Edge-based Energy Management for Smart Homes

Xia, Chunqiu; Li, Wei; Chang, Xiaomin; Delicato, Flávia C.; Yang, Ting; Zomaya, Albert Y.

doi:10.1109/dasc/picom/datacom/cyberscitec.2018.00-19

Cited by 23 publications

(10 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Edge-computing is also advocated in [126]. The authors implemented a load-shifting mechanism, which allows non-time-constraint applications to be moved from rush hours to off-peak hours.…”

Section: Use Of Nilm In Hemsmentioning

confidence: 99%

NILM Techniques for Intelligent Home Energy Management and Ambient Assisted Living: A Review

et al. 2019

View full text Add to dashboard Cite

The ongoing deployment of smart meters and different commercial devices has made electricity disaggregation feasible in buildings and households, based on a single measure of the current and, sometimes, of the voltage. Energy disaggregation is intended to separate the total power consumption into specific appliance loads, which can be achieved by applying Non-Intrusive Load Monitoring (NILM) techniques with a minimum invasion of privacy. NILM techniques are becoming more and more widespread in recent years, as a consequence of the interest companies and consumers have in efficient energy consumption and management. This work presents a detailed review of NILM methods, focusing particularly on recent proposals and their applications, particularly in the areas of Home Energy Management Systems (HEMS) and Ambient Assisted Living (AAL), where the ability to determine the on/off status of certain devices can provide key information for making further decisions. As well as complementing previous reviews on the NILM field and providing a discussion of the applications of NILM in HEMS and AAL, this paper provides guidelines for future research in these topics.

show abstract

“…Edge-computing is also advocated in [126]. The authors implemented a load-shifting mechanism, which allows non-time-constraint applications to be moved from rush hours to off-peak hours.…”

Section: Use Of Nilm In Hemsmentioning

confidence: 99%

NILM Techniques for Intelligent Home Energy Management and Ambient Assisted Living: A Review

et al. 2019

View full text Add to dashboard Cite

show abstract

“…There are authors [20][21][22][23] who study energy management in the Smart home environment. Collotta and Pau [20] propose an energy management system for smart homes, using Bluetooth Low Energy (BLE) [21] for communications, together with a home energy management (HEM) scheme.…”

Section: Related Workmentioning

confidence: 99%

Artificial intelligent system for multimedia services in smart home environments

et al. 2021

View full text Add to dashboard Cite

Internet of Things (IoT) has introduced new applications and environments. Smart Home provides new ways of communication and service consumption. In addition, Artificial Intelligence (AI) and deep learning have improved different services and tasks by automatizing them. In this field, reinforcement learning (RL) provides an unsupervised way to learn from the environment. In this paper, a new intelligent system based on RL and deep learning is proposed for Smart Home environments to guarantee good levels of QoE, focused on multimedia services. This system is aimed to reduce the impact on user experience when the classifying system achieves a low accuracy. The experiments performed show that the deep learning model proposed achieves better accuracy than the KNN algorithm and that the RL system increases the QoE of the user up to 3.8 on a scale of 10.

show abstract

“…Xia et al [46] proposes an energy management framework based on Edge Computing. The main focus of this research is the amortization of deployed IoT devices to obtain efficient energy savings.…”

Section: Edge Computing Iot and Smart Energymentioning

confidence: 99%

Edge Computing, IoT and Social Computing in Smart Energy Scenarios

Sittón-Candanedo

Alonso

García

et al. 2019

Sensors

View full text Add to dashboard Cite

The Internet of Things (IoT) has become one of the most widely research paradigms, having received much attention from the research community in the last few years. IoT is the paradigm that creates an internet-connected world, where all the everyday objects capture data from our environment and adapt it to our needs. However, the implementation of IoT is a challenging task and all the implementation scenarios require the use of different technologies and the emergence of new ones, such as Edge Computing (EC). EC allows for more secure and efficient data processing in real time, achieving better performance and results. Energy efficiency is one of the most interesting IoT scenarios. In this scenario sensors, actuators and smart devices interact to generate a large volume of data associated with energy consumption. This work proposes the use of an Edge-IoT platform and a Social Computing framework to build a system aimed to smart energy efficiency in a public building scenario. The system has been evaluated in a public building and the results make evident the notable benefits that come from applying Edge Computing to both energy efficiency scenarios and the framework itself. Those benefits included reduced data transfer from the IoT-Edge to the Cloud and reduced Cloud, computing and network resource costs.

show abstract

Edge-based Energy Management for Smart Homes

Cited by 23 publications

References 19 publications

NILM Techniques for Intelligent Home Energy Management and Ambient Assisted Living: A Review

NILM Techniques for Intelligent Home Energy Management and Ambient Assisted Living: A Review

Artificial intelligent system for multimedia services in smart home environments

Edge Computing, IoT and Social Computing in Smart Energy Scenarios

Contact Info

Product

Resources

About