Non -residential buildings contribute to around 20% of the total energy consumed in Eu- rope . This consumption continues to increase globally . Smart building proposals (focused on Nearly Zero Energy Buil ding (NZEB ), air quality monitoring, energy saving with thermal comfort, etc.) were already necessary before 2020, and the pandemic has made this research and development area more essential. Furthermore, the need to meet the Sustainable Development Goals (SDG) and obtain technological solutions based on the Internet of Things (IoT) requires holistic contributions through real installations that serve as spaces for measuring, testing, study and research. This article proposes a “measure –analyse –decide and a ct” methodology to quantify the Smart Readiness Indi- cator (SRI) for university buildings as a reference environment for energy efficiency and COVID -19 prevention models. Two conceptual spaces (physical and digital) within two dimensions (users and infrastructures) are designated over a n IoT three -level model (information acquisition, interopera- ble communication , and data -driven decision). A n IoT ecosystem ( sensoriZAR ) was implemented as a proof -of-concept of a smart campus at the University of Zar agoza , Spain. Focused on CO 2 and energy consumption monitoring, the results show ed effectiveness through real installations , demonstrat ing the IoT potential as SDG -enabling technologies. These contributions allow not only experimental lab tests (from the authors ’ expertise in several specialties of Industrial, Mechanical, Design, Thermal, Electrical, Electronic, Computer and Telecommunication Engineering) but also a reference model for direct application in academic works, research projects and institutional init ia- tives, extendable to professional environments, buildings and cities. Keywords: Internet of Things (IoT) for Sustainable Development Goals (SDG) ; university campus as living lab; energy efficiency building ; Nearly Zero Energy Building (NZEB); digital twins ; smart building technologies ; Smart Readiness Indicator (SRI) ; service design thinking ; educational spaces 1. Introduction Non -residential buildings ( e.g., public institutions, industrial factories, government buildings) contribute to around 20% of the total energy consumed in Europe, according to a 2020 special report by the European Court of Auditors. This consumption continues to increase globally [1]. Non -residential buildings, given th eir savings potential, should have priority con sideration in the European Union (EU) 2030 a genda to meet the Sustain- able Development Goals (SDG s) with the commitment to reduce energy consumption by more than 20% by 2020 and 32.5% by 2030 [2]. The EU says in its “ Renovation wave for Europe ” strategy [3] that a trend of renewal for Europe allows “ greening our buildings, creating jobs, and improving our lives ”. Its goal is to double the annual energy renewal rates in the next ten years. In 2014– 2020, the EU allocated approximately €14 billion to improving the energy efficiency of buildings. In addition, member states budgeted €5.4 billion in national co -financing for the improvement of all types of buildings. These reno- vations will improve the quality of life for the people who live in or/and use building s, preP rint · IoT as SDG Enabling Technology towards SRI for University Buildings 2 of 18 reduce greenhouse gas emissions and create up to 160 ,000 additional green jobs in the construction sector . Therefore, energy efficiency in buildings is becoming a determining factor for investment and financing in our environment . Furthermore, this strate gy is re- inforced with the action plan “European Green Deal”: an agreement to promote the effi- cient use of resources towards a clean circular economy , restore biodiversity and reduce pollution [4]. The plan describes how to obtain a just and inclusive trans ition for the EU to be climate neutral by 2050, following the proposal of the “European Climate Law” to turn this political commitment into a legal obligation [5]. In the last 20 –30 years, notable advances have been made in building energy simula- tion, but much less progress has been made in terms of measuring, analysing and learning about the real operation of the building -facilities integration. Therefore, a contributor to- ward these objectives is the use of continuous improvement cycles based on the “meas- ure–analyse –decide and act” methodology [ 6]. Thanks to analytical procedures for meas- urement and data -driven decisions, the corresponding audits of energy parameters in buildings can be carried out to achieve compliance with national and international regu- lations, meet goals established in the SDGs, and propose cost -effective solutions , such as the one presented in this article. The proposed methodology align s with the Strategy for Energy Rehabilitation in the Building Sector in Spain (ERESEE) [7 ], which pro poses a logical order for the actions: ( 1) reduc e demand through energy efficiency actions , (2) use highly efficient systems and ( 3) provid e the final energy through renewable energies, mainly produced in situ. Figure 1 shows the main technological advances and tools necessary to move towards climate neu- trality in the building sector from 2030 and reach a zero -emissions level by 2050. Following the ERESEE strategy, each milestone in Figure 1 occupies a circle, lo cated on the date of its maturity and proportional to its contribution. Moreover, the grey circles can be consid-ered information technology (IT) subareas , showing the need to monitor the facilities to quantify the ir performance and efficiency before and af ter the actions. To demonstrate the success of the improvements, data comparisons , quantitative conclusions and the publi- cation of results are essential to replicate the improvements in multiple areas. Figure 1. ERESEE strategy. Milestones towards climate neutrality , adapted from [ 7]. At present, the proposal for cost -effective solutions developed on data -based deci- sion-making involves technologies based on the Internet of Things (IoT) preferably [8]. IoT is one of the technological paradigms destined to exponentially increase the