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Bloom filter–based efficient broadcast algorithm for the Internet of things

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Show simple item record Talpur, Anum Shaikh, Faisal K. Newe, Thomas Sheikh, Adil A. Felemban, Emad Khelil, Abdelmajid 2018-01-03T10:03:06Z 2018-01-03T10:03:06Z 2017
dc.description peer-reviewed en_US
dc.description.abstract In the Internet of things, a large number of objects can be embedded over a region of interest where almost every device is connected to the Internet. This work scrutinizes the broadcast overhead problem in an Internet of things network, containing a very large number of objects. The work proposes a probabilistic structure (bloom filter)-based technique, which uses a new broadcast structure that attempts to reduce the number of duplicate copies of a packet at every node. This article utilizes a clustering concept to make the broadcast efficient in terms of memory space, broadcast overhead, and energy usage. The unique idea of a bloom-based network uses a filter to incorporate neighbor information when taking a forwarding decision to reduce broadcast overhead. The simulation results show that parallel broadcasting among different clusters and the use of a bloom filter can achieve a reduction in broadcast overhead from hundreds to ones and tens, when compared with a conventional non-bloom-based broadcast algorithm and a bloom-based algorithm. In addition, it helps to reduce energy usage evenly throughout the network, 1/100 times, when compared with conventional broadcast (non-bloom-based) and, 1/10 times, when compared with bloom-based broadcast. This increases the lifetime of a network by having control over network density usage and communications overhead as a result of broadcasting. en_US
dc.language.iso eng en_US
dc.publisher SAGE en_US
dc.relation.ispartofseries International Journal of Distributed Sensor Networks;13 (12)
dc.subject internet of things en_US
dc.subject broadcast en_US
dc.subject bloom filter en_US
dc.subject cluster en_US
dc.title Bloom filter–based efficient broadcast algorithm for the Internet of things 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
dc.identifier.doi 10.1177/1550147717749744
dc.contributor.sponsor Long-Term National Plan for Science,Technology and Innovation (LTNPSTI) en_US
dc.contributor.sponsor King AbdulAziz City for Science and Technology (KACST), Kingdom of Saudi Arabia. en_US
dc.relation.projectid 10-INF1236-10 en_US
dc.rights.accessrights info:eu-repo/semantics/openAccess en_US
dc.internal.rssid 2735780

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