Towards a context-driven platform using IoT and big data technologies for energy efficient buildings

Bakhouya, Mohamed; NaitMalek, Y.; Elmouatamid, Abdellatif; Lachhab, Fadwa; Berouine, Anass; Boulmrharj, Sofia; Ouladsine, Radouane; Felix, Vanessa G.; Zine-Dine, Khalid; Khaidar, Mohammed; Elkamoune, N.

doi:10.1109/cloudtech.2017.8284744

Cited by 24 publications

(18 citation statements)

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“…However, the conventional BMS's isolated communication protocols and static rules are unable to respond to real-time building conditions [39], as the data processing technique is primarily based on SQL database or even spreadsheets statistics which is pretty primitive, inefficient, resource-demanding and costly [40] [41] and not intended for analytics [42]. The building services could be improved by developing more accurate context using recent IoT and BD technologies [43] [44]. Especially, IoT-enabled smart networks capable of integrating dynamic control strategies and new efficient and effective algorithms to deal with large influx of data, hold the promise of improved system reliability, greater energy efficiency, and lower costs for consumers [39] [40] [25].…”

Section: Assessing the Potential Value Of Bda Adoption For The Uk's Fmentioning

confidence: 99%

Drivers and Challenges Associated With the Implementation of Big Data Within U.K. Facilities Management Sector: An Exploratory Factor Analysis Approach

Konanahalli

Marinelli

Oyedele

2022

IEEE Trans. Eng. Manage.

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Section: Assessing the Potential Value Of Bda Adoption For The Uk's Fmentioning

confidence: 99%

Drivers and Challenges Associated With the Implementation of Big Data Within U.K. Facilities Management Sector: An Exploratory Factor Analysis Approach

Konanahalli

Marinelli

Oyedele

2022

IEEE Trans. Eng. Manage.

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“…In parallel to this progress, big data technologies have been developed and deployed so rapidly and are relying heavily on cloud computing infrastructures for both storage and data processing. In many studies, these technologies are considered among the most remarkable technologies for developing context‐driven applications and services in many domains such as transportation, health, and energy –. In other words, big data technologies have been some of the current and future research frontiers.…”

Section: Introductionmentioning

confidence: 99%

“…These data streams have to be first pre‐processed locally before sending it to the cloud infrastructures using IoT platforms (eg, ThingSpeak) and via wireless technologies (eg, Wifi and LTE) for storage and processing using machine learning algorithms (eg, deep learning). These heavy processing and high communication factors are, however, serious challenges for the development of large‐scale IoT applications over cloud computing environments …”

Section: Introductionmentioning

confidence: 99%

“…In many studies, these technologies are considered among the most remarkable technologies for developing context-driven applications and services in many domains such as transportation, health, and energy. [7][8][9] In other words, big data technologies have been some of the current and future research frontiers. They revolutionize many fields, including business, scientific research, and public administration.…”

Section: Introductionmentioning

confidence: 99%

“…These heavy processing and high communication factors are, however, serious challenges for the development of large-scale IoT applications over cloud computing environments. 7,9 The aim of this special issue is to present recent contributions and results in the fields of cloud computing, IoT and big data applications, systems architecture and services, virtualization, security and privacy, high-performance computing, and applications, with an emphasis on how to build cloud computing platforms with real impacts.…”

Section: Introductionmentioning

confidence: 99%

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Cloud computing and big data: Technologies and applications

Zbakh

Bakhouya

Essaaidi

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A platform architecture for occupancy detection using stream processing and machine learning approaches

Elkhoukhi

NaitMalek

Bakhouya

et al. 2019

Concurrency and Computation

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SummaryContext‐awareness in energy‐efficient buildings has been considered as a crucial fact for developing context‐driven control approaches in which sensing and actuation tasks are performed according to the contextual changes. This could be done by including the presence of occupants, number, actions, and behaviors in up‐to‐date context, taking into account the complex interlinked elements, situations, processes, and their dynamics. However, many studies have shown that occupancy information is a major leading source of uncertainty when developing control approaches. Comprehensive and real‐time fine‐grained occupancy information has to be, therefore, integrated in order to improve the performance of occupancy‐driven control approaches. The work presented in this paper is toward the development of a holistic platform that combines recent IoT and Big Data technologies for real‐time occupancy detection in smart building. The purpose of this work focuses mainly on the presence of occupants by comparing both static and dynamic machine learning techniques. An open‐access occupancy detection dataset was first used to assess the usefulness of the platform and the effectiveness of static machine learning strategies for data processing. This dataset is used for applications that follow the strategy aiming at storing data first and processing it later. However, many smart buildings' applications, such as HVAC and ventilation control, require online data streams processing. Therefore, a distributed real‐time machine learning framework was integrated into the platform and tested to show its effectiveness for this kind of applications. Experiments have been conducted for ventilation systems in energy‐efficient building laboratory (EEBLab) and preliminary results show the effectiveness of this platform in detecting on‐the‐fly presence of occupants, which is required to either make ON or OFF the system and then activate the corresponding embedded control technique (eg, ON/OFF, PID, state‐feedback).

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Towards a context-driven platform using IoT and big data technologies for energy efficient buildings

Cited by 24 publications

References 0 publications

Drivers and Challenges Associated With the Implementation of Big Data Within U.K. Facilities Management Sector: An Exploratory Factor Analysis Approach

Drivers and Challenges Associated With the Implementation of Big Data Within U.K. Facilities Management Sector: An Exploratory Factor Analysis Approach

Cloud computing and big data: Technologies and applications

A platform architecture for occupancy detection using stream processing and machine learning approaches

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