Multi-Criteria Decision Analysis of Wireless Technologies in WPANs for IoT-Enabled Smart Buildings in Tourism

Abstract

The increasing demand for energy-efficient and interconnected smart buildings, particularly in the tourism sector, has driven the adoption of advanced wireless technologies. IoT technologies are crucial in this evolution, improving modern buildings’ functionality and operational efficiency. This study investigates the utilization of various wireless technologies within Wireless Personal Area Networks (WPANs), including Bluetooth BLE 4.2, Bluetooth BLE 5.0, ZigBee, and Z-Wave, in smart buildings. A multiple-criteria decision-making (MCDM) approach, specifically the PIPRECIA-S model, was applied to evaluate these technologies based on criteria such as device connectivity, mobility, low energy consumption, scalability, flexibility, and interoperability. Simulations using the PIPRECIA-S model were conducted to assess technology performance across various real-world scenarios. The results indicate that ZigBee (0.2942) and Bluetooth BLE 5.0 (0.2602) provide superior performance in terms of energy efficiency and scalability, followed by Z-Wave (0.2550) and Bluetooth BLE 4.2 (0.1906). These findings provide decision-makers with data-driven recommendations for selecting the most suitable wireless technologies for smart buildings.

1. Introduction

The development of the Internet of Things (IoT) technology is revolutionizing various industries [1]. For the advancement of the tourism industry, the development of smart buildings is of great importance [2]. In tourism, smart buildings represent advanced infrastructural facilities [3] equipped with modern technologies that enable efficient resource management, enhanced security, an energy-sustainable environment, and increased guest comfort [4,5,6]. Through the integration of IoT solutions, smart buildings in hotels, resorts, and tourist complexes can automate many functions [7], from energy management to guest service personalization, significantly enhancing the stay experience with minimal human intervention [8]. In the tourism industry, where comfort and safety are key to guest satisfaction, the application of smart buildings is becoming increasingly important [9]. Wireless technologies enable the connection of various devices and systems within these buildings, such as climate control sensors, surveillance systems, access control devices, and smart appliances in rooms [10]. This connectivity not only increases efficiency in facility management but also provides personalized services that can enhance the overall guest experience.

Smart buildings in the tourism sector pose a challenge regarding the selection of appropriate wireless technologies that can meet the specific needs of this industry [11]. For example, the requirement for high connectivity of devices, which enable guests to seamlessly utilize technology throughout their stay, or mobility, which facilitates personalized services regardless of location within the facility. Low energy consumption is crucial for maintaining energy-efficient buildings [12], while scalability allows for the easy expansion of systems with additional devices as the needs of the property grow [13,14]. Flexibility supports the application of these technologies across various tourism environments [15], and interoperability ensures the seamless functioning of diverse devices and systems [16].

Despite advances in IoT and smart building technologies, there is a lack of research that explicitly addresses the decision-making challenges related to selecting the most suitable wireless technology in tourism-focused smart buildings. While some studies have explored specific technologies, such as Bluetooth or ZigBee, there remains a gap in evaluating these technologies holistically based on multiple criteria relevant to the tourism industry. This gap in the literature highlights the need for a comprehensive framework to assist decision-makers in making informed technology choices.

Decision-making theories offer a robust framework for structured decision-making when selecting optimal technologies in complex systems, such as smart buildings in tourism [17]. The decision-making process often requires the consideration of numerous criteria, including technological, economic, ethical, political, legal, and social factors. Thus, simple, systematic, and logical methods are essential for identifying and choosing the most suitable option that meets real-world requirements [18]. Operations research, a scientific discipline developed to assist in decision-making, aims to find the best solution (referred to as the optimal solution) to the problem under consideration. The goal is not merely to improve the current state but to identify the best possible course of action [19].

The PIPRECIA-S model (Pivot Pairwise Relative Criteria Importance Assessment with Scale) emerges as an effective approach for accurately determining the importance of various criteria [20]. In this context, the model facilitates a detailed analysis and comparison of wireless technologies to select those that best meet the specific requirements of smart buildings in the tourism industry, considering connectivity, mobility, energy consumption, scalability, flexibility, and interoperability.

This study aims to fill the aforementioned knowledge gap by applying the PIPRECIA-S model to assess and rank wireless technologies within Wireless Personal Area Networks (WPANs) for IoT-enabled smart buildings in tourism. By addressing the key criteria relevant to the tourism sector, the research contributes to the growing body of knowledge by offering a structured decision-making framework that guides the optimal implementation of IoT systems in tourism-focused smart buildings.

The objective of this study is to evaluate and select the most suitable wireless technology within Wireless Personal Area Networks (WPANs) for IoT integration in smart buildings in the tourism sector by applying decision-making theories, with a particular focus on the PIPRECIA-S model. Through a thorough analysis of the defined criteria, the paper provides guidelines for the optimal implementation of IoT systems in smart buildings, which in turn enhances the functioning, efficiency, and long-term sustainability of tourism facilities.

