Earticle

This study investigates a Cloud?Edge-sensors infrastructure using M/M/c/K queuing theory to analyze agricultural data systems’ performance. It focuses on optimizing data handling and evaluates the system configuration impacts on performance. The model significantly enhances efficiency and scalability, minimizing the need for extensive physical infrastructure. Analysis shows over 90% utilization in both layers, highlighting the model’s applicability to various IoT applications. The M/M/c/K queuing model addresses scalability and real-time data processing challenges in agricultural cloud?edge-sensor networks, improving over traditional methods lacking dynamic scalability. Designed for optimized resource use and reduced data handling delays, this model proves crucial in precision agriculture, where timely data is essential for decision-making. Its versatility extends to various agricultural applications requiring efficient real-time analysis and resource management.

Nowadays, wireless sensor network applications have been used in several important areas, such as healthcare, military, critical infrastructure , environmental monitoring, and manufacturing. However, due to the limitation of WSNs in terms of memory, energy, computation, and scalability, efficient management of the large number of WSNs data in these areas is an important issue to deal with. There is a need for a high-performance computing and massive storage infrastructure for real-time processing and storing of the data as well as analysis of the information. In this paper, we present a framework of sensor cloud computing and service to manage monitoring sensor data for the design build projects. With this sensor cloud framework, the entire project participants can efficiently share information to identify and solve issues in the design build phase.

Boltwood 구름센서는 구름으로부터 복사되는 적외선을 감지하여 구름의 유무와 많고 적음을 판별할 수 있는 기상센서의 한 종류이 다. 이 구름센서는 한국천문연구원이 진행하고 있는 국가현안과제의 일환인 우주물체 전자광학 감시체계 시스템(OWL, Optical Wide-field patroL)에 사용될 계획이다. 실제 시스템 적용에 앞서, Boltwood 구름센서를 충북대학교 천문대에 설치, 약 2주간 구름센 서의 구름감지 성능 시험을 위한 관측을 진행하였다. 구름센서의 성능과 비교할 대상으로 충북대학교 천문대에 현재 설치, 운영 중인 구름량 측정을 위한 CCD 관측시스템을 이용하였다. 성능 테스트 결과, 하늘과 지상의 온도차이와 측광 자료의 별 개수간 명확한 상관관계가 도출되지 못했다. 그 원인으로는 시험 환경상의 문제와 Boltwood 구름센서의 내부 알고리즘 및 하드웨어에 대한 정보공 개가 제한 때문인 것으로 판단된다. 이 논문에서는 Boltwood 구름센서와 CCD 관측시스템의 구름지수를 비교, 분석한 과정과 그 상세 결과를 제시하고자 한다.

The Boltwood Cloud Sensor is meteorological sensor that is used to estimate an amount of clouds in the sky. This sensor will be installed for OWL(Optical Wide-field patroL) telescope and observatory system of Korea Astronomy and Space Science. Before applying this sensor to an observatory system, we performed test observations at Chungbuk University Observatory at Jincheon, Chungbuk. During the test run, a significant correlation between air temperature difference and the number of visible stars recorded in the CCD frames has not been found. This preliminary result can be attributed to test environment of the observation and our lack of knowledge on calculation algorithm as well as the hardware system of the Boltwood Cloud Sensor.In this paper, we present the procedure and the result of the performance test employing the cloud sensor.

Congestion control is one of the most important factors in ensuring secure traffic. This paper is a study of Congestion Control in War State Battle Field using cloud sensor for collision detection and prevention. Cloud sensor uses common parameters such as total nodes, minimum speed, maximum speed, available mines (bombs), and distance variation to prevent collision of tanks on the battlefield. The main advantage of cloud sensors is that it allows easily gathering, accessing, processing, storing, sharing and searching for sensor data. Cloud sensors will be placed in a particular space that will notice the fastness and voice of siren at a specific threshold. Existing cloud based traffic control schemes are susceptible to various congestions such as upcoming vehicle control and priority vehicle control. The main reason for success of collision attack is the highest congestion reuse rate. This research based on congestion control in War State Battle Field. Battle Field is the basement of military action and is very substantial for officers to considerate and establishes the entire use of it in the conclusion- making. For evaluation of this approach, a scenario of battlefield has been considered in experimental analysis.The objective of this paper is to develop a new technique for avoiding the traffic collision in battlefield and to evaluate the proposed technique in the Java virtual environment.

