A Set-Theoretic Method for Parametric Uncertainty Analysis in Markov Reliability and Reward Models

Dhople, Sairaj V.; Chen, Yu Christine; Domínguez-García, Alejandro D.

doi:10.1109/tr.2013.2270421

Cited by 18 publications

(6 citation statements)

References 16 publications

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“…denotes the first order partial derivative w.r.t. p R [33]. The same result applies for the k-th order derivative w.r.t.…”

Section: ) Large Deviations Principle (Ldp) For the Asymptotic Missesupporting

confidence: 52%

Efficiency and Detectability of Random Reactive Jamming in Carrier Sense Wireless Networks

Weber

2019

IEEE Trans. Commun.

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A natural basis for the detection of a wireless random reactive jammer (RRJ) is the perceived violation by the detector (typically located at the access point (AP)) of the carrier sensing protocol underpinning many wireless random access protocols (e.g., WiFi). Specifically, when the wireless medium is perceived by a station to be busy, a carrier sensing compliant station will avoid transmission while a RRJ station will often initiate transmission. However, hidden terminals (HTs), i.e., activity detected by the AP but not by the sensing station, complicate the use of carrier sensing as the basis for RRJ detection since they provide plausible deniability to a station suspected of being an RRJ. The RRJ has the dual objectives of avoiding detection and effectively disrupting communication, but there is an inherent performance tradeoff between these two objectives. In this paper we capture the behavior of both the RRJ and the compliant stations via a parsimonious Markov chain model, and pose the detection problem using the framework of Markov chain hypothesis testing. Our analysis yields the receiver operating characteristic (ROC) of the detector, and the optimized behavior of the RRJ. While there has been extensive work in the literature on jamming detection, our innovation lies in leveraging carrier sensing as a natural and effective basis for detection.N. An and S. Weber are with the Reactive jamming, Markov chain model, large deviations principle, hypothesis testing I. INTRODUCTIONJamming attacks are a widely recognized threat to wireless networks. As a type of denialof-service attack, wireless jamming leverages the broadcast nature of the wireless medium and emits jamming signals either to prevent other (compliant) users from accessing the network, or to corrupt ongoing transmissions. There are three major types of jammers [2]: i) constant jammer, which constantly sends jamming signals, ii) random jammer, which randomly alternates between jamming and idle states, and iii) reactive jammer, which emits jamming signals upon sensing any ongoing traffic over the wireless channel. Compared with the first two types, the reactive jammer (RJ) is more sophisticated in that it achieves high jamming efficiency by only disrupting ongoing transmissions, which in general also lowers the risk of detection [2]. A RJ faces an inherent tradeoff in the dual objectives of effectively degrading network throughput and in avoiding detection: as the "aggressiveness" of the jamming is increased, it increases the effectiveness of the disruption, but at the same time increases the ease with which behavior not compliant with carrier sensing is detected. This detection is often based upon changes in network performance statistics such as the packet delivery rate (PDR), received signal strength (RSS), packet delivery delay, etc. However, the presence of hidden terminals (HT), i.e., transmissions detectable by the access point (AP) but not the sensing station, complicates the jamming detection problem, as the AP cannot always disambiguate whet...

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“…denotes the first order partial derivative w.r.t. p R [33]. The same result applies for the k-th order derivative w.r.t.…”

Section: ) Large Deviations Principle (Ldp) For the Asymptotic Missesupporting

confidence: 52%

Efficiency and Detectability of Random Reactive Jamming in Carrier Sense Wireless Networks

Weber

2019

IEEE Trans. Commun.

