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The future of mobile and internet technologies are manifesting advancements beyond the existing scope of science. The concepts of automated driving, augmented-reality, and machine-type-communication are quite sophisticated and require an elevation of the current mobile infrastructure for launching. The fifth-generation (5G) mobile technology serves as the solution, though it lacks a proximate networking infrastructure to satisfy the service guarantees. Multi-access Edge Computing (MEC) envisages such an edge computing platform. In this survey, we are revealing security vulnerabilities of key 5G-based use cases deployed in the MEC context. Probable security flows of each case are specified, while countermeasures are proposed for mitigating them.

Smart city has obtained increasing attention from both academic and industry which has the potential to improve human living standards. A smart city consists of a great number of smart devices which are generating large amounts of data and emerging applications all the time. However, the computing capacity of smart devices are limited. Fortunately, the emergence of MEC can solve the above problem. However, the resources of edge servers in MEC are limited and the capabilities of edge servers are heterogeneous. It is important to improve the average resource utilization of all edge servers and keep load balancing of edge server cluster simultaneously. On the other hand, quite a few numbers of applications are delay-sensitive, it is necessary to ensure the security of these applications. In this paper, we consider joint optimization of mobile device and edge server in MEC-enabled smart city, improving the overall performance of the system. Technically, a new multi-objective computation offloading method is implemented to reduce time consumption, energy consumption, and keep load balancing of edge servers, as well as increase average resource utilization of edge servers while meeting the deadline constraint of delay-sensitive applications. Sufficient experiments have been conducted to prove the effectiveness and superiority of our proposed method in different scenarios.

This paper mainly investigates the energy-efficient and secure offloading problem in air-to-ground Mobile Edge Computing (MEC) networks. The proposed efficient offloading mechanism is as per the requirements of the heterogeneous tasks of ground users. Further, the optimizing offloading rate, offloading object, and channel access jointly formulate system energy consumption and eavesdropping rate minimization. A distributed two-stage offloading scheme is proposed for achieving the sub-optimal solution for the Mixed-integer Nonlinear Programming (MINLP) problem. Finally, simulation results demonstrate that the proposed scheme is superior to several benchmark schemes.

Control systems achieve the desired performance with the model-based controller if the dynamical model of the actual plant is given with sufficient accuracy. However, if there exists a difference between the actual plant and its model dynamics, the model-based controller does not work well and does not achieve the intended desired performance. A model error compensator (MEC) is proposed for overcoming the model error in our previous study. Attaching the compensator for the model error to the actual plant, the output trajectory of the actual plant is made close to that of its model. Then, from the controller, the apparent difference in the dynamics can be smaller, and performance degradation is drastically reduced. MEC is useful for various control systems such as non-linear systems and the control systems with delay, and so on. In this paper, we propose an original design method of the filter parameters in MEC for systems with polytopic-type uncertainty and disturbances. First, we show an analysis method about the robust performance of MEC for the system with the polytopic type uncertainty based on an linear matrix inequality problem. The gain parameters in MEC is designed using particle swarm optimization and the presented analysis method. The effectiveness of the design method for the system with polytopic-type uncertainty and disturbance is evaluated using numerical examples.

Vehicles will be wirelessly connected in the future and they will be able to exchange information with other vehicles and their surroundings for safer and more efficient driving. 5G communication systems have introduced advanced functionalities and radio solutions to support connected, cooperative, and automated mobility (CCAM) services with demanding Quality of Service (QoS) requirements. However, interoperability among involved stakeholders, seamless connectivity and the uninterrupted delivery of real-time services across borders are issues that should be carefully analyzed for the realization of crossborder CCAM services. This paper provides an overview of key standardization bodies by analyzing recent work on key technologies to provide cross-border connectivity services for CCAM. Standardization gaps and regulatory barriers that may affect fast and efficient adoption of 5G-enabled CCAM services, are also discussed.

Multi-Access Edge Computing (MEC) is an extension of cloud computing that aims to provide computation, storage, and networking capabilities at the edge of the network in close proximity to end-users. The MEC architecture supports applications and services that bridge between cloud computing and end-users. The architecture includes devices and systems that are interconnected, layered, and flexibly deployed. As a result of the technological advancements, MEC is facing a myriad of highly sophisticated threats. This paper provides a review of MEC Architecture, use cases, conceptual guidelines for MEC security architecture, security and privacy techniques, and identifies current and future challenges, their implications, and approaches to overcome the challenges. This research examined significant threats, described the MEC architecture, identified the susceptible functional layers, the different categories of threats, and the potential security safeguards. The research recommends that MEC providers should implement multiple layers of security controls to mitigate targeted attacks.

