Smart City Gnosys
Smart city article details
| Title | Implementation And Analysis Of Fog Node-Assisted Scheduling And Optimization Of Resource Allocation And Utilization |
|---|---|
| ID_Doc | 30492 |
| Authors | Sharma N.; Sharma D. |
| Year | 2024 |
| Published | International Journal of Computer Networks and Applications, 11, 6 |
| DOI | http://dx.doi.org/10.22247/ijcna/2024/51 |
| Abstract | The Internet of Things (IoT) has recently become popular for collecting and storing data in third-party datasets. When combined with IoT devices, fog computing (FC) efficiently manages large data volumes and processing demands. However, concerns persist regarding privacy, edge node latency, data security, and energy consumption. With the increasing automation in smart cities, the workload for fog nodes (FNs) is developing, and additional FNs are needed. The optimal allocation of resources is essential in addressing the resource allocation (RA) issues in executing IoT applications within FC. To tackle this, the mixed integer linear Ant Lion optimizer (MILALO) model has been deployed to optimize resource allocation, reduce execution time, and conserve energy in fog computing. The proposed model overcomes challenges by optimizing resource allocation, reducing execution time, and conserving energy in fog computing. It targets efficient resource utilization and enhances scheduling, optimization, and cloud resource management to improve overall time and energy consumption. This model mediates between the network and users to process and present results by constructing an allocation matrix for the allocator. Simulations confirm the effectiveness of the MILALO model, with demonstrated 20-25% cloud optimization improvement and 50-60% reduction in time and energy consumption. It conducts a thorough assessment of the proposed model's effectiveness through key performance indicators such as execution time (ET), energy consumption (EC), and resource utilization (RU). Finally, a detailed comparative analysis against established techniques enriches the discussion, providing valuable insights into the superiority of the proposed technique. © EverScience Publications. |
| Author Keywords | Energy Consumption (EC); Execution Time (ET); Fog Computing (FC); Internet of Things (IoT); Mixed integer linear Ant Lion optimizer (MILALO); Resource Allocation (RA) |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
4182  | 0.884 | Kumar S.; Singh P.; Singh A. | A Review Of Optimized Computational Strategies For Iot: Cloud, Fog, And Edge Computing Approaches | Proceedings of 5th International Conference on Pervasive Computing and Social Networking, ICPCSN 2025 (2025) |
| 61636 | 0.876 | Shingare H.; Kumar M. | Whale Optimization-Based Task Offloading Technique In Integrated Cloud-Fog Environment | Lecture Notes in Networks and Systems, 547 (2023) |
| 23348 | 0.876 | Puttaswamy N.G.; Murthy A.N. | Energy Optimization In Smart Networks Using Machine Learning-Driven Fog Computing To Reduce Unnecessary Cloud Data Transmission | Engineering, Technology and Applied Science Research, 15, 3 (2025) |
| 26780 | 0.876 | Apat H.K.; Goswami V.; Sahoo B.; Barik R.K.; Saikia M.J. | Fog Service Placement Optimization: A Survey Of State-Of-The-Art Strategies And Techniques | Computers, 14, 3 (2025) |
| 46065 | 0.875 | Jamil B.; Ijaz H.; Shojafar M.; Munir K.; Buyya R. | Resource Allocation And Task Scheduling In Fog Computing And Internet Of Everything Environments: A Taxonomy, Review, And Future Directions | ACM Computing Surveys, 54, 11s (2022) |
| 26767 | 0.875 | Gokulkannan S.; Kiranshankar S.; Kishore S.; Lanitha B. | Fog Environment For Smart Cities With Multi-Level Resource Sharing Framework | Proceedings of the 2023 2nd International Conference on Electronics and Renewable Systems, ICEARS 2023 (2023) |
| 38240 | 0.873 | Aldossary M. | Multi-Layer Fog-Cloud Architecture For Optimizing The Placement Of Iot Applications In Smart Cities | Computers, Materials and Continua, 75, 1 (2023) |
| 48508 | 0.871 | Wang Y.; Shafik W.; Seong J.-T.; Al Mutairi A.; SidAhmed Mustafa M.; Mouhamed M.R. | Service Delay And Optimization Of The Energy Efficiency Of A System In Fog-Enabled Smart Cities | Alexandria Engineering Journal, 84 (2023) |
| 2379 | 0.871 | De Queiroz T.A.; Canali C.; Iori M.; Lancellotti R. | A Location-Allocation Model For Fog Computing Infrastructures | CLOSER 2020 - Proceedings of the 10th International Conference on Cloud Computing and Services Science (2020) |
| 40898 | 0.87 | Negi V.; Joshi D.; Sharma A. | Optimizing Task Allocation In Fog-Based Iot For Smart City Solutions | Citizen-Centric Artificial Intelligence for Smart Cities (2025) |