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“…For example [63] used statistical estimates of failure probability and the expected economic impact of failures in multi‐objective optimization, simultaneously minimising the maintenance costs and the predicted cost of unexpected failures to form an integrated framework of transformer maintenance management. In [64], Markov reliability and reward models are used to perform component failure and repair rates and follow the effect of parametric uncertainty in reliability and performability indices and the proposed method applied on sample system to apply preventative maintenance of an electric‐power distribution transformer. Paper [65] determines the optimum maintenance strategy and optimum power flow control based on condition monitoring and diagnostic results of the operating power apparatus.…”

Section: Components Of Distribution Systemsmentioning

confidence: 99%

Asset management and maintenance programming for power distribution systems: A review

Mirhosseini

Keynia

2021

IET Generation Trans & Dist

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Security and continuous proper functioning of the power system is a vital issue for supplying demands. Providing an acceptable level of reliability and necessity of that in all parts of the power system, from generation to distribution, has always been, and remains a major concern for network managers. Asset management and performing regular and routine maintenance activities has a considerable impact on ensuring system reliability, reducing expensive crashes, and preventing shutdowns. Up to now, much research has been done on the proper planning of maintenance in different parts of the network with a focus on cost savings, improved system performance and reliability, and reduced shutdowns. The purpose of this article is to review the findings of the study and to provide a more comprehensive view of the maintenance planning issue.

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“…In the case of power systems described by nonlinear differential algebraic equations (DAEs), reachability analysis is often limited to small 1-2 bus systems [48]. For larger power systems, the reachable set can be efficiently approximated by ellipsoidal techniques for linearized system models [49,50] and second-order Taylor-series approximations [51].…”

Section: ______________________________________________________________________________________________________mentioning

confidence: 99%

Validation capabilities of the NIST campus power system to evaluate distributed control of grid-edge distributed energy resources

Anand¹,

Ehsan²,

Freiheit³

et al. 2021

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This report outlines National Institute of Standards and Technology (NIST)'s validation tools developed to help address the smart grid's emergent needs and complement research and standards coordination efforts. Rapid advances have enabled an opportunity for the grid to autonomously and simultaneously control generation, balance load, and regulate power quality. The grid will need this coordinated control to regulate power and voltage in realtime, to meet fast-changing power demands, especially with increasing distributed energy resources. To do this, the grid needs measurements from across the distribution network to accurately assess demand and prevent negative impact of variable grid-edge generation. Additionally, such a coordinated control system depends on a new operating paradigm with two-way information flows between domains of the grid. The different operational domains and the information flows between them are outlined in the NIST Framework and Roadmap of Smart Grid Interoperability Standards, Release 4.0 [1].The coordinated control mentioned above requires improved observability of the state of distributed resources and depends on marshalling increasingly decentralized control systems. To implement such a system and meet system level performance objectives, distribution system operators (DSOs) need an understanding of the bandwidth, security and integrity considerations while also ensuring reliability at the grid's edges. Industry is also concerned with how to represent uncertainties like latency and cyber-vulnerabilities in a complex cyber-physical system. Additionally, industry is concerned that standards are focused more on devices than on coordinated control systems.Thus, this report presents an overview of a data acquistion and control system designed by NIST as a validation tool and deployed on the NIST campus in Gaithersburg, MD. This system is comprised of interoperating computing resources, cybersecurity infrastructure, communication links and data interfaces to both take measurements and to transmit control inputs to commonly used distribution system control components. An important proof of concept included in the aforementioned validation system is a data pipeline optimized to stream data from a 5 MW PV inverter and multiple sources of synchronized phasor data. This pipeline utilizes industry standard information modeling and communication standards with the intent of being compatible with industry standard testing equipment. This capability demonstrates how DSOs could test current standards against functionality -such as phasor based control of distribution circuits.

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A Set-Theoretic Method for Parametric Uncertainty Analysis in Markov Reliability and Reward Models

Cited by 18 publications

References 16 publications

Efficiency and Detectability of Random Reactive Jamming in Carrier Sense Wireless Networks

Efficiency and Detectability of Random Reactive Jamming in Carrier Sense Wireless Networks

Asset management and maintenance programming for power distribution systems: A review

Validation capabilities of the NIST campus power system to evaluate distributed control of grid-edge distributed energy resources

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