Smart homes require every device inside them to be connected with each other at all times, which leads to a lot of power wastage on a daily basis. As the devices inside a smart home increase, it becomes difficult for the user to control or operate every individual device optimally. Therefore, users generally rely on power management systems for such optimization but often are not satisfied with the results. In this paper, we present a novel multi-objective reinforcement learning framework with two-fold objectives of minimizing power consumption and maximizing user satisfaction. The framework explores the trade-off between the two objectives and converges to a better power management policy when both objectives are considered while finding an optimal policy. We experiment on real-world smart home data, and show that the multi-objective approaches: i) establish trade-off between the two objectives, ii) achieve better combined user satisfaction and power consumption than single-objective approaches. We also show that the devices that are used regularly and have several fluctuations in device modes at regular intervals should be targeted for optimization, and the experiments on data from other smart homes fetch similar results, hence ensuring transfer-ability of the proposed framework.
{"title":"Multi-objective Reinforcement Learning based approach for User-Centric Power Optimization in Smart Home Environments","authors":"Saurabh Gupta, Siddhant Bhambri, Karanveer Dhingra, Arun Balaji Buduru, P. Kumaraguru","doi":"10.1109/SMDS49396.2020.00018","DOIUrl":"https://doi.org/10.1109/SMDS49396.2020.00018","url":null,"abstract":"Smart homes require every device inside them to be connected with each other at all times, which leads to a lot of power wastage on a daily basis. As the devices inside a smart home increase, it becomes difficult for the user to control or operate every individual device optimally. Therefore, users generally rely on power management systems for such optimization but often are not satisfied with the results. In this paper, we present a novel multi-objective reinforcement learning framework with two-fold objectives of minimizing power consumption and maximizing user satisfaction. The framework explores the trade-off between the two objectives and converges to a better power management policy when both objectives are considered while finding an optimal policy. We experiment on real-world smart home data, and show that the multi-objective approaches: i) establish trade-off between the two objectives, ii) achieve better combined user satisfaction and power consumption than single-objective approaches. We also show that the devices that are used regularly and have several fluctuations in device modes at regular intervals should be targeted for optimization, and the experiments on data from other smart homes fetch similar results, hence ensuring transfer-ability of the proposed framework.","PeriodicalId":385149,"journal":{"name":"2020 IEEE International Conference on Smart Data Services (SMDS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130015181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-19DOI: 10.1109/SMDS49396.2020.00022
Chathura Widanage, Niranda Perera, V. Abeykoon, Supun Kamburugamuve, Thejaka Amila Kanewala, Hasara Maithree, P. Wickramasinghe, A. Uyar, Gurhan Gunduz, G. Fox
The amazing advances being made in the fields of machine and deep learning are a highlight of the Big Data era for both enterprise and research communities. Modern applications require resources beyond a single node's ability to provide. However this is just a small part of the issues facing the overall data processing environment, which must also support a raft of data engineering for pre- and post-data processing, communication, and system integration. An important requirement of data analytics tools is to be able to easily integrate with existing frameworks in a multitude of languages, thereby increasing user productivity and efficiency. All this demands an efficient and highly distributed integrated approach for data processing, yet many of today's popular data analytics tools are unable to satisfy all these requirements at the same time. In this paper we present Cylon, an open-source high performance distributed data processing library that can be seamlessly integrated with existing Big Data and AI/ML frameworks. It is developed with a flexible C++ core on top of a compact data structure and exposes language bindings to C++, Java, and Python. We discuss Cylon's architecture in detail, and reveal how it can be imported as a library to existing applications or operate as a standalone framework. Initial experiments show that Cylon enhances popular tools such as Apache Spark and Dask with major performance improvements for key operations and better component linkages. Finally, we show how its design enables Cylon to be used cross-platform with minimum overhead, which includes popular AI tools such as PyTorch, Tensorflow, and Jupyter notebooks.
{"title":"High Performance Data Engineering Everywhere","authors":"Chathura Widanage, Niranda Perera, V. Abeykoon, Supun Kamburugamuve, Thejaka Amila Kanewala, Hasara Maithree, P. Wickramasinghe, A. Uyar, Gurhan Gunduz, G. Fox","doi":"10.1109/SMDS49396.2020.00022","DOIUrl":"https://doi.org/10.1109/SMDS49396.2020.00022","url":null,"abstract":"The amazing advances being made in the fields of machine and deep learning are a highlight of the Big Data era for both enterprise and research communities. Modern applications require resources beyond a single node's ability to provide. However this is just a small part of the issues facing the overall data processing environment, which must also support a raft of data engineering for pre- and post-data processing, communication, and system integration. An important requirement of data analytics tools is to be able to easily integrate with existing frameworks in a multitude of languages, thereby increasing user productivity and efficiency. All this demands an efficient and highly distributed integrated approach for data processing, yet many of today's popular data analytics tools are unable to satisfy all these requirements at the same time. In this paper we present Cylon, an open-source high performance distributed data processing library that can be seamlessly integrated with existing Big Data and AI/ML frameworks. It is developed with a flexible C++ core on top of a compact data structure and exposes language bindings to C++, Java, and Python. We discuss Cylon's architecture in detail, and reveal how it can be imported as a library to existing applications or operate as a standalone framework. Initial experiments show that Cylon enhances popular tools such as Apache Spark and Dask with major performance improvements for key operations and better component linkages. Finally, we show how its design enables Cylon to be used cross-platform with minimum overhead, which includes popular AI tools such as PyTorch, Tensorflow, and Jupyter notebooks.","PeriodicalId":385149,"journal":{"name":"2020 IEEE International Conference on Smart Data Services (SMDS)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126504449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
On behalf of the Organizing Committee, it is my pleasure to welcome all our distinguished speakers and authors to 2020 9th International Conference on Industrial Technology and Management (ICITM 2020) from February 11-13, 2020 in Oxford, UK. The idea of the conference is for scientists, scholars, engineers and students from the universities and those who from the industry all around the world to present ongoing research activities, and hence to foster research relations between the universities and the industry. This conference provides opportunities for the delegates to exchange new ideas and application experiences face to face, to establish business or research relations and to find global partners for future collaboration.
{"title":"Message from the Chairs","authors":"R. Gundlach","doi":"10.1109/RE.2005.40","DOIUrl":"https://doi.org/10.1109/RE.2005.40","url":null,"abstract":"On behalf of the Organizing Committee, it is my pleasure to welcome all our distinguished speakers and authors to 2020 9th International Conference on Industrial Technology and Management (ICITM 2020) from February 11-13, 2020 in Oxford, UK. The idea of the conference is for scientists, scholars, engineers and students from the universities and those who from the industry all around the world to present ongoing research activities, and hence to foster research relations between the universities and the industry. This conference provides opportunities for the delegates to exchange new ideas and application experiences face to face, to establish business or research relations and to find global partners for future collaboration.","PeriodicalId":385149,"journal":{"name":"2020 IEEE International Conference on Smart Data Services (SMDS)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123673833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Message from the Technical Committee Chair on Services Computing of IEEE Computer Society","authors":"","doi":"10.1109/edge.2019.00010","DOIUrl":"https://doi.org/10.1109/edge.2019.00010","url":null,"abstract":"","PeriodicalId":385149,"journal":{"name":"2020 IEEE International Conference on Smart Data Services (SMDS)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114108910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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