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With the growing development of 5G and its new services, edge computing is becoming the cornerstone of the ongoing network transformation. Its integration into 5G network infrastructure brings new research opportunities related to the design and implementation of high-performance systems, enabling the accomplishment of the three main promises of 5G: very high speed, low latency, and massive connectivity. This trend has generated strong interest in realizing effective life cycle management of latency-sensitive edge applications in order to achieve a high level of QoS while ensuring the service continuity in the case of user mobility. This article deals with the relocation of edge services, commonly called edge relocation, which aims to relocate edge application instances between edge clusters in order to ensure uninterrupted service. To achieve our objective, we propose a cloud-native edge-enabled 5G solution that complies with ETSI and 3GPP standards. The latter performs pre-relocation and relocation of edge applications in order to support the service continuity. Various scenarios of edge delocation are discussed, and illustrative use cases are provided. Next, a proof of concept is described to demonstrate how edge relocation can be implemented and leveraged to ensure service continuity in the case of infrastructure performance degradation. Finally, unique research opportunities are identified. [ABSTRACT FROM AUTHOR]

Deterministic executions of computation-intensive and delay-sensitive tasks are vital for realizing Industry 4.0. Multi-access edge computing (MEC) enables critical tasks to be offloaded to the network edge for quickly responding and processing tasks locally. However, during workload peaks at the resource-limited edge, the best effort IP forwarding in the typical network architecture incurs long-tail latency at both the access and interconnection networks of MEC systems. The long-tail latency results in strict service level agreements (SLAs) unable to be guaranteed in end-to-end communication. In this article, we propose a task deterministic network (TDN) architecture that provides communication with bounded low latency and zero jitter for critical tasks among MEC systems. In TDN, the cross-domain collaboration mechanisms of the layer 2/layer 3 deterministic networking and 5G are jointly designed to eliminate the long-tail latency in typical MEC system networks. In addition, we design a seamless working scheme for the time-sensitive networking controller, the MEC orchestrator, and other coordinators to enable edge collaboration and global deterministic forwarding. Moreover, the main functions of TDN are detailed, including clock synchronization, network slicing, cycle mapping, and multi-layer collaboration. Finally, we present simulation results to substantiate the performance of TDN for accommodating critical service flows among MEC systems. [ABSTRACT FROM AUTHOR]

The cloud native approach is rapidly transforming how applications are developed and operated, turning monolithic applications into microservice applications, allowing teams to release faster, increase reliability, and expedite operations by taking full advantage of cloud resources and their elasticity. At the same time, “fog computing” is emerging, bringing the cloud toward the edge, near the end user, in order to increase privacy, improve resource efficiency, and reduce latency. Combining these two trends, however, proves difficult because of four fundamental disconnects between the cloud native paradigm and fog computing. This article identifies these disconnects and proposes a fog native architecture along with a set of design patterns to take full advantage of the fog. Central to this approach is turning microservice applications into microservice workflows, constructed dynamically by the system using an intent-based approach taking into account a number of factors such as user requirements, request location, and available infrastructure and microservices. The architecture introduces a novel softwarized fog mesh facilitating both inter-microservice connectivity, external communication, and end-user aggregation. Our evaluation analyzes the impact of distributing microservice-based applications over a fog ecosystem, illustrating the impact of CPU and network latency and application metrics on perceived quality of service of fog native workflows compared to the cloud. The results show the fog can offer superior application performance given the right conditions. [ABSTRACT FROM AUTHOR]

5G-enabled Tactile Internet (TI) will rely on sensors, actuators, and ultra-reliable and ultralow latency communication for providing haptic human-machine interaction services. However, stringent requirements in terms of high reliability and sub-millisecond latency pose many daunting challenges that have limited the development of Tl services. In this article, we shed light on the design of edge computing for Tl industrial applications. We discuss major industrial use cases that will be transformed with the development of TI. We thoroughly present the benefits and challenges of the processing of Tl computation-in-tensive tasks in edge computing servers. Moreover, we highlight the current designs of edge resource allocation algorithms for tasks offloading and haptic and non-haptic feedback delivery in Tl industrial applications. Furthermore, we thoroughly discuss the current missing building blocks in edge computing to unleash TI industrial applications. Finally, open challenges that emerge when designing edge computing for supporting Tl industrial applications are discussed. [ABSTRACT FROM AUTHOR]

Internet of Everything (IoE) applications such as haptics, human-computer interaction, and extended reality, using the sixth-generation (6G) of wireless systems have diverse requirements in terms of latency, reliability, data rate, and user-defined performance metrics. Therefore, enabling IoE applications over 6G requires a new framework that can be used to manage, operate, and optimize the 6G wireless system and its underlying IoE services. Such a new framework for 6G can be based on digital twins. Digital twins use a virtual representation of the 6G physical system along with the associated algorithms (e.g., machine learning, optimization), communication technologies (e.g., millimeter-wave and terahertz communication), computing systems (e.g., edge computing and cloud computing), as well as privacy and security-related technologists (e.g., blockchain). First, we present the key design requirements for enabling 6G through the use of a digital twin. Next, the architectural components and trends such as edge-based twins, cloud-based-twins, and edge-cloud-based twins are presented. Furthermore, we provide a comparative description of various twins. Finally, we outline and recommend guidelines for several future research directions. [ABSTRACT FROM AUTHOR]

New paradigm shifts and 6G technological rev-olution in vehicular services have emerged toward unmanned driving, automated transportation, and self-driving vehicles. As the technology for autonomous cars becomes mature, real challeng-es come from reliable, safe, real-time connected transportation operations to achieve ubiquitous and prompt information exchanges with massive connected and autonomous vehicles. This article introduces novel wireless distributed architectures that embed the edge computing capability inside software-defined vehicular networking infrastructure. Such edge networks consist of open-loop grant-free communications and computing-based control frameworks, which enable dynamic eco-routing with ultra-low latency and mobile data-driven orchestration. Thus, this work advanc-es the frontiers of machine learning potential and next-generation mobile systems in vehicular net-working applications. [ABSTRACT FROM AUTHOR]