connec- tivity of various devices . A main strength of IoT is its high impact on the daily behaviour of potential users [ 9]. The unstoppable growth of IoT devices (currently more than 30 bil- lion connected in the world) provides an incalculable amount of data that, if correctly transmitted and processed (through cloud computing and similar technologies), will turn into usable information [1 0]. This information, conveniently stored, visuali sed and ana- lysed (using Artificia l Intelligence, Machine Learning, Process Mining, etc.), will generate preP rint · IoT as SDG Enabling Technology towards SRI for University Buildings 3 of 18 knowledge that can produce positive feedback in the acquisition and processing of the data and intelligent decision -making based on valuable data [1 1]. According to the United Nations E ducational, Scientific and Cultural Organization (UNESCO ), sustainable and in- novative solutions and systems require efficient, transparent and dynamic scientific ef- forts [1 2]. The objectives are industrial and commercial technological development and innovation guaranteeing the protection and confidentiality of data ; and promot ing the open availability of data such that third parties can offer valu able services, especially tech- nological innovation and open science in university and research environment s [13]. Following the first two points of the ERESSE strategy (demand reduction and perfor- mance increase), a relevant element is the ventilation load (which constitutes between 30% and 70% of demand). The CO 2 measurement is an essential factor in the current COVID - 19 pandemic and a key parameter for Demand- Controlled Ventilation (DCV) as a smart ventilation strategy [ 14]. Natural ventilation systems (simply , the opening of windows) do not allow adequate control over the entrance of outside air, making it di fficult to de- termine if the ventilation flow is meeting the needs of the indoor environment. In addition, natural ventilation eliminates the potential use of filtration systems’ heat recovery systems with thermal energy . Given that university buildings are characteri sed by significant in- termittency or variability in level s of occupancy, it is crucial to improve the performance of the mechanical ventilation automation and control system, adapting the outdoor air flows to the actual occupation of each premises , or thermal zone s [15]. Therefore, the most appropriate control in volves sensors that measure parameters of indoor air quality (CO 2) [16]. This improvement measure is expected to significantly impact university building rehabilitation plans. Since this research area is an emerging field, the available literature is still limited . Nonetheless, some related words can be highlighted. In [17], assuming this scope is a rel- atively new development, a comprehensive review was detailed by analysing the poten- tial of connecting Building Information Modelling (BIM) and smart buildings with IoT - based data sources. In [18], several examples of IoT implementation ( with in the last five years) in residential and commercial buildings were reviewed. Compared to the reviewed literature, the approach in this paper advances in the integration of open -source tools, open data and open hardware with ultra -low consumption technologies , such that this knowledge can be available to the population. Even so, several work s have been pub- lished on open -source IoT devices in academic buildings. In [19], a hardware IoT infra- structure was proposed for monitoring school buildings in Greece. A study by [20] devel- oped an IoT lab system to monitor the overall activities of the lab. The differential value of th e contribution s of this paper is that the entire buildings (not only researching labs) become an experimental test bed for the teaching /learning of current and future genera- tions. Thus, the methodology proposed constitute s a reference model for direct applica- tion in academic works, researching projects and institutional initiatives, extendable to professional environments , healthy buildings and cities . Finally, to promote energy efficiency through smart building technologies and fol- lowing the European Energy Performance of Buildings Directive (EPBD) [ 21], a key ele- ment is the Smart Readiness Indicator (SRI) [ 22]. This indicator qualifies the capacity of a building (or building unit) to adapt its general performance to the occu pant needs (also optimi sing energy efficiency) and to allow energy flexibility in the performance according to the network parameters. SRI is essential to make building owners and occupants aware of the value hidden in building automation and the digital s upervision of building tech- nical systems, providing considerable savings thanks to the new intelligent functionalities [23]. Previous work on the application of the SRI has been sought. Only one has been found [24], the authors evaluated the SRI of two bui ldings and detected some weak points in the methodology. This paper advances in this line, evaluating the SRI of the university buildings and especially taking advantage of this useful tool to propose ideas for retrofit- ting actions. preP rint · IoT as SDG Enabling Technology towards SRI for University Buildings 4 of 18 Smart building proposals , focused on Nearly Z ero Energy Buildings ( NZEB s), air- quality monitoring, energy saving s with thermal comfort, etc., were necessary before 2020, and the pandemic has made this research and development area more essential. Therefore, cost -effective and scalable solutions are required to apply smart building tech- nologies in different contexts, encompassing global scenarios. This article proposes a “measure– analyse –decide and act” methodology to quantify the SRI from a holistic per- spec tive. The research group includes a rchitecture and several specialities of industrial, mechanical, design, thermal, electrical, electronic, computer and telecommunication engi- neering. This transversal perspective is intended to serve as a reference environ ment of measur ements , testing, study and research for energy efficiency and COVID -19 preven- tion models . The Materials and Methods section details two conceptual spaces (for users and infrastructures, within two dimensions ( physical and digital) , over a n IoT three -level model (information acquisition, interoperable communication, and data -driven decision). The Results section shows several success stories, focused on CO 2 and energy consump- tion monitoring, through real installations as a proof -of-concept of IoT as SDG -enabling technologies. Finally, the Discussion section contains a critical discussion that supports the Conclusions section and proposes further studies. 