Stochastic backlog and delay bounds of generic rate-based AIMD congestion control scheme in cognitive radio sensor networks

Esmaeelzadeh, Vahid; Berangi, Reza; Hosseini, Elahe Sadat; Akan, Özgür B.

doi:10.1016/j.pmcj.2015.02.005

Cited by 6 publications

(11 citation statements)

References 34 publications

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“…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]. With regard to the applica-17 tion, opportunistic spectrum access (OSA) feature of sensor 18 nodes in CRSNs can decrease the collision and retransmission 19 probabilities in the environments with bursty traffic which 20 is common in sensor networks.…”

Section: Introductionmentioning

confidence: 99%

“…In [15], the optimality of simple rate adjustment tech-107 niques is investigated in CRSNs. In [16], the stochastic back-108 log and delay bounds of generic AIMD congestion control 109 schemes in CRSNs are modeled based on stochastic network 110 calculus (SNC) [17]. Ref.…”

Section: Introductionmentioning

confidence: 99%

“…Ref. [16] models the backlog and delay 111 bounds with the given sending rate distribution (probability 112 mass function) of source nodes in CRSNs through moment 113 generating function (MGF)-based theories in SNC. However, 114 there is no modeling of sending rate distribution of CR source 115 sensors based on queue length distribution and the attain-116 able sending rate of MAC layer in CRSNs that is the main con-117 tribution of this study.…”

Section: Introductionmentioning

confidence: 99%

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

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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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

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“…Recently, CRSN has attracted much attention of researches from the world, and some literatures including routing algorithms are also published [6,[14][15][16][17][18][19][20][21][22][23][24]. Taking account of the shortcomings of the existing clustering routing algorithm in WSN and CRN, the paper proposes a low-energy adaptive uneven clustering hierarchy (LEAUCH) for CRSN, which can not only consider the advantage of channel resources brought by cognitive function in CRSN but also exploit the uneven clustering method based on the channel resources.…”

Section: Introductionmentioning

confidence: 99%

LEAUCH: low-energy adaptive uneven clustering hierarchy for cognitive radio sensor network

Pei

Han

Sun

et al. 2015

J Wireless Com Network

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The integration of wireless sensor network (WSN) and cognitive radio (CR) technology enables a new paradigm of communication: cognitive radio sensor networks (CRSN). The existing WSN clustering algorithm cannot consider the advantage of channel resource brought by CR function in CRSN, and the CR network (CRN) clustering algorithm is designed based on the infinite energy nodes; thus both algorithms cannot operate with energy efficiency in CRSN. The paper proposes a low-energy adaptive uneven clustering hierarchy for CRSN, which can not only consider the advantage of the channel resource in reducing the energy consumption but also employ uneven clustering method for balancing the energy consumption among the cluster heads under multiple hops transmission means. Simulation results show that compared with the existing several typical clustering algorithms including WSN and CRSN clustering algorithms, low-energy adaptive clustering hierarchy (LEACH), HEED, energy-efficient unequal clustering (EEUC), cognitive LEACH (CogLEACH), and distributed spectrum-aware clustering (DSAC), the proposed algorithm can not only efficiently balance the energy consumption among cluster heads and network load in CRSN but also remarkably prolong the network lifetime.

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“…Therefore, an appropriate admission control mechanism is needed to decide whether a new connection should be accepted or rejected considering the PUs activity in CR channels and other network constraints. In general, connection admission control (CAC) is a proactive type of congestion control mechanism [4,5] to prevent congestion. This mechanism reduces packet end-to-end delay and packet loss probability and hence improves real-time performance [6].…”

Section: Introductionmentioning

confidence: 99%

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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Stochastic backlog and delay bounds of generic rate-based AIMD congestion control scheme in cognitive radio sensor networks

Cited by 6 publications

References 34 publications

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

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

LEAUCH: low-energy adaptive uneven clustering hierarchy for cognitive radio sensor network

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

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