War State Battle Field is the basement of the military action. Battle Field is very significant for commanders to considerate and make full use of it in decision-making. This is basically depend on the vehicular traffic control in war state battle field. In this the tanks represents a vechicle. A tanks traffic on war state battle field is a vital problem and is seemly a major pretend to conclusion makers. In this research paper we will try to bring the scenario of battle field arena where the traffic consists of the vehicles called tanks. So the main focus in this research is put on the collision detection of the tanks among each other using cloud sensor by controlling the congestion in the battlefield.

The Internet of Things (IoT) is a new evolution in technological advancement taking place in the world today. This paradigm allows physical world objects in our surroundings to be connected to the Internet. This idea comes to life by utilizing two architecture; the Sensing Entity in the environment that collects data and connects itself to the cloud and the Cloud Service that hosts the data from the environment. The combination of wireless sensor networks and cloud computing is becoming a popular strategy for the IoT era. The cold chain requires controlled environment for sensitive products in order for them to be fit for use. The monitoring process is the only assurance which tells if a certain process has been carried out successfully. Taking advantage of IoT and its benefits to monitor cold chain logistics will result in better management and product handling. This paper looks at a system comprising of Arduino wireless sensor network and Xively sensor cloud which can be an ideal system to monitor temperature and humidity of cold chain logistics.

The interaction between wireless sensors such as Internet of Things (IoT) and Cloud is a new paradigm of communication virtualization to overcome resource and efficiency restriction. Cloud computing provides unlimited platform, resources, services and also covers almost every area of computing. On the other hand, Wireless Sensor Networks (WSN) has gained attention for their potential supports and attractive solutions such as IoT, environment monitoring, healthcare, military, critical infrastructure monitoring, home and industrial automation, transportation, business, etc. Besides, our virtual groups and social networks are in main role of information sharing. However, this sensor network lacks resource, storage capacity and computational power along with extensibility, fault-tolerance, reliability and openness. These data are not available to community groups or cloud environment for general purpose research or utilization yet. If we reduce the gap between real and virtual world by adding this WSN driven data to cloud environment and virtual communities, then it can gain a remarkable attention from all over, along with giving us the benefit in various sectors. We have proposed a Pub/Sub-based sensor virtualization framework Cloud environment. This integration provides resource, service, and storage with sensor driven data to the community. We have virtualized physical sensors as virtual sensors on cloud computing, while this middleware and virtual sensors are provisioned automatically to end users whenever they required. Our architecture provides service to end users without being concerned about its implementation details. Furthermore, we have proposed an efficient content-based event matching algorithm to analyze subscriptions and to publish proper contents in a cost-effective manner. We have evaluated our algorithm which shows better performance while comparing to that of previously proposed algorithms.

Recently, introduced a cloud computing technology to the IT industry, smart phones, it has become possible connection between mobility terminal such as a tablet PC. For dissemination and popularization of movable wireless terminal, the same operation have focused on a viable mobile cloud in various terminal. Also, it evolved Wireless Sensor Network(WSN) technology, utilizing a Body Sensor Network(BSN), which research is underway to build large Ubiquitous Sensor Network(USN). BSN is based on large-scale sensor networks, it integrates the state information of the patient's body, it has been the need to build a managed system. Also, by transferring the acquired sensor information to HIS (Hospital Information System), there is a need to frequently monitor the condition of the patient. Therefore, In this paper, possible sensor information exchange between terminals in a mobile cloud environment, by integrating the data obtained by the body sensor HIS and interoperable data DBaaS (DataBase as a Service) it will provide a base of mBodyCloud System. Therefore, to provide an integrated protocol to include the sensor data to a standard HL7(Health Level7) medical information data.

Recently, introduced a cloud computing technology to the IT industry, smart phones, it has become possible connection between mobility terminal such as a tablet PC. For dissemination and popularization of movable wireless terminal, the same operation have focused on a viable mobile cloud in various terminal. Also, it evolved Wireless Sensor Network(WSN) technology, utilizing a Body Sensor Network(BSN), which research is underway to build large Ubiquitous Sensor Network(USN). BSN is based on large-scale sensor networks, it integrates the state information of the patient's body, it has been the need to build a managed system. Also, by transferring the acquired sensor information to HIS(Hospital Information System), there is a need to frequently monitor the condition of the patient. Therefore, In this paper, possible sensor information exchange between terminals in a mobile cloud environment, by integrating the data obtained by the body sensor HIS and interoperable data DBaaS(DataBase as a Service) it will provide a base of mBodyCloud System. Therefore, to provide an integrated protocol to include the sensor data to a standard HL7(Health Level7) medical information data.

