “Convergence of information-centric networks and edge intelligence for iov: Challenges and future directions,” Future Internet, vol. 14, no. 7, p. 192, 6 2022. [Online]. Available: http://dx.doi.org/10.3390/fi14070192

    2022 doi

  67. [74]

    Hybrid human-artificial intelligence,

    L. Chen, H. Ning, C. Nugent, and Z. Yu, “Hybrid human-artificial intelligence,”Computer, 2020. [Online]. Available: https://ieeexplore.ieee.org/abstract/ document/9153308/

    2020

  68. [76]

    Hybrid AI for IoT Ac- tionable Insights & Real-Time Data-Driven Networks,

    L. Hugo, G. Mina, and K. R., “Hybrid AI for IoT Ac- tionable Insights & Real-Time Data-Driven Networks,” International Workshop on Self-Supervised Learning, 2022

    2022

  69. [77]

    Gpt-in- the-loop: Supporting adaptation in multiagent systems,

    N. Nascimento, P. Alencar, and D. Cowan, “Gpt-in- the-loop: Supporting adaptation in multiagent systems,” inProceedings - 2023 IEEE International Conference on Big Data, BigData 2023. IEEE, 12 2023, pp. 4674–4683. [Online]. Available: http://dx.doi.org/10. 1109/BigData59044.20...

    2023

  70. [78]

    Available: http://dx.doi.org/10.1109/ TWC.2024.3396276

    [Online]. Available: http://dx.doi.org/10.1109/ TWC.2024.3396276

    2024

  71. [79]

    A paradigm shift in crisis management: The nexus of agi-driven intelligence fusion networks and blockchain trustworthiness,

    Y . Yue and J. Z. Shyu, “A paradigm shift in crisis management: The nexus of agi-driven intelligence fusion networks and blockchain trustworthiness,” Journal of Contingencies and Crisis Management, vol. 32, no. 1, 1 2024. [Online]. Available: http://dx.doi.org/10.1111/1468-5973.12541

    2024

  72. [80]

    Guest editorial special section on hybrid human-artificial intelligence for multimedia computing,

    R. Hamzaoui, H. Ning, C. Wang, R. Malekian, and W. Ding, “Guest editorial special section on hybrid human-artificial intelligence for multimedia computing,”IEEE Transactions on . . ., 2021. [Online]. Available: https://ieeexplore.ieee.org/abstract/ document/9502424/

    2021

  73. [81]

    Artificial general intelligence (agi)-native wireless systems: A journey beyond 6g,

    W. Saad, O. Hashash, C. K. Thomas, C. Chaccour, M. Debbah, N. Mandayam, and Z. Han, “Artificial general intelligence (agi)-native wireless systems: A journey beyond 6g,”arXiv.org, 2024. [Online]. Available: https://arxiv.org/abs/2405.02336

    2024 arXiv

  74. [82]

    Interactive ai with retrieval- augmented generation for next generation networking,

    R. Zhang, H. Du, Y . Liu, D. Niyato, J. Kang, S. Sun, X. Shen, and H. V . Poor, “Interactive ai with retrieval- augmented generation for next generation networking,” IEEE Network, vol. 38, no. 6, pp. 414–424, 2024. [Online]. Available: http://dx.doi.org/10.1109/MNET. 2024.3401159

    2024

  75. [83]

    Ai-native memory: A pathway from llms to- wards agi,

    J. Shang, Z. Zheng, X. Ying, F. Tao, and Mindverse Team, “Ai-native memory: A pathway from llms to- wards agi,”arXiv.org, 2024

    2024

  76. [84]

    Genainet: Enabling wireless collective intelligence via knowledge transfer and reasoning,

    H. Zou, Q. Zhao, L. Bariah, Y . Tian, M. Bennis, S. Lasaulce, M. Debbah, and F. Bader, “Genainet: Enabling wireless collective intelligence via knowledge transfer and reasoning,”arXiv.org, 2024. [Online]. Available: https://arxiv.org/abs/2402.16631 PREPRINT ARXIV 30

    2024 arXiv

  77. [85]

    Integration of mixture of experts and multimodal generative ai in internet of vehicles: A survey,

    M. Xu, D. Niyato, J. Kang, Z. Xiong, A. Jamalipour, Y . Fang, D. I. Kim, Xuemin, and Shen, “Integration of mixture of experts and multimodal generative ai in internet of vehicles: A survey,”arXiv.org, 2024. [Online]. Available: https://arxiv.org/abs/2404.16356

    2024 arXiv

  78. [86]

    Virtual cities: From digital twins to autonomous ai societies,

    A. Nechesov, I. Dorokhov, and J. Ruponen, “Virtual cities: From digital twins to autonomous ai societies,” IEEE Access, 2025

    2025

  79. [87]

    Predicting behavior,

    A. Abbasi, R. Y . Lau, and D. E. Brown, “Predicting behavior,”IEEE Intelligent Systems, vol. 30, no. 3, pp. 35–43, 2015

    2015

  80. [88]

    Thinking in the network,

    S. G ´alik, “Thinking in the network,”Central European Journal of Communication, vol. 13, no. 27, pp. 446– 459, 2020

    2020

  81. [89]

    The future of social networks on the internet: The need for semantics,

    J. Breslin and S. Decker, “The future of social networks on the internet: The need for semantics,”IEEE Internet Computing, vol. 11, no. 6, pp. 86–90, 2007

    2007

  82. [90]

    Future trends for human-ai collaboration: A comprehensive taxonomy of ai/agi using multiple intelligences and learning styles,

    A. Cichocki and A. P. Kuleshov, “Future trends for human-ai collaboration: A comprehensive taxonomy of ai/agi using multiple intelligences and learning styles,” Computational Intelligence and Neuroscience, 2020

    2020

  83. [91]

    Agi: Artificial general intelligence for education,

    E. Latif, G. Mai, M. Nyaaba, X. Wu, N. Liu, G. Lu, S. Li, T. Liu, and X. Zhai, “Agi: Artificial general intelligence for education,”arXiv.org, 2023

    2023

  84. [92]

    C2 design considerations for federated ai/ml systems,

    J. R. Michaelis, A. Morelli, and M. Ali, “C2 design considerations for federated ai/ml systems,” inArtificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV, T. Pham, L. Solomon, and M. E. Hohil, Eds. SPIE, 7 2022, p. 66. [Online]. Available: htt...

