**3. Corviale, a machine for living and adapting over time**

The Corviale district is one of the most famous and well-known symbols in the production of public cities in Rome and beyond. It is part of the first Peep and is designed for about 6800 inhabitants, with public facilities of 50 square metres per inhabitant, far above the legal minimum of 18 square metres. Of the 60 hectares of land, 36% is used for services and only 7% for buildings. This is the most striking feature of the Corviale, a single urban building almost a kilometre long and nine stores high, with a street of shops and public services, professional studios and community spaces running through its interior. It acts as a barrier at the edge of the built-up city and faces west, towards the sea and the Roman landscape, the Valle dei Casali. This creates a scenario in which nature, agricultural and urban activities merge, forming an entirely modern landscape with the Roman countryside. A neighbourhood characterised by an extreme duality between density and rarefied has come to be seen as a radical architectural exercise (**Figure 1**). The kilometre-long residential building is counterpointed by a smaller building arranged at a 45° angle to the main building and crossed by an internal street that serves the shops and ends in the facilities connecting the different buildings and the different functions, the school, the market, the multipurpose areas and the sports facilities. Looking at the concept of a residential machine, it is undoubtedly the Roman quarter that comes closer to this theory than the others. However, the solution chosen by the planners, led by Mario Fiorentino, aims to refer to the characteristics of the site and reinforce the landscape dimension of the urban. The definition of the boundary, a double boundary, that of the building, closed in on itself, and that of the city, the last bulwark built to the west in front of the landscape, form the most important architectural feature of the neighbourhood (**Figure 2**). The dimensions of the building remain one of the most important urban signs of the city of Rome. The dimensions of the building are even more pronounced as they cause the features of the landscape. Here, the echo of a duality between the building and the emptiness of the landscape reinforces the dimensions of the one urban system—the building. Given the vastness of the emptiness of the landscape and the horizon, the house-city aqueduct stands out as a symbol of redemption for the working class who are the least of these—a popular house with a unique aesthetic.

Throughout its life, the building has undergone a series of signs and adjustments that represent a continuous deviation from the planners' intentions; in some cases, the residents reject the sophisticated architectural solutions. The management of a building of this complexity was not in the hands of the regional authority that manages public housing in Rome and Lazio (Ater), and the housing machine immediately yielded to the adjustments and changes that created space between the rooms abandoned to neglect, especially the non-residential rooms used temporarily for residential

**Figure 1.** *Corviale. Credit: Julian Schubert.*

**Figure 2.** *Corviale. Credit: Laboratorio di Città Corviale.*

purposes. The failure to open public facilities was the leading cause of the conversion of the fourth floor into temporary accommodation. This phenomenon can also be observed in other public housing neighbourhoods in Rome, for example, in Lurentino


1 Analysis of influencing factors of hotel indoor environment

**H1.** *Hotel air quality environment will have a positive impact on customers' subjective evaluation of the hotel's indoor environment.*

**H2.** *Hotel thermal environment will have a positive impact on customers' subjective evaluation of the hotel's indoor environment.*

**H3.** *Hotel acoustic environment will have a positive impact on the subjective evaluation of the hotel's indoor environment by customers.*

**H4.** *Hotel light environment will have a positive impact on customers' subjective evaluation of the hotel's indoor environment.*

**H5.** *Hotel non-light visual factor environment will have a positive impact on customers' subjective evaluation of the hotel's indoor environment.*

 2 Hotel modern simple style room type and indoor environment influencing factors

Many scholars have found that modern simple style achieves harmony in visual effects with simple and smooth lines, black, white, gray, and log colors, and other non-light visual factors; in terms of light environment factors such as space lighting, it combines overall and local lighting to satisfy the owner. Many studies have confirmed that non-light visual factors and light environment have a greater impact on customers' subjective ratings of the hotel's modern simple style room type C [39,40]. Based on the above research results and questionnaire survey results, the following research hypotheses are proposed:

