1.1. Research Motivation and Scope
Smart cities must be large towns able to sustain their citizens’ incremental needs while promoting environmental sustainability. With the emergence of new information and communication technologies (ICTs), such as the Internet of Things (IoT) and big data, smart cities are closer to this realization. However, the deployment of such an amount of technology in a wide geographical area requires experimentation and testing. Consequently, our research proposes to create smart campuses (SCs) to experiment with the deployment of these ICT technologies [1
]. The aim is to support the efficient management of a “small” smart city. In the context of an SC, we consider the needs of students and campus staff while improving environmental sustainability.
This way, we narrow the scope of the present paper by focusing on two properties: students’ comfort and energy efficiency. We aim to integrate the ICTs to monitor and manage both of them; therefore, IoT devices are responsible for detecting comfort levels and energy efficiency on the campus and take consequent corrective action. We propose to conceptualize groups of smart devices that could be used to achieve a determined goal by acting as physical-world proxies for agents. For instance, an agent is responsible for improving energy efficiency and comfort in a given classroom, and it senses and actuates on the physical world (e.g., classrooms) through IoT sensors and actuators.
According to Eurostat and the European Commission report in Education and Training Monitor 2019, more than 31% of the European population is currently enrolled in educational programs. This percentage only includes physical-based learning. However, in recent years remote learning and distance education have grown significantly [3
]. Hence, more than 138 million European people spend a considerable amount of their time in educational facilities (schools, universities, colleges, etc.). Most of these facilities were constructed a long time ago to rapidly address the educational needs of growing local populations due to the societal changes in which young adults began to complete a full education plan: primary school, high school, and university/vocational training. At that time, educational institutions were large infrastructures to allocate all students, faculty members, and staff. However, little or no attention was paid to the overall comfort of these environments—understood as a measure that balances the wellbeing of all users, the efficiency of the processes involved, and the pro-environmental footprint of their facilities.
Recent studies have suggested that comfort in educational environments is a critical parameter for the success of learning and the evolution of society [4
]. Comfort is usually related to individual and isolated parameters such as air quality, temperature, or noise [5
]. Measuring these parameters can be tackled seamlessly with unobtrusive equipment as an enabler to obtaining reasonable—yet incomplete—partial conclusions [6
]. Indeed, much effort has been made to improve ICT-based solutions in the direction of more accurate and more complete systems (e.g., including more local variables) [7
]. However, these recurrent solutions typically fail at quantifying the side effects of measuring comfort involving external parameters to the educational environment that still have a great impact on its associated issues (e.g., overall sustainability, energy efficiency, learning and teaching performance, etc.). For instance, they are unable to address dilemmas such as whether it would be worth increasing the energy consumption to keep the optimal thermal conditions in order to ensure an improvement in the students’ academic output or not.
In essence, current ICT-based proposals to monitor comfort either do not deal collectively with the vast amount of internal and external parameters to measure them, or only provide local (i.e., partial) qualitative views of comfort as they are more focused on keeping the technological paradigm of cost-effectiveness [5
]. Hence, existing developments are incremental, concerning a conceptual and technological paradigm that remains unchanged. Understanding, monitoring, predicting, and optimizing comfort in educational environments requires a holistic and cross-layer view able to frame and quantify the dynamic and nonlinear relations of their involved users [8
]. Indeed, addressing the comfort in educational facilities cannot be tackled in a linear way since several interdependent parts are continuously changing. Therefore, it is safe to say that comfort in educational environments has remained under-sampled for years mostly due to the complexity of objectively quantifying and acting on it.
Specifically, authors have examined, measured, and analyzed all the potential external (e.g., available open data, weather information, architectural issues, etc.) and internal (e.g., thermal or acoustic data) variables affecting such comfort to (1) quantify, monitor, predict and optimize comfort in physical and, eventually, virtual educational environments; (2) enhance overall sustainability and (3) overcome potential issues in the teaching-learning process. The proposed structural model of our SC will help to predict the impact of the distinct institutional policies on comfort and, as such, it will encourage drivers to address changes such as conducting active learning methodologies, adopting eco-friendly initiatives to reduce environmental footprint toward carbon neutrality, or incorporating renewable energies to save natural resources.