2. Application in Tourism

Smart buildings in the tourism industry represent the pinnacle of modern technology, enabling enhanced hospitality, increased energy efficiency, and the optimization of operational processes in tourist facilities such as hotels, resorts, and conference centers [21]. The specificity of smart buildings in this sector lies in their ability to combine guest comfort and safety with operational efficiency and sustainability for facility managers [22].

2.1. Specificities of Smart Buildings in Tourism

Smart buildings in tourism utilize technologies for the automation and personalization of services to meet the needs of each guest [23]. These buildings are equipped with various sensors and devices that allow for the control of climate, lighting, security systems, and entertainment systems within rooms. Through IoT technology, each guest can tailor their stay [24], such as adjusting the room temperature or light intensity, using smart devices or mobile applications [25].

One of the main advantages of smart buildings in tourism is the ability to remotely manage and monitor all systems within the facility. This capability allows operators to optimize energy consumption, customize services according to guest expectations, and respond quickly to any issues [26]. For example, presence sensors can automatically turn off air conditioning or lights when a guest leaves the room, significantly reducing unnecessary energy consumption.

2.2. Technologies Used in Smart Buildings

The characteristics of smart buildings are based on the following criteria [27]:

• High-Quality Building Features: These include reduced energy demand, increased use of local renewable energy sources, and a healthy and comfortable indoor environment for occupants.
• Dynamic Operability: This allows users to control energy usage while optimizing comfort, indoor air quality, and operational efficiency.
• Sustainability of Energy Composition: This involves optimizing the functioning of interconnected energy systems and external infrastructure.

To achieve these goals, some key technologies include the following:

• Wireless Networks (Wi-Fi, ZigBee, Bluetooth) [28]: Wireless networks enable the interconnection of all IoT devices within a building, facilitating seamless communication between sensors, building management systems, and applications used by guests and staff.
• Sensors and Actuators [29]: Sensors collect data on indoor conditions such as temperature, humidity, lighting, and occupancy, while actuators enable automatic responses to this data, such as adjusting heating, ventilation, and air conditioning (HVAC) systems.
• Building Energy Management Systems (BEMSs) [30]: These systems optimize energy consumption by monitoring and controlling all energy users in the building, thereby reducing costs and increasing energy efficiency.
• Access Control and Security Systems [31]: Technologies such as RFID cards, biometrics, and surveillance cameras ensure secure access for guests and staff while improving overall building security. Smart locks managed via mobile applications provide a higher level of service to guests and improve management performance in accommodation facilities.
• Cloud Computing and Big Data [32]: The processing of large volumes of data collected from various systems within the building enables detailed analyses that can improve operational processes, personalize guest experiences, and predict maintenance needs.

2.3. The Importance of Smart Buildings in Tourism

In the tourism industry, where market competition and guest expectations are exceptionally high, smart buildings can serve as a key differentiating factor [33]. The personalization of services achieved through smart technologies enhances guest satisfaction, which can directly influence their loyalty and likelihood of returning [34]. Additionally, reducing operational costs through the optimization of resources such as energy and water significantly contributes to the profitability of tourism establishments [35,36].

Smart buildings also enable tourism facilities to adapt to changing market needs. The flexibility and scalability of the technologies used in these buildings allow for the easy introduction of new functionalities, such as digital key systems or advanced AI-based services, further enhancing their attractiveness and functionality [37].

Today, smart buildings represent the future of the tourism industry, enabling facilities to offer a superior guest experience while reducing costs and increasing efficiency [38]. Smart buildings are capable of executing advanced decision-making algorithms and utilizing existing data recorded by their monitoring systems to predict potential upcoming occurrences related to the building and its systems. These buildings operate under optimal conditions to efficiently and economically meet the needs of the building and its systems [39].

It is important to note that technological advancements in buildings cannot compensate for deficiencies resulting from poor decisions in urban planning or building design. The quality of urban spaces, buildings, or structures is the outcome of the quality of the plan or design—for example, in the case of buildings, this includes optimal orientation, location, and organization of primary functions, as well as the selection of construction materials [27]. Therefore, it is essential to consider all criteria (from the initial concept to the final construction) that influence the quality of the project and align with market needs.

3. Previous Research

3.1. Review of Decision-Making Theories in the Context of Smart Buildings

Engineering problems, particularly those related to building design, have become increasingly complex. This intricate process demands the integration and collaboration of engineers from various disciplines, architects, owners, equipment manufacturers, and others. Each participant possesses specialized knowledge and is therefore responsible for their respective portion of the design to meet all industry-required goals and criteria. Notably, some of these goals may conflict, such as energy savings versus increasing user comfort levels. Meanwhile, some objectives are more critical than others, like an architect’s desire to achieve a greater impact through the building’s form compared to a mechanical engineer’s need for more space above the ceiling for equipment. A lack of a clearly understood strategy from the outset among the key stakeholders in this integrated process can easily lead to an outcome that not only fails to meet all participants’ expectations but also almost guarantees the expenditure of money and resources on objectives that may not be the most crucial for the team [39].

Existing decision-making models still often overlook critical aspects, such as the need for dynamic adaptability in response to changing technological advancements or evolving user requirements. These limitations highlight the importance of developing more flexible models capable of addressing both current and future challenges.

