Blackout Avoidance and Energy Saving Services with Alert Aggregation

2012 ◽  
Vol 3 (4) ◽  
pp. 40-56
Author(s):  
Erica Fong ◽  
Dickson K.W. Chiu ◽  
Haiyang Hu ◽  
Yi Zhuang ◽  
Hua Hu
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Real Time ◽  
Mobile Devices ◽  
Critical Energy ◽  
The Internet ◽  
Monitor System ◽  
Energy Usage ◽  
Huge Number ◽  
Smart Meters

Peak electricity demands from huge number of households in a mega-city would cause contention, leading to potential blackout. This paper proposes bi-directional collaboration via a Smart Energy Monitor System (SEMS) between consumers and energy suppliers, exchanging real-time energy usage data with smart meters over the Internet and mobile devices for well-informed decisions and even predictions. The authors further propose the use of an Alert Management System (AMS) to monitor and aggregate critical energy consumption events for this purpose.

Designing Mobile Collaborative Applications for Cloud Environments

2016 ◽  
pp. 34-60 ◽  
Author(s):  
Nadir Guetmi ◽  
Abdessamad Imine
Keyword(s):  
Energy Consumption ◽  
Real Time ◽  
Mobile Devices ◽  
Design Patterns ◽  
Self Control ◽  
Network Coverage ◽  
Collaborative Editing ◽  
Collaborative Applications ◽  
Cloud Environments ◽  
Continuous Use

Mobile devices have experienced a huge progress in the capacity of computing, storage and data visualization. They are becoming the device of choice for operating a large variety of applications while supporting real-time collaboration of people and their mobility. Despite this progress, the energy consumption and the network coverage remain a serious problem against an efficient and continuous use of these mobile collaborative applications and a great challenge for their designers and developers. To address these issues, this chapter describes design patterns that help modelling mobile collaborative applications to support collaboration through the cloud. Two levels are presented: the first level provides self-control to create clones of mobile devices, manage users' groups and recover failed clones in the cloud. The second level supports group collaboration mechanisms in real-time. These design patterns have been used as a basis for the design of a mobile collaborative editing application.

Energy Conservation in the Era of Ubiquitous Computing

2019 ◽  
pp. 1142-1151
Author(s):  
P. P. Abdul Haleem
Keyword(s):  
Energy Consumption ◽  
Ubiquitous Computing ◽  
Mobile Devices ◽  
Exponential Growth ◽  
Carbon Emission ◽  
The Internet ◽  
Conservation Of Energy ◽  
Limited Power ◽  
Widespread Availability ◽  
Combined Impact

Widespread availability and affordability of devices and easy accessibility of internet has accelerated the pace of ubiquitous computing. Connectivity to the internet has resulted in an exponential growth in terms of content and traffic available on the internet. When the barriers such as type of devices, location, time, and format have become meaningless in the era of ubiquitous computing; the issue of energy consumption and resultant carbon emission is a matter of concern. Energy consumption is an issue in ubiquitous computing, as the majority of the devices involved will be mobile devices that depend on the limited power offered by the battery inside the device. Carbon emission is a concern due to the combined impact made by the devices hooked over to the internet. This chapter discusses the issues related to energy consumption for various activities when the services offered by the internet are availed. The chapter also discusses the challenges to be overcome to achieve conservation of energy consumed by the internet and devices.

scholarly journals HEMS-IoT: A Big Data and Machine Learning-Based Smart Home System for Energy Saving

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1097 ◽  
Author(s):  
Isaac Machorro-Cano ◽  
Giner Alor-Hernández ◽  
Mario Andrés Paredes-Valverde ◽  
Lisbeth Rodríguez-Mazahua ◽  
José Luis Sánchez-Cervantes ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Efficiency ◽  
Big Data ◽  
Energy Consumption ◽  
Energy Saving ◽  
Smart Home ◽  
User Preferences ◽  
The Internet ◽  
Automation Systems ◽  
Home Energy Management

Energy efficiency has aroused great interest in research worldwide, because energy consumption has increased in recent years, especially in the residential sector. The advances in energy conversion, along with new forms of communication, and information technologies have paved the way for what is now known as smart homes. The Internet of Things (IoT) is the convergence of various heterogeneous technologies from different application domains that are used to interconnect things through the Internet, thus allowing for the detection, monitoring, and remote control of multiple devices. Home automation systems (HAS) combined with IoT, big data technologies, and machine learning are alternatives that promise to contribute to greater energy efficiency. This work presents HEMS-IoT, a big data and machine learning-based smart home energy management system for home comfort, safety, and energy saving. We used the J48 machine learning algorithm and Weka API to learn user behaviors and energy consumption patterns and classify houses with respect to energy consumption. Likewise, we relied on RuleML and Apache Mahout to generate energy-saving recommendations based on user preferences to preserve smart home comfort and safety. To validate our system, we present a case study where we monitor a smart home to ensure comfort and safety and reduce energy consumption.