Wireless communication with unmanned aerial vehicles (UAVs) has appeared as a promising technique for many application scenarios in future communication networks. In this article, we provide a vision of four UAV-enabled aerial computing (AC) architectures. Specifically, these architectures have attracted growing attention in recent years for decreasing data processing delay and enhancing computation performance via integrating UAVs into AC systems. We first study the benefits and application scenarios of UAV-enabled AC networks. Then we emphasize the efficient computation and communication design of UAV-enabled AC networks by discussing the potential research issues and key design considerations in a comprehensive manner. In addition, extensive case study simulations show the performance superiority of UAV-enabled AC networks over conventional infrastructure-based mobile edge computing networks. To provide an instructive guide for future research work, we discuss the technical challenges and open issues. [ABSTRACT FROM AUTHOR]

The evolution of 5G network service ecosystems raises the need for the discovery of services among multiple (edge) clouds with potentially heterogeneous resources and features. Besides 5G service deployment, such a service discovery architecture could provide the means for cross-service communication (CSC), that is, the consumption of a network service (NS) by another NS, empowering the service capabilities of the latter. This article presents a distributed service discovery framework for network service marketplaces (NSMs) in edge computing. We exemplify the operation of a new distributed service discovery mechanism that relies on a cache-based protocol for the discovery of NSs among edge clouds. We also present a multi-criteria ranking mechanism, which identifies the most suitable edge cloud according to the client's functional, performance, and cost requirements. Our evaluation results indicate that the proposed service discovery mechanism (SDM) yields service discovery effectiveness on par with other approaches, while drastically reducing the communication overhead. [ABSTRACT FROM AUTHOR]

This article discusses an Ultra-Reliable Low-La-tency Communication (URLLC) implementation on a 5G New Radio (NR) virtual radio access Network (vRAN). The vRAN application runs on a commercial off-the-shelf (COTS) test platform, on which 3GPP 5G NR RAN Layer-1 (L1) and Layer-2 (L2) containerized network functions (CNFs) are implemented. To demonstrate capability and scalability, the CNFs are dimensioned to support up to three 100 MHz cells. URLLC and enhanced mobile broadband (eMBB) slices are implemented to partition L1 processing for mixed use cases. This article illustrates an advantage of virtualization in managing L1 processing demand by L1 slice-scaling. It also describes a methodology for optimal cell capacity dimensioning based on connected URLLC users' service level agreements (SLAs) in terms of latency, reliability, and throughput. Using analysis of the test results, the article discusses maintaining a critical time budget of downlink (DL) and uplink (UL) L1 processing pipelines and points out techniques to increase L1 processing capacity to achieve URLLC SLA while serving eMBB users. The article concludes that a 5G NR vRAN application running on COTS hardware enables public/private URLLC verticals including mobile edge computing, cloud gaming, AR/VR, industrial automation, and others. [ABSTRACT FROM AUTHOR]

The fifth generation (5G) of mobile communications introduces improvements on many fronts when compared to its previous generations. Besides the performance enhancements and new advances in radio technologies, it also integrates other technological domains, such as cloud-to-things continuum and artificial intelligence. In this work, the 5G-DIVE Elastic Edge Platform (DEEP) is proposed as the linking piece for the integration of these technological domains, making available an intelligent edge and fog 5G end-to-end solution. This solution brings numerous benefits to vertical industries by enabling streamlined, abstracted, and automated management of their vertical services, thus contributing to the introduction of novel services, cost savings, and improved time to market. Preliminary validation of the proposed platform is performed through a proof of concept, along with a qualitative analysis of its benefits for Industry 4.0 and autonomous drone scouting vertical industries. [ABSTRACT FROM AUTHOR]

Integrating the Internet of Things (IoT) and edge computing in Edge-IoT systems, converged with machine intelligence, has the potential to enable a wide range of applications in smart homes, factories, and cities. Edge-IoT can connect many diverse devices, and IoT asset owners can run heterogeneous IoT systems supported by various vendors or service providers (SPs), using either cloud or local edge computing (or both) for resource assistance. The existing methods typically manage the systems as separate vertical silos, or in a vendor-/SP-centric way, which suffers from significant challenges. In this article, we present a novel owner-centric management paradigm named ORCA to address the gaps left by the owner-centric paradigm and empower IoT asset owners to effectively identify and mitigate potential issues in their own network premises, regardless of vendors'/SPs' situations. ORCA aims to be scalable and extensible in assisting IoT owners to perform intelligent management through a behavior-oriented and data-driven approach. ORCA is an ongoing project, and the preliminary results indicate that it can significantly empower IoT system owners to better manage their IoT assets. [ABSTRACT FROM AUTHOR]

Smart railway is in the process of vigorous development based on the advancement of numerous emerging and advanced techniques, such as artificial intelligence and big data. However, a smart railway network is exceedingly time-varying and complicated, which presents great challenges to power allocation. Deep reinforcement learning is capable of effectually improving the intelligence as well as cognition of high-speed railway network, which helps to optimize the power allocation problems with both time-varying and complicated characteristics. In this article, we first provide an overview of the existing power allocation methods, including advantages, disadvantages, and complexity, as well as characteristics. Then we investigate an innovative power allocation algorithm based on multi-agent deep recurrent deterministic policy gradient (MADRDPG), which is capable of learning power decisions from past experience instead of an accurate mathematical model. Finally, numerical results indicate that the performance of the MADRDPG-based method significantly outperforms existing state-of-the-art methods in terms of spectrum efficiency and execution latency. [ABSTRACT FROM AUTHOR]

