A SPN-Based Performance Evaluation of Wireless Sensor Networks

Wu, Zhengxuan; Hong, Zhiguo

doi:10.1109/isecs.2009.36

Cited by 4 publications

(4 citation statements)

References 7 publications

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“…The reliability and availability of IP multi media subsystem is analyzed using Markov Chain [41] The availability of mobile cloud computing system is analyzed by modeling the physical components using CTMC [98] A Markov Chain based approach is presented to analyze the reliability of smart grid station having dependent failures [208] An MS-exchange email server having uncertain failure rates and repair rates is modeled and analyzed [202] The availability analysis of Eucalyptus cloud computing is presented [41] The availability of mobile cloud computing system is analyzed by modeling the physical components using CTMC Data Communication Networks/ Concurrent Failutes [114] The survivability analysis of VADHOC network, in terms of message lost, is modeled using Markov reward modeling and analysis is done using SHARPE software [209] A reliable WSN system is presented for safety applications and its reliability analysis is done using Markov Chain and analysis is done in SHARPE software [116] The reliability analysis of VoIP network is performed by simulating its RBD model using RAPTOR tool [210] The reliability and availability analysis of nanosatellites based spacecrafts-swarm is performed using Markov Chain and analysis is done through Monte Carlo simulation Data Communication Network/ Network Protocols [211] The reliability and rapidness analysis of fast and secure protocol, in term of successful delivery and time elapsed, is performed using Markov Chain and analysis is done in PRISM model checker [212] The survivability analysis of WSN, in terms of data loss and frequency of failures, is performed using PRISM model checker [213] The dependability properties of wireless group communication are verifies using ETMCCC model checker Data Communication System/ Unreliable Components [214] A finite retrial system with unreliable and multi servers, is analyzed through Petri Nets [199] WLAN based data communication systems are presented for CTBC and are analyzed through Petri Nets [186] The survivability analysis of cellular network, in terms of excess delay due to failure and call blocking, is performed using Petri Nets [215] [216] Erlang-B and Erlang-C traffic scenarios are created and thus reliability is evaluated [217] WSNs are modeled and analyzed through SPN to observe the effects of data packet number, throughput and arrival rate [218] A timed rotation token protocol is analyzed for the reliability analysis of FDDI using Petri Nets [219] Two types of networks architectures are modeled for a file server system and are analyzed using Petri Nets …”

Section: Analytical Methods With Markov Chain Based Analysismentioning

confidence: 99%

“…Using this approach, the reliability analysis of finite source retrial system that possesses unreliable hardware and multi servers is presented in [214]. Some other prominent works in this direction include the reliability analysis of the data communication systems of the WLAN based train control system [199], cellular networks [186] and WSNs [215] [216] [217]. Moreover, some network protocols, like courier and FDDI token ring protocol, have also been analyzed using the Petri Net approach [227] [218].…”

Section: Formal Methods With Markov Chainmentioning

confidence: 99%

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Reliability modeling and analysis of communication networks

Ahmed

Hasan

Pervez

et al. 2017

Journal of Network and Computer Applications

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In recent times, the functioning of various aspects of modern society-ranging from the various infrastructural utilities such as electrical power, water to socio-economical aspects such as telecommunications, business, commerce, education-has become critically reliant on communication networks, and particularly on the Internet. With the migration of critical facilities to the Internet, it has become vitally important to ensure the reliability and availability of networks. In this paper, we study various modeling and analysis techniques that can aid in the study of reliability of communication networks. In this regard, we provide background on the modeling techniques (such as reliability block diagrams, fault trees, Markov chains, etc.) and analysis techniques (such as mathematical analytical methods, simulation methods, and formal methods). Apart from providing the necessary background, we also critically evaluate the pros and cons of different approaches, and provide a detailed survey of their applications in communication networks. To the best of our knowledge, this is the first in-depth review of the application of reliability modeling and analysis techniques in communication networks. work, the ability of a network to maintain an acceptable reliability level under extreme environmental conditions and assess the impact of design changes on the reliability of the overall network [17]. Similarly, the availability prediction allows us to evaluate various maintenance design options to achieve the desired availability of the network [18].The qualitative and quantitative reliability analysis requires the selection of an appropriate mathematical modeling and analysis technique. The modeling technique must be able to effectively capture the important parameters of the real system and the analysis technique should be capable of providing insights into the system behavior without running (or executing) the real system. There are numerous techniques that can provide analysis in the early phase, when only initial details are available of the design, and there are other techniques that cater the analysis of the later design phases, when more precise implementation details are available [16]. Some of the most widely used formalisms/techniques for modeling reliability and availability of communication networks are: Reliability Block Diagram (RBD) [19], Fault Tree (FT) [20], and Markov Chain (MC) [21]. Traditionally, the models developed using these techniques are analyzed using analytical methods or simulation tools.Recently, the computer networks community has shown great interest in the utilization of formal methods [22] as a reliability analysis technique for communication networks. Formal methods use mathematical logic to precisely model the system's intended behavior and deploy mathematical reasoning to construct an irrefutable proof that the given system satisfies its requirements. This kind of mathematical modeling and analysis makes formal methods an accurate and rigorous analysis method compared to the traditional a...