Research of Paddle Wave-Maker Control System Based on Jonswap Spectrum

2014 ◽  
Vol 1006-1007 ◽  
pp. 679-684
Author(s):  
Xue Fei Huang ◽  
Mao Pang
Keyword(s):  
Control System ◽  
Energy Consumption ◽  
Energy Saving ◽  
Real Time ◽  
Personal Computer ◽  
Control Algorithm ◽  
Change Rate ◽  
The Real ◽  
Wave Maker ◽  
Jonswap Spectrum

According to large energy consumption, overload and re-reflection problemr, the control algorithm of wave-maker has been improved based on Jonswap spectrum. The control algorithm is comprised of the offline calculation of the control waveform and the real-time governing of the wave paddle. Offline algorithm is implemented on a personal computer, whereas paddle control is realized on a programmable automation controller. The experiments results show that the speed is continuous and stable, change rate of load has been optimized obviously and achieves the purpose of energy saving.

scholarly journals Intelligent spacing selection model under energy-saving constraints for the selection of communication nodes in the Internet of Things

2021 ◽  
Author(s):  
Jing-Shu Sun ◽  
Teng Zhu ◽  
Marcin Wozniak
Keyword(s):  
Energy Consumption ◽  
Internet Of Things ◽  
Energy Saving ◽  
Transition Amplitude ◽  
Selection Model ◽  
Link Quality ◽  
The Internet ◽  
The Internet Of Things ◽  
Spacing Selection ◽  
Selection Of

AbstractCurrent IoT communication node spacing selection process show may potential areas for improvements such as high delay ratio, high total energy consumption ratio, confusion of the optimal communication information band, intelligent spacing node design under the constraints of the energy-saving selection of IoT communication. Based on energy-saving constraints, the link status between nodes is evaluated through link stability and link quality. In order to prevent the generation of serious noisy nodes and frequency hopping data, the interference nodes under the intrusion of the Internet of Things are identified by determining transition amplitude of the noise nodes in the transmission data sequence. Finally, according to the calculation results of the optimal communication node selection, the design of the intelligent spacing selection model for the communication nodes of the Internet of Things is realized. The simulation results show that the established model not only reduces energy consumption of nodes, shortens the average transmission delay of nodes, but also improves anti-interference effect of node spacing selection.

scholarly journals An Internet of Things Wireless Sensor Network Data Exchange Model Based on Hierarchical Address Automatic Configuration and Header Compression Encoding Strategy

2017 ◽  
Vol 13 (07) ◽  
pp. 140 ◽  
Author(s):  
Yuankun Yang ◽  
Yongqing Ji
Keyword(s):  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Internet Of Things ◽  
Real Time ◽  
Sensor Network ◽  
Data Exchange ◽  
Wireless Sensor ◽  
The Internet ◽  
Network Data ◽  
Sink Node

<p><span style="font-size: medium;"><span style="font-family: 宋体;">To explore the wireless sensor network data exchange model, an addressing strategy is applied to the Internet of Things, and the real-time communication between the underlying wireless sensor network and the Internet based on the IEEE 802.15.4 protocol is realized. In addition, Hierarchical address auto configuration strategy is adopted. First of all, inside the bottom network, it allows nodes to use link local address derived by 16-bit short address for data packet transmission. Secondly, Sink node in each underlying network accesses to the global routing prefix through the upper IP router, and combined with interface identifier, it forms Sink node global address, and realizes wireless sensor network and Internet data exchange. The research results show that the strategy has certain superiority in network cost, throughput, energy consumption and other performances. In summary, the proposed addressing strategy has the characteristics of effectively integrating heterogeneous networks, reducing system energy consumption, increasing network throughput and ensuring real-time system performance for the future Internet of things.</span></span></p>

scholarly journals Time-Pattern Profiling from Smart Meter Data to Detect Outliers in Energy Consumption