Autonomous flight for UAVs relies on visual information for avoiding obstacles and ensuring safe collision-free flight. In addition to visual clues, safe UAVs often need connectivity with the ground station. In this article, we study the synergies between vision and communications for edge-computing-enabled UAV flight. By proposing a framework of edge computing assisted autonomous flight (ECAAF), we illustrate that vision and communications can interact with and assist each other with the aid of edge computing and offloading, and further speed up UAV mission completion. ECAAF consists of three functionalities that are discussed in detail: edge computing for 3D map acquisition, radio map construction from the 3D map, and online trajectory planning. During ECAAF, the interactions of communication capacity, video offloading, 3D map quality, and channel state of the trajectory form a positive feedback loop. Simulation results verify that the proposed method can improve mission performance by enhancing connectivity. Finally, we conclude with some future research directions. [ABSTRACT FROM AUTHOR]

Edge intelligence, also called edge-native artificial intelligence (AI), is an emerging technological framework focusing on seamless integration of AI, communication networks, and mobile edge computing. It has been considered to be one of the key missing components in the existing 5G network and is widely recognized to be one of the most sought after functions for tomorrow's wireless 6G cellular systems. In this article, we identify the key requirements and challenges of edge-native AI in 6G. A self-learning architecture based on self-supervised generative adversarial nets is introduced to demonstrate the potential performance improvement that can be achieved by automatic data learning and synthesizing at the edge of the network. We evaluate the performance of our proposed self-learning architecture in a university campus shuttle system connected via a 5G network. Our result shows that the proposed architecture has the potential to identify and classify unknown services that emerge in edge computing networks. Future trends and key research problems for self-learning-enabled 6G edge intelligence are also discussed. [ABSTRACT FROM AUTHOR]

The recent breakthrough in artificial intelligence (AI), especially deep neural networks (DNNs), has affected every branch of science and technology. Particularly, edge AI has been envisioned as a major application scenario to provide DNN-based services at edge devices. This article presents effective methods for edge inference at resource-constrained devices. It focuses on device-edge co-inference, assisted by an edge computing server, and investigates a critical trade-off among the computational cost of the on-device model and the communication overhead of forwarding the intermediate feature to the edge server. A general three-step framework is proposed for the effective inference: model split point selection to determine the on-device model, communication-aware model compression to reduce the on-device computation and the resulting communication overhead simultaneously, and task-oriented encoding of the intermediate feature to further reduce the communication overhead. Experiments demonstrate that our proposed framework achieves a better tradeoff and significantly reduces the inference latency than baseline methods. [ABSTRACT FROM AUTHOR]

5G technology is expected to enable many innovative applications in different verticals. These applications have heterogeneous performance requirements (e.g., high data rate, low latency, high reliability, and high availability). In order to meet these requirements, 5G networks endorse network flexibility through the deployment of new emerging technologies, mainly network slicing and mobile edge computing. This article introduces a distributed blockchain-enabled network slicing (DBNS) framework that enables service and resource providers to dynamically lease resources to ensure high performance for their end-to-end services. The key component of our framework is the global service provisioning (GSP), which provides admission control for incoming service requests along with dynamic resource assignment by means of a blockchain-based bidding system. The goal is to improve users' experience with diverse services and reduce providers' capital and operational expenditures. [ABSTRACT FROM AUTHOR]

Network slicing is set out to address crucial needs of 5G, including support for multi-service provisioning. Focusing on resource and performance isolation, as well as security concerns associated with multi-tenancy, existing management and orchestration (MANO) frameworks typically offer network slices in the form of isolated bundles of computing, storage, and network resources across the network infrastructure, including the edge. However, network slice instantiation in its prevailing form raises significant concerns related to performance and resource utilization, hindering potential business-to-business (B2B) synergies. In this article, we discuss a new aspect of network slicing, optimized cross-slice communication (CSC). We argue that multi-tenancy and service co-location open up unique opportunities for B2B interactions, inter-service communications, and service composition, especially in the domain of edge computing and location-based services. In this context, we present optimized edge slice orchestration (MESON), a MANO-based architecture for optimized CSC in edge clouds. We discuss the main architecture components and descriptors, as well as the steps required for the discovery of CSC-enabled services and the establishment of optimized CSC among co-located slices. [ABSTRACT FROM AUTHOR]

Edge computing has already shown its potential benefits to support the delay-sensitive and computation-intensive service provisioning in the Internet-of-Things-based environments. Simultaneously, artificial intelligence is expected to enhance the cognizance and intelligence of edge computing, resulting in a new paradigm, intelligent edge computing. However, as is evident, several new security and privacy-related issues will arise due to the distinct characteristics and working principles of intelligent edge computing. In this article, we focus on malicious attacks targeting the intelligent engines of edge computing. These attacks counteract the gains from intelligent systems and also compromise the edge computing system. We provide the main factors that are responsible for the security and privacy-related challenges in intelligent edge computing. Finally, the potential research directions in privacy and security-related issues are outlined for intelligent edge computing. [ABSTRACT FROM AUTHOR]

The surging numbers of wireless devices and the accompanying exponential growth of user traffic have led edge and fog computing to become essential components in future access networks. By bringing more processing toward the edge of the network, they enable the realization of various demanding applications while supporting numerous resource-constrained devices. Their integration with passive optical networks and their huge offered bandwidth has therefore attracted much research interest. In this article, we review these computing concepts and examine key considerations for their integration with long-reach optical access networks when the employed upstream bandwidth allocation is either centralized or decentralized. [ABSTRACT FROM AUTHOR]

The MEC paradigm calls for a distribution of computational capacity at the network's edges. While MEC will play a key role in future 5G deployments, it will take some time until the existing 4G networks evolve into a full 5G system. A challenge exists to devise a transition mechanism that allows MEC features to be seamlessly integrated in the current 4G networks. This article introduces a lightweight, ETSI-compliant MEC solution for 4G and 5G networks. The proposed solution, which we name LightEdge, has the main goal of immediately making available the features and capabilities of edge clouds to mobile users. This article reports on the design and implementation of LightEdge and on its evaluation in a practical latency-sensitive use case. [ABSTRACT FROM AUTHOR]

Fog computing is considered to be a key enabling technology for future networks. By broadening the cloud computing services to the network edge, fog computing can support various emerging applications such as IoT, big data, and blockchain with low latency and low bandwidth consumption cost. To achieve the full potential of fog computing, it is essential to design an incentive mechanism for fog computing service providers. Auction is a promising solution for the incentive mechanism design. However, it is challenging to design an optimal auction that maximizes the revenue for the providers while holding important properties: IR and IC. Therefore, this article introduces the design of an optimal auction based on deep learning for the resource allocation in fog computing. The proposed optimal auction is developed specifically to support blockchain applications. In particular, we first discuss resource management issues in fog computing. Second, we review economic and pricing models for resource management in fog computing. Third, we introduce fog computing and blockchain. Fourth, we present how to design the optimal auction by using deep learning for the fog resource allocation in the blockchain network. Simulation results demonstrate that the proposed scheme outperforms the baseline scheme (i.e., the greedy algorithm) in terms of revenue, and IC and IR violations. Thus, the proposed scheme can be used as a useful tool for the optimal resource allocation in general fog networks. [ABSTRACT FROM AUTHOR]

Emerging edge computing has seen latency- sensitive services moving rapidly from cloud to the edge to take advantage of its close vicinity to end users, while cloud is retained for carrying out latency-insensitive and computation-intensive tasks. When more edge computing service providers come to the market, the network will become increasingly more fragmented because of their proprietary services and policies deployed in the network. This means that the Internet can become more cumbersome and riskier as there will be more tiers and potential vulnerabilities that could be exploited. To tackle this issue, we envisage a federated service chaining paradigm in which operators can share and put service functions in other participants' networks so as to improve resource utilization, collaboratively mitigate cyber threats, and enable service innovations. In this position article, building on our past experience in enabling federated cloud infrastructure and heterogeneous service chaining, we present a Federated Service Chaining architecture followed by discussions of its key components. Several key research challenges are described for the successful realization of such architecture. We hope this article can open a discussion in the research community and generate enough research interest to significantly advance this field. [ABSTRACT FROM AUTHOR]

Computation offloading through stateless applications is gaining momentum thanks to the emergence of serverless frameworks with inherent scalability properties. However, adoption of a serverless framework in an edge computing system requires careful consideration to keep its advantages unscathed. In the cloud, micro-services are scaled automatically according to demands, but in edge computing this would incur a significantly higher cost than in a data center and cannot be as fluid. This is especially relevant in scenarios where edge nodes are spread across large areas and have relatively small computation capabilities. In this article we propose to overcome this issue by adapting the allocation of demands to the currently allocated micro-services at short timescales, with two alternative mechanisms designed for different target scenarios, both aimed at enabling distributed computing environments. The proposed solution can be integrated within the ETSI MEC standard, which specifies a reference architecture and open service interfaces. Our contribution is validated in a proof-of-concept scenario with a prototype implementation released as open source. [ABSTRACT FROM AUTHOR]

MCS is an emerging paradigm that leverages the pervasiveness of mobile, wearable, and vehicle-mounted devices to collect data from urban environments for ubiquitous service provisioning. In order to manage MCS application data streams efficiently, a scalable computing infrastructure hosting heterogeneous and distributed resources is critical. FC, as a geo-distributed computing paradigm, is a key enabler for this requirement as it bridges cloud servers and smart mobile devices. Research on the integration of MCS with FC has recently started to be explored, recognizing the requirements of MCS and their coexistence with cyber-physical systems. In this article, we analyze the state of the art of FC solutions in MCS systems. After a brief overview of MCS, we emphasize the link between MCS and FC. We then investigate the existing fog-based MCS architectures in detail by focusing on their building blocks, as well as the challenges that remain unaddressed. Our detailed review on the subject results in a taxonomy of FC solutions in MCS systems. In particular, we highlight the node structures, the information exchanged, the resource and service management, and the type of solutions adopted concerning privacy and security. Moreover, we provide a thorough discussion on the open issues and challenges by reporting useful insights for researchers in MCS and FC. [ABSTRACT FROM AUTHOR]

Recent advances in wireless communication, sensing, computation and control technologies have paved the way for the development of a new era of Internet of Vehicles (IoV). Demanded by the requirements of information-centric and data-driven intelligent transportation systems (ITS), it is of great significance to explore new paradigms of IoV in supporting large-scale, real-time, and reliable information services. In this article, we propose a hierarchical system architecture, which aims at synthesizing the paradigms of software defined networking and fog computing in IoV and best exploiting their synergistic effects on information services. Specifically, a four-layer architecture is designed, comprising the application layer, the control layer, the virtualization layer, and the data layer, with objectives of enabling logically centralized control via the separation of the control plane and the data plane; facilitating adaptive resource allocation and QoS oriented services based on network functions virtualization and network slicing, and enhancing system scalability, responsiveness, and reliability by exploiting the networking, computation, communication, and storage capacities of fog-based services. On this basis, we further analyze newly arising challenges and discuss future research directions by presenting a cross-layer protocol stack. Finally, for the proof of concept, we implement the system prototype and give two case studies in real-world IoV environments. The results of field tests not only demonstrate the great potential of the new architecture, but also give insight into the development of future ITS. [ABSTRACT FROM AUTHOR]

Information-centric networking (ICN) is an important trend that will impact the future of the Internet. ICN caters to large content consumption patterns while achieving high performance. New features in the information-centric future Internet, such as caching, name-based routing, and content-based security, bring novel challenges to a decentralized environment. On one hand, the processing capabilities on the edge in an information-centric future Internet need to implement smart analysis for large quantities of content. On the other hand, the computational and storage resources need to be configured and controlled on demand and based on cognition of the content from users. To address these challenges, this article proposes a fog-computing- enabled cognitive network functions virtualization approach for an information-centric future Internet. We first propose an on-demand caching function virtualization scheme and design a communication scheme between the fog nodes and the future Internet nodes for the forwarding process. Then, to attain smart control for related operations (i.e., routing, cache policy, and security), we propose a control function virtualization approach. Finally, a cognitive resource configuration mechanism is proposed. The simulation results show the advantages and efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

MEC is an emerging paradigm that utilizes computing resources at the network edge to deploy heterogeneous applications and services. In the MEC system, mobile users and enterprises can offload computation-intensive tasks to nearby computing resources to reduce latency and save energy. When users make offloading decisions, the task dependency needs to be considered. Due to the NP-hardness of the offloading problem, the existing solutions are mainly heuristic, and therefore have difficulties in adapting to the increasingly complex and dynamic applications. To address the challenges of task dependency and adapting to dynamic scenarios, we propose a new DRL-based offloading framework, which can efficiently learn the offloading policy uniquely represented by a specially designed S2S neural network. The proposed DRL solution can automatically discover the common patterns behind various applications so as to infer an optimal offloading policy in different scenarios. Simulation experiments were conducted to evaluate the performance of the proposed DRL-based method with different data transmission rates and task numbers. The results show that our method outperforms two heuristic baselines and achieves nearly optimal performance. [ABSTRACT FROM AUTHOR]

The first generation of IoT was developed and deployed all over the world by connecting devices with common functionalities that were not sufficiently efficient or reliable for use in dynamic situations that require adaptive solutions. However, these fundamental IoT functions and services mainly targeted stable environments; there is consequently a strong need for the next generation of IoT services to be smarter, faster, and more reliable. We believe that the hyper-connected IoT ecosystem on fog platforms with contextual AI technologies is a promising solution. In this work, we introduce the EiF, a flexible fog computing framework that runs on IoT gateways with adaptive AI services fostered on the cloud. Our approach can be viewed as an integration of three emerging technologies, namely IoT, fog, and AI. Generally, EiF virtualizes an IoT service layer platform for fog nodes, and provides functions to manage and orchestrate various fog nodes; upon service virtualization and orchestration, AI services are fostered within both the federated cloud and distributed edge side and are deployed on fog nodes. We demonstrate the feasibility of EiF via the example of intelligent traffic flow monitoring and management. [ABSTRACT FROM AUTHOR]

The rise of smart health promotes ubiquitous healthcare services with the adoption of information and communication technologies. However, increasing demands of medical services require more computing and storage resources in proximity of medical users for intelligent sensing, processing, and analysis. Fog computing emerges to enable in situ data processing and service provision for smart health in proximity of medical users, exploiting a large number of small-scale servers. In this article, we investigate fog-enabled smart health toward cooperative and secure healthcare service provision. Specifically, we first introduce the overall infrastructure and some promising applications, including emergent healthcare service, health risk assessment, and healthcare notification. We then discuss the challenges of fog-enabled smart health from the perspectives of cooperation and security. A case study is presented to demonstrate efficient and secure health data sharing through naive Bayes classification and attribute-based encryption with assistance from fog computing. Finally, by exploring interesting future directions, more attention can be attracted to this emerging area. [ABSTRACT FROM AUTHOR]

Deploying NFV technologies to the edge networks has attracted growing attention in the stateof- the-art studies. In this article, we first review the most recent work on the topic of applying NFV to edge networks. Then, we identify that an urgent research challenge is to provide proactive failure recovery (shorten as failover hereafter) mechanism for the NFV-enabled distributed edge computing. To address this issue, we propose a novel management architecture that supports the proactive failover mechanism while provisioning NFV services in distributed edge computing. Simulation results show that the proposed proactive failover mechanism outperforms the reactive manner significantly in terms of latency spending on failover operations. We hope this article can spur deeper studies on the proactive intelligent resilience mechanism for deploying NFV in distributed edge computing and other related edge intelligence topics. [ABSTRACT FROM AUTHOR]

The convergence of communication and computing (COM2P) has been taken as a promising solution for the sustainable development of mobile communication systems. The introduction of fog computing in future mobile networks makes COM2P possible. This article provides an overview on fog computing enabled mobile communication networks (FogMNW), including network architecture, system capacity and resource management. First, this article analyzes the heterogeneity of FogMNW with both advanced communication techniques and fog computing. Then a heterogeneous communication and hierarchical fog computing network architecture is proposed. With both communication and computing resources, FogMNW is enabled to achieve much higher capacity than conventional communication networks. This has been well demonstrated by the coded multicast scheme. Furthermore, a systematic management of communication and computing resources is necessary for FogMNW. By exploiting the communication load diversity in N cells, a communication load aware CLA scheme can achieve much higher computing resource efficiency than comparing schemes. The performance gap increases with N, and CLA can improve efficiency by more than 100 percent when there are 14 cells. [ABSTRACT FROM AUTHOR]

As the ratification of 5G New Radio technology is being completed, enabling network architectures are expected to undertake a matching effort. Conventional cloud and edge computing paradigms may thus become insufficient in supporting the increasingly stringent operating requirements of intelligent IoT devices that can move unpredictably and at high speeds. Complementing these, the concept of fog emerges to deploy cooperative cloud-like functions in the immediate vicinity of various moving devices, such as connected and autonomous vehicles, on the road and in the air. Envisioning the gradual evolution of these infrastructures toward an increasingly denser geographical distribution of fog functionality, we in this work put forward the vision of dense moving fog for intelligent IoT applications. To this aim, we review the recent powerful enablers, outline the main challenges and opportunities, and corroborate the performance benefits of collaborative dense fog operation in a characteristic use case fe [ABSTRACT FROM AUTHOR]

The multi-access edge computing (MEC) architectural model has fostered the development of new human-driven edge computing (HEC) frameworks that extend the coverage of traditional MEC solutions leveraging people roaming around with their devices. HEC is a well-suited architecture for human-centered technologies such as mobile crowdsensing (MCS) as it allows conveying and distributing sensing tasks at the edges of the network, also enabling (local) sensing data collection from devices. This article, through the joint use of HEC and MCS paradigms, introduces a new social-driven edge computing architecture based on incentives and centrality measures. The core idea is to add social MEC (SMEC) nodes to complement the traditional edge nodes (i.e., the main actors of the middle layer of the standard MEC architecture), acting as bridges between other devices and the cloud. The principle that underlies the SMEC selection is based on the attitude of the users in performing tasks and on their measures of centrality. In addition, we report extensive experimental results based on co-location traces and cooperativeness scores extracted from the ParticipAct living lab, a well-known MCS dataset based on data collected between 2013 and 2015 from 170 students of the University of Bologna, that show how the selection based on centrality measurements returns greater benefits than simple selection based on cooperativeness scores. [ABSTRACT FROM AUTHOR]

The proliferation of IoTs beside the emergence of various cloud services push the horizon of edge computing. By offering cloud capabilities at the network edge closer to mobile devices, edge computing is a promising paradigm to resolve several vital challenges in IoTs, such as bandwidth saturation, energy constraints, low latency transmission, and data security and privacy. To provide a comprehensive understanding of edge computing supported by the integration of IoTs and cloud computing, that is, CoTs, this article first discusses some distinct research directions in CoTs with respect to edge computing. Given the significance of energy efficiency and sustainability of edge deployment in CoTs, we put forward a green and sustainable virtual network embedding framework for cooperative edge computing in wireless-optical broadband access networks. Specifically, we leverage a reliability function to confirm the number of backup edge devices, and embed virtual networks onto the suitable edge devices in CoTs. Finally, several research challenges and open issues are discussed. [ABSTRACT FROM AUTHOR]

Vehicular edge computing is essential to support future emerging multimedia-rich and delay-sensitive applications in vehicular networks. However, the massive deployment of edge computing infrastructures induces new problems including energy consumption and carbon pollution. This motivates us to develop BEGIN (Big data enabled EnerGy-efficient vehIcular edge computiNg), a programmable, scalable, and flexible framework for integrating big data analytics with vehicular edge computing. In this article, we first present a comprehensive literature review. Then the overall design principle of BEGIN is described with an emphasis on computing domain and data domain convergence. In the next section, we classify big data in BEGIN into four categories and then describe their features and potential values. Four typical application scenarios in BEGIN including node deployment, resource adaptation and workload allocation, energy management, and proactive caching and pushing, are provided to illustrate how to achieve energy-efficient vehicular edge computing by using big data. A case study is presented to demonstrate the feasibility of BEGIN and the superiority of big data in energy efficiency improvement. Finally, we conclude this work and outline future research open issues. [ABSTRACT FROM AUTHOR]

The volume of data traffic has increased exponentially due to the explosive growth of IoT devices and the arrival of many new IoT applications. Due to the large volume of data generated from IoT devices, limited bandwidth, high latency, and realtime analysis requirements, the conventional centralized network architecture cannot meet users requirements. Intensive real-time data analysis is one of the major challenges in current state-ofthe- art centralized architectures due to the ubiquitous deployment of different types of sensors. To address the current challenges and adhere to the principles of architectural design, we are proposing a SoftEdgeNet model, which is a novel SDN-based distributed layered network architecture with a blockchain technique for a sustainable edge computing network. At the fog layer, we introduce an SDN-based secure fog node architecture to mitigate security attacks and provide real-time analytics services. We are also proposing a flow rule partition, and allocation algorithm at the edge of the network. The evaluation result shows our proposed model allows for a significant improvement of the interactions in real time data transmission. In terms of the ability to mitigate flooding attacks, the bandwidth is maintained above 9 Mb/s until the attack rates exceed 2000 PPS in the hardware environment and the bandwidth remained almost unchanged in the software environment. In the case of scalability of the proposed model, our proposed algorithm has performed better and proceeded linearly with the increase in traffic volume. [ABSTRACT FROM AUTHOR]

Remarkable advancements in embedded systems- on-a-chip have significantly increased the number of commercial devices that possess sufficient resources to run full-fledged operating systems. This change has extended the potential of the IoT. Many early IoT devices could only collect and send data for analysis. However, the increasing computing capacity of today's devices allow them to perform complex computations on-site, resulting in edge computing. Edge computing extends cloud computing capabilities by bringing services close to the edge of a network and thus supports a new variety of services and applications. In this work, we investigate, highlight, and report on recent advances in edge computing technologies with respect to measuring their impact on IoT. We establish a taxonomy of edge computing by classifying and categorizing existing literature, and by doing so, we reveal the salient and supportive features of different edge computing paradigms for IoT. Moreover, we present the key requirements for the successful deployment of edge computing in IoT and discuss a few indispensable scenarios of edge computing in IoT. Several open research challenges are also outlined. [ABSTRACT FROM AUTHOR]

Fog computing has emerged as a promising solution for the IoT and next generation mobile networks. As an extension to cloud computing, it enables service provisioning along the continuum from the cloud to things to reduce latency and bandwidth demands, and empower end users in their vicinity. Such a cloud-to-thing service continuum requires full technology support in infrastructure, platform, software and service levels. This article proposes FA2ST and its architecture to underpin a multi-level system of fog computing services for end-to-end support of IoT applications. It presents the concept of FA2ST and describes its architecture, main features, a use case in a vertical industry, and a performance study. [ABSTRACT FROM AUTHOR]

With the growing adoption of dash cameras, we are seeing great potential for innovations by analyzing the video collected from vehicles. On the other hand, transmitting and analyzing a large amount of video, especially high-resolution video in real time, requires a lot of communications and computing resources. In this work, we investigate the feasibility and challenges of applying vehicular fog computing for real- time analytics of crowdsourced dash camera video. Instead of forwarding all the video to the cloud, we propose to turn commercial fleets (e.g., buses and taxis) into vehicular fog nodes, and to utilize these nodes to gather and process the video from the vehicles within communication ranges. We assess the feasibility of our proposal in two steps. First, we analyze the availability of vehicular fog nodes based on a real-world traffic dataset. Second, we explore the serviceability of vehicular fog nodes by evaluating the networking performance of fog-enabled video crowdsourcing over two mainstream access technologies, DSRC and LTE. Based on our findings, we also summarize the challenges to largescale real-time analytics of crowdsourced videos over vehicular networks. [ABSTRACT FROM AUTHOR]

Edge computing is expected to enable the cloud computing services in proximity and leverage the computational capacities at the edge of a network. Driven by massive data-hungry and energy-hungry applications, future wireless networks will need to provide various cloud-based multimedia services in a "greener" manner. However, limited computing capacity, wireless resources, and power supply prevent edge computing from evolving and maturing in terms of high sustainability and energy efficiency. In this article, we attempt to further improve the energy-efficient communication in mobile edge computing by proposing a device-to-device (D2D) offloading architecture. The D2D technique, in line with mobile edge computing, is responsible for offloading wireless traffic as well as computational tasks. Cooperative relay-aided transmission is also used in this architecture to pursue further improvements in a multi-cell scenario. Finally, offloading and balancing technologies are introduced to increase the total energy efficiency, decrease the inter/ intra-cell interference, deal with traffic/computational congestion, and provide better services to end users. [ABSTRACT FROM AUTHOR]

The world of connected devices has led to the rise of the Internet of Things paradigm, where applications rely on multiple devices, gathering and sharing data across highly heterogeneous networks. The variety of possible mechanisms, protocols, and hardware has become a hindrance in the development of architectures capable of addressing the most common IoT use cases, while abstracting services from the underlying communication subsystem. Moreover, the world is moving toward new strict requirements in terms of timeliness and low latency in combination with ultra-high availability and reliability. Thus, future IoT architectures will also have to support the requirements of these cyber-physical applications. In this regard, edge computing has been presented as one of the most promising solutions, relying on the cooperation of nodes by moving services directly to end devices and caching information locally. Therefore, in this article, we propose a modular and scalable architecture based on lightweight virtualization. The provided modularity, combined with the orchestration supplied by Docker, simplifies management and enables distributed deployments, creating a highly dynamic system. Moreover, characteristics such as fault tolerance and system availability are achieved by distributing the application logic across different layers, where failures of devices and micro-services can be masked by this natively redundant architecture, with minimal impact on the overall system performance. Experimental results have validated the implementation of the proposed architecture for on-demand services deployment across different architecture layers. [ABSTRACT FROM AUTHOR]

The evolution and sophistication of cyber-attacks need resilient and evolving cybersecurity schemes. As an emerging technology, the Internet of Things (IoT) inherits cyber-attacks and threats from the IT environment despite the existence of a layered defensive security mechanism. The extension of the digital world to the physical environment of IoT brings unseen attacks that require a novel lightweight and distributed attack detection mechanism due to their architecture and resource constraints. Architecturally, fog nodes can be leveraged to offload security functions from IoT and the cloud to mitigate the resource limitation issues of IoT and scalability bottlenecks of the cloud. Classical machine learning algorithms have been extensively used for intrusion detection, although scalability, feature engineering efforts, and accuracy have hindered their penetration into the security market. These shortcomings could be mitigated using the deep learning approach as it has been successful in big data fields. Apart from eliminating the need to craft features manually, deep learning is resilient against morphing attacks with high detection accuracy. This article proposes an LSTM network for distributed cyber-attack detection in fog-to-things communication. We identify and analyze critical attacks and threats targeting IoT devices, especially attacks exploiting vulnerabilities of wireless communications. The conducted experiments on two scenarios demonstrate the effectiveness and efficiency of deeper models over traditional machine learning models. [ABSTRACT FROM AUTHOR]

Edge computing extends the capabilities of computation, network connection, and storage from the cloud to the edge of the network. It enables the application of business logic between the downstream data of the cloud service and the upstream data of the Internet of Things (IoT). In the field of Industrial IoT, edge computing provides added benefits of agility, real-time processing, and autonomy to create value for intelligent manufacturing. With the focus on the concept of edge computing, this article proposes an architecture of edge computing for IoT-based manufacturing. It also analyzes the role of edge computing from four aspects including edge equipment, network communication, information fusion, and cooperative mechanism with cloud computing. Finally, we give a case study to implement the active maintenance based on a prototype platform. This article aims to provide a technical reference for the deployment of edge computing in the smart factory. [ABSTRACT FROM AUTHOR]

Recently, edge computing has emerged as a promising computing paradigm to meet stringent quality-of-service requirements of an increasing number of latency-sensitive applications. The core principle of edge computing is to bring the capability of cloud computing in close proximity to mobile devices, sensors, actuators, connected things and end users, thereby supporting various types of services and applications at the network edge. In this article, we design capacity-centric FiWi broadband access networks enhanced with edge computing as well as resulting fiber backhaul sharing and computation offloading capabilities. More specifically, we introduce the concept of FiWi enhanced two-level edge computing at the access edge cloud and metro edge cloud. To guarantee low end-to-end latency, we propose a TDMA based polling scheme for resource management. Furthermore, given the vital importance of experimentally demonstrating the potential and practical limitations of edge computing, we develop an experimental testbed for edge computing across converged FiWi broadband access networks. The proof-ofconcept demonstration of the testbed is studied in terms of response time and response time efficiency of both edge clouds, including their respective energy consumption. [ABSTRACT FROM AUTHOR]

While applications of the heterogeneous Internet of Things (IoT) proliferate, their performance requirements in terms of latency and jitter become more stringent and difficult to be met by the traditional cloud computing paradigm. As a result, a new paradigm called multi-access edge computing has emerged in recent years. It complements the centralized cloud platform by providing additional resources at the edge of radio access networks, and provides better support of IoT applications. One of the challenges of supporting IoT applications in mobile edge computing (MEC) is security. In this article, we will scrutinize the security issues of two IoT applications that can exploit the benefits of MEC. The first one is an environment perception system based on an industrial IoT network. The second one, which is gaining momentum fast, is mobile IoT based on a network of unmanned aerial vehicles. [ABSTRACT FROM AUTHOR]

Blockchain, as the backbone technology of the current popular Bitcoin digital currency, has become a promising decentralized data management framework. Although blockchain has been widely adopted in many applications (e.g., finance, healthcare, and logistics), its application in mobile services is still limited. This is due to the fact that blockchain users need to solve preset proof-of-work puzzles to add new data (i.e., a block) to the blockchain. Solving the proof of work, however, consumes substantial resources in terms of CPU time and energy, which is not suitable for resource-limited mobile devices. To facilitate blockchain applications in future mobile Internet of Things systems, multiple access mobile edge computing appears to be an auspicious solution to solve the proof-of-work puzzles for mobile users. We first introduce a novel concept of edge computing for mobile blockchain. Then we introduce an economic approach for edge computing resource management. Moreover, a prototype of mobile edge computing enabled blockchain systems is presented with experimental results to justify the proposed concept. [ABSTRACT FROM AUTHOR]

The ever increasing mobile data demands have posed significant challenges in the current radio access networks, while the emerging computation- heavy Internet of Things applications with varied requirements demand more flexibility and resilience from the cloud/edge computing architecture. In this article, to address the issues, we propose a novel air-ground integrated mobile edge network (AGMEN), where UAVs are flexibly deployed and scheduled, and assist the communication, caching, and computing of the edge network. Specifically, we present the detailed architecture of AGMEN, and investigate the benefits and application scenarios of drone cells, and UAV-assisted edge caching and computing. Furthermore, the challenging issues in AGMEN are discussed, and potential research directions are highlighted. [ABSTRACT FROM AUTHOR]

Edge computing has emerged as a promising paradigm for delay-sensitive and context-aware IoT data analytics, through migrating data processing from the cloud to the edge of the network. However, traditional solutions adopting homomorphic encryption to achieve data protection and aggregation at edge servers are infeasible because of their heavy computational overhead. How to preserve data privacy while guaranteeing data utility in edge computing becomes an extremely important problem for IoT data analytics. In this article, we propose a local differential privacy obfuscation (LDPO) framework for IoT data analytics to aggregate and distill the IoT data at the edge without disclosing users' sensitive data. We first introduce the architecture and benefits of the LDPO framework, followed by some technical challenges in guaranteeing its performance. Then we present a preliminary implementation of the LDPO framework, and validate its performance in terms of privacy preservation level and data utility using real-world apps and datasets. Some future directions are finally envisioned for further research. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) has emerged as a promising paradigm to realize user requirements with low-latency applications. The deep integration of multi-access technologies and MEC can significantly enhance the access capacity between heterogeneous devices and MEC platforms. However, the traditional MEC network architecture cannot be directly applied to the Internet of Vehicles (IoV) due to high speed mobility and inherent characteristics. Furthermore, given a large number of resource-rich vehicles on the road, it is a new opportunity to execute task offloading and data processing onto smart vehicles. To facilitate good merging of the MEC technology in IoV, this article first introduces a vehicular edge multi-access network that treats vehicles as edge computation resources to construct the cooperative and distributed computing architecture. For immersive applications, co-located vehicles have the inherent properties of collecting considerable identical and similar computation tasks. We propose a collaborative task offloading and output transmission mechanism to guarantee low latency as well as the application- level performance. Finally, we take 3D reconstruction as an exemplary scenario to provide insights on the design of the network framework. Numerical results demonstrate that the proposed scheme is able to reduce the perception reaction time while ensuring the application-level driving experiences. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC), which enables delay-sensitive computing tasks to be executed at network edges, has been proposed to accommodate the explosive growth of the Internet of Things. However, the growing demands for massive connectivity, ultra-low latency, and high reliability in various emerging resource-hungry and computation-intensive applications pose new challenges in network access capacity. This motivates us to conceive a new MEC framework from an air-ground integration perspective. First, we present a review of the state-of-the-art literature. Then the architecture and technological benefits of the proposed framework are elaborated. Next, four typical use cases are introduced, and a case study is conducted to demonstrate the significant performance improvements in computation capability and communication connectivity based on real-world road topology. Finally, we present challenges and research directions, and conclude this article. [ABSTRACT FROM AUTHOR]