Pith. sign in

REVIEW 4 major objections 5 minor 73 references

Connectivity for AI enabled cities -- A field survey based study of emerging economies

T0 review · 4 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read A Kathmandu field survey finds connectivity gaps, not lack of demand, are what block AI-enabled cities in emerging economies.

desk verdict A likeable but thin field survey: the Kathmandu data are new and plausible, yet the missing methodology and a toy simulation leave the paper short of a research contribution. read the letter →

arxiv 2501.09479 v1 pith:BNX5MHCE submitted 2025-01-16 cs.CY

Dibakar Das , Jyotsna Bapat , Angeliki Katsenou , Sushmita Shrestha This is my paper · ORCID
classification cs.CY
keywords artificialintelligencesmartcitiestelecommunicationsinfrastructureinternetaffordabilityWi-FicongestiondigitaldivideemergingeconomiesKathmandusurvey
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

Before AI-enabled urban services can succeed in emerging economies, the connectivity problem has to be solved first. This paper argues that a field survey of three Kathmandu neighborhoods — a commercial hub, a low-income settlement, and a high-income settlement — shows residents already use the internet heavily and uniformly want faster, cheaper, more reliable service. The authors claim the main barrier is not lack of demand but inadequate telecommunications infrastructure plus a price structure that pushes users onto congested shared Wi-Fi, and they simulate how that congestion develops when cellular data costs ten times as much as Wi-Fi. Because more than three billion people are unserved or underserved and most of the world's population lives in emerging economies, the paper concludes that closing this gap is a precondition for AI applications in urban areas.

What carries the argument

The empirical engine is a deliberately chosen field survey of three Kathmandu areas — Asan, a commercial hub; Bansighat, a low-income settlement; and Kusunti, a high-income area — capturing age, education, gender, occupation, internet usage, and complaints. The explanatory mechanism is a techno-economic simulation of Wi-Fi congestion that assumes cellular data costs ten times as much as Wi-Fi and declares congestion when average bandwidth per user drops below 10 Mbps; the simulation reproduces the alternating crowding and retreat the survey respondents described. This pricing mechanism is what carries the paper's claim that affordability, not just infrastructure, drives network quality.

What would settle it

Run continuous speed tests in Asan, Bansighat, and Kusunti during the evening and weekend hours when residents reported slowdowns, and compare measured per-user bandwidth to the survey responses; if average bandwidth stays above 10 Mbps during those peak periods, the paper's congestion diagnosis would be directly contradicted. A complementary test would lower cellular data prices in one neighborhood while holding a matched comparison neighborhood unchanged and observe whether Wi-Fi congestion actually decreases, as the pricing mechanism predicts.

Watch

Extended reading notes

Core claim

On the paper's own terms, the discovery is that affordable, reliable connectivity is the missing precondition for AI-enabled cities in emerging economies. The Kathmandu survey finds that in all three settings people use the internet for communication, entertainment, banking, learning, and business, and that they consistently report slowdowns, buffering, and high costs, with congestion worst in the evenings and on weekends. The accompanying simulation shows that when cellular data is priced at ten times Wi-Fi, users crowd onto shared Wi-Fi, average bandwidth per user falls below the 10 Mbps congestion threshold, and the cycle of crowding, retreat, and return repeats, matching the survey's complaints. The paper therefore concludes that proper pricing of internet services is key to avoiding congestion and that network investment, public or public-private, is needed to make AI-driven urban life a reality in emerging countries.

Load-bearing premise

The load-bearing premise is that the three deliberately chosen Kathmandu neighborhoods, and residents' self-reported complaints about internet service, stand for urban connectivity problems across emerging economies; the paper provides no sampling frame, response rate, or independent network measurements to anchor that extrapolation.

Editorial extensions

If this is right

  • If correct, the price gap between cellular data and shared Wi-Fi is an actionable lever: repricing cellular data could relieve congestion even before new infrastructure is built.
  • If correct, AI applications in urban governance, healthcare, finance, and public safety will remain unreliable in emerging-economy cities until last-mile networks and investment gaps are closed.
  • If correct, network slicing and spectrum sharing could protect essential services by giving them guaranteed quality even when consumer traffic overloads shared connections.
  • If correct, free or subsidized internet for essential services would help bridge the digital divide, but only if capacity and pricing are managed to avoid recreating the congestion loop.
  • If correct, the Kathmandu pattern should be found in other dense emerging-economy cities with similar income mixes, making it a general barrier rather than a local one.

