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In light of the recent advancements made in IT, many researchers are studying and exploring ways to minimize damage from fire disasters using artificial intelligence and cloud technology. With the introduction of edge computing, firedisaster response software systems have made significant progress. However, existing studies often do not consider the response to a sudden power supply cut-off due to fire. In this study, we propose a container migration scheme based on the first-fit-decreasing algorithm of bin-packing problem and 0-1 knapsack algorithm to provide fault tolerance for containers running on edge servers that are powered off.

5G 이동 통신 서비스가 제공됨에 따라 다양한 서비스를 초저지연으로 사용자에게 제공하려는 노력이 진행되고 있다. 이는 네트워크 코어에서 클라우드 컴퓨팅을 제공하는 대신에 사용자 인근에서 고성능 컴퓨팅 서비스를 제공하는 에지 컴퓨팅에 대한 관심을 불러 일으키고 있다. 본 논문은 에지 컴퓨팅의 실현을 위한 필수 장비인 마이크로 데이터센 터의 운영 및 관리 방안을 제시한다. 먼저, 에지 컴퓨팅의 기능 구조와 배치 방안을 제시한다. 다음으로 에지 컴퓨팅을 위한 마이크로 데이터센터의 요구사항과 이에 따른 운영 및 관리 방안을 제시한다. 마지막으로 마이크로 데이터센터의 자원을 효율적으로 관리하기 위해서 수집 및 감시해야 하는 자원 관리 아이템을 제시하고, 에너지 효율을 측정할 수 있는 성능 지표를 제안한다.

As 5G mobile communication services are provided, efforts are being made to provide various services to users with ultra-low latency. This raises interest in edge computing, which can provide high performance computing services near users instead of cloud computing at the network core. This paper presents an efficient operation and management scheme of a micro data center, which is an essential equipment for realizing edge computing. First, we present the functional structure and deployment plan of edge computing. Next, we present the requirements for the micro data centers for edge computing and the operation and management scheme accordingly. Finally, in order to efficiently manage resources in the micro data centers, we present resource management items to be collected and monitored, and propose a performance indicator to measure the energy efficiency.

본 논문에서는 엣지 컴퓨팅 장치에서 쿠버네티스의 네트워크 성능 제어 방식이 실제 컨테이너 네트워크 성능에 미치 는 영향을 실험을 통해 제시한다. 컨테이너의 네트워크 성능은 엣지 컴퓨팅 환경에서 서비스 품질에 큰 영향을 미치 는 요소로 네트워크 성능이 저하되는 경우 데이터 전송에 걸리는 시간이 늘어나고 사용자 요청에 대한 응답시간이 길어지는 등의 서비스 품질 저하를 야기할 수 있다. 본 논문의 실험 결과, 엣지 컴퓨팅 환경에서 쿠버네티스의 네트 워크 성능 제어 방식을 적용하는 경우, 1) 사용자가 요청한 네트워크 성능에 달성하는데 상당한 시간이 소요되며, 2) 요청한 네트워크 성능 수치를 제대로 달성하지 못하는 문제가 있음을 확인하였다. 이를 통해, 쿠버네티스를 이용 한 네트워크 성능 제어를 위해서는 엣지 컴퓨팅 환경의 특성을 고려한 새로운 기법의 개발이 필요함을 알 수 있다.

This paper empirically demonstrates the ramifications of Kubernetes' network performance control methodologies on the network performance of containers within edge computing devices. Our experimental findings corroborate that the application of Kubernetes' network performance control methodologies within edge computing environments is characterized by: 1) a significant temporal investment required to attain user-requested network performance, and 2) inadequacies in achieving the stipulated network performance metrics. Thus, it is evident that the development of novel techniques tailored to the distinctive attributes of edge computing environments is imperative for proficient network performance control leveraging Kubernetes.

