Enhancing Public Safety Situational Awareness Using Edge Intelligence

Authors

  • Pedro Lira Federal University of Rio Grande do Norte
  • Stefano Loss Federal University of Rio Grande do Norte
  • Karine Costa Federal University of Rio Grande do Norte
  • Daniel Araújo Federal University of Rio Grande do Norte
  • Aluizio Rocha Neto Federal University of Rio Grande do Norte
  • Nelio Cacho Federal University of Rio Grande do Norte
  • Thais Batista Federal University of Rio Grande do Norte
  • Everton Cavalcante Federal University of Rio Grande do Norte
  • Frederico Lopes Federal University of Rio Grande do Norte
  • Eduardo Nogueira Federal University of Rio Grande do Norte

DOI:

https://doi.org/10.64552/wipiec.v11i1.88

Keywords:

public safety, situational awareness, edge intelligence, stream analytics

Abstract

Real-time video analytics powered by artificial intelligence (AI) enables public safety agents to effectively perceive and respond to dynamic environments. However, processing large-scale video streams introduces computational and latency challenges. This work presents a framework that combines edge and cloud computing to facilitate efficient AI-based processing of video streams for public safety applications. We evaluated the framework’s performance in a face recognition task by comparing edge and cloud processing. Our initial results demonstrate that edge processing achieves lower total latency compared to cloud processing despite higher inference times, primarily due to reduced transmission overhead. The framework also achieves high accuracy in recognition tasks, though with trade-offs in recall.

References

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Published

2025-09-02

How to Cite

Lira, P., Loss, S., Costa, K., Araújo, D., Rocha Neto, A., Cacho, N., Batista, T., Cavalcante, E., Lopes, F., & Nogueira, E. (2025). Enhancing Public Safety Situational Awareness Using Edge Intelligence. WiPiEC Journal - Works in Progress in Embedded Computing Journal, 11(1), 4. https://doi.org/10.64552/wipiec.v11i1.88

Issue

Vol. 11 No. 1 (2025): WiPiEC Journal — Special Issue

Section

Short Paper

License

Copyright (c) 2025 Pedro Lira, Stefano Loss, Karine Costa, Daniel Araújo, Aluizio Rocha Neto, Nelio Cacho, Thais Batista, Everton Cavalcante, Frederico Lopes, Eduardo Nogueira

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