Reading between the lines

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

  • A direct test the paper does not report: measure objective throughput in the same neighborhoods at the same peak times and compare it with the survey's perceived slowdowns, since perception and measured bandwidth can diverge.
  • The pricing mechanism implies a testable elasticity: if cellular data prices drop in one neighborhood but not in a matched control, Wi-Fi congestion should fall measurably in the treated area, which would confirm the causal story.
  • The paper's call for free internet for essential services sits in tension with its own congestion simulation; a free tier would need prioritization or capacity guarantees, otherwise it may simply move the congestion problem.
  • The survey's three purposively selected sites make the generalization to all emerging economies a hypothesis rather than an established fact; a multi-city, random-sample survey with objective network data would be the natural next study.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper argues that the success of AI-enabled urban applications in emerging economies depends on reliable, affordable telecommunications connectivity. After a broad review of AI in urban governance, healthcare, sustainability, labor, and economics, it presents a survey conducted in three Kathmandu neighborhoods (Asan, Bansighat, Kusunti) and claims that residents aspire to high-speed, reliable, low-cost internet. It also introduces a techno-economic simulation in Section 12.5 intended to show that cellular data priced much higher than Wi-Fi leads to Wi-Fi congestion, and it concludes that proper pricing of internet services is key to avoiding network congestion. The paper's central message is that infrastructure investment, affordability, and QoS are prerequisites for AI-enabled cities in emerging economies.

Significance. If the empirical claims were fully supported, the paper would make a useful contribution to the digital-divide and smart-city literature by grounding connectivity barriers in field data from an under-studied city. The qualitative descriptions of user experiences, the reported cost differential between Wi-Fi and cellular data, and the identified congestion and reliability complaints are plausible and policy-relevant. The paper also usefully connects a set of AI-for-urban-life applications to the often-overlooked network infrastructure constraint. However, the paper's empirical core is not currently verifiable: the survey methodology is undocumented, the demographic figures contain internal inconsistencies, and the Section 12.5 simulation is not reproducible. The paper does not ship machine-checked proofs, code, or a full instrument, so its value rests entirely on the adequacy of the reported survey and simulation, which are at present insufficiently described.

major comments (4)
  1. [Section 12 and Section 13] The paper does not report the survey sample size, sampling method, respondent selection criteria, questionnaire, response rate, or analysis procedure. The only methodological information is that three Kathmandu locations were chosen and that respondents were asked about their internet experiences. Without these details, the reader cannot assess selection bias, question wording effects, or whether the responses support the strong generalization in Section 13 that 'the people of emerging economies have high aspirations' for high-speed internet. Please add a complete methods subsection, including the instrument, the number of respondents per site, and the recruitment procedure.
  2. [Section 12, Figures 1 and 2] The reported demographic distributions are internally inconsistent: the percentages in Figure 1 (45+39+14+20) sum to 118%, and those in Figure 2 (8+70+3+17+10) sum to 108%. These sums cannot both be valid percentage distributions, so the demographic context of the survey is unclear. Please correct the values, state the exact category definitions, and explain how rounding was handled.
  3. [Section 12.5] The conclusion that 'proper pricing of internet services is key to avoid network congestion' rests on a simulation that is not described in sufficient detail to be checked or reproduced. The text gives only two assumptions (cellular data priced at ten times Wi-Fi, and congestion defined as average bandwidth per user below 10 Mbps) and reports no model equations, demand distribution, user switching rule, network capacity parameters, calibration to Kathmandu prices or traffic, or sensitivity analysis. The statement that the phenomenon 'was observed in the deployed network' is anecdotal and does not substitute for validation. Please either provide the full model with calibration and sensitivity results, or reframe the conclusion as a hypothesis with explicit limitations.
  4. [Abstract, Section 11, and Section 13] The paper generalizes from three purposively selected neighborhoods in a single city to 'emerging economies' in the title, abstract, and discussion. There is no evidence that Asan, Bansighat, and Kusunti are representative of the diversity of urban connectivity conditions across emerging economies, and the survey captures perceived QoS rather than objective network measurements. Please narrow the scope of the claims to Kathmandu or provide a reasoned sampling justification and external validity evidence.
minor comments (5)
  1. [Keywords and Section 1] The keyword 'emerging economics' should be 'emerging economies', and Section 1 contains the phrase 'In all workplace' which should be 'In the workplace'.
  2. [Section 12.5] The sentence 'proper pricing is internet services is key' contains a typo and should read 'proper pricing of internet services is key'.
  3. [Figures 5 and 6] The word clouds and word-frequency plots would benefit from a description of the text-processing steps (stopword removal, stemming, part-of-speech filtering) and the number of responses used to generate them.
  4. [Reference [38]] Reference [38] is a book review of Zuboff's 'The Age of Surveillance Capitalism'; as cited, it does not directly support the claim about legal frameworks for AI in Section 6. Please replace it with a primary source on AI regulation or clarify the connection.
  5. [Section 12.1 and 12.2] Several statements such as 'One-third of them use mobile banking applications' and 'On average, users are paying NPR 1430/month' would be more useful if accompanied by the relevant sample sizes and the number of respondents making each statement.