2. Materials and Methods Learning implies a performance: a factory (from Latin , facere). The university envi- ronment has traditionally been seen as a support for knowledge exchange. However, as the COV ID-19 pandemic has shown, digital technologies allow new teaching /learning methodologies in digital spaces. Thus, rethink ing university spaces is necessary, as places for not only information exchange and production but also meeting s and dialogue, in which there is learning by doing. To achieve this objective, this article first proposes a strategic contribution of concep- tual spaces (physical and d igital) to integrate smart NZEB s and IoT ecosystems as SDG - enabling technologies. A second executive contribution is an IoT three- level model (data acquisition, interoperable information and knowledge by data -driven decision s) to build a methodology based on continuous cycles of “measure –analyse –decide and act”. This iterative methodology allows for efficient adaptation to new building contexts and uses or unexpected circumstances. 2.1. Conceptual Spaces This proposal relates physical space and digital spac e based on methodologies such as Community [2 5] and CUA [2 6], which are related with technological Service Design Thinking models [27]. In physical space , movements and interactions occur with environ- mental parameters. In digital space , the information obtained by our senses is completed from large amounts of data, acquired in real time by sensors and transformed into infor- mation, knowledge and valuable services). As outlined in Figure 2, physical space includes smart sensors ( e.g., CO 2, humidity, temperature, light, presence and energy consumption) and user experiences ( e.g., ventilation, thermal comfort, consumption and cost saving s, and energy efficiency). In parallel, digital space includes system s and networks ( e.g., inter- connection devices, IoT gateways, databases, cloud computing services and visualis ation dashboards) to create a digital education ecosystem with customi sed services , such as on - line classrooms, e -learning tools, virtual teaching technologies and smart campus es, for students, teachers and university staff. The challenge is to act on physical space from digital space. This means to project and install a complete network of sensors and data acquisition devices to monitor, control and visuali se space behav iours. The results lead to data -driven decisions, energy savings, energy efficiency and cost -effective solutions , provid ing knowledge -based value. preP rint · IoT as SDG Enabling Technology towards SRI for University Buildings 5 of 18 Figure 2. Infrastructures and users as conceptual spaces (physical and digital) . 2.1.1. Physical Space In this work, at the infrastructure level , several sensors were included to measure CO 2 level (ppm), relative humidity (%), temperature (°C), light (lumens), presence (pax) and energetic consumption (kWh) (see Figure 2) . At the user level (the dimension in which peo- ple move and perceive these air conditions, humidity and lighting, thermal comfort, etc.), several of the services include look ing for location indications, request ing information about open/closed windows or empty/occupied classrooms and offices and having secu- rity in case of emergency, among others. In short, our user experience—at individual and collective level s—within a building is influenced by internal and external conditions , such as lighting or weather , and generates an impact both on cost savings and energy efficiency (see Figure 2). Thus, th is evolution of university buildings towards an experimental lab test launch es transversal actions, improves the response to society, and reduces the envi- ronment al impact while increas ing the economic and social impact on the SDGs . With th is information and real -time responses , better experiences can be promoted. In addition, the use of spaces can be optimised , improv ing their habitability, indoor climate and meeting accommodations . Within the university environment is a catalyst for dialogue, activity and the exchange of ideas and knowledge. Smart physical spaces allow tangible interaction with the building with the following value -added services: • Parameteri se, characteri se and model each o ne space, studying its status and behav- iour for different uses and external conditions. • Monitor energy consumption, weather variables, installation performance, etc. to move towards smart buildings. • Incorporate renewable energies and visuali se their utilisation , production, consump- tion, efficiency, savings, etc. • Follow up indoor thermal conditions, indoor air quality, occupancy levels, etc. • Assess and e xtrapolate the obtained results to added -value SDG -aligned services , such as smart use, energy efficiency and rehabilitation, and predictive and adaptive maintenance. • Improve DCV, as smart ventilation strategy, based on the patterns obtained from the measured data (CO 2, humidity, temperature, etc.). preP rint · IoT as SDG Enabling Technology towards SRI for University Buildings 6 of 18 • Produce renewable (phot ovoltaic) energy from the smart and digital infrastructures. • Transform traditional spaces into green and environmentally sustainable spaces for digital education. • Facilitate the decision -making processes for the smart use of every space. 