Earticle

클라우드 컴퓨팅과 사물인터넷 기술의 발전과 함께 클라우드 컴퓨팅의 분산 확장 모델인 포그 컴퓨팅이 주목받으면 서 IoT 기반 대규모 분산 환경에서 유용하게 활용되고 다양한 연구가 이루어지고 있다. 그러나 이러한 환경의 네트 워크와 엣지 장비의 취약점을 악용한 사이버 공격 가능성이 증가하며 보안 문제가 더욱 복잡해지고 있다. 이에 본 연구에서는 TON_IoT 데이터셋을 활용한 지도학습 기반 IDS와 디셉션 기법을 결합한 보안 아키텍처를 제시한다. 특정 칼럼의 라벨 인코딩 과정에서 키워드 기반 전처리를 수행하고, 시뮬레이션 데이터를 학습에 포함하여 IDS 성 능을 개선하였다. 또한, 포그 컴퓨팅 환경에 적합한 침입 탐지 시스템 모델을 선정하기 위해 추론 시간을 분석하고 지연 시간을 줄이기 위한 리다이렉션 방법을 제시하였다. 그러나 MitM 데이터의 과적합 문제, 제로데이 공격 탐지 한계, 리다이렉션 방법에 대한 추가 검증은 추후 연구에서 해결해야 할 과제이다.

As cloud computing and Internet of Things technologies advance, fog computing as a distributed extension model of cloud computing is gaining attention and is being widely used in large-scale IoT-based distributed environments with many research efforts underway. However, these environments face increasing cybersecurity challenges due to vulnerabilities in networks and edge devices. To address these issues, this study presents a security framework integrating a supervised learningbased intrusion detection system trained on the TON_IoT dataset with deception techniques. The IDS performance was improved by performing keyword-based preprocessing during label encoding of specific columns and including simulation data in training. And to select an IDS model suitable for the fog computing environment, we analyzed the inference time and proposed a redirection method to reduce latency. However, overfitting of MitM data, limitations in detecting zero-day attacks, and further validation of redirection methods are challenges for future works.

포그 컴퓨팅은 클라우드 기반 IoT 시스템에서 발생하는 네트워크 트래픽 문제를 해결하기 위해 등장한 새로운 패러다 임이다. 현재 대부분의 포그 컴퓨팅 시스템 연구에서는 포그 노드의 역할이 IoT 장치 데이터를 저장해주는 역할만 수 행하고 있어서 중앙 서버의 역할을 대체하여 수행하기에는 역부족이다. 본 논문에서 소개하는 DDS(Data Distribution Service) 기반 포그 플랫폼, DFog(EchoDDS Fog Platform)는 기존 연구에서와는 달리 IoT 장치 데이터 저장뿐만 아니라, IoT 장치의 상태를 모니터링하고 필요에 따라 사용자 또는 관리자 개입 없이 실시간 IoT 장 치 관리 기능을 제공한다. 또한, 중앙 서버를 통해 관리자가 개별 IoT 장치를 연결하고 모니터링할 수 있도록 원격 제 어 기능을 제공함으로써, 대규모 IoT 시스템에 적용할 수 있도록 하였다. 더 나아가, 포그 컴퓨팅 시스템의 유동적인 네트워크 형성을 위해 발간/구독 기반 통신 미들웨어인 DDS를 적용하여 포그 노드 성능과 확장성을 향상시켰다. 이 러한 기능과 성능을 확인하기 위해, IoT 시스템 프로토타입을 구현하였으며, 주요 기능에 대한 검증을 수행하였다.

Fog computing is a new paradigm that has emerged to solve the network traffic problem arising from cloud server based IoT systems. Most of the current fog computing research provide fog nodes with capability of storing IoT data. This is not enough to replace the central server's role for IoT systems. Unlike the previous research, the DDS (Data Distribution Service) based fog platform, DFog (EchoDDS Fog Platform) introduced in this paper, not only stores IoT device data, but also monitors the state of the IoT device and manages the devices realtime without user or administrator intervention as needed. In addition, by providing a remote control function to enable administrators to connect and monitor individual IoT devices through a central server, it is applicable to large-scale IoT systems. Furthermore, the performance and scalability of fog nodes are improved by applying DDS, a publishing/subscription-based communication middleware which forms a flexible network of fog nodes and servers. Prototype IoT systems were implemented using DFog in order to confirm feasibility of these functions and performances.