Overall, our research proposes a radical paradigm shift and the use of IoT technology in monitoring and optimizing comfort in university learning environments, where the frame for analysis and modeling of the comfort parameter holistically covers the internal and external meta-dimensions, as a whole, that characterize the socio-environmental interactions of three strategic stakeholders: teaching and learning community, facility management staff, and energy providers. If these dimensions, and their impact on comfort, were defined, quantified, and validated through innovative scientifically-grounded methods, this would drive the conception of a new technology able to transform the current generation of comfort analysis in physical and virtual educational environments. This achievement will endow them with a completely novel functionality to improve their sustainability while helping to understand, design, populate, monitor, and perceive comfortable learning environments.
1.2. The Importance of the University in the Promotion of Sustainability
Universities and colleges play a crucial role in the development of knowledge and innovation, especially in more environmentally benign technologies and goods to promote sustainable living [9
]. They represent vital places to explore, test, develop, and communicate the necessary conditions for effective and sustainable change [10
]. Many universities and colleges are similar to micro cities because of their population, size, and the many different types of activities happening on campus. According to the literature, a sustainable university is “a higher educational institution that addresses, involves and promotes, on a regional or a global level, the minimization of negative environmental, economic, societal, and health effects generated in the use of their resources in order to fulfill its functions of teaching, research, outreach and partnership, and stewardship in ways to help society make the transition to sustainable lifestyles” [12
Although universities acknowledge their roles in our present culture, there is a part of university life that has been rendered a mystery and has never truly been solved universally among universities: sustainable development. Sustainable development is defined as “development that meets the needs of the present without compromising the ability of future generations to meet their own needs” [13
]. Since sustainability is an issue of present-day and future societies, it is crucial that places of learning, such as universities, play a critical role in teaching sustainability to citizens who will be the future decision-makers. Sustainability practices begin at the university level by adapting environmentally sustainable policies and expanding to local, regional, national and international levels [14
Since graduates of any discipline will need knowledge and skills related to sustainability, the challenges and possible solutions should be integrated within the main functions of a university: the development of an interdisciplinary curriculum, environmental literacy, sustainable academic research, sustainable physical operations of the campus, and collaboration amongst universities. The common ground of sustainable practices is the ethical and moral responsibility of universities to be leaders in promoting sustainability [15
]. Campus sustainability has become an issue of global concern for university policymakers and planners as a result of the realization of the impacts the activities and operations of universities have on the environment. Generating more sustainable campus life, including actual innovative campus projects and administrative policies, creates opportunities for students within sustainability [18
Due to their unique position, universities and colleges play a key role in educating the future generations of citizens who will have expertise in all fields of the labor market. This role includes both the promotion of environmental literacy among students and research in sustainability, as well as a contrived effort to decrease the university’s impact on the environment [19
]. Although universities worldwide are constantly improving their vision and curricula to address future sustainability challenges, there is still much work to do. The goal of sustainability education is to give students knowledge and skills and help them find solutions to environmental, health-related, and economic challenges [20
]. Another important element in the methodology used for teaching students about sustainability is the need to undertake hands-on projects to ensure the students’ understanding of the challenges and possible solutions. Self-sustainable campuses with many projects (e.g., composting, rooftop gardens and solar panels) teach students about sustainability and require the active work of the students. Students who participate in planning, building, and maintaining these projects will be more likely to develop lifelong sustainability habits.
1.3. The Statement for Our Smart Campus Comfort Challenge
The main goal of the Advanced Training in Health Innovation Knowledge Alliance (ATHIKA) [21
] is to use knowledge transfer to duplicate, yet also locally customize, sustainability innovations undertaken by diverse institutions. The ATHIKA project will build a set of advanced training programs involving academia, public administrations, SMEs (Small and Medium Enterprises), start-ups, and health business consultants. The variety of profiles of the project partners will provide an overall perspective of the sector and will enable the identification of its most urgent challenges. They will guide and coach students and researchers during the development of novel technical and ethical-compliance solutions to implement ICT solutions in the health sector, especially the solutions related to the smart campus (SC) ATHIKA challenge. Authors envisage that the accurate monitoring, analysis, prediction, and management of comfort will lead to a reduction in the overall environmental footprint of educational environments while increasing the comfort of their users.
