Edge computing: A survey

Special Issue on Recent Trends and Future of Fog and Edge Computing, Services and Enabling Technologies

The upcoming 5th Generation (5G) mobile networks will be different from the previous mobile network generations in the fact that it will enable the mobile networks industry, besides offering superior broadband services, to enhance Internet of Things (IoT) industries such as vehicular communication system, factory automation, smart healthcare system and many more. Many of these use cases have challenging and quite often contradicting requirements in terms of data rate, latency, throughput and so on. This suggests that 5G mobile networks need to adopt flexible models that can adapt to different IoT device and traffic requirements. Consequently, a fresh look into how mobile networks are currently designed and deployed is needed. Historically, mobile networks have relied on the axiomatic role of cells as the cornerstone of the Radio Access Networks (RAN). Mobile network systems have witnessed several recent trends such as the increased heterogeneity in heterogeneous types of IoT services infrastructure and spectrum as well as the rise of different traffic types with different Quality of Services (QoS) requirements. In this direction, this thesis focuses on improving the performance of cell-edge users or IoT devices in 5G mobile networks by initially implementing the network slicing management approach, particularly as, with the fast growth of IoT, billions of devices will join the internet in the next few years. Hence, the latest 5G mobile technologies expected to offer massive connectivity and management ability of high volume of data traffic at the presence of immense interferences from a mobile network of IoT devices. Further, it will face challenges due to congestion and overload of data traffic due to a humongous number of IoT devices. Besides, these devices likely to demand high throughput, low latency and high level of reliability especially for critical real-time smart systems in density and small zone, such as in Vehicular Communication System (VCS), these vehicles mainly rely on connectivity aspects. Furthermore, IoT devices transmit small and large-sized packets with different radio resource requirements. For example, Smart Healthcare System (SHS) devices transmit small-sized of a data with utilizing a small portion of Physical Resource Block (PRB) as the smallest radio resource unit, which is allocated to a single device for data transmission in 5G mobile networks. In the IoT services with transmitting a small-sized data, the capacity of the PRB is not fully utilized, which causes wastage and unfairness of using PRB among these IoT devices or services. The novelties made in this thesis significantly advance a Slice Allocation Management (SAM) model based on critical services such as (VCS) to satisfy low latency demand. The proposed model aims at providing dedicated slices based on service requirements such as expected low latency for (VCS). To ensure such performance to data traffic of IoT devices in Uplink (UL)of Relay Node (RN) cells in the 5G mobile networks by slicing the RAN, along with assigning the nearest Mobile Edge Computing (MEC) with isolating slices depend on technical and QoS requirements for each IoT nodes. Also, this thesis proposes a Data Traffic Aggregation (DTA) model for efficient utilization of the smallest untie of PRB by aggregating the data traffics of several IoT devices, which can support IoT node throughput such as SHS. Also, this thesis presents a comprehensive comparison of the packet scheduling mechanisms include Priority Queuing (PQ), First-In-First-Out (FIFO) and Weighted Fair Queuing (WFQ) applied based on data traffic slicing model through RN cells. These thesis models are validated through the OPNET simulator to measure the performance of the SAM and DTA Models along with the assessment of packet scheduling mechanism. The simulation considers IoT devices in various smart systems such as VCS, SHS and smartphones also, different protocols include Simple Mail Transfer Protocol (SMTP), File Transfer Protocol (FTP), and Voice over Internet Protocol (VoIP) and Real-time Transport Protocol (RTP). Simulation results show a significant improvement in IoT nodes packets transmission via RNs and Donor eNodeB (DeNB) cells, in My SAM Model scenario comparing with other scenarios. The model has improved such as End-to-End (E2E) delay in FTP node by reaching 1ms, loading in VoIP node by 80% and throughput of all nodes in the uplink side of networks by 66%. In addition, the results display significant impact of IoT data traffic with different priority, networks E2E performance is improved by aggregating data traffic of several IoT devices with DTA model, which is determined by simulating several scenarios, considerable performance improvement is achieved in terms of averages cell throughput, upload response time, packet E2E delay and radio resource utilization. Finally, the result found PQ packet scheduling mechanism as the appropriate scheduling mechanism in case of supporting several of priorities queuing for data traffic.

