Modeling Wireless Sensor Networks Using Finite-Source Retrial Queues with Unreliable Orbit

Wüchner, Patrick; Sztrik, János; Meer, Hermann de

doi:10.1007/978-3-642-25575-5_7

Cited by 17 publications

(9 citation statements)

References 15 publications

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“…For computing the steady-state probabilities and the system characteristics, the MOSEL-2 software tool is used. These computations are similar to the ones described in, for example [20].…”

Section: X(t) = (Y(t) Q(t); C(t) O(t))supporting

confidence: 56%

Performance Modeling of Finite-Source Cognitive Radio Networks

et al. 2016

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This paper deals with performance modeling aspects of radio frequency licensing. The utilization of mobile cellular networks can be increased by the idea of the cognitive radio. Licensed users (Primary Users -PUs) and normal users (Secondary Users -SUs) are considered. The main idea is, that the SUs are able to access to the available non-licensed radio frequencies. A finite-source retrial queueing model with two non independent frequency bands (considered as service units) is proposed for the performance evaluation of the system. A service unit with a priority queue and another service unit with an orbit are assigned to the PUs and SUs, respectively. The users are classified into two classes: the PUs have got a licensed frequency, while the SUs have got a frequency band, too but it suffers from the overloading. We assume that during the service of the non-overloaded band the PUs have preemptive priority over SUs. The involved inter-event times are supposed to be independent and exponentially distributed random variables. The novelty of this work lies in the fact that we consider the effect of retrial phenomenon of SUs in performance modeling of radio frequency licensing by using a finite-source queueing model which takes the unreliability of radio transmission into account for the first time. In the literature, most work studied the performance of cognitive radio networks under a mixed spectrum environment of licensed and unlicensed bands where the blocked SUs and the preempted SUs are forced to leave the system forever when there are no idle channels in the system. But in practical situation, the blocked SUs and the preempted SUs may do not leave the system forever and try to continue their services after random amount of time. By the help of an appropriate continuous time Markov chain using MOSEL (MOdeling Specification and Evaluation Language) tool several numerical examples are provided showing the effects of different input parameters on the main performance measures of the cognitive radio networks. Our primary focus is to determine an optimal number of SUs, where at the * University of Debrecen, Hungary, E-mail: almasi.bela,berczes.tamas, kuki.attila, sztrik.janos@inf.unideb.hu † Beijing Jiaotong University, China E-mail: jtwang@bjtu.cn DOI: 10.14232/actacyb.22.3.2016.5 618 B. Almási, T. Bérczes, A. Kuki, J. Sztrik, J. Wang secondary band the gained utilization, that is when switching to the cognitive radio, has a maximum value.

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“…For computing the steady-state probabilities and the system characteristics, the MOSEL-2 software tool is used. These computations are similar to the ones described in, for example [20].…”

Section: X(t) = (Y(t) Q(t); C(t) O(t))supporting

confidence: 56%

Performance Modeling of Finite-Source Cognitive Radio Networks

et al. 2016

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“…These computations are described in eg. [17,18]. When we have calculated the distributions defined above, the most important steady-state system characteristics can be obtained in the following way:…”

Section: System Modelmentioning

confidence: 99%

Performance Modeling of Finite-Source Retrial Queueing Systems with Collisions and Non-reliable Server Using MOSEL

Bérczes

Sztrik

Tóth

et al. 2017

Communications in Computer and Information Science

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Abstract. In this paper we investigate a single-server retrial queueing system with collision of the customer and an unreliable server. If a customer finds the server idle, he enters into service immediately. The service times are independent exponentially distributed random variables. During the service time the source cannot generate a new primary call. Otherwise, if the server is busy, an arriving (primary or repeated) customer involves into collision with customer under service and they both moves into the orbit. The retrial time of requests are exponentially distributed. We assume that the server is unreliable and could be break down. When the server is interrupted, the call being served just before server interruption goes to the orbit. Our interest is to give the main steady-state performance measures of the system computed by the help of the MOSEL tool. Several Figures illustrate the effect of input parameters on the mean response time.

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“…Because of the fact, that the state space of this chain is very large, it is rather difficult to calculate the system measures in the traditional way of writing down and solving the underlying steady-state equations. To simplify this procedure we used the software tool MOSEL (Modeling, Specification and Evaluation Language), see Begain et al [5], to formulate the model and to obtain the performance measures as it has been done in, for example [7], [9].…”

Section: System Modelmentioning

confidence: 99%

“…However, during the breakdown only the service is stopped all other operations ( request generations, retrials ) are continued and after repair the service is resumed. The proposed new model is originated from complex retrial queueing systems treated in, for example [1], [2], [3], [6], [8], [9].…”

Section: Introductionmentioning

confidence: 99%

A queueing model to study the effect of network service breakdown in a CogInfoCom system

Kuki

Bérczes

Almási

et al. 2013

2013 IEEE 4th International Conference on Cognitive Infocommunications (CogInfoCom)

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Abstract-In this paper we introduce a finite source retrial queueing model to investigate the performance measures of a cognitive infocommunication system subject to random network communication breakdowns. The sources of the queueing model represent the communication entities, which are divided into two classes: The first class is the class of "Intelligent sources", where the source entity is able to get information about the state of the network environment, and so they are able to retry the started request in the case of special communication problems. The second class is the "Normal sources" (e.g. sensors), where the source entity starts the network communication, but is not able to sense the state of the network environment, so it is unable to retry the transmission in the case of special problems or errors.A novel Markovian model is constructed focusing on the question how the breakdowns of the communication network ("Network Cloud") influence the system's performance assuming that the service can be in 3 different states, fully operational, limited operation, broken offering different communication capabilities to the "Sources".The main interest of the present paper is to investigate the main steady-state performance measures of the system. To achieve this goal the MOSEL (MOdeling, Specification and Evaluation Language) tool is used to formulate and solve the problem. At the end of the paper several sample numerical examples will be shown illustrating the effect of the server's failure rate, for example on the mean number of customers in the system, mean orbit size, mean response and waiting time of the requests.

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Modeling Wireless Sensor Networks Using Finite-Source Retrial Queues with Unreliable Orbit

Cited by 17 publications

References 15 publications

Performance Modeling of Finite-Source Cognitive Radio Networks

Performance Modeling of Finite-Source Cognitive Radio Networks

Performance Modeling of Finite-Source Retrial Queueing Systems with Collisions and Non-reliable Server Using MOSEL

A queueing model to study the effect of network service breakdown in a CogInfoCom system

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