AU2020101173A4 - Advance metering infrastructure system for large scale iot networks data collection by streaming - Google Patents
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/02—Protocols based on web technology, e.g. hypertext transfer protocol [HTTP]
- H04L67/025—Protocols based on web technology, e.g. hypertext transfer protocol [HTTP] for remote control or remote monitoring of applications
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W84/00—Network topologies
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- H—ELECTRICITY
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- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/38—Services specially adapted for particular environments, situations or purposes for collecting sensor information
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W88/00—Devices specially adapted for wireless communication networks, e.g. terminals, base stations or access point devices
- H04W88/16—Gateway arrangements
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
- Y04S20/30—Smart metering, e.g. specially adapted for remote reading
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Abstract
ADVANCE METERING INFRASTRUCTURE SYSTEM FOR LARGE
SCALE IOT NETWORKS DATA COLLECTION BY STREAMING
ABSTRACT
The Internet of Things (IoT) sector is overgrowing beyond research and is still a
primary industrial market with investors and fast-growing operators of large-area
networks. This emerging field is motivated by technologies that significantly
enhance the IoT infrastructure for obtaining substantial, high-value providers to
users. IoT infrastructure ranges from small scale to large scale. The advanced
metering infrastructure (AMI) is the key component of the smart grid, which
invariably consists of three components, such as the data management system, the
network infrastructure, and the energy storage devices. The invention approaches
the rigorous computation based on deployment of NB-IoT using real-world meter
readings called as, an Advanced Metering Infrastructure (AMI). Advanced metering
infrastructure (AMI) is accountable to accumulate, calculate, and review power
consumption details, and forwarding this information among smart meter and data
concentrator. The meter data management system examines the metering
information using Apache Flink software to formulate the data in the specific format
for the billing server. The billing server generates transparent billing information to
the user about the usage and cost of usage. This innovation scrutinizes the
effectiveness of NB-IoT network elsewhere in the modem environment, and
pertinently investigates the capability of NB-IoT to endorse and to enhance AMI.
1| P a g e
ADVANCE METERING INFRASTRUCTURE SYSTEM FOR LARGE
SCALE IOT NETWORKS DATA COLLECTION BY STREAMING
DIAGRAM
User
Siiirt
B
Data
Conicentrato 8S e
Home Network
NB-IOT Utiity Providers
Gate y Cloud Server
Home Network NB-JOT Network
Fig 1: Data flow diagram
1 P a g e
Description
User
Siiirt B Data Conicentrato 8S e
Home Network
NB-IOT Utiity Providers Gate y Cloud Server
Home Network NB-JOT Network
Fig 1: Data flow diagram
1 Pa g e
Field of Invention:
This invention suggests a new approach called an advanced metering infrastructure system for large scale IoT systems to collect data using the streaming process. Advanced metering infrastructure (AMI) is an integration of smart meters, communicative networks, and data management systems, which ensures duplex interaction among utilities and customers. The different types of sensors, such as gas, water, electricity, are used, and smart meters, are used to collect all the sensed information. The Narrowband IoT is proposed to connect all the neighboring intelligent meters and data concatenators in a mesh topology fashion. This IoT network provides wide-area coverage, enhances the power consumption of devices, system capacity, and spectrum efficiency.
Background and prior art of the invention:
Presently, IoT devices are generally utilized in a different range of challenging application domains, including e-Health, smart environments, smart cities, smart building, and farming. The significant information gathering made possible by IoT technology prompts the development of a colossal extend of sensor-oriented applications.
IoT is a developing step for large-scale data collection sensor networks. Since the sensor network is modern technology, its use is subject to various limitations, including the adaptability of network scaling using existing methods. Many significant challenges need to be discussed to enable the network to play their role effectively. This involves the optimum installation of the sinks, the reduction of energy consumption, and the lifespan of the sensors.
Jawurek et al. considered privacy-enhancing strategies for records using anonymization or pseudo-anonymization. However, there is a significant
1|Page possibility that an interloper might degrade these security measures and access data sensitive to privacy.
Chu et al. developed anomaly-based and signature-based strategies for the IDS network along with a one-pass approach. IDS is classified into two modules, such as a passive data streaming module that utilize a detection based on the signature and an active module that employs anomalies-based detection. A one-pass algorithm called FP-Stream is proposed for the implementation of the architecture.
Simone Cirani et al. suggested an IoT-Oauth-based-Authorization-Service (OAS) design targeting IoT applications based on HTTP and confined application protocol platforms to use an open authorization rapid prototyping for IoT applications based on HTTP and constrained application protocol services to ensure open and secure services in IoT scenarios with lower processing loads and to configure fine-grained access control policies remotely.
Jiang et al. achieved a robust performance, confidentiality query strategy for encoded, multidimensional broad metering reading, to resolve how encoded multifaceted metering reading in an unsecured distributed system environment is efficiently requested.
Martin et al. discussed that data completion depends on factors such as work measures, obtained missing load metrics based on work reporting.