Decision-making theories play a crucial role in engineering and technical disciplines [40], particularly in the context of decision-making within complex systems such as smart buildings. Various decision-making models, such as the Analytic Hierarchy Process (AHP), Analytic Network Process (ANP), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and multicriteria compromise ranking (VIKOR), have been widely applied for technology selection and the evaluation of various aspects within smart buildings.

These models facilitate the evaluation of multiple criteria, aiding in the identification of optimal solutions in scenarios where multiple competing alternatives exist. However, a common shortcoming of these models is their reliance on static criteria, which may not fully reflect the dynamic nature of smart building environments. For instance, in studies addressing the energy efficiency of smart buildings, decision-making models are utilized for the selection of optimal HVAC systems [41] and insulation materials [42], as well as for the choice of internet platforms [43]. Additionally, models such as AHP and TOPSIS are frequently employed for the assessment of security and safety systems [44] in smart buildings. Yet, these models do not always account for real-time data integration or the evolving demands of smart infrastructure, which represents a significant gap in existing research.

3.2. Application of the PIPRECIA-S Model in Technology Evaluation

The PIPRECIA-S is a relatively new decision-making model that has proven to be an effective tool for evaluating and ranking alternatives based on the relative importance of criteria. The advantage of PIPRECIA-S lies in its simplicity of use and ease of understanding [45]. This model provides a straightforward yet effective method for assessing and prioritizing criteria within the context of multi-criteria decision-making [46]. The weighting of criteria is crucial for evaluating and prioritizing the criteria used in decision-making processes [47]. This method involves pairwise comparisons of criteria to determine the degree of importance of one criterion relative to others. Its application has been recognized in various industries, including construction, manufacturing, and the energy sector, as well as in the field of information technology [48].

However, a limitation of the PIPRECIA-S model is its dependence on expert judgment, which may introduce bias into the decision-making process. Additionally, the application of the PIPRECIA-S model in the context of smart buildings and wireless technologies for IoT integration is still in its early stages, indicating the need for further research.

So far, the PIPRECIA model has been used for assessing various criteria in the context of selecting optimal solutions for the choice of electronic learning materials [49], for research in sustainable development [50], decision-making in the IT industry [51], as well as in studies focused on the evaluation of cognitive skills [52]. Additionally, the PIPRECIA-S method has been applied to the selection of vehicle tracking systems [53], as well as in the application of information technologies in storage systems [54].

While both AHP and TOPSIS are well-established models used in decision-making, the PIPRECIA-S model offers several distinct advantages. Unlike AHP, which requires complex hierarchical structuring and consistency checks, PIPRECIA-S simplifies the evaluation process by using pairwise comparisons that are easier to perform and interpret. Moreover, PIPRECIA-S allows for more flexibility in assigning weights to criteria based on real-world importance, whereas TOPSIS relies on a more rigid ideal solution approach. This flexibility is particularly valuable in dynamic environments like smart buildings, where the relative importance of criteria such as energy efficiency, scalability, and security may shift depending on technological advancements or user needs. Furthermore, PIPRECIA-S is less computationally intensive compared to these traditional models, making it more accessible for practical applications in engineering.

3.3. Application of Decision-Making Theories in the Selection of Wireless Technologies for Smart Buildings

Wireless technologies play a central role in the functioning of smart buildings, enabling connectivity between various IoT devices and systems [55]. Previous research has demonstrated that the selection of appropriate wireless technology is crucial for achieving optimal functionality and efficiency in smart buildings [2].

Decision-making theories are widely applied in the process of evaluating and selecting wireless technologies for smart buildings [56]. For example, AHP and ANP models have been used to assess IoT communication technologies [57] based on criteria such as communication, technology, privacy and security, job, legal regulations, and culture [57]. In the context of IoT systems, models such as TOPSIS and VIKOR have been employed to analyze and evaluate communication protocols and standards, including HTTP, MQTT, DDS, XMPP, AMQP, and CoAP [20,58]. Despite their widespread use, many of these models do not fully account for the interoperability challenges posed by emerging wireless standards, nor do they address the trade-offs between scalability and energy efficiency.

3.4. Importance of Further Research

While many decision-making theories have been applied in the context of wireless technologies and smart buildings, the use of the PIPRECIA-S model in this domain remains limited. One of the main shortcomings identified in existing research is the lack of real-world case studies that validate the theoretical models. This paper aims to fill this gap by implementing the PIPRECIA-S model for the evaluation and selection of wireless technologies that best meet the specific needs of smart buildings. Further research in this area could contribute to the development of optimized IoT systems in smart buildings, resulting in improved efficiency, enhanced security, and a better user experience. The authors hope that this work will inspire other researchers to conduct similar studies that can accelerate and contribute to the development of smart cities.

4. Methodology and Materials

4.1. Simplified Method for Assessing the Relative Importance of Criteria (PIPRECIA-S)

The PIPRECIA-S method was selected to facilitate the determination of criteria weight coefficients. Unlike the original PIPRECIA method, in the PIPRECIA-S method, the importance of each criterion is compared to the importance of the first criterion. The main advantage of the PIPRECIA-S method is its simplicity and ease of application in group decision-making processes. However, unlike the extended PIPRECIA method (PIPRECIA-E) [20] and the AHP method [59], PIPRECIA-S does not include a consistency check, which is its key drawback.

