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.