A modified energy efficient relay selection for cognitive radio sensor networks

被引:0
|
作者
Prasad, L. V. R. Chaitanya [1 ,2 ]
Kamatham, Yedukondalu [3 ]
Sunehra, Dhiraj [4 ]
机构
[1] Sreenidhi Inst Sci & Technol, Dept ECE, Hyderabad, Telangana, India
[2] JNTUH, Dept ECE, Hyderabad, Telangana, India
[3] CVR Coll Engn, Dept ECE, Hyderabad, Telangana, India
[4] JNTUH Univ Coll Engn, Dept ECE, Jagtial, Telangana, India
关键词
Cognitive radio; Sensor networks; Clustering; Cluster head weight; Cluster head selection; Energy efficiency; Spectrum availability; Fuzzy logic; CLUSTERING APPROACH; WSN;
D O I
10.1007/s11276-023-03466-7
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:6537 / 6554
页数:18
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