Selecting Key Smart Building Technologies for UAE Prisons by Integrating Analytical Hierarchy Process (AHP) and Fuzzy-TOPSIS

Abstract

Prisons are the structures used for incarcerated inmates and are often overcrowded and understaffed. This often leads to inhumane conditions and increased violence. Smart building technologies can help to alleviate these problems to some extent and improve communication between staff and prisoners. However, selecting appropriate smart building technology for prison building requires significant effort, knowledge, and experience. The current study aims to develop a decision-making model for selecting smart building technologies for UAE prisons following the analytical hierarchy process (AHP) and fuzzy-TOPSIS. The results of AHP revealed that for the main criteria, economical criteria were the highest ranked with a global weight of 0.228, followed by technology and engineering criteria (global weights of 0.203 and 0.200, respectively). For sub-criteria, prison category and security was the highest ranked criterion with a global weight of 0.082 followed by antihacking capability (0.075). Concerning the final ranking of smart building technologies by fuzzy-TOPSIS, the safety and security system was the highest-ranked technology (Ci = 0.970), followed by the fire protection system (Ci = 0.636) and information and communication information network system (Ci = 0.605). To conclude, the current findings will assist UAE policymakers and prison authorities to select the most appropriate smart building technologies for UAE prison buildings.

1. Introduction

The construction sector is increasingly incorporating smart building technologies that enable a building to be more efficient, effective, and sustainable. By integrating systems, such as heating, ventilation, and air conditioning (HVAC), lighting control, security, communication, and facilities management, smart buildings can save resources and reduce costs while providing a better experience for occupants [1]. Smart building technologies comprise smart building management systems (BMSs), the Internet of Things (IoT), and sensor technology [2]. They provide numerous benefits to the building administrators and end-users, especially by enhancing the energy efficiency of smart buildings, thus generating less waste and ensuring environmental sustainability [3,4].

Prisons are facilities used to detain and reform individuals for some time as punishment for violating the law [5]. Prisons serve important functions in society because they handle inmates, including persons convicted of dangerous crimes. Prisons, punitive centers, and other correctional facilities play an important role in the criminal justice system and social order by providing an environment for the correction and healing of convicted persons [6]. Although smart building technologies are mainly used in residential and commercial buildings [7,8,9], many smart building technologies can offer numerous benefits for prison facilities by enhancing surveillance, control, environmental sustainability, and economic efficiency. For example, enhanced energy-saving techniques could offer significant operational savings for prisons [10]. Technologies, such as rainwater harvesting, the use of biomass energy, anaerobic digestion facilities, and forward-thinking design, are now incorporated into prison buildings.

Moreover, modern IT technologies for smart buildings could facilitate tracking inmates using sensors, detecting the human presence in banned areas, and identifying behavior patterns using databases [11]. In particular, the intelligent monitoring system is one of the important components of the smart building technology in prisons, which includes video monitoring, perimeter alarm, access control, monitoring intercom, prison security inspection, fence power grid prevention, patrol, emergency alarm, and interview recording [12]. An intelligent monitoring system enables action recognition as well as the real-time detection and analysis of human behaviors by adopting deep learning techniques, such as neural networks [13]. The automation of data generation could facilitate making complex decisions that enhance building safety, efficiency, and comfort [14]. According to Minoli [15], smart building information networks can be useful for law enforcement because they can link inmates to other criminals. This could enhance safety through monitoring, increase efficiency, and reduce operational costs.

Despite the importance and potential benefits of applying smart building technologies in prisons, selecting appropriate smart building systems for prison buildings is difficult, especially for the UAE prison sector. Prisons are rarely connected to technological development, especially digital media that seem evacuated from prison [16]. In the United Arab Emirates, although smart building technologies have been widely adopted as the country experienced unprecedented growth and innovation in the past decade, most of the smart building innovations have been directed towards commercial and residential projects and public infrastructure while excluding correctional facilities. Furthermore, different types of prisons require different technologies, and no technology can be suitable for all prisons. Thus, multiple criteria decision-making (MCDM) should be involved to select the most appropriate technologies.