The mine improves the response speed of the IoT control system by sinking computing services into the MEC Server. In this case, computing resources are scattered and it is difficult to cope with the changing computing requirements of mobile terminal devices. To solve this problem, we optimize from the supply side by build P.M.R (problem of maximum revenue) problem. In P.M.R, we comprehensively consider the air rate of wireless communication, network delay, computing resource demand and other factors that affect the offloading task of terminals. To obtain the relative optimal solution of P.M.R, we construct a task unloading algorithm based on particle swarm optimization. And we set up a simulation experiment environment according to the mine network parameters. The experimental results show that this task unloading algorithm can effectively improve the efficiency of computing facilities in the system and achieve load balancing.

Multi-Access Mobile Edge Computing (MEC) is proclaimed as a key technology for reducing service processing delays in 5G networks. One of the use cases in MEC is content caching as a way of bringing resources closer to the end-users. Consequently, both latency and QoE are reduced. However, MEC has a limited storage space compared to the cloud. Therefore, there is a need to effectively manage the cache storage. This article proposes and evaluates a novel scheme (PCR) that combines proactive prediction, collaboration among MECs and replacement algorithm to manage content caching in MEC. Results show that the proposed replacement scheme outperforms conventional baseline content caching algorithms LFU, LRU, MQ, FBR, LFRU. This has been validated with experimental results using a real dataset (MovieLens20M dataset) and comparison with contemporary Long Short Term Memory (LSTM) based caching algorithm.

Virtual reality (VR) video services are becoming increasingly popular in Internet of Things (IoT) owing to its immersive experience. The realization of immersive experience relies on extremely large amount of data transmitted, restricting its application. To meet the transmission requirement of VR video services, this paper designs a user-centric network based on the mobile edge computing (MEC) framework, where the 2D/3D field of view (FOV) files can be cached and the projection process from 2D FOV to 3D FOV can be computed at the VR device or MEC servers collaboratively. Based on this, a cooperative caching and computing-offloading strategy is formulated as a decision matrix, and its optimal closed-form expression is obtained by minimizing the transmission requirement under the strict service time constraint to realize the trade-off between communication, caching and computing (3C trade-off). Besides, the effect of the transmission requirement on the network density is also analyzed to provide useful guidelines for network design. Simulation results demonstrate that the proposed method can achieve quasi-optimal performance compared with other contrasting schemes.

Network slicing and Multiple-Access Edge Computing (MEC) are key technologies in fifth-generation (5G) networks. The flexible programmability of network slicing and the decentralization of MEC facilitate the deployment of Information-Centric Networking (ICN). The caching feature of ICN can provide users with low-latency data services. Although many existing works have addressed the cache deployment problem or the cache optimization problem, most of them do not consider the issue of caching resource allocation in the dynamic and hierarchical environment. Dynamic deployment of cache nodes can improve the operator’s revenue as much as possible while accurately allocating the caching resources can reduce the user-requested latency. Therefore, in this study, a problem of the operator’s expected revenue maximization is presented in an environment combining dynamic deployment of the MECs and the caching-enabled node ICN-Gateway (ICN-GW). To solve this problem, we propose an optimal stopping theory (OST)-based dynamic hierarchical caching resources allocation (ODH-CRA) algorithm. The algorithm consists of three parts. Firstly, an Integer Linear Programming (ILP) solution is proposed to determine the optimal deployment of the MECs. This method determines the optimal location and number of the MECs by considering deployment costs and service requirement costs synthetically. Secondly, a redeployment technique based on the OST is proposed to determine the best redeployment time of the MECs according to the values of latency violations and the service latency requirements. Finally, an improved elite genetic algorithm (IEGA) is proposed to find the optimal solution of the hierarchical caching resource allocation. This method searches the optimal scheme by maximizing the operator’s revenue joint caching costs and energy consumption. Ultimately, we perform a series of simulation experiments to compare the proposed method’s performance to dynamic and hierarchical methods. Our solution can effectively reduce the latency for users’ requesting, improve the revenue of ICN Communication Service Provider (ICSP), and provide an effective caching resource allocation scheme for the next generation of Internet of Things (IoT) networks.

