An adaptive clustering approach for the management of dynamic systems

Ragusa, Carmelo; Liotta, Antonio; Pavlou, George

doi:10.1109/jsac.2005.857203

Cited by 35 publications

(21 citation statements)

References 14 publications

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“…Although, there is no optimal approach proposed to configure the parameter B, our simulation results in Section V demonstrate excellent performance when using the configuration B = ⌊M/L⌋. It might also be worth mentioning that the idea of subcarrier clustering has recently received a lot of interests particularly for improving the communication quality and spectral efficiency in cognitive communications [30]- [33]. However, in our work, the subcarrier clustering is for improving the performance of spectrum sensing.…”

Section: A Sensing Of Cp-based Multi-carrier Signalsmentioning

confidence: 84%

Cluster-Based Differential Energy Detection for Spectrum Sensing in Multi-Carrier Systems

Cheraghi

Tafazolli

et al. 2012

IEEE Trans. Signal Process.

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Abstract-This paper presents a novel differential energy detection scheme for multi-carrier systems, which can form fast and reliable decision of spectrum availability even in very low signalto-noise ratio (SNR) environment. For example, the proposed scheme can reach 90% in probability of detection (PD) and 10% in probability of false alarm (PFA) for the SNRs as low as −21 dB, while the observation length is equivalent to 2 multi-carrier symbol duration. The underlying initiative of the proposed scheme is applying order statistics on the clustered differential energy-spectral-density (ESD) in order to exploit the channel frequency diversity inherent in high data-rate communications. Specifically, to enjoy a good frequency diversity, the clustering operation is utilized to group uncorrelated subcarriers, while, the differential operation applied onto each cluster can effectively remove the noise floor and consequently overcome the impact of noise uncertainty while exploiting the frequency diversity. More importantly, the proposed scheme is designed to allow robustness in terms of both, time and frequency offsets. In order to analytically evaluate the proposed scheme, PFA and PD for Rayleigh fading channel are derived. The closed-form expressions show a clear relationship between the sensing performance and the cluster size, which is an indicator of the diversity gain. Moreover, we are able to observe up to 10 dB gain in the performance compared to the state-of-the-art spectrum sensing schemes.Index Terms-Differential, energy detection, low signal-tonoise ratio (SNR), multi-carrier, spectrum sensing.

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Section: A Sensing Of Cp-based Multi-carrier Signalsmentioning

confidence: 84%

Cluster-Based Differential Energy Detection for Spectrum Sensing in Multi-Carrier Systems

Cheraghi

Tafazolli

et al. 2012

IEEE Trans. Signal Process.

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“…Examples of these applications are: Peer-to-Peer (P2P) overlay networks [7], distributed computing [8], mobile ad hoc networks (MANET) [9]- [11], failure detection [12,13], exascale high performance computing [14][15][16], data mining [17]- [18], data aggregation [19][20][21][22], and wireless sensor networks (WSN) [23][24][25].…”

Section: Epidemic Aggregation Protocolsmentioning

confidence: 99%

Accuracy of Distributed Systems Towards Industry 4.0: Smart Grids and Urban Drainage Systems Case Studies

Poonpakdee¹

2018

GEOMATE

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ABSTRACT:With significantly improving the overall manufacturing operations commonly known in smart factory practices, vertical and horizontal integrations of various components are introduced throughout the entire value chain. The proliferation of smart factory practices introduces the fourth industrial revolution called Industry 4.0. Industry 4.0 creates new challenges and the application of networked manufacturing systems is one of the important features required to handle the systems in an efficient way by communicating and processing information to each other. Distributed systems provide coordination to allow the global information to be available for a better decision making and consequently achieving high efficiency. This connectivity helps for a better decision making and therefore achieve high efficiency. However, the evaluation on the accuracy of global information between different architectures of distributed systems (centralized systems and decentralized systems) has little work mentioning in the state of current manufacturing systems under the conception of Industry 4.0. As a result, this research will fill the gap by providing an accuracy of the decentralized system over the centralized system together with their sensitivity analysis. In this study, the decentralized system is built based on the concept of Epidemic protocols or Gossip-based protocols, while the centralized system is a simple client-server. The epidemic protocol is a bio-inspired communication paradigm that imitates the behavior of virus when the outbreak occurs in a community. The accuracy in both systems has been monitored by means of simulations. The effects of message loss to the accuracy of centralized and decentralized systems are studied. By comparing the system accuracy between both systems, it was found that the accuracy of the decentralized system is generally more accurate when the system is used for a long period. The accuracy tends to be lower down especially when the information is not completely distributed, while the accuracy of the centralized system receives an excessive suffer from message loss.

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“…The closest works to the one presented here are [26] and [27]. Ragusa et al [26] propose a heuristic approach for partitioning the network into a number of k clusters selfmanaged through mobile agents.…”

Section: Related Workmentioning

confidence: 99%

“…Ragusa et al [26] propose a heuristic approach for partitioning the network into a number of k clusters selfmanaged through mobile agents. Each agent migrates to more efficient positions within the cluster after deriving the median position in a centralized manner.…”

Section: Related Workmentioning

confidence: 99%

“…As service facilities move inside the network, rballs are partitioned or merged based on the outcome of the centralized approaches. The main difference between [26], [27] and the work presented here, amounts to the requirement for local information in order to select more efficient positions for the mobile agents in [26] and to incorporate classical centralized solution approaches in [27]. Instead, the proposed service migration approach relies on strictly local information; this local information is passively collected by the service facility itself, as opposed to requiring the deployment of a mechanism that provides both the demand and topology information within each cluster as in [26] and [27].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Scalable service migration in autonomic network environments

Οικονόμου

Stavrakakis

2010

IEEE J. Select. Areas Commun.

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Abstract-Service placement is a key problem in communication networks as it determines how efficiently the user service demands are supported. This problem has been traditionally approached through the formulation and resolution of large optimization problems requiring global knowledge and a continuous recalculation of the solution in case of network changes. Such approaches are not suitable for large-scale and dynamic network environments. In this paper, the problem of determining the optimal location of a service facility is revisited and addressed in a way that is both scalable and deals inherently with network dynamicity. In particular, service migration which enables service facilities to move between neighbor nodes towards more communication cost-effective positions, is based on local information. The migration policies proposed in this work are analytically shown to be capable of moving a service facility between neighbor nodes in a way that the cost of service provision is reduced and -under certain conditions -the service facility reaches the optimal (cost minimizing) location, and locks in there as long as the environment does not change; as network conditions change, the migration process is automatically resumed, thus, naturally responding to network dynamicity under certain conditions. The analytical findings of this work are also supported by simulation results that shed some additional light on the behavior and effectiveness of the proposed policies.

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An adaptive clustering approach for the management of dynamic systems

Cited by 35 publications

References 14 publications

Cluster-Based Differential Energy Detection for Spectrum Sensing in Multi-Carrier Systems

Cluster-Based Differential Energy Detection for Spectrum Sensing in Multi-Carrier Systems

Accuracy of Distributed Systems Towards Industry 4.0: Smart Grids and Urban Drainage Systems Case Studies

Scalable service migration in autonomic network environments

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