However, the MCDM process is challenging because various factors must be considered when deciding [17]. First and foremost, it is important to note that the primary purpose of prisons is to house inmates convicted of a crime. However, prisons are also responsible for the safety and security of inmates and staff and the rehabilitation of inmates. Therefore, prisons must be equipped with the latest technology to provide a safe and secure environment. In addition, as many different types of smart building technologies are available, it is important but challenging to determine which ones are best suited for use in a prison setting. A suitable decision-making model is thus needed to make the selection process easier. One of the key challenges in designing and deploying smart building technologies in prison buildings is the need to select the right technologies and systems for the specific needs of the building. MCDM methods are designed to help decision-makers evaluate options against multiple criteria and can be a valuable tool in selecting smart building technologies.

While MCDM methods can help to overcome some of the challenges in selecting smart building technologies, they are not without their challenges. One of the key challenges of using MCDM methods is that they require a significant amount of data and information about the technologies being considered. These data can be difficult to obtain and may not be available for all smart technologies. Further, MCDM methods are complex and time-consuming, making it difficult to select smart building technologies regularly. Though the MCDM process is challenging, it is the best tool for making decisions about smart building technologies because the process allows for all of the relevant factors to be considered and for a decision based on the best available options.

The technology needs of any prison facility are unique and ever-changing, but there are some general trends that can be observed in terms of the types of technology that are being used in prisons around the world. One of the most important trends is the increasing use of IoT, AI, and smart technologies in all aspects of prison life, from inmate tracking and security to inmate rehabilitation and education. The implementation of these technologies in UAE prisons can have a number of benefits, including improved security, increased efficiency, and enhanced rehabilitative potential. However, selecting the right technology solution can be a challenge, given the sheer number of options on the market. One way to narrow down the field is to use a decision-making tool, such as the analytical hierarchy process (AHP) or fuzzy-TOPSIS. These methods can help to identify the key factors that need to be considered when making a decision, and they can also provide a quantitative basis for comparing different options. Ultimately, the decision of which smart building technologies to implement in UAE prisons will come down to a number of factors, including the specific needs of the prison, the budget, and the preferences of the decision-makers. However, using a decision-making tool, such as AHP or fuzzy-TOPSIS, can help to ensure that the decision is based on a systematic analysis of the available options, and not simply on personal preference. Therefore, this study aimed to select suitable smart building technologies using a multicriteria decision-making model integrating AHP and fuzzy-TOPSIS. The AHP is a well-known decision-making model that considers quantitative and qualitative factors. Fuzzy-TOPSIS is a newer model that uses fuzzy logic to deal with imprecise or uncertain data. AHP and fuzzy-TOPSIS effectively select smart building technologies for prison use [18].

2. Literature Review

The smart building concept is the future of the construction industry which has grown rapidly after the advance in information technology. Smart buildings provide sustainability by improving human well-being and the overall living standard of residents. So, the main focus of smart buildings has been to increase end-user satisfaction by providing the ultimate living experience [19]. Another important characteristic of smart buildings is the integration of automation and human control in the same building. Even now, traditional buildings are being converted into smart buildings by implementing smart building technologies [20].

Smart buildings have been widely adopted in various parts of the world. Modern buildings incorporate basic sensor technologies, the internet of things, monitoring, and control systems to enhance performance [21]. For instance, the Duke Energy Center in North Carolina is one of the best examples of the smart buildings concept. The building has automated blinds that adjust with the sun’s movement to improve lighting. Its intelligent building captures real-time data and generates useful reports contributing to operational efficiencies [22]. UAE is leading the GCC region in the adoption of smart buildings. Prominent smart buildings in the country include Rosewood Hotel, Masdar Institute, and the World Trade Centre Abu Dhabi [23]. Smart buildings are also popular in Australia, Europe, Latin America, Africa, and Asia. Countries, such as Australia, China, Singapore, and India, are leading the smart buildings revolution in Asia-Pacific. Thus, smart buildings are now a global phenomenon with widespread adoption and applications.

The selection criteria for smart buildings vary depending on the location and type of the building. Among critical selection criteria, the safety and security of smart buildings should be critical for any smart building. To meet this purpose, access control systems such as logging and biometrics offer better security for buildings [24], while gas leak detection systems enhance a facility’s safety [25]. Working efficiency in the form of energy savings is also considered an important criterion for smart buildings and has been well-researched. Indeed, the pursuit of energy efficiency is one of the primary selection criteria for smart building projects. Buildings installed with power meters and occupancy sensors can adjust lighting and thermal conditions with changes in occupancy, thus saving energy [25]. Under UAE conditions, for instance, Masdar Smart City has achieved complete energy efficiency through the harnessing of solar energy [26]. Environmental sustainability is the key focus of architects and builders, so smart buildings are considered environmentally sustainable, and this key criterion makes smart buildings distinct from traditional buildings. Smart buildings maintain sustainability by reducing greenhouse emissions, increasing recycling levels, and adopting renewable energy sources [24,26,27].