The edge cache is an effective way to reduce the heavy traffic load and the end-to-end latency in radio access networks (RANs) for supporting a number of critical Internet of Things (IoT) services and applications. It has been verified to provide high spectral efficiency (SE), high energy efficiency (EE), and low latency. Along with several key techniques that have been applied, such as device-to-device communication and predictive caching, the edge cache techniques in RANs for IoT are becoming diversified. This paper comprehensively surveys the recent advances of the edge cache in RANs, including the key techniques and the corresponding performances. In particular, the key techniques are presented from the viewpoints of the deployment location of edge caches, content placement strategy, and coded caching. An advanced hierarchical edge cache structure is presented, and the main impacts on SE, EE, and latency of the key techniques are mainly summarized. Several open issues and challenges are identified as well to spur future investigations, in which the joint optimization of radio and cache resources, the edge cache with mobile edge computing and network intelligence, privacy, and security are discussed.

Edge and fog computing for IoT: A survey on current research activities & future directions

The Internet of Things (IoT) is expected to grow faster than any other category of connected devices. IoT allows any device with an on-and-off switch to connect to the internet—a concept that has the ability to greatly change our lives and work. These modern systems collect inherently complex data streams due to the volume, velocity, value, variety, variability, and veracity of data, which leads to incremental growth in data traffic on networks and in the cloud. To fulfill the requirements of IoT, including geodistribution, low latency, location awareness, and mobility support, a new paradigm is proposed: edge computing. In edge computing, substantial computing and storage resources are placed at the edge of the network in mobile devices or sensors. The term “edge” is taken from network diagrams; normally, the edge of a network diagram represents the point at which data traffic enters or leaves the workable network. Using the concept of edge computing, an organization can shift huge amounts of data into processed data near the data origin, which helps to reduce data traffic in the network’s central repository (called the “cloud”). Edge computing uses a variety of data reduction techniques close to the data source at the network edge, including data pre-processing, local storage, and filtering. This approach can prevent some critical issues, such as I/O bottlenecks, storage and bandwidth limitations, data traffic increments, and high energy costs. A major advantage of edge computing is improvement of the request-response delay to milliseconds. Edge computing also supports security and network challenges. However, two major obstacles exist toward achieving the benefit of network-edge computing. First, the most efficient algorithms for data reduction in time series (one of the most common types of data in IoT) were developed to work posteriori upon big datasets, but they cannot make decisions for each incoming data item. Secondly, the state of the art lacks systems that can apply any of the possible data reduction methods without adding significant delays or major reconfigurations. Edge computing has also inherited some of the challenges of cloud computing, including data abstraction, naming, and programmability. This chapter presents a detailed taxonomic discussion of edge computing, along with its challenges, opportunities, and data reduction methods.

Edge computing as a promising technology provides lower latency, more efficient transmission, and faster speed of data processing since the edge servers are closer to the user devices. Each edge server with limited resources can offload latency-sensitive and computation-intensive tasks from nearby user devices. However, edge computing faces challenges such as resource allocation, energy consumption, security and privacy issues, etc. Auction mechanisms can well characterize bidirectional interactions between edge servers and user devices under the above constraints in edge computing. As demonstrated by the existing works, auction and mechanism design approaches are outstanding on achieving optimal allocation strategy while guaranteeing mutual satisfaction among edge servers and user devices, especially for scenarios with scarce resources. In this paper, we introduce a comprehensive survey of recent researches that apply auction approaches in edge computing. Firstly, a brief overview of edge computing including three common edge computing paradigms, i.e., cloudlet, fog computing and mobile edge computing, is presented. Then, we introduce fundamentals and backgrounds of auction schemes commonly used in edge computing systems. After then, a comprehensive survey of applications of auction-based approaches applied for edge computing is provided, which is categorized by different auction approaches. Finally, several open challenges and promising research directions are discussed.