정보기술의 발달과 스마트한 서비스의 활성화로 인해서 다양한 스마트기기가 네트워크에 연결되는 사물인터넷 기술이 지속적으로 발전해오고 있다. 기존의 사물인터넷 구조에서는 클라우드 컴퓨팅 기술을 기반으로 중앙 집중형으로 데이터를 처리해왔으나, 단일 장애 지점, 종단간 전송 지연, 보안에 대한 우려가 있다. 이러한 문제를 해결하기 위해서 탈중앙화된 블록체인 기술을 사물인터넷에 적용할 필요가 있다. 하지만 많은 사물인터넷 기기들은 컴퓨팅 성능이 부족 하여 블록 채굴과 같은 막대한 자원이 소요되는 일을 처리하기에 어려움이 있다. 이를 극복하기 위해서 본 논문은 컴퓨 팅 자원이 부족한 사물인터넷 기기에서도 블록체인 기술을 적용할 수 있는 에지 컴퓨팅 기술 기반의 사물인터넷 구조를 제안한다. 본 논문은 또한 에지 컴퓨팅 기반의 사물인터넷에서의 블록체인의 동작 절차를 제시한다.

Thanks to the development of information technology and the vitalization of smart services, the Internet of Things (IoT) technology, in which various smart devices are connected to the network, has been continuously developed. In the legacy IoT architecture, data processing has been centralized based on cloud computing, but there are concerns about a single point of failure, end-to-end transmission delay, and security. To solve these problems, it is necessary to apply decentralized blockchain technology to the IoT. However, it is hard for the IoT devices with limited computing power to mine blocks, which consumes a great amount of computing resources. To overcome this difficulty, this paper proposes an IoT architecture based on the edge computing technology that can apply blockchain technology to IoT devices, which lack computing resources. This paper also presents an operaional procedure of blockchain in the edge computing-based IoT architecture.

본 도시 교통 혼잡은 간선도로망 전반에서 장시간의 지연, 배출 증가, 그리고 안전성 저하를 초래한다. 본 논문 은 스마트 폴 엣지 컴퓨팅과 연합형 다중 에이전트 강화학습을 결합한 교차로 제어용 엣지-네이티브 프레임워크를 제안 한다. 각 교차로의 경량 에이전트는 폴 탑재 센서로부터 로컬 학습을 수행하고, 프라이버시를 보존하는 연합 절차가 네 트워크 전역에서 주기적으로 모델 업데이트를 집계한다. 통신 계층은 저지연 로컬 V2X 교환을 위한 IEEE 802.11p와 모니터링 및 모델 조정을 위한 MQTT 5.0을 통합한다. 평가는 802.11p와 NR-V2X 링크 동작을 반영하기 위해 VEINS(OMNeT++–SUMO)와 Simu5G를 결합한 디지털 트윈-인-더-루프 환경에서 수행된다. 배출량은 SUMO의 HBEFA 기반 모형으로 추정한다. 고정주기, 감응식, 비연합형 MARL 기준선과 비교할 때, 제안 방법은 네트워크 전역 평균 지연과 대기행렬 길이를 줄이는 동시에 CO₂와 NOx를 낮출 잠재력을 보인다. 본 논문은 아키텍처, 학습 및 통신 설계, 그리고 시나리오 구성·평가지표·통계 검정을 위한 재현 가능한 프로토콜을 상세히 기술한다.

Urban traffic congestion leads to prolonged delays, elevated emissions, and diminished safety across arterial networks. This paper proposes an edge‑native framework for intersection control that combines smart‑pole edge computing with federated multi‑agent reinforcement learning. Lightweight agents at each intersection learn locally from on‑pole sensors while a privacy‑preserving federated procedure periodically aggregates model updates across the network. The communication layer integrates IEEE 802.11p for low‑latency local V2X exchange and MQTT 5.0 for monitoring and model coordination. Evaluation is conducted in a digital‑twin‑in‑the‑loop environment that couples VEINS (OMNeT++–SUMO) with Simu5G to account for 802.11p and NR‑V2X link behavior. Emissions are estimated using SUMO’s HBEFA‑based model. Compared with fixed‑time, actuated, and non‑federated MARL baselines, the proposed approach shows the potential to reduce network‑wide average delay and queue length while simultaneously lowering CO₂ and NOx. The paper details the architecture, the learning and communication design, and a reproducible protocol for scenario construction, metrics, and statistical testing.