The procedure for determining the weight coefficients of criteria using the PIPRECIA-S method consists of five steps, which are outlined below [60].

Step 1. Selection of evaluation criteria $C_j$. This step involves defining the criteria $C_j, j=1,\dots , n$ where $n$ is the number of criteria taken into account when solving the problem. Criteria can be determined using the literature and/or with the help of expert opinions.

Step 2. Determining the relative importance of criteria $s_j$. First, the reference criterion is established ($C_1$), which is used as a basis for comparison. Starting with the second criterion, each criterion $C_j$ is assigned a relative importance value s_j based on Equation (1). So, every criterion $C_j$ is compared with the reference criterion $C_1$.

$$s_j=\left\{\begin{array}{c}>1, C_j>C_1\\ =1, C_j=C_1\\ <1, C_j<C_1\end{array}\right.$$

(1)

If criterion $C_j$ is more important than criterion $C_1$, it is assigned a value $s_j$ greater than 1. If criterion $C_j$ is less important than criterion $C_1$, it is assigned a value less than 1. If criteria $C_1$ and $C_j$ are equally important, both criteria are assigned an importance value of 1. The values $s_j$ fall within the interval [0.6, 1.4]. Value $s_1$ is always 1 and represents the assessment of the importance of the reference criterion $C_1$.

Step 3. The value of coefficient $k_j$ is calculated based on Equation (2).

$$k_j=\left\{\begin{array}{c}1, j=1\\ 2-s_j, j>1\end{array}\right.$$

(2)

Step 4. The value of coefficient $q_j$ is calculated based on Equation (3).

$$q_j=\left\{\begin{array}{c}1, j=1\\ {\displaystyle \frac{q_{j-1}}{k_j}}, j>1\end{array}\right.$$

(3)

Step 5. Calculating the relative weight $w_j$ of the criteria. Based on Equation (5), the relative weight of each criterion $w_j$ is calculated, where $0\le w_j\le 1$ and ${\sum }_{k=1}^n{w}_k=1$.

$$w_j={\displaystyle \frac{q_j}{{\sum }_{k=1}^n{q}_k}}$$

(4)

Once this step is complete, the process of determining the weight values of the criteria is finished.

4.2. Evaluation Criteria

The PIPRECIA-S method, which was used to determine the weight of the criteria, allows experts to express their opinions and evaluate the importance of the criteria in a simpler way than the AHP method, which is also based on pairwise comparisons.

To determine the criteria that are important for the selection of appropriate wireless technology in the concept of smart buildings, both the literature and the opinions of experts in this field were utilized. The following five criteria were defined: device connectivity, mobility, low power consumption, scalability, and flexibility.

Device Connectivity: This criterion assesses how well wireless technologies allow different IoT devices to connect and talk to each other, as well as how well they can share data with central systems for processing and analysis. This criterion encompasses several aspects, including compatibility with various device types, connection stability and reliability (frequency of interruptions, latency, and resistance to interference), data transfer speed, range, and security (level of protection during data transmission, including encryption and resistance to unauthorized access).

Mobility: This criterion evaluates the ability of an IoT device to move freely while maintaining a stable wireless connection. This criterion includes several aspects, such as uninterrupted movement (the ability of the device to remain connected without losing signal while moving through different spaces), transmission speed during motion, automatic switching between networks without disrupting the user experience, the range of the wireless technology, and the compactness and portability of the device.

Low Power Consumption: This criterion evaluates the efficiency of wireless technologies in terms of energy consumption. Low power consumption is key to wireless technologies because it directly affects device longevity, sustainability, and user experience, especially for devices that rely on battery power. This criterion includes several aspects, such as energy consumption efficiency during data transmission (very important for devices such as sensors and IoT devices), battery life, energy efficient modes (low consumption mode when the device is not active), and protocol optimization.

Scalability: This criterion evaluates the system’s ability to effectively support a growing number of connected devices or an increase in traffic volume without losing performance or stability. Scalability is key to wireless technologies because it allows networks to grow and adapt to ever-increasing demands, whether by adding more users and devices or covering larger geographic areas. This criterion includes several aspects, such as the number of connected devices (network capacity), network architecture (the flexibility of network design to expand to support more devices or a greater range of coverage—e.g., by adding new access points), resource optimization (the technology’s ability to efficiently use available resources, e.g., frequency range and channel capacity, even when the number of devices or traffic increases), scaling performance (maintaining stable transmission speed, low latency, and connection reliability even when the network expands or the load increases), ease of integration and management (the ability to easily add new devices or nodes to the network without complex procedures or the need for major changes in the existing infrastructure), and scaling costs.

