Methods for predicting possible failures in smart home systems and analyzing the data required for this have been considered in the study. A study of machine learning methods has been carried out: their features, advantages, and disadvantages have been identified, the metrics of each method have been studied, and the effectiveness of methods for predicting failures in a smart home has been established. It has been found that the Long Short-Term Memory (LSTM) model is distinguished by its ability to work with data sequences and store information for a long time. The characteristics of the LSTM method and its algorithm have been studied in detail. The study emphasizes the importance of collecting and processing various data, such as sensor data, energy consumption, and information about devices and users. The results of the study can be useful for the further development of smart home control systems to improve their reliability and efficiency.
Smart houses are becoming increasingly
common thanks to the development of the
Internet of Things (IoT) and smart technologies
[
However, as the complexity of these systems
grows, the likelihood of failures or problems
increases. Network instability, software errors,
and faulty devices can all lead to unpredictable
situations that affect the usability and security
of a smart home [
Therefore, predicting failures in a smart home is a relevant issue, and it is appropriate to conduct such a prediction using machine learning methods. The use of machine learning algorithms allows for analyzing large amounts of data; and identifying deviations and patterns that precede failures. This approach allows predicting possible problems and taking measures to prevent them before they occur.
Today, smart home failure prediction is mostly based on reactive data analysis. This means that systems detect anomalous situations or failures after they occur, which can make it difficult to avoid potential problems.
However, using machine learning methods, such as classification, clustering, and prediction algorithms, it is possible to develop systems that can predict failures in a smart home in advance.
Such systems use data analysis from sensors,
IoT devices, control systems, energy
consumption, and other data to identify
patterns and anomalies that may precede
disruptions [
This area of research is still evolving, but it promises to improve smart home control systems by enabling them to predict and prevent possible failures in advance, providing greater reliability and security for users.
Machine learning techniques can help detect and avoid some disruptions before they occur or restore system operations faster after a failure. They can provide early detection of anomalies in performance, which will prevent problems from occurring or respond quickly to them, which in turn will help reduce the impact of these failures on the smart home.
Machine learning algorithms are compared on four different data representations: original, balanced, normalized, and standardized. The original data is unchanged from the selected data except for the removal of timestamp values. Balancing is performed by undersampling the faultless data in the training data.
Failure-free data inputs are randomly selected and removed from the dataset, resulting in the same number of failures and no failures.
Insufficient sampling can erase important information from the data, leading to poor algorithm performance. The main advantage of data balancing is that it reduces the resources and time required to train algorithms. Data normalization refers to the scaling of feature values in the range from zero to one. Scaling is performed using (1) separately for each feature by finding its minimum and maximum values.
The data is also standardized by considering
each feature separately. The feature values are
subtracted from the mean and then divided by
the standard deviation, as shown in (2). These
results in values centered around zero with unit
dispersion. An additional pre-processing step
that is performed before normalizing and
standardizing the data is the conversion of the
time features of the day of the week and the
hour. To indicate that the difference between
the hours 23 and 0 is the same as the difference
between 22 and 23, the values are converted to
cyclic representations using the Fourier
transform [
x − min max− min . xn = x −
2 x xsin = sin , N
2 x xcos = cos
N where х is the feature value, min is the minimum value of the feature, max is the maximum value of the feature, µ is the average value of the feature, σ is the standard deviation of a feature, and N is the total number of different values of the features.
Some of the algorithms may have problems with dimensionality and run much slower than other algorithms. The problem can be solved by reducing the number of dimensions using principal component analysis. The method is only applicable to specific algorithms and specific data representations, depending on the speed of learning and prediction. In addition, some of the algorithms work only with a certain input format.
Many machine learning algorithms can be
used to predict device failures. They can differ
in many properties and features [
The algorithms were selected based on several criteria: 1. Supervised learning: based on the assumption that the data to be used have been chosen. 2. Practical use: some of the algorithms are used more than others for predictive maintenance. 3. Diversity: algorithms were chosen to represent different families, tasks, and functions, such as online learning or prediction over time.