This article presents a framework for improved and efficient video delivery in scenarios featuring users moving at high speed (e.g., trains), leveraging on dynamic Multi-access Edge Computing (MEC) enabled 5G network capabilities. The framework is location-aware and it allows the content to efficiently follow the users, conserving load usage on network and computational resources, by placing virtualized Content Delivery Network (vCDN) nodes at edge sites. The nodes are controlled by the framework's centralized control unit, which is able to dynamically and preemptively deploy virtulized resources, as the train moves. The framework is capable of segmenting video content and placing the specific portion of content that a user is likely to consume across a set of dynamically deployed vCDN nodes, associated to the coverage section the train is currently passing. A proof of concept was implemented and evaluated, where the benefits of using this framework are assessed. Results showed that the proposed system was able to reduce the load on the core network by 10.9 percent and maximize the cache hit ratio to a value of 99.8 percent.

An important component of complex integrated energy supply systems is the electrification of mobile power facilities of the agro-industrial complex, which will significantly affect the synthesis of energy systems. The basis of mobile energy facilities are multifunctional energy technology complexes (MEC) of the traction-energy concept with multi-channel distribution of energy flows of different physical nature. Based on the logical analysis of the mobile MEC, a technological scheme consisting of three subsystems is constructed: 1) factors of external conditions; 2) general design and layout solutions; 3) energy and operational properties of the mobile MEC. Energy connections are established between these subsystems and the system elements. A differentiated method for evaluating the energy efficiency of using alternative fuels and an original mathematical model of the generalized mobile MEC have been developed, which will allow determining the type of MEC, optimal design and layout solutions, operating modes and parameters at the design stage.

Mobile edge computing (MEC) is an effective assistant technology that can overcome some defects of cloud computing. For the sake of alleviating the clashes between the capability constraint of cloudlets and the needs of mobile devices (MDs) for reducing executing latency as well as decreasing the power consumption of MDs, a user-oriented use case in the MEC named computation offloading is taken into consideration. Computation offloading is capable of effectively making the MEC adapt to the resources of cloudlets and MDs in different environments, and it is very beneficial to the development of the internet of things. Owing to the finite computation capabilities of the MDs and the resources of cloudlets are heterogeneous and limited; a three-objective model is established to optimise the time consumption, and the energy consumption of MDs as well as the load balancing of cloudlets jointly. Technically, the authors propose an effective multi-user multi-application computation offloading method in the multi-cloudlet environment on the basis of improved non-dominated sorting genetic algorithm III. Finally, comprehensive experiments and analysis were conducted to validate the effectiveness and efficiency of the proposed method.

The demand for digital media services is increasing as the number of wireless subscriptions is growing exponentially. In order to meet this growing need, mobile wireless networks have been advanced at a tremendous pace over recent days. However, the centralized architecture of existing mobile networks, with limited capacity and range of bandwidth of the radio access network and low bandwidth back-haul network, can not handle the exponentially increasing mobile traffic. Recently, we have seen the growth of new mechanisms of data caching and delivery methods through intermediate caching servers. In this paper, we present a survey on recent advances in mobile edge computing and content caching, including caching insertion and expulsion policies, the behavior of the caching system, and caching optimization based on wireless networks. Some of the important open challenges in mobile edge computing with content caching are identified and discussed. We have also compared edge, fog and cloud computing in terms of delay. Readers of this paper will get a thorough understanding of recent advances in mobile edge computing and content caching in mobile wireless networks.