Zhou et al. have developed the virtualization remedy for the communication network, and the ledge enlarges the one should provide access to the demand balancing issues.
Waalinder et al. performed with Advanced Metering Infrastructure (AMI) and though emphasizes data analysis and public networks, which provide end-user benchmarks for wearable decisions on energy usage.
Jiang et al. formulated a high level of efficiency and privacy-preserving Query method against authenticated multidimensional massive metering data to implement how to accurately query encoded multifaceted metering records contained in an unauthorized diverse system environment.
21Page
Liu et al. devised a lambda model to detect abnormal patterns of consumption, with the idea of getting smart energy management decision-making.
Di Francesco et al. provided a detailed research study and categorization of the data gathering methodology with portable elements in Wireless Sensor Networks. Along with sensors, there has been a recent propensity to use specialized aerial devices such as drones for data accession. With this perspective, stochastic optimization techniques premised on bacterial foraging modeling have been adequately utilized to accumulate an enormous volume of information in LS WSNs. Ang et al. suggested an approach to capture Big Data in LS-WSNs using smart vendors to accelerate the usage of energy due to optimizing the collection of Big Data in the IoT system. The authors have deployed information gathering methods using an invoice mule and a smart gateway, which are feasibly utilized components of data analysis models in sensing devices.
Inoubli W et al. provided a valuable concept about Big Data processing architectures and their effectiveness in the Smart City environment. Big Data processing frameworks are classified according to their programming model, data source type, and supported programming languages. The method is concentrated on the technical features of the computed constellation. However, certain key factors of Big Data computation in IoT applications are not covered.
Veiga J discussed Big Data computing platforms such as Apache Hadoop, Apache Spark, and Apache Flink. It exploits potential technologies such as Terasort, WordCount, Grep, PageRank, and K-means for evaluation and provides contrasting knowledge about the outcome of such platforms. The drawback of this method is the different programming model for assessed platforms which significantly impacts the description of correlated benchmarks. The data set is not outlined, and the modifications of application scenarios and their data variables for both batch and stream evaluation in different platforms are not properly defined.
Singh D et al. stated the essential characteristics of Big Data analytics platforms such as interoperability, flaw sufferance, and affordability and exploit k-means for their evaluations. However, this approach doesn't examine specific characteristics of IoT applications such as throughput, delay, and usage of resources.
31Page
The objective of the invention:
The objective is
* To focus the streaming-based modeling and statistics based on Apache Flink, which enables the rapid disseminated internet-based reviews. * To offer a fine-tuned computation, based on a real paradigm that is performed with actions accumulated from a real-time advanced metering infrastructure. * To suggest real-time observation of utilities to customers that enables them to track their usage and generates incorporated bill of water, gas, and electricity. * To maintain metering data and bill payments by providing high-security mechanisms against de-anonymization attacks. * To promote clarity on monthly gas, water, and electricity bills via affording an entirely appropriate amount based on consumption. * To ensure the users for adapting their lifestyles to decline the regular utility bills based on meter readings.
Summary of the invention:
Smart Grid is a duplex data network system that impedes the smart convergence of conventional electricity production, sustainable energy generation, dissemination generation, energy storage systems, as well as other systems, transfer management, distribution, and mandate. This offers enormous benefits, including improved efficiency and responsiveness, increased organizational efficiency, cost-effective self-management, and smarter quality assurance, through automated control and communication technologies.
The advanced metering infrastructure as among the major control systems of the smart grid not only aims at obtaining all the meter readings from smart meters rather than to enforce an input signal and provides guidance such that precise and efficient protection steps are taken in the Smart Electric Grid. The propagation of these data streams among all elements entirely depends on the network infrastructure.
41Page
Advanced Metering Infrastructure (AMI) is generally liable, but not exclusive to smart electricity, gas, water meters, and other sensors that are used to estimate light, pressure, and temperature can indeed be encased and typically termed as smart gadgets.
Smart metering is the utilization of intelligent meters, mainly in advanced metering infrastructure (AMI) installations in quick grid projects, intelligent homes, smart buildings, and the development of smart cities. Intelligent meters facilitate users to inspect their power, water and gas consumption, and viewing. These smart water, gas, and electricity sensors manage drinking water quality, temperature, pressure, electricity, gas consumption.
Narrow Band IoT (NB-IoT) is equipped to accommodate a considerable amount of devices, low latency, low power consumption, extreme penetration, and very low cost equipment. The new narrower NB-IoT interface is the core feature of the LTE system that can be connected to GSM and LTE networks. The most significant characteristics of NB-IoT are enriched distribution and reduced usage of water, gas, and power.
The smart meters and data concentrators generally interact directly with a utility company, which incorporates applications such as Apache Flink, storm, Azure cloud to interpret the readings and then revert to the user in an accessible and structured format. This approach provides transparency data about their usage, and users can realize how their consumption compares to city statistics and history of usage that declines the consumption of water, gas, and power and saves money. Leak detectors that are beneficial for everyone to detect and prevent defective plug, pipe, and hose or clogged home appliance in their home at some point.