Flexibility: This criterion evaluates the technology’s ability to adapt to different requirements, conditions, applications, and environments. This criterion includes the following aspects: support for different protocols and standards (the ability of the device to work with multiple wireless standards and to easily switch between them depending on the needs of the application), flexibility in configuration (ability to easily configure devices, change parameters, and adapt to specific conditions, such as adjusting signal strength), support for different applications and usage scenarios (the ability of the technology to be effective in different scenarios, such as IoT, smart homes, vehicles, or high-load networks in business environments), scalability in performance (the ability for the technology to easily adapt to changes in traffic volume, number of users, or required resources, which enables optimal performance under different conditions), and ease of integration with existing systems (the flexibility of the technology to integrate with existing solutions, platforms, or infrastructures without the need for significant changes).

4.3. Ranking Scale

For each of the above criteria, a ranking scale will be used to enable an objective and consistent evaluation of wireless technologies. The proposed scale is shown in

Table 1. Ranking scale.

Description	Rating	PIPRECIA-S Scale
Very bad	1	0.60
Bad	2	0.80
Satisfying	3	1.00
Good	4	1.20
Excellent	5	1.40

Source: Authors’ research.

.

The PIPRECIA-S method uses a specific rating scale to determine the relative importance of criteria. In the PIPRECIA-S method, the values usually range from 0.6 to 1.4, based on the scale shown in

Table 2. Piprecia-S grading values.

Criterion Value	Description of the Importance of Criteria
0.6	The criterion is much less important than the reference one.
0.8	The criterion is somewhat less important than the reference one.
1.0	The criterion has the same importance as the reference criterion (neutral value).
1.2	The criterion is somewhat more important than the reference one.
1.4	The criterion is much more important than the reference one.

.

Values less than 1.0 indicate reduced importance relative to the reference criterion, while values greater than 1.0 indicate increased importance. To relate the rating scale from 1 to 5 with the PIPRECIA scale from 0.6 to 1.4, it is possible to make a recalculation that allows the use of the known rating scale while maintaining the principles of the PIPRECIA method as shown in Table 1. This scale has been adapted for ease of use during the evaluation by experts, who are not familiar with the PIPRECIA-S decision-making method.

4.4. Prioritization of Criteria

Defining the priority criteria (device connectivity, mobility, low energy consumption, scalability, flexibility, and interoperability) is the task of the project investor. In accordance with the specific needs of the project, technical requirements, and the financial possibilities of the project, the investor defines the priorities. The main goal is to enable the investor to systematically determine the weight coefficients of the criteria through a simple process of comparing the importance of the criteria and selecting the appropriate wireless technology in smart buildings intended for the tourism sector. By making this choice, the technical aspects of the system can be aligned with the company’s goals and strategies. This ensures that the solution works well, is reliable, and will last for a long time.

Table 3. Relative importance of weighted criteria for the selection of wireless technology.

Notation	Criteria	Grades
C1	Mobility	1
C2	Device Connectivity	5
C3	Low Power Consumption	4
C4	Scalability	3
C5	Flexibility	2

Source: Authors’ research.

shows one possible case of ranking criteria according to importance in the process of selecting wireless technology for smart buildings in tourism. The criteria were ranked by experts in the fields of information technology, tourism, construction and architecture, energy efficiency, security and protection, and economics. The authors of this paper, who are also experts from the aforementioned fields, ensured consideration of the unique needs and challenges of each field. Each expert independently evaluated the criteria according to their importance for decision-making in their field. After the initial evaluation, the results were used as input to the PIPRECIA method, where the weights of the criteria were adjusted through an iterative process until a consensus was reached.

In the initial phase, each expert ranked the criteria on a scale of 1 to 5, where 1 indicated the lowest priority and 5 the highest. The ranking results were then aggregated, and the average scores were used as initial weights in the PIPRECIA method. Participants had the opportunity to adjust their ratings based on the results of the aggregation in the next step, allowing them to strike a balance between different perspectives. The criteria’s final weights, which are the result of this process, represent a consensus that reflects a multidisciplinary approach to research and decision-making.

Although the priorities in the table are defined based on the specific needs of the study, it is important to note once again that the ranking of these criteria may differ depending on the specific requirements, context, and goals of individual cases. As a result, the ranking should be adjusted to meet the specific needs of investors and the project’s specifics.

4.5. Selection of Wireless Technologies and Their Evaluation

The research analyzed four wireless technologies for use in smart buildings in tourism, namely, Bluetooth V4.2, Bluetooth Low Energy V5.0, ZigBee, and Z-Wave. These technologies are highlighted for their broad application in smart building automation, optimization for low energy consumption, and specific features suitable for IoT applications. These specific technologies were selected because they are widely adopted in the industry for their balance between cost-efficiency, energy consumption, and compatibility with various IoT devices, making them strong candidates for smart building applications. The described wireless communication technologies provide reliable and energy-efficient device interaction, which is especially important in tourism, where reducing operational costs is a priority. Bluetooth, ZigBee, and Z-Wave support various network topologies, such as mesh networks, offer high levels of security, and are widely utilized across multiple industries, making them highly compatible with smart buildings. Despite their advantages, these technologies come with certain limitations. For instance, alternatives like LoRaWAN and Wi-Fi, which offer greater range and bandwidth, were not considered due to their higher energy consumption and suboptimal performance in short-range IoT applications. Consequently, Bluetooth, ZigBee, and Z-Wave are identified as the most suitable for smart building applications in the tourism sector.