Circularity Check

1 steps flagged · score 6.0 of 10

Techno-economic simulation in §12.5 builds its conclusion into its inputs: the assumed 10x price ratio and the 10 Mbps congestion threshold force the Wi-Fi congestion result, so the claim that 'proper pricing is key' is not independently derived.

  1. self definitional [Section 12.5, 'Techno-economic simulation of bandwidth utilization and network congestion', paragraph spanning Figures 7-8]
    "Cellular data was assumed to be priced at ten times that of Wi-Fi. Network congestion is declared when the average bandwidth per user is less than 10 Mbps. ... Situation improves temporarily when users retreat from Wi-Fi due to acute congestion. However, the same cycle repeats because the lower cost drives users again back to Wi-Fi network after some time. ... This result shows that proper pricing is internet services is key to avoid network congestion since the latter can have negative impact the essential services provided in urban areas."

    The simulation's two inputs already contain the conclusion. It assumes cellular data is priced ten times higher than Wi-Fi, assumes users therefore prefer Wi-Fi, and defines congestion as average bandwidth per user below 10 Mbps. Given these inputs, Wi-Fi congestion and the retreat-and-return cycle are forced by construction: cheaper Wi-Fi attracts users, and the fixed Wi-Fi capacity divided among more users falls below the declared threshold. The sentence 'This result shows that proper pricing is internet services is key to avoid network congestion' restates the model's assumptions as an output rather than deriving the policy claim from calibrated data, user behavior observations, or sensitivity analysis.

full rationale

The survey portion of the paper is empirical and not circular: it reports self-reported experiences and aspirations from three purposively selected Kathmandu areas, and the conclusion that residents want affordable, high-speed internet is a summary of those responses rather than a derivation that reduces to its inputs. The one load-bearing circular step is the techno-economic simulation in Section 12.5. There, 'congestion' is defined as per-user bandwidth below 10 Mbps, and a 10x cellular/Wi-Fi price differential is assumed to drive users to Wi-Fi; the plotted congestion and the repeated retreat/rejoin cycles are logical consequences of those assumptions, so the 'result' that pricing is key to avoiding congestion is equivalent to the model's inputs by construction. No calibration, demand model, or sensitivity analysis is supplied. The paper also contains a self-citation to reference [69] (Katsenou is a co-author), but that citation is not load-bearing for the main argument and does not create circularity. There are data-quality concerns, such as percentages in Figures 1 and 2 summing above 100 percent, but those are empirical reporting issues rather than circularity. Overall, the central survey finding has independent content, but one claimed numerical 'result' reduces by construction, yielding a partial circularity score of 6.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The paper introduces no new entities. Its small simulation relies on two hand-set free parameters, and its qualitative conclusions rest on assumptions about representativeness and self-report validity.

free parameters (2)
  • Cellular-to-WiFi price ratio = 10x
    Set by hand in Section 12.5 to generate congestion; no calibration or sensitivity analysis is provided.
  • Congestion bandwidth threshold = 10 Mbps average per user
    Chosen in Section 12.5 to define network congestion; no basis or error analysis is provided.
assumptions (4)
  • domain assumption Self-reported internet quality problems reflect actual network conditions.
    Survey quotes in Section 12 are treated as evidence of infrastructure inadequacy without independent network measurements.
  • domain assumption The three chosen neighborhoods are representative of emerging-economy urban connectivity.
    Section 12 says the locations were chosen to represent commercial, low-income, and high-income settings, but no sampling frame or justification for representativeness is given.
  • domain assumption High-quality connectivity is a necessary precondition for AI-enabled city applications.
    The paper assumes this throughout Sections 9 to 13; it is the paper's framing rather than a proven result.
  • domain assumption The simulation's user behavior model is realistic.
    Users migrating to cheaper Wi-Fi and causing congestion cycles are modeled without user behavior data or validation in Section 12.5.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Connectivity for AI enabled cities -- A field survey based study of emerging economies." pith.science (2026). https://pith.science/paper/BNX5MHCE

