Smart Campus Applications: A Literature Review on Operations Research and Big Data
Corresponding email: moseslsinggih@ie.its.ac.id
Published at : 28 May 2025
Volume : IJtech
Vol 16, No 3 (2025)
DOI : https://doi.org/10.14716/ijtech.v16i3.6803
Singgih, IK, Prabowo, AR, Soegiharto, S, Singgih, ML & Dharma, FP 2025, ‘Smart campus applications: a literature review on transportation research and big data’, International Journal of Technology, vol. 16, no. 3, pp. 796-824
| Ivan Kristianto Singgih | 1. Department of Industrial Engineering, University of Surabaya, Surabaya 60293, Indonesia. 2. The Indonesian Researcher Association in South Korea (APIK), Seoul, 07342, South Korea. 3. Kolaborasi Ris |
| Aditya Rio Prabowo | Department of Mechanical Engineering, Universitas Sebelas Maret, Surakarta 57126, Indonesia |
| Stefanus Soegiharto | Department of Industrial Engineering, University of Surabaya, Surabaya 60293, Indonesia |
| Moses Laksono Singgih | Department of Industrial and Systems Engineering, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia |
| Fajar Pitarsi Dharma | Department of Industrial and Systems Engineering, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia |
Industry 4.0-related technologies are being implemented to improve societal well-being, and smart campus is a significant application area. Smart campus integrates various technologies for online class management, energy usage observation, warning systems, smart vehicles, and people flow management. Therefore, this study aimed to list and classify surveys on smart campus applications. Unlike previous reviews, a broad range of topics, and characteristics of smart campus applications were explored. A total of 43 studies were identified and screened using Google Scholar database according to the PRISMA framework. An overview of each application topic was provided, detailing its functions, related data, and technologies used. In addition, transportation problems related to these applications were identified, offering areas for future studies to optimize smart campus systems.
Review; Smart campus; Survey; Transportation
The rapid development of IoT, big data, and optimization techniques offers significant opportunities for new applications in universities. There is a necessity to improve education quality because the sector is an important source of innovation and a key factor in the development of smart cities (Zhuhadar et al., 2017). A smart campus is defined as a facility that combines technologies with physical infrastructure to improve decision-making and learning quality (Min-Allah and Alrashed, 2020). Moreover, the use of these technologies helps identify hidden problems and propose effective solutions to users (Berawi, 2022), which contribute to a more comfortable and convenient education (Muhammad et al., 2017). Effective education requires a conducive environment, such as appropriate lighting and temperature (Hakim et al., 2021). Apart from focusing on users, it is important to ensure the education facility management supports the concept of green campus and environment preservation (Fatriansyah et al., 2021). These issues can be addressed by collecting and integrating real-time data, and enabling fast decision-making (Singgih et al., 2016).
The current study was conducted to facilitate the understanding of recent developments in smart campus topics and propose better or new applications. In this context, understanding the main characteristics of smart campus system can promote its implementation by decision-makers. After providing an overview of all relevant topics, potential transportation problems within the smart campus are addressed. Meanwhile, the importance of identifying and solving problems in smart system has motivated studies in various fields, including smart logistics (Feng and Ye, 2021), smart manufacturing (Khakifirooz et al., 2019), smart waste collection (Jorge et al., 2022), and smart grid (Anjos and Gómez, 2017). Addressing operations research problems in the smart campus offers several benefits, namely (1) efficient and effective decision-making that reduces operational costs and time using the collected big data and (2) leveraging operations research computational methods to process big data for valuable decision-making. In addition, operations methods in smart system studies have been used to (1) minimize energy consumption or cost (Nutakki and Mandava, 2023; Mohajer and Mousavi, 2023) and (2) maximize user comfort (Khan et al., 2023). Therefore, effectively identifying and solving the operations research problems (transportation issues, in the context of the current study) can support smart campus to improve education quality, promote innovations, and ensure sustainability.