4.5.1. Bluetooth Overview

Bluetooth is a short-range wireless communication protocol that uses transceivers on the 2.4 GHz ISM frequency. By utilizing FHSS (Frequency Hopping Spread Spectrum), devices can communicate without requiring a direct line of sight. The frequency range is split into 79 channels, each 1 MHz wide, and data transmission occurs at intervals of 625 microseconds, with channel changes happening 1600 times per second [61]. A typical Bluetooth network consists of one master and up to seven slave devices, with the master dictating the frequency hopping sequence. Multiple picogrids can operate in close proximity, as each employs a unique hopping sequence. Bluetooth’s key advantages include its ability to quickly transmit speech and data in real time, which has made it popular in telecommunication systems due to its compact design, flexibility, and affordability [62].

Classic Bluetooth (BR/EDR) supports constant wireless connections in a point-to-point topology, with speeds ranging from 0.7 to 2.1 Mbps and coverage of up to 100 m, making it ideal for applications requiring rapid data exchange [63]. Bluetooth Low Energy (LE) offers lower energy consumption, extended range, and higher speeds, reaching up to 2 Mbps. BLE operates on 40 channels with 2 MHz bandwidth each, enhancing its transmission speed and making it especially useful in IoT applications where energy efficiency is crucial [63]. Furthermore, the introduction of BLE mesh topology increased network reliability by reducing vulnerability to node failures. As Bluetooth evolved from version 4.1 to 5.0, significant advancements were made, including doubling data rates, quadrupling range, and increasing broadcasting capacity [61,63].

4.5.2. ZigBee Overview

ZigBee is designed for wireless sensor and remote-control applications, adhering to the IEEE 802.15.4 standard [64]. It is optimized for low-rate personal area networks (LR-WPANs), supporting small data transfers over short distances [62]. ZigBee networks typically span 10 to 20 m but can cover longer distances using mesh topology. Operating across several ISM bands (868 MHz, 915 MHz, and 2.4 GHz), ZigBee supports three types of devices: coordinators, routers, and end devices, and enables star, tree, and mesh network topologies [65,66,67,68].

The 2.4 GHz band allows for data rates up to 250 kbps, suitable for applications such as smart homes, offices, and infrastructure [66]. ZigBee’s ability to accommodate a large number of devices—up to 653,356 within a network—sets it apart from technologies like BLE 4.2, which supports only seven devices. Additionally, ZigBee devices are low-power, typically consuming around 1 mW, making them suitable for battery-powered applications with lifespans of several years [65]. Its self-healing mechanism in mesh networks further enhances communication reliability, ensuring stable device connections even in high-interference environments.

4.5.3. Z-Wave Overview

Z-Wave is a low-power wireless technology primarily designed for smart home automation and IoT applications. It operates using PSK and GFSK modulation techniques, achieving data rates up to 100 kbps [69]. Like ZigBee, Z-Wave utilizes mesh topology, where controllers manage devices through a hierarchical structure. The network includes a primary controller that coordinates communication with secondary controllers and devices. Slave devices execute commands and forward messages to extend the controller’s communication range.

Z-Wave networks rely on two key identifiers: the Home ID and the Node ID, ensuring proper network access and management of devices [70]. Operating in the 900 MHz range, Z-Wave experiences minimal interference, contributing to its reliability in smart home systems. While Z-Wave’s advantages include energy efficiency, ease of use, and support for a large number of devices, vulnerabilities include the master controller’s constant need for internet connectivity and potential security risks from unauthorized network access [71].

4.5.4. Evaluation of Wireless Technologies

Table 4. Evaluation of technologies in accordance with the criteria.

Specifications	Bluetooth BLE 4.2	Bluetooth BLE 5.0	ZigBee	Z-Wave
Mobility	Middle (3)	Very high (5)	Very high (5)	Medium (3)
Device Connectivity	Tall (4)	Very high (5)	Tall (4)	Tall (4)
Low Power Consumption	Medium (3)	Tall (4)	Very high (5)	Tall (4)
Scalability	Low (2)	Medium (3)	Very high (5)	Very high (5)
Flexibility	Medium (3)	Middle (3)	Very high (5)	Very high (5)

Source: Authors’ research.

shows the results of the assessment of wireless technologies in relation to the selected criteria.

5. Research Results

Table 5. Relative importance of the considered wireless technologies in terms of criterion C₁—Mobility.

		sj	kj	qj	wj
A1	Bluetooth BLE 4.2		1	1	0.19
A2	Bluetooth BLE 5.0	1.4	0.60	1.67	0.31
A3	ZigBee	1.4	0.60	1.67	0.31
A4	Z-Wave	1	1.00	1.00	0.19
				5.33	1.00

Source: Authors’ research.

shows the relative importance of the considered wireless technologies in terms of criterion C₁—Mobility, based on which the following conclusions are drawn:

• Bluetooth BLE 4.2 gets a medium rating for mobility (3), which means it provides solid connectivity for devices on the go but with limitations compared to newer versions.
• Bluetooth BLE 5.0 achieves a score of 5 (very high), standing out as a great option for mobility with improved data rates and greater range.
• ZigBee also gets a score of 5 (very high) because it provides very good support for device mobility, especially in networks with many connected devices.
• Z-Wave has a score of 3 (medium), indicating that it is a solid choice, but with limitations in terms of range and speed compared to ZigBee and Bluetooth BLE 5.0.