Among various types of facilities, prisons serve important functions in society because they handle inmates, including persons convicted of dangerous crimes. Prisons, punitive centers, and other correctional facilities play an important role in the criminal justice system and social order by providing an environment for the correction and healing of convicted persons. They serve three main functions protecting the public, punishing offenders, and rehabilitating convicts [6]. Prison must be segregated to ensure that hard-core criminals and first-time offenders of minor crimes do not mix [28]. However, monitoring and segregation are difficult to implement in old prison facilities. Thus, there is a need to design and develop modern prison buildings that will reduce costs while at the same time increasing security, performance, and maintenance [29]. In most societies, prison buildings are designed and constructed to accommodate and secure persons that the society needs to be protected [30]. Most countries have highly incarcerated prison systems characterized by high-security prison buildings [31].

Smart prisons require a unique set of selection criteria for selecting smart building technologies as they are the places for the detention of high-risk offenders and differ from commercial and residential buildings. Thus, smart correctional facilities require enhanced surveillance and monitoring of inmates and greater control over security features through building automation [32]. Accordingly, when selecting smart building technologies for prison buildings, some additional criteria should be considered apart from the traditional ones of safety, security, and building efficiency. For example, a smart prison building should enable administrators to easily locate and monitor the inmates from their offices. Not much research has been conducted to understand the specific selection criteria for smart building technologies that could be implemented in prison facilities.

Rationale of the Study

In light of the high recidivism rates in the UAE, there is a clear need to reform the prison system. One way to do this is by improving the conditions within prisons. Creating a more comfortable and stimulating environment can lead to a decrease in recidivism rates. This can be achieved through the implementation of smart building technologies. Many smart building technologies are available, and it can be difficult to determine which would be the most beneficial for UAE prisons. This study used AHP and fuzzy-TOPSIS to identify key smart building technologies for UAE prisons. The AHP is a decision-making tool that allows for the systematic selection of alternatives, taking into account both qualitative and quantitative factors. Fuzzy-TOPSIS is a variation of the TOPSIS method, which is a multi-criteria decision-making tool. It uses fuzzy set theory to handle imprecision and uncertainty.

3. Materials and Methods

3.1. Multicriteria Decision-Making Techniques

MCDM is a process for handling problems with multiple conflicting criteria [33]. The goal of MCDM is to find the best possible solution to a problem, given the constraints and objectives of the decision-maker. MCDM can be used in various situations, from simple personal decisions to large-scale organizational decision-making. In all cases, MCDM can help to identify and assess the options and to find the best possible solution. MCDM methods are used in various fields, such as engineering, economics, management, and political science [34,35,36,37]. MCDM methods can be used to determine the best possible compromise solution between multiple criteria, i.e., the solution best meets the needs of all parties involved. MCDM can also be used to find the best possible solution when there is no clear preference for any one criterion. In this case, the goal is to find the best solution overall [17,36].

There are various MCDM methods, each with its strengths and weaknesses. Some of the more popular methods are AHP, Multi-Attribute Utility Theory (MAUT), and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). However, there are also a few other notable MCDM methods that are worth mentioning. These include the ELECTRE method, the PROMETHEE technique, and the SAW (simple additive weighting) method. The ELECTRE method is a family of outranking methods that were developed in the 1970s. The PROMETHEE technique is a multiple-criteria decision-making method that was developed in the 1980s. The SAW method is a simple and popular method that assigns weights to criteria and sums the weighted scores of alternatives. While the AHP and fuzzy TOPSIS are the most widely used MCDM methods, the ELECTRE, PROMETHEE, and SAW methods are also worth considering [13,34,38,39].

The two most crucial and widely used MCDM methods are AHP and fuzzy TOPSIS. The AHP is a systematic process involving four steps: developing a decision hierarchy, constructing a set of pairwise comparisons, checking the consistency of judgments, and prioritizing analysis [40]. The AHP method gives specific numerical weights representing each smart technology’s relative importance and associated selection criteria concerning the goal. There are many different ways to determine the relative importance of alternatives. However, AHP is often seen as the most reliable method because it takes into account a variety of factors and allows for a more objective determination of importance. AHP can also be used in various situations and for various problems [41,42]. AHP also provides qualitative and quantitative approaches to solving complex decision problems [43].

