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.

/pdf/big-data-stream-analysis-a-systematic-literature-review-39e2p1a0n6.pdf

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

Micro-grid systems have been recently emerged for efficient integration and management of renewable energy sources, buildings’ equipment (e.g. ventilation; lighting; heating, ventilation, and air c...

https://journals.sagepub.com/doi/pdf/10.1177/0959651819856251

An energy management platform for micro-grid systems using Internet of Things and Big-data technologies:

Recent advances in pervasive technologies, such as wireless, ad hoc networks, and wearable sensor devices, allow the connection of everyday things to the Internet, commonly denoted as the Internet of Things (IoT). The IoT is seen as an enabler to the development of intelligent and context-aware services and applications. However, handling dynamic and frequent context changes is a difficult task without a real-time event/data acquisition and processing platform. Big data technologies and data analytics have been recently proposed for timely analyzing information (i.e., data, events) streams. The main aim is to make users' life more comfortable according to their locations, current requirements, and ongoing activities. In this article, combining IoT techniques and Big data technologies into a holistic platform for continuous and real-time health-care data monitoring and processing is introduced. Real-testing experiments have been conducted and results are reported to show the usefulness of this platform in a real-case scenario.

D. Elouadghiri

Papers

Towards a Real-time Occupancy Detection Approach for Smart Buildings