@misc{pith2026250109479,
  author       = {Pith},
  title        = {Pith review of: Connectivity for AI enabled cities -- A field survey based study of emerging economies},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/BNX5MHCE}},
  note         = {Machine review of arXiv:2501.09479}
}
read the original abstract

The impact of Artificial Intelligence (AI) is transforming various aspects of urban life, including, governance, policy and planning, healthcare, sustainability, economics, entrepreneurship, etc. Although AI immense potential for positively impacting urban living, its success depends on overcoming significant challenges, particularly in telecommunications infrastructure. Smart city applications, such as, federated learning, Internet of Things (IoT), and online financial services, require reliable Quality of Service (QoS) from telecommunications networks to ensure effective information transfer. However, with over three billion people underserved or lacking access to internet, many of these AI-driven applications are at risk of either remaining underutilized or failing altogether. Furthermore, many IoT and video-based applications in densely populated urban areas require high-quality connectivity. This paper explores these issues, focusing on the challenges that need to be mitigated to make AI succeed in emerging countries, where more than 80% of the world population resides and urban migration grows. In this context, an overview of a case study conducted in Kathmandu, Nepal, highlights citizens' aspirations for affordable, high-quality internet-based services. The findings underscore the pressing need for advanced telecommunication networks to meet diverse user requirements while addressing investment and infrastructure gaps. This discussion provides insights into bridging the digital divide and enabling AI's transformative potential in urban areas.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

73 extracted references · 47 canonical work pages

  1. [1]

    (2019) Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again

    Topol, E.J. (2019) Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Basic Books

    work page 2019

  2. [2]

    Brynjolfsson, E., & McAfee, A. (2014). The second machine age: work, progress, and prosperity in a time of brilliant technologies. First edition. New York, W.W . Norton & Company

    work page 2014

  3. [3]

    Davenport, T., Guha, A., Grewal, D. et al. How artificial intelligence will change the future of marketing. J. of the Acad. Mark. Sci. 48, 24–42 (2020). https://doi.org/10.1007/s11747-019-00696-0

    doi:10.1007/s11747-019-00696-0 2020

  4. [4]

    and Campos-Cordobés, S

    Torre-Bastida, A.I., Del Ser, J., Laña, I., Ilardia, M., Bilbao, M.N. and Campos-Cordobés, S. (2018), Big Data for transportation and mobility: recent advances, trends and challenges. IET Intell. Transp. Syst., 12: 742-755. https://doi.org/10.1049/iet-its.2018.5188

    work page arXiv 2018

  5. [5]

    D’ Alberto, R., & Giudici, H. (2023). A sustainable smart mobility? Opportunities and challenges from a big data use perspective. Sustainable Futures, 6(100118), 100118. doi:10.1016/j.sftr .2023.100118

    work page arXiv 2023

  6. [6]

    Liyanage, S., Abduljabbar, R., Dia, H., & Tsai, P .-W. (2022). AI-based neural network models for bus passenger demand forecasting using smart card data. Journal of Urban Management , 11(3), 365 –380. doi:10.1016/j.jum.2022.05.002

    work page doi:10.1016/j.jum.2022.05.002 2022

  7. [7]

    Ahmad, K., Maabreh, M., Ghaly, M., Khan, K., Qadir, J., & Al-Fuqaha, A. (2022). Developing future human- centered smart cities: Critical analysis of smart city security, Data management, and Ethical challenges. Computer Science Review, 43(100452), 100452. doi:10.1016/j.cosrev.2021.100452

    work page arXiv 2022

  8. [8]

    PLoS Med 15(11): e1002689

    Vayena E, Blasimme A, Cohen IG (2018) Machine learning in medicine: Addressing ethical challenges. PLoS Med 15(11): e1002689. https://doi.org/10.1371/journal.pmed.1002689

    work page doi:10.1371/journal.pmed.1002689 2018

Show all 73 references
  1. [9]

    Keesara, S., Jonas, A., & Schulman, K. (2020). Covid -19 and Health Care’s Digital Revolution. New England Journal of Medicine, 382(23), e82. doi:10.1056/NEJMp2005835

    2020 doi

  2. [10]

    A., & Niu, H

    Omitaomu, O. A., & Niu, H. (2021). Artificial Intelligence Techniques in Smart Grid: A Survey. Smart Cities, 4(2), 548–568. doi:10.3390/smartcities4020029

    2021 doi

  3. [11]

    Stecuła, K., Wolniak, R., & Grebski, W. W . (2023). AI-driven urban energy solutions —from individuals to society: A review. Energies, 16(24), 7988. doi:10.3390/en16247988