2.1.2. Digital Space In this work, at the infrastructure level , several systems and networks were included for various purposes, such as air conditioning based on weather and setting lighting levels according to classroom occupation and timetables . This is achieved through IoT gateways, interoperable databases, cloud computing services, visuali sation dashboards, etc. (see Fig- ure 2). At the user level ( the dimension in which information managed at the infrastructure level evolve s into knowledge), several smart servic es of digital education were included: on-line classroom s, e-learning tools, virtual teaching, smart campus es, etc. (see Figure 2). This digital space can be a teaching /learning environment that integrates full–, semi – and non– face-to-face methodologies, i ncorporating technological tools, such as e -learning platforms or on -line meeting tools. This flexible adaptation to the specific needs of every user profile ( faculties , staff, students and visitors) may allow some of the following fea- tures : • A unified and friendly environment to easily access a virtual and customi sed class- room, teaching -learning contents, university management, etc. • Open tools to improve consolidated environments. • Visibility of class offerings, including their methodologies and transdisciplinary learning, to enable students to choose their own curricula. • Transparency of the quality teaching offer ings, facilitating the integration and access of incoming students regarding their interests, training objectives, etc. • An active, adaptive and transparent environment for university management, us ing metrics and quality results. • Digital twins as virtual support to show available services in a unified and harmo- nised way to the university community. • Integrat ion of all digital spaces (classrooms, offices, laboratories, webinars, etc.) to interconnect users (students, teachers and university staff) with academic and man- agement activities. • Access , in a continuous, adaptive, transparent and user -friendly way , to heterogen e- ous services classes, practices, jobs, projects, tutorials, evaluation, reservations, no-tices and messaging, through open and geopositioned data. 2.2. IoT Three -Level Model From previously detailed concepts of physical and digital spaces , this article pro poses an IoT three -level model: data –information –knowledge , as outlined in Figure 3. In this con- text, we consider ed the definitions for data, information and knowledge. Data consists of raw facts represented by numbers, sounds, images, measurements, etc. Information is the collection of data organi sed and processed to provide additional value beyond the indi- vidual facts . The added value depends on the relationships defined and identified in the data set. Finally , Knowledge is the understanding of a set of i nformation and the ways that information can be made useful to support specific tasks or make decisions, i.e., to decide and act. preP rint · IoT as SDG Enabling Technology towards SRI for University Buildings 7 of 18 Figure 3. IoT three-level model for “measure –analyse –decide and act” methodology . 2.2.1. Data Level Data are obtained from different kinds of sensors and systems that measure various parameters of interest , such as the following : • CO 2, lighting levels, temperature and humidity in different locations of the university buildings , e.g., corridors, offices, classrooms, restaurants and librar ies, to assess ther- mal comfort and air quality conditions and determine when to use air conditioning systems or natural ventilation (opening windows, ventilation systems, etc.). • Number/presence of people in the different rooms , obtained by different Quick Re- sponse (QR) code verification systems and mobile devices monitoring. • Real -time e lectricity consumption, identify ing unforeseen consumption, detect ing unwanted ignitions, analysing specific information through Supervisory Cont rol and Data Acquisition (SCADA) systems, such as temperatures in the heat -transfer fluids in supply and return. • Electricity generation levels of the photovoltaic panels in order to analyse the effi- ciency of the renewable energies. • Timetables and other par ameters related to the mobility of the people in the univer- sity buildings , such as heat maps and access frequencies. As shown in Figure 3, these collected data are sent to the following communication level s by various interconnect ed devices through their associated connectivity technolo- gies, such as Bluetooth, WiFi and LoRaWAN , among others. In more detail, in the Archi- tecture subsection, a six -layer ecosystem based on this IoT three- level model, developed as a proof -of-concept of the methodo logy proposed in this paper, is also described. Ac- quisition and Ingestion layers of this six -layer system corresponds to the data level. Thus, Acquisition layer is composed of a network of different kinds of sensors and routers preP rint · IoT as SDG Enabling Technology towards SRI for University Buildings 8 of 18 whose main purpose is obtain ing data and measuring the different parameters described above. Furthermore, the entire process of installation and calibration of sensors and infra- structures were analysed in detail . For example, to guarantee the accuracy of CO 2 sensors, the calibratio n protocol included two criteria. First , from the technical information pro- vided by device manufacturers, a device uncertainty value was determined to be ±50 ppm. Second , the dispersion of the measurement variation was studied through a reference de- vice (t esto480©) with a calibration certificate [ 28]. The revision thresholds were then es- tablished as +65 ppm (upper limit) and −55 ppm (lower limit) with respect to the reference device. Figure 4 shows the dispersion of the measurement variation for several sensors (indicated by their location codes ( floor.space as 0.01, 0.02, etc.). Thus, those sensors that were not between the calibration thresholds (0.01, 0.04 and 3.09) required calibration . Fur- thermore, all collected data are sent to the Info rmation level (see Section 2 .2.2) through the Ingestion layer (see Architecture subsection), which is composed by a set of gateways and streaming techniques to enable the subsequent data processing. Figure 4. Calibration thresholds respect the certified reference device for CO 2 measurement . 