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The rapid evolution of communication systems towards 5G and 6G promises unprecedented performance improvements in data throughput, latency, and reliability, making them ideal for supporting next-generation Smart City infrastructure. This paper outlines a Smart City Development and Deployment platform built on a high-performance 5G/6G system with Multiple Access Edge Computing (MEC) capabilities. The platform achieves over 1 Gbps downlink speed for a single user, paired with single-digit application layer latencies. Leveraging advanced multi-level AI systems distributed across autonomous platforms, MEC, and cloud infrastructure, the system enhances control, decision-making, and resource allocation within the Smart City framework. Sharing platform data across systems further improves safety, reduces collision risks, and lightens the load on AI/ML-based object recognition. The paper culminates with the “LifeLine” concept as a use case, showcasing a real-world application in emergency response and public safety systems.

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A Smart City Development and Deployment Platform Using a High-Performance 5G/6G System

  • 会议论文

  • 发表日期：

    2025年

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  • Jeffrey Wallace,
  • Miroslav Vuković,
  • Toni Karimović,
  • William Edwards

摘要

The rapid evolution of communication systems towards 5G and 6G promises unprecedented performance improvements in data throughput, latency, and reliability, making them ideal for supporting next-generation Smart City infrastructure. This paper outlines a Smart City Development and Deployment platform built on a high-performance 5G/6G system with Multiple Access Edge Computing (MEC) capabilities. The platform achieves over 1 Gbps downlink speed for a single user, paired with single-digit application layer latencies. Leveraging advanced multi-level AI systems distributed across autonomous platforms, MEC, and cloud infrastructure, the system enhances control, decision-making, and resource allocation within the Smart City framework. Sharing platform data across systems further improves safety, reduces collision risks, and lightens the load on AI/ML-based object recognition. The paper culminates with the “LifeLine” concept as a use case, showcasing a real-world application in emergency response and public safety systems.