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Open research questions in IoT and Edge/Fog Computing

498 gap statements mined from IoT and Edge/Fog Computing papers in our 7.9M-paper local library, which holds 4,219 papers on the topic — drawn mostly from each paper's own stated research gap, future-work, challenge and limitation notes, and its abstract. The ones listed below are a selection still marked open; each names the study that raised it, with a DOI link where the paper has one.

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What the literature leaves open

  • There is a need for an algorithm that can balance global exploration and local exploitation. Existing algorithms may not be effective for complex and large-scale optimization problems.

    Logistics-Supply-Chain-Enhanced Human Urbanization Algorithm for Global Optimization and Engineering Applications · 2026 · DOI

  • Limited energy, communication overhead, scalability, preserving privacy, and learning in an intelligent decentralized fashion are major challenges in WSN-IoT ecosystems. Conventional federated learning methods are not suitable for WSN-IoT ecosystems due to their energy constraints, mobility constraints, and failure tolerance.

    An energy-aware federated intelligence framework for sustainable WSN-IoT ecosystems · 2026 · DOI

  • Latency is critical for responsiveness in edge-cloud systems. The growing heterogeneity of devices, dynamic workload demands, and the need for real-time processing create significant constraints. Efficient coordination across architectural layers is essential to maintaining service reliability and performance.

    Edge and Cloud Computing in Smart Cities · 2025 · DOI

  • Existing surveys have limitations in terms of real-world deployments and experimental results. The need for efficient coordination across architectural layers is essential to maintaining service reliability and performance.

    Edge and Cloud Computing in Smart Cities · 2025 · DOI

  • Existing benchmarking suites and tools for edge and IoT scenarios require manual setup and do not focus on concurrent automation. There is a lack of a framework that can securely automate the benchmarking of Edge AI devices at scale. Prior research typically assumes the user runs each benchmark on each device and often measures one device at a time.

    SHEAB: A Novel Automated Benchmarking Framework for Edge AI · 2025 · DOI

  • Existing trust management models lack scalability for large SIoT environments. There is a need for a lightweight trust evaluation model that can efficiently manage heterogeneous resource constraints in SIoT.

    MMTE: Micro-Moment Based Lightweight Trust Evaluation Model with Trust Spheres for Scalable Social IoT · 2025 · DOI

  • Data Privacy and Security Challenges - Implementing robust security measures and ensuring user data privacy throughout the system life cycle remains an ongoing challenge. Computational Requirements - Running complex AI algorithms and maintaining a high-fidelity DT may require significant computational resources, potentially limiting the scalability of the E-ICF in resource-constrained environments.

    Enhancing Healthcare through Sensor-Enabled Digital Twins in Smart Environments: A Comprehensive Analysis · 2024 · DOI

  • Managing data processing at the edge layer remains a significant challenge for numerous global applications. Ensuring the confidentiality of data at rest is particularly essential in scenarios where a device could be compromised.

    A Survey of Machine Learning in Edge Computing: Techniques, Frameworks, Applications, Issues, and Research Directions · 2024 · DOI

  • The paper highlights the need for robust access control mechanisms to ensure that only authorized users or systems can access data. The paper notes that trust is obtained by ensuring data integrity is mandatory, by enforcing that personal information remains reliable and unmodified during storage and data transmission.

    Edge Computing in Healthcare: Innovations, Opportunities, and Challenges · 2024 · DOI

  • The limitations of 5G networks have led to the investigation of 6G networks as the future solution. The fundamental privacy and security issues related to 6G technology need to be addressed. The paper identifies the need for a novel method in blockchain 6G-based wireless network security management and optimization.

    Blockchain 6G-Based Wireless Network Security Management with Optimization Using Machine Learning Techniques · 2024 · DOI

  • The need for a synergistic framework that combines the advantages of edge and cloud computing. The lack of optimized workload management algorithms and resource allocation strategies to fully realize the potential of hybrid edge-cloud frameworks. The need for balanced energy consumption and bandwidth optimization in sensor networks.

    Edge-Cloud Synergy for AI-Enhanced Sensor Network Data: A Real-Time Predictive Maintenance Framework · 2024 · DOI

  • The lack of experimental validation of IIoT architectures, - the need for functional architectures to deploy and organize hardware and software in IIoT.

    Implementation and Experimental Application of Industrial IoT Architecture Using Automation and IoT Hardware/Software · 2024 · DOI

  • Privacy concerns surrounding data collection, storage, and utilization in smart city environments underscore the need for developing privacy-preserving technologies and regulatory frameworks. Energy efficiency and sustainability emerge as critical imperatives.

    Secure, Sustainable Smart Cities and the Internet of Things: Perspectives, Challenges, and Future Directions · 2024 · DOI

  • The study suggests the need for continuous monitoring and security system maintenance to effectively handle new threats and vulnerabilities. The study implies the need for further research on the application of IoMT algorithms in various fields, including data analysis, natural language processing, control systems, predictive modeling, and decision assistance.