Based on these criteria, ten algorithms have
been selected, nine of which are implemented
as classification algorithms and one as a time
series regression algorithm (Tables 1 and 2)
[
Decision tree ensemble, where multiple trees are combined to avoid overlearning and improve accuracy [
An algorithm that determines the optimal boundary of separation between classes using support vectors
[
A classification method that uses a logistic function to determine the probability of an object belonging to a
certain class [
An optimization algorithm that uses a gradient to find the minimum of a loss function with a randomly
selected subset of data [
A neural network with one or more hidden layers is used for classification and regression based on
weighting coefficients [
A type of recurrent neural network designed to store and use information over a long period to predict
failures or events [
Accuracy represents the percentage of
correctly classified cases in the total number of
cases, which gives a general idea of the
algorithm’s accuracy [
Using Bayes’ theorem for probabilistic classification Determining the optimal boundary of separation between classes
Filtering anomalies, detecting failure patterns Classification of anomalies, forecasting failures Determining the probability of an Failure classification, object belonging to a certain class anomaly detection Stochastic Using a gradient to optimize the Gradient Descent loss function Model training, failure analysis Application area Detecting anomalies, predicting failures
Advantages Easy to implement, no training required Detecting anomalies, predicting failures Failure prediction, anomaly detection
High accuracy, and consistency of solutions High accuracy, less tendency to overlearn Efficiency for small data, simplicity of the model Efficiency in large spaces, flexibility Interpretability, ease of implementation Fast learning, efficient for big data
Disadvantages Sensitivity to emissions, high computational costs A large number of hyperparameters, training time A large number of hyperparameters, complexity of interpretation The predictions are not flexible enough Requirements for data preparation, high complexity of customization Requires a linear separation surface Requirements for hyperparameters, tendency to stutter Requires a lot of data for training, training time High computational costs, the complexity of setup Т = (TP + TN) / (FP + FN + TP + TN), (4) where TP is true positive (correctly categorized positive ones), TN is true negative (correctly categorized negative ones), FP is false positive (incorrectly categorized positive ones), and FN is false negative (incorrectly categorized negative ones).
The precision determines the percentage of correctly identified positive classes among all identified positive classes, which is useful for working with uneven classes.
Recall displays the percentage of correctly identified positive classes among all actual positive classes, which is important for identifying important cases that have been missed.
F1-average is a score that uses the harmonic mean between accuracy and completeness to understand how well the model solves the classification task.
1 = 2 +
ROC-AUC measures the area under the ROC curve and evaluates the model’s performance depending on different classification thresholds, helping to determine its ability to make correct predictions.
1 ROC − AUC = R(S) d(S) 0 (TP + FN ) . method are usually used, since the ROC-AUC formula itself is an integral.
The completeness (R) and frequency (S) are determined using a confusion matrix for binary classification.
Indicator S is calculated using formula (6), and the frequency is calculated using formula (9).
S =
FP FP + TN .
(9)
The Confusion Matrix provides detailed information about the real and predicted classes, which helps to estimate the level of correctness and errors for each class, which is important when analyzing the model.
The Confusion Matrix helps to evaluate the performance of a classification model by visualizing real and predicted values. It is the basis for calculating various metrics, such as accuracy, sensitivity, specificity, F1 score, and others.
These metrics are crucial for evaluating the
effectiveness of algorithms and choosing the one
that is most suitable for a particular task,
depending on the requirements and needs [
Table 3 shows the performance of different machine learning methods by the main metrics considered.
Table 3 and Fig. 1 show the performance of different machine learning methods in terms of the main metrics such as precision, accuracy, classification accuracy, completeness, F1-mean, and ROC-AUC. The score of “High,” “Medium”, or “Very High” in the “Performance” column is a generalized characterization of the methods’ performance based on these metrics.
The model itself consists of the following formulas: 1. Forgetting the previous memory state: 2. Defining what will be updated in memory:
C t = f t * C t- 1+ it * C t . 4. Update the output value: The study has found that the LSTM model is distinguished by its ability to work with data sequences and store information for a long time. This makes LSTM effective for analyzing time series, such as sensor data in a smart home, where information is usually sequential in time.
The LSTM model is capable of storing information for a long time, allowing it to effectively understand and analyze a sequence of real-time sensor data. By using mechanisms that ensure that some information is forgotten and others are retained, the LSTM can take into account long-term dependencies and the importance of individual events in time series. LSTM can adapt to and learn from different amounts of data, including large amounts of data from smart home sensors, which allows for more accurate predictions of failures. The LSTM model can adapt to changing conditions and detect changes in time series, which allows for predicting failures and anomalies in real-time. LSTM can process a variety of data types (text, numbers, sequences, etc.), making it versatile for use in various forecasting and analysis scenarios.
That is why it is not surprising that this algorithm showed the best results for predicting failures in a smart home.
