Experimental evaluation demonstrates significant reductions in response time, network traffic, and service latency while improving throughput and resource utilization compared with conventional cloud-only approaches, and confirms that intelligent fog–cloud orchestration provides an effective solution for supporting real-time IoT applications.
The results of this study demonstrate that, when combined with hardware-based edge intelligence, AI-powered cloud analytics can significantly enhance the responsiveness, scalability, and decision accuracy in cyber-physical infrastructure systems.
Naveen, Satyam Kumar Sainy· International Journal on Eng...· 0 citations
This review examines emerging trends in Artificial Intelligence (AI)-driven resource management within this continuum, with a focus on three directions: the transition from centralized to distributed and collaborative intelligence, cross-domain adaptation and knowledge transfer for heterogeneous IoT applications, and t...
Zhi-Yu Wang, Nilotpal Kapri, L. Bittencourt et al.· Frontiers in The Internet of...· 1 citation
Multi-Access Edge Computing (MEC) places computation, storage, and service capabilities close to users and connected devices. This proximity supports latency-sensitive Artificial Intelligence (AI) inference and Internet of Things (IoT) services while reducing backhaul traffic and dependence on distant clouds. This pape...
Keren Sara John, M. F. Abd Malek, Halima Asif· International Conferences on...· 0 citations
A comparative analytical framework called EDGE-LAT is presented for systematically evaluating the existing latency-aware task offloading and resource allocation approaches in IoT–Fog–Cloud environments and shows that the EDGE-LAT analytical framework identifies better latency-aware strategies under the evaluated simula...
Sharmila Patil-Karpe, Anjali R. Deshpande, Rupali Shelke· Journal of High Speed Networ...· 0 citations
A hardware-based, efficient task offloading framework using an IoT-Fog-Cloud architecture, which reduces response latency at the fog layer and includes a latency comparison between fog-layer processing time and cloud-layer response time.
Syed Faizan Haider· Journal of IoT-based Distrib...· 0 citations
A hardware-based, efficient task offloading framework using an IoT-Fog-Cloud architecture, which reduces response latency at the fog layer and includes a latency comparison between fog-layer processing time and cloud-layer response time.
Nadar Akshayashree Stephan Selvaraj, Maya S. Nair· Journal of IoT-based Distrib...· 0 citations
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