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Open research questions in Internet of Things and AI

57 unresolved questions extracted from the limitations and future-work sections of 221 Internet of Things and AI papers in our library. Each links back to the study that raised it.

What the literature leaves open

  • Further research is needed to develop more interpretable AI models. There is a need for more studies on the application of AI in healthcare supply chains.

    Artificial Intelligence-Based Healthcare Systems: A Review of Machine Learning, Deep Learning, Data Analytics, Supply Chain Management, and Electrical Engineering Technologies · 2026 · DOI

  • There is a need for more comprehensive reviews of AI in healthcare. Prior work has focused on specific medical applications, rather than providing a broad overview.

    Artificial Intelligence-Based Healthcare Systems: A Review of Machine Learning, Deep Learning, Data Analytics, Supply Chain Management, and Electrical Engineering Technologies · 2026 · DOI

  • Cyber threats affecting patient safety and information security in digital hospitals. The complexity of digital hospital systems, including electronic medical records, networked diagnostic platforms, cloud services, remote monitoring, and connected biomedical equipment. The need for regulatory compliance in digital hospitals.

    AI-Enabled Cyber Resilience for Healthcare: Predictive Threat Detection and Risk Prioritization in Kingdom of Saudi Arabia Digital Hospitals · 2026 · DOI

  • Data privacy and AI ethics are significant challenges associated with the adoption of Web 4.0. Infrastructure demands and the digital divide are also major challenges that need to be addressed. The paper also highlights the risk of autonomous systems making unethical decisions without human oversight.

    Web 4.0 and Beyond: Toward a Symbiotic and Emotionally Intelligent Internet Architecture · 2026 · DOI

  • The paper suggests that future research should focus on the development of Web 5.0, which aims to establish a fully symbiotic relationship between humans and machines. The study also highlights the need to address the challenges associated with the adoption of Web 4.0, such as data privacy and AI ethics.

    Web 4.0 and Beyond: Toward a Symbiotic and Emotionally Intelligent Internet Architecture · 2026 · DOI

  • To monitor the advanced AI spread and sustained productivity effects. To study the impact of AI on the digital economy in other countries.

    Role of Artificial Intelligence in Digital Economy · 2026 · DOI

  • The need for forward-thinking regulation. The need for emphasis on human-AI collaboration.

    Role of Artificial Intelligence in Digital Economy · 2026 · DOI

  • The pursuit of Artificial General Intelligence raises profound interdisciplinary questions. Integrating AI with quantum computing, IoT, and green technologies highlights the critical need for sustainable, human-centered development.

    Future Directions in Artificial Intelligence · 2026 · DOI

  • The need for responsible development and governance has become paramount. AI faces critical challenges related to transparency, bias, security, and ethical concerns.

    Future Directions in Artificial Intelligence · 2026 · DOI

  • Limited digital literacy and inadequate infrastructure in rural areas hinder the widespread adoption of AI-enabled banking services. AI systems are vulnerable to cyberattacks, data breaches, and unauthorised access. AI models may inherit biases from training data, leading to discriminatory decisions.

    Artificial Intelligence in Indian Banking: Opportunities, Challenges and Future Prospects – A Review · 2026 · DOI

  • Limited comparative studies between public and private sector banks. The need for explainable AI frameworks and robust governance mechanisms. The need for careful consideration of the challenges and limitations of AI adoption in Indian banking.

    Artificial Intelligence in Indian Banking: Opportunities, Challenges and Future Prospects – A Review · 2026 · DOI

  • The development of computer networks was challenging due to the lack of fundamental difference between data, voice, and video communications. The integration of geographically dispersed computing facilities was a challenge. The development of local area networks was a challenge due to bandwidth limitations.

    A Revise Study of Computer Networks · 2026 · DOI

  • Alert fatigue, - Lack of context, - Evolving log templates, - Sparse labels, - Imbalanced incident classes

    AI-Enabled Cloud Operations for Predictive Monitoring, Automation, and Service Reliability · 2026 · DOI

  • The need for data communications among geographically dispersed machines. The need for collaboration and sharing of resources among machines.

    A Revise Study of Computer Networks · 2026 · DOI

  • 10. 1. Although the study provides significant information on the utilization of the Artificial Intelligence-based command and control systems in the financial security, there are some limitations that need to be identified. 2. To begin with, the study is carried out on a sample size of 250 respondents which might not be facing enough population of financial service users. Hence, the findings are largely the views of the chosen participants and might not be applicable to the whole population.

