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Defence against black hole and selective forwarding attacks for medical WSNs in the IoT

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Show simple item record Mathur, Avijit Newe, Thomas Rao, Muzaffar 2016-04-18T15:56:10Z 2016-04-18T15:56:10Z 2016
dc.description peer-reviewed en_US
dc.description.abstract Wireless sensor networks (WSNs) are being used to facilitate monitoring of patients in hospital and home environments. These systems consist of a variety of different components/sensors and many processes like clustering, routing, security, and self-organization. Routing is necessary for medical-based WSNs because it allows remote data delivery and it facilitates network scalability in large hospitals. However, routing entails several problems, mainly due to the open nature of wireless networks, and these need to be addressed. This paper looks at two of the problems that arise due to wireless routing between the nodes and access points of a medical WSN (for IoT use): black hole and selective forwarding (SF) attacks. A solution to the former can readily be provided through the use of cryptographic hashes, while the latter makes use of a neighbourhood watch and threshold-based analysis to detect and correct SF attacks. The scheme proposed here is capable of detecting a selective forwarding attack with over 96% accuracy and successfully identifying the malicious node with 83% accuracy. en_US
dc.language.iso eng en_US
dc.publisher MDPI en_US
dc.relation.ispartofseries Sensors;16, 118
dc.subject routing attacks en_US
dc.subject black hole en_US
dc.subject selective forwarding en_US
dc.subject sensor networks en_US
dc.subject medical WSN en_US
dc.subject IoT-Internet of Things en_US
dc.title Defence against black hole and selective forwarding attacks for medical WSNs in the IoT en_US
dc.type info:eu-repo/semantics/article en_US
dc.type.supercollection all_ul_research en_US
dc.type.supercollection ul_published_reviewed en_US 2016-04-18T15:35:50Z
dc.description.version PUBLISHED
dc.identifier.doi 10.3390/s16010118
dc.rights.accessrights info:eu-repo/semantics/openAccess en_US
dc.internal.rssid 1632224
dc.internal.copyrightchecked Yes
dc.description.status peer-reviewed

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