5G시대의 도래와 더불어 엣지 컴퓨팅 기술의 중요성이 최근 다시 한번 더 부각되고 있다. 엣지 컴퓨팅 기술은 클라 우드의 발전과 더불어 클라우드의 기능을 보강하고 정보 전달에 있어서의 효율성 증대를 위해 지속적으로 상호 발전 해 오고 있다. 최근 엣지 컴퓨팅은 클라우드와의 협력뿐만 아니라 5G통신에서도 핵심적인 요소기술로 자리매김해 가고 있다. 올해 발표된 3GPP 표준(Rel-16)에서도 초저지연을 특징으로 하는 5G통신의 호처리 품질을 보장하기 위해서도 엣지 컴퓨팅 개념의 도입이 확대되고 있는 추세라고 본다. 또한 엣지 컴퓨팅은 지속적으로 진화하고 있는 IoT플랫폼 기능과 인공지능 기술들을 엣지 디바이스와 연결해 주는 데 있어서도 그 역할이 더욱 강화되어 가고 있 다. 본 논문에서는 엣지 컴퓨팅의 초기 태동 배경과 기술 발전 흐름을 다시 한번 정리해 보고, 연관된 기술들과의 개념적인 차이점과 특징을 살펴보고자 한다. 끝으로 엣지 컴퓨팅의 대표적인 상용화 사례와 향후 전망에 대해서도 고찰해 보았다.

With the advent of the 5G era, the importance of edge computing technology has become more prominent in recent years. With the evolution of the cloud, edge computing technology continues to evolve to enhance the capabilities of the cloud and increase efficiency in information delivery. In recent years, edge computing has become a key element in 5G communication as well as cooperation with the cloud. In the 3GPP standard (Rel-16) announced this year, the introduction of edge computing concepts is also seen as expanding in order to ensure the quality of the 5G communication characterized by ultra-low latency. In addition, edge computing is strengthening its role in connecting the ever-evolving IoT platform functions and artificial intelligence technologies with edge devices. In this paper, we will revisit the early beginnings of edge computing and the technological developments, and examine conceptual differences and characteristics of the technologies involved. Finally, we also looked at some of the typical commercialization cases of edge computing and the future prospects.

본 연구는 포그 기반 IoT 환경에서 Roy 등의 격자 기반 하이브리드 인증 프로토콜을 재평가하고, 손상격리 중심의 개선 설계를 제안한다. 기존 프로토콜은 핸드오버 시 장기 자격증명 복제, 정적 식별자로 인한 링크가능성, fail-safe 단계의 공유 비밀 재사용으로 인한 손상 전파 한계를 보인다. 이에 본 논문은 장기 비밀 직접 전달을 제거하고, 제한적 가명, 단기 인증 토큰, 분리된 복구 토큰, 이벤트 기반 신뢰 업데이트를 도입한 개선안을 제안한다. 평가 결과, 제안 방식은 적당한 오버헤드로 재생 공격 저항성, 프라이버시, 손상 격리 능력을 크게 향상시킨다.

This study re-evaluates Roy et al.’s lattice-based hybrid authentication protocol for fog-based IoT and proposes a compromise-containment-oriented improvement. The original protocol exhibits vulnerabilities, including long-term credential duplication during handover, identity linkability via static identifiers, and compromise propagation through shared-secret reuse. To address these, the proposed design eliminates direct long-term secret transfer, introducing bounded pseudonyms, short-lived authorization tokens, isolated recovery tokens, and event-based trust updates. Evaluations confirm that the improved protocol significantly enhances replay resistance, privacy, and compromise containment with moderate overhead.

IoT 분야의 기술과 동향은 IoT Device와 IoT Gateway의 각각의 플랫폼이나 표준에 초점에 맞춰 발전하고 있다. 본 논문에서는 IoT 기술을 활용하여 IoT Device, IoT Gateway, Cloud Service 들의 역할에 대해 정리하여 Smart IoT Gateway Platform을 제시하고 있다. IoT Device들로 존재 할 수 있는 다양한 플랫폼(Curie Board, Android등)이 하는 역할과 이를 연결, 관리 및 제어하는 Smart IoT Gateway, IoT Device와 Smart IoT Gateway로 구성되어있는 환경에 맞는 Cloud Service의 Architecture에 대해 제시하고, 프로토타입 형태로 Smart IoT Gateway Platform을 구 현하고, 고객 및 관리자 니즈에 맞는 Cloud Service들을 제시하였다.

Technology and trends in the IoT field are being developed focusing on the respective platforms and standards of IoT devices and IoT gateways. In this paper, we propose Smart IoT Gateway Platform by summarizing the role of IoT Device, IoT Gateway, and Cloud Service using IoT technology. We present the roles of various platforms (Curie Board, Android, etc.) of IoT Device and present Smart IoT Gateway that connects, manages and controls IoT devices. In addition, we propose the architecture of Cloud Service for environment that consists of IoT Device and Smart IoT Gateway. In this paper, we implemented the Smart IoT Gateway Platform as a prototype and presented the cloud services for the customer and manager needs.

With the increasing advancement in the applications of the Internet of Things (IoT), the integrated Cloud Computing (CC) faces numerous threats such as performance, security, latency, and network breakdown. With the discovery of Fog Computing these issues are addressed by taking CC nearer to the Internet of Things (IoT). The key functionality of the fog is to provide the data generated by the IoT devices near the edge. Processing of the data and data storage is done locally at the fog node rather than moving the information to the cloud server. In comparison with the cloud, Fog Computing delivers services with high quality and quick response time. Hence, Fog Computing might be the optimal option to allow the Internet of Things to deliver an efficient and highly secured service to numerous IoT clients. It allows the administration of the services and resource provisioning outside CC, nearer to devices, at the network edge, or ultimately at places specified by Service Level Agreements (SLA’s). Fog Computing is not a replacement to CC, but a prevailing component. It allows the processing of the information at the edge though still delivering the option to connect with the data center of the cloud. In this paper, we put forward various computing paradigms, features of fog computing, an in-depth reference architecture of fog with its various levels, a detailed analysis of fog with IoT, various fog system algorithms and also systematically examine the challenges in Fog Computing which acts as a middle layer between IoT sensors or devices and data centers of the cloud.

The Internet of Things (IoT) is one of the main enablers for situation awareness needed in accomplishing smart cities. IoT devices, especially for monitoring purposes, have stringent timing requirements which may not be met by cloud computing. This deficiency of cloud computing can be overcome by fog computing for which fog nodes are placed close to IoT devices. Because of low capabilities of fog nodes compared to cloud data centers, fog nodes may not be deployed with all the services required by IoT devices. Thus, in this article, we focus on the issue of fog service placement and present the recent research trends in this issue. Most of the literature on fog service placement deals with determining an appropriate fog node satisfying the various requirements like delay from the perspective of one or more service requests. In this article, we aim to effectively place fog services in accordance with the pre-obtained service demands, which may have been collected during the prior time interval, instead of on-demand service placement for one or more service requests. The concept of the logical fog network is newly presented for the sake of the scalability of fog service placement in a large-scale smart city. The logical fog network is formed in a tree topology rooted at the cloud data center. Based on the logical fog network, a service placement approach is proposed so that services can be placed on fog nodes in a resource-effective way.

Numerous consensus mechanisms have been suggested to cater to the specific characteristics of fog computing. To comprehensively understand their unique features, performance, and applications in fog computing, it is crucial to conduct a systematic analysis of these mechanisms. For this study, 79 relevant articles were carefully selected based on predefined criteria. Among these articles, 35 employed work-proof-based consensus mechanisms, 24 utilized voting-based mechanisms, and 22 adopted capability-based mechanisms. Among the 26 identified consensus mechanisms, proof of work remains the most prevalent one. It is important to note that the scope of this paper is limited to the research available in the predominant databases at the time of writing. Future research may expand to include additional databases and more recent literature in this domain.

