CrowWhale-ETR: CrowWhale optimization algorithm for energy and trust aware multicast routing in WSN for IoT applications

WSN serves as a medium for linking the physical and information network of IoT. Energy and trust are the two major factors that facilitate reliable communication in the network. During multicast routing, the BS engages in forwarding the data securely to the multiple destinations through the intermed...

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Bibliographic Details
Published inWireless networks Vol. 26; no. 6; pp. 4011 - 4029
Main Authors Shende, Dipali K., Sonavane, S. S.
Format Journal Article
LanguageEnglish
Published New York Springer US 01.08.2020
Springer Nature B.V
Subjects
Algorithms
Communications Engineering
Computer Communication Networks
Computer simulation
Data transmission
Electrical Engineering
Energy
Energy management
Engineering
IT in Business
Multicasting
Networks
Nodes
Optimization
Wireless networks
Wireless sensor networks
CSA
WSN
CrowWhale-ETR
WOA
Multicast routing
Trust
Optimization
IoT
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Summary:WSN serves as a medium for linking the physical and information network of IoT. Energy and trust are the two major factors that facilitate reliable communication in the network. During multicast routing, the BS engages in forwarding the data securely to the multiple destinations through the intermediate nodes, which is the major challenge in IoT. The paper addresses the challenges through proposing an energy-aware multicast routing protocol based on the optimization, CrowWhale-ETR, which is the integration of CSA and WOA based on the objective function designed with the energy and trust factors of the nodes. Initially, the trust and energy of the nodes are evaluated for establishing the routes that is chosen optimally using CWOA. This optimally chosen path is used for the data transmission, in which energy and trusts of the individual nodes are updated at the end of the individual transmission, in such a way the secure nodes can be selected, and which improves the secure communication in the network. The simulation is analyzed using 50 and 100 nodes in terms of the performance measures. The proposed method acquired the minimal delay of 0.2729 and 0.3491, maximal detection rate of 0.6726, maximal energy of 66.4275 and 71.0567, and maximal throughput of 0.4625 and 0.8649 in the presence and absence of attacks with 50 nodes for analysis.
ISSN:1022-0038
1572-8196
DOI:10.1007/s11276-020-02299-y