Learning automaton‐based self‐protection algorithm for wireless sensor networks
Wireless sensor networks (WSNs) have been widely used for many applications such as surveillance and security applications. Every simple sensor in a WSN plays a critical role and it has to be protected from any attack and failure. The self‐protection of WSNs focuses on using sensors to protect thems...
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Online Access: | https://doi.org/10.1049/iet-net.2018.0005 |
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doaj-3fac42ad5d5845f2ae7251ae3b20590d2021-09-08T13:49:14ZengWileyIET Networks2047-49542047-49622018-09-017535336110.1049/iet-net.2018.0005Learning automaton‐based self‐protection algorithm for wireless sensor networksHabib Mostafaei0Mohammad S. Obaidat1Department of EngineeringRoma Tre UniversityRomeItalyDepartment of ECENazarbayev University, Astana, Kazakhstan and King Abdullah II School of Information Technology, Universality of JordanJordanWireless sensor networks (WSNs) have been widely used for many applications such as surveillance and security applications. Every simple sensor in a WSN plays a critical role and it has to be protected from any attack and failure. The self‐protection of WSNs focuses on using sensors to protect themselves to resist against attacks targeting them. Therefore, it is necessary to provide a certain level of protection to each sensor. The authors propose an irregular cellular learning automaton (ICLA)‐based algorithm, which is called SPLA, to preserve sensors protection. Learning automaton at each cell of ICLA with proper rules aims at investigating the minimum possible number of nodes in order to guarantee the self‐protection requirements of the network. To evaluate the performance of SPLA, several simulation experiments were carried out and the obtained results show that SPLA performs on average of 50% better than maximum independent set and minimum connected dominating set algorithms in terms of active node ratio and can provide two times reduction in energy consumption.https://doi.org/10.1049/iet-net.2018.0005self‐protection algorithmwireless sensor networkssecurity applicationsirregular cellular learning automatonICLASPLA |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Habib Mostafaei Mohammad S. Obaidat |
spellingShingle |
Habib Mostafaei Mohammad S. Obaidat Learning automaton‐based self‐protection algorithm for wireless sensor networks IET Networks self‐protection algorithm wireless sensor networks security applications irregular cellular learning automaton ICLA SPLA |
author_facet |
Habib Mostafaei Mohammad S. Obaidat |
author_sort |
Habib Mostafaei |
title |
Learning automaton‐based self‐protection algorithm for wireless sensor networks |
title_short |
Learning automaton‐based self‐protection algorithm for wireless sensor networks |
title_full |
Learning automaton‐based self‐protection algorithm for wireless sensor networks |
title_fullStr |
Learning automaton‐based self‐protection algorithm for wireless sensor networks |
title_full_unstemmed |
Learning automaton‐based self‐protection algorithm for wireless sensor networks |
title_sort |
learning automaton‐based self‐protection algorithm for wireless sensor networks |
publisher |
Wiley |
series |
IET Networks |
issn |
2047-4954 2047-4962 |
publishDate |
2018-09-01 |
description |
Wireless sensor networks (WSNs) have been widely used for many applications such as surveillance and security applications. Every simple sensor in a WSN plays a critical role and it has to be protected from any attack and failure. The self‐protection of WSNs focuses on using sensors to protect themselves to resist against attacks targeting them. Therefore, it is necessary to provide a certain level of protection to each sensor. The authors propose an irregular cellular learning automaton (ICLA)‐based algorithm, which is called SPLA, to preserve sensors protection. Learning automaton at each cell of ICLA with proper rules aims at investigating the minimum possible number of nodes in order to guarantee the self‐protection requirements of the network. To evaluate the performance of SPLA, several simulation experiments were carried out and the obtained results show that SPLA performs on average of 50% better than maximum independent set and minimum connected dominating set algorithms in terms of active node ratio and can provide two times reduction in energy consumption. |
topic |
self‐protection algorithm wireless sensor networks security applications irregular cellular learning automaton ICLA SPLA |
url |
https://doi.org/10.1049/iet-net.2018.0005 |
work_keys_str_mv |
AT habibmostafaei learningautomatonbasedselfprotectionalgorithmforwirelesssensornetworks AT mohammadsobaidat learningautomatonbasedselfprotectionalgorithmforwirelesssensornetworks |
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1717762354387615744 |