After analysis by using Apache Flink, the information feeds to the billing server to generate the billing information directly to the user. The billing information provides transparency about the usage and the corresponding amount to the user.
Statement of the invention:
In the era of smart grids, smart metering necessitates not only measuring electricity, gas, water, or heat consumption, and interacting from the meter to the data gateway or cloud. Real-time disclosure of energy demand directly to consumers enhances consumer awareness, reduces waste, and lower costs. 51Page
Smart meters are used to evaluate and track power usage that ensures utility services and utility vendors to analyze their contribution and insistence in the actual environment. The significant benefits of smart metering systems are low costs, high flexibility, relatively low spoofing, and reduce carbon impacts. It implies a more accurate measurement of energy and reduces energy consumption.
We claim that,
• The different types of sensors are used for sensing meter data from their corresponding meters, such as water, gas, and electricity. • Smart Meters are integrated with every home environment that used by utility providers to determine power, gas, and water usage metrics either explicitly or via Meter Concentrator Units. Power utilization readings can be represented as a stream, a soluble series of actions. • A data concentrator is used to analyze and communicate this information to the central utility database. • A cloud server is used to store metering readings from the entire home area network, which connected via narrowband IoT. • Apache Flink is a platform and decentralized processing system for vigorous computing over unconstrained and constrained sensor information. • Touch screen electronic device with such as smartphones, laptops, tablets, etc. with high-speed internet or Wi-Fi. • The billing server is used to generate the billing information to the user that provides clarity of usage and total amount to pay.
Brief description of the Drawings:
Fig 1: Data flow Diagram
Fig 2: NB-IoT architecture
A detailed description of the drawing:
Figure 1 illustrates the flow diagram of the proposed methodology. In the figure, the individual home includes the different types of sensors to obtain the meter readings from their corresponding meter devices. The sensors used for electricity, gas, and water are connected to smart meters via wireless or wired network
61 P a g e networks. The smart meters from neighboring areas and data concentrators are further connected via Narrowband IoT.
Narrowband IoT has impressive efficiency in the presence of penetration, capability, and power accumulation. Narrowband IoT is a wireless network advance technology with low power and massive range that provides improved battery life and enhances coverage gain. The cloud server is used to store all the metering data. The security mechanisms are applied to secure the metering data from various attacks, such as the de-anonymization attack.
Before feeding the data to the billing server, Apache Flink is used for analyzing the metering data. The Meter data management system includes a pool of data, configuration, managing the data, and provides the security mechanisms for metering data.
Figure 2 explains the Narrow-band IoT methodology. NB-IoT network based on the integrated packet-system and amendments made for modular IoT, the modular IoT user plane has progressed to enhance packet system, and the control plane emerged improves the packet system. Any of the planes explores the optimal path for user data packets and control, both up-link and down-link. The data packet affords the optimal path for the chosen plane.
71Page
Claims (7)
1. The different types of sensors are used for sensing meter data from their corresponding meters, such as water, gas, and electricity.
2. Smart Meters are integrated with every home environment that used by utility providers to determine power, gas, and water usage metrics either explicitly or via Meter Concentrator Units. Power utilization readings can be represented as a stream, a soluble series of actions.
3. A data concentrator is used to analyze and communicate this information to the central utility database.
4. A cloud server is used to store metering readings from the entire home area network, which connected via narrowband IoT.
5. Apache Flink is a platform and decentralized processing system for vigorous computing over unconstrained and constrained sensor information.
6. Touch screen electronic device with such as smartphones, laptops, tablets, etc. with high-speed internet or Wi-Fi.
7. The billing server is used to generate the billing information to the user that provides clarity of usage and total amount to pay.
1| P a g e
ADVANCE METERING INFRASTRUCTURE SYSTEM FOR LARGE 28 Jun 2020
SCALE IOT NETWORKS DATA COLLECTION BY STREAMING
DIAGRAM 2020101173
Fig 1: Data flow diagram
1|Page
Fig 2: NB-IoT architecture
2|Page
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113159475A (en) * | 2020-12-04 | 2021-07-23 | 中国国家铁路集团有限公司 | Infrastructure full life cycle monitoring platform and method |
| CN113485897A (en) * | 2021-07-05 | 2021-10-08 | 建信金融科技有限责任公司 | Data processing method and device |
-
2020
- 2020-06-28 AU AU2020101173A patent/AU2020101173A4/en not_active Ceased
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113159475A (en) * | 2020-12-04 | 2021-07-23 | 中国国家铁路集团有限公司 | Infrastructure full life cycle monitoring platform and method |
| CN113159475B (en) * | 2020-12-04 | 2024-03-15 | 中国国家铁路集团有限公司 | Infrastructure full life cycle monitoring platform and method |
| CN113485897A (en) * | 2021-07-05 | 2021-10-08 | 建信金融科技有限责任公司 | Data processing method and device |
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