CogNS: A Simulation Framework for Cognitive Radio Networks

Esmaeelzadeh, Vahid; Berangi, Reza; Sebt, Seyyed Mohammad; Hosseini, Elahe Sadat; Parsinia, Moein

doi:10.1007/s11277-013-1184-y

Cited by 26 publications

(16 citation statements)

References 18 publications

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“…The obtained results through models are verified using simulations by CogNS simulation framework [13] which is a simulation framework for cognitive radio networks based on Network Simulator 2 (NS2) [18]. Default simulation settings and the configuration parameters of the CRSN are summarized in 1− Ω 8 Figure 3: The embedded DTMC of sending rate process based on generic AIMD rate adjustment with maximum sending rate = 8, increasing factor INC = 1, and decreasing factor DEC = 2.…”

Section: Simulation Results and Verificationsmentioning

confidence: 99%

On the Optimality of Generic Rate-Based AIMD and AIAD Congestion Control Schemes in Cognitive Radio Sensor Networks

Esmaeelzadeh

Berangi

2015

International Journal of Distributed Sensor Networks

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Investigating the optimality and the modeling of congestion control schemes is crucial in order to provide quality of service (QoS) for various applications in cognitive radio sensor networks (CRSNs). This paper develops an analytical framework to study the optimality of rate-based generic AIMD and AIAD congestion control schemes. In this way, a congestion model is introduced to describe the congestion behavior of CRSNs. A semi-Markov chain (SMC) is proposed to model the steady-state sending rate distribution of source nodes based on the congestion model. The optimality of generic AIMD and AIAD, based on the proposed models, is analyzed in order to maximize the defined rate-congestion ratio (RCR). The analytical results are verified through various NS2-based simulations in CRSNs.

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Section: Simulation Results and Verificationsmentioning

confidence: 99%

On the Optimality of Generic Rate-Based AIMD and AIAD Congestion Control Schemes in Cognitive Radio Sensor Networks

Esmaeelzadeh

Berangi

2015

International Journal of Distributed Sensor Networks

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“…If a pri- 10 mary user is appeared in the licensed band, CR user leaves 11 the spectrum immediately [1]. Cognitive radio technology 12 can be used in wireless sensor networks (WSNs) to over- 13 come spectrum shortage problem and reserve the limited 14 resources of sensors in WSNs [3]. Wireless sensor networks 15 with CR-equipped sensor nodes are called cognitive radio 16 sensor networks (CRSNs) [3].…”

Section: Introductionmentioning

confidence: 99%

“…However, 35 there is a limited amount of work on the performance eval- 36 uation of transport layer protocols in CRSNs. Most of pre-37 vious studies [4][5][6][7][8][9][10][11][12][13][14] concentrated on the simulation-based 38 performance evaluation of transport layer protocols. How-39 ever, analytical investigation is required to model the per- 40 formance of congestion control schemes in CRSNs for the 41 following motivations: (1) transport-level delay (delay over- 42 head of transport layer protocols) depends on the sending 43 rate of source nodes in this layer.…”

Section: Introductionmentioning

confidence: 99%

“…An equation-138 based transport protocol for cognitive radio networks is pro-139 posed in [13]. Authors in [14] evaluate TCP end-to-end de-140 lay, throughput and packet drop probability with regard to 141 packet size, sensing time, sensing accuracy and activities of 142 primary users. However, most of studies have focused on the 143 simulation-based performance evaluation of transport layer 144 protocols in CRNs.…”

Section: Introductionmentioning

confidence: 99%

“…where l is node queue length and Q l is the probability that 378 the queue length equals l. In the operating mode, a CR node operates based on a 385 multichannel MAC protocol of which its behavior is inspired 386 from the CSMA/CA mechanism [14]. 387 The CR node first senses the spectrum and selects a chan-388 nel which is free of primary users.…”

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confidence: 99%

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Modeling of rate-based congestion control schemes in cognitive radio sensor networks

et al. 2016

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a b s t r a c tPerformance evaluation of transport layer protocols in cognitive radio sensor networks (CRSNs) is useful to provide quality-of-service for real-time reliable applications. This paper develops an analytical framework to model the steady-state sending rate of collecting cognitive radio (CR) sensors in rate-based generic additive-increase multiplicative-decrease (AIMD) and additive-increase additive-decrease (AIAD) congestion control schemes. Evolution process of sending rate is modeled by a discrete time Markov chain (DTMC) in the terms of queue length. We model the queue length distribution of a CR node by a semi-Markov chain (SMC) with assuming general probability density functions (PDFs) of input rate and attainable sending rate of the node. These PDFs are derived based on the parameters of MAC and physical layers and CRSN configuration. The proposed models are verified through various simulations.

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A correlation‐based and spectrum‐aware admission control mechanism for multimedia streaming in cognitive radio sensor networks

Hosseini

Esmaeelzadeh

Berangi

et al. 2015

Int J Communication

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SUMMARYBandwidth management and traffic control are critical issues to guarantee the quality of service in cognitive radio networks. This paper exploits a network load refinement approach to achieve the efficient resource utilization and provide the required quality of service. A connection admission control approach is introduced in cognitive radio multimedia sensor networks to provide the data transmission reliability and decrease jitter and packet end-to-end delay. In this approach, the admission of multimedia flows is controlled based on multimedia sensors' correlation information and traffic characteristics. We propose a problem, connection admission control optimization problem, to optimize the connection admission control operation. Furthermore, using a proposed weighting scheme according to the correlation of flows issued by multimedia sensors enables us to convert the connection admission control optimization problem to a binary integerprogramming problem. This problem is a kind of a Knapsack problem that is solved by a branch and bound method. Simulation results verify the proposed admission control method's effectiveness and demonstrate the benefits of admission control and traffic management in cognitive radio multimedia sensor networks.

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CogNS: A Simulation Framework for Cognitive Radio Networks

Cited by 26 publications

References 18 publications

On the Optimality of Generic Rate-Based AIMD and AIAD Congestion Control Schemes in Cognitive Radio Sensor Networks

On the Optimality of Generic Rate-Based AIMD and AIAD Congestion Control Schemes in Cognitive Radio Sensor Networks

Modeling of rate-based congestion control schemes in cognitive radio sensor networks

A correlation‐based and spectrum‐aware admission control mechanism for multimedia streaming in cognitive radio sensor networks

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