Thai Minh Truong

Latency and connectivity problems in traditional cloud computing platforms regularly hinder the use of Internet of Things (IoT) systems for smart agriculture, especially in remote rural areas. Network quality is an essential element of centralized architectures since they require continuous telemetry transmission to support real-time control decisions. This sensitivity leads to delayed control responses and possible over-control of high-capacity farm machinery, raising operating expenses. To solve these problems, this paper proposes a lightweight edge computing architecture based on embedded microservices for fast, reliable, and automated control. The thresholds for different environmental conditions are set directly on the ESP32 microcontroller, and a wireless sensor network is used to create a fully local control loop. This configuration enables edge devices to autonomously and accurately recognize anomalies in environmental conditions at the monitoring site, eliminating the need to await cloud server processing. The experimental system was built to demonstrate the benefits of on-premises processing over cloud-based processing by carefully measuring trigger latency and fault tolerance under different network conditions in rural areas. The results indicate that the Edge architecture reliably lowers network round-trip latency, reducing overall trigger latency by 40.09% compared to the cloud platform. Without network-related trigger delays, the system lets local hardware make control decisions quickly and reliably, even if the network goes down completely. Additionally, the architecture substantially decreases unnecessary energy consumption by heavy agricultural actuators, such as aeration pumps used in aquaculture farms. In summary, this decoupled architecture improves real-time response and energy efficiency. It also makes it easier to add fog computing and distributed artificial intelligence to smart agriculture systems.

Smart campus is developed to deliver intelligent, user-centric services by leveraging IoT and big data to optimize the management of resources, spaces, and campus-wide activities. Its architecture relies on three core technological pillars: (1) Internet of Things (IoT) for collecting real-time data from the physical environment, (2) cloud computing for processing and storing both spatial and non-spatial data at scale, and (3) intelligent analytics that apply machine learning and data mining for automated decision-making and anomaly detection. Among these, spatial data, especially indoor spatial data, plays a vital role in enabling services such as indoor navigation, resource allocation, and environmental monitoring. However, the lack of standardization and poor interoperability with IoT systems remain key barriers to the effective use of indoor spatial data. To overcome this, this paper proposes a unified approach that leverages the Indoor Mapping Data Format (IMDF) as part of the spatial data infrastructure (SDI) for smart campuses. By integrating IMDF with the OGC SensorThings API, referred to as the digital nervous system (DNS) in a smart campus architecture, the approach helps build a flexible, real-time responsive indoor mapping system. This solution aims to standardize and optimize the connection between IoT data and indoor maps, thereby improving user experience and operational efficiency of smart campuses.

The rapid advancement of Internet of Things (IoT) technologies presents significant opportunities to enhance agriculture and aquaculture practices. This paper proposed a software architecture designed based on microservices to address the complex and evolving requirements of contemporary agriculture and aquaculture systems locations. These systems demand a versatile platform capable of integrating a wide range of functionalities, including environmental monitoring, aquaculture management, and comprehensive record-keeping for crops and aquatic life. The proposed IoT system employs a microservice architecture that ensures modularity, scalability, and flexibility. This approach facilitates the seamless integration of new services and features, easy adaptation to existing systems, and efficient reuse of established components. The microservice-based architecture has been successfully implemented in the TC2 project at Can Tho University under the Japan International Cooperation Agency (JICA) initiative. The project is in remote areas through the deployment of a network of sensor and actuator nodes, supported by a robust backend and user-friendly Front End applications. This implementation demonstrated notable improvements in operational efficiency and resource management. By leveraging edge computing for real-time data processing and integrating cloud-based analytics, the system achieved reduced response times and minimized data transmission costs. The modularity of the microservices architecture also enabled easy scalability and adaptability to change environmental conditions. The success of this implementation highlights the potential of microservices-based IoT solutions to drive innovation and enhance practices in agriculture and aquaculture, laying a strong foundation for future advancements in these fields.

This article presents an environment monitoring model for agricultural production applied to farmers in the Mekong Delta, Viet Nam. The proposed model's special feature is a system combining techniques such as IoT, agent-based, sensor networks, and data warehouses. Successfully deploying the model in monitoring the water level, and the environmental factors of the rice soil such as NPK, pH, EC, temperature, and soil moisture not only aids managers and farmers monitor environmental quality indicators in real time, but also experts to analyze data, give warnings and appropriate response solutions in agricultural production according to the pivot strategy towards Agriculture 4.0 proposed by the Minister of the Ministry of Agriculture and Rural Development of Viet Nam.

Mô hình lúa - tôm là mô hình canh tác truyền thống của nông dân huyện An Biên, tỉnh Kiên Giang, hoạt động khai thác mô hình chủ yếu từ kinh nghiệm dân gian, hiệu quả kinh tế thấp. Công nghệ IoT và mạng cảm biến được ứng dụng trong quản lý môi trường trong mô hình canh tác lúa - tôm ở huyện An Biên tỉnh Kiên Giang nhằm xây dựng hoàn thiện quy trình kỹ thuật vận hành mô hình, nâng cao lợi nhuận cho người canh tác lúa tôm. Trong thực nghiệm, hệ thống quan trắc ứng dụng công nghệ IoT và mạng cảm biến được dùng để thu thập, giám sát và quản lý dữ liệu của các yếu tố môi trường (độ mặn, pH, NH4, DO, nhiệt độ) trong mô hình canh tác lúa - tôm ở 4 xã của huyện thuộc huyện An Biên đã được triển khai thiết kế, lắp đặt và vận hành. Hệ thống này giúp cho nông dân chủ động quyết định thời gian phù hợp để cấp nước thêm cho ruộng tạo sự ổn định của chất lượng nước trong ruộng nuôi, nâng được tỷ lệ sống của tôm,...