A new analytical model for enhancing performance of cognitive radio user’s transmissions in the internet of things

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Pashmforoush S.; Emrani Zarandi A.A.

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Journal Paper

Paper Information

Journal: JOURNAL OF ELECTRONIC AND CYBER DEFENCE Year:2021 | Volume:9 | Issue:3 Page(s): 73-82

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Title

A new analytical model for enhancing performance of cognitive radio user’s transmissions in the internet of things

Author(s)

Pashmforoush S. | Emrani Zarandi A.A. | Issue Writer Certificate

Keywords

Internet of ThingsQ4

Transport protocolQ4

ThroughputQ4

Cognitive Radio Sensor NetworkQ4

Markov ChainQ4

Packet loss probabilityQ4

Primary userQ4

Secondary userQ4

End to end delayQ4

Abstract

Internet of Things (IoT) constitutes networked devices that can gather and exchange information. Thescarcity of the available spectrum used by a large number of devices in IoT is a challenge. The Cognitive Radiotechnology has emerged as a promising solution to overcome spectrum scarcity in a resource-constrained wireless sensor network. The prioritized spectrum access is the key to maintain the efficiency of CRSN. Modelling the prioritized spectrum access policy is a significant need to analyze a CRSN. However, in CRSN, TCP experiences in severe Throughput reduction since it cannot differentiate between the packet loss due to SU’s transmission-blocking and packet loss due to congestion. In this paper, two significant events are investigated that caused Secondary user blocking. In additiona Discrete-Time Markov Chain (DTMC) is proposed to describe the spectrum usage by both primary and Secondary users, which is used to estimate the TCP Throughput and End to end delay. The experimental resultsbased on the NS2 confirms the accuracy of the proposed model and show that the Throughput and the average end-to-end delay are improved based on the proposed DTMC model comparing with some Transport protocol in the cognitive radio networks. The performance results through simulation show that the proposed model achieves up to 20% improvement of the Throughput comparing with the classical TFRC and TFRC-CR respectively.

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