The increasing challenges caused by contaminated soil and frequent climate changes have significantly impacted traditional agricultural systems. Indoor aquaponic systems, which utilize nutrient-rich fish tank water to cultivate plants, present a viable solution by eliminating the need for soil and providing a controlled environment that mitigates the effects of climate change. However, maintaining a balanced nutrient composition for fish and plants remains challenging. Integrating the Internet of Things (IoT) into aquaponic systems enables realtime monitoring of nutrient levels, ensuring optimal system maintenance. In this paper, we present the development of a portable and low-cost Aquaponic Monitoring System designed to automate nutrient data collection, reducing the need for manual labor while enhancing system efficiency. This system not only addresses the current challenges but also opens up exciting possibilities for further optimization, ultimately increasing yield and sustainability in aquaponic farming.

The fast development of smart cities includes edging technologies such as Digital Twin (DT) that plays a critical role in developing and managing smart cities by providing dynamic, virtual representations of physical assets, systems, and processes. These digital replicas are continuously updated with real-time data collected from Internet of Things (IoT) devices, such as sensors, smart devices, connected vehicles, and monitoring systems, to enable city planners, policymakers, and operators to monitor, analyze, and optimize urban environments with unprecedented precision. To gain meaningful insights from collected data, it is necessary to have robust database systems capable of efficiently storing, organizing, and retrieving large volumes of information. Databases serve as the backbone of smart city applications, ensuring that IoT-generated data can be accessed quickly and reliably to support analytics, predictive modeling, and real-time operations. This paper examines MongoDB, a widely used nonrelational database technology, with the objective of exploring various database architectures and features. The analysis focuses on how these architectural choices influence the performance of smart city applications, providing insights into the factors that should be considered when selecting a database solution for IoTdriven environments.

The Internet of Vehicles (IoV), as one subset of the Internet of Things (IoT) in the smart transportation area, integrates vehicle networks with sensors and actuators. By connecting all sensors to the network, the IoV enables smart transportation (i.e., autonomous vehicles) and makes smart cities a reality. In smart transportation systems, roadside units (RSUs) capture all vehicle information and serve as gateways. However, smart transportation infrastructure has yet to mature in the current stage. RSUs are insufficient to support all vehicles. Meanwhile, the low computational capability of vehicles makes it challenging to recompute the driving route as the road environment changes. To address the problem of insufficient RSU coverage, one protocol called IEEE 802.11p enables vehicle-to-vehicle communication using relays. Nonetheless, data transfer among vehicles via relays is still time-consuming for a large-scale transportation network. To deal with the above issues, in this paper, we propose an IoV framework using digital twins (DTs) to digitize the IoV environment and assign nearby IoT gateways compatible with the RSU communication protocol. This framework lets DTs update the vehicle’s driving route based on real-time information. With a case study, we evaluate the efficacy of DT-assisted IoV based on communication latency and vehicle driving efficiency. Our evaluation results confirm that the proposed framework can efficiently enhance communication latency when the relay needs to pass through two or more vehicles and reduce travel time when vehicles receive updated route information at intersections.