In recent times, Edge computing technology is rapidly growing at a faster rate offering users a better performance for processing real-time applications in comparison with the traditional cloud computations. In the edge computing model, there are edge nodes grouped together, forming edge clusters for application services. The development of service migration technology in the Kubernetes environment complements the reliability and stability of the edge cloud management during the migration process. It automatically replicates the data changes on the edge server, and ultimately, there is no data loss or service failure, ensuring that the system is fully operational. In this paper, the proposed method of migration aims to provide uninterrupted service operation between the edge nodes in the event of a service failure. The preliminary experimental result performs the health check of containers in the pod which is used to identify the cause of service failure.

최근 횡단보도 및 교차로에서 보행자 충돌사고 예방의 중요성이 증가하고 있다. C-ITS 서비 스에서 이러한 사고를 줄이기 위하여 보행자 충돌방지 경고 서비스를 제시하고 있다. 그러나 현재 C-ITS 표준에 따른 보행자 충돌방지 경고 서비스는 현장의 카메라에서 보행자를 바로 검 출하여 서비스를 제공하는 것이 아니라 관제 센터의 영상분석 서버에서 보행자를 검출하고 ITS 시스템과 연계하여 서비스를 제공하기 때문에 실시간성을 만족하기 어렵다. 본 논문에서 는 엣지 카메라를 이용하여 현장에서 보행자를 검출하고 검출결과를 V2X 인프라를 통해 바로 운전자에게 제공하는 보행자 충돌방지 경고 시스템을 제안하고, 구현한 후 성능 평가를 시행 하였다. 평가 결과, 최악의 상황에서도 보행자 충돌방지 경고 메시지를 C-ITS 표준에서 요구하 고 있는 300ms 이내의 지연시간을 만족하여 전달할 수 있음을 확인하였다.

The prevention of pedestrian accidents in crosswalks and intersections is very important. The C-ITS services provide a warning service for preventing accidents between cars and pedestrians. In the current pedestrian collision prevention warning service according to the C-ITS standard, however, it is difficult to provide real-time service because it detects pedestrians from a video-analysis server in the control center and sends service messages through the ITS system. This paper proposes a pedestrian collision-prevention warning system that detects pedestrians in the local field using an edge camera and sends a warning message directly to the driver through a roadside unit. An evaluation showed that the proposed system could deliver the pedestrian collision prevention-warning message to the driver satisfying the delay time within the 300 ms required by the C-ITS standard, even in the worst case.

Future internet aims to function as a neutral in-network storage and computation platform, essential for enabling 6G and beyond wireless use cases. Information-Centric Networking and Edge Computing are key paradigms driving this vision by offering diversified services with fast response times across heterogeneous networks. This approach requires effective coordination to dynamically utilize resources like links, storage, and computation in near real-time within a non-homogenous and distributed computing environment. Additionally, networks must be aware of resource availability and reputational information to manage unknown and partially observed dynamic systems, ensuring the desired Quality of Experience (QoE). This paper provides a comprehensive evaluation of edge computing technologies, starting with an introduction to its architectural frameworks. We examine contemporary research on essential aspects such as resource allocation, computation delegation, data administration, and network management, highlighting existing research gaps. Furthermore, we explore the synergy between edge computing and 5G, and discuss advancements in 6G that enhance solutions through edge computing. Our study emphasizes the importance of integrating edge computing in future considerations, particularly regarding sustainable energy and standards.

Closed Circuit Television (CCTV) based Surveillance has become the fundamental part of the security Systems. In most cases, surveillance feeds are only used as evidence. The emergence of Edge Computing gives hope for enabling real time surveillance systems that focuses on prevention of crimes. The proposed architecture consists of a Convolutional Neural Network (CNN) enabled in an edge device, with reduced computational complexity, which classifies various actions like Pulling, pushing and other hand movements and locates the identified activities in the image frame using bounding boxes. The proposed architecture gives an alert whenever a suspicious activity is detected. The system was found efficient when validated against the Dataset taken from the SRM IST Campus.