Since satellite images generally include clouds in the atmosphere, it is essential to detect or mask clouds before satellite image processing. Clouds were detected using physical characteristics of clouds in previous research. Cloud detection methods using deep learning techniques such as CNN or the modified U-Net in image segmentation field have been studied recently. Since image segmentation is the process of assigning a label to every pixel in an image, precise pixel-based dataset is required for cloud detection. Obtaining accurate training datasets is more important than a network configuration in image segmentation for cloud detection. Existing deep learning techniques used different training datasets. And test datasets were extracted from intra-dataset which were acquired by same sensor and procedure as training dataset. Different datasets make it difficult to determine which network shows a better overall performance. To verify the effectiveness of the cloud detection network such as Cloud-Net, two types of networks were trained using the cloud dataset from KOMPSAT-3 images provided by the AIHUB site and the L8-Cloud dataset from Landsat8 images which was publicly opened by a Cloud-Net author. Test data from intra-dataset of KOMPSAT-3 cloud dataset were used for validating the network. The simulation results show that the network trained with KOMPSAT-3 cloud dataset shows good performance on the network trained with L8-Cloud dataset. Because Landsat8 and KOMPSAT-3 satellite images have different GSDs, making it difficult to achieve good results from cross-sensor validation. The network could be superior for intra-dataset, but it could be inferior for cross-sensor data. It is necessary to study techniques that show good results in cross-senor validation dataset in the future.

Sensor networks are increasingly integral to modern applications such as industrial automation, environmental monitoring, and smart infrastructure. As these networks grow in scale and complexity, the volume, variety, and velocity of generated data introduce significant challenges for traditional Sensor Information Systems (SIS). These challenges include scalability limitations, high operational costs, heterogeneous data processing, and inefficient resource allocation. This paper presents a cloud-based architecture model for sensor information systems to address these issues. The proposed model integrates cloud computing and sensor virtualization technologies to construct a flexible, scalable, cost-effective SIS. The architecture features a multi-layered structure, including a secure user front-end, application management, service orchestration, and a virtualization layer that abstracts physical sensor resources. This enables transparent access to data and dynamic provisioning of computational and storage resources. By leveraging the elasticity and service-oriented nature of cloud computing, the model supports real-time processing and efficient management of sensor data. Sensor virtualization further enhances system flexibility by representing one physical sensor as multiple virtual instances, enabling resource sharing and dynamic scaling based on demand. The proposed system is designed to support a wide range of sensor network configurations?wired or wireless, homogeneous or heterogeneous?and to improve performance through dynamic load balancing, efficient data handling, and user-centric service delivery. Overall, this architecture offers a generalizable and adaptive solution for the evolving demands of sensor-based applications, positioning itself as a promising approach to modern sensor information system design.

An Internet of Things (IOT) sensor network is an effective solution for monitoring environmental conditions. However, IOT sensor networks generate massive data such that the abilities of massive data storage, processing, and query become technical challenges. To solve the problem, a Hadoop cloud platform is proposed. Using the time and workload genetic algorithm (TWLGA), the data processing platform enables the work of one node to be shared with other nodes, which not only raises efficiency of one single node but also provides the compatibility support to reduce the possible risk of software and hardware. In this experiment, a Hadoop cluster platform with TWLGA scheduling algorithm is developed, and the performance of the platform is tested. The results show that the Hadoop cloud platform is suitable for big data processing requirements of IOT sensor networks.

최근 다양한 센서를 활용한 보안 감시 시스템의 수요가 증가하면서 센서 데이터의 효율적인 관리 또한 중요해지고 있다. 본 논문에서는 높은 확장성 대비 낮은 비용이 장점인 클라우드 환경을 적용한 센서 클라우드 시스템을 설계한다. 본 시스템에서는 옥내에 분산되어 있는 센서 네트워크가 침입자를 감지하여 클라우드 게이트웨이를 통해 센서 클라우드로 센서 데이터를 전달한다. 전달된 센서 데이터는 Apache Hadoop 을 기반으로 하는 데이터 서버에 분산 저장된다. 또한 본 시스템은 센서 데이터를 실시간으로 파악하기 위한 시스템 인터페이스를 포함한다.