2.2.2. Information Level These sensors and systems typically do not save measured data, but they are con- nected to various networks in which nodes receive and process = transform the data into information. These networks use different protocols to transmit data among nodes , such as a Programmable Logic Controller (PLC), depending on their features ( consumption , cost, etc.). Moreover, network protocols used by these devices are not normally based on Transmission Co ntrol Protocol and Internet Protocol (TCP/IP) standards, which are gen- erally used to optimi se and manage the storage and retrieval of data and information . Thus, sensor networks end up in a concentrating element ( e.g., SCADA system or Lora Server node) that acts as an IoT gateway to send the data to a TCP/IP network achieving the interoperability of the communication (see Figure 3). In the proposed six -layer ecosys- tem (see Architecture subsection), the Processing and Storage layers correspond to t his level. In more detail, different processes based on Extraction, Transformation and Load (ETL) rules are applied to save and store the organised data by considering different rela- tionships relevant for the domain considered. In addition , data are associ ated with metadata according to the best practices and standards defined by FAIRMODE, W3C, OGC -INSPIRE and other relevant specifications for the domain considered. Moreover, before proceeding to the next level, all information is analysed by iterative proc essing for consistency and reliability. Suitable data with a certain level of quality and appropriate data organi sation are key to the generation of knowledge, avoid ing bias or poor conclu- sions, because missing, incorrect or in valid data can compromise the result [12]. Thus, preP rint · IoT as SDG Enabling Technology towards SRI for University Buildings 9 of 18 statistical techniques to identify outliers and missing data are applied to verify the quality of the data and adjust, if necessary . 2.2.3. Knowledge Level The general purpose of this level is the development of services to predict, analyse and visuali se the use and performance of non -residential buildings by considering differ- ent models and parameters (see Analysis and Visualization layers in the Architecture sub- section) . These services generally make use of High -Performance Computin g (HPC) tech- nologies when a large set of heterogeneous and historical data is considered. In particular, three types of services are proposed: • Services to detect inadequate operations in the facilities , such as malfunctioning sys- tems, equipment that should be turned off at certain time slots, lower than expected performance, and high energy consumption. • Predictive services with multiple applications, such as those able to predict the real performance of the facilities under specific circumstances and comfort parameters for users. For this purpose, learning how the non -residential buildings work in real sce- narios is required. Thus, monitoring and acquiring historical data of the buildings and their context is key , e.g., data concerning the energy consumption of heaters and pumps, thermal inertias, and parameters about the passive behaviour of different rooms and buildings. • Prescriptive or recommendation services that support suitable data -driven decision - making. For example, knowledge about the behaviour of Heating, Ventilating and Air Conditioning (HVAC) can help determine the timetable to switch the facilities and equipment of the buildings on/off . As another example, Model Predictive Con- trol (MPC) supports an automatic and efficient contro l of the facilities (under a series of registry inputs) by considering the predictions obtained with the models. To develop these services, different types of models are considered: physical (white- box) models, models based only on data (black -box) and hyb rid (grey -box) models [2 9]. Hybrid models are often considered the most effective at balanc ing the trade -off between computational costs and prediction accuracy. A crucial task for the success of these ser- vices is the verification and validation/analysis o f the considered models to determine the quality of the results. 3. Results Following the “measure– analyse –decide and act” methodology, an IoT ecosystem (named sensoriZAR) was implemented as a proof -of-concept of a smart campus at the Uni- versity of Zaragoza , Spain. This smart campus includes three buildings: Ada Byron (built in 1998 with 14, 000 m2 for 1000 students and 250 university staff), Torres Quevedo (built in 1986 with 21, 000 m2 for 1500 students and 350 university staff) and Ag ustín de Betancourt (built in 1999 with 27, 000 m2 for 2000 students and 450 university staff). This wo rk details the obtained results, focused on CO 2 and energy consumption monitoring, during the re- cent design, development, implementation and technical val idation of sensoriZAR. 3.1. Architecture Firstly, the architecture of the sensoriZAR IoT ecosystem is presented, which includes both the infrastructure and the technological tools to create and manage all the processes within the “measure– analyse –decide and act” methodology. This architecture was de- signed to store a large volume of heterogeneous data (acquired at high speed), transform it into information, and provide added -value knowledge. The architecture is made up of six layers (see Figure 5 ): acquisition, ingestion, processing, storage, analysis and visuali- sation. • Acquisition. The first step is the origin of the data: where it is generated, at what speed, with what quality, etc. Firstly, th e acquisition layer solves the heterogeneity preP rint · IoT as SDG Enabling Technology towards SRI for University Buildings 10 of 18 of sensors, brands, models, manufacturers, etc. Secondly, this layer harmoni ses the connectivity problem between heterogeneous technologies, protocols, interconnec- tion devices, etc. that will communicate in an interoperable way with the IoT gateway at the next layer . Figure 5. Architecture and functional diagram of sensoriZAR . • Ingestion. The ingestion layer is responsible for the data through the IoT gateway. It can include a wide variety of techniques , such as publication, subscription, batch pro- cesses and continuous flows. For this work, the proposed ingestion layer consist ed of an IoT