Design and implementation of a power consumption management system for smart home over fog-cloud computing

Chen, Yan-Da; Azhari, Muhammad; Leu, Jenq‐Shiou

doi:10.1109/igbsg.2018.8393553

Cited by 30 publications

(10 citation statements)

References 8 publications

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“…In ref. [46], the authors showed that they considered fog cloud computing as the Open Fog reference architectural smart home system. The simulation system has not, however, been tested in a realistic environment; the analysis lacked a functional evaluation.…”

Section: Knowledge and Objectivesmentioning

confidence: 99%

A Novel Smart Energy Management as a Service over a Cloud Computing Platform for Nanogrid Appliances

et al. 2020

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There will be a dearth of electrical energy in the world in the future due to exponential increase in electrical energy demand of rapidly growing world population. With the development of Internet of Things (IoT), more smart appliances will be integrated into homes in smart cities that actively participate in the electricity market by demand response programs to efficiently manage energy in order to meet this increasing energy demand. Thus, with this incitement, the energy management strategy using a price-based demand response program is developed for IoT-enabled residential buildings. We propose a new EMS for smart homes for IoT-enabled residential building smart devices by scheduling to minimize cost of electricity, alleviate peak-to-average ratio, correct power factor, automatic protective appliances, and maximize user comfort. In this method, every home appliance is interfaced with an IoT entity (a data acquisition module) with a specific IP address, which results in a wide wireless system of devices. There are two components of the proposed system: software and hardware. The hardware is composed of a base station unit (BSU) and many terminal units (TUs). The software comprises Wi-Fi network programming as well as system protocol. In this study, a message queue telemetry transportation (MQTT) broker was installed on the boards of BSU and TU. In this paper, we present a low-cost platform for the monitoring and helping decision making about different areas in a neighboring community for efficient management and maintenance, using information and communication technologies. The findings of the experiments demonstrated the feasibility and viability of the proposed method for energy management in various modes. The proposed method increases effective energy utilization, which in turn increases the sustainability of IoT-enabled homes in smart cities. The proposed strategy automatically responds to power factor correction, to protective home appliances, and to price-based demand response programs to combat the major problem of the demand response programs, which is the limitation of consumer’s knowledge to respond upon receiving demand response signals. The schedule controller proposed in this paper achieved an energy saving of 6.347 kWh real power per day, this paper achieved saving 7.282 kWh apparent power per day, and the proposed algorithm in our paper saved $2.3228388 per day.

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Section: Knowledge and Objectivesmentioning

confidence: 99%

A Novel Smart Energy Management as a Service over a Cloud Computing Platform for Nanogrid Appliances

et al. 2020

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“…In this sense, fog-cloud analytics is formed; resulting insights from cloud analytics are sent back to edge analytics. In Reference [12], the authors presented a simulated OpenFog reference architecture-based smart home system considering fog-cloud computing. However, the simulated system was not evaluated in a realistic environment; a practical evaluation was lacking in the study.…”

Section: Related Workmentioning

confidence: 99%

“…As a result, edge analytics that supports immediately analytical, interpretable, and real-time actionable data insights at IoT sources is necessary for latency-sensitive user-centric IoT applications in smart homes, where AI models are trained in cloud analytics and then deployed on-site on IoT end devices as edge analytics at the edge of the internet. In this sense, fog-cloud analytics is formed [12,13].…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of Cloud Analytics-Assisted Smart Power Meters Considering Advanced Artificial Intelligence as Edge Analytics in Demand-Side Management for Smart Homes

Chen

Lin

Kung

et al. 2019

Sensors

142

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In a smart home linked to a smart grid (SG), demand-side management (DSM) has the potential to reduce electricity costs and carbon/chlorofluorocarbon emissions, which are associated with electricity used in today’s modern society. To meet continuously increasing electrical energy demands requested from downstream sectors in an SG, energy management systems (EMS), developed with paradigms of artificial intelligence (AI) across Internet of things (IoT) and conducted in fields of interest, monitor, manage, and analyze industrial, commercial, and residential electrical appliances efficiently in response to demand response (DR) signals as DSM. Usually, a DSM service provided by utilities for consumers in an SG is based on cloud-centered data science analytics. However, such cloud-centered data science analytics service involved for DSM is mostly far away from on-site IoT end devices, such as DR switches/power meters/smart meters, which is usually unacceptable for latency-sensitive user-centric IoT applications in DSM. This implies that, for instance, IoT end devices deployed on-site for latency-sensitive user-centric IoT applications in DSM should be aware of immediately analytical, interpretable, and real-time actionable data insights processed on and identified by IoT end devices at IoT sources. Therefore, this work designs and implements a smart edge analytics-empowered power meter prototype considering advanced AI in DSM for smart homes. The prototype in this work works in a cloud analytics-assisted electrical EMS architecture, which is designed and implemented as edge analytics in the architecture described and developed toward a next-generation smart sensing infrastructure for smart homes. Two different types of AI deployed on-site on the prototype are conducted for DSM and compared in this work. The experimentation reported in this work shows the architecture described with the prototype in this work is feasible and workable.

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“…The applications using mobile fog nodes include SWAMP [27], protecting the Android devices with the GPUaided antivirus [28], observation of traffic by drones [29], managing power usage [30], delivery by drones [31], managing congestion of traffic [32], driving autonomously [33], cognitively assisting by wearable [34], smart street lamp [35], and live broadcast of videos [36]. Several fog applications use mobile fog nodes such as sensors, laptops, and drones having limited processing capacity.…”

Section: Introductionmentioning

confidence: 99%

D-GENE: Deferring the GENEration of Power Sets for Discovering Frequent Itemsets in Sparse Big Data

et al. 2020

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Sparseness is the distinctive aspect of big data generated by numerous applications at present. Furthermore, several similar records exist in real-world sparse datasets. Based on Iterative Trimmed Transaction Lattice (ITTL), the recently proposed TRICE algorithm learns frequent itemsets efficiently from sparse datasets. TRICE stores alike transactions once, and eliminates the infrequent part of each distinct transaction afterward. However, removing the infrequent part of two or more distinct transactions may result in similar trimmed transactions. TRICE repeatedly generates ITTLs of similar trimmed transactions that induce redundant computations and eventually, affects the runtime efficiency. This paper presents D-GENE, a technique that optimizes TRICE by introducing a deferred ITTL generation mechanism. D-GENE suspends the process of ITTL generation till the completion of transaction pruning phase. The deferral strategy enables D-GENE to generate ITTLs of similar trimmed transactions once. Experimental results show that by avoiding the redundant computations, D-GENE gets better runtime efficiency. D-GENE beats TRICE, FP-growth, and optimized versions of SaM and RElim algorithms comprehensively, especially when the difference between distinct transactions and distinct trimmed transactions is high.INDEX TERMS Big data applications, pattern recognition, association rules, frequent item set mining, IoT.

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Design and implementation of a power consumption management system for smart home over fog-cloud computing

Cited by 30 publications

References 8 publications

A Novel Smart Energy Management as a Service over a Cloud Computing Platform for Nanogrid Appliances

A Novel Smart Energy Management as a Service over a Cloud Computing Platform for Nanogrid Appliances

Design and Implementation of Cloud Analytics-Assisted Smart Power Meters Considering Advanced Artificial Intelligence as Edge Analytics in Demand-Side Management for Smart Homes

D-GENE: Deferring the GENEration of Power Sets for Discovering Frequent Itemsets in Sparse Big Data

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