Vehicular Ad hoc NETworks (VANET) are becoming popular due to the emergence of the Internet of\ud Things and ambient intelligence applications. In such networks, secure resource sharing functionality is\ud accomplished by incorporating trust schemes. Current solutions adopt peer-to-peer technologies that can\ud cover the large operational area. However, these systems fail to capture some inherent properties of\ud VANETs, such as fast and ephemeral interaction, making robust trust evaluation of crowdsourcing\ud challenging. In this article, we propose MobileTrust – a hybrid trust-based system for secure resource\ud sharing in VANETs. The proposal is a breakthrough in centralized trust computing that utilizes cloud and\ud upcoming 5G technologies in order to provide robust trust establishment with global scalability. The ad hoc\ud communication is energy-efficient and protects the system against threats that are not countered by the\ud current settings. To evaluate its performance and effectiveness, MobileTrust is modelled in the SUMO\ud simulator and tested on the traffic features of the small-size German city of Eichstatt. Similar schemes are\ud implemented in the same platform in order to provide a fair comparison. Moreover, MobileTrust is deployed\ud on a typical embedded system platform and applied on a real smart car installation for monitoring traffic and\ud road-state parameters of an urban application. The proposed system is developed under the EU-founded\ud THREAT-ARREST project, to provide security, privacy, and trust in an intelligent and energy-aware\ud transportation scenario, bringing closer the vision of sustainable circular economy.

openaire: EC/H2020/761898/EU//MATILDA The need for faster and higher-capacity networks that can sustain modern, high-demanding applications has driven the development of 5G technology. Moreover, support for low-latency communication (1ms -10ms) is one of the main requirements of 5G systems. Multi-access Edge Computing (MEC) has been seen as a key component to attain the 5G objectives, since it allows hosting and executing critical services at the vicinity of users, thus reducing the latency to its minimum. Motivated by the evolution of real-time applications, we propose and evaluate two different mechanisms to improve the end-user experience by leveraging container-based live migration technologies. The first solution is aware of the users' mobility patterns, while the other is oblivious to the users' paths. Our results show approximately 50 percent reduction in downtime, which demonstrates the efficiency of the proposed solutions compared to prior works using similar underlying technology, i.e., LXC or Docker.

Vehicle-to-Everything (V2X) requirements from cooperative autonomous driving can be characterized as ultra-reliable, low latency, high traffic, and high mobility. These requirements introduce great challenges in the air interface and protocol stack design, resource allocation, network deployment, and all the way up to mobile (or multi-access) edge computing (MEC), cloud and application layer. In this paper, we present a cooperative autonomous driving oriented MEC-aided 5G-V2X prototype system design and the rationale behind the design choices. The prototype system is developed based on a next-generation radio access network (NG-RAN) experimental platform, a cooperative driving vehicle platoon, and an MEC server providing high definition (HD) 3D dynamic map service. Field tests are conducted and the results demonstrate that the combination of 5G-V2X, MEC and cooperative autonomous driving can be pretty powerful. Considering the remaining challenges in the commercial deployment of 5G-V2X networks and future researches, we propose two artificial intelligence (AI) based optimization tools. The first is a deep-learning-based tool called deep spatio-temporal residual networks with a permutation operator (PST-ResNet). By providing city-wide user and network traffic prediction, PST-ResNet can help to reduce the capital expense (CAPEX) and operating expense (OPEX) costs of commercial 5G-V2X networks. The second is a swarm intelligence based optimization tool called subpopulation collaboration based dynamic self-adaption cuckoo Search (SC-DSCS), which can be widely used to solve complex optimization problems in future researches. The effectiveness of proposed optimization tools is verified by real-world data and benchmark functions.

The distributed cooperative offloading technique with wireless setting and power transmission provides a possible solution to meet the requirements of next-generation Multi-access Edge Computation (MEC). MEC is a model which avails cloud computing the aptitude to smoothly compute data at the edge of a largely dense network and in nearness to smart communicating devices (SCDs). This paper presents a cooperative offloading technique based on the Lagrangian Suboptimal Convergent Computation Offloading Algorithm (LSCCOA) for multi-access MEC in a distributed Internet of Things (IoT) network. A computational competition of the SCDs for limited resources which tends to obstructs smooth task offloading for MEC in an IoT high demand network is considered. The proposed suboptimal computational algorithm is implemented to perform task offloading which is optimized at the cloud edge server without relocating it to the centralized network. These resulted in a minimized weighted sum of transmit power consumption and outputs as a mixed-integer optimization problem. Also, the derived fast-convergent suboptimal algorithm is implemented to resolve the non-deterministic polynomial-time (NP)-hard problem. In conclusion, simulation results are performed to prove that the proposed algorithm substantially outperforms recent techniques with regards to energy efficiency, energy consumption reduction, throughput, and transmission delay performance.

