With the advancement of Internet-of-Things (IoT) technology, various domains, such as smart factories and smart cars have used this new technology to provide value-added services. In addition, technologies, such as multiaccess edge computing (MEC) and network slicing provide another opportunity for the IoT to support more advanced and real-time services that could not have been previously supported. However, the simple integration of such technologies into the IoT does not take the full advantage of MEC and network slicing or the reduction of latency and traffic prioritization, respectively. Therefore, there is a strong need for an efficient integration mechanism for IoT platforms to maximize the benefit of using such technologies. In this article, we introduce a novel architectural framework that enables the virtualization of an IoT platform with minimum functions to support specific IoT services and host the instance in an edge node, close to the end user. As the instance provides its service at the edge node, where the MEC node and network slice are located, the traffic for the end user does not need to traverse back to the cloud. This architecture guarantees not only low latency but also efficient management of IoT services at the edge node. To show the feasibility of the proposed architecture, we conducted an experimental evaluation by comparing the transmission time of both IoT services running on the central cloud and those using sliced IoT functions in the edge gateway. The results show that the proposed architecture provides twice as much transmission speed as that from the conventional cloud-based IoT platform.

The Internet of Things (IoT) allows machines and devices in the world to connect with each other and generate a huge amount of data, which has a great potential to provide useful knowledge across service domains. Combining the context of IoT with semantic technologies, we can build integrated semantic systems to support semantic interoperability. In this paper, we propose an integrated semantic service platform (ISSP) to support ontological models in various IoT-based service domains of a smart city. In particular, we address three main problems for providing integrated semantic services together with IoT systems: semantic discovery, dynamic semantic representation, and semantic data repository for IoT resources. To show the feasibility of the ISSP, we develop a prototype service for a smart office using the ISSP, which can provide a preset, personalized office environment by interpreting user text input via a smartphone. We also discuss a scenario to show how the ISSP-based method would help build a smart city, where services in each service domain can discover and exploit IoT resources that are wanted across domains. We expect that our method could eventually contribute to providing people in a smart city with more integrated, comprehensive services based on semantic interoperability.

Like what happened to the Internet of Things (IoT), smart cities have become abundant in our lives as well. One of the smart city definitions commonly used is that smart cities solve city problems to enhance citizens’ life quality and make cities sustainable. From the perspective of information and communication technologies (ICT), we think this can be done by collecting and analyzing data to generate insights. The City Data Hub, which is a standard-based city data platform that has been developed, and a couple of problem-solving examples have been demonstrated. The key elements for smart city platforms have been chosen and they have been included in the core architecture principles and implemented as a platform. It has been proven that standard application programming interfaces (APIs) and common data models with data marketplaces, which are the keys, increase interoperability and guarantee ecosystem extensibility.

Object detection supported by unmanned aerial vehicles (UAVs) has generated significant interest in recent years including applications, such as surveillance, search for missing persons, traffic, and disaster management. Location awareness is a challenging task, particularly, the deployment of UAVs in a global positioning system (GPS) restricted environment or GPS sensor failure. To mitigate this problem, we propose LocateUAV, a novel location awareness framework, to detect UAV’s location by processing the data from the visual sensor in real time using a lightweight convolutional neural network (CNN). Assuming that the drone is in an IoT environment, first, the object detection technique is applied to detect the object of interest (OOI), namely, signboard. Subsequently, optical character recognition (OCR) is applied to extract useful contextual information. In the final step, the extracted information is forwarded to the map application programming interface (API) to locate the UAV. We also present a newly created data set for LocateUAV, which comprises challenging scenarios for context analysis. Moreover, we also compress an existing lightweight model up to 45 MB for efficient processing over UAV, which is 19.5% when compared with the size of the original model. Finally, an in-depth comparison of various trained and efficient object detection and OCR techniques is presented to facilitate future research on the development of flex drone that can extract information from the surroundings of a location in a GPS-restricted environment.

In this pandemic situation caused by a novel coronavirus disease in 2019 (COVID-19), an electronic support system that can rapidly and accurately perform epidemic investigations, is needed. It would systematically secure and analyze patients' data (who have been confirmed to have the infection), location information, and credit card usage. The "Infectious Disease Prevention and Control Act" in South Korea, established a legal basis for the securement, handling procedure, and disclosure of information required for epidemic investigations. The Epidemic Investigation Support System (EISS) was developed as an application platform on the Smart City data platform. The EISS performed the function of inter-institutional communication which reduced the processing period of patients' data in comparison to other methods. This system automatically marked confirmed cases' tracking data on a map and hot-spot analysis which lead to the prediction of areas where people may be vulnerable to infection. The EISS was designed and implemented for use during an epidemic investigation to prevent the spread of an infectious disease, by specifically tracking confirmed cases of infection.

Gesture recognition is crucial in computer vision-based applications, such as drone control, gaming, virtual and augmented reality (VR/AR), and security, especially in human–computer interaction (HCI)-based systems. There are two types of gesture recognition systems, i.e., static and dynamic. However, our focus in this paper is on dynamic gesture recognition. In dynamic hand gesture recognition systems, the sequences of frames, i.e., temporal data, pose significant processing challenges and reduce efficiency compared to static gestures. These data become multi-dimensional compared to static images because spatial and temporal data are being processed, which demands complex deep learning (DL) models with increased computational costs. This article presents a novel triple-layer algorithm that efficiently reduces the 3D feature map into 1D row vectors and enhances the overall performance. First, we process the individual images in a given sequence using the MediaPipe framework and extract the regions of interest (ROI). The processed cropped image is then passed to the Inception-v3 for the 2D feature extractor. Finally, a long short-term memory (LSTM) network is used as a temporal feature extractor and classifier. Our proposed method achieves an average accuracy of more than 89.7%. The experimental results also show that the proposed framework outperforms existing state-of-the-art methods.