messaging server (MQTT broker), an event platform (Kafka) and a device management application (ThingsBoard). Thus, ingestion works through components specifically created for data collection with non- native sources or with indirect or restricted access. • Processing. After the ingestion layer, th e processing layer allows data transforming, if necessary, for later storage. Data processing can be implement ed by batch or flow proce ssing. Multiple technologies can perform this task, each with a specific purpose. Some technologies used in this project ( all open -source d) were Spark Streaming (for real-time , scalable data processing), Airflow (to manage, monitor and plan work- flows) and Spark (as a clustered computing framework for distributed data pro-cessing). • Storage. Generated data needs a medium storage, for subsequent consultation and analysis. To handle a large data set, distributed storage systems, object repositories or specific st orage technologies , such as graph and time -series databases , are required. Because of the data heterogeneity, this work used various storage technologies , in- cluding Kafka (as a distributed storage for raw data), PostgreSQL (as an object -ori- ented relational database management system), TimeScaleDB (as an optimi sed data- base for storing data as time series) and Cassandra (as a distributed storage, specific for time series). Thanks to this versatility, a broad integration of systems and services was achieved, along with the scalability, availability and reliability of the entire IoT ecosystem. preP rint · IoT as SDG Enabling Technology towards SRI for University Buildings 11 of 18 • Analysis. Many technologies exist for the extraction of characteristics, analysis and experimentation with the stored data. Because the tools are open -source , using stand- ard formats and protocols, the architecture is flexible enough to apply different tech- nologies depending on the analysis objective. T his project , specifically for the heter- ogeneous features of the collected data, combined several big data analysis te chnol- ogies, including Amazon Web Services (as tools and services set for cloud compu- ting), Amazon S3 (as a specific service for secure objects management in the cloud), Ceph (as an open analysis system, defined by software, specific for large amounts of distributed data) and influxdb (as a reliable management system to visualis e data time series, specific for IoT) , among others . • Visualis ation. Finally, the visuali sation layer is compatible with various display tech- nologies, panels, browsers, etc. For this w ork, two environments were integrated: ThingsBoard (for short -term monitoring and control) and Grafana (for structured in- formation from different data sources). 3.2. Infrastructures and Users Secondly , following the conceptual spaces proposal, this architecture was implemented at infrastructure and user level s, detailing in both cases their physical and digital space s. At the infrastructure level , Table 1 describes several alternatives to set -up implemen- tation by electing the best sui tability and minimal cost. Firstly, wired solutions were dis- carded because of their installation problem s and combinations with wired sensor s. Wire- less gateway s were also discarded due to their connectivity problem s. Following this, wireless sensors were a nalysed with both commercial solutions and open IoT cloud net- works. This last option was chosen for its good scalability and high adaptability with a medium cost. The wireless technology chosen was LoRaWAN because of its excellent in- door range, ubiquitous connectivity and high technological adoption [ 30]. Table 1. Costs and range comparative for set -up election. Set-up Sensor Cost (€) Connectivity Cost (€) Indoor Range (m) Disadvantages Advantages wired sensor wired gateway 10–25 150–250 100–250 installation low cost reliability wired sensor wireless gateway 10–25 150–250 15–40 connectivity low cost high adaptability LoRaWAN sensor commercial solutions 250–400 450–900 1000 –1500 high cost low adaptability good scalability LoRaWAN sensor open IoT network 250–400 250–400 1000 –1500 medium cost good scalability high adaptability With these premises, meaningful variables were real -time monitored from wired and wireless sensors. Wired sensors (over 300) were connected to the SCADAs to provide the air-quality parameters detailed in [ 31]. Wireless LoRAWAN sensors (46) were ad- hoc in- stalled to provide temperature, humidity, barometric pressure and CO2 level. The se-lected sensors were SenseCAP AU915 [ 32] and Aranet 4 Pro [3 3]. Their features, technical specifications and device details are summarized in [ 32] and [33] , respectively. Figure 6 shows several examples of their real installation in the three buildings of sensoriZAR: 15 in Ada Byron , 12 in Torres Quevedo , 19 in Agustín de Betancourt . At the user level , several user profiles were considered with different roles and inter- actions with sensoriZAR: ● Directorate and research —responsible for long term vision, coordination and system design. ● IT and IoT units —responsible for computer networks, servers and IoT infrastructure implementation. preP rint · IoT as SDG Enabling Technology towards SRI for University Buildings 12 of 18 ● Technical, security, green office units —responsible for building subsystems (lighting, HVAC, alarms, etc.) ● Maintenance and concierge —responsible for daily operation, data monitoring and problem solving. ● Students and university staff —main users of the system as data consumers and gen- erators. Figure 6. Sensecap [32] and Aranet [33] LoRaWAN sensors and their real installation in sensoriZAR. Each user profile requires different outputs from the system. For example, IT and IoT units need to monitor infrastructure to ensure the correct functioning of the system. Tech- nical units are the main data producers that aim to consume big data generated f rom other units. Maintenance and concierges are notified when an issue needs to be addressed (e.g., CO 2 level above 800 ppm or a malfunctioning sensor). Finally, students and staff are in- terested in their closest environments (or where they are heading), e.g. , the current