    A Novel Cloud-Based IoT Framework for Secure Health Monitoring · 2024 · DOI

  • The paper identifies the gap in current research, emphasizing the need for a platform for discourse and innovation. It highlights the importance of balancing technological advancements with human elements, such as citizen engagement and socio-economic implications. The paper emphasizes the need for urgent, comprehensive, and collaborative action towards achieving the triple bottom line of sustainability.

    Smart Technologies for Sustainable Urban and Regional Development · 2024 · DOI

  • We compare our work with existing surveys, highlight its specific focus on the FL-FC-IoT-DT convergence, and identify open challenges and future research directions toward secure, scalable, and intelligent IoT ecosystems.

    Federated Learning in Fog Computing Within IoT Environments: An Up-to-Date and Comprehensive Survey · 2026 · DOI

  • Recently, one of the main challenges facing the smart grid is insufficient computing resources and intermittent energy supply for various distributed components (such as monitoring systems for renewable energy power stations).

    Providing robust and low-cost edge computing in smart grid: An energy harvesting based task scheduling and resource management framework · 2025 · DOI

  • Finally, we highlight the open challenges and future research directions that will advance the field of edge LLM inference.

    Efficient Inference for Edge Large Language Models: A Survey · 2025 · DOI

  • However, the dropped task ratio metric has not been studied thoroughly in literature.

    Joint Optimization of Completion Ratio and Latency of Offloaded Tasks With Multiple Priority Levels in 5G Edge · 2025 · DOI

  • However, rigorous research to develop appropriate data privacy solutions in the healthcare sector is still lacking.

    A Review of Privacy and Security of Edge Computing in Smart Healthcare Systems: Issues, Challenges, and Research Directions · 2024 · DOI

  • No publicly available dataset has been identified that provides optimizer-derived scheduling labels with pre-execution task features for heterogeneous fog environments.

    A labeled dataset for resource scheduling in heterogeneous IoT-fog-cloud computing systems · 2026 · DOI

  • Reproducible benchmarking and model development using the provided datasets, - Investigation of other scheduling algorithms and techniques, - Application of the FogMLS datasets to real-world IoT-fog-cloud…

    A labeled dataset for resource scheduling in heterogeneous IoT-fog-cloud computing systems · 2026 · DOI

  • Development of lightweight AI algorithms - Optimizing AI algorithms for efficient processing on resource-constrained devices.

    Enhancing Healthcare through Sensor-Enabled Digital Twins in Smart Environments: A Comprehensive Analysis · 2024 · DOI

  • Resource-constrained IoT devices have limited computing power, small memory capacities, and lack the self-sufficient intelligence to process raw data locally and make independent decisions - High latency and bandwidth saturation with traditional cloud computing - Security and privacy concerns with edge…

    A Survey of Machine Learning in Edge Computing: Techniques, Frameworks, Applications, Issues, and Research Directions · 2024 · DOI

  • This article connects representative architectures, mission use cases, and open challenges for GS-centric, GS-assisted, and fully on-orbit FL.

    Federated Learning for Orbital Edge Computing: Architectures, Opportunities, and Open Challenges · 2026 · DOI

Most-cited papers in IoT and Edge/Fog Computing

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Open questions raised by papers listed on this page include: (1) There is a need for an algorithm that can balance global exploration and local exploitation (“Logistics-Supply-Chain-Enhanced Human Urbanization Algorithm for Global…”, 2026); (2) There is a lack of a framework that can securely automate the benchmarking of Edge AI devices at scale (“SHEAB: A Novel Automated Benchmarking Framework for Edge AI”, 2025); (3) There is a need for a lightweight trust evaluation model that can efficiently manage heterogeneous resource constraints in SIoT (“MMTE: Micro-Moment Based Lightweight Trust Evaluation Model with Trust Spheres…”, 2025); (4) The dropped task ratio metric has not been studied thoroughly in literature (“Joint Optimization of Completion Ratio and Latency of Offloaded Tasks With…”, 2025); (5) The lack of optimized workload management algorithms and resource allocation strategies to fully realize the potential of hybrid edge-cloud frameworks (“Edge-Cloud Synergy for AI-Enhanced Sensor Network Data: A Real-Time Predictive…”, 2024). 498 gap statements have been mined from IoT and Edge/Fog Computing papers in our 7.9M-paper local library, which holds 4,219 papers on the topic; the gaps come from whichever of those papers state one. They are mostly the research gaps the authors state and the papers' abstracts, plus future-work, limitations and challenges passages. The ones listed below are a selection still marked open; each names the study that raised it, with a DOI link where the paper has one, so you can read the original claim in context.

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