**H6a.** *The hotel's light environment will have a positive impact on customers' subjective evaluation of the hotel's modern simple style room type.*

**H6b.** *The non-light visual factors of the hotel will have a positive impact on customers' subjective evaluation of the hotel's modern simple style room type.*

 3 Hotel British pastoral-style room type and indoor environment influencing factors Many studies have confirmed the important influence of non-light visual factors on customers' subjective evaluation of British pastoral-style room type [41], but few studies have studied the effect of light factors and air quality on the creation of a quiet, natural, pleasant, and relaxed atmosphere in British pastoral-style room type [42]. Through the analysis of KH Coder text of online evaluation, this study finds that the two factors of light and air quality have a great influence on the hotel's British pastoral-style room type, and then put forward the following assumptions:

**H7a.** *The light environment of the hotel will have a positive impact on customers' subjective evaluation of the hotel's British pastoral-style room type.*

**H7b.** *The air quality environment of the hotel will have a positive impact on customers' subjective evaluation of the hotel's British pastoral-style room type.*

 4 Hotel modern Japanese-style room type and indoor environmental influencing factors Studies have shown that modern Japanese-style room type uses a variety of retro furniture such as tatami and fan-shaped windows as decorations to highlight elegance and simplicity and go deep into the realm of Zen [43,44]. Therefore, the adjustment of factors such as room temperature and wind speed included in the thermal environment may be of important significance and the Japanese-style house type pays special attention to the use of "light", most of them are mainly warm-toned lights. So, putting forward the following assumptions:

**H8a.** *The light environment of the hotel will have a positive impact on customers' subjective evaluation of the hotel's modern Japanese-style room type.*

**H8b.** *The thermal environment of the hotel will have a positive impact on customers' subjective evaluation of the hotel's modern Japanese-style room type.*

 5 Hotel indoor environment and customer subjective evaluation

Based on the analysis of five influencing factors in IEQ theory, hypotheses are put forward:

**H9.** *The indoor environment of the hotel will have a positive impact on the overall customer subjective evaluation of the hotel.*

### *3.2. Data Analysis Method*

The framework of this research is shown in Figure 6 below. First, the reliability, validity, and factor analysis of the questionnaire were carried out. After testing the feasibility of factor analysis of the questionnaire, a demographic descriptive analysis of customer reviews was carried out. Then, principal component analysis (PCA) was carried out to determine the influence of various factors of the hotel's indoor environment on the subjective evaluation of tourists [43,45]. The study further applied factor analysis to eliminate the redundancy of correlated variables to reduce the number of available composite data [46]. To reveal the significant influence, significant difference, and joint distribution of different decoration styles on the subjective evaluation of the hotel, the study used SPSS to carry out single factor ANOVA, independent sample *t*-test, and further analysis of the data using the custom table to obtain in-depth data analysis conclusions and verify the hypothesis.

**Figure 6.** SPSS analysis frame diagram.

#### *3.3. Evaluation of the Reliability and Validity of the Scale*

#### (1) Reliability test

This study mainly used SPSS 25.0 to test the reliability of the scale. The larger the Cronbach's Alpha value, the greater the reliability of the scale. The coefficient should not be less than 0.6, and the questionnaire should be readjusted if it is below 0.6. The test results showed that the coefficient of the actual satisfaction scale was 0.962, and the coefficient of each sub-scale must be greater than 0.8, and most of them were greater than 0.9; then according to the criterion that the overall correlation coefficient (CITC) of the project proposed by the scholar Churchill should not be less than 0.5, the model was trusted. The results showed that the CITC values were all above 0.7, which meant that the scale of this research had a high degree of internal consistency and a small error value in the survey of hotel user groups, that is, the reliability of the questionnaire was high, and the reliability was strong.