    2022 doi

  85. [93]

    Neuro-symbolic explainable artificial intelligence twin for zero-touch ioe in wireless network,

    M. S. Munir, K. T. Kim, A. Adhikary, W. Saad, S. Shetty, S. B. Park, and C. S. Hong, “Neuro-symbolic explainable artificial intelligence twin for zero-touch ioe in wireless network,”IEEE Internet of Things Journal, vol. 10, no. 24, pp. 22 451–22 468, 12 2023. [Online]. Availab...

    2023

  86. [95]

    Vermesan, R

    O. Vermesan, R. John, P. Pype, G. Daalderop, M. Ashwathnarayan, R. Bahr, T. Karlsen, and H. E. Sand,Internet of Vehicles – System of Systems Distributed Intelligence for Mobility Applications. Springer International Publishing, 2021, pp. 93–

    2021

  87. [96]

    Ai embodiment through 6g: Shaping the future of agi,

    L. Bariah and M. Debbah, “Ai embodiment through 6g: Shaping the future of agi,”IEEE Wireless Communications, vol. 31, no. 5, pp. 1–8, 2024. [Online]. Available: http://dx.doi.org/10.1109/MWC. 015.2300521

    2024 doi

  88. [97]

    Causal reasoning: Charting a revolutionary course for next-generation ai-native wireless networks,

    C. K. Thomas, C. Chaccour, W. Saad, M. Debbah, and C. S. Hong, “Causal reasoning: Charting a revolutionary course for next-generation ai-native wireless networks,” IEEE Vehicular Technology Magazine, vol. 19, no. 1, pp. 16–31, 2024. [Online]. Available: http://dx.doi.org/ 10.1...

    2024

  89. [98]

    A survey of data semantization in internet of things,

    F. Shi, Q. Li, T. Zhu, and H. Ning, “A survey of data semantization in internet of things,”Sensors, 2018. [Online]. Available: https://www.mdpi.com/1424-8220/ 18/1/313

    2018

  90. [99]

    Iot-enabled social relationships meet artificial social intelligence,

    S. Dhelim, H. Ning, F. Farha, L. Chen, L. Atzori, and M. Daneshmand, “Iot-enabled social relationships meet artificial social intelligence,”IEEE Internet of Things Journal, 2021. [Online]. Available: https: //ieeexplore.ieee.org/abstract/document/9434396/

    2021

  91. [100]

    An ar- chitecture for aggregating information from distributed data nodes for industrial internet of things,

    T. Zhu, S. Dhelim, Z. Zhou, S. Yang, and ..., “An ar- chitecture for aggregating information from distributed data nodes for industrial internet of things,”Cyber- Enabled Intelligence, 2019

    2019

  92. [101]

    A distributed video analytics architecture based on edge-computing and federated learning,

    A. B. Sada, M. A. Bouras, J. Ma, H. Runhe, and H. Ning, “A distributed video analytics architecture based on edge-computing and federated learning,” in 2019 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Co...

    2019

  93. [103]

    Capd: a context-aware, policy-driven framework for secure and resilient iobt operations,

    S. S. L. Chukkapalli, A. Joshi, T. Finin, and R. F. Erbacher, “Capd: a context-aware, policy-driven framework for secure and resilient iobt operations,” inArtificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV, T. Pham, L. Solomon, and M. E. H...

    2022 doi

  94. [104]

    An attention mechanism inspired selective sensing framework for physical-cyber mapping in internet of things,

    H. Ning, X. Ye, A. B. Sada, L. Mao, and M. Daneshmand, “An attention mechanism inspired selective sensing framework for physical-cyber mapping in internet of things,”IEEE Internet of Things . . .,

  95. [105]

    Available: https://ieeexplore.ieee.org/ abstract/document/8765791/

    [Online]. Available: https://ieeexplore.ieee.org/ abstract/document/8765791/

  96. [147]

    Available: http://dx.doi.org/10.1007/ 978-3-030-76493-7 4

    [Online]. Available: http://dx.doi.org/10.1007/ 978-3-030-76493-7 4

  97. [2019]

    Available: https://www.sciencedirect

    [Online]. Available: https://www.sciencedirect. com/science/article/pii/S1877050919302662

  98. [2020]

    Available: https://ieeexplore.ieee.org/ abstract/document/9239362/

    [Online]. Available: https://ieeexplore.ieee.org/ abstract/document/9239362/

  99. [2021]

    Available: https://ieeexplore.ieee.org/ abstract/document/9611543/

    [Online]. Available: https://ieeexplore.ieee.org/ abstract/document/9611543/

  100. [2024]

    Available: http://dx.doi.org/10.1177/ PREPRINT ARXIV 28 26339137231222481

    [Online]. Available: http://dx.doi.org/10.1177/ PREPRINT ARXIV 28 26339137231222481

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.