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A Low-Cost Jamming Detection Approach Using Performance Metrics in Cluster-Based Wireless Sensor Networks.

Carolina Del-Valle-Soto1, Carlos Mex-Perera2, Juan Arturo Nolazco-Flores3

  • 1Facultad de Ingeniería, Universidad Panamericana, Álvaro del Portillo 49, Zapopan 45010, Mexico.

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Summary
This summary is machine-generated.

This study introduces an energy-efficient jamming detection algorithm for Wireless Sensor Networks (WSNs). The algorithm effectively identifies jamming-affected nodes with minimal power consumption, enhancing network security and performance.

Keywords:
cluster-based protocolsenergyjammingrouting protocolswireless sensor networks

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Security

Background:

  • Wireless Sensor Networks (WSNs) are crucial for the Internet of Things (IoT), facing challenges in energy efficiency and data availability.
  • WSNs are vulnerable to jamming attacks, which disrupt communication and increase node power consumption, negatively impacting network performance.
  • Existing methods for detecting anomalous behavior in WSNs can lead to increased power usage, counteracting the goal of energy efficiency.

Purpose of the Study:

  • To propose a simple, energy-efficient jamming detection algorithm for Wireless Sensor Networks.
  • To evaluate the algorithm's effectiveness in detecting jamming-affected nodes with minimal energy expenditure.
  • To assess the performance of the proposed algorithm across various cluster-based routing protocols.

Main Methods:

  • An exhaustive study of performance metrics related to routing protocols and node energy consumption was conducted.
  • A novel jamming detection algorithm was developed, focusing on minimal energy expenditure for detection.
  • The algorithm's detection capabilities were evaluated using four cluster-based protocols: PEGASIS, TEEN, LEACH, and HPAR.
  • Real-world experimentation was performed using Zigbee and LoRa wireless protocols.

Main Results:

  • The proposed algorithm successfully detects areas of nodes affected by jamming.
  • The algorithm achieves detection with minimal energy expenditure, crucial for battery-operated WSNs.
  • Performance analysis revealed varying impacts of jamming on different cluster-based protocols, guiding protocol selection.
  • Experimental validation with Zigbee and LoRa confirmed the algorithm's practical applicability.

Conclusions:

  • The developed jamming detection algorithm offers an energy-efficient solution for securing WSNs against interference attacks.
  • The findings provide valuable insights into the performance of different WSN routing protocols under jamming conditions.
  • This research contributes to enhancing the reliability and longevity of WSNs in the Internet of Things ecosystem.