Internet of Things (IoT) enables different smart environment objects to communicate without involving humans. Recently, IoT has started a new challenge within the healthcare sector called the Internet of Medical Things (IoMT). The huge amounts of data generated by IoMT entities need to be analyzed in real-time to improve the performance and quality of service of the IoMT applications. Mobile Edge Computing-enabled 5G system is shown as a successful paradigm to address such an obstacle. Numerous frameworks are introduced in literature based on this idea. This paper presents a thorough discussion of MEC-based IoMT healthcare systems.

In many of 5G applications, end devices with lack of computing power often need to carry out heavy computations involving multimedia data. Edge computing has emerged as a promising solution to circumvent scarce resources at end devices, with moderate delays compared to cloud computing. In this work, we study the problem of offloading video processing tasks to edge servers. To this end, we develop a deep reinforcement learning based method for selecting either local or edge server to process video frames. We demonstrate the performance of our method through experiments with video frame transform tasks.

The emergence of Multi-Access Edge Computing (MEC) technology aims to extend cloud computing capabilities to the edge of the wireless access networks, i.e., closer to the end-users. Thus, MEC-enabled 5G wireless systems are envisaged to offer real-time, low-latency, and high-bandwidth access to the radio network resources. Thus, MEC allows network operators to open up their networks to a wide range of innovative services, thereby giving rise to a brand-new ecosystem and a value chain. Furthermore, MEC as an enabling technology will provide new insights into coherent integration of Internet of Things (IoT) in 5G wireless systems. In this context, this paper expounds the four key technologies, including Network Function Virtualization (NFV), Software Defined Networking (SDN), Network Slicing and Information Centric Networking (ICN), that will propel and intensify the integration of MEC IoT in 5G networks. Moreover, our goal is to provide the close alliance between MEC and these four driving technologies in the 5G IoT context and to identify the open challenges, future directions, and concrete integration paths.

This paper introduces a Deep Reinforcement Learning (DRL)-based framework to enhance Network Function Parallelism (NFP) in Mobile Edge Computing (MEC). Leveraging Network Function Virtualization (NFV), the proposed framework optimizes service delay by solving a fairness-aware throughput maximization problem for service function chain placement. It aims to maximize the long-term cumulative reward while satisfying Quality of Service (QoS) requirements. The framework also preserves resources for future requests by efficiently managing the initialized network functions distribution. Simulation results demonstrate the superior performance of the proposed framework across various metrics. Specifically, our framework improves the average delay and deployment rate by 1.2% and 2.4% compared to the existing best method.

Edge computing has emerged as a promising computing paradigm that enables real-time data processing and analysis closer to the data source and boosts decision-making applications in a safe manner. On the other hand, the microservice is a new type of architecture that can be dynamically deployed, migrating across edge clouds on demand. Therefore, the combination of these two technologies can provide numerous benefits, including improved performance, reduced latency, and better resource utilization. In this paper, we present a thorough analysis of state-of-the-art research on the use of microservices in edge computing environments. We take into consideration several distinct microservice research directions, including coordination, orchestration, repositories, scheduling, autoscaling, deployment, resource management, and different security issues. Furthermore, we explore the potential applications of microservices in edge computing across various domains. Finally, the unsolved research issues and future directions of emerging trends in this area are also discussed.

Edge computing has emerged as a promising paradigm for addressing the latency, bandwidth, and scalability challenges associated with traditional cloud-centric architectures. Computation offloading, the process of transferring computational tasks from edge devices to more powerful remote servers or cloud infrastructure, plays a crucial role in optimizing performance and resource utilization in edge computing systems. However, traditional computation offloading techniques often face limitations related to latency, network dependency, and scalability. In this survey, we explore the integration of digital twin (DT) technology into edge computing environments to empower intelligent computation offloading decisions. DTs, virtual representations of physical entities or systems that mirror their real-world counterparts, offer opportunities to enhance situational awareness, optimize resource allocation, and enable more informed decision-making at the edge. We provide a comprehensive overview of DTs empowered intelligent computation offloading, covering the fundamentals of DTs, traditional computation offloading techniques, and their limitations in edge computing. Additionally, we discuss how DTs can address these challenges and improve computation offloading strategies, along with practical applications and use cases across various domains. Finally, we identify open research challenges and opportunities for future exploration in this emerging field. Through this survey, we aim to provide researchers, practitioners, and stakeholders with insights into the potential of DTs to revolutionize computation offloading for edge computing and drive innovation in this rapidly evolving area.