CO 2 level in the room. In order to satisfy all needs, the user interface is able to provide a visual one- glance and colour -based perspective of how all the spaces in the buildings are working (se e Figure 7). The l eft area of Figure 7 shows all the sensors installed in the smart campus, grouped by building and ordered by floors. A green circle means good performance with a CO 2 level between 390 and 700 ppm ; yellow means necessary ventilation with a CO 2 level between 700 and 850 ppm ; red mean s a non- secure area with a CO 2 level higher than 850 ppm ; the tool icon means a necessary revision with CO 2 level lower than 390 ppm. The r ight area of Figure 7 shows in detail every real -time measurement from the selected sensor and the CO 2 level (ppm), t emperature (°C), relative humidity (%) and presence (pax). Furthermore, different mechanisms can be used to download data for third -party use and data- drive n decisions. This hierarchical navigation for each building’s space with detailed visualis ation (all variables and short -term data) was useful as indicated by users in the first user -experience validations. preP rint · IoT as SDG Enabling Technology towards SRI for University Buildings 13 of 18 Figure 7. Dashboard for real -time visuali sation and data -driven decisions . 3.3. Success Stories as a Proof -of-Concept of a Smart Campus The aim of this work serves as both a proof -of-concept of a smart campus and an application model in academic works, research projects and institutional initiatives; sev- eral success stories are presented. The f irst examples are focused on energy consumption monitoring and, derived from the COVID -19 situation, CO 2 evolution as analysed in a specific classroom . On the one hand, to quantify the instantaneous and seasonal value of the campus performance, the main indicators of the heat pumps were analysed : Energy Efficiency Ra- tio (EER), Coefficient Of Performance (COP), Seasonal EER (SEER), and Seasonal COP (SCOP). The buildings operate with an open -cycle geothermal heat pump system, using groundwater (30 m deep). As shown in Figure 8 (with resu lts obtained from data analysis from the last three years), the groundwater temperature is stable, although chang es ac- cording to the season. Furthermore, from the SCADA analysis, EER is roughly 6, a value useful as a real -time indicator. Thus, EER evolutio n shows the influence of maintenance operations or variations in boundary conditions (refrigerant change, exchangers cleaning, compressor repair, machine parameters programming, etc.) . This indicator contribute s to improving the SRI of the building in the heating and cooling system. Figure 8. Temperature (taken and returned to the ground) and flow rate measured in the ground- water used by the heat pump. preP rint · IoT as SDG Enabling Technology towards SRI for University Buildings 14 of 18 These studies have also allowed measurements of Energy Performance Indicators (EnPIs), as ISO 50001:2018 [34] recommends, such as the above -mentioned SEER and SCOP and Energy Baseline s (EnBs). These indicators serve as quantitative tools to annu- ally audit the installation and track both the device deterioration and the improvements implemented each year. As shown in Figure 9 (detailing EnBs for one of the campus build- ing from 2015 to 2018), the consumption baseline holds a consistent behaviour . Thanks to the proposed methodology of persistent data collection and analysis, inefficiencies in the operation of hy draulic water pumps were detected from the groundwater to plate ex- changers —the pumps operated for more hours than necessary for the air conditioning service with a significant electrical overrun (which was later reduced with remarkable savings). Figure 9 . Energy Baseline (EnBs) of Betancourt building (2015 –2018). Another key point of efficiency improvement through the proposed methodology was the smart management that kept the secondary circuit pumps running longer (after the primary circuit stop) to continuously harness thermal energy from the heat -transfer fluid in the building. On the other hand, the “measure– analyse –decide and act” methodology applied to spaces has shown the significance of situations of energy inefficiency. Some examples in- clude the effective occupation of university spaces, detection of unnecessarily air- condi- tioned areas outside work ing hours and unnecessary illumination of empty areas , among others . The use of intelligent systems allows the detect ion in real time of inefficiencies that condition the operation of buildings, and that taking measures allow s mak ing their man- agement more efficient. Figure 10 shows a comparative analysis of the energy consump-tion (electricity in blue and gas in red) of the different university building s. As Figure 10 illustrates, the diversity of construction and facilities of the university ’s building is very wide. Until the implementation of intelligent monitoring systems, the consumption data of the buildings were obtained from the energy billing and the information provided from the general electrical totalizers of the buildings. Current real -time monitoring is automat- ically integrated with SCADA through Siemens Sentron Pac 3200 digital energy multime- ter [3 5]. These consumptions have even been compared to standards levels: 238 kWh/m 2·year (following Technical Building Code , CTE [ 36], as the green threshold) and 120 kWh/m2·year (following Passivhaus international recommendation, as the orange threshold). As shown in Figure 10, the three buildin gs of the proposed proof -of-concept preP rint · IoT as SDG Enabling Technology towards SRI for University Buildings 15 of 18 of a smart campus ( Torres Quevedo , Agustín de Betancourt and Ada Byron in black) fulfil the CTE standard. These comparative studies allow the detect ion of both behavioral anoma- lies and a correct performance. Even so, they must be completed with intelligent monitor- ing systems such as those carried out with the aim of being able to deepen the inefficien- cies and in the possible solutions [ 37]. Figure 10. Primary energy consumption (kWh/ m2 · year ) for different buildings, indicating the smart campus fulfils the standard