Efficient Vision-based Smart Meter Reading Network

Efficient Vision-based Smart Meter Reading Network

Ching-Han Chen (Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan), Ching-Yi Chen (Department of Information and Telecommunications Engineering, Ming Chuan University, Taoyuan, Taiwan), Chih-Hsien Hsia (Department of Electrical Engineering, Chinese Culture University, Taipei, Taiwan) and Guan-Xin Wu (Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan)
Copyright: © 2017 |Pages: 15
DOI: 10.4018/IJWSR.2017010104
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For building the big data collection infrastructure, a vision-based smart meter-reading network and its gateway is provided for a community gas supply system that uses traditional mechanical meters. In the network architecture, the gas meter readings are captured by embedded image sensor nodes and then transmitted to a newly designed gateway for image recognition and are collected in the embedded database of gateway. The Web-based monitoring system designed using HTML5 is applicable to a mobile device which allows a user to monitor household gas consumption and history and allows a gas company to develop an effective energy management system to analyze community users' energy consumption models using the big data collected in the database.
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1. Introduction

Internet of Things (IoT) is a novel information technology that brings entity objects into the Internet, allows new communication between people and things and gives a new Information Technology (IT) communications model for the real world, in which all online users can exchange information at any time and in any place (Lu & Neng, 2010). IoT relies on various sensors, controls and communicaitons components as well as information processing devices, such as infrared sensor, Radio Frequency Identification (RFID), and the Global Positioning System (GPS) to produce interconnected networks in which the internet is incorporated to allow ubiquitous computing (Li, Huang & Wang, 2011). However, there are various issues for IoT if a large number of physical devices are connected. Such as, huge data traffic, security, Quality of Service (QoS), identity management, scalability, interoperability, and energy loss.

The Wireless Sensor Network (WSN) (Guo, Wang, Li & Lee, 2014; Xie & Wang, 2014), also known as the lowest layer in IoT architecture and the perception layer, consists of sensors for information retrieval and recognition and is important trend in smart grid applications (Ou, Zhen, Li, Zhang & Zeng, 2012; Wu, 2011). In a smart grid, all components are connected to and communicate with one another for sharing of collected information, which allows information to be communicated that allows better performance for an electrical power system. In this regard, smart meters are critical components in the smart grid and are used to measure or record various states, including electricity consumption, service power and the capability of communicating with other components. Information is transmitted to a computer for processing, or to generate analyses that show the status of the energy consumption for each household, which allows energy administration strategies to be conceived.

Currently, traditional mechanical meters are used to record household power or gas consumption and display readings using scales or digits. In this regard, an electrical or gas utility company still relies on inefficient manual meter-readings, which do not allow analysis of energy consumption or efficient management of an energy supply system, because traditional watt-hour meters or gas meters cannot communicate with the outside world, to allow information exchange. It is also impracticable and expensive to replace most traditional watt-hour meters or gas meters with advanced meters. In order to increase the performance of a traditional watt-hour meter, in (Chen & Yang, 2012) designed a single-phase programmable logic controller watt-hour meter that uses chip technology, for which an analog to digital converter transforms the sampled current signals, including a user’s power consumption data, to digital signals, but this was not compatible with the existing meter-reading system of a utility company. In (Tewolde, Fritch & Longtin, 2011-Tewolde & Longtin, 2010) used a high-definition encoder to collect gas flow data and to transmit gas meter-readings to a server, for storage or computing, but other invasive devices had to be installed in a traditional meter. In (Hsia, Sheu & Chang, 2013), an arrow sensor is used to detect water meter-readings and the method is inexpensive, but extra devices must be installed in a water meter.

To transmit the data that is read by a remote meter-reading system to the next layer through a wireless transmission interface, in (Zheng, Zhou & Zhang, 2010) proposed a remote meter-reading system that uses Bluetooth and General Packet Radio Service (GPRS) technologies, whereby Bluetooth is built on the personal local network, which requires the data collector and the sensor nodes to be matched. In (Zhang & Liu, 2010-Zhang & Zhang, 2010), the performance of ZigBee-based transmission or infrared-based transmission, both of which feature longer latency, is shown to be unsatisfactory. In (Li, Hu, Huang & He, 2011) proposed a Wi-Fi based wireless-sensor smart grid architecture, in which the equipment installed inside a building communicates through a Wi-Fi network. The data transfer charges that lead to higher operation costs in these studies arise because of the long-distance or wireless technologies that use GPRS, 3G or WiMAX for data transmission (Shen, Tan, Wang et al., 2015; Ren, Shen, Wang et al., 2015).

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