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Smart city article details
| Title | Traffic-Aware Network Slicing For Smart Cities: A Machine Learning Framework For Gbr And Non-Gbr Traffic Classification And Resource Optimization |
|---|---|
| ID_Doc | 58685 |
| Authors | Dubey M.; Singh A.K.; Bhushan A.; Mishra R. |
| Year | 2025 |
| Published | International Journal of System Assurance Engineering and Management |
| DOI | http://dx.doi.org/10.1007/s13198-025-02841-1 |
| Abstract | Smart cities are central to driving national progress. Integrates a wide range of applications, including vehicle-to-everything (V2X) communication, surveillance, healthcare, entertainment, and so on. Effective implementation of these applications and seamless user experience within a smart city framework hinges on two critical requirements: accommodating diverse smart city use cases within specific QoS requirements and prioritizing essential services over lower, lesser-priority services. Network slicing, a core 5G capability, offers an impressive solution to meet these stringent demands by customizing network resources. To assign priority based traffic through network slicing, the proposed framework follows a two-fold approach to assigning priority based network resources. In the initial step, smart city traffic was classified into Guaranteed Bit Rate (GBR) and non-GBR (NGBR) categories using an ensemble-based Bagged Decision Trees (BDT). Then we refine classification by further classifying GBR and NGBR traffic into three critical service categories: Enhanced Mobile Broadband (eMBB), Massive Machine-Type Communications (mMTC), and Ultra-Reliable and Low Latency Communications (uRLLC) and achieved 98.45% accuracy to ensure priority handling of essential smart city services. Classified traffic was then utilized for resource distribution using a priority based heuristic approach. To assess the efficacy of this classification, we designed and compared a framework for two scenarios: a Best Effort Scenario (BES) and a Network Slicing Scenario (NSS). The proposed NSS showcased an improvement of 63.77% to optimize network resources. This approach demonstrates an effective solution for resource optimization for urban services, particularly for prioritized critical smart city applications. © The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2025. |
| Author Keywords | 5G Network slicing; GBR and Non-GBR; Smart city resource allocation; Smart city traffic classification |
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