Along with the proliferation of the Internet of Things (IoT) and the surge in the use of artificial intelligence (AI), Edge Computing has proved considerable success in reducing latency, network traffic consumption, and security risks. The convergence of AI and Edge Computing, emerging a brand-new paradigm called edge intelligence, has been expected to unleash the full potential of intelligent IoT services. Unfortunately, integrating AI and Edge Computing into IoT is highly challenging due to the concerns over IoT device performance, energy efficiency, and privacy. In this paper, we present brainyEdge, an AI-enabled framework for edge devices able to jointly satisfy the Quality of Experience (QoE) criteria of IoT applications. We enhanced the intelligence of AI models operating at edges by designing a learning procedure consisting of transfer learning and incremental learning to dynamically retrain the models with personalized and incremental data locally stored. These data are classified into private data permanently stored in edges and public data shared in the cloud. This increases the edge-cloud collaboration level while preserving data privacy. To minimize the network cost of deploying the models to edge devices, we developed a lightweight deployment paradigm supporting cloud-compression and edge-decompression based on a user-desired compression ratio. Our prototype-based evaluation results indicate the superiority of brainyEdge over a typical edge-cloud paradigm.

The entire world progression has ceased with the unexpected outbreak of the COVID-19 pandemic, and urges the requirement for contact-less and autonomous services and applications. Realizing these predominantly Internet of Things (IoT) based applications demands a holistic pervasive computing infrastructure. In this paper, we conduct a survey to determine the possible pervasive approaches for utilizing the Multi-Access Edge Computing (MEC) infrastructure in realizing the requirements of emerging IoT applications. We have formalized specific architectural layouts for the considered IoT applications, while specifying network-level requirements to realize such approaches; and conducted a simulation to test the feasibility of proposed MEC approaches.

This paper investigates QoS-aware computation offloading issues for mobile edge computing in the 6G network. To minimize the end-to-end delay, we harness the Information-Centric Network (ICN) to ensure resource-constrained mobile user offloading computation-sensitive tasks in a distributed manner. Then, a two-stage approach based on a Multi-Agent Reinforcement Learning (MARL) algorithm entwined with optimization-embedding offloading ratio is proposed to enhance server selection for load balancing. Numeral results demonstrate that, with reference to a workshop-scale scenario, the proposed method can achieve outperformed performance in reducing delay and balancing loads on edge servers than the other four baseline schemes.

In the centralized architecture of traditional mobile cellular network, every traffic of mobile terminals has to traverse the centered network core and then deliver to the corresponding service mobile terminals. Since the traffic load is concentrated on the central network core, it is difficult to deliver the explosive amount of network traffic and guarantee latency requirements of diversifying applications. In order to solve this problem, the mobile edge computing (MEC) architecture that distributes the traffic load by locating the computing and caching server closer to the terminal is currently being developed. In this paper, the effect of distribution of mobile traffic with MEC is evaluated in terms of throughput enhancements and latency reduction. The simulations were conducted using Mininet to determine what performance gains can be obtained when applying the MEC architecture to the mobile cellular network. As a result, under the same link bandwidth condition, MEC architecture provides an increase in data rates of approximately 265% for high-quality video transmission and about 162% for low-quality video transmission, and shows the reduced delay of packet arrival times.

Despite the rapid growth of the Internet industry, the provision of full Internet service to remote regions is still challenging. As a solution, the combination of Low Earth Orbit (LEO) satellite communication and Mobile Edge Computing (MEC) is gaining attention. However, considering the high speed of LEO satellites in network environments remains a significant challenge. To this end, this paper introduces a dynamic computation offloading and resource allocation framework in the LEO satellite MEC architecture. Using Lyapunov optimization, we propose an efficient DCOOL algorithm to minimize average power consumption and propagation delay constrained by queue stability. Finally, comparative analysis and simulations demonstrate the superior performance of DCOOL while achieving lower power consumption and stable workload processing.