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Smart city article details
| Title | A Review Of Optimized Computational Strategies For Iot: Cloud, Fog, And Edge Computing Approaches |
|---|---|
| ID_Doc | 4182 |
| Authors | Kumar S.; Singh P.; Singh A. |
| Year | 2025 |
| Published | Proceedings of 5th International Conference on Pervasive Computing and Social Networking, ICPCSN 2025 |
| DOI | http://dx.doi.org/10.1109/ICPCSN65854.2025.11035529 |
| Abstract | The growing Internet of Things deployment demanded massive data creation that needs rapid computational processing methods to handle and decide data with speed. This paper investigates performance enhancement methods that reduce latency, enhance scalability, and optimize power usage in cloud, fog, and edge computing environments. Cloud computing faces limitations in meeting real-time operational requirements for IoT applications because it cannot provide the required real-time operational requirements. Fog and edge computing systems effectively process IoT source data in local areas, thus making them suitable for critical IoT applications for smart cities as well as healthcare systems and autonomous vehicles. This paper evaluates state-of-the-art research about load balancing and task offloading techniques and resource allocation methods and energy-efficient models that come from diverse computing paradigms. This paper discusses essential research challenges that include interoperability issues and security needs and demands an all-encompassing optimized system. This paper examines real-life deployment difficulties experienced by active models, which stem from installation obstacles that hinder scalability while simultaneously requiring secure distributed structural applications. Research professionals, along with practitioners who work on IoT-enabled computing systems requiring optimization, should refer to this review for foundational knowledge. © 2025 IEEE. |
| Author Keywords | Bald Eagle Search (BES); Cloud Computing; Edge Computing; Energy Efficiency; Fault Tolerance; Fog Computing; Internet of Things (IoT); Load Balancing; Machine Learning in IoT; Makespan Reduction; Optimization Techniques; Resource Allocation; Task Scheduling; Whale Optimization Algorithm (WOA) |
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