IoT big data analytics for smart homes with fog and cloud computing

Big data and IoT-based applications in smart environments: A systematic review

Recently, big data streams have become ubiquitous due to the fact that a number of applications generate a huge amount of data at a great velocity. This made it difficult for existing data mining tools, technologies, methods, and techniques to be applied directly on big data streams due to the inherent dynamic characteristics of big data. In this paper, a systematic review of big data streams analysis which employed a rigorous and methodical approach to look at the trends of big data stream tools and technologies as well as methods and techniques employed in analysing big data streams. It provides a global view of big data stream tools and technologies and its comparisons. Three major databases, Scopus, ScienceDirect and EBSCO, which indexes journals and conferences that are promoted by entities such as IEEE, ACM, SpringerLink, and Elsevier were explored as data sources. Out of the initial 2295 papers that resulted from the first search string, 47 papers were found to be relevant to our research questions after implementing the inclusion and exclusion criteria. The study found that scalability, privacy and load balancing issues as well as empirical analysis of big data streams and technologies are still open for further research efforts. We also found that although, significant research efforts have been directed to real-time analysis of big data stream not much attention has been given to the preprocessing stage of big data streams. Only a few big data streaming tools and technologies can do all of the batch, streaming, and iterative jobs; there seems to be no big data tool and technology that offers all the key features required for now and standard benchmark dataset for big data streaming analytics has not been widely adopted. In conclusion, it was recommended that research efforts should be geared towards developing scalable frameworks and algorithms that will accommodate data stream computing mode, effective resource allocation strategy and parallelization issues to cope with the ever-growing size and complexity of data.

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Big data stream analysis: a systematic literature review

Past research has shown that occupancy information can be used to reduce building energy consumption through occupant-based controls and by mitigating wasteful occupant behavior. In this study, we investigate the dynamic relationship between WiFi connection counts (as a proxy to occupancy) and building electricity consumption across four building typologies (office, lab, health center, and library). Our findings based on one year of data show a strong positive linear correlation between electricity consumption and WiFi count across all four building when the building is in operation. The data exploration also indicates higher interactions between occupants with the plug and lighting loads in office and lab space types as compared to in a health center and a library. Next, using principal component analysis (PCA) for feature extraction followed by Density-based spatial clustering of applications with noise (DBSCAN), we show that distinct clusters could be generated, characterized by an increase in the between-cluster variance and smaller within-cluster variation. Lastly, we apply linear regression to manifest how the clustering results can be used to better model the variables.

Building occupancy and energy consumption: Case studies across building types

The emergence of radio frequency (RF) dependent device-free indoor occupancy detection has seen slow acceptance due to its high fragility. Experimentation shows that an RF-dependent occupancy detector initially performs well in the room to be sensed. However, once the physical arrangement of objects changes in the room, the performance of the classifier degrades significantly. To address this issue, we propose BLECS, a Bluetooth-dependent indoor occupancy detection system which can adapt itself in the dynamic environment. BLECS uses a reinforcement learning approach to predict the occupancy of an indoor environment and updates its decision policy by interacting with existing IoT devices and sensors in the room. We tested this system in five different rooms for 520 hours in total, involving four occupants. Results show that, BLECS achieves 21.4% performance improvement in a dynamic environment compared to the state-of-the-art supervised learning algorithm with an average F1 score of 86.52%. This system can also predict occupancy with a maximum 89.23% F1 score in a completely unknown environment with no initial trained model.

BLE Can See: A Reinforcement Learning Approach for RF-based Indoor Occupancy Detection

Towards a Real-time Occupancy Detection Approach for Smart Buildings

Context-awareness is crucial for leveraging energy-efficient buildings by developing intelligent control approaches in which sensing and actuation tasks are performed according to the contextual changes This could be done by including the users' actions and behaviours in up-to-date context taking into account the complex interlinked elements, situations, processes, and their dynamics In this paper, we introduce a holistic platform that integrates recent sensing/actuating and Big data technologies for monitoring and data processing The main aim is to develop context-driven control approaches whereby energy consumption, production, and storage could be controlled according to actual situations (eg, occupancy, occupant behaviour patterns, energy production patterns, and weather data) A platform prototype was deployed in our university test site Experiments have been conducted and preliminary results show the usefulness of this holistic platform for monitoring and data processing in energy efficient buildings

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

Autonomous monitoring of electricity consumption requires a metering platform for recording and processing electrical data Recent advances in IoT tools and Big data technologies allow now the development of holistic platforms for real-time data monitoring and processing In this paper, we shed further more light on the integration of these technologies into a holistic platform for real-time sensing, processing, and data visualization A platform prototype was developed and tested for detailed profiling of energy consumption in buildings It allows measuring and monitoring the electric power consumption of individual appliances A testbed was deployed in our university building and results are reported to show the usefulness of the proposed platform

A smart metering platform using big data and IoT technologies

In this work, a Hardware-In-the-Loop (HIL) framework is introduced for the implementation and the assessment of predictive control approaches in smart buildings. The framework combines recent Internet of Things (IoT) and big data platforms together with machine-learning algorithms and MATLAB-based Model Predictive Control (MPC) programs in order to enable HIL simulations. As a case study, the MPC algorithm was deployed for control of a standalone ventilation system (VS). The objective is to maintain the indoor Carbon Dioxide (CO2) concentration at the standard comfort range while enhancing energy efficiency in the building. The proposed framework has been tested and deployed in a real-case scenario of the EEBLab test site. The MPC controller has been implemented on MATLAB/Simulink and deployed in a Raspberry Pi (RPi) hardware. Contextual data are collected using the deployed IoT/big data platform and injected into the MPC and LSTM machine learning models. Occupants’ numbers were first forecasted and then sent to the MPC to predict the optimal ventilation flow rates. The performance of the MPC control over the HIL framework has been assessed and compared to an ON/OFF strategy. Results show the usefulness of the proposed approach and its effectiveness in reducing energy consumption by approximately 16%, while maintaining good indoor air quality.

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A. Berouine

Papers

Towards a Real-time Occupancy Detection Approach for Smart Buildings

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

A smart metering platform using big data and IoT technologies

Internet-of-Things Based Hardware-in-the-Loop Framework for Model-Predictive-Control of Smart Building Ventilation