Purpose of review. The paper provides an in-depth exploration of the integration of Internet of Things (IoT) technologies with cloud, fog, and edge computing paradigms, examining the transformative impact on computational architectures. Approach to review. Beginning with an overview of IoT's evolution and its surge in global adoption, the paper emphasizes the increasing importance of integrating cloud, fog, and edge computing to meet the escalating demands for real-time data processing, low-latency communication, and scalable infrastructure in the IoT ecosystem. The survey meticulously dissects each computing paradigm, highlighting the unique characteristics, advantages, and challenges associated with IoT, cloud computing, edge computing, and fog computing. The discussion delves into the individual strengths and limitations of these technologies, addressing issues such as latency, bandwidth consumption, security, and data privacy. Further, the paper explores the synergies between IoT and cloud computing, recognizing cloud computing as a backend solution for processing vast data streams generated by IoT devices. Review results. Challenges related to unreliable data handling and privacy concerns are acknowledged, emphasizing the need for robust security measures and regulatory frameworks. The integration of edge computing with IoT is investigated, showcasing the symbiotic relationship where edge nodes leverage the residual computing capabilities of IoT devices to provide additional services. The challenges associated with the heterogeneity of edge computing systems are highlighted, and the paper presents research on computational offloading as a strategy to minimize latency in mobile edge computing. Fog computing's intermediary role in enhancing bandwidth, reducing latency, and providing scalability for IoT applications is thoroughly examined. Challenges related to security, authentication, and distributed denial of service in fog computing are acknowledged. The paper also explores innovative algorithms addressing resource management challenges in fog-IoT environments. Conclusions. The survey concludes with insights into the collaborative integration of cloud, fog, and edge computing to form a cohesive computational architecture for IoT. The future perspectives section anticipates the role of 6G technology in unlocking the full potential of IoT, emphasizing applications such as telemedicine, smart cities, and enhanced distance learning. Cybersecurity concerns, energy consumption, and standardization challenges are identified as key areas for future research.

Video streaming and computer games are among the most popular and highest bandwidth consuming media in the Internet. Video contents consume around 70% of the total bandwidth usage in the Internet today. Advancements in media generation tools, high processing power, and high speed connectivity have enabled generation of live, interactive, multi-view media generation. Cognitive assisted and online multi-player gaming have unlocked new horizons for gaming experience. However, such interactive gaming, and multi-view and 360-degree view videos, are currently limited by delay intolerance and excessive bandwidth usage. Edge computing is the name of a set of new technologies, such as cloudlets, micro data centers, fog, and mobile edge computing. It aims to provide storage and computational resources near to user at the network edge, to minimize latency and response time. Edge computing is foreseen as a significant enabler of Internet of Every Thing (IoET) era by extending the cloud services and resources at the end of the network to deliver very low latency and real-time communication. It can provide significant services to video and gaming applications and enable new stream of interactive multimedia era. In this paper, we highlight some of the potentials and prospects of edge computing for interactive media, and present some preliminary works in the area. We shed light on how edge computing can be used to tackle various challenges faced by todays interactive media application. We also present the benefits of using edge computing to save cost, bandwidth, and energy in multimedia applications, video streaming, and transcoding.

The COVID‐19 pandemic has brought profound changes in people's live and work. It has also accelerated the development of education from traditional model to online model, which is particularly important in preschool education. Preschoolers communicate with teachers through online video, so how to provide high quality and low latency online teaching has become a new challenge. In cloud computing, users offload computing tasks to the cloud to meet the high computing demands of their devices, but cloud‐based solutions have led to huge bandwidth usage and unpredictable latency. In order to solve this problem, mobile edge computing (MEC) deploys the server at the edge of the network to provide the service with close range and low latency. In task scheduling, edge computing (EC) devices have rational thinking, and they are unwilling to collaborate with MEC server to perform tasks due to their selfishness. Therefore, it is necessary to design an effective incentive mechanism to encourage the collaboration of EC devices. Through analysis of MEC server and EC devices, we propose a distributed task scheduling algorithm—Stackelberg game approach based on alternating direction method of multipliers, which selects the appropriate incentive mechanism to encourage the collaboration of EC devices. The experimental results demonstrate that the proposed approach can rapidly converge to a certain accuracy within 40 iterations, and in incentive mechanism comparison and quality of experience, the proposed approach also has a good performance in anti‐jitter and low latency.

