Framework for Cross-Layer Optimization in Cognitive Radio Network

Pandit, Shweta; Singh, Ghanshyam

doi:10.1007/978-3-319-53147-2_10

Cited by 2 publications

(1 citation statement)

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“…The method focuses on enhancing the system throughput and speed of data transmission. Cross-layer architectures for cognitive radio networks have been discussed in [4]. It gives layer-centric and centralized solutions for cross-layer design problems.…”

Section: Introductionmentioning

confidence: 99%

A Novel Duty Cycle Based Cross Layer Model for Energy Efficient Routing in IWSN Based IoT Application

2022

KSII TIIS

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Wireless Sensor Network (WSN) is considered as an integral part of the Internet of Things (IoT) for collecting real-time data from the site having many applications in industry 4.0 and smart cities. The task of nodes is to sense the environment and send the relevant information over the internet. Though this task seems very straightforward but it is vulnerable to certain issues like energy consumption, delay, throughput, etc. To efficiently address these issues, this work develops a cross-layer model for the optimization between MAC and the Network layer of the OSI model for WSN. A high value of duty cycle for nodes is selected to control the delay and further enhances data transmission reliability. A node measurement prediction system based on the Kalman filter has been introduced, which uses the constraint based on covariance value to decide the scheduling scheme of the nodes. The concept of duty cycle for node scheduling is employed with a greedy data forwarding scheme. The proposed Duty Cycle-based Greedy Routing (DCGR) scheme aims to minimize the hop count, thereby mitigating the energy consumption rate. The proposed algorithm is tested using a real-world wastewater treatment dataset. The proposed method marks an 87.5% increase in the energy efficiency and reduction in the network latency by 61% when validated with other similar pre-existing schemes.

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Section: Introductionmentioning

confidence: 99%