A Cognitive Radio-Based Energy-Efficient System for Power Transmission Line Monitoring in Smart Grids

Ahmed, Saeed; Lee, Young Doo; Hyun, Seung-Ho; Koo, Insoo

doi:10.1155/2017/3862375

Cited by 9 publications

(11 citation statements)

References 30 publications

(35 reference statements)

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“…This analysis shows that the traffic management has the on-off structure. Compared with subproblem (10), subproblem (11) is more complicated and it will be solved in the next subsection. It should be noticed that the APRA algorithm is an online algorithm without any statistical information about PLC channel process, queue process and traffic arrival process, which makes it can be easily implemented in practice.…”

Section: Algorithm 1 Adaptive Plc Resource Allocation (Apra) Algorithmmentioning

confidence: 99%

“…In the following, we aim to solve subproblem (11), which is an MINO problem. Some similar resource allocation strategies can be found in [27,28] without considering the customisation requirements of the different service types, and in [29] with separately allocating the subchannel and controlling the transmit power.…”

Section: Joint Subchannel and Power Allocationmentioning

confidence: 99%

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Adaptive resource management for cognitive power line communication system

Fu¹,

Wang²,

Xu³

et al. 2022

IET Communications

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The authors investigate the adaptive resource allocation problem for cognitive power line communication system. The dynamic optimisation model is employed to maximise the energy‐aware network utility, which is defined as the difference of the weighted network throughput and the power consumption. Then, the authors decompose the stochastic optimisation into a traffic management subproblem and a joint subchannel and power allocation subproblem. Since the joint subchannel and power allocation subproblem is a mixed integer non‐linear optimisation, a continuity relaxation and Lagrange dual method is proposed to obtain the optimal solution of the mixed integer non‐linear optimisation subproblem. After that, the authors develop an adaptive power line communication resource allocation algorithm to cope with the power line communication network dynamics only according to the current network state information. Finally, we derive a trade‐off mechanism between network utility and network delay, in which the network delay is proportional to the control parameter V and the network utility is proportional to the parameter 1/V$1/V$. Simulation results not only show the effectiveness but also verify the theoretical analysis of our proposed algorithm.

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Section: Algorithm 1 Adaptive Plc Resource Allocation (Apra) Algorithmmentioning

confidence: 99%

Section: Joint Subchannel and Power Allocationmentioning

confidence: 99%

Adaptive resource management for cognitive power line communication system

Fu¹,

Wang²,

Xu³

et al. 2022

IET Communications

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“…Integration of state-of-the-art computing and bi-directional communications technologies with the existing power infrastructure realizes the concept of the smart grid (SG) [1,2]. However, increased dependence on communications technologies is intensifying the SG's vulnerability to cyber-attacks.…”

Section: Introductionmentioning

confidence: 99%

“…Seven worst-reconstructed features (random attack on 40% of the features in a standard IEEE 14-bus system) with the GDAE scheme.Next, to show the reconstruction performance in the overall dataset, we present average error sum (EAS) as another performance gauge. The average error sum is given as follows:1 , where m is the number of features and e i is the reconstruction error for the ith feature. The EAS is measured in megawatts (MW).Figures 4-11show the EAS for the various DAE schemes for the 20% and 40% fixed-attacked features from the standard IEEE 14-, and 39-bus systems.…”

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confidence: 99%

Mitigating the Impacts of Covert Cyber Attacks in Smart Grids Via Reconstruction of Measurement Data Utilizing Deep Denoising Autoencoders

et al. 2019

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As one of the most diversified cyber-physical systems, the smart grid has become more decumbent to cyber vulnerabilities. An intelligently crafted, covert, data-integrity assault can insert biased values into the measurements collected by a sensor network, to elude the bad data detector in the state estimator, resulting in fallacious control decisions. Thus, such an attack can compromise the secure and reliable operations of smart grids, leading to power network disruptions, economic loss, or a combination of both. To this end, in this paper, we propose a novel idea for the reconstruction of sensor-collected measurement data from power networks, by removing the impacts of the covert data-integrity attack. The proposed reconstruction scheme is based on a latterly developed, unsupervised learning algorithm called a denoising autoencoder, which learns about the robust nonlinear representations from the data to root out the bias added into the sensor measurements by a smart attacker. For a robust, multivariate reconstruction of the attacked measurements from multiple sensors, the denoising autoencoder is used. The proposed scheme was evaluated utilizing standard IEEE 14-bus, 39-bus, 57-bus, and 118-bus systems. Simulation results confirm that the proposed scheme can handle labeled and non-labeled historical measurement data and results in a reasonably good reconstruction of the measurements affected by attacks.

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“…Researchers in both industry and academia have proposed workable solutions to reduce network operational costs through the installation of energy-efficient hardware [5], but this approach does not seem to solve the problem completely. Reducing network operational costs by installing more energyefficient hardware somehow requires a compromise between the energy cost and the user coverage drop.…”

mentioning

confidence: 99%

QoS provisioning and energy saving scheme for distributed cognitive radio networks using deep learning

Hlophe

Maharaj

2020

J. Commun. Netw.

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One of the major challenges facing the realization of cognitive radios (CRs) in future mobile and wireless communications is the issue of high energy consumption. Since future network infrastructure will host real-time services requiring immediate satisfaction, the issue of high energy consumption will hinder the full realization of CRs. This means that to offer the required quality of service (QoS) in an energy-efficient manner, resource management strategies need to allow for effective trade-offs between QoS provisioning and energy saving. To address this issue, this paper focuses on single base station (BS) management, where resource consumption efficiency is obtained by solving a dynamic resource allocation (RA) problem using bipartite matching. A deep learning (DL) predictive control scheme is used to predict the traffic load for better energy saving using a stacked auto-encoder (SAE). Considered here was a base station (BS) processor with both processor sharing (PS) and first-come-first-served (FCFS) sharing disciplines under quite general assumptions about the arrival and service processes. The workload arrivals are defined by a Markovian arrival process while the service is general. The possible impatience of customers is taken into account in terms of the required delays. In this way, the BS processor is treated as a hybrid switching system that chooses a better packet scheduling scheme between mean slowdown (MS) FCFS and MS PS. The simulation results presented in this paper indicate that the proposed predictive control scheme achieves better energy saving as the traffic load increases, and that the processing of workload using MS PS achieves substantially superior energy saving compared to MS FCFS.

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A Cognitive Radio-Based Energy-Efficient System for Power Transmission Line Monitoring in Smart Grids

Cited by 9 publications

References 30 publications

Adaptive resource management for cognitive power line communication system

Adaptive resource management for cognitive power line communication system

Mitigating the Impacts of Covert Cyber Attacks in Smart Grids Via Reconstruction of Measurement Data Utilizing Deep Denoising Autoencoders

QoS provisioning and energy saving scheme for distributed cognitive radio networks using deep learning

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