Smart City Gnosys
Smart city article details
| Title | Fog Service Placement Optimization: A Survey Of State-Of-The-Art Strategies And Techniques |
|---|---|
| ID_Doc | 26780 |
| Authors | Apat H.K.; Goswami V.; Sahoo B.; Barik R.K.; Saikia M.J. |
| Year | 2025 |
| Published | Computers, 14, 3 |
| DOI | http://dx.doi.org/10.3390/computers14030099 |
| Abstract | The rapid development of Internet of Things (IoT) devices in various smart city-based applications such as healthcare, traffic management systems, environment sensing systems, and public safety systems produce large volumes of data. To process these data, it requires substantial computing and storage resources for smooth implementation and execution. While centralized cloud computing offers scalability, flexibility, and resource sharing, it faces significant limitations in IoT-based applications, especially in terms of latency, bandwidth, security, and cost. The fog computing paradigm complements the existing cloud computing services at the edge of the network to facilitate the various services without sending the data to a centralized cloud server. By processing the data in fog computing, it satisfies the delay requirement of various time-sensitive services of IoT applications. However, many resource-intensive IoT systems exist that require substantial computing resources for their processing. In such scenarios, finding the optimal computing node for processing and executing the service is a challenge. The optimal placement of various IoT applications services in heterogeneous fog computing environments is a well-known NP-complete problem. To solve this problem, various authors proposed different algorithms like the randomized algorithm, heuristic algorithm, meta heuristic algorithm, machine learning algorithm, and graph-based algorithm for finding the optimal placement. In the present survey, we first describe the fundamental and mathematical aspects of the three-layer IoT–fog–cloud computing model. Then, we classify the IoT application model based on different attributes that help to find the optimal computing node. Furthermore, we discuss the complexity analysis of the service placement problem in detail. Finally, we provide a comprehensive evaluation of both single-objective and multi-objective IoT service placement strategies in fog computing. Additionally, we highlight new challenges and identify promising directions for future research, specifically in the context of multi-objective IoT service optimization. © 2025 by the authors. |
| Author Keywords | cloud computing; fog computing; IoT; multi-objective optimization; service placement; single objective optimization |
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