Table 6. Relative importance of the considered wireless technologies in terms of criterion C₂—Device Connectivity.

		sj	kj	qj	wj
A1	Bluetooth BLE 4.2		1	1	0.19
A2	Bluetooth BLE 5.0	1.4	0.60	1.67	0.32
A3	ZigBee	1.2	0.80	1.25	0.24
A4	Z-Wave	1.2	0.80	1.25	0.24
				5.17	1.00

Source: Authors’ research.

shows the relative importance of the considered wireless technologies in terms of criterion C₂—Device Connectivity, based on which the following conclusions are drawn:

• Bluetooth BLE 4.2 is rated high (4), providing reliable device connectivity but with limitations compared to BLE 5.0.
• For connecting devices, Bluetooth BLE 5.0 gets a 5 (very high), allowing for better capacity and stability in terms of the number of devices it can support.
• ZigBee achieves a score of 4 (high), which means that it enables very good device connectivity, especially in networks with many sensors.
• Z-Wave also gets a 4 (high), indicating solid support for connecting devices, especially in home automation.

Table 7. Relative importance of the considered wireless technologies in terms of criterion C₃—Low Power Consumption.

		sj	kj	qj	wj
A1	Bluetooth BLE 4.2		1	1	0.19
A2	Bluetooth BLE 5.0	1.2	0.80	1.25	0.24
A3	ZigBee	1.4	0.60	1.67	0.32
A4	Z-Wave	1.2	0.80	1.25	0.24
				5.17	1.00

Source: Authors’ research.

shows the relative importance of the considered wireless technologies in terms of criterion C₃—Low Power Consumption, based on which the following conclusions are drawn:

• Bluetooth BLE 4.2 gets a rating of medium (3), which means it’s relatively energy efficient, but not as much as newer standards.
• Bluetooth BLE 5.0 is rated high (4), thanks to improvements in energy efficiency compared to the previous version.
• ZigBee receives a rating of very high (5), standing out as an extremely energy-efficient technology, ideal for battery-powered devices.
• Z-Wave also receives a high (4) rating, indicating its high energy efficiency and suitability for long-term IoT applications.

Table 8. Relative importance of the considered wireless technologies in terms of criterion C₄—Scalability.

		sj	kj	qj	wj
A1	Bluetooth BLE 4.2		1	1	0.19
A2	Bluetooth BLE 5.0	1	1.00	1.00	0.19
A3	ZigBee	1.4	0.60	1.67	0.31
A4	Z-Wave	1.4	0.60	1.67	0.31
				5.33	1.00

Source: Authors’ research.

shows the relative importance of the considered wireless technologies in terms of criterion C₄—Scalability, based on which the following conclusions are drawn:

• Bluetooth BLE 4.2 receives a rating of low (2), indicating a limited ability to support a large number of devices on the network.
• Bluetooth BLE 5.0 is rated medium (3), showing improved but not optimal scalability compared to specialized IoT technologies.
• ZigBee receives a rating of very high (5), making it extremely scalable for networks with many devices.
• Z-Wave also scores a 5 (very high), indicating its ability to support many devices in a network without significant performance decline.

Table 9. Relative importance of the considered wireless technologies in terms of criterion C₅—Flexibility.

		sj	kj	qj	wj
A1	Bluetooth BLE 4.2		1	1	0.19
A2	Bluetooth BLE 5.0	1	1.00	1.00	0.19
A3	ZigBee	1.4	0.60	1.67	0.31
A4	Z-Wave	1.4	0.60	1.67	0.31
				5.33	1.00

Source: Authors’ research.

shows the relative importance of the considered wireless technologies in terms of criterion C₅—Flexibility, based on which the following conclusions are drawn:

• Bluetooth BLE 4.2 gets a rating of medium (3), which means it provides basic flexibility but with limitations in adapting to different environments.
• Bluetooth BLE 5.0 is rated medium (3), showing solid flexibility, but not as much as ZigBee and Z-Wave offer.
• ZigBee gets a 5 (very high) because it offers exceptional flexibility, ideal for various IoT applications in smart buildings.
• Z-Wave also gets a 5 (very high), making it an extremely flexible solution for deployment in a variety of environments.

These descriptive evaluations provide a clear insight into how each of these technologies fulfills the key criteria for smart building applications, particularly in the tourism industry.

Each of these technologies has its strengths and weaknesses depending on the specific needs of smart buildings. ZigBee and Z-Wave are generally better suited for applications that require low power consumption and high scalability, while Bluetooth BLE 5.0 offers significant improvements in device mobility and connectivity, which can be beneficial in many smart building tourism applications.

Table 10. Summary of all rankings.