Meanwhile, it is also recommended to consider a fuzzy environment to improve the decision-making process. Decision makers desire to make decisions in fuzzy environments about the flexibility to use different linguistic variables [44]. The fuzzy technique is useful in multicriteria decision-making because it allows decision-makers to rank alternatives through numerical evaluations. Fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) is a widely used method of MCDM that helps in the objective and systematic evaluation of alternatives based on multiple criteria [45]. Fuzzy TOPSIS was first coined by Chen [46]. In the decision-making model, TOPSIS has been applied in various fields because it scrutinizes multiple criteria and gives the best technique to suit the nature of the building [47]. TOPSIS could help solve multicriteria group decision-making, where a group must decide based on some criteria. Still, such approaches do not have the same perception toward weighing the criteria of that decision-making process. To address that problem, TOPSIS was based on the principle of finding the longest distance of the most negative solution, namely a solution to maximize cost and minimize the benefits or of finding the shortest distance of the most positive solution to minimize the cost and maximize the benefit [48].

3.2. Procedure of MCDM Process for the Selection of Smart Building Technologies

3.2.1. Step 1. Identification of Smart Building Technologies

Smart building technologies to be used in UAE prisons were identified by semi-structured interviews and questionnaire survey conducted in the prior stage of this study [49,50]. The interview was accomplished by the participation of fourteen interviewees from the top management working in the UAE prison and construction sectors. Subsequently, 238 participants, including people with good knowledge and experience of prisons and construction, completed a questionnaire survey. As a result, nine smart building technologies were selected for the pilot survey involving AHP and Fuzzy-TOPSIS. These smart technologies include the lighting system, fire protection system, safety and security system, HVAC, vertical transportation system, information and communication network system, electrical system, building automation system, and hydraulic and drainage system.

3.2.2. Step 2. Identification of Criteria and Sub-Criteria for Smart Building Technologies

The next step of the decision-making process is identifying the criteria and sub-criteria for smart building technologies. This was accomplished based on the literature review, interviews, and survey [49,50]. By collating evidence from these broad sources, a questionnaire was developed, comprising pairwise comparisons of main criteria, sub-criteria and smart building technologies using Expert Choice online software and provided to 14 experts from the top management level working in the construction and prison sector of the UAE. The demographic information of experts revealed that 11 out of 14 were male; half had master’s degrees, whereas five experts had bachelor’s degrees. The 11 experts had more than ten years of experience serving in higher positions in the government and private sectors. The experts were briefed about the study’s objectives and asked to compare the weights of each criterion and sub-criteria on a 9-point Saaty scale (

Table 1. Saaty’s 9-point scale.

Point	Scale	Reciprocal Definition
1	1	Equally preferred
3	1/3	Moderately preferred
5	1/5	Strongly preferred
7	1/7	Very strongly preferred
9	1/9	Extremely preferred

) [41].

3.2.3. Step 3. Deriving the Fuzzy Weights of Criteria Using the AHP

The AHP was employed to derive the weights of all pairwise comparisons from the questionnaire survey, resulting in local and global weights for each criterion and sub-criteria. The pairwise comparison result of experts (k) on the relative weight of criteria i over sub-criteria j is given by ${ã}_{ij}\left(\mathit{k}\right)$. The linguistic terms were mapped onto fuzzy triangular numbers using the constructs shown in Figure 1 below.

3.2.4. Step 4. Aggregating the Fuzzy Weights from All Decision-Makers

The weights of different criteria obtained from the AHP process were aggregated to incorporate multiple decision-makers. This involved pooling the decision makers’ opinions on the relative weights of different criteria to get the aggregated fuzzy rating for each alternative. This process facilitated the identification of the aggregate fuzzy rating ${\mathit{\chi }}_{\mathit{i}\mathit{j}}$ for alternative ${\mathit{A}}_{\mathit{j}}$ under criterion ${\mathit{C}}_{\mathit{j}}$. For k number of decision makers, the rating and weight of each rating are given by the following model:

$$\begin{gathered} {\mathit{\chi }}_{\mathit{i}\mathit{j}}=\frac{\mathbf{1}}{\mathit{K}}\lfloor \mathit{\chi }\frac{\mathbf{1}}{\mathit{i}\mathit{j}}+\mathit{\chi }\frac{\mathbf{2}}{\mathit{i}\mathit{j}}+\cdots \mathit{\chi }\frac{\mathit{K}}{\mathit{i}\mathit{j}}\rfloor \\ {Ŵ}_i=\frac{\mathbf{1}}{\mathit{K}}\lfloor Ŵ\frac{\mathbf{1}}{\mathit{j}}+Ŵ\frac{\mathbf{2}}{\mathit{j}}+\cdots Ŵ\frac{\mathit{K}}{\mathit{j}}\rfloor \end{gathered}$$

where ${\mathit{\chi }}_{\mathit{i}\mathit{j}}$ is the rating and ${Ŵ}_i$ is the weight.