최근 다양한 센서를 활용한 보안 감시 시스템의 수요가 증가하면서 센서 데이터의 효율적인 관리 또한 중요해지고 있다. 본 논문에서는 높은 확장성 대비 낮은 비용이 장점인 클라우드 환경을 적용한 센서 클라우드 시스템을 설계한다. 본 시스템에서는 옥내에 분산되어 있는 센서 네트워크가 침입자를 감지하여 클라우드 게이트웨이를 통해 센서 클라우드로 센서 데이터를 전달한다. 전달된 센서 데이터는 Apache Hadoop 을 기반으로 하는 데이터 서버에 분산 저장된다. 또한 본 시스템은 센서 데이터를 실시간으로 파악하기 위한 시스템 인터페이스를 포함한다.

사회기반 시설물의 노후화에 대응해 이상 징후를 파악하고 유지보수를 위한 최적의 의사결정을 내리기 위해선 디지털 기반 SOC 시설물 유지관리 시스템의 개발이 필수적인데, 디지털 SOC 시스템은 장기간 구조물 계측을 위한 IoT 센서 시스템과 축적 데이터 처리를 위한 클라우드 컴퓨팅 기술을 요구한다. 본 연구에서는 구조물의 다물리량을 장기간 측정할 수 있는 IoT센서와 클라우드 컴퓨팅을 위한 서버 시스템을 개발하였다. 개발 IoT센서는 총 3축 가속도 및 3채널의 변형률 측정이 가능하고 24비트의 높은 해상도로 정밀한 데이터 수집을 수행한다. 또한 저전력 LTE-CAT M1 통신을 통해 데이터를 실시간으로 서버에 전송하여 별도의 중계기가 필요 없는 장점이 있다. 개발된 클라우드 서버는 센서로부터 다물리량 데이터를 수신하고 가속도, 변형률 기반 변위 융합 알고리즘을 내장하여 센서에서의 연산 없이 고성능 연산을 수행한다. 제안 방법의 검증은 2개소의 실제 교량에서 변위계와의 계측 결과 비교, 장기간 운영 테스트를 통해 이뤄졌다.

It is important to develop a digital SOC (Social Overhead Capital) maintenance system for preemptive maintenance in response to the rapid aging of social infrastructures. Abnormal signals induced from structures can be detected quickly and optimal decisions can be made promptly using IoT sensors deployed on the structures. In this study, a digital SOC monitoring system incorporating a multimetric IoT sensor was developed for long-term monitoring, for use in cloud-computing server for automated and powerful data analysis, and for establishing databases to perform : (1) multimetric sensing, (2) long-term operation, and (3) LTE-based direct communication. The developed sensor had three axes of acceleration, and five axes of strain sensing channels for multimetric sensing, and had an event-driven power management system that activated the sensors only when vibration exceeded a predetermined limit, or the timer was triggered. The power management system could reduce power consumption, and an additional solar panel charging could enable long-term operation. Data from the sensors were transmitted to the server in real-time via low-power LTE-CAT M1 communication, which does not require an additional gateway device. Furthermore, the cloud server was developed to receive multi-variable data from the sensor, and perform a displacement fusion algorithm to obtain reference-free structural displacement for ambient structural assessment. The proposed digital SOC system was experimentally validated on a steel railroad and concrete girder bridge.

센서 네트워크는 다수의 센서들로부터 수집된 각종 데이터를 저장하고, 처리하며, 이를 분석하여 사용자에게 유용한 정보를 제공하는 유용 시스템으로 다양한 분야에서 활용된다. 이러한 센서 네트워크의 구성시 통신 서버와 데이터베이스 서버 모두 클라우드 PaaS를 이용함으로써, 시스템의 비용 절감 및 안정화를 도모할 수 있다. 본 논문에서는 이동체의 위치 정보뿐만 아니라 각종 센서 데이터를 위한 클라우드 서비스로 처리하여 사용자에게 판정 정보를 제공하기 위해서 UDIPSN (센서 네트워크를 제공하는 사용자 결정 정보)을 구현하였다. 마지막으로 제안된 시스템의 동작이 정상적으로 이루어짐을 보였다.

Sensor networks are utilized in various fields as the useful system that provides the useful information to the user, by storing, processing and analyzing the various data collected from sensors. In construction of such sensor networks, by utilizing cloud PaaS for both communication server and the database server, the cost reduction and stabilization of the system can be achieved. In this paper, UDIPSN (User Decision Information Providing Sensor Network) was implemented to provide the decision information to the users by being treated as a cloud service for the location information of a moving object as well as various sensor data. Finally, we showed that the operation of the proposed system was performed properly.

