Research on IoT Architecture and Application Scheme for Smart Grid

Sun, Donglei; Li, Wenjing; Yao, Xianjiong; Liu, Hui; Chai, Jinlong; Xie, Kunyi; Zhu, Liang; Feng, Lei

doi:10.1007/978-981-15-3753-0_90

Cited by 6 publications

(4 citation statements)

References 4 publications

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“…Among these, D Queue is a direct function of network congestion which occurs in M/M/1 model due to low capacity of the backhaul network and can be computed in Eq. (2).…”

Section: Problem Formulation and Methodologymentioning

confidence: 99%

A QoS-Aware Data Aggregation Strategy for Resource Constrained IoT-Enabled AMI Network in Smart Grid

Khan,

Shirazi,

Adeel

et al. 2023

IEEE Access

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Emerging Internet of Things (IoT) technologies and applications have enabled the Smart Grid Utility control center to connect, monitor, control and exchange data between the smart appliances, smart meters (SMs), data concentrators (DCs) and control center server (CCS) over the Internet. In particular, DC receives different Advanced Metering Infrastructure (AMI) applications data from multiple SMs for processing, queuing, aggregation, and forwarding onward towards the CCS over the things networking. However, DCs are expensive component of the AMI network. Recently, SMs are used as relay-devices to accomplish a cost-effective AMI network infrastructure and avoid the DC placement and bottleneck problem. However, SMs are recourse constrained (limited CPU, RAM, storage, and network capacity) intelligent devices which faces numerous communication challenges during outage conditions and summer peak hours where bulk amount of data with different traffic rates and latency are exchanged with the Utility control center. Therefore, an efficient data aggregation at relay-devices is required to deal with high volume of data exchange rates in order to optimize the constrained-resources of the AMI network. In this article, we propose a hybrid data aggregation strategy implemented on an aggregator-head (AH) in the clustering topology which performs data aggregation on the Interval Meter Reading (IMR) application data. AH induction greatly reduce the workload of the cluster-heads (CHs), and efficiently utilizes the constrained-resource of AMI devices in a cost effective-manner. The proposed strategy is evaluated for different existing approaches using the CloudSim simulation tool. Experimental and simulation results are obtained and compared which show the effectiveness of the proposed strategy such that limited resources are optimized, CH workload is minimized, and QoS of AMI applications are maintained.

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“…Among these, D Queue is a direct function of network congestion which occurs in M/M/1 model due to low capacity of the backhaul network and can be computed in Eq. (2).…”

Section: Problem Formulation and Methodologymentioning

confidence: 99%

A QoS-Aware Data Aggregation Strategy for Resource Constrained IoT-Enabled AMI Network in Smart Grid

Khan,

Shirazi,

Adeel

et al. 2023

IEEE Access

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“…Therefore, E2E latency and reliability for industrial automation can be achieved by realizing the Tactile Internet. The work in [42] and [43] introduced interesting applications, including smart grid and 2).…”

Section: A Tactile Internet Use Casesmentioning

confidence: 99%

QoS Provisioning: Key Drivers and Enablers Toward the Tactile Internet in Beyond 5G Era

et al. 2022

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The Tactile Internet has become a revolution for Internet technology, greatly improving the transmission of skill sets (audio, video, text, and haptics) over communication channels compared with traditional triple-play data (audio, video, text). It is a strong candidate to support next-generation delay-sensitive and loss-intolerant smart applications. However, stringent requirements for the Tactile Internet, including ultra-low latency, ultra-high reliability, high availability, and ultra-security, present critical challenges to ensure Quality of Service (QoS). Consequently, several approaches have been proposed to meet these QoS requirements. This article reviews QoS provisioning approaches for the Tactile Internet. First, we present key concepts for the fifth-generation and beyond technologies, Tactile Internet, and haptic communication. Second, we discuss the Tactile Internet use cases along with strict QoS requirements. Third, we classify existing solutions, including haptic codecs, control system designs, hybrid schemes, and intelligent prediction models; provide in-depth discussion regarding these approaches to improve QoS for the Tactile Internet applications; and investigate strengths and weaknesses for each proposed solution. Finally, we present open research challenges and discuss potential future research avenues to realize the Tactile Internet services.

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“…In Smart Grid, SMs [33] are typically installed at low altitudes in homes and buildings and play an important role in making decisions such as the demand and supply of electricity in Smart Grid. These SMs measure the power consumption of the home appliances at fixed-time intervals, as well as on demand, and exchange these measurements with the Utility provider via a shared communication network.…”

Section: Smart Metermentioning

confidence: 99%

“…In this section, we briefly present the working of our proposed optimization model to clarify the concept and validate the findings extracted from a small-scale residential area, as visualized in Figure 2. The IoT-based [33] Smart Grid hierarchical architecture is composed of SMs, DC, and the main CCS. In the clustering topology, SMs are configured to submit IMR data at fixed time intervals (e.g., 60 min) regularly and other metering data as needed to their respective CHs, which then transmit them to the DC.…”

Section: A Walk-through Examplementioning

confidence: 99%

QoS-Aware Cost Minimization Strategy for AMI Applications in Smart Grid Using Cloud Computing

Khan

Umar

Shirazi

et al. 2022

Sensors

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Cloud computing coupled with Internet of Things technology provides a wide range of cloud services such as memory, storage, computational processing, network bandwidth, and database application to the end users on demand over the Internet. More specifically, cloud computing provides efficient services such as “pay as per usage”. However, Utility providers in Smart Grid are facing challenges in the design and implementation of such architecture in order to minimize the cost of underlying hardware, software, and network services. In Smart Grid, smart meters generate a large volume of different traffics, due to which efficient utilization of available resources such as buffer, storage, limited processing, and bandwidth is required in a cost-effective manner in the underlying network infrastructure. In such context, this article introduces a QoS-aware Hybrid Queue Scheduling (HQS) model that can be seen over the IoT-based network integrated with cloud environment for different advanced metering infrastructure (AMI) application traffic, which have different QoS levels in the Smart Grid network. The proposed optimization model supports, classifies, and prioritizes the AMI application traffic. The main objective is to reduce the cost of buffer, processing power, and network bandwidth utilized by AMI applications in the cloud environment. For this, we developed a simulation model in the CloudSim simulator that uses a simple mathematical model in order to achieve the objective function. During the simulations, the effects of various numbers of cloudlets on the cost of virtual machine resources such as RAM, CPU processing, and available bandwidth have been investigated in cloud computing. The obtained simulation results exhibited that our proposed model successfully competes with the previous schemes in terms of minimizing the processing, memory, and bandwidth cost by a significant margin. Moreover, the simulation results confirmed that the proposed optimization model behaves as expected and is realistic for AMI application traffic in the Smart Grid network using cloud computing.

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Research on IoT Architecture and Application Scheme for Smart Grid

Cited by 6 publications

References 4 publications

A QoS-Aware Data Aggregation Strategy for Resource Constrained IoT-Enabled AMI Network in Smart Grid

A QoS-Aware Data Aggregation Strategy for Resource Constrained IoT-Enabled AMI Network in Smart Grid

QoS Provisioning: Key Drivers and Enablers Toward the Tactile Internet in Beyond 5G Era

QoS-Aware Cost Minimization Strategy for AMI Applications in Smart Grid Using Cloud Computing

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