Literature Review
This study was compared with previous reviews on smart universities, as shown in Table 1. The reviews were searched using the Google Scholar database through the Publish or Perish 8 search engine (Harzing, 2023) with the keywords “smart AND (university OR campus)”. Based on Table 1, this study was novel for several reasons, namely (1) provides a clear and comprehensive list of characteristics for each smart university application, (2) includes a larger number of studies, and (3) covers a broader range of smart university application topics.
The contributions are as follows:
Studies on smart campus topics are listed and classified based on their application types and detailed characteristics (available functions, considered information, software, and hardware).
Possible definitions of transportation problems are proposed, which can serve as a basis for future studies to develop and apply appropriate solution methods.
Table 1 Novelty comparison with related review papers
The target studies were screened and selected using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) (Page et al., 2021), as shown in Figure 1. PRISMA is a well-established method (Murray, 2022) that (1) facilitates the reproduction of review due to its standardized review method (Liu et al., 2021) and (2) ensures a systematic and thorough review that guarantees quality (Lescinsky et al., 2022). At the time of the manuscript submission, the PRISMA framework had been cited over 43,000 times. Using PRISMA, the process started by identifying the source used to search for the papers, listing the number of papers found, and detailing the criteria for screening, leading to the final list for discussion.
Studies were searched using the Google Scholar database through the Publish or Perish 8 search engine (Harzing, 2023), with a maximum of 200 results due to journal page limitations. The keywords “smart AND university OR campus” were used, and studies between 1973 to 2022 were initially found. During the first screening, studies were filtered based on titles and abstracts, reducing to 92 studies. A total of 73 was obtained after removing inaccessible studies. In the final screening process, full texts were reviewed, and studies not related to smart campuses were excluded, resulting in 43 studies published between 2001 and 2020. Subsequently, review and classification were carried out and the results were verified. This review was not registered, and no protocol was prepared.
Figure 1 Review methodology in this study
General Overview of the Review Results
In this section, the statistics of the 43 reviewed studies were presented, with the majority having a significant number of citations. Table 2 presents the 30 most-cited while Figure 2 shows the number of published papers per year for all 43 selected studies.
The studies were classified based on smart campus application types, as shown in Table 3. These classifications were derived from previous reviews (listed in Table 1) as follows, (1) indoor/outdoor and energy monitoring (Min-Allah and Alrashed, 2020; Abuarqoub et al., 2017; Alghamdi and Shetty, 2016), (2) equipment monitoring and control (Abuarqoub et al., 2017; Min-Allah and Alrashed, 2020; Alghamdi and Shetty, 2016), (3) people flow monitoring (Muhammad et al., 2017), (4) class management (Min-Allah and Alrashed, 2020; Muhammad et al., 2017; Abuarqoub et al., 2017; Alghamdi and Shetty, 2016), (5) room and resource management (Min-Allah and Alrashed, 2020), (6) personnel activity and behavior management, (7) security (Min-Allah and Alrashed, 2020; Muhammad et al., 2017), (8) finance (Muhammad et al., 2017), (9) health and hygiene (Muhammad et al., 2017; Abuarqoub et al., 2017; Alghamdi and Shetty, 2016), (10) communication and data (Muhammad et al., 2017). Further subtopics within these classifications were defined and listed in Figure 3, ensuring that each class differed.
Table 4 presents the classification of the studies. Based on information from the listed papers, each smart campus application in Table 3 is detailed in Table 5.
The list includes functions of each application, distinguishing between manual and automatic features, and provides details on the relevant information considered.
Figure 3 provides the percentage of studies for each smart campus topic. This percentage represents the number of studies in each smart campus topic divided by the total number, namely 43. Insights that could be extracted from the reviewed studies related to smart campus concepts and applications are elaborated into the following points.
Table 2 Top 30 highly cited papers
Figure 2 Number of selected studies per year after the screening
4.1. Good Insights from the Studies
The variety of system purposes, along with the hardware and software used in the studies, showed that implementing a smart campus was a practical reality. Several surveys provided detailed explanations of how the hardware and software were selected and how the database used in smart campus systems was designed. The main challenge was designing an effective method to extract and analyze the data appropriately and provide suitable solutions to relevant decision-makers or users ( Channamally et al., 2025; Hakim et al., 2023).
