You only look once (YOLO) is being used as the most popular object detection software in many intelligent video applications due to its ease of use and high object detection precision. In addition, in recent years, various intelligent vision systems based on high-performance embedded systems are being developed. Nevertheless, the YOLO still requires high-end hardware for successful real-time object detection. In this paper, we first discuss real-time object detection service of the YOLO on AI embedded systems with resource constraints. In particular, we point out the problems related to real-time processing in YOLO object detection associated with network cameras, and then propose a novel YOLO architecture with adaptive frame control (AFC) that can efficiently cope with these problems. Through various experiments, we show that the proposed AFC can maintain the high precision and convenience of YOLO, and provide real-time object detection service by minimizing total service delay, which remains a limitation of the pure YOLO.

The growing concern about Indoor Air-Quality has accelerated the development of small, low-cost air-quality monitoring systems. These systems are capable of monitoring various indoor air pollutants in real time, notifying users about the current air-quality status and gathering the information to the central server. However, most Internet of Things (IoT)-based air-quality monitoring systems numerically present the sensed value per pollutant, making it difficult for general users to identify how polluted the air is. Therefore, in this paper, we first introduce a tiny air-quality monitoring system that we developed and, based on the system, we also test the applicability of the comprehensive Air-Quality Index (AQI), which is widely used all over the world, in terms of its capacity for a comprehensive indoor air-quality indication. We also develop design considerations for an IoT-based air-quality monitoring system and propose a real-time comprehensive indoor air-quality level indication method, which effectively copes with dynamic changes and is efficient in terms of processing and memory overhead.

Recently, the development of the Internet of Things (IoT) applications has become more active with the emergence of low-power wide-area network (LPWAN), which has the advantages of low-power and long communication distance. Among the various LPWAN technologies, long-range wide-area network (LoRaWAN, or LoRa) is considered as the most mature technology. However, since LoRa performs uplink-oriented communication to increase energy efficiency, there is a restriction on the downlink function from the network server to the end devices. In this paper, we propose cooperative downlink listening to solve the fundamental problem of LoRa. In particular, the proposed scheme can be extended to various communication models such as groupcasting and geocasting by combining with the data-centric model. Experiments also show that the proposed technology not only significantly reduces network traffic compared to the LoRa standard, but also guarantees maximum energy efficiency of the LoRa.

In order to construct a successful Internet of things (IoT), reliable network construction and maintenance in a sensor domain should be supported. However, IEEE 802.15.4, which is the most representative wireless standard for IoT, still has problems in constructing a large-scale sensor network, such as beacon collision. To overcome some problems in IEEE 802.15.4, the 15.4e task group proposed various different modes of operation. Particularly, the IEEE 802.15.4e deterministic and synchronous multichannel extension (DSME) mode presents a novel scheduling model to solve beacon collision problems. However, the DSME model specified in the 15.4e draft does not present a concrete design model but a conceptual abstract model. Therefore, in this paper we introduce a DSME beacon scheduling model and present a concrete design model. Furthermore, validity and performance of DSME are evaluated through experiments. Based on experiment results, we analyze the problems and limitations of DSME, present solutions step by step, and finally propose an enhanced DSME beacon scheduling model. Through additional experiments, we prove the performance superiority of enhanced DSME.

In recent years, the governments in many countries have recognized the importance of data in boosting their economies. As a result, they are implementing the philosophy of open government data (OGD) to make public data easily and freely available to everyone in standardized formats. Because good quality OGD can boost a country’s economy, whereas poor quality can jeopardize its efficient use and reuse, it is very important to maintain the quality of data stored in open government data portals (OGDP). However, most OGDPs do not have a feature that indicates the quality of the data stored there, and even if they do, they do not provide real-time service. Moreover, most recent studies focused on developing approaches to quantify the quality of OGD, either qualitatively or quantitatively, but did not offer an approach to automatically calculate and visualize it in real-time. To address this problem to some extent, this paper proposes a framework that can automatically assess the quality of data in the form of a data completeness ratio (DCR) and visualize it in real-time. The framework is validated using the OGD of South Korea, whose DCR is displayed in real-time using the Django-based dashboard.

Journal of Information Processing Systems