3.2.5. Step 5. Assessing the Suitability of Smart Technology Applications in Prisons Using the Fuzzy TOPSIS

The fuzzy TOPSIS method was used to assess the suitability of different smart building technologies for applications in different prison scenarios. The selection of a smart technology application involved calculating the fuzzy closeness coefficient of each alternative using the following equation:

$$\begin{gathered} {\mathit{d}}_{\mathit{i}}^{*}={\displaystyle \sum }_{\mathit{j}=\mathbf{1}}^{\mathit{n}}\mathit{d}({ṽ}_{\mathit{i}\mathit{j}},{ṽ}_{\mathit{i}\mathit{j}}^{*}), \mathit{i}=\mathbf{1},\mathbf{2},\mathbf{\dots },\mathit{m} \\ {\mathit{d}}_{\mathit{i}}^{-}={\displaystyle \sum }_{\mathit{j}=\mathbf{1}}^{\mathit{n}}\mathit{d}({ṽ}_{\mathit{i}\mathit{j}},{ṽ}_{\mathit{i}\mathit{j}}^{-}), \mathit{i}=\mathbf{1},\mathbf{2},\mathbf{\dots },\mathit{m} \end{gathered}$$

where (${\mathit{d}}_{\mathit{i}}^{*}, {\mathit{d}}_{\mathit{i}}^{-})$ is the distance between two fuzzy numbers

A closeness coefficient ($\mathit{C}{\mathit{C}}_{\mathit{i}}$) was used to rank the order of alternatives:

$$\mathit{C}{\mathit{C}}_{\mathit{i}}=\frac{{\mathit{d}}_{\mathit{i}}^{-}}{{\mathit{d}}_{\mathit{i}}^{*}+{\mathit{d}}_{\mathit{i}}^{-}}, \mathit{i}=\mathbf{1},\mathbf{2},\mathbf{\dots }, \mathit{m}.$$

A smart technology application for prison buildings was considered more suitable if its fuzzy closeness was higher.

4. Results

4.1. Results of AHP

4.1.1. Weights of Main Criteria

There were six main criteria whose rating was assessed by the experts through pairwise comparison, and results are demonstrated in

Table 2. Weights of main criteria and sub-criteria.

Main Criteria	Weights	Sub-Criteria	Local Weighs	Rank	Global Weights	Rank
Engineering	0.200	Working efficiency	0.319	1	0.064	6
		Responsiveness	0.157	3	0.031	14
		Smart maintenance program	0.147	5	0.029	16
		Availability of spare parts	0.150	4	0.030	15
		System integration	0.226	2	0.045	12
Environment and waste management	0.093	Energy consumption and water conservation efficiency	0.314	3	0.029	16
		Materials used for durability and recycling	0.318	2	0.030	15
		Indoor Environmental Quality	0.368	1	0.034	13
Economical	0.228	Economic performance and Affordability	0.248	2	0.057	8
		Initial costs, operational and maintenance costs	0.294	1	0.067	5
		Life cycle costs	0.215	4	0.049	10
		Suppliers reliability	0.243	3	0.055	9
Socio-cultural	0.079	Respect and integration to building context	0.276	2	0.022	18
		Health and sanitation	0.255	3	0.020	19
		Compatibility with local heritage values “local traditions and customs.”	0.161	4	0.013	20
		Prisoners classification	0.308	1	0.024	17
Technological	0.203	Anti-hacking capability	0.370	1	0.075	2
		Allow for further upgrade	0.335	2	0.068	4
		Smart technology brand and warranty	0.294	3	0.060	7
Architectural and design	0.197	Land use and site selection	0.237	3	0.047	11
		Compliance with design and sustainability codes and standards	0.348	2	0.069	3
		Consider prison category and security level	0.415	1	0.082	1

. It is evident from the findings that economical criteria were the highest ranked criteria, with a global weight of 0.228 for the selection of suitable smart building technologies in prison buildings of UAE. The pairwise decision matrix revealed that technology was the second highest ranked criterion, with a global weight of 0.203, whereas engineering criteria ranked third, with a global weight of 0.200. Architectural and design criteria were considered the fourth highest-ranked criteria, gaining a global weight of 0.197 (Table 2). The experts assessed socio-cultural criteria as the lowest ranked criteria, with a global weight of 0.079, followed by environment and waste management (global weight of 0.093).

