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Related Experiment Videos

Efficient Actor Recovery Paradigm for Wireless Sensor and Actor Networks.

Reem K Mahjoub1, Khaled Elleithy2

  • 1Department of Computer Science, University of Bridgeport, 126 Park Avenue, Bridgeport, CT 06604, USA. rmahjoub@my.bridgeport.edu.

Sensors (Basel, Switzerland)
|April 20, 2017
PubMed
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This study introduces an Efficient Actor Recovery (EAR) paradigm to enhance wireless sensor and actor networks (WSANs). EAR minimizes node failures and ensures continuous traffic forwarding for improved Quality-of-Service (QoS).

Area of Science:

  • Computer Science
  • Network Engineering

Background:

  • Wireless Sensor and Actor Networks (WSANs) operate in challenging environments, leading to a high potential for node failures.
  • Node failures in WSANs can stem from power issues, hardware malfunctions, software errors, or connectivity problems, impacting overall network performance and Quality-of-Service (QoS).

Purpose of the Study:

  • To propose an Efficient Actor Recovery (EAR) paradigm designed to mitigate node failures and ensure reliable traffic forwarding in WSANs.
  • To enhance the Quality-of-Service (QoS) by proactively addressing critical node failures and maintaining network stability.

Main Methods:

  • The proposed Efficient Actor Recovery (EAR) paradigm integrates three key algorithms: Node Monitoring and Critical Node Detection (NMCND), Network Integration and Message Forwarding (NIMF), and Priority-Based Routing for Node Failure Avoidance (PRNFA).
Keywords:
RSSIWSANWSNactordata recoverylatencynode failuresensorwireless sensor actor networkwireless sensor network

Related Experiment Videos

  • NMCND identifies and replaces critical nodes before complete failure. NIMF optimizes packet forwarding by determining the optimal source (actor or sensor). PRNFA prioritizes packet transmission based on data significance to ensure efficient routing.
  • The effectiveness of the EAR paradigm and its algorithms was validated through simulations using OMNET++.

    • Main Results:

    • The EAR paradigm successfully guarantees contention-free traffic-forwarding capacity by proactively managing node failures.
    • The NMCND algorithm effectively monitors nodes and replaces critical ones, preventing complete network disruption.
    • NIMF and PRNFA contribute to balanced network performance and sustained network operation by controlling forwarding rates and prioritizing critical data.

    Conclusions:

    • The Efficient Actor Recovery (EAR) paradigm offers a robust solution for enhancing the reliability and performance of wireless sensor and actor networks.
    • The integrated approach of monitoring, backup, intelligent forwarding, and priority-based routing significantly improves WSAN resilience against node failures.
    • The proposed methods are effective in maintaining network functionality and QoS in demanding operational environments.