Automatic identification system (AIS) is a vessel radio navigation equipment that has been determined by international maritime organization (IMO). Historical AIS data can be utilized for anomaly detection, trajectory prediction, and vessel trajectory planning. These benefits can be achieved by identifying the vessel's trajectory pattern through trajectory clustering. However, more effort is needed in trajectory clustering using AIS data due to their large volume and the significant number of deficiencies. In addition, trajectory clustering cannot be directly applied to trajectory data, which also applies to vessel trajectory. Therefore, we propose a trajectory clustering framework by combining douglas peucker (DP), longest common subsequence (LCSS), multi-dimensional scaling (MDS), and density-based spatial clustering of applications with noise (DBSCAN). Our experiments, carried out with AIS data for the Lombok Strait, Indonesia, showed that the trajectory compression with DP significantly accelerates the similarity measurement process. Moreover, we found that the LCSS is the optimal algorithm for similarity measurement of vessel trajectories based on AIS data. We also applied the right combination of MDS and DBSCAN in density-based clustering. The proposed framework can distinguish trajectoriess in different directions, identify the noise, and produce good quality clusters in relatively fast total processing time.

Smart cities today can utilize vehicular delay tolerant networks (VDTN) to col-lect data from connected-objects in the environment for various delay-tolerant applications. They can take advantage of the available intelligent transportation systems (ITS) infrastructures to deliver data to the central server. The system can also exploit multiple and diverse mobility patterns found in cities, such as privately owned cars, taxis, public buses, and trams, along with their vehicle-to-everything (V2X) communications capabilities. In the envisioned convergence between the ITS and V2X, we believe that a simple and efficient routing protocol can be deployed for the delay-tolerant data delivery, contrary to the implemen-tation of optimized solutions that might be resource-demanding and difficult to standardize. In this paper, we analyzed the performances of four baseline VDTN routing protocols, namely: direct delivery, first contact, epidemic, and spray and wait, to understand their strengths and weaknesses. Our simulation results high-lighted the trade-off between distinct approaches used by those protocols and pointed out some gaps that can be refined. This study provides new interesting ideas and arguments towards developing a simple, efficient, and high-performing routing protocol for data collection in smart cities.

Automatic Identification System (AIS) is radio navigation equipment for a vessel that has been required by the International Maritime Organization (IMO). The AIS dataset contains vessel information and vessel position. Various analyses can utilize the availability of AIS's extensive data history. In those analyses, it is necessary to know the vessel's trajectory pattern. With the development of data mining techniques, vessel trajectory patterns can be obtained by clustering. However, AIS data cannot be directly used in the clustering process. Data pre-processing is required due to the complexity of the trajectory data and the need to reduce noises in AIS data with large sizes. This study proposes a pre-processing model with data cleaning, trajectory extraction, and trajectory compression stages. Results show that the proposed model can reduce noise and, at the same time, reduce rows that will affect the following clustering process.

Automatic Identification System (AIS) data contains static and dynamic information for identification, tracking, and collision avoidance of vessels, as well as management of maritime activities. In the utilization of AIS data, a data pipeline framework is needed which includes the process of capturing messages, translating messages, detecting data corruption, cleaning damaged data, reconstructing trajectories, visualizing data, and storing data for analysis needs. Usually, the analysis and storage of AIS data are done offline which makes it not conducive to understanding vessel dynamics. In addition, large AIS data contains incomplete and noisy information that affects the quality of the trajectory. In this context, this article has proposed an AIS data pipeline framework in the form of pre-processing and real-time trajectory reconstruction. Pre-processing is intended to eliminate data anomalies caused by transmission errors. While the trajectory reconstruction process to overcome missing values due to noise is based on the Cubic Spline interpolation technique. All processes in the data pipeline framework are streamed using Apache Kafka. The simulation results show that the proposed AIS data pipelines framework has succeeded in visualizing the data flow in the form of a map application and trajectory reconstruction in real-time.

The rapid growth of population and urbanization poses significant challenges to global food security, particularly in urban areas. The conversion of agricultural land into residential and infrastructure zones reduces local food production capacity, while climate change exacerbates uncertainties in crop yields. To address these challenges, IoT-based vertical farming has emerged as an innovative solution to enhance efficiency and sustainability in food production systems. IoT technology enables vertical farming systems to monitor and control environmental variables such as temperature, humidity, lighting, and nutrient levels in real-time through sensors connected to artificial intelligence. The collected data is analyzed to optimize plant growth, minimize resource waste, and maximize crop yields while reducing energy consumption. Additionally, integrating IoT with automated irrigation systems and energy-efficient LED lighting further enhances water and electricity efficiency. From a sustainability perspective, IoT-based vertical farming allows for year-round food production without relying on vast land areas or favorable weather conditions. This research further explores how IoT contributes to improving resource efficiency, environmental sustainability, and the economic and social impacts of vertical farming. . Based on the research findings, the implementation of IoT in vertical farming has proven to be highly beneficial in enhancing sustainability and resource efficiency in urban food production. Through real-time monitoring and automated control systems, IoT enables precise regulation of key environmental factors such as temperature, humidity, lighting, and nutrient levels, ensuring optimal plant growth with minimal resource wastage.