Machine learning for smart building applications: Review and taxonomy

© 2019 Association for Computing Machinery. The use of machine learning (ML) in smart building applications is reviewed in this article. We split existing solutions into two main classes: occupant-centric versus energy/devices-centric. The first class groups solutions that use ML for aspects related to the occupants, including (1) occupancy estimation and identification, (2) activity recognition, and (3) estimating preferences and behavior. The second class groups solutions that use ML to estimate aspects related either to energy or devices. They are divided into three categories: (1) energy profiling and demand estimation, (2) appliances profiling and fault detection, and (3) inference on sensors. Solutions in each category are presented, discussed, and compared; open perspectives and research trends are discussed as well. Compared to related state-of-the-art survey papers, the contribution herein is to provide a comprehensive and holistic review from the ML perspectives rather than architectural and technical aspects of existing building management systems. This is by considering all types of ML tools, buildings, and several categories of applications, and by structuring the taxonomy accordingly. The article ends with a summary discussion of the presented works, with focus on lessons learned, challenges, open and future directions of research in this field

Context-awareness for mobile sensing: a survey and future directions

The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions

Detailed Case Studies

Wireless body area networks (WBANs) are one of the key technologies that support the development of pervasive health monitoring (remote patient monitoring systems), which has attracted more attention in recent years. These WBAN applications requires stringent security requirements as they are concerned with human lives. In the recent scenario of the corona pandemic, where most of the healthcare providers are giving online services for treatment, DDoS attacks become the major threats over the internet. This chapter particularly focusses on detection of DDoS attack using machine learning algorithms over the healthcare environment. In the process of attack detection, the dataset is preprocessed. After preprocessing the dataset, the cleaned dataset is given to the popular classification algorithms in the area of machine learning namely, AdaBoost, J48, k-NN, JRip, Random Committee and Random Forest classifiers. Those algorithms are evaluated independently and the results are recorded. Results concluded that J48 outperform with accuracy of 99.98% with CICIDS dataset and random forest outperform with accuracy of 99.917, but it takes the longest model building time. Depending on the evaluation performance the appropriate classifier is selected for further DDoS detection at real-time

Occupant-Centric Simulation-Aided Building Design Theory, Application, and Case Studies

This book promotes occupants as a focal point for the design process

Subzone control method of stratum ventilation for thermal comfort improvement

The conventional control method of a collective ventilation (e.g., stratum ventilation) controls the averaged thermal environment in the occupied zone to satisfy the averaged thermal preference of a group of occupants. However, the averaged thermal environment in the occupied zone is not the same as the microclimates of the occupants, because the thermal environment in the occupied zone is not absolutely uniform. Moreover, the averaged thermal preference of the occupants could deviate from the individual thermal preferences, because the occupants could have different individual thermal preferences. This study proposes a subzone control method for stratum ventilation to improve thermal comfort. The proposed method divides the occupied zone into subzones, and controls the microclimates of the subzones to satisfy the thermal preferences of the respective subzones. Experiments in a stratum-ventilated classroom are conducted to model and validate the Predicted Mean Votes (PMVs) of the subzones, with a mean absolute error between 0.05 scale and 0.14 scale. Using the PMV models, the supply air parameters are optimized to minimize the deviation between the PMVs of the subzones and the respective thermal preferences. Case studies show that the proposed method can fulfill the thermal constraints of all subzones for thermal comfort, while the conventional method fails. The proposed method further improves thermal comfort by reducing the deviation of the achieved PMVs of subzones from the preferred ones by 17.6%–41.5% as compared with the conventional method. The proposed method is also promising for other collective ventilations (e.g., mixing ventilation and displacement ventilation)

Comparison of two approaches for web-based 3D visualization of smart building sensor data

Abstract. This thesis presents a comparative study on two different approaches for visualizing sensor data collected from smart buildings on the web using 3D virtual environments. The sensor data is provided by sensors that are deployed in real buildings to measure several environmental parameters including temperature, humidity, air quality and air pressure. The first approach uses the three.js WebGL framework to create the 3D model of a smart apartment where sensor data is illustrated with point and wall visualizations. Point visualizations show sensor values at the real locations of the sensors using text, icons or a mixture of the two. Wall visualizations display sensor values inside panels placed on the interior walls of the apartment. The second approach uses the Unity game engine to create the 3D model of a 4-floored hospice where sensor data is illustrated with aforementioned point visualizations and floor visualizations, where the sensor values are shown on the floor around the location of the sensors in form of color or other effects. The two approaches are compared with respect to their technical performance in terms of rendering speed, model size and request size, and with respect to the relative advantages and disadvantages of the two development environments as experienced in this thesis