Earticle

Blockchain has become the central research to enable data processing in edge computing. Considering that edge computing primarily deals with sensitive IoT data, privacy is an essential factor. Blockchain solves the secure data propagation problem among dynamically located edge nodes. However, blockchain enables public access for all connected nodes, thus leaving data privacy as an open issue. In this paper, we propose a smart-contract-based solution for controlling the data accesses in the public blockchain.

Existing simulators for performance analysis of resource management techniques in edge computing have a limitation: they lack horizontal management features such as inter-server clustering and container registry placement. To address this issue, this paper proposes EdgeNet, a new simulator specialized for modeling of network overhead and server clustering algorithm in edge computing environments. EdgeNet provides a Python library that can be used to develop leader election algorithms for clustered server groups, facilitating research into horizontal resource management approaches that were previously difficult to study.

본 논문에서는 실시간 엣지 컴퓨팅 서비스를 위해 엣지 서버단에서 다량의 엣지 디바이스들의 데이터를 고속으로 처리하는 실시간성을 보장하고, 엣지 컴퓨팅 시스템의 효율적 운영, 최적의 자원관리 및 기능 제어를 수행할 수 있 는 시스템 소프트웨어를 설계하고, 실시간 성능을 제공할 수 있는 OpenEOE라 명명된 기본 구조를 제시하였다. 제 안 시스템 검증을 위한 성능 평가 결과, OpenEOE는 기존 리눅스 배포판인 우분투 시스템 대비, 부하없는 상황과 부하 상황에서 상대적으로 저지연 서비스를 제공할 수 있음을 확인하였다.

In this paper, for real-time edge computing service, the edge server side guarantees real-time processing of a large amount of data from edge devices at high speed, and it is a system that can perform efficient operation of edge computing systems, optimal resource management and function control. We designed the software and presented a basic structure named OpenEOE that can provide real-time performance. As a result of the performance evaluation for verification of the proposed system, it was confirmed that OpenEOE can provide relatively low-latency service under load and no load compared to the Ubuntu system, which is the existing Linux distribution.

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.

Beyond 6G services and applications demand high and efficient processing capacity due to the massive connectivity of users equipment (UEs). However, the high computational capability and energy consumption of UEs are limited, which becomes a main challenge to overcome. Multi-access edge computing (MEC) has recently been studied widely as it can potentially assist complex tasks executed at UEs. Furthermore, several techniques have been proposed to optimize task offloading among users. Thus, another challenge in MEC is emerging due to the fact that mobile users do not always have a line-of-sight (LoS) to the base station (BS) due to the blocking object. Therefore, it can affect users data rate and result in incremental energy consumption. This research introduces the concept of reconfigurable intelligence surfaces (RIS) to support multiple-input-single-output (MISO) base stations (BS) in both uplink (UL) and downlink (DL) using BCD algorithms. While previous studies concentrate on enhancing task offloading and neglecting inter-user interference, this study suggests an optimization approach for UL and DL data rates, as well as minimizing task offloading delays. The results indicate that optimizing task placement, phase shift, and precoding can reduce the duration of task offloading.

Multi-access Edge Computing (MEC) has emerged as a novel and efficient technology to enable a new breed of time sensitive applications in the 5G era. By installing small computing infrastructures at the network edges, it solves the current centralized structure problem of the cloud infrastructure (i.e., high end-to-end communication latency between a user equipment and the cloud). As users move across different sites over time, continuous seamless service support to the users is also required. A service migration in 5G MEC is a promising approach for continuous seamless service support by migrating active services to a new MEC host near the current user location. In this paper, a comparison result for the service migration implemented either in a virtual machine or container is presented. The motivation behind this paper is to understand the fundamental performance differences between the virtual machine and container for the service migration in 5G MEC.

This paper presents a test bed demonstration of NeuroEdge computing for face recognition using a novel neuromorphic chip- NM500. First, a general description and important specifications of the NM500 are presented. Second, a face recognition test-bed case study is used to demonstrate the efficacy and efficiency of the chip. Neuromorphic technology offers scalability and consistent recognition time, which is required by real-time networked systems, and presents a considerable advantage for real-time computations, making them virtually independent of the dataset size. In this study, intelligent edge computing technology was introduced using NeuroEdge. The performance was verified using a face recognition test. The results demonstrated that using neuromorphic technology, such as the NM500 chip, saves the time needed for training systems and does not impose the burden of requiring many datasets for effective training.