IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Fault Tolerance Model for Efficient Actor Recovery Paradigm in WSAN

Fault Tolerance Model for Efficient Actor Recovery Paradigm in WSAN
View Sample PDF
Author(s): Reem Khalid Mahjoub (University of Bridgeport, Bridgeport, USA)and Khaled Elleithy (University of Bridgeport, Bridgeport, USA)
Copyright: 2021
Pages: 18
Source title: Research Anthology on Architectures, Frameworks, and Integration Strategies for Distributed and Cloud Computing
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-7998-5339-8.ch022

Purchase

View Fault Tolerance Model for Efficient Actor Recovery Paradigm in WSAN on the publisher's website for pricing and purchasing information.

Abstract

Wireless sensor and actor networks (WSAN) is an area where sensors and actors collaborate to sense, handle and perform tasks in real-time. Thus, reliability is an important factor. Due to the natural of WSAN, actor nodes are open to failure. Failure of actor nodes degrades the network performance and may lead to network disjoint. Thus, fault tolerance techniques should be applied to insure the efficiency of the network. In an earlier work, the authors proposed an efficient actor recovery paradigm (EAR) for WSAN which handles the critical actor node failure and recovery while maintaining QoS. EAR is supported with node monitoring and critical node detection (NMCND), network integration and message forwarding (NIMF), priority-based routing for node failure avoidance (PRNFA) and backup selection algorithms. In this article, the authors extend the work by adding a fault tolerance mathematical model. By evaluating the model, EAR shows to manage fault tolerance in deferent levels. To evaluate the effectiveness, the EAR fault tolerance is evaluated by simulation using OMNET++ Simulation. In addition, EAR reliability is measured and compared with RNF, DPCRA, ACR, and ACRA.

Related Content

Sushruta Mishra, Sunil Kumar Mohapatra, Brojo Kishore Mishra, Soumya Sahoo. © 2021. 24 pages.
Carlos Santos, Helena InĂ¡cio, Rui Pedro Marques. © 2021. 16 pages.
Akash Chowdhury, Swastik Mukherjee, Sourav Banerjee. © 2021. 26 pages.
Stojan Kitanov, Toni Janevski. © 2021. 28 pages.
Ramesh C. Poonia, Linesh Raja. © 2021. 27 pages.
Jens Kohler, Thomas Specht. © 2021. 27 pages.
Jagdish Chandra Patni. © 2021. 15 pages.
Body Bottom