Developing unified frameworks that integrate security

The gap

Evidence profile

Sourced from the stated research gap and future-work section and abstract of the source papers, classified as general, spanning 5 journals. Those papers have been cited 17 times in total.

Supporting evidence — 5 representative gaps

• Edge Computing as a Modern Trend in Information Technology: A Comprehensive Review. (2026) · International Journal of Creative and Open Research in Engineering and Management · doi

  The limitations of traditional cloud computing architectures in meeting the stringent latency, bandwidth, and privacy requirements of next-generation applications. The need for a comprehensive review of edge computing as a modern trend in information technology. The importance of outlining future research directions, including the convergence of edge computing with artificial intelligence and 6G networks.

  generalstated research gapevidence 5/5
  Keywords: limitations traditional cloud computing architectures meeting stringent latency
• INTELLIGENT EDGE: A SURVEY OF ARTIFICIAL INTELLIGENCE INTEGRATION IN ELECTRONIC EMBEDDED SYSTEMS (2026) · JOURNAL OF RESEARCH PERSPECTIVES IN MULTIDISCIPLINARY SCIENCE EDUCATION AND TECHNOLOGY (RPSET) · doi

  The integration of AI into embedded systems is a relatively new field, with many challenges and limitations remaining. The development of efficient algorithms, specialized hardware, and innovative software toolchains is necessary to enable intelligent edge computing. The identification of persistent challenges and future research directions is crucial to advancing the field.

  generalstated research gapevidence 5/5
  Keywords: integration embedded systems relatively new field many challenges
• Secure, Intelligent, and Energy-Efficient Architectures for Next-Generation Smart Homes: A Review (2026) · Scientific Journal of Computer Science · doi

  Developing unified frameworks that integrate security, automation, and energy management. Designing lightweight and energy-efficient machine learning models for edge devices. Enhancing interoperability through standard protocols and middleware solutions. Conducting large-scale real-world experiments to validate proposed systems.

  generalfuture-work sectionevidence 5/5
  Keywords: developing unified frameworks integrate security automation energy management
• Cloud Based IoT Data Analytics Platform (2026) · Iconic Research and Engineering Journals · doi

  One possible enhancement is the integration of advanced machine learning and artificial intelligence algorithms to provide more accurate predictions and automated decision-making. Another future enhancement is the implementation of edge computing, where some data processing is performed directly on IoT devices before sending it to the cloud.

  generalfuture-work sectionevidence 5/5
  Keywords: one possible enhancement integration advanced machine learning artificial
• Paradigm Shift Toward Distributed Learning in IoT Intelligence: A Comprehensive Survey of Opportunities and Challenges (2026) · IEEE Internet of Things Journal · cited 17× · doi

  The main outcomes of this study include: (i) a comprehensive taxonomy characterizing enabling technologies for distributed edge intelligence, (ii) a comparative synthesis of representative works highlighting common architectural patterns and evaluation practices, and (iii) the identification of research gaps, critical trade-offs, and open challenges, particularly related to model robustness, energy efficiency, data heterogeneity, and secure real-time inference.

  generalabstractevidence 3/5
  Keywords: main outcomes include comprehensive taxonomy characterizing enabling technologies distributed edge intelligence comparative synthesis representative works

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