4.2. Weakness of the Studies
Future studies were recommended to consider the following points:
Clearly defining the problem (objectives, input data) and providing a comprehensive software and hardware design to implement the smart campus concept. Some of the reviewed studies lacked comprehensive information, making the concept challenging to understand or implement effectively. Designing procedures that ensure reproducibility is important to produce good solutions for the benefit of many people (Camprodon et al., 2019).
Appropriate recognition and comparison with similar previous studies are necessary. Several studies proposed new smart campus applications without thorough comparison with existing ones. For example, using certain machine learning techniques without extensive comparison experiments with other methods. Constructing a comprehensive database to record each implementation of a topic could facilitate future updates.
Engaging users more in the smart campus system design and implementation is crucial (Kaisermayer et al., 2024; Chen et al., 2014). Effective system design should commence with user requirement identification, followed by benefit analysis for various system actors (campus as the regulator and individuals within the campus as users), and continuously improving the system design and operation. Campus should consider applying a more decentralized approach, allowing users to access open data and propose various solutions for smart campus topics. This approach can facilitate the development of smart campus system.
It is crucial to conduct studies that address more integrated problems. Most of the reviewed studies discussed each smart campus topic separately, potentially leading to decisions that optimize only a part of the whole campus system. The following are examples of integrated studies worth exploring, (1) route recommendation based on the populated area or predicted weather, (2) integrated room scheduling and energy-saving initiatives, and (3) people grouping suggestions to balance density levels (from the formed activities) across the whole campus area.
In the integrated problems, synchronization between decision-making in the related subproblems becomes important to ensure the good performance of the whole system (Zhuang et al., 2025; Saletti et al., 2022).
4.3. Managerial Insights for Smart Campus Technology Applications
It is essential to consider the following before decision-makers apply the smart campus architectures presented in Table 5.
Digital vs Smart
Being smart differs from being digital. Simply having the hardware installed to collect big data in the smart campus is insufficient to improve quality without the ability to analyze data effectively, specifically providing important information to users (Sumanthi, 2025; Coccoli et al., 2014).
Table 3 Classification of smart campus studies.
Table 4 Classification of studies based on subtopics
Combination of Historical and Real-Time Data
Considering that each user might utilize data differently, it is important to provide (1) historical data of specific user and other users with similar characteristics, and (2) real-time data to identify the current situation and make optimal decisions (Alvarez-Campana et al., 2017). A good example was the previously proposed framework (Singgih, 2021), where historical data were used to predict system performance, while real-time statistics were continuously updated and utilized to make the best decisions based on the system's dynamic behavior.
Data Security and User Right Issues
Discussions around data security and user rights issues are crucial. Most studies did not address how users are notified and asked for consent before sharing data. It is crucial to clearly discuss the types of data disclosed and data access privileges for each user type when data are shared anonymously.
Table 5 Characteristics of each smart campus application
Table 5 Characteristics of each smart campus application (cont.)
Table 5 Characteristics of each smart campus application (cont.)
Table 5 Characteristics of each smart campus application (cont.)
Table 5 Characteristics of each smart campus application (cont.)
Table 5 Characteristics of each smart campus application (cont.)
Table 5 Characteristics of each smart campus application (cont.)
Table 5 Characteristics of each smart campus application (cont.)
Table 5 Characteristics of each smart campus application (cont.)
Table 5 Characteristics of each smart campus application (cont.)
Table 5 Characteristics of each smart campus application (cont.)
Table 5 Characteristics of each smart campus application (cont.)
Potential for Combining Operations Research and Machine Learning Techniques for Solving Problems in Smart Campus
Most of the studies listed primarily discussed the smart campus concept and its applications, with a focus on machine learning methods. These studies used various approaches to combine operations research and machine learning techniques. Examples include:
- Implementing machine learning to predict certain data, which is subsequently used as input to solve operations research problem. For instance, predicting factors contributing to high throughput and using this information to optimize a production system (Singgih, 2021).