Potentials, trends, and prospects in edge technologies: Fog, cloudlet, mobile edge, and micro data centers

2. Medical information systems

Building effective and scalable systems that can handle enormous volumes of data processing and provide low-latency processing and real-time analytics may be accomplished by combining cloud and edge computing. Edge and cloud computing are both used in hybrid cloud computing. We investigated the various ways that cloud and edge computing may be coupled in this research report. The names "cloud," "edge," "cloud and edge," "edge as a service," "mobile edge computing," and "fog computing" were all used to characterise these techniques. When choosing a strategy, organisations should carefully consider their needs because each strategy has benefits and drawbacks of its own. We also reviewed a range of academic articles that discuss the advantages and disadvantages of using a hybrid cloud/edge computing architecture. The papers show how cloud and edge computing may be applied to support special use cases in a variety of industries, including manufacturing, transportation, and healthcare. The issues that need to be resolved include those related to connectivity, security, complexity, cost, and scalability, to name just a few. If organisations wish to guarantee the success of their cloud and edge computing solutions, they must carefully tackle the aforementioned concerns. Looking ahead, we can see that the field of cloud and edge computing has a lot of potential for advancement and innovation. This can only happen with the development of intelligent edge devices, new security and privacy technologies, and the optimisation of cloud and edge computing for 5G networks. We could anticipate even more substantial developments in the integration of cloud and edge computing as time goes on. These innovations will make it possible to introduce creative and original use cases in a variety of business situations.

Task offloading paradigm in mobile edge computing-current issues, adopted approaches, and future directions

A review of edge computing: Features and resource virtualization

As urbanization accelerates, smart cities are emerging as innovative ecosystems that integrate technology to address challenges related to sustainability, mobility, and infrastructure. Among these technologies, edge computing has gained prominence as a transformative solution to optimize data processing and resource management in urban environments. This paper explores the role of edge computing in enabling efficient, real-time decision-making by bringing computational power closer to data sources. Unlike traditional cloud-centric models, edge computing reduces latency, enhances data security, and improves bandwidth utilization by distributing data processing across decentralized nodes. The integration of edge computing in smart cities supports various applications, including intelligent transportation systems, energy-efficient smart grids, and real-time public safety monitoring. By processing data locally, edge devices can handle massive volumes of information generated by Internet of Things (IoT) devices, ensuring seamless service delivery without overwhelming centralized systems. Furthermore, this decentralized approach enhances resilience by reducing dependency on remote servers, a crucial factor for mission-critical urban applications. A significant focus of this paper is on resource management, particularly the allocation of computational resources across edge nodes. Strategies such as dynamic resource scheduling, load balancing, and adaptive task offloading are analyzed for their effectiveness in maintaining operational efficiency. Moreover, the research highlights the importance of leveraging machine learning and artificial intelligence algorithms within edge computing frameworks to predict traffic patterns, optimize energy consumption, and enhance waste management systems. Security and privacy concerns, often considered barriers to edge computing adoption, are addressed through advanced encryption techniques and secure communication protocols. This paper also evaluates challenges associated with edge computing deployment, such as hardware limitations, interoperability issues, and the need for robust regulatory frameworks. Case studies from leading smart city projects illustrate successful implementations and offer insights into overcoming these obstacles. In addition to technical aspects, this research underscores the socioeconomic benefits of edge computing in urban settings. Improved public services, reduced environmental impact, and cost-effective infrastructure management demonstrate the potential of edge computing to revolutionize city living. By enabling real-time analytics and localized decision-making, edge computing supports a more responsive and adaptive urban ecosystem. The findings presented in this paper emphasize the critical role of edge computing in bridging the gap between urban challenges and technological solutions. As cities continue to evolve, adopting edge computing technologies will not only enhance operational efficiency but also foster innovation, sustainability, and inclusivity. Future research directions include exploring hybrid models combining edge and cloud computing, advancing hardware capabilities, and developing standardized frameworks to accelerate adoption. This paper contributes to the growing body of knowledge on edge computing, offering a comprehensive analysis of its applications, challenges, and potential in shaping the future of smart cities. By optimizing data processing and resource management, edge computing emerges as a cornerstone technology for creating smarter, more resilient urban environments.