Opportunities and Challenges of Wireless Sensor Networks in Smart Grid

Güngör, Vehbi Çağrı; Lü, Bin; Hancke, Gerhard P.

doi:10.1109/tie.2009.2039455

Cited by 1,169 publications

(589 citation statements)

References 26 publications

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“…These properties of CR appear to match up well with the basic IWSN requirements: redundancy, tolerance to interference [4], [5], [25], [13] timely transmission, reduced latency, reduced retransmissions, lower frame loss [5], [25], [13], and increased robustness in communication links due to changing environment, network topology or node location [4], [5], [25], [13].…”

Section: Benefits and Limitations Of Cognitive Radio Approaches Inmentioning

confidence: 61%

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Using Cognitive Radio for Interference-Resistant Industrial Wireless Sensor Networks: An Overview

Chiwewe

Mbuya

Hancke

2015

IEEE Trans. Ind. Inf.

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138

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Abstract-Industrial wireless sensor networks have to contend with environments that are usually harsh and time varying. Industrial wireless technology, such as WirelessHART and ISA 100.11a, also operates in a frequency spectrum utilised by many other wireless technologies, and with wireless applications rapidly growing it is possible that multiple heterogeneous wireless systems would need to operate in overlapping spatiotemporal regions. Interference such as noise or other wireless devices affects connectivity and reduces communication link quality. This negatively affects reliability and latency, which are core requirements of industrial communication. Building wireless networks that are resistant to noise in industrial environments and can coexist with competing wireless devices in an increasingly crowded frequency spectrum is challenging. To meet these challenges, we need to consider the benefits that approaches finding success in other application areas can offer industrial communication. Cognitive radio methods offer a potential solution to improve resistance of industrial wireless sensor networks to interference. Integrating cognitive radio principles into the lower layers of industrial wireless sensor networks can enable devices to detect and avoid interference and potentially opens the possibility of utilising free radio spectrum for additional communication channels. This improves resistance to noise and increases redundancy in terms of channels per network node or adding additional nodes. In this paper, we summarise cognitive radio methods relevant to industrial applications, covering cognitive radio architecture, spectrum access and interference management, spectrum sensing, dynamic spectrum access, game theory and cognitive radio network security.Index Terms-Cognitive radio, industrial wireless sensor networks, Internet of Things, spectrum management, spectrum sensing.

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Section: Benefits and Limitations Of Cognitive Radio Approaches Inmentioning

confidence: 61%

“…DSSS can address a crowded spectrum but is far from sufficient [25]. Should the power of the interfering signal fall within the jamming margin then DSSS can remove interference completely.…”

Section: ) Spread Spectrum Techniquesmentioning

confidence: 99%

Using Cognitive Radio for Interference-Resistant Industrial Wireless Sensor Networks: An Overview

Chiwewe

Mbuya

Hancke

2015

IEEE Trans. Ind. Inf.

Self Cite

138

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“…One of them is the work of Gungor et al [7], which uses LQI and RSSI metrics to evaluate the quality of a radio link. RSSI is the estimate of the signal power while LQI is used as chip error rate.…”

Section: Related Workmentioning

confidence: 99%

Measurable Security, Privacy and Dependability in Smart Grids

Noll

Garitano

Fayyad

et al. 2015

JCSM

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This paper presents a methodology for assessing security, privacy and dependability (SPD) of embedded systems. The methodology, developed through the European collaboration SHIELD, is applied for the smart grid network as deployed in the South of Norway. Three Smart Grid use cases are analysed in detail, being billing, home control and alarm.The SHIELD methodology uses a Multi-Metrics approach to evaluate the system SPD level during running processes and compares it with use case goals for S, P, and D. The simplicity, applicability, and scalability of the suggested Multi-Metrics approach is demonstrated in this paper. It shows that a single configuration is not sufficient to satisfy the given goals for all use cases.

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“…Moreover, if multiple DG units are installed, optimal approach for placement of DGs in order to maintain the stability and reliability of the system become more crucial. The Smart Grid is a modern electric power grid infrastructure for improved efficiency, reliability and safety, with smooth integration of renewable and alternative energy sources, through automated control and modern communications technologies [1].…”

Section: Introductionmentioning

confidence: 99%

Efficient Energy Management System for Smart Grid Using Bacterial Foraging Optimization Technique

Raviprabhakaran¹

2017

ijecs

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Distributed devices in smart grid systems are decentralized and connected to the power grid. The connection made through different types of equipment transmits. This produces the numerous energy losses, when power flows from one bus to another bus. The most efficient approaches to reduce energy losses is to integrate the renewable energy resources in Distributed Generation's (DG's). The uncertainty of DG may cause instability issues. The major issue includes congestion in the power grid due to the sudden power consumption by the customers, which affects the efficient energy delivery. Energy management with DG regulation is one of the most efficient solutions to solve these instability issues. In the considered power system with DG's and consumers, the Locational Marginal Pricing (LMP) based unified Energy Management System (uEMS) model is considered. This model increases the profit benefits for DG's and increases the stability of Distributed Energy System (DES). In this paper, the Bacterial Foraging Optimization (BFO) is employed to reduce losses i.e. based on Loss Reduction Allocation (LRA) method. Using LRA method the energy loss reduction is calculated and this model accurately rewards DG contribution and offers a good competitive market. Moreover, the entire DG's profit is increase by the BFO technique. The IEEE 37 bus feeder system is to be considered to validate the proposed uEMS model to increase the DG system stability. Furthermore, this implementation gives the idea of formulating efficient energy management system of future Indian scenario.

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Opportunities and Challenges of Wireless Sensor Networks in Smart Grid

Cited by 1,169 publications

References 26 publications

Using Cognitive Radio for Interference-Resistant Industrial Wireless Sensor Networks: An Overview

Using Cognitive Radio for Interference-Resistant Industrial Wireless Sensor Networks: An Overview

Measurable Security, Privacy and Dependability in Smart Grids

Efficient Energy Management System for Smart Grid Using Bacterial Foraging Optimization Technique

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