4.1.2. Weights of Sub-Criteria

The local and global weights of 22 sub-criteria assessed through pairwise comparisons have been demonstrated in Table 2. It is worth mentioning that the local weight of a sub-criteria is the degree to which that sub-criteria is important concerning the other sub-criteria within the same main criterion. It is important to consider the local weight of a sub-criteria when creating or modifying a scoring system, as it can help ensure that the final score assigned accurately reflects each sub-criteria’s importance. In engineering criteria, working efficiency was considered the most significant sub-criteria with a local weight of 0.319, followed by system integration (local weight of 0.226). Indoor environment quality was the most significant sub-criteria, with a local weight of 0.314 in environment and waste management main criteria. Concerning economical main criteria, initial, operational, and maintenance costs were considered the highest-ranked sub-criteria with a local weight of 0.294. Under socio-cultural main criteria, prisoners classification was considered the most significant sub-criteria with a local weight of 0.309. Ant-hacking capability topped the list of sub-criteria in the main technological criteria with a local weight of 0.370. Concerning the main architectural and design criteria, the experts significantly demanded that the prison category and security level (local weight of 0.415) be considered before designing any prison building. In a decision matrix, the global weight of a sub-criteria refers to its importance across all the criteria. The weight of each sub-criteria is multiplied by their respective criteria to determine the global rank. Among all the tested criteria, to consider prison design and security level was considered the highest priority sub-criteria with a global weight of 0.082, followed by anti-hacking capability (global weight of 0.075) and compliance with design and sustainability codes and standards (global weight of 0.069) (Table 2).

4.1.3. Weights of Alternatives

The global weights of smart building technologies have been documented in

Table 3. Final weights of alternatives based on the AHP method.

Alternatives	Priorities (%)	Global Weights
Safety and security system	18.74	0.187
Fire protection system	14.69	0.147
Heat, ventilation, and air-conditioning system (HVAC)	13.32	0.133
Information and communication network system	12.94	0.129
Electrical system	11.02	0.110
Building automation system	10.07	0.101
Hydraulic and drainage system	7.15	0.072
Lighting system	6.00	0.060
Vertical transportation system	6.05	0.060

. The highest-ranked smart building technology was the safety and security system, with a global weight of 0.187, followed by the fire protection system having a global weight of 0.147. In contrast, the least-ranked smart building technology, as indicated by the AHP method, was the lightning system, with a global weight of 0.060. The priorities of the participants revealed that safety and security was ranked by 18.74% of experts, whereas fire protection system by 14.69%. The experts emphasized lighting and vertical transportation system the least (6.00% and 6.05%, respectively).

4.2. Results of Fuzzy TOPSIS

A fuzzy TOPSIS approach was implemented to prioritize the smart building technologies following the weights of criteria and subcriteria of AHP. The consistency index revealed that safety and security was prioritized as the top-ranked smart building technology (Ci = 0.970) for prison buildings of UAE, followed by the fire protection system (0.636) and information and communication network system (0.605). The fuzzy TOPSIS analysis revealed that the electrical system was the least-ranked smart building technology (0.270), followed by the hydraulic and drainage system (0.331) (

Table 4. Fuzzy-TOPSIS ranking of smart building technologies in prisons.

Alternatives	Ci	Rank
Safety and Security System	0.970	1
Fire Protection System	0.636	2
Information and Communication Network System	0.605	3
Heat, ventilation, and air-conditioning system (HVAC)	0.586	4
Building Automation System	0.579	5
Lighting system	0.576	6
Vertical Transportation System	0.405	7
Hydraulic and Drainage System	0.331	8
Electrical System	0.270	9

).

5. Discussion

5.1. Ranking of Selection Criteria

This study evaluated the significance of different smart building technologies for use in UAE prisons following certain criteria and sub-criteria. A total of 14 experts presented their knowledge and experience to prioritize the weights of the main and sub-criteria. The AHP was used to assess the weights of all criteria after a pairwise comparison matrix.