(2) Validity test

The KMO test and Bartlett's test of sphericity were used to analyze whether factor analysis was possible. The results of the KMO test showed that the value of KMO was 0.958. At the same time, the results of the Bartlett sphericity test showed that the significant *p* value was 0.000 \*\*\*, infinitely close to 0, and the level was significant, rejecting the null hypothesis, indicating that this questionnaire had validity, and there was the correlation between variables, which was suitable for factor analysis. The factor analysis results showed that there was at least one value greater than 0.4 in the loading values of all the variables in the row, so the variables were reasonable and did not need to be deleted.

(3) Test the convergent validity of the model

Confirmatory factor analysis is performed on the convergent validity of the measurement model. Further evaluation of the structural model can only be performed if the fit of the measurement model meets acceptable standards. The test results showed that the standardized factor loadings of all dimensions were above 0.7 and were significant; the compositional reliability CR values were all higher than 0.8, and the average variance extraction (AVE value) was greater than 0.5. Therefore, the model had good convergent validity.

#### *3.4. Demographic Description of the Sample*

First, study participants were screened according to the inclusion criteria; second, questionnaires were collected through screening questions, 856 questionnaires were initially collected, and after excluding outliers, a total of 710 valid questionnaires were used for data analysis. Among them, the majority of respondents were women, accounting for 62%; the age group accounted for the largest proportion of the 18–29-year-old group (93%) because the way such as giving gifts was more attractive to young women; most respondents had a bachelor's degree (88%). For profession, middle school students (84%) accounted for the majority; 77% of all valid participants had a monthly income of less than 3000; this study was mainly based on college students. To be realistic, there are no barriers to the survey respondents, so there are some non-college student groups of respondents. The specific results can be seen in the following Figure 7:

Additional relevant demographic information (Figure 8) was measured to further understand respondents. Respondents mostly chose the modern simple style (60.6%), followed by the British pastoral style (22.7%) and the modern Japanese style (16.7%); most respondents traveled with friends (40.8%); leisure travel was the reason why most respondents stayed in hotels (79.3%).

**Figure 8.** Chart of other population data indicators.


**About the Editors**

**Carlos Tavares Calafate** (Full Professor) is a member of the Department of Computer Engineering at the Technical University of Valencia (UPV) in Spain. He graduated with honors in Electrical and Computer Engineering at the University of Oporto (Portugal) in 2001. He completed his PhD in Informatics at the Technical University of Valencia in 2006, where he has worked since 2002. His research interests include ad hoc and vehicular networks, UAVs, smart cities and IoT, QoS, network protocols, video streaming, and network security. To date, he has published more than 400 articles, several of which have been journals, including IEEE Transactions on Vehicular Technology, IEEE Transactions on Mobile Computing, IEEE/ACM Transactions on Networking, Elsevier Ad Hoc Networks and IEEE Communications Magazine. He is an associate editor for several international journals, and has participated in the TPC of more than 250 international conferences. He is ranked among the top 100 Spanish researchers in the Computer Science and Electronics field. He is also a founding member of the IEEE SIG on Big Data with Computational Intelligence.

**Mauro Tropea** (Post Doc Researcher) works in the DIMES Department at the University of Calabria in Italy. He received his master's degree in April 2003 and PhD in January 2009, both in computer science engineering, at the University of Calabria, Cosenza, Italy. From May 2008 to October 2008, he was a visiting researcher at the Telecommunication Department of ESA ESTEC Noordwijk, The Netherlands. Since 2003, he has been with the telecommunications group of the DIMES Department, and, since September 2018, he has been a postdoctoral researcher there. His research interests include satellite communications, QoS architectures, bio-inspired algorithms, VANETs, FANETs, hierarchical networks, and multicasting.


**5.3 Data clustering**

The k-means clustering is a method of vector quantization that aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean based on a similarity metric [20].

Let *S* ¼ *f* 1, *f* 2, *::* … *f n* and *K* ¼ *C*1,*C*2, *:* … *Cn* then *Ci* 6¼ *ϕ*, *Ci* ∩*Cj* ¼ *ϕ*, and ∪*K i*¼1*Ci* ¼ *S*, where *i*, *j* ¼ 1, 2, … *K*, and *i* 6¼ *j*.