    2023 doi

  4. [13]

    Ahmed, K., Kumar Dubey, M., Kumar, A., & Dubey, S. (2024). Artificial intelligence and IoT driven system architecture for municipality waste management in smart cities: A review. Measurement: Sensors , 36, 101395. doi:10.1016/j.measen.2024.101395

    2024

  5. [14]

    Broadband Infrastructure, Access and Use

    World Bank. (2023). "Broadband Infrastructure, Access and Use" Available at: https://www.worldbank.org/en/topic/digital/brief/digital-infrastructure

    2023

  6. [15]

    Connecting the Unconnected

    IEEE (2024). "Connecting the Unconnected", Available at: https://ctu.ieee.org/

    2024

  7. [16]

    Verbraeken, J., Wolting, M., Katzy, J., Kloppenburg, J., Verbelen, T., & Rellermeyer, J. S. (2020). A Survey on Distributed Machine Learning. ACM Comput. Surv., 53(2). doi:10.1145/3377454

    2020 doi

  8. [17]

    Kitchin, R. (2014). The data revolution: Big data, open data, data infrastructures & their consequences. SAGE Publications Ltd, https://doi.org/10.4135/9781473909472

    2014 doi

  9. [18]

    Zook, M. (2017). Crowd-sourcing the smart city: Using big geosocial media metrics in urban governance. Big Data & Society, 4(1). https://doi.org/10.1177/2053951717694384

    2017 doi

  10. [19]

    Crawford, Kate & Paglen, Trevor . (2021). Excavating AI: the politics of images in machine learning training sets. AI & SOCIETY. 10.1007/s00146-021-01162-8

    2021 doi

  11. [20]

    Zhang, Z., Lin, X., & Shan, S. (2023). Big data-assisted urban governance: An intelligent real-time monitoring and early warning system for public opinion in government hotline. Future Generations Computer Systems: FGCS, 144, 90–104. doi:10.1016/j.future.2023.03.004

    2023 doi

  12. [21]

    C., Diop, E

    Koumetio Tekouabou, S. C., Diop, E. B., Azmi, R., Jaligot, R., & Chenal, J. (2022). Reviewing the application of machine learning methods to model urban form indicators in planning decision support systems: Potential, issues and challenges. Journal of King Saud University - Co...

    2022

  13. [22]

    Hashem, I. A. T., Chang, V., Anuar, N. B., Adewole, K., Yaqoob, I., Gani, A., … Chiroma, H. (2016). The role of big data in smart city. International Journal of Information Management , 36(5), 748 –758. doi:10.1016/j.ijinfomgt.2016.05.002

    2016 doi

  14. [23]

    H., Weedon, Z., Yigitcanlar, T., Sanchez, T., Corchado, J

    Son, T. H., Weedon, Z., Yigitcanlar, T., Sanchez, T., Corchado, J. M., & Mehmood, R. (2023). Algorithmic urban planning for smart and sustainable development: Systematic review of the literature. Sustainable Cities and Society, 94(104562), 104562. doi:10.1016/j.scs.2023.104562

    2023

  15. [24]

    Noble, S. U. (2018). Algorithms of Oppression: How Search Engines Reinforce Racism. NYU Press

    2018

  16. [25]

    P ., Li, S., Cao, Z., Hu, P

    Zhao, A. P ., Li, S., Cao, Z., Hu, P . J.-H., Wang, J., Xiang, Y., … Lu, X. (2024). AI for science: Predicting infectious diseases. Journal of Safety Science and Resilience, 5(2), 130–146. doi:10.1016/j.jnlssr .2024.02.002

    2024 doi

  17. [26]

    Esteva, A., Robicquet, A., Ramsundar, B. et al. A guide to deep learning in healthcare. Nat Med 25, 24–29 (2019). https://doi.org/10.1038/s41591-018-0316-z

    2019 doi

  18. [27]

    Prediction of patient flow in the emergency department using explainable artificial intelligence

    Moreno-Sánchez PA, Aalto M, van Gils M. Prediction of patient flow in the emergency department using explainable artificial intelligence. DIGITAL HEALTH. 2024;10. doi:10.1177/20552076241264194

    2024 doi

  19. [28]

    Gupta, A., & Katarya, R. (2020). Social media based surveillance systems for healthcare using machine learning: A systematic review. Journal of Biomedical Informatics , 108(103500), 103500. doi:10.1016/j.jbi.2020.103500

    2020

  20. [29]