The economical criteria were ranked the most significant for the main criteria, whereas the socio-cultural criteria were the least significant. Initial costs of the smart building technology and operational and maintenance costs were deemed the highly significant subcriteria within the economical criteria. The previous studies [8,43] demonstrated that, when selecting smart building technology, another important point to consider is the maintenance cost, as smart technology requires regular updates and replacements [43]. These costs are also associated with the type of building. For some technologies, such as advanced building automation systems and smart thermostats, the cost goes up in larger buildings and down in smaller ones. Advanced building automation systems are more cost-effective when installed in larger buildings than in smaller ones. On the other hand, sensors and controls that are installed throughout a building, like smart thermostats, are less expensive for small buildings than for large ones [51]. Another important subcriteria of the economical criteria was the economic performance and affordability of the smart building technology. It has been observed that while selecting smart building technology, the potential economic benefits against the upfront costs should also be weighed. In many cases, the economic benefits of improved energy efficiency and reduced operating costs will outweigh the cost of the technology. In addition to economic performance, organizations also consider the affordability of smart building technology. The affordability of smart building technology has improved in recent years as the cost of the technology has decreased, and financing options have become more available [52,53].

The economical criteria were followed by engineering criteria which consist of five sub-criteria, i.e., working efficiency, responsiveness, smart maintenance program, availability of spare parts, and system integration. Of these sub-criteria, working efficiency was considered the highest-ranked sub-criteria for selecting smart building technologies. As buildings are becoming increasingly complex, selecting the most efficient and effective technology is essential to ensuring the project’s success. In another study, work efficiency’ was continuously perceived as the most important main criterion for several IB systems [43]. Modern smart buildings adopt advanced computing technologies to achieve optimum performance in terms of comfort, control, and sustainability [27]. The intelligent design and placement of HVAC control systems can save considerable energy in heating, cooling, and ventilation fan operation. Smart building controls for HVAC can use CO₂ levels, occupancy data, temperature, humidity and air quality to optimize the area [51]. High-performance building systems are described as those that promote the reduction of energy demand, involve the utilization of locally generated renewable energy, and enhance the health and comfort of indoor environments for the occupants [27]. Computer networking and sensor technology advances have created new capabilities for buildings to meet and even anticipate users’ needs, reduce operating costs, and increase efficiency [24].

Another significantly crucial criterion for the selection of smart building technologies was related to technology consisting of three subcriteria. The anti-hacking capability of a smart building was perceived to be the most significant technology criterion. Smart buildings are nowadays equipped with various sensors and systems that work together to detect potential security threats. These systems mitigate the potential threats through physical security measures, such as locks and alarms, or through more subtle means like adjusting the temperature or lighting to make it more difficult for intruders to hide [54]. IoT devices are becoming more prevalent in smart buildings. While their convenience is undeniable, their security risks are also a major concern. One way to mitigate these risks is to use a secure and light IoT protocol (SLIP). SLIP was designed with security in mind and is much lighter and faster than similar protocols. This makes it ideal for IoT devices, which often have limited resources [55]. The other important technology criteria were the availability of spare parts for smart building technology and the brand and warranty of smart building technology. When it comes to smart building technology, it is necessary to check the brand and warranty. Spending a lot of money on smart building technology is a significant investment. Smart building technology is often complicated and sensitive, meaning that any issues that arise can be very costly to fix. Moreover, many manufacturers require that the warranty be valid to provide support or service [49].

5.2. Selection of Smart Building Technologies

Concerning the smart building technologies used in UAE prisons, nine technologies were selected based on selection criteria through a questionnaire survey [49]. Based on the Fuzzy-TOPSIS decision-making model, safety and security system was ranked as the most significant smart building technology because a smart prison should be equipped with an intelligent security system that helps to protect the inmates and property within the building. The widely adopted security systems of a prison building include CCTV cameras, access control systems, intruder alarm systems, and suppression systems. Safety and security always remain the major concern of smart buildings for prison facilities. A smart prison requires various smart building technologies for the safety and security of the inmates incorporated in its BMS. Some security systems include remote monitoring, automated data collection on inmates and the building, using RFID to track and control violent inmates, and interception of illegal calls. Smart building security systems can be customized to the specific needs of a prison building. They can be integrated with other systems, such as fire and life safety, to provide a higher level of protection [56]. Modern IT technologies, such as artificial intelligence, have facial recognition capabilities, facilitating easier identification of inmates for easier behaviour control. The usefulness of IoT technology in developing prisoner escape alert and prevention system has been well documented. Smart buildings should be equipped with a system to enhance the function of monitoring and control of inmates [57].

