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<p>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.</p>

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Traffic-aware network slicing for smart cities: a machine learning framework for GBR and non-GBR traffic classification and resource optimization

  • 期刊论文

  • 发表日期：

    2025年6月9日

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  • Monika Dubey,
  • Ashutosh Kumar Singh,
  • Aditya Bhushan,
  • Richa Mishra

摘要

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.