- Integrating machine learning-trained operators into metaheuristic algorithm for solving operations research problem (autonomous ridesharing operations) (Bongiovanni et al., 2022). These operators were trained using big data from ridesharing cases.
- Using machine learning techniques to solve operations research problems, such as reinforcement learning (Madabathula et al., 2025; Mazyavkina et al., 2021). This approach is often used when decisions need to be made quickly while maintaining acceptable quality.
Considering these combinations can help reduce costs (often used to evaluate operational decisions) by leveraging big data and machine learning-based data analysis results.
Figure 3 Number of studies for each smart campus topic
5. Transportation Issues in Smart Campus
This section provided a detailed information on transportation problems arising from the listed smart campus topics. Previous studies have shown the importance of addressing operations research issues in smart system, evident in smart manufacturing (Serrano-Ruiz et al., 2022), home appliances (Maurya and Nanda, 2023), and traffic lights (Younes et al., 2023). Moreover, addressing operations research problem is essential to optimize system operations and reduce total costs. Transportation-related studies were selected based on the following reasons, (1) routing problems have been studied with various variants over the years by researchers in the operations research community (Soares et al., 2023), (2) several new solution methods have been proposed in the last decade (Fleckenstein et al., 2023), and (3) transportation is a crucial aspect of sustainability (Mubarak and Rahman, 2020), as emissions from the transportation sector constitute a quarter of total emissions, particularly in Europe (Leviakangas and Ahonen, 2021). Based on these reasons, discussions were limited to the operations research issues related to transportation aspects. Dealing with transportation-related smart campus topics becomes possible due to (1) the advancement of information technology, e.g., online systems with recorded historical data (Handojo et al., 2023; Singgih and Kim, 2020), and (2) the vast development of more efficient optimization techniques (Singgih and Singgih, 2024) and revolutionary machine learning approaches (Singgih and Singgih, 2024). A representative overview of existing studies for each selected smart campus topic was provided, along with the input data, objectives, and decision variables (output) for each topic that follows a basic representation for a mathematical model (Riaventin et al., 2021; Singgih et al., 2021a; 2021b).
5.1. Vehicle Parking Location Recommendation
Parking lots at a smart campus use sensors to collect various data, including air quality and usage (Abdeen et al., 2021). Leveraging the collected data helps to understand parking behavior based on historical data, predict future usage, and optimize selection for vehicle users. An example of decision-making based on the collected data was presented in (Wu et al., 2021).
Operation research problems for parking lot recommendation in smart campus can be described as follows:
Input data:
Utilization of each parking slot (current status, historical data).
Historical data on users' recently preferred parking slots.
Current users' location and destination (e.g., building, meeting area, classroom).
Required time to park the car considering the current parking lot congestion level.
Objectives:
Minimizing the total walking distance/time for the users from the parking lot to destination.
Maximizing utilization considering potential future parking slot selections and opportunity loss based on users’ preferences.
Decision variables (output):
Best candidates for parking lot for the users, with information of their detailed convenience level (travel distance/time, congestion level).
5.2. Public Transportation Deployment
The traditional approach for planning and deploying the movements of public transportation assets relies on static routes and schedules. While these routes may be determined based on historical data of users’ movements, the drawback of this system lies in its predetermined routes for the whole day. This rigidity may not allow for optimal utilization of transportation assets based on hourly demand fluctuations. Flexible routes matching the real-time demand of the travelers within the campus are crucial. To achieve this, continuous collection of travelers’ data is essential (using WiFi connection established within the campus area or deploying sensors at strategic locations). These route and schedule updates can be easily communicated to users through smartphones.
An illustration of an intelligent transportation system incorporating the considered data was presented in (Cheng et al., 2022; Rojas et al., 2020). The deployment of public transportation could be designed as follows:
Input data:
Current routes and schedules of the same type of transportation assets.
Station locations, current routes, and schedules of different types of transportation assets. Examples of different transportation assets within a campus are buses and bicycles. Information on these different transportation assets can complement each other and influence travelers’ decision-making.
Real-time information on travelers at each station (number of travelers and the estimated waiting time before departure).
Congestion levels on roads within the transportation network.
Real-time travelers’ selection probabilities for different types of transportation assets (Su et al., 2022). This decision is significantly influenced by the current environment (weather, road congestion) and system conditions (movements of transportation assets, transportation assets, breakdowns).
Real-time updates on the activities travelers can participate after using the transportation assets.
Objectives:
Minimizing the waiting times of the travelers.
Maximizing the deviation between the occupancy level and the transportation assets' capacity. Overcrowded transportation assets reduce the comfort level of travelers.
Decision variables (output):
Updated routes and schedule of the transportation assets.
5.3. Route Recommendation based on Vehicle/People Flow Monitoring
Route recommendations for each person or users' vehicle should be provided based on real-time information on congestion of the area or road network to be traveled. The system can function effectively only when data are collected comprehensively, and algorithms with short computational times are used for route finding. Several routing (rerouting) procedures based on collected data were presented in (Mushtaq et al., 2023; Rahman et al., 2021).
The route recommendation based on vehicle/people flow monitoring results can be described as follows:
Input data:
People and vehicle density in the surrounding area.
Scheduled routes of other vehicles passing the area.
Historical data of individual travel speeds (estimated values based on period and area information).
Users’ preferences regarding which facility to pass (or not pass) during the movement.
Objectives:
Minimizing the travel time of the user or vehicle.
Decision variables (output):
Recommended routes for the user or vehicle.
Suggested travel speed required to avoid congestion.
Users’ satisfaction level when traveling through the suggested route, based on the facilities passed.
5.4. Sensor-based Waste Pickup
For waste collection at a smart campus, real-time information regarding waste bin fill levels (weight and volume as shown in (Henaien et al., 2024; Roy et al., 2022; Lozano et al., 2018) is crucial for scheduling effective collecting truck routes. The number of required truck routes can be reduced by ensuring that trucks pick up almost-filled waste bins. This smart system also ensures the comfort level of people on campus, as waste levels impact campus cleanliness and aesthetics (especially when bins are predicted to overflow, leading to waste accumulation outside the bins).
This sensor-based waste pickup process is described as follows:
Input data:
Locations of waste bins.
Current levels and predicted fill levels of waste bins.
Objectives:
Minimizing the total travel times of trucks.
Decision variables (output):
Waste collecting schedule (routes and departure times).
In conclusion, this study compiled smart campus papers, identifying 43 studies between 2001 and 2020. The surveys were subsequently classified into 10 main classes (each comprising several subtopics), and detailed information on functions, considered information, used software, and hardware was provided. The most frequently discussed topics included class management, indoor/outdoor, and energy monitoring. Moreover, transportation issues relevant to smart campuses were addressed. This study had the following limitations, namely (1) detailed data transfer scheme and their relation to hardware and software for each smart campus application were not included, and (2) recent studies were not incorporated into the review. The following steps were proposed for future studies, (1) formulating each proposed transportation problem, (2) designing the software and hardware required to obtain solutions for each problem, (3) implementing the proposed systems at campus to validate the technologies and ensure stakeholders’ benefits, and (4) conducting review without limiting the maximum number of search results to incorporate and analyze more recent studies.
There is no acknowledgement for this study.
Ivan Kristianto Singgih: Data curation, Formal analysis, Investigation, Software, Visualization, Writing – original draft, Writing – review & editing; Aditya Rio Prabowo: Resources, Validation; Stefanus Soegiharto: Project administration, Writing – review & editing; Moses Laksono Singgih: Methodology, Conceptualization, Supervision, Writing – review & editing; Fajar Pitarsi Dharma: Validation.
Conflict of Interest
There is no conflict of interests.
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