The application placement strategies in the hierarchical fog-cloud environment based on the directed acyclic graph (DAG) for rapid execution is an NP-hard optimization problem. Although heuristics have been proposed to generate sub-optimal solutions based on non-preemptive placement policy, merging multiple DAG-based applications into one application is simple but ineffective for minimizing the overall makespan due to the lack of consideration of fairness among multiple DAG-based applications. We design an application placement strategy based on dynamic scheduling that can effectively utilize the schedule gaps in the virtual machines of the different layers to minimize the makespan that meets the deadlines.

The advent of Network Function Virtualization (NFV) technology has brought flexible provisioning to Fog?Cloud Computing-based Networks (FCCNs) for enterprises to outsource their network functions to data center networks. Service Function Chaining (SFC) is a networking concept in NFV by which traffic is steered through an ordered set of Virtual Network Functions (VNFs) composing an end-to-end service. When hundreds of users outsource their network functions to FCCN, the optimal placement of VNFs in the network becomes important for assembling SFCs with the aim of resource utilization efficiency. Motivated by the scalability shortcomings of existing schemes, we propose Deep Reinforcement Learning (DRL)-based approaches by simultaneously considering parallelized SFC and reuse of VNFs to solve this problem, i.e., Asynchronous Advantage Actor?Critic (A3C). A parallelized SFC consists of several sub-SFCs, which can reduce delay and guarantee availability. Also, reuse of preliminary VNFs in SFC placement can improve computation acceleration. The proposed scheme pursues the maximization of the long-term cumulative reward for the trade-off between Quality of Service (QoS) and service cost. The results of the experiments show that the proposed scheme performs better than the state-of-the-art methods.

Throughout the world, aging populations and doctor shortages have helped drive the increasing demand for smart healthcare systems. Recently, these systems have benefited from the evolution of the Internet of Things (IoT), big data, and machine learning. However, these advances result in the generation of large amounts of data, making healthcare data analysis a major issue. These data have a number of complex properties such as high-dimensionality, irregularity, and sparsity, which makes efficient processing difficult to implement. These challenges are met by big data analytics. In this paper, we propose an innovative analytic framework for big healthcare data that are collected either from IoT wearable devices or from archived patient medical images. The proposed method would efficiently address the data heterogeneity problem using middleware between heterogeneous data sources and MapReduce Hadoop clusters. Furthermore, the proposed framework enables the use of both fog computing and cloud platforms to handle the problems faced through online and offline data processing, data storage, and data classification. Additionally, it guarantees robust and secure knowledge of patient medical data.

4차 산업혁명시대 핵심인 IoT(사물인터넷) 기술이 발전하면서 다양한 적용 분야가 생겨나고 있으며, 그에 따른 서비스를 이용하는 사용자 수도 대폭 증가하고 있다. 주변 환경에 흩어져 있는 수많은 IoT 디바이스들에 의해 생성되는 실시간 센싱 데이터들을 실시간 클라우드 컴퓨팅 환경에 전송하여 저장하는 것은 시간 및 저장 공간에 대한 효율성이 적합 하지 않다. 따라서 이러한 문제들을 해결하기 위해서 응답시간을 최소화 하면서 처리 시간이 효율적으로 관리가 될 수 있도 록 하는 포그 컴퓨팅이 제안되었다. 그러나 포그 컴퓨팅이라는 새로운 패러다임에 대한 보안 요구사항이 아직 정립되지 않고 있다. 클라우드 끝단에 있는 센서노드들은 컴퓨팅 파워가 높지 않기 때문에 보안 모듈을 적용하기가 어렵고, 보안 모듈 적용 시에 경량화된 프로토콜 적용을 통하여 보안과 효율성을 수립하여야 한다. 따라서 이 논문에서는 포그 컴퓨팅에 대한 보안 모델 제시와 포그 컴퓨팅에 최적화된 보안 요구사항을 제시하여 안전한 포그 컴퓨팅 발전에 기여한다.

IoT technology is developing with various application areas in 4th Industrial revolution. There are many users using the application services. Sensing data from various environment need to be transferred to cloud computing storage and store in the cloud storage. However, physical distance from the end node to cloud computing storage is far away, and it is not efficient to transfer data from sensors and store the sensing data in the cloud storage whenever sensing data happen. Therefore, Fog computing is proposed to solve these problems which can process and store the sensing data. However, Fog computing is new emerging technology, there is no standard security model and requirements. This research proposes to security requirements and security model for Fog computing to establish a secure and efficient cloud computing environment.

The advancement of the Internet and mobile technology is driving the enablement of the IoT and cloud computing paradigm. However, IoT platforms has security issues because of its centralized system, and the Cloud computing faces technological limitations due to data explosion caused by IoT and the emergence of latency-sensitive application. Blockchain platforms are being developed based on cryptocurrencies because it is in prospect but usage is limited in field of financial. Also the fog computing has not yet been widely used because it is not clear who operates and manages a fog computing infrastructure. In this paper, a user friendly fog computing architecture is proposed to enhance the performance and feasibility of fog computing and mapping identification with IP address to blockchain network..In the proposed architecture, the blockchain records mapping data sevurely and users register their fog device in the fog portal, and the fog portal acts as an intermediary between the resources of each local network and the IoT service.

스마트팜이나 스마트시티와 같은 IoT 시스템이 보편화되면, 많은 센서 노드들로부터 수집된 대량의 데이터가 인터 넷 내 서버로 전송되기 때문에 네트워크 트래픽 폭증, 전달 지연, 서버 부하증가 문제가 발생한다. 이러한 문제를 완화하기 위해 IoT 시스템과 서버와 사이에 데이터를 저장하는 포그 컴퓨팅 개념이 제안된 바 있다. 본 연구에서는 포그 노드의 소프트웨어 플랫폼을 구현하여 스마트팜(smart farm) 시험 구현물에 적용해 봄으로써, 포그 노드를 사용하는 경우 위에서 나열된 문제를 해결할 수 있음을 확인하였다. 포그 노드 플랫폼을 이용했을 때 IoT 장치를 제 어하는데 걸리는 시간이 기존 IoT–서버 방식보다 더 낮아지는 것을 확인하였으며, 인터넷 내부 트래픽 폭증, 부하 증가 문제를 해결할 수 있음을 확인하였다. 또한 포그 노드의 기본 기능인 IoT 데이터 저장뿐만 아니라, 실시간 원 격제어, 긴급 알림, 데이터 시각화의 기능을 본 논문의 포그 노드에 구현해 봄으로써 보다 지능적인 IoT 제어가 가 능함을 보였다.

As IoT systems such as smart farms and smart cities become popular, a large amount of data collected from many sensor nodes is sent to a server in the Internet, which causes network traffic explosion, delay in delivery, and increase of server’s workload. To solve these problems, the concept of fog computing has been proposed to store data between IoT systems and servers. In this study, we implemented a software platform of the fog node and applied it to the prototype smart farm system in order to check whether the problems listed above can be solved when using the fog node. When the fog node is used, the time taken to control an IoT device is lower than the response time of the existing IoT device-server case. We confirmed that it can also solve the problem of the Internet traffic explosion and the workload increase in the server. We also showed that the intelligent control of IoT system is feasible by having the data visualization in the server and real time remote control, emergency notification in the fog node as well as data storage which is the basic capability of the fog node.

IoT 기술 발전으로 기기 간 실시간 데이터 교환 및 자동화된 의사결정이 가능해지면서 다양한 산업 현장에서 IIoT 사용이 폭발적으로 증가하고 있다. IIoT 사용이 기하급수적으로 늘어남에 따라 서로 다른 통신 방법 및 인증 체 계를 갖고 있는 기기 간 상호 통신하는 경우가 발생한다. 도메인 간 인증 시 인증 오류 및 보안 위협이 발생하기 때문 에, 안전한 도메인 간 인증 방법이 필요하다. 본 논문에서는 도메인 간 인증 모델을 제시하고 블록체인 암호화 기법을 기반으로 하여 경량성과 안전성이 보장되는 인증기법을 제안한다. 제안 기법은 가상 아이디를 통해 익명성을 보장하고, 인증 과정에서 ECDSA 기반의 전자 서명을 응용하여 중간자 공격, 재전송공격, 위장 공격 위험에 안전한 인증기법을 제안하였다. 또한 스칼라 지수와 이중 선형 페어링 연산을 사용해 경량화 시스템에 적합함을 보였으며, 기존 연구들과 시간 복잡도 비교 분석 결과 효율성을 보였다.

Rapid technological advances have driven explosive IIoT growth across industries, enabling real-time data exchange and automated device decisions. However, cross-domain communication between devices using diverse protocols and authentication methods creates serious security risks, including authentication failures and cyberattacks. To address these issues, This paper proposes a lightweight, blockchain-based cross-domain authentication model for heterogeneous IIoT environments. It ensures user anonymity with virtual IDs, uses ECDSA to resist various attacks, and employs scalar indices and dual pairing for optimization in resource-constrained systems.

Recently, research is being conducted on the rapid service provision and reliability of the instance-based rather than the existing IP-based structure. Research is mainly conducted through Block cloud, a platform that combines service-centric networking (SCN) and blockchain. In addition, the Internet of Things network has been proposed as a fog computing environment in the structure of the existing cloud computing. Fog computing is an environment suitable for real-time information processing. In this paper, we propose a new Internet network structure based on fog computing that requires real-time for rapid processing of IoT services. The proposed system applies IoTA, the third-generation blockchain based on DAG, to the block cloud. In addition, we want to propose a basic model of the object block chain and check the application services of electric vehicles.

The majority of existing falling recognition techniques provide service by recognizing only that the falling occurred. However, it is important to recognize not only the occurrence of falling but also the situation before and after the falling, as well as the location of the falling. In this paper, we design and propose the falling notification service system to recognize and provide service. This system uses the acceleration sensor of the smartphone to recognize the occurrence of a falling and the situation before and after the falling. In order to check the location of falling, GPS sensor data is used in the Google Map API to map to the map. Also, a crosswalk map converted into grid-based coordinates based on the longitude and latitude of the crosswalk is stored, and the locations before and after falling are mapped. In order to reduce the connection speed and server overload for real-time data processing, fog computing and cloud computing are designed to be distributed processing.

Cloud computing has evolved into a core computing infrastructure for the internet that encompasses content, as well as communications, applications and commerce. By providing powerful computing and communications capabilities in the palm of the hand everywhere with a variety of smart devices, mobile applications such as virtual reality, sensing and navigation have emerged and radically changed the patterns people live. The data that is generated is getting bigger. Cloud computing, on the other hand, has problems with system load and speed due to the collection, processing and control of remote data. To solve this problem, fog computing has been proposed in which data is collected and processed at an edge. In this paper, we propose a system that dynamically selects a fog server that acts as a cloud in the edge. It serves as a mediator in the cloud, and provides information on the services and systems belonging to the cloud to the mobile device so that the mobile device can act as a fog. When the role of the fog system is complete, we provide it to the cloud to virtualize the fog. The heterogeneous problem of data of mobile nodes can be solved by using mDBaaS (Mobile DataBase as a Service) and we propose a system design method for this.

To understand whether Falling, which is one of the causes of injuries, occurs, various behavior recognition research is proceeding. However, in most research recognize only the fact that Falling has occurred and provide the service. As well as the occurrence of the Falling, the risk varies greatly based on the type of Falling and the situation before and after the Falling. Therefore, when Falling occurs, it is necessary to infer the user's current situation and provide appropriate services. In this paper, we propose to base on Fog Computing and Cloud Computing to design Context-aware System using analysis of behavior data and process sensor data in real-time. This system solved the problem of increase latency and server overload due to large capacity sensor data.