Optimized Data Acquisition Point Placement for an Advanced Metering Infrastructure Based on Power Line Communication Technology

Aalamifar, Fariba; Lampe, Lutz

doi:10.1109/access.2018.2865592

Cited by 13 publications

(10 citation statements)

References 29 publications

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“…Figure 13 indicates that the CPIs with the changes of communication paths are enhanced; therefore, the proposed CPI and the number of each communication path used can be adopted to find the optimal communication path of each meter. This paper provides an effective mechanism to assess and supervise the communication performance of AMI, the optimal communication path selection integrating with other routing algorithms [12][13][14][15][16][17][18], [31][32][33] will be investigated in the future.…”

Section: A Simple Six-meter Casementioning

confidence: 99%

“…Energies 2018, 11, x FOR PEER REVIEW 9 of 17 of AMI, the optimal communication path selection integrating with other routing algorithms [12][13][14][15][16][17][18], [31][32][33] will be investigated in the future. of AMI, the optimal communication path selection integrating with other routing algorithms [12][13][14][15][16][17][18], [31][32][33] will be investigated in the future.…”

Section: A Simple Six-meter Casementioning

confidence: 99%

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Communication Performance Assessment for Advanced Metering Infrastructure

et al. 2018

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Advanced Metering Infrastructure (AMI), the foundation of smart grids, can be used to provide numerous intelligent power applications and services based on the data acquired from AMI. Effective and efficient communication performance between widely-spread smart meters and Data Concentrator Units (DCUs) is one of the most important issues for the successful deployment and operation of AMI and needs to be further investigated. This paper proposes an effective Communication Performance Index (CPI) to assess and supervise the communication performance of each smart meter. Some communication quality measurements that can be easily acquired from a smart meter such as reading success rate and response time are used to design the proposed CPI. Fuzzy logic is adopted to combine these measurements to calculate the proposed CPI. The CPIs for communication paths, DCUs and whole AMI can then be derived from meter CPIs. Simulation and experimental results for small-scale AMIs demonstrate the validity of the proposed CPI. Through the calculated CPIs, the communication performance and stability for AMI can be effectively assessed and supervised.

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Section: A Simple Six-meter Casementioning

confidence: 99%

Section: A Simple Six-meter Casementioning

confidence: 99%

Communication Performance Assessment for Advanced Metering Infrastructure

et al. 2018

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“…With this clustering topology in place, data from SMs traverse three tiers to reach the CCS and vice versa. At the lower tier, SMs generate two types of AMI traffic [24][25][26][27], namely, normal and critical traffic. The normal traffic, such as IMRs, is sent to the AH for traffic aggregation, while the critical traffic (delay-sensitive traffic) is sent directly to the CH for onward transmission.…”

Section: Introductionmentioning

confidence: 99%

A QoS-Aware Machine Learning-Based Framework for AMI Applications in Smart Grids

et al. 2021

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The Internet of things (IoT) enables a diverse set of applications such as distribution automation, smart cities, wireless sensor networks, and advanced metering infrastructure (AMI). In smart grids (SGs), quality of service (QoS) and AMI traffic management need to be considered in the design of efficient AMI architectures. In this article, we propose a QoS-aware machine-learning-based framework for AMI applications in smart grids. Our proposed framework comprises a three-tier hierarchical architecture for AMI applications, a machine-learning-based hierarchical clustering approach, and a priority-based scheduling technique to ensure QoS in AMI applications in smart grids. We introduce a three-tier hierarchical architecture for AMI applications in smart grids to take advantage of IoT communication technologies and the cloud infrastructure. In this architecture, smart meters are deployed over a georeferenced area where the control center has remote access over the Internet to these network devices. More specifically, these devices can be digitally controlled and monitored using simple web interfaces such as REST APIs. We modify the existing K-means algorithm to construct a hierarchical clustering topology that employs Wi-SUN technology for bi-directional communication between smart meters and data concentrators. Further, we develop a queuing model in which different priorities are assigned to each item of the critical and normal AMI traffic based on its latency and packet size. The critical AMI traffic is scheduled first using priority-based scheduling while the normal traffic is scheduled with a first-in–first-out scheduling scheme to ensure the QoS requirements of both traffic classes in the smart grid network. The numerical results demonstrate that the target coverage and connectivity requirements of all smart meters are fulfilled with the least number of data concentrators in the design. Additionally, the numerical results show that the architectural cost is reduced, and the bottleneck problem of the data concentrator is eliminated. Furthermore, the performance of the proposed framework is evaluated and validated on the CloudSim simulator. The simulation results of our proposed framework show efficient performance in terms of CPU utilization compared to a traditional framework that uses single-hop communication from smart meters to data concentrators with a first-in–first-out scheduling scheme.

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“…In view of the characteristics of the equal-scatter sampling method [34][35][36][37], this paper proposes a pseudoanalytical mix sampling technique to reduce the variance, and further improve the sampling efficiency of assessing processes. More specifically, the scheme first selects those optimal extraction components, and enumerates the states of the components and their probabilities.…”

Section: Introductionmentioning

confidence: 99%