threshold . Finally, and as an example of the importance of facility analysis thanks to monitoring and follow -up, all this analysis show ed the improvement s obtained as detailed in Figure 11 in terms of economic cost and energy consumption. Figure 11 shows a reduction of more than 50% w hen evolving from buildings that use gas boiler (for heating and cool- ing) with compression chillers with outdoor air focus (20 .9–27.4 €/m2 and 414 kWh/m2) to HVAC buildings with heat pump s using groundwater (10 –14 €/m2 and 194 kWh/m2). Figure 11. Economic cost (€/m2 · year ) and energy consumption ( kWh/m2 · year ) in university buildings: improvement obtained in the smart campus . Derived from the COVID -19 situation, the system proved its utmost relevance to tackle high- risk situations. Figure 12 shows the CO 2 evolution in a specific classroom and the effectiveness of the system when the value approached the 700 ppm level but stayed preP rint · IoT as SDG Enabling Technology towards SRI for University Buildings 16 of 18 below the 800 ppm thresh old. This generated invaluable trust of the institution, not only of the students but also o f the staff. Figure 12. CO 2 evolution in a specific classroom . 4. Discussion The results of the proof -of-concept of a smart campus demonstrate that quantitative measurements are key element s for subsequent analys es and decision -making processes . The information collected through real -time monitoring of university buildings show ed both the data utility for continuous improvement and the need to increase the number of measurement devices to cover all campus spaces. Furthermore, the proposed approach is committed to open science in order to foster verifiable scientific knowledge subject to scrutiny and critique, the reproducibility of the experimental results, and rob ust decision - making processes. Measur ements of CO 2, temperature and humidity (among other key parameters) in every space work toward an objective and well -founded approach to establishing air con- ditioning schedules ( analysing the inertia of the building) a nd HVAC improvements. Thus, the proposed methodology assesses the performance of the installation and gener- ates quantitative indicators of the operation system, following the SRI perspective that the EPBD recommends [ 21]. Having quantitative patterns of HV AC behaviour will allow for the adjust ment of the ventilation systems . For example, open ing the windows (losing ther- mal comfort) in those spaces can be avoided with a correct ventilation air supply through HVAC ducts and diffusers. Furthermore, the digitis ation of the entire ecosystem will al- low data- driven decisions to move towards the automation of DCV systems. Likewise, to obtain higher energy efficiency, both the main building energy con- sum ption and performance HVAC indicators (COP and EER) should be mo nitor ed. Ad- ditional ventilation control, such as the better use of free- cooling hours (night and early morning) with outdoor air in summer, along with local demand control based on air -qual- ity sensors (CO 2) with damper -regulated local flow regulated ( level 4, maximum level, SRI ventilation) , can optimi se ventilation consumption. SRI of some university buildings has begun to be evaluated to contribute to transver- sal know ledge of smart campus. The first evaluated building has obtained a value of SRI = 16,45 (over 100). The use of this methodology has provided a large number of ideas for improvement in terms of monitoring, regulation and control, to improve the capacity of the building to adapt to the demands. These lessons learned lead us to continue taking steps towards digital twins. Experi- ments are being carried out to obtain predictive models to characteri se spaces using solid CO 2 pellets and analysing the evolution of CO 2 reduction curves in the classroom. These models are valuable for users with new (collaborative and interactive through digital tech- nologies) teaching /learning methodologies . In addition, the models help infrastructures with smart automation, regulation and control , becoming more efficient, effective, flexible and sustainable. preP rint · IoT as SDG Enabling Technology towards SRI for University Buildings 17 of 18 5. Conclusions Post -pandemic challenges to fulfil SDGs and improve energy efficiency require ho- listic contributions and data -based methodologies as it is proposed in this work. The n eed to measure to turn data into know ledge and quantify key efficiency indicators, such as SRI, is essential to make data -driven decisions. This paper contributed an open data IoT ecosystem ( sensoriZAR) , defining two conceptual spaces (physical and digital) based on Ser- vice Design Thinking, within two dimensions (for users and for infrastructures) over a n IoT three- level model (data, information and knowledge). A s a proof -of-concept of IoT and SDG -enabling technologies towards NZEB, the results showed that evolving to a smart campus as exper imental lab tests can achieve energy efficiency, performance audits, ultra -low consumption, and cost savings. Furthermore, this proposal of a university ref- erence model (for academic works, researching projects, institutional initiatives, etc.) is extendab le to smart buildings and cities and compatible with complementary proposals from other universities and initiatives to establish transversal contacts (through articles like this one) for joint collaborations. Author Contribution s: This research group is m ultidisciplinary and all the authors have contrib- uted in the entire manuscript. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Data Availability Statement: All the published data can be found in sensoriZAR ecosystem (https://sensorizar.unizar.es/ (accessed on 07/07/2021). ), Green University Office (https://ofici- naverde.unizar.es/ (accessed on 07/07/2021). ) and Open Data and Transparency Portal University of Zaragoza (https://datuz.unizar.es/ (accessed on 07/07/2021). ). Acknowledgments: Authors thanks to Engineering and Architecture School (EINA), Green Univer- sity Office and University of Zaragoza for their support in this work. Conflicts of Interest: The authors declare no conflict of interest.