IoT ◽  
2020 ◽  
Vol 1 (1) ◽  
pp. 92-108
Author(s):  
William Hurst ◽  
Casimiro A. Curbelo Montañez ◽  
Nathan Shone
Keyword(s):  
Energy Consumption ◽  
Spatial Clustering ◽  
Smart Meter ◽  
Energy Usage ◽  
Smart Meters ◽  
Time Pattern ◽  
Behavioural Patterns ◽  
Utility Companies ◽  
The Uk ◽  
The Internet Of Things

Smart meters have become a core part of the Internet of Things, and its sensory network is increasing globally. For example, in the UK there are over 15 million smart meters operating across homes and businesses. One of the main advantages of the smart meter installation is the link to a reduction in carbon emissions. Research shows that, when provided with accurate and real-time energy usage readings, consumers are more likely to turn off unneeded appliances and change other behavioural patterns around the home (e.g., lighting, thermostat adjustments). In addition, the smart meter rollout results in a lessening in the number of vehicle callouts for the collection of consumption readings from analogue meters and a general promotion of renewable sources of energy supply. Capturing and mining the data from this fully maintained (and highly accurate) sensing network, provides a wealth of information for utility companies and data scientists to promote applications that can further support a reduction in energy usage. This research focuses on modelling trends in domestic energy consumption using density-based classifiers. The technique estimates the volume of outliers (e.g., high periods of anomalous energy consumption) within a social class grouping. To achieve this, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Ordering Points to Identify the Clustering Structure (OPTICS) and Local Outlier Factor (LOF) demonstrate the detection of unusual energy consumption within naturally occurring groups with similar characteristics. Using DBSCAN and OPTICS, 53 and 208 outliers were detected respectively; with 218 using LOF, on a dataset comprised of 1,058,534 readings from 1026 homes.

scholarly journals Green distributed algorithm for energy saving in IP wired networks using sleep scheduling

2021 ◽  
Vol 11 (6) ◽  
pp. 5160
Author(s):  
Mohammed Hussein ◽  
Wisam Alabbasi ◽  
Ahmad Alsadeh
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Learning Algorithm ◽  
Critical Issue ◽  
The Internet ◽  
Great Effort ◽  
Network Nodes ◽  
Backbone Networks ◽  
Wired Networks ◽  
Traffic Matrix

Energy saving has become a critical issue and a great challenge in the past few decades, and a great effort as well is being made to reduce consumed energy. The Internet forms a major source for energy consumption. Therefore, in this work we propose an algorithm for energy saving in distributed backbone networks, the reduced energy consumption (RedCon) algorithm. In this paper, we introduce a new version for saving energy on the Internet by switching off underutilized links and switching on idle links when the network is overloaded in a distributed manner over the network nodes based on LSA messages and without any knowledge of the traffic matrix. Our algorithm is more accurate and outperforms other algorithms with its time checks and advanced learning algorithm.

scholarly journals A Novel IoT Based Smart Energy Meter with Backup Battery

2021 ◽  
pp. 357-364
Author(s):  
Osman Yakubu ◽  
C. Narendra Babu ◽  
C. Osei Adjei
Keyword(s):  
Energy Consumption ◽  
Real Time ◽  
Energy Demand ◽  
Single Phase ◽  
Data Repository ◽  
Smart Meters ◽  
Cloud Data ◽  
Energy Meter ◽  
Consumption Data ◽  
Utility Companies

Energy consumption is currently on the ascendency due to increased demand by domestic and industrial consumers. The quest to ensure that consumers manage their consumption and the utility companies also monitor consumers to manage energy demand and production resulted in smart energy meters which are able to transmit data automatically at certain intervals being introduced. These Smart Meters are still fraught with challenges as consumers are unable to effectively monitor their consumption and the meters are also expensive to deploy. This research aims to present a novel IoT based Smart Energy Meter that will gather consumption data in real time and transmit it to a cloud data repository for storage and analysis. The novelty of this inexpensive system is the introduction of an ADM25SC Single Phase DIN-RAIL Watt-hour Energy Meter which sends power to the microcontroller and also the introduction of a backup battery that keeps the meter on for some time to transmit outage data during power outages. Data gathered from the proposed IoT based Smart Energy Meter for a period is compared against that of the same period from a Smart G meter, a widely used energy meter, and is found to be very close confirming the accuracy of the IoT based Smart Energy Meter.

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