최근 센서와 이동 통신 기기의 발전은 의료 및 관련 분야에 많은 가능성을 제공하고 있다. 그러나 이에 발생하는 데이터는 메타데이터, 규격, 단위 등에서 일치하기는 어렵다. 여러 규격의 센서에서 발생하는 데이터를 효율적으로 이용하기 위해 통합은 필요하다. 이에 따라 우리는 본 논문에서는 기존 센서와 신규 센서에서 발생하는 데이터를 통합할 수 있는 방안으로 온톨로지를 이용하는 방법을 제안하고자 한다. 온톨로지는 기본 항목과 센서의 항목을 매핑하고, 추가적으로 타입과 구조적 차이에 대한 부분도 포함한다. 매핑은 메타데이터 간의 매핑과 데이터 간의 매핑으로 구분한다. 이러한 방법으로 생성된 표준 항목이 서비스 간 데이터 교환의 형식이 됨으로써 센서 발생하는 이질적 문제를 해결할 수 있다.

Recently, the development of sensors and the mobile communication device offers a number of possibilities in the medical and related fields. However, this data is generated, it is difficult to match the metadata and standard units. The data integration is required to use the data generated by the different specifications of the sensor efficiently. Accordingly, in this paper we propose a method using an ontology as a method to integrate the data generated by the existing sensors and the new sensor. The ontology is mapping to the standard item and sensors, also include a type and structural difference. The mapping is comprised of two : data mapping, and metadata mapping. There are standard items that are created in this way, type of data exchange between services. This can solve the heterogeneous problem generated by sensors.

조선해양산업의 용접 자동화는 숙련 인력 부족과 고위험 환경 극복을 위해 협동로봇 중심으로 발전하고 있으나 선박블록 내부 공간을 계측하기 위한 3차원 비전 센서의 경우 계측 거리에 따라 품질이 저하되는 문제가 있다. 본 연구는 협동로봇 용접 자동화를 위해 3차원 포인트 클라우드 기반의 V-개선 용접선 검출 알고리즘을 제안하였으며 특히 자동화 용접에 필수적인 1m 미만 근거리에서 비전 센서 기술에 따른 계측 정밀도 및 검출된 평면의 개선각을 정량적으로 비교 검증하였다. 대중적으로 활용되고 있는 Active IR Stereo와 Time-of-Flight(ToF) LiDAR 센서를 400mm, 600mm, 800mm 거리에서 90° 개선각 시편으로 비교 평가한 결과 Active IR Stereo 센서는 삼각 측량 원리의 한계로 인한 데이터 왜곡으로 상당한 각도 오차를 보였으며 800mm에서는 개선면 검출에 실패하였다. 반면 ToF LiDAR 센서는 데이터 왜곡에 강건하여 400mm에서 4.4°의 가장 낮은 평균 개선각 오차를 기록했으며 모든 거리에서 안정적으로 평면을 검출하였다. 이를 통해 근거리 V-개선 형상 계측에는 ToF LiDAR 방식이 Active IR Stereo 방식보다 높은 정밀도를 제공하여 용접선 검출에 더 적합함을 정량적으로 검증하였다.

Welding automation in the shipbuilding industry is advancing, centered on collaborative robots, to overcome skilled labor shortages and high-risk environments. However, three-dimensional(3D) vision sensors used for measuring ship block interiors present quality degradation issues depending on the measurement distance. This study proposes a 3D point cloud-based V-groove weld line detection algorithm for collaborative robot welding automation. In particular, this study quantitatively compares sensor technologies to identify one that provides the high precision required for automated welding at close ranges of less than 1 m. A comparative evaluation of Active IR Stereo and Time-of-Flight (ToF) LiDAR sensors was performed at distances of 400, 600, and 800 mm using a 90° groove angle specimen. The results show that the Active IR Stereo sensor exhibited critical angle errors due to data distortion caused by the limitations of the triangulation principle and that it failed to detect the groove plane at 800 mm. Conversely, the ToF LiDAR sensor demonstrated robusnesst against data distortion, with the lowest mean groove angle error of 4.4° recorded at 400 mm, and stably detected the planes at all distances. This quantitatively demonstrates that the ToF method provides significantly higher reliability and precision than the Active IR Stereo method for close-range V-groove measurements.