    Dissecting racial bias in an algorithm used to manage the health of populations

    Ziad Obermeyer et al. Dissecting racial bias in an algorithm used to manage the health of populations. Science 366,447-453(2019).DOI:10.1126/science.aax2342

    2019 doi

  21. [30]

    Hsu, J.-Y., Wang, Y.-F., Lin, K.-C., Chen, M.-Y., & Hsu, J. H.-Y. (2020). Wind Turbine Fault Diagnosis and Predictive Maintenance Through Statistical Process Control and Machine Learning. IEEE Access , 8, 23427 –23439. doi:10.1109/ACCESS.2020.2968615

    2020

  22. [31]

    (10 2019)

    Wang, H., Lei, Z., Zhang, X., Zhou, B., & Peng, J. (10 2019). A review of deep learning for renewable energy forecasting. Energy Conversion and Management, 198, 111799. doi:10.1016/j.enconman.2019.111799

    2019

  23. [32]

    Kaginalkar, A., Kumar, S., Gargava, P ., & Niyogi, D. (2021). Review of urban computing in air quality management as smart city service: An integrated IoT, AI, and cloud technology perspective. Urban Climate, 39(100972), 100972. doi:10.1016/j.uclim.2021.100972

    2021

  24. [33]

    Douglas, I., Anderson, P .M.L., Goode, D., Houck, M.C., Maddox, D., Nagendra, H., & Tan, P .Y. (Eds.). (2020). The Routledge Handbook of Urban Ecology (2nd ed.). Routledge. https://doi.org/10.4324/9780429506758

    2020 doi

  25. [34]

    Mohamed, O. Y. A., & Zahidi, I. (2024). Artificial intelligence for predicting urban heat island effect and optimising land use/land cover for mitigation: Prospects and recent advancements. Urban Climate, 55, 101976. doi:10.1016/j.uclim.2024.101976

    2024

  26. [35]

    Mehlin, V., Schacht, S., & Lanquillon, C. (2023). Towards energy -efficient Deep Learning: An overview of energy-efficient approaches along the Deep Learning Lifecycle. arXiv [Cs.LG] . Retrieved from http://arxiv.org/abs/2303.01980

    2023 arXiv

  27. [36]

    Tu, X., Mallik, A., Chen, D., Han, K., Altintas, O., Wang, H., & Xie, J. (2024). Unveiling Energy Efficiency in Deep Learning: Measurement, Prediction, and Scoring across Edge Devices. Proceedings of the Eighth ACM/IEEE Symposium on Edge Computing , 80 –93. Presented at the Wi...

    2024

  28. [37]

    Van Wynsberghe, A. (2021). Sustainable AI: AI for sustainability and the sustainability of AI. AI and Ethics, 1(3), 213–218. doi:10.1007/s43681-021-00043-6

    2021 doi

  29. [38]

    Mendoza, C. (2022). The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power: by Shoshana Zuboff, New York, Public Affairs, 2019, 704 pp., €20.00, ISBN 978 -1610395694. Church, Communication and Culture, 7(2), 452–455. https://doi.org/10.10...

    2022

  30. [39]

    Proposal for a Regulation Laying Down Harmonized Rules on Artificial Intelligence (Artificial Intelligence Act)

    European Commission. (2021). "Proposal for a Regulation Laying Down Harmonized Rules on Artificial Intelligence (Artificial Intelligence Act)." https://digital-strategy.ec.europa.eu/en/library/proposal- regulation-laying-down-harmonised-rules-artificial-intelligence

    2021

  31. [40]

    (01 2017)

    Voigt, P ., & Bussche, A. (01 2017). The EU General Data Protection Regulation (GDPR): A Practical Guide . doi:10.1007/978-3-319-57959-7

    2017 doi

  32. [41]

    California Consumer Privacy Act of 2018

    California Legislative Information. (2018). "California Consumer Privacy Act of 2018." https://oag.ca.gov/privacy/ccpa

    2018

  33. [42]

    AI for Good: Global Summit

    United Nations. (2024). "AI for Good: Global Summit." Available at: https://aiforgood.itu.int

    2024

  34. [43]

    Good Society

    Cath, C., Wachter, S., Mittelstadt, B., Taddeo, M., & Floridi, L. (2018). Artificial Intelligence and the “Good Society”: the US, EU, and UK approach. Science and Engineering Ethics , 24(2), 505 –528. doi:10.1007/s11948-017-9901-7

    2018 doi

  35. [44]

    Dafoe, A. (2018). AI governance: a research agenda. Governance of AI Program, Future of Humanity Institute, University of Oxford: Oxford, UK, 1442, 1443

    2018

  36. [45]

