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VistaScan: Optimizing Internet-Wide Scanning Through Visibility-Aware Distributed Task Allocation.

Luolin Hu1, Fan Shi1, Yi Shen1

  • 1College of Electronic Engineering, National University of Defense Technology, Hefei 230031, China.

Sensors (Basel, Switzerland)
|March 14, 2026
PubMed
Summary
This summary is machine-generated.

VistaScan enhances internet scanning by using node visibility awareness for efficient, scalable network measurement. This novel distributed system achieves near-optimal coverage with reduced probe volume and faster completion times.

Keywords:
distributed scanninginternet measurementnetwork architecturenode visibilitytask allocationvisibility consistencyvisibility matrix

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Area of Science:

  • Computer Science
  • Network Security
  • Distributed Systems

Background:

  • Internet scanning is crucial for security and network measurement but faces visibility limitations.
  • Centralized scanners have single points of failure, while naive distributed methods are inefficient.

Purpose of the Study:

  • To introduce VistaScan, a novel distributed scanning system.
  • To leverage node visibility awareness for efficient and scalable Internet-wide scanning.
  • To address visibility heterogeneity in global networks.

Main Methods:

  • Developed VistaScan, a distributed system utilizing node visibility awareness.
  • Constructed a Visibility Matrix via efficient anchor probing.
  • Implemented a load-aware task allocation mechanism for intelligent block assignment and filtering.

Main Results:

  • VistaScan outperformed five baseline methods in global, regional, and Special-Block tasks.
  • Achieved near-optimal coverage (97.58%-99.05%) with 64-93% less probe volume.
  • Reduced completion time by 13-19× and demonstrated cross-port generalizability.

Conclusions:

  • VistaScan offers a highly efficient solution for Internet-scale distributed measurement.
  • Established a theoretical foundation based on visibility consistency for intelligent scanning.
  • Advanced scanning practices towards intelligent, low-overhead, and sustainable methods.