Avaa käsikirjoitus, kun julkaistu | openaire: EC/H2020/761898/EU//MATILDA Network slicing offers numerous benefits, particularly the ability to deliver highly customizable services to new industry sectors that have been unserved or inadequately served by current mobile network operators. Among new industry use cases that are targeted by the fifth generation (5G) mobile systems, there exist scenarios that go beyond what the current device-centric mobility approaches can support. The mobility of low latency communication services, shared by a group of moving devices, e.g., autonomous vehicles that share sensor data, is a prime example of these cases. These use cases' demands for ultra-low latency can be addressed by leveraging the Multi-Access Edge Computing (MEC) concept, techniques for live migration of virtual resources, Software Defined Networking (SDN), and network slicing. In this article, we define different slice mobility patterns, different methods for grouping users, and different triggers for network slice mobility. Furthermore, we evaluate the mobility of services and network slices based on the simultaneous migrations of multiple containers.

Mobile edge computing (MEC) enhanced satellite based internet of things (SAT-IoT) is an important complement for terrestrial networks based IoT, especially for the remote and depopulated areas. For MEC enhanced SAT-IoT networks with multiple satellites and multiple satellite gateways, the coupled user association, offloading decision, computing and communication resource allocation should be jointly optimized to minimize the latency and energy cost. In this paper, the latency and energy optimization for MEC enhanced SAT-IoT networks are formulated as a dynamic mixed-integer programming problem, which is hard to obtain the optimal solutions. To tackle this problem, we decompose the complex problem into two sub-problems. The first one is computing and communication resource allocation with fixed user association and offloading decision, and the second one is joint user association and offloading with optimal resource allocation. For the sub-problem of resource allocation, the optimal solution is proven to be obtained based on Lagrange multiplier method. And then, the second sub-problem is further formulated as a Markov decision process (MDP), and a joint user association and offloading decision with optimal resource allocation (JUAOD-ORA) is proposed based on deep reinforcement learning (DRL). Simulation results show that the proposed approach can achieve better long-term reward in terms of latency and energy cost.

Producción Científica, Next generation of Internet of Things (IoT) services imposes stringent requirements to the future networks that current ones cannot fulfill. 5G is a technology born to give response to those requirements. However, the deployment of 5G is also accompanied by profound architectural changes in the network, including the introduction of technologies like multi-access edge computing (MEC), software defined networking (SDN), and network function virtualization (NFV). In particular, NFV poses diverse challenges like virtual network function (VNF) placement and chaining, also called VNF-mapping. In this paper, we present an algorithm that solves VNF-placement and chaining in a metro WDM optical network equipped with MEC resources. Therefore, it solves the VNF-mapping in conjunction with the virtual topology design of the underlying optical backhaul network. Moreover, a version of the method providing protection against node failures is also presented. A simulation study is presented to show the importance of designing the three problems jointly, in contrast to other proposals of the literature that do not take the design of the underlying network into consideration when solving that problem. Furthermore, this paper also shows the advantages of using collaboration between MEC nodes to solve the VNF-mapping problem and the advantage of using shared protection schemes. The new algorithm outperforms other proposals in terms of both service blocking ratio, and number of active CPUs (thus reducing energy consumption). Finally, the impact of deploying different physical topologies for the optical backhaul network is also presented., Ministerio de Economía, Industria y Competitividad (grant TEC2017-84423-C3-1-P), Ministerio de Industria, Comercio y Turismo (grant BES 2015-074514), Spanish Thematic Network (contract RED2018-102585-T), INTERREG V-A España-Portugal (POCTEP) program (project 0677_DISRUPTIVE_2_E)

This paper investigates a cache-aided mobile edge computing (MEC) network, where the source offloads the computation task to multiple destinations with computation capacity, with the help of a cache-aided relay. For the proposed cache-aided MEC networks, two destination selection criteria have been proposed to maximize the computation capacity of the selected destination, the channel gain of relay link and the channel gain of direct link, respectively. Similarly, three destination selection criteria have been proposed for the cache-free MEC networks based on the computation capacities of destinations and the channel gains of transmission links, respectively. To evaluate the system performance regarding the latency constraint, we provide the outage probability for the proposed network which is defined based on the transmission-plus-computation time. Our analysis suggests that caching can significantly alleviate the impact of increasing the size of computation task, since only half of the transmission time of cache-free network is required. However, the cache-aided network can not fully exploit the signal from both direct and relay links, thus the improvement by caching is less significant in the high signal-to-noise ratio (SNR) region, compared with the cache-free network employing the destination with maximal channel gain of direct link. Numerical results are given to validate our analysis.

