LoRaWANs play a critical role in various applications such as smart farming, industrial IoT, and smart cities. The strategic placement of gateways significantly influences network performance optimization. This study presents a comprehensive analysis of the tradeoffs between system costs and bitrate maximization for selecting optimal gateway locations in LoRaWANs. To address this challenge, a rigorous mathematical model is formulated to incorporate essential factors and constraints related to gateway selection. Furthermore, we propose an innovative metaheuristic algorithm known as the M-VaNSAS algorithm, which effectively explores the solution space and identifies favorable gateway locations. The Pareto front and TOPSIS methods are employed to evaluate and rank the generated solutions, providing a robust assessment framework. Our research findings highlight the suitability of a network model comprising 144 gateways tailored for the Ubon Ratchathani province. Among the evaluated algorithms, the M-VaNSAS method demonstrates exceptional efficiency in gateway location selection, outperforming the PSO, DE, and GA methods.

The adoption of smart agricultural technology in rural areas is still limited in terms of network infrastructure supported. As a result, farmers continue to practice traditional farming that mainly focuses on human labor and requires experience in planning the production of agricultural products in unstable weather conditions, which makes the farmers highly risky. Currently, long range (LoRa) technology is a smart agriculture support tool that will enable the Internet of Things devices to a large number of end nodes distributed over a wide geographical area. They could access cloud computing from a long distance, kilometers, for processing via long range wide area network (LoRaWAN) communication protocol. When choosing a multiple gateway location for LoRaWAN networks, big networks must consider the spatial distribution of clients, radio signal propagation, and the cap on the number of devices served access. In this study, a mathematical model is developed to optimize coverage. The LINGO modeling program, an exact software method, was used to test the model. The findings indicated that the best six gateways at the optimal LoRaWAN gateway location. The gateways can provide signal coverage for all end nodes and can manage the capacity of the LoRaWAN gateway to support the proper number of end nodes.

This study investigates the various factors affecting the adoption of cloud computing in Thai SMEs by evaluating and contrasting the significance of these adoption factors. A comprehensive sample consisting of 384 SMEs from Northeast Thailand participated in the study by responding to detailed questionnaires that were designed for robust data collection. We employ various statistical approaches, including T and F tests, which incorporate ANOVA and MANOVA procedures, to examine the gathered data. The analysis results indicate an agreement among SMEs on the benefits that information technology provides to their business operations. This encompasses elements such as swift access to critical information, enhanced time efficiency in many procedures, and increased data accuracy, all of which are vital for effective corporate administration. Additionally, SMEs demonstrate a positive perception of cloud applications, particularly in areas such as problem-solving capabilities, data storage solutions, and their alignment with the specific operational needs of businesses. Furthermore, the diverse experiences and backgrounds of SME owners in Thailand play a crucial role in shaping their perceptions and attitudes toward the adoption of Information and Communication Technology (ICT), as well as their overall use of cloud computing. This dynamic interplay between individual experiences and technological adoption highlights the complexity of integrating cloud computing into the operational framework of SMEs throughout the region.

A Smart University serves as a crucial component in enabling the implementation of intelligent systems and driving innovation. Many international universities have taken the lead in initiating Smart University projects to align with the ever-changing educational landscape. Thailand is actively dedicated to pursuing a transformative agenda that is consistent with the 20-year national strategy (2018-2037) with the aim of promoting sustainable development. This study aims to examine the performance of the Wi-Fi network at Mahasarakham University in Thailand, with the objective of identifying factors that influence the delivery of network services, user satisfaction and network coverage. The findings will provide valuable information to enhance the system. The study focuses on assessing user behaviors related to network usage and evaluating user satisfaction across three dimensions. Wi-Fi network service, security system and service venue/facilities. In addition, the NetSpot software tool is used to measure the extent of Wi-Fi signal coverage. The research seeks to evaluate the effectiveness of the university's Wi-Fi network in supporting the transition towards a Smart University. The study's target population consists of students, professors and staff members at Mahasarakham University during the academic year 2022, which is a total of 45,426 individuals. The findings will provide valuable guidance to university management, enabling them to plan, analyze and design an efficient and effective smart IT infrastructure to facilitate the development of students, faculty and university staff toward a Smart University.

This study investigates a theory-driven causal model explaining the determinants of Smart Campus success in Thailand, drawing on the Technology–Organization–Environment (TOE) framework. Data were collected from 96 senior administrators across 126 higher education institutions using a validated questionnaire (IOC ≥ 0.50; Cronbach’s alpha = 0.992). Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) reveal that technological, organizational, and environmental factors each exert significant positive influences on four performance domains—economic, social, environmental, and governance—that together define Smart Campus success. The structural model shows a strong fit (χ² = 988.222, p = 0.0532; CFI = 0.998; TLI = 0.980; SRMR = 0.017; RMSEA = 0.059), and all sixteen hypotheses were supported (p < 0.05). Organizational readiness emerged as the most influential determinant, particularly for social outcomes (β = 0.326), emphasizing the importance of leadership commitment, institutional culture, and internal preparedness in digital transformation. Among the outcome domains, economic performance contributed most strongly to Smart Campus success (β = 0.416). Overall, the findings confirm the relevance of the TOE framework for understanding digital transformation in higher education and provide actionable guidance for policymakers and university leaders seeking to enhance innovation capacity, competitiveness, and sustainable development in Thai and similar institutional contexts.

Cybersecurity effectiveness in digitally intensive university environments depends not only on technological safeguards but also on how individuals enact protective behaviors within decentralized systems. While prior research has largely emphasized compliance intention, limited empirical attention has examined how behavioral mechanisms translate into measurable confidentiality, integrity, and availability (CIA) outcomes in smart universities. This study develops and tests an integrated structural model grounded in the Theory of Planned Behavior and Social Cognitive Theory to examine how contextual exposure, cognitive resources, and motivational processes jointly influence security outcomes. Using structural equation modeling (SEM), data from 540 respondents across multiple higher education institutions were analyzed. Behavioral intention (β = 0.489) emerges as the strongest predictor of CIA, followed by self-efficacy (β = 0.190). Cybersecurity knowledge influences CIA indirectly through attitudes and intention rather than through a dominant direct path. Technological exposure (β = 0.250) and social norms (β = 0.540) primarily strengthen knowledge formation. The model demonstrates strong empirical fit (CFI = 0.997; RMSEA = 0.057; SRMR = 0.015). These findings show that CIA protection in smart universities emerges through structured cognitive–motivational pathways in which awareness is transformed into capability and intention, rather than through technological exposure alone.