    An Analysis of Artificial Intelligence -Driven Command and Control in Financial Security and Fraud Detection with special reference to Palghar District. · 2026 · DOI

  • This research presents a novel framework that integrates Artificial Intelligence and Blockchain technologies to address the challenges of deepfake detection and digital identity verification. The proposed system leverages the strengths of CNN and LSTM models to analyze multimedia content and detect inconsistencies, while the TrustScore mechanism enhances the interpretability of the results. The use of blockchain ensures that the verification data is stored in a secure and immutable manner, providing transparency and trust. The results of our experiment show that the system is really accurate and does well in many different tests. By using federated learning, we can make the system even better at handling lots of data and keeping information private, which makes it a good choice for using in the real world. This research helps us create online systems that are safe and trustworthy by giving us a complete solution that combines smart detection with secure verification. It's like having a strong shield that protects our digital world from harm. The system is designed to be flexible and can work well in many different situations, which is important for making sure it can be used in lots of different ways. Overall, our research is an important step towards creating a safer and more trustworthy digital world, and we're excited to see how it can be used in the future. Future work will focus on improving the efficiency of the system, reducing computational costs, and extending the framework to support real-time streaming applications. Additionally, efforts will be made to enhance the robustness of the model against adversarial attacks and to explore the use of advanced blockchain technologies for improved scalability. Overall, the proposed framework represents a significant step forward in the field of cybersecurity and digital identity verification. Our tests have proven that our system makes a big difference in how accurately it detects things and how reliable it is. Using blockchain technology stops people from messing with the data, and adding AI means the system can keep learning and getting better at finding new threats. So, we can trust that the system will work well and quickly spot any potential problems. By combining blockchain and AI, we've made the system more trustworthy and better at detecting issues. This means we can have faith in its performance and know it will keep getting better over time.

    Blockchain-Integrated AI Cybersecurity Framework for Deepfake Detection and Secure Digital Identity Verification · 2026 · DOI

  • The paper acknowledges the lack of cohesive policies on ethical AI use in supply chains but does not delineate which regulatory frameworks (fairness thresholds, transparency standards, audit protocols) should be prioritized or how they should be operationalized across different supply chain optimization domains such as routing, inventory allocation, and supplier selection.

    Ethics of AI-based supply chain optimization: a better balance between efficiency and fairness · 2026 · DOI

  • While the paper notes that half of logistics employees will require job reskilling due to automation, it does not identify which specific supply chain roles (warehouse management, procurement, last-mile delivery) require targeted reskilling programs or benchmark the effectiveness of different reskilling approaches in supply chain contexts.

    Ethics of AI-based supply chain optimization: a better balance between efficiency and fairness · 2026 · DOI

  • Further investigation may include the improvement of the existing model through the integration of other renewable energy sources like solar and wind energy for greater sustainability of IoT devices. Blockchain technology could be used to ensure secure updates to federated learning models. Future studies could also consider building sophisticated lightweight and explainable AI models to enhance decision-making processes. It is also recommended that real-time applications of the model in extensive smart cities or industries should be considered to understand more about the working of the IoT system. Lastly, adaptive systems could be incorporated into the model to make sure that energy consumption is optimal at all times.

    Enhancing Sustainability in Green Electronics through Federated Learning for Distributed IoT Systems · 2026 · DOI

  • The paper suggests that future research should focus on the development of more effective and efficient ICT systems. The paper highlights the potential of intelligent ICT systems in various sectors, including healthcare and education. The paper suggests that future research should explore the use of cyber-physical systems, the Internet of Things (IoT), and digital twin layers.

    Recent Advances and Challenges in Intelligent ICT Systems: A Comprehensive Review · 2026 · DOI

  • The lack of a universally accepted definition of intelligence. The need for more research on the integration of AI and other advanced technologies into ICT systems.

    Recent Advances and Challenges in Intelligent ICT Systems: A Comprehensive Review · 2026 · DOI

  • Data privacy and cybersecurity are major challenges. Ethical governance, algorithmic bias, and environmental sustainability are also significant concerns. The study highlights the need for addressing these challenges in the development of sustainable and intelligent systems.

    Convergence of Green Energy, Intelligent Computing, and Advanced Engineering Systems: Emerging Trends, Challenges, and Future Directions · 2026 · DOI

  • The study identifies a need for interdisciplinary technological advancements and sustainable intelligent systems. The convergence of green energy, intelligent computing, and advanced engineering systems is a key area of focus.

    Convergence of Green Energy, Intelligent Computing, and Advanced Engineering Systems: Emerging Trends, Challenges, and Future Directions · 2026 · DOI

  • Further research is needed to improve the efficiency and reliability of power grids using AI. Further research is needed to develop newer ideas like digital twins, blockchain energy trading, and vehicle-to-grid systems.

    How Artificial Intelligence Is Changing the Way We Manage Power Grids and Clean Energy: A Review · 2026 · DOI

  • The lack of historical data available for training AI models is a major issue. The quality of data is a major concern, with sensor readings potentially being wrong due to faulty equipment or communication errors.

    How Artificial Intelligence Is Changing the Way We Manage Power Grids and Clean Energy: A Review · 2026 · DOI

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57 open questions have been extracted from the limitations and future-work passages of 221 Internet of Things and AI papers in our library. Each one below links back to the study that raised it, so you can read the original claim in context.

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