    Macrorie, R., Marvin, S., & While, A. (2019). Robotics and automation in the city: a research agenda. Urban Geography, 42(2), 197–217. https://doi.org/10.1080/02723638.2019.1698868

    2019

  37. [46]

    Chibani, A., Amirat, Y., Mohammed, S., Matson, E., Hagita, N., & Barreto, M. (2013). Ubiquitous robotics: Recent challenges and future trends. Robotics and Autonomous Systems, 61(11), 1162-1172

    2013

  38. [47]

    M., Aggarwal, N., Cowls, J., Morley, J., Taddeo, M., & Floridi, L

    Tan, Z. M., Aggarwal, N., Cowls, J., Morley, J., Taddeo, M., & Floridi, L. (2021). The ethical debate about the gig economy: A review and critical analysis. Technology in Society, 65, 101594

    2021

  39. [48]

    Bessen, J. (2018). AI and Jobs: the role of demand. doi:10.3386/w24235

    2018 doi

  40. [49]

    P ., van Alstyne, M

    Choudary, S. P ., van Alstyne, M. W., & Parker, G. (2016). Platform Revolution: How Networked Markets Are Transforming the Economy--and How to Make Them Work for You. https://wwnorton.com/books/Platform- Revolution

    2016

  41. [50]

    The Future of Jobs Report 2020

    World Economic Forum. (2020). "The Future of Jobs Report 2020." Available at: https://www.weforum.org/publications/the-future-of-jobs-report-2020/

    2020

  42. [51]

    The geography of AI

    Muro, M., & Liu, S. (2021), “The geography of AI”, Available at: https://www.brookings.edu/articles/the- geography-of-ai/

    2021

  43. [52]

    Shen, X., Gao, J., Wu, W., Li, M., Zhou, C., & Zhuang, W. (2022). Holistic Network Virtualization and Pervasive Network Intelligence for 6G. IEEE Communications Surveys & Tutorials , 24(1), 1 –30. doi:10.1109/COMST .2021.3135829

    2022

  44. [53]

    G., Dustdar, S., Aral, A., … Dustdar, S

    Meuser, T., Lovén, L., Bhuyan, M., Patil, S. G., Dustdar, S., Aral, A., … Dustdar, S. (2024). Revisiting Edge AI: Opportunities and Challenges. IEEE Internet Computing, 28(4), 49–59. doi:10.1109/MIC.2024.3383758

    2024

  45. [54]

    Hazra, A., Rana, P ., Adhikari, M., & Amgoth, T. (2023). Fog computing for next-generation Internet of Things: Fundamental, state -of-the-art and research challenges. Computer Science Review , 48, 100549. doi:10.1016/j.cosrev.2023.100549

    2023

  46. [55]

    E., Rezaei, M., & Leung, H

    Fazel, E., Najafabadi, H. E., Rezaei, M., & Leung, H. (2023). Unlocking the power of mist computing through clustering techniques in IoT networks. Internet of Things, 22, 100710. doi:10.1016/j.iot.2023.100710

    2023

  47. [56]

    Hosen, A. S. M. S., Sharma, P . K., & Cho, G. H. (2022). MSRM-IoT: A Reliable Resource Management for Cloud, Fog, and Mist -Assisted IoT Networks. IEEE Internet of Things Journal , 9(4), 2527 –2537. doi:10.1109/JIOT.2021.3090779

    2022

  48. [57]

    Talal, M., Garfan, S., Qays, R., Pamucar, D., Delen, D., Pedrycz, W., … Simic, V. (2025). A comprehensive systematic review on machine learning application in the 5G -RAN architecture: Issues, challenges, and future directions. Journal of Network and Computer Applications , 23...

    2025

  49. [58]

    Banabilah, S., Aloqaily, M., Alsayed, E., Malik, N., & Jararweh, Y. (2022). Federated learning review: Fundamentals, enabling technologies, and future applications. Information Processing & Management , 59(6), 103061. doi:10.1016/j.ipm.2022.103061

    2022

  50. [59]

    Wang, Q., Guo, Y., Wang, X., Ji, T., Yu, L., & Li, P . (2020). AI at the edge: Blockchain -empowered secure multiparty learning with heterogeneous models. IEEE Internet of Things Journal, 7(10), 9600-9610

    2020

  51. [60]

    Quan, X., & Solheim, M. C. W. (2023). Public -private partnerships in smart cities: A critical survey and research agenda. City, Culture and Society, 32, 100491. doi:10.1016/j.ccs.2022.100491

    2023

  52. [61]

    M., Hasan, M

    Ghazal, T. M., Hasan, M. K., Alzoubi, H. M., Alshurideh, M., Ahmad, M., & Akbar, S. S. (2023). Internet of Things Connected Wireless Sensor Networks for Smart Cities. In M. Alshurideh, B. H. Al Kurdi, R. Masa’deh, H. M. Alzoubi, & S. Salloum (Eds.), The Effect of Information T...

