Implementation of smart home service over web of object architecture

Lee, NamKyung; Lee, HyungKeuk; Lee, HyunWoo; Ryu, Won

doi:10.1109/ictc.2015.7354778

Cited by 6 publications

(2 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…SQLite database system was used for the implementation and different tests for the validation. Lee et al [18] proposed a web services architecture for home service environments. Three layers shape the architecture: (1) information layer, (2) management layer, and (3) presentation layer.…”

Section: Related Workmentioning

confidence: 99%

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

et al. 2020

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%

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

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Böcker et al 2010General, health B2C Brotman et al 2015H o m e B 2 C Buchanan and McMenemy (2012) Health, library B2C Buettner 2016General B2C Byun and Park 2011Building Both Calza et al 2015Manufacturing Both Cardozo and Clarke 2015C i t y B 2 C Cellary 2013Manufacturing B2C Chatterjee and Armentano 2015H e a l t h B 2 C Chen et al 2012General B2C Chu and Lin 2011General Both Ciortea et al (2016) General B2C Dawid et al (2016) General B2C De Oliveira and Silva (2015) G Website/app B2C Holgado and Macchi (2014) Manufacturing B2B Hong et al (2014) Website/app B2C Jerald et al (2016) General Both Kamp et al (2016) Manufacturing B2B Kennedy and Keskin (2016) Manufacturing B2C Keskin and Kennedy (2015) Manufacturing Both Kim et al (2015) Health, Home B2C Knote and Blohm (2016) General B2C Kölmel and Bulander (2015) Manufacturing Both Korzun et al (2015) H e a l t h B 2 C Kryvinska et al (2008) Telecommunications B2C Kuebel and Zarnekow (2015) H o m e B o t h Kwak et al (2014) General B2C Kynsilehto and Olsson (2011) General B2C Kynsilehto and Olsson (2012) General, home B2C Lê Tuán et al 2012C i t y B 2 C Lebedev et al (2016) H e a l t h B 2 C Lee et al 2010General B2C Lee et al (2012) General B2C Lee et al (2015) H o m e B 2 C Lee et al (2016) General B2C Lesjak et al (2014) Manufacturing B2B Lesjak et al (2015) Manufacturing B2B Li et al (2015) H e a l t h B 2 B …”

Section: Bothmentioning

confidence: 99%

Focusing the customer through smart services: a literature review

Dreyer

Olivotti

Lebek³

et al. 2019

Electron Markets

View full text Add to dashboard Cite

Smart services serve customers and their individual, continuously changing needs; information and communications technology enables such services. The interactions between customers and service providers form the basis for co-created value. A growing interest in smart services has been reported in the literature in recent years. However, a categorization of the literature and relevant research fields is still missing. This article presents a structured literature search in which 109 relevant publications were identified. The publications are clustered in 13 topics and across five phases of the lifecycle of a smart service. The status quo is analyzed, and a heat map is created that graphically shows the research intensity in various dimensions. The results show that there is diverse knowledge related to the various topics associated with smart services. One finding suggests that economic aspects such as new business models or pricing strategies are rarely considered in the literature. Additionally, the customer plays a minor role in IS publications. Machine learning and knowledge management are identified as promising fields that should be the focus of further research and practical applications. Concrete ideas for future research are presented and discussed and will contribute to academic knowledge. Addressing the identified research gaps can help practitioners successfully provide smart services.

show abstract