The clustering method works as follows:

Randomly initialize K:

When deploying k-mean to cluster IoT traffic, we reduce the dimension data to quantize the data into single-dimensional data. We classify the traffic flow depending on similar characteristics and identify clusters of homogeneous traffic flows and define their borders. It needs to have high intra-cluster homogeneity and inter-cluster heterogeneity.

For determining the optimal (fitting) value of K, we use the elbow algorithm repeatedly applying different values of K and plotting their heterogeneity. When the curve begins to flatten, it reaches the optimal value of K.

#### **5.4 Dimensionality reduction**

After clustering the data, initially, we have n states ð Þ *S*1, *S*2, … , *Sn* , applying the dimension reduction technique results in a new set of m states ð Þ *s*1, *s*2, … , *sm* where ð Þ *m* <*n* , *si* ¼ *fi* ð Þ *S*1, *S*2, … , *Sn* and *fi* represents a mapping function. Thus, its idea is to transform the massive data from a high-dimensional space, like IoT data, into a k-dimensional sub-space by partitioning the data-space into fully connected states. The low-dimensional form holds the top eigenvector v which has the meaningful features of the real data ideally close to its natural dimension [22]. The new set of features is extracted by some functional mapping. In this model, we considered the principal component analysis (PCA) and calculated it using the singular value decomposition (SVD) as the PCA presents a structure for reducing data dimensionality by outlining the maximum variation in the data.

To achieve dimension reduction by applying PCA, it requires placing the eigenvalues from the highest to lowest by their value. This ordering stores the elements in order of weight to the variance of the initial data matrix. This will allow us to drop the less important elements. Thus, we keep most of the information and lose a little noise. We can reduce the dimension of the original data. For instance, for any data of d dimensions, we only take the first r eigenvectors:

$$\frac{\sum a\_r}{\sum a\_d} = \frac{a\_1 + a\_2 + \dots + a\_r}{a\_1 + a\_2 + \dots + a\_d} \tag{1}$$

$$a = a\_1, a\_2, \dots, a\_r \tag{2}$$

**Definition 1** [23]: For any matrix Y ¼ *y*1, *y*2, … , *yn* of the size *K* � *d* can be re-written as *Y* ¼ *USVT* where, U is an orthonormal matrix of size *K* � *r*, S is a diagonal matrix of size *r* � *r*, V is a matrix of eigenvectors of size *r* � *d* (a column is an eigenvector) (see **Figure 2**).

*An Effective Method for Secure Data Delivery in IoT DOI: http://dx.doi.org/10.5772/intechopen.104663*

**Figure 2.** *Singular value decomposition of Y [23].*

Assume that the data matrix Y is centered, i.e., the column means have been subtracted to be equal to zero. The covariance matrix (C) is calculated by:

$$C = \frac{\mathbf{X}^T \mathbf{X}}{K - 1} \tag{3}$$

Because the covariance matrix is symmetric, it can be diagonalized by:

$$\mathbf{C} = \mathbf{V} \mathbf{L} \mathbf{V}^T \tag{4}$$

where V is an eigenvectors matrix and L is a diagonal matrix with eigenvalues *λi*. The eigenvectors are called principal axes of the data, and, the data projections on the principal axes are called principal components [24]. After obtaining the singular value decomposition, C is defined by:

$$\mathbf{C} = \frac{\mathbf{V} \mathbf{S} \mathbf{U}^T \mathbf{U} \mathbf{S} \mathbf{V}^T}{K - 1} \tag{5}$$

$$\mathbf{H} = \mathbf{V} \frac{\mathbf{S}^2}{K - \mathbf{1}} \mathbf{V}^T \tag{6}$$

As the result, the eigenvectors of C are the same as the matrix V (the right singular vectors of Y) and the eigenvalues of C can be defined from the singular values *λi*.