The fire protection system was ranked the second most significant smart building technology for UAE prisons as a smart fire protection system can not only detect a fire, but also provide information that can help firefighters to move quickly and efficiently extinguish the fire. A fire protection system is one of the most important safety features in any building. Smart buildings equipped with advanced fire safety features can help to prevent and manage fires. The fire protection system in a smart building consists of advanced fire detection and early warning systems, automatic fire suppression systems, and emergency evacuation plans, procedures, and workflows [58,59].

The information and communication network system was the next highest-ranked smart building technology for UAE prisons. The effective management of prisons requires establishing an efficient information and communication network system. These systems allow for video conferencing, electronic monitoring, and other technological advances that improve communication and security within the prison. There are a variety of ways they are used in smart prisons. For example, these systems can be used to monitor inmates and keep track of their whereabouts; provide guidance and support to inmates as they attempt to reintegrate into society, and to keep families and friends informed about an inmate’s progress and whereabouts [49,60].

Some other smart building technologies, such as HVAC, building automation system, lighting system, and vertical transportation system, were considered moderately important by fuzzy-TOPSIS because these technologies are most commonly used in prisons and other smart buildings. A smart prison must have a heating, ventilation, and air-conditioning system (HVAC) that allows for temperature control and monitoring of individual rooms [61,62]. The smart prison should also have automated lighting controls that alert staff when a room is not occupied during daytime hours to reduce energy costs [63,64]. The vertical transportation system has a series of ropes that transport inmates between floors in an elevator-like fashion rather than stairs, which can be dangerous for inmates, especially those carrying heavy items or escorting new inmates [65]. The building automation system monitors heating, ventilation, and air-conditioning equipment and electrical, plumbing, and fire/life safety systems; providing building engineers with real-time data on building performance. It also controls various relevant devices, such as lighting and HVAC systems, through connectivity to a security operations centre [66,67].

The electrical system consisting of automated light switching and automatic gate opening and locking are also considered crucial for smart prisons [11]. The electrical system was identified as the least significant smart building technology for UAE prisons, followed by hydraulic and drainage system and as weighted by the experts. The primary reason for this is that the UAE prisons are already very energy efficient and have little need for further improvement. In terms of the hydraulic and drainage system, the experts noted that the UAE prisons have been investing in upgrading these systems for several years and have made significant progress in this area [49,50].

6. Conclusions

UAE prisons are constantly trying to increase their inmates’ living conditions and security. They must carefully select the most appropriate smart building technologies to do this. This research presented an AHP Fuzzy-TOPSIS MCDM framework to help select smart building technologies suitable for UAE prisons. The findings revealed that safety and security was the most significant smart building technology, followed by the fire protection system. In contrast, the lightning system was assessed as the least significant smart building technology for UAE prisons, followed by the hydraulic and drainage systems. It was also found that economical criteria were ranked highly significant and socio-cultural criteria the least significant. Initial and operational and maintenance costs were deemed most significant within economical criteria. The anti-hacking capability of a prison building was highly ranked under engineering criteria as well as automation in UAE prisons which contain five subcriteria: working efficiency, responsiveness, smart maintenance program availability of spare parts, and system integration. Our experts considered working efficiency the highest-ranked sub-criteria for selecting smart building technologies for UAE prisons.

The Implications of smart building technologies in UAE prisons for government and private industry are significant. For industry, stakeholders can use smart building technologies to monitor their premises and deter and detect criminal activity. Regarding energy efficiency, businesses can use smart building technologies to reduce their energy consumption and costs. For the government, it is imperative to consider the use of smart building technologies in prisons to improve the conditions of prisons and the safety of prisoners. Governments must ensure that prisoners can access smart building technologies to improve their rehabilitation prospects.

The current study revealed useful insights into selecting smart building technologies for UAE prisons based on the AHP and fuzzy-TOPSIS. However, there are still areas of improvement. One area of future research is the impact of smart building technologies on prison inmate behaviour. The literature shows that smart building technologies can positively impact inmate behaviour. However, more research is needed to understand the long-term effects of these technologies on inmate behaviour. Another area of future research is the impact of smart building technologies on prison staff. There is evidence that these technologies can reduce the workload of prison staff. Therefore, the future research should focus on developing more effective ways to use smart building technologies to generate revenue for the UAE government.