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Section: Analytical Methods With Markov Chain Based Analysismentioning

confidence: 99%

Section: Formal Methods With Markov Chainmentioning

confidence: 99%

Reliability modeling and analysis of communication networks

Ahmed

Hasan

Pervez

et al. 2017

Journal of Network and Computer Applications

View full text Add to dashboard Cite

show abstract

“…Petri Net [15] and their extension (like SPN and GSPN) [16] is frequently used in Dependability modeling and evaluation. In this domain, Sheldon et al [17] focus on the specification and assessment of SPN models [18] to evaluate the design of an embedded system for reliability and availability. He propose an approach to model and evaluate dynamic driving regulation (DDR) system to improve vehicle deductibility (anti-skid, -slip and steering assist).…”

Section: Introductionmentioning

confidence: 99%

The EventB2PN Tool: From Event-B specification to Petri Nets through model transformation

Garoui

Mazigh

Koukam

2015

2015 IEEE/ACIS 16th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distribu

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Formal methods, in particular the B Method and its extension Event-B are used to validate the system correct ness and to verify its properties. The weak point here is that Event-B that don't support quantitative analysis of performance and/or dependability. For this reason, we transform an Event-B specification to another formalism which automatically transform and allows us to evaluate the performance and/or dependability attributes. In this paper, we propose an algorithm which offers the transformation rules. This algorithm is implemented within our transformation tool, EventB2PN, to generate the transformation process from Event-B specification to Petri Net (PN) structure. EventB2PN tool use XML file for Event-B specification and stan dard PNML file for PN which can be analyzed with quantitative analysis tool. A small case study of repaired system has been conducted to show the benefits and the strong points in ours works.

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“…In order to overcome the limitations of Markov models in analyzing the performance of concurrent system with wide area traffic [7] , Fernando J. and David M., et al first developed handoff and dynamic channel allocation models using Petri Nets (PN) for cellular CDMA system and showed its significant advantages over MC and state equations approaches [8] . Then, Stochastic Petri Net (SPN)-based performance evaluation approaches were presented in ad hoc networks [9] , wireless sensor networks [10] and web services [11] .…”

Section: Introductionmentioning

confidence: 99%

Modeling radio resource management in cognitive radio system based on OFDMA using colored Petri Net

Zhang

Yang

et al. 2010

J. Electron.(China)

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Cognitive Radio (CR) system based on Orthogonal Frequency Division Multiple Access (OFDMA), such as Wireless Regional Area Networks (WRAN) and Worldwide Interoperability for Microwave Access (WiMAX), often attempt to improve performance via dynamic radio resource management, which is characterized as concurrent processing of different traffic and nondeterministic system capacity. It is essential to design and evaluate such complex system using proper modeling and analysis tools. In the previous work, most of the communication systems were modeled as Markov Chain (MC) and Stochastic Petri Nets (SPN), which have the explicit limitation in evaluating adaptive OFDMA CR system with wide area traffic. In this paper, we develop an executable top-down hierarchical Colored Petri Net (CPN) model for adaptive OFDMA CR system, and analyze its performance using CPN tools. The results demonstrate that the CPN can model different radio resource management algorithms in CR Systems, and the CPN tools require less computational effort than Markov model using Matlab, with its flexibility and adaptability to the traffics which arrival interval and processing time are not exponentially distributed.

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A SPN-Based Performance Evaluation of Wireless Sensor Networks

Cited by 4 publications

References 7 publications

Reliability modeling and analysis of communication networks

Reliability modeling and analysis of communication networks

The EventB2PN Tool: From Event-B specification to Petri Nets through model transformation

Modeling radio resource management in cognitive radio system based on OFDMA using colored Petri Net

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