An Integrated IoT Architecture for Smart Metering

Lloret, Jaime; Tomás, Jaime Prohens; Cánovas, Alejandro; Parra, Lorena

doi:10.1109/mcom.2016.1600647cm

Cited by 238 publications

(107 citation statements)

References 8 publications

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“…Equations (5) and (6) provide the transmission energy consumed in the periods of P 1 and P 2 , respectively. Equations (7) and (8) provide the received energy consumed in the periods P 1 and P 2 .…”

Section: Determining the Period Of The Receiving Node Based On The Ammentioning

confidence: 99%

Dynamic Duty-Cycle MAC Protocol for IoT Environments and Wireless Sensor Networks

Kim

Kang

2019

Energies

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This paper proposes a new protocol that can be used to reduce transmission delay and energy consumption effectively. This will be done by adjusting the duty-cycle (DC) ratio of the receiver node and the contention window size of the sender node according to the traffic congestion for various devices in the Internet of Things (IoT). In the conventional duty-cycle MAC protocol, the data transmission delay latency and unnecessary energy consumption are caused by a high collision rate. This is because the receiver node cannot sufficiently process the data of the transmitting node during the traffic peak time when the transmission and reception have the same duty-cycle ratio. To solve this problem, this paper proposes an algorithm that changes the duty-cycle ratio of the receiver and broadcasts the contention window size of the senders through Early Acknowledgment (E-ACK) at peak time and off/peak time. The proposed algorithm, according to peak and off/peak time, can transmit data with fewer delays and minimizes energy consumption.Energies 2019, 12, 4069 2 of 13 an environment with traffic congestion causes problems of transmission delay, and the entire service life of the application is shortened due to the collision. Thus, the use of the existing duty-cycle (DC) MAC protocol [5] is not suitable. Therefore, it is necessary to develop a protocol for effective event detection [6] and data collection [7] considering various traffic conditions. For example, if the total number of wake-ups to listen to an application is determined [8], the existing duty-cycle MAC occurs irrespective of the congestion occurring at the time of the traffic. This reduces data collisions and transmission delays if the receiver wakes up frequently only at times in heavy traffic.The proposed algorithm increases the number of wake-up times by reducing the sleep time of the receiver at the peak traffic time and reduces the number of wake-up in the opposite case. This demonstrates reduced energy consumption. This paper proposes a new duty-cycle based MAC protocol suitable for congested WSNs and IoT. According to the degree of network traffic congestion, the duty-cycle ratio of the receiver and the contention window size information of the senders are broadcast through E-ACK (Early Acknowledgment), which reduces the transmission latency and waste of energy.The composition of the paper is described in detail in Section 1, followed by a summary of related work in Section 2. Section 3 proposes the operation procedure of the MAC layer. In Section 4, mathematical modeling is performed to predict the proposed algorithm and performance. In Section 5, we compare the performance of the proposed algorithm with the energy consumption and average service latency. Finally, Section 6 presents conclusions and future research topics.

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Section: Determining the Period Of The Receiving Node Based On The Ammentioning

confidence: 99%

Dynamic Duty-Cycle MAC Protocol for IoT Environments and Wireless Sensor Networks

Kim

Kang

2019

Energies

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“…Lloret proposed an integrated Internet of things (IoT) infrastructure for smart meter networks to be deployed in smart cities. They analyzed the communication protocol, the data format, the data gathering procedure, and the decision system based on big data treatment, considering electricity, water, and gas smart meters.…”

Section: Background and Related Workmentioning

confidence: 99%

“…We selected papers that were mainly related to the time requirements of applications, the use of simulation and WSNs-based smart grids as discussed below. Lloret 7,8 proposed an integrated Internet of things (IoT) infrastructure for smart meter networks to be deployed in smart cities. They analyzed the communication protocol, the data format, the data gathering procedure, and the decision system based on big data treatment, considering electricity, water, and gas smart meters.…”

Section: Background and Related Workmentioning

confidence: 99%

Deploying wireless sensor networks–based smart grid for smart meters monitoring and control