The LSTM model has the following elements at each time step t: 1. Input data xt 2. Previous output ht−1 3. Previous memory state Ct−1 4. Gates: • Forget gate ft • Input gate it • Output gate ot.
ht = ot * tg (C t). where ft is the forget gate, which decides that the previous state should be forgotten, it is the input gate, which determines how much of the new input will be added to the memory state, ̃ is a new candidate for the memory state, Ct is memory status, ot is output gate, determines which output will be next, xt is input on a time step t, ht−1 is preliminary output on the time step t−1, W and а b is weights and displacements to be taught during the training process.
These equations allow the LSTM to determine what information to forget, what to keep, and how to use it to generate output values.
The step-by-step algorithm for training an LSTM model includes the following steps: 1. Data preparation: • Input: receive a set of data containing time series or sequences. • Data preparation: normalization, and conversion of data format to meet model requirements. 2. Building an LSTM architecture: • Creating a model: using machine learning libraries to build an LSTM network. • Defining parameters: number of layers, number of neurons in each layer, activation functions, etc. 3. Data separation: • Training and testing sets: splitting data into training and testing sets to evaluate model performance. 4. Model training: • Model training: fitting LSTM to training data using backpropagation. • Model evaluation: assessing whether the model’s predictions match the actual data on the test set. 5. Evaluation and improvement of the model: • Analyzing the results: reviewing the forecast results and assessing their accuracy. • Improving the model: use optimization techniques, change hyperparameters, or modify the architecture to improve results. 6. Testing and forecasting: • Failure prediction: applying a trained model to new data to predict failures in a smart home. 7. Evaluation of the results: • Performance evaluation: comparing forecasts with real data to determine the accuracy and efficiency of the model. 8. Maintaining and improving the model: • Continuous learning: collecting new data and improving the model based on it to keep forecasts up-to-date.
This is a general description of the LSTM learning algorithm for predicting failures in a smart home.
The approach presented in this study takes advantage of LSTM to predict time series. The LSTM is implemented using Keras (a high-level neural network interface that simplifies the process of creating and training artificial neural networks; it is a machine learning library that runs on the Tensorflow, Theano, and Microsoft Cognitive Toolkit frameworks) as a sequential model with two LSTM layers and a dense output layer. It receives a sequence of data inputs (normalized feature values without rejections) and outputs a sequence of failure values.
It was decided to use a single data input, as this significantly reduces the training time and is sufficient for the algorithm to recognize failure patterns. The length of the source sequence determines the duration of the runtime. Prediction performance is tested on three different input sequences: 1 (1 second), 300 (5 minutes), and 1800 (30 minutes). As a result, three different LSTM models were built. For training, we used a dataset with 70% failures, creating a split into training and test sets in the proportion of 25–75% without mixing. In addition, the data was prepared by creating output sequences for each data record, which were then used for training.
Based on the conducted research, a data prediction platform has been developed. Once the system has been successfully integrated into the smart home, the implementation process takes place, when the system becomes an active part of the home environment. However, this is only the beginning: further support, optimization, and continuous improvement of the system play a key role in ensuring its long-term and efficient operation in a smart home, adapting to changing needs and conditions (Fig. 2).
Energy efficiency
Automation
Security
Comfort management
Resource forecasting and optimization
Responding to changes System without machine learning Machine learning system
Machine learning helps to avoid certain problems by analyzing previous data and recognizing patterns, but it cannot predict absolutely all possible scenarios, especially if they arise from certain unpredictable factors or third-party interventions.
A smart home system that uses machine
learning methods proves to be better than a
system without this technology (as the study
results show, the performance of a smart home
using failure prediction methods gives an
average of 22% better result compared to a
similar system without prediction—Fig. 3).
Machine learning allows the system to adapt to
changes in the environment and user
requirements, respond more quickly to new
conditions, and optimize resource use. This
helps to improve the system’s efficiency in
managing energy, comfort, safety, and user
satisfaction [
Machine learning allows the system to
predict and avoid failures, which ensures
greater reliability and durability of the system.
This approach also allows for increased
automation, helping the system perform
routine tasks without user intervention [
The study results have shown a wide range of modern technologies, sensors, and control systems used in smart homes. The overview has shown that existing technologies have the potential to improve convenience, security, and energy efficiency.
The analysis of available machine learning methods indicates their potential in predicting and managing risks in smart homes. The considered models have shown high accuracy in predicting failures.
The following areas can be considered for further development of this work: • Improving machine learning methods to increase the accuracy of failure prediction. • In-depth study of the impact of the introduction of machine learning systems on the functioning of a smart home.
Based on the obtained data, it is possible to build a methodology for predicting failures in a smart home, which will be the direction of the authors’ next research.