$$
\lambda\_i = \frac{s\_i^2}{(K-1)} \lambda\_i \tag{7}
$$

The principal components are defined by:

$$\mathbf{V}\mathbf{V} = \mathbf{U}\mathbf{S}\mathbf{V}^T\mathbf{V} = \mathbf{U}\mathbf{S} \tag{8}$$

In short, the PCA is calculated as follows:

Based on Oja's algorithm for stochastic PCA optimization [25], the primary concept of our algorithm is to implement stochastic m updates by uniformly sampling the columns *yi* at random, and reduce the variance of these updates.

$$X\_t' = X\_{t-1} + \eta y\_{i\_\theta} y\_{i\_t}^T X\_{t-1} \tag{9}$$

$$X\_t = \frac{1}{||X\_t'||} X\_t \tag{10}$$

We use the variance-reduced stochastic schemes for convex optimization [23] to reduce the stochastic variance. Let *B* ¼ 1 *nXXT*; then the updates in each iteration can be rewritten of our algorithm as

$$X' = (I + \eta \mathcal{B}) X\_{t-1} + \eta \left( \mathcal{Y}\_i \mathcal{Y}\_{i\_t}^T - \mathcal{B} \right) \left( X\_{t-1} - \overline{X}\_{f-1} \right) \tag{11}$$

$$X\_t = \frac{1}{||X\_t'||} X\_t \tag{12}$$

The algorithm is burst into periods f = 1, 2, 3,. .., wherein all period we do a single exact power iteration by computing *U*. The steps to solve the problem are explained by the pseudo-code in Algorithm 1.

**Algorithm 1**: Dimensionality Reduction Algorithm.

$$\begin{array}{ll}\textbf{Input Matrix } Y = (\boldsymbol{\nu}\_{1}, \ldots, \boldsymbol{\nu}\_{n}). \\\textbf{Output Matrix } \overline{X}\_{f}. \\\textbf{1: }\textbf{Initialize } \text{Orthonormal Matrix } \overline{X}\_{k \times d}. \\\textbf{2: }\textbf{For } f = 1, 2, 3 \ldots K \quad \textbf{Do} \\\textbf{3: }\overline{X}\_{i} = \frac{1}{n} \sum\_{j} \boldsymbol{\nu}\_{i} (\boldsymbol{y}\_{i}^{T} \overline{X}\_{f-1}) \\\textbf{4: }\overline{X}\_{0} = \overline{X}\_{f-1} \\\textbf{5: }\textbf{For } t = 1, 2, 3, \ldots m \quad \textbf{Do} \\\textbf{6: }B\_{t-1} = U U^{T}, \text{ where} \\\textbf{U} \boldsymbol{Y}^{T} \text{ is an decomposition of } X\_{t-1}^{T} \overline{X}\_{f-1} \\\textbf{7: }\textbf{Set }\boldsymbol{t}\_{i} \in \textbf{1, 2, 3, \ldots n} \text{ uniformly at random} \\\textbf{8: }\boldsymbol{X}\_{t}^{\prime} = \boldsymbol{X}\_{t-1} + \eta \left( \boldsymbol{y}\_{i} \left( \boldsymbol{y}\_{i}^{T} \boldsymbol{X}\_{t-1} - \boldsymbol{y}\_{i}^{T} \overline{X}\_{f-1} \boldsymbol{B}\_{t-1} \right) + \overline{\boldsymbol{\mathcal{U}}} \boldsymbol{B}\_{t-1} \right) \\\textbf{9: }\boldsymbol{X}\_{t} = \boldsymbol{X}\_{t}^{\prime} \left( \boldsymbol{X}^{T} \boldsymbol{\boldsymbol{t}}\_{i}^{\prime} \right)^{1/2} \\\textbf{10: }\textbf{is is to ensure that } \boldsymbol{W}\_{t} \text{ has orthonormal columns} \\\textbf{11} \boldsymbol{\overline{X}\_{f} = \boldsymbol{X}\_{m} \\\textbf{12: end for} \end{array}$$