A modified energy efficient relay selection for cognitive radio sensor networks

The cognitive radio sensor network (CRSN) faces additional difficulties in developing topology maintenance methods. Spectrum-aware clustering techniques, which depend on channel availability, have existed in the past, satisfactory clustering may not be possible. Due to the sensor nodes’ restricted a...

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Bibliographic Details
Published in:Wireless networks 2024-10, Vol.30 (7), p.6537-6554
Main Authors: Prasad, L. V. R. Chaitanya, Kamatham, Yedukondalu, Sunehra, Dhiraj
Format: Article
Language:English
Subjects:
Availability
Clustering
Cognitive radio
Communications Engineering
Computer Communication Networks
Electrical Engineering
Energy sources
Engineering
Error analysis
Fuzzy logic
IT in Business
Network latency
Networks
Nodes
Relay
Residual energy
Sensors
Topology
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Summary:The cognitive radio sensor network (CRSN) faces additional difficulties in developing topology maintenance methods. Spectrum-aware clustering techniques, which depend on channel availability, have existed in the past, satisfactory clustering may not be possible. Due to the sensor nodes’ restricted ability to transmit data due to energy resources, CRSNs have an energy efficiency problem.As a result of the failure of the clusterheads (CH) the activity of clustered networks stopped. To extend the lifespan of the network, a reliable selection of CHs should be carried out in the network. Utilizing the weighted clustering parameter, an adaptive clustering approach is suggested. Queue length, spectrum availability, node coverage and residual energy are all factors that are taken into account while choosing a CH. Every node’s cluster head weight (CHW) is calculated using the proposed technique, and this CHW is used to choose which CHs to use for each node. The suggested approach includes a quality relay node selection mechanism using fuzzy logic for inter- and intra-cluster communications after clustering and CH selection. As inputs for selecting a relay, the channel quality, traffic index and connection error rate are used by the fuzzy system. The proposed method is superior to the current clustering methods for CRSN when studying the simulation data produced in terms of residual energy, end-to-end latency, routing overhead, network throughput, and longevity.
ISSN:1022-0038
1572-8196
DOI:10.1007/s11276-023-03466-7