		C1	C2	C3	C4	C5
		0.17	0.29	0.22	0.17	0.14
A1	Bluetooth BLE 4.2	0.19	0.19	0.19	0.19	0.19
A2	Bluetooth BLE 5.0	0.31	0.32	0.24	0.19	0.19
A3	ZigBee	0.31	0.24	0.32	0.31	0.31
A4	Z-Wave	0.19	0.24	0.24	0.31	0.31

Source: Authors’ research.

shows a summary of the ranking, while

Table 11. The final ranking.

		C1	C2	C3	C4	C5	Overall Score	Final Rank
A1	Bluetooth BLE 4.2	0.03	0.06	0.04	0.03	0.03	0.1906	4
A2	Bluetooth BLE 5.0	0.05	0.09	0.05	0.03	0.03	0.2602	2
A3	ZigBee	0.05	0.07	0.07	0.05	0.05	0.2942	1
A4	Z-Wave	0.03	0.07	0.05	0.05	0.05	0.2550	3

Source: Authors’ research.

shows the final order of ranking of the analyzed wireless technologies based on the importance of the given criteria.

6. Discussion

Using the PIPRECIA-S evaluation method, the authors conclude that Bluetooth BLE 5.0 and ZigBee represent the best options for wireless technologies in smart buildings, considering criteria such as device connectivity, mobility, energy consumption, scalability, and flexibility. These two technologies stand out thanks to their advanced functionality and ability to respond to the complex requirements of smart buildings. Bluetooth BLE 5.0 provides an improved data transfer rate and better range compared to the older BLE 4.2, while ZigBee enables stable connections through a network topology that supports a large number of devices, making it highly suitable for scalable systems.

In addition, Bluetooth BLE 5.0 is designed with an emphasis on low power consumption, which is crucial for battery-powered devices, while ZigBee also ensures long-term energy efficiency. Compared to them, Z-Wave, although reliable in certain aspects, has limitations in the number of devices it can connect and somewhat higher power consumption, while BLE 4.2, with basic features, does not offer the same advanced capabilities as BLE 5.0.

However, it is important to consider the potential limitations of applying these technologies in real-world scenarios. For instance, while Bluetooth BLE 5.0 and ZigBee offer excellent performance in terms of energy efficiency and scalability, the cost of implementing these technologies in large-scale smart building projects could be significant. The need for extensive infrastructure, particularly for ZigBee’s mesh network setup, can increase installation and maintenance expenses. Additionally, compatibility issues with existing systems and the potential need for device upgrades may further impact overall project costs.

Therefore, we recommend (Figure 1) Bluetooth BLE 5.0 and ZigBee as the overall best choices for implementation in smart buildings, where the priorities are connectivity for a large number of devices, low energy consumption, and system flexibility. Decision-makers can use these results to choose the most appropriate technology, taking into account the specific requirements of their project.

In this context, the use of decision theory is indispensable because it enables a systematic approach to evaluating different technologies in accordance with select criteria. In engineering applications, where decisions are often complex and require the balancing of various factors, decision theory provides a tool for making optimal decisions. In this study, the PIPRECIA-S method enabled a detailed analysis and comparison of wireless technologies, which led to the identification of the best options for smart buildings, further confirming the importance of decision theory in engineering practice.

Future research could explore the cost–benefit analysis of wireless technology implementation in smart buildings, addressing both the technical performance and economic feasibility to provide a more holistic approach to decision-making.

7. Conclusions

The choice of wireless technologies for smart buildings plays a key role in optimizing the energy efficiency and functionality of modern buildings. Research that included Bluetooth BLE 4.2, Bluetooth BLE 5.0, ZigBee, and Z-Wave showed that each of these technologies has its own advantages and limitations when analyzed according to the criteria of device connectivity, mobility, power consumption, scalability, and flexibility.

Bluetooth BLE 5.0 stands out as the most advanced technology in terms of data transfer speed, range, and energy efficiency, making it an excellent choice for applications that require high connectivity and low power consumption. ZigBee, on the other hand, provides robust networking performance with a large number of devices and low power consumption, making it suitable for complex networks in smart buildings. Z-Wave, while reliable and effective in-home automation, has limitations in the number of devices it can connect. Bluetooth BLE 4.2, although less advanced compared to BLE 5.0, provides basic functionalities that are still useful in certain cases.

The analysis of the results indicates that Bluetooth BLE 5.0 and ZigBee offer the best balance between the criteria of connectivity, mobility, energy consumption, scalability, and flexibility. These technologies are the optimal choice for smart buildings that strive for a high level of integration and energy efficiency. Z-Wave and BLE 4.2, have their advantages but may not be the most suitable for all scenarios and have limitations compared to BLE 5.0 and ZigBee; however, they are still used where the requirements are fewer.

For decision-makers, the results of this research provide important guidelines for selecting the appropriate wireless technology depending on specific project requirements and priorities. The choice between Bluetooth BLE 5.0 and ZigBee should be driven by the need for high connectivity and low power consumption. Furthermore, other factors such as scalability and flexibility must be considered to ensure optimal implementation and long-term sustainability of smart building solutions.

Future research could focus on exploring the integration of these wireless technologies with emerging IoT standards and platforms. Additionally, further studies could investigate the challenges of deploying these technologies in large-scale, multi-building smart environments, addressing issues such as network interoperability, security, and maintenance.