Souza

Moreira

Rodrigues

et al. 2018

Int J Communication

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Summary Transmission and distribution systems for electricity have undergone a technological revolution in terms of operation and management using computer networks, automation, remote sensing, and information and communication technologies to improve the performance of digital electronic meters. This work describes the integration of a wireless sensor networks (WSNs)–based communication system with an electrical energy‐measurement structure, to verify the feasibility of large‐scale installation of intelligent electronic meters in low‐voltage consumer units. The study is based on simulations, using Castalia, considering 2 scenarios, the first in a flat network and the second in a hierarchical network of WSNs to analyze the feasibility of sending messages from intelligent electronic meters to the concessionaires through a ZigBee network.In addition, the time requirements of the IEC 61850 standard for sending and receiving manufacturing message specifications and generic object‐oriented substation event type messages are verified. This work demonstrated the technical feasibility of using WSNs for different node densities by region and evaluated the location of the sink node, and adequate infrastructures for WSNs were found. This extends time checks for both vertical (usually for supervision) and horizontal (used for protection) messages. The proposed model has great potential to use a WSN infrastructure and to evaluate if this infrastructure allows data transmission of the protocols used in smart grids, mainly verifying the requirements of transmission times required by each application.

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“…Smart grid circumstances employ various Internet of Thing (IoT) devices to provide efficient energy management for electric power consumers [1]. A smart meter is a representative IoT device that measures electrical energy data of customers in real time and transmits these data through network communication.…”

Section: Introductionmentioning

confidence: 99%

Development of Operational Strategies of Energy Storage System Using Classification of Customer Load Profiles under Time-of-Use Tariffs in South Korea

2020

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This study proposes a methodology to develop adaptive operational strategies of customer-installed Energy Storage Systems (ESS) based on the classification of customer load profiles. In addition, this study proposes a methodology to characterize and classify customer load profiles based on newly proposed Time-of-Use (TOU) indices. The TOU indices effectively distribute daily customer load profiles on multi-dimensional domains, indicating customer energy consumption patterns under the TOU tariff. The K-means and Self-Organizing Map (SOM) sophisticated clustering methods were applied for classification. Furthermore, this study demonstrates peak shaving and arbitrage operations of ESS with current supporting polices in South Korea. Actual load profiles accumulated from customers under the TOU rate were used to validate the proposed methodologies. The simulation results show that the TOU index-based clustering effectively classifies load patterns into 'M-shaped' and 'square wave-shaped' load patterns. In addition, the feasibility analysis results suggest different ESS operational strategies for different load patterns: the 'M-shaped' pattern fixes a 2-cycle operation per day due to battery life, while the 'square wave-shaped' pattern maximizes its operational cycle (a 3-cycle operation during the winter) for the highest profits. Energies 2020, 13, 1723 2 of 17 algorithms [6]. Lee et al. classified the electric power consumption characteristics of industrial customers using the standard industry classification code in South Korea [7]. Bidoki et al. applied different clustering algorithms based on K-means, weighted fuzzy mean K-means, modified follow leader (MFTL), self-organizing map (SOM), and layer algorithm to classify load curves of different types of customers, and the results from comparing the clustering performances utilized to determine the adaptive clustering algorithms [8]. Zhou et al. proposed the five-stage process model based on K-means, SOM, Fuzzy c-average (FCM), and hierarchical clustering algorithms to analyze the impact of electric power suppliers and their consumers in smart grid circumstances [9]. Abubaker classified load profiles achieved from electricity consumers in the Tulkarm district based on the K-means algorithm [10].These clustering-based approaches for analyzing customer load profiles can be applied to the development of effective strategies for energy management in smart grid circumstances. The representative strategy utilizes the Energy Storage System (ESS) installed for electricity consumers [11]. The ESS is a device that enables the storage of electrical energy during off-peak times and supplies the stored energy at the requested time to reduce electricity costs for the customer. The South Korean government had established a long-term roadmap and supporting policies to increase the penetration level of ESS, and, consequently, South Korea has been positioned as the second largest nameplate capacity of ESS since 2016 [12].Several studies have proposed methodologies to optimize and deve...

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An Integrated IoT Architecture for Smart Metering

Cited by 238 publications

References 8 publications

Dynamic Duty-Cycle MAC Protocol for IoT Environments and Wireless Sensor Networks

Dynamic Duty-Cycle MAC Protocol for IoT Environments and Wireless Sensor Networks

Deploying wireless sensor networks–based smart grid for smart meters monitoring and control

Development of Operational Strategies of Energy Storage System Using Classification of Customer Load Profiles under Time-of-Use Tariffs in South Korea

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