This report outlines the evolving landscape of edge and fog computing research, as reflected in high-quality, peer-reviewed article titles from 2015 to 2025. It identifies key trends, shifts in focus, and areas of growing interest over time.
2015-2016: The Genesis of Distributed Computing at the Edge
This period marks the initial conceptualization and introduction of "Fog Computing" and "Edge Computing" as extensions to traditional cloud infrastructure, particularly in the context of the burgeoning Internet of Things (IoT). Researchers were primarily focused on defining these new paradigms and highlighting their potential.
Titles like "The Benefits of Self-Awareness and Attention in Fog and Mist Computing" (2015) and "A Cloud Visitation Platform to Facilitate Cloud Federation and Fog Computing" (2015) show an early exploration of distributed computing beyond the centralized cloud, with terms like "mist computing" appearing alongside "fog." By 2016, the focus sharpened on the potential for IoT, as seen in "Fog Computing: Helping the Internet of Things Realize Its Potential" and "The Promise of Edge Computing." The "Internet of Things and Edge Cloud Computing Roadmap for Manufacturing" (2016) indicates early thoughts on sector-specific applications. The emphasis was on introducing these concepts and outlining their general advantages, often in relation to the IoT.
2017-2018: Defining Architectures and Identifying Early Use Cases
The research landscape significantly expanded during these years, with a clear shift from simply defining concepts to exploring architectural specifics and identifying tangible applications. The term "Mobile Edge Computing" (MEC) gained prominence, and the integration of edge computing with cellular networks and smart cities became a notable theme.
Many titles from 2017, such as "The Emergence of Edge Computing," "Edge Computing," and "Fog Computing," suggest a continued effort to formalize and solidify these terms. However, a strong push towards application is evident with "Real-Time Video Analytics: The Killer App for Edge Computing" (2017) and "Fog Computing for Smart Living" (2017). Integration with network infrastructure also became crucial, highlighted by "Edge Computing and the Role of Cellular Networks" (2017) and discussions on "Energy efficient cloud computing based radio access networks in 5G" (2017). By 2018, concerns around security and coordination started appearing, with "Safe, Secure Executions at the Network Edge: Coordinating Cloud, Edge, and Fog Computing" and "Resource Management for Edge Computing in Internet of Things (IoT)." Smart city applications were also a growing area, as seen in "An Edge Cloud-Assisted CPSS Framework for Smart City" (2018) and "SensingBus: Using Bus Lines and Fog Computing for Smart Sensing the City" (2018).
2019-2020: Operationalizing and Securing the Edge
This period saw research mature beyond initial definitions and early applications, moving towards the practical challenges of deploying, managing, and securing edge and fog computing systems. The integration with 5G networks became a more concrete aspect, and specific industry applications gained traction.
Titles like "Research for practice: edge computing" (2019) and "Edge Computing Platforms and Protocols" (2019) signify a focus on practical implementation. The critical role of edge in enabling resilient IoT was recognized ("Creating a Resilient IoT With Edge Computing," 2019). Resource management and optimization, particularly for task offloading, became central concerns, as indicated by "Edge Cloud Offloading Algorithms: Issues, Methods, and Perspectives" (2019) and "Resource Allocation for Mobile Edge Computing Systems" (2020). Security emerged as a more explicit theme with "Secure and Safe Edge Computing for the Internet-of-Things" (2020) and "Fog Computing as Privacy Enabler" (2020), even integrating with blockchain for cyber-physical systems ("Blockchain and Fog Computing for Cyberphysical Systems: The Case of Smart Industry," 2020). Furthermore, the role of edge computing in 5G deployment was actively explored ("How will edge computing shape the 5G deployment? The hardware acceleration use case," 2019; "Edge computing infrastructure for 5G networks: a placement optimization solution," 2020).
2021-2022: Advanced Resource Management and AI Integration
The focus deepened on optimizing core functionalities, with a noticeable trend towards leveraging Artificial Intelligence (AI) and Machine Learning (ML) for resource management and task offloading. Research also diversified into more specific application domains and explored the complexities of distributed and collaborative edge environments.
The theme of optimization and intelligent resource management is prominent: "DROO: Integrated Learning and Optimization for Edge Computing Offloading" (2021), "Machine Learning-Assisted Resource Management in Edge Computing Systems" (2021), and "Intelligent resource management and optimization in cloud-edge computing" (2021). Specific applications like vehicular edge computing ("Computation Offloading and Retrieval for Vehicular Edge Computing," 2021) and UAVs ("Unmanned aerial vehicle-enabled mobile edge computing for 5G and beyond," 2021) gained traction. The concept of the "cloud-edge continuum" was further refined, including discussions on service migration and hierarchical optimization, as seen in "A Survey of Hierarchical Energy Optimization for Mobile Edge Computing" (2021) and "Optimization and orchestration in multi-tier edge computing" (2021). New applications like "Multimedia Data Analysis With Edge Computing" (2021) and "ISOBlue Avena: A Framework for Agricultural Edge Computing and Data Sovereignty" (2022) showcase the growing versatility of edge computing.
2023-2024: Towards Autonomous and Hardware-Accelerated Edge Systems
This period is characterized by a proliferation of comprehensive surveys, indicating a more established and mature field. There's a strong emphasis on integrating advanced AI/ML techniques for autonomous operation, exploring specialized hardware for performance, and defining the future of the "Cloud-to-Edge Computing Continuum," with an eye towards 6G.
Surveys dominate the titles, covering broad topics like "Computational Resource Allocation in Fog Computing: A Comprehensive Survey" (2023) and specific ones like "Topology-aware Federated Learning in Edge Computing: A Comprehensive Survey" (2024). AI and Machine Learning are no longer just tools but central to edge computing, with titles like "Edge Computing with Artificial Intelligence: A Machine Learning Perspective" (2023) and "Optimizing Distributed Machine Learning on User-Variant Edge Computing Systems" (2023). Hardware considerations are also more explicit, including "Approximation Opportunities in Edge Computing Hardware" (2023) and "Reconfigurable Approximating Accelerators for Edge Computing" (2024). The emergence of "Serverless Edge Computing" (2023) signifies a new operational paradigm. The expansion to future networks is evident with "Edge Computing in the Internet of Things: A 6G Perspective" (2024). Overall, the research points towards more sophisticated, intelligent, and performance-optimized edge deployments.
2025: Future Trajectories and Comprehensive Overviews
The future-dated articles indicate a continued trend towards consolidating knowledge through comprehensive overviews and pushing the boundaries of AI integration and continuum management.
Titles such as "Distributed Machine Learning in Edge Computing: Challenges, Solutions and Future Directions" and "Fog Computing Technology Research: A Retrospective Overview and Bibliometric Analysis" highlight a clear intention to synthesize existing research and chart future directions for the field. The concept of the "cloud edge computing continuum" is explored with new networking paradigms, as seen in "Edge2LoRa: a new paradigm for enabling cloud edge computing continuum over LoRaWAN." This suggests a mature field that is both looking back to learn from its journey and looking forward to integrate with the next generation of networking technologies and advanced AI applications.