    2023 doi

  53. [62]

    W ., Giannotti, F., Pozdnoukhov, A., Bazzani, A., Wachowicz, M., … Portugali, Y

    Batty, M., Axhausen, K. W ., Giannotti, F., Pozdnoukhov, A., Bazzani, A., Wachowicz, M., … Portugali, Y. (2012). Smart cities of the future. The European Physical Journal Special Topics , 214(1), 481 –518. doi:10.1140/epjst/e2012-01703-3

    2012 doi

  54. [63]

    R., Singh, V

    Singh, P . R., Singh, V. K., Yadav, R., & Chaurasia, S. N. (2023). 6G networks for artificial intelligence-enabled smart cities applications: A scoping review. Telematics and Informatics Reports , 9, 100044. doi:10.1016/j.teler .2023.100044

    2023

  55. [64]

    The Digital Economy: The Role of the Internet in Development

    World Bank. (202 3). "The Digital Economy: The Role of the Internet in Development." Available at: https://www.worldbank.org/en/publication/digital-progress-and-trends-report

  56. [65]

    ARTIFICIAL INTELLIGENCE THE NEXT DIGITAL FRONTIER?

    Bughin, J., et al. (2018). " ARTIFICIAL INTELLIGENCE THE NEXT DIGITAL FRONTIER?" McKinsey Global Institute. Available at: https://www.mckinsey.com/~/media/McKinsey/Industries/Advanced%20Electronics/Our%20Insights/How %20artificial%20intelligence%20can%20deliver%20real%20value%...

    2018

  57. [66]

    D., Sun, Y., & Ileberi, E

    Mienye, I. D., Sun, Y., & Ileberi, E. (2024). Artificial intelligence and sustainable development in Africa: A comprehensive review. Machine Learning with Applications, 18, 100591. doi:10.1016/j.mlwa.2024.100591

    2024

  58. [67]

    K., Conrad, J

    Villata, S., Araszkiewicz, M., Ashley, K., Bench-Capon, T., Branting, L. K., Conrad, J. G., & Wyner, A. (2022). Thirty years of artificial intelligence and law: the third decade. Artificial Intelligence and Law, 30(4), 561–

    2022

  59. [68]

    Zhang, S. (2019). An Overview of Network Slicing for 5G. IEEE Wireless Communications, 26(3), 111–117. doi:10.1109/MWC.2019.1800234

    2019

  60. [69]

    Mavromatis, I., Katsenou, A., Bardhi, K., Xenos, E., & Simeonidou, D. (2024). Connecting the Unconnected: A DT Case Study of Nomadic Nodes Deployment in Nepal. arXiv [Cs.NI] . Retrieved from http://arxiv.org/abs/2411.09380

    2024 arXiv

  61. [70]

    -C., & Li, S

    Zhang, L., Xiao, M., Wu, G., Alam, M., Liang, Y. -C., & Li, S. (2017). A Survey of Advanced Techniques for Spectrum Sharing in 5G Networks. IEEE Wireless Communications , 24(5), 44 –51. doi:10.1109/MWC.2017.1700069

    2017

  62. [71]

    J., Gao, B., Bian, K., & Lehr, W

    Bhattarai, S., Park, J.-M. J., Gao, B., Bian, K., & Lehr, W. (2016). An Overview of Dynamic Spectrum Sharing: Ongoing Initiatives, Challenges, and a Roadmap for Future Research. IEEE Transactions on Cognitive Communications and Networking, 2(2), 110–128. doi:10.1109/TCCN.2016.2592921

    2016

  63. [72]

    H., Vahid, S., Triantafyllopoulou, D., Lee, H., & Moessner, K

    Tehrani, R. H., Vahid, S., Triantafyllopoulou, D., Lee, H., & Moessner, K. (2016). Licensed Spectrum Sharing Schemes for Mobile Operators: A Survey and Outlook. IEEE Communications Surveys & Tutorials , 18(4), 2591–2623. doi:10.1109/COMST.2016.2583499

    2016

  64. [591]

    doi:10.1007/s10506-022-09327-6

    doi

  65. [5967]

    doi:10.1016/j.jksuci.2021.08.007

    2021 doi

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.