In this paper, we present the development and implementation of novel and advanced healthy SC by using comfort as a quality metric, based on ICT that relies on greater interaction between healthcare professionals, education communities, and technological experts. Available SC data are becoming massive, and needs to be handled in controlled environments, under proper ethical criteria. The goal is to establish a challenge-based learning program where teams of students from various disciplines and countries will compete to find solutions for our SC challenge. The devised solutions, or pretotypes, have been developed into prototypes, following a technology coaching (supported by universities) and the application-oriented coaching (conducted by the target company). This program will be used to reduce the learning and experience curve associated with targeting, developing, and implementing sustainability projects in university settings. The current paper introduces the research carried out in the smart campus challenge within the ATHIKA Erasmus+ project [21
Reaching a comfortable and responsive SC implies focusing on the two interrelated concepts: “smartness”, mainly related to addressing the problems cities face with the aid of information and communication technologies (ICT), and “healthy sustainability”, emphasizing citizens’ inclusion (students and faculty) and social wellbeing (social dimension), ecosystem protection (environmental dimension) and boosting of the local economy (economic dimension) [22
Nowadays, new ICTs make the real-time monitoring of university campus conditions possible. A variety of sensors and intelligent devices deployed throughout the campus can monitor pollution, noise, natural or artificial risks as well as epidemics, and manage public spaces and facilities to reduce or avoid negative impacts on educational community health. Our SC challenge also aims to build a platform capable of assisting contemporary university campuses in transforming towards sustainable and comfortable campuses by exploiting data from both existing data sets and on-field sensors. The proposed approach is based on an interdisciplinary digital twin modeling that can be integrated into existing decision support systems by providing quantitative hints and suggestions on architecting and ICT engineering sustainable policies. Using novel trends in ICTs—such as cloud computing, big data, artificial intelligence and Internet of Things—to process, visualize and analyze real-time data is now feasible to accurately monitor citizens and their interactions with the physical infrastructures, and thus, identify, learn, and act to improve the future public health conditions.
In fact, ATHIKA aims to (1) explore innovative approaches to contribute to the sustainable campus transformation, employing technologically advanced pedagogy in a multi-disciplinary way through ICT engineering and architecture frameworks, (2) propose innovative good practices for managing a university campus, involving data-driven sustainable products and service outcomes in order to support environmental policymaking and (3) use novel edge computing architectures for advanced submetering and distributed hybrid intelligence algorithms [23
]. Nevertheless, in this paper, the authors introduce a quantitative and measurable definition of comfort, together with the first-ever accurate and unbiased measurement of the concept. It includes the development of computational models and low-cost infrastructures for automated, resilient, and reliable data acquisition, storage, processing, and visualization of comfort. The innovative and scientifically grounded technologies of our proposal have been validated in our real-world university campus.
1.4. Framework-Based Methodology
Smart cities are usually associated with complex systems [24
]. Complex systems are defined as systems formed by heterogeneous elements that interact with each other and their environment [25
]. The diversity of these elements, the non-linearity of relationships between them and the multiple influences of the environment determine their complexity [28
]. Indeed, the level of complexity of smart cities and their ability to achieve urban sustainability has called for debate [29
]. Additionally, adding smartness to the city leads to an increase in complexity—and more complexity requires more energy [30
]. Therefore, in light of the debate surrounding the sustainability of smart cities and with the acknowledgment that smart campuses are similar to small smart cities [1
]—thus, potentially able to shed light on the debate—the methodological framework used in this work considers the smart campus as a complex system.
Under the umbrella of complexity theory comes the framework of complex adaptive systems (CAS) [25
]. CAS refers to systems that involve “a large number of components, often called agents, which interact and adapt or learn” [32
]. General top-level properties and features such as self-similarity, complexity, emergence and, self-organization induce CAS to be considered as an appropriate framework for the methodological sequence of the presented research project proposal on comfort in educational environments: agents (i.e., teaching and learning community, facility managers, and energy providers) and the system (i.e., physical and virtual educational environments) are adaptive, and the system is a complex self-similar collectivity of interacting, adaptive agents.
In juxtaposition with the vision of smart campuses as CAS, some authors model the IoT—an enabler technology for SCs—as a complex system too [30
]. To exemplify our SC modeling approach, we consider the increase in students’ comfort and energy efficiency. We allocate each space (e.g., classroom) with an agent with two goals. The first, concerning students’ comfort, the second, aiming at energy efficiency. The agent is responsible for sensing different properties of both students and classrooms through IoT sensors, gathering contextual information, and acting according to the desired level of comfort and energy efficiency through IoT devices. Therefore, we allocate several agents in the campus.
Agents in a multi-agent system (MAS) cooperate to maximize their goal [37
]. For example, given a determinate number of students in a classroom, the agent sets a level of comfort for the classroom. At the same time, the agent sets a determinate energy efficiency goal. Then, the agent needs to carry out actions to achieve a reasonable level of students’ comfort and energy efficiency. Additionally, the environment in which the agent operates might be modified by other agents and external factors. Modification by other agents might be due to their operation in other spaces (e.g., spaces on the same floor or building), and modification by external factors might be due to weather conditions, for example.
With regard to the characterization of the hierarchical structure of the system comprehended by IoT devices and agents (in our framework, guardians), we add a higher-level module providing a decision support system: the wise module. Therefore, IoT devices, the guardian module, and the wise module have a hierarchical relationship in the digital twin as well. IoT devices are deployed in a zone or section of an SC building, and the guardian perceives and acts on the physical world using those devices; therefore, the relationship between the guardian and the IoT devices is one-to-many. In turn, the wise module is connected to the guardians in a one-to-many relationship and contains the support decision system to coordinate the guardians, so they operate towards a common goal: students’ comfort and energy efficiency.
Essentially, at a lower scale, an IoT-enabled device is a system of software and hardware components; at an upper scale, in consideration of the model we propose, devices (sensors and actuators) cooperate to enable an agent to sense and actuate on the physical world (guardian), zooming out, agents in a MAS form a system (wise module), and beyond these scales, more systems of systems arise.
In addition, regarding the interaction between agents in a CAS and their implementation using ICTs, we now set our focus on the relationship between agents. The authors in [38
] compare network and complexity theories and define CAS as “a pattern of relationships among adaptive, self-organizing and interdependent elements (agents)”. As stated, our technological framework is under the umbrella of IoT technologies among other novel ICTs. To frame the relationships between agents—and the organizing dynamics of their relationships—we use the Social Internet of Things (SIoT) paradigm.
The SIoT [39
] promotes a scalable and flexible network structure between things. It enables things to be part of a social network to search for required services or things. The search is influenced by the trust assigned, subjectively or objectively, to each thing. In an SC, sensors and actuators might be placed at relevant locations such as classrooms. Then, according to the proposed SIoT relationships, sensors and actuators in a classroom create social relationships between each other, either by their closeness in space (called co-location relationships) or by their need to cooperate and work together to achieve a certain goal (called co-work relationships). Moreover, in the presence of an agent per classroom (the guardian), they create a hierarchical relationship; the agent on top, sensors, and actuators (things) at the bottom.
Some properties of our study (e.g., air quality, humidity, and temperature) might share a greater space than a classroom. For example, the temperature in a classroom dissipates and affects other classrooms in the same building. Consequently, when considering the spaces and locations in a building, agents need to cooperate to achieve balance and to improve students’ comfort and energy efficiency, agents in different classrooms and spaces cooperate and create SIoT relationships between them by using the wise
module. Furthermore, agents need to perceive the state of the physical world to validate that their acts work towards the desired behaviors in the digital twin model. In a large deployment, communication between all agents would create communication overhead. To reduce this overhead, the state of the world should be perceived within the agents’ neighborhood [33
Concluding, the SC is considered a small smart city in the scope of our research. SCs, similar to smart cities, are CAS but on a smaller scale, where heterogeneous elements adapt, interact, and create a pattern of relationships. The main elements in our SC model are IoT devices, guardians and the wise module, which have been modeled in the digital twin environment. The guardians and the wise module create relationships, interact, and adapt, whereas IoT devices (which may have limited resources) create relationships and interact. Additionally, we frame the potential relationships under the IoT paradigm called SIoT. The SIoT aims to provide a scalable and flexible network of things to facilitate their search and discovery, both processes influenced by security-related trust mechanisms. Those interactions are depicted in Figure 1
1.5. IoT Platforms
In the literature, there are only a few papers that present descriptions of current SC proposals [8
]. Nevertheless, authors in [41
] have carried out extensive research on previous SC designs and have encountered several examples. There are SCs based on the development of an open data platform or based on cloud computing, service-oriented architecture, and IoT platforms.
As stated before, the main principle of communication inside an IoT system implies that each collector node must “speak” the same language. In IoT, this is a big issue since there is a deluge of devices, each with its own language that does not follow the standards [44
]. However, this compatibility problem is solved through a middleware [37
] (i.e., a software that provides interoperability between incompatible devices and applications). In the literature, IoT middleware solutions are sometimes referred to as IoT platforms or IoT middleware platforms because generally, the middleware is a platform. However, as it is proven in this project, other middleware tools exist, such as building information modeling (BIM) or computational simulation software, which can act as a middleware [47
Various IoT platforms can be generally categorized into four categories known as (1) public traded IoT cloud platforms, (2) open source IoT cloud platforms, (3) developer friendly IoT cloud platforms, and (4) end to end connectivity IoT cloud platforms [50
]. Table 1
describes various platforms in each of these categories that could be used in deployments of smart cities and IoT environments [21