used to help estimate and control power usage. To optimize this convenience, reduce expenses adapting to requirements of its residents, the SSRB requires sophisticated tools to understand, anticipate and make intelligent decisions. SSRB must also provide a variety of wearable sensor data linked to its patients and produce new remote sensors. SSRB algorithms include estimation, decision analysis, robots, smart devices, wireless sensor networks, interactive, web computing and cloud computing and include several other developments. Cognitive maintenance of offices is necessary in several SSRB programs for starters, fitness, safety, energy management, illumination, repair, the elderly and digital entertainment through these technologies.
1.2 Related Works
While several SB-focused survey papers have been released, none focuses on the role of data analysis and ML within SBs. All the relevant survey papers are comprehensively presented in Table 1.1.
Table 1.1 Report data of a survey.
| Cite | Purpose | Limitations |
| Chan et al. [12] | A country and continent arranged project SH Review as well as the associated technologies for monitoring systems and assistive robotics. | It not emphasized on the importance of ML and big data analytics, it does not review and classify the papers according to the applications of SH |
| Alam et al. [13] | Research objectives and services-based review of SH projects; namely, comfort, healthcare, and security. | It not emphasized on the importance of ML and big data analytics for SB. |
| Lobaccaro et al. [14] | Review of existing software, hardware, and communications control systems for S.H and smart grid. | It not emphasized on the importance of ML and big data analytics. It also does not focus on reviewing and categorizing papers according to the applications of SH. |
| Pan et al. [15] | The energy efficiency and the vision of microgrids topics research review in SBs. | The emphasis of the paper is not the ML and big data analytics for SB services. It does not consist of the other applications of SB rather than energy efficiency. |
| Ni et al. [16] | Propose a classification of activities considered in SH for older peoples independent living, they also classify sensors and data processing techniques in SH. | Does not cover all the services in SH. It also does not categorize the research according to different ML model styles. |
| Rashidi and Mi-hailidis [17] | Review AAL technologies, tools, and techniques. | The paper focuses only on AAL in healthcare, and does not cover the other applications in SH or SB; in addition, there is no classifying of the researches according to ML model styles. |
| Peetoom et al. [18] | The monitoring technologies that detect ADL or significant events in SH based review. | Does not focus on the role of ML in SB. |
| Salih and Abraham [19] | The ambient intelligence assisted healthcare monitoring focuses only on AAL in healthcare, and does not cover the other applications in SH or SB in the review. | The challenges and the future research directions in the field not covered in the research. |
| Perera et al. [20] | Discuss and analyzed the works in context awareness from an IoT perspective | Not emphasized specifically on the SB domain and its application services. |
| Tsai et al. [21] | Data mining technologies for IoT applications data reviewed. | SB applications not emphasized. |
| Mahdavinejad et al. [22] | Discussed and analyzed some ML methods applied to IoT data by studying smart cities as a use case scenario. | Not concentrated on SB and its applications as a use case. |
Chan et al. in 2008 [12] gave a description of intelligent home study. It even speaks about smart and friendly robotics. The article examines the nation and the continent’s smart home programs. Alam et al. [13] presented information on sensors, apps, algorithms and protocols of communication used in smart homes. The paper explores intelligent homes focused on their facilities and study aims: protection, fitness and comfort.
Lobaccaro et al. [14] shared the notion of a smart house but smart grid technology and address obstacles, advantages and potential developments of intelligent home technology. Pan et al. [15] analyzed the research of SBs with microgrids on efficient energy usage. The study explores subjects for analysis and latest developments in SBs and microgrid vision.
For multiple study articles research on making the autonomous lives of seniors for smart homes simpler has been checked. Ni et al. [16] have reported on sensing machine features including practices which can help elderly people reside peacefully in intelligent residences. Rashidi and Mihailidis provided a study on environmental assistance systems for elderly people [17]. Peetoom et al. [18] concentrated software tracking that understands householder existence, including reduced identification and changes of safety condition. Salih et al. [19] proposed a health-assisted urban knowledge report surveillance system identifying different methods included in current research literature, as well as connectivity and wireless sensor network technology.
1.3 Machine Learning
A brief list of the different algorithms for machine learning [49] in sustainable and resilient building is obtained below.
Decision Tree—Decision Tree is a supervised learning system used for classification or regression. A training model is built in Decision Tree Learning and the importance of the results is determined through the learning decision rules derived from the data attributes. In Big data there are many drawbacks to these decision tree algorithms. Firstly, if the data are very large, it is very time to build a decision tree. Secondly, there is no optimal solution to the distribution of data that contributes to higher communication cost.
Support Vector Machine (SVM)—Support Vector Machine is a supervised learning approach that can be used for either regression or classification. When used on big data, due to its high machine complexity, the SVM technique is not successful. The demand for measurement and storage is increased considerably for enormous amount of data.
K-Nearest Neighbor (KNN)—For regression and classification problems, K-Nearest Neighbor (KNN) algorithms are used. KNN approaches are using data and graded use similar steps to different data points. The information is reserved for the class with the closest neighbors. The value of k increases with the increase of the number of closest neighbors. KNN is not realistic on big data applications because of the high cost of calculation and memory.
Naive Bayes Classifier—For classification function Naive Bayes Classifier is commonly used. For any class or data point that belongs to a certain class, they define membership probabilities. The most probable class is the one with the highest likelihood. The efficiency of Naive Bayes is not possible in text classification tasks due to text redundant