Multi-access edge computing (MEC) comes with the promise of enabling low-latency applications and of reducing core network load by offloading traffic to edge service instances. Recent standardization efforts, among which the ETSI MEC, have brought about detailed architectures for the MEC. Leveraging the ETSI model, in this paper we first present a flexible, yet full-fledged, MEC architecture that is compliant with the standard specifications. We then use such architecture, along with the popular OpenAir Interface (OAI), for the support of automotive services with very tight latency requirements. We focus in particular on the Extended Virtual Sensing (EVS) services, which aim at enhancing the sensor measurements aboard vehicles with the data collected by the network infrastructure, and exploit this information to achieve better safety and improved passengers/driver comfort. For the sake of concreteness, we select the intersection control as an EVS service and present its design and implementation within the MEC platform. Experimental measurements obtained through our testbed show the excellent performance of the MEC EVS service against its equivalent cloud-based implementation, proving the need for MEC to support critical automotive services, as well as the benefits of the solution we designed. This work was supported by the European Commission through the H2020 5G-TRANSFORMER project (Project ID 761536). The work of Christian Vitale was also supported by the European Union’s Horizon 2020 Research and Innovation Programme under Grant 739551 (KIOS CoE) and from the Republic of Cyprus through the Directorate General for Euro-pean Programmes, Coordination, and Development.

5G mobile network technology together with edge computing will create new opportunities for developing novel road safety services in order to better support connected and automated driving in challenging situations. This paper studies the feasibility and benefits of localized mobile network edge applications for supporting vehicles in diverse conditions. We study a particular scenario, where vehicle sensor data processing, required by road safety services, is installed into the mobile network edge in order to extend the electronic horizon of the sensors carried by other vehicles. Specifically, we focus on a LiDAR data-based obstacle warning case where vehicles receive obstacle warnings from the mobile network edge. The proposed solution is based on a generic system architecture. In this paper, we first evaluate different connectivity and computing options associated with such a system using ns-3 simulations. Then, we introduce a proof-of-concept implementation of the LiDAR-based obstacle warning scenario together with first results from an experimental evaluation, conducted both in a real vehicle testbed environment and in a laboratory setting. As a result, we obtain first insights on the feasibility of the overall solution and further enhancements needed.

Multi-Access Edge Computing (MEC) is one of the emerging key technologies in Fifth Generation (5G) Mobile Networks, providing reduced end-to-end latency for applications and reduced load in the transport network. This paper is about user privacy in MEC within 5G. We consider a basic MEC usage scenario, where the user accesses an application hosted in the MEC platform via the radio access network of the Mobile Network Operator (MNO). First, we create a system model based on this scenario, then define the adversary model and privacy requirements for this system model. Second, we introduce a privacy-preserving access solution for the system model and analyze the solution against the privacy requirements.

Mobile edge computing (MEC) focuses on transferring computing resources close to the user's device, and it provides high-performance and low-delay services for mobile devices. It is an effective method to deal with computationally intensive and delay-sensitive tasks. Given the large number of underutilized computing resources for mobile devices in urban areas, leveraging these underutilized resources offers tremendous opportunities and value. Considering the spatiotemporal dynamics of user devices, the uncertainty of rich computing resources and the state of network channels in the MEC system, computing resource allocation in mobile devices with idle computing resources will affect the response time of task requesting. To solve these problems, this paper considers the case in which a mobile device can learn from a neighboring IoT device when offloading a computing request. On this basis, a novel self-adaptive learning of task offloading algorithm (SAda) is designed to minimize the average offloading delay in the MEC system. SAda adopts a distributed working mode and has a perception function to adapt to the dynamic environment in reality, it does not require frequent access to equipment information. Extensive simulations demonstrate that SAda achieves preferable latency performance and low learning error compared to the existing upper bound algorithms.

MEC technology provides a distributed computing environment in 5G mobile networks for application and service hosting. It allows customers with different requirements and professional competencies to use the services offered by external suppliers. We consider a service access control framework on 5G MEC networks that is efficient, flexible, and user-friendly. Its central element is the MEC Enabler, which handles AAA requests for stakeholders accessing services hosted on the edge servers. The JSON Web Token (JWT) open standard is a suitable tool for the MEC Enabler to manage access control credentials and transfer them securely between parties. In this paper, in the context of access control, we propose the token reference pattern called JSON MEC Access Token (JMAT) and analyze the effectiveness of its available protection methods in compliance with the standard requirements of MEC-hosted services in 5G networks.

With the explosive growth of smart devices and mobile applications, mobile core networks face the challenge of exponential growth in traffic and computing demand. Edge caching is one of the most promising solutions to the problem. The main purpose of edge caching is to place popular content that users need at the edge of the network, borrow free space to reduce user waiting time, and lighten the network load by reducing the amount of duplicate data. Due to the promising advantages of edge caching, there have been many efforts motivated by this topic. In this paper, we have done an extensive survey on the existing work from our own perspectives. Distinguished from the existing review articles, our work not only investigates the latest articles in this area, but more importantly, covers all the researches of the total process of edge caching from caching placement optimization, policy design, to the content delivery process. In particular, we discuss the benefits of caching placement optimization from the perspective of different stakeholders, detail the delivery process, and conduct in-depth discussions from the five phases, i.e., requested content analysis, user model analysis, content retrieval, delivery, and update. Finally, we put forward several challenges and potential future directions, and hope to bring some ideas for the follow-up researches in this area.

Artificial Intelligence (AI) has become a major innovative force and a major pillar in the fourth industrial revolution. This trend has been acknowledged by the European Commission, who has pointed out how high-performance, intelligent, and secure networks are fundamental for the evolution of the multiservice Next Generation Internet (NGI). While great progress has been done in the accuracy and performance of AI-enabled platforms, their integration in autonomous decision-making and critical systems requires end-to-end quality assurance. AI@EDGE addresses these challenges harnessing the concept of “reusable, secure, and trustworthy AI for network automation”. To this end, AI@EDGE targets significant breakthroughs in two fields: (i) general-purpose frameworks for closed-loop network automation capable of supporting flexible and programmable pipelines for the creation, utilization, and adaptation of the secure, reusable, and trustworthy AI/ML models; and (ii) converged connect-compute platform for creating and managing resilient, elastic, and secure end-to-end slices supporting a diverse range of AI-enabled network applications. Cooperative perception for vehicular networks, secure, multi-stakeholder AI for Industrial Internet of Things, aerial infrastructure inspections, and inflight entertainment are the uses cases targeted by AI@EDGE to maximise its commercial, societal, and environmental impact. Index Terms—AI, 5G, MEC, automation, disaggregated RANs, ML-based security, hardware acceleration, serverless platforms.

International audience; Traditional loss-based Congestion Control Algorithms (CCAs) suffer from performance issues over wireless networks mostly due to their inability to distinguish wireless random losses from congestion losses. Different loss discrimination algorithms have been proposed to tackle this issue but they are not efficient for 4G networks since they do not consider the impact of various link layer mechanisms such as adaptive modulation and coding and retransmission techniques on congestion in LTE Radio Access Networks (RANs). We propose MELD (MEC-based Edge Loss Discrimination), a novel server-side loss discrimination mechanism that leverages recent advancements in Multi-access Edge Computing (MEC) services to discriminate packet losses based on real-time RAN statistics. Our approach collects the relevant radio information via MEC's Radio Network Information Service and uses it to correctly distinguish random losses from congestion losses. Our experimental study made with the QUIC transport protocol shows over 80% higher goodput when MELD is used with NewReno and 8% higher goodput when used with Cubic.

In this work, the design of the hardware architecture to implement an algorithm for optimizing the Hydrogen Productivity Rate (HPR) in a Microbial Electrolysis Cell (MEC) is presented. The HPR in the MEC is maximized by the golden section search algorithm in conjunction with a super-twisting controller. The development of the digital architecture in the implementation step of the optimization algorithm was developed in the Very High Description Language (VHDL) and synthesized in a Field Programmable Gate Array (FPGA). Numerical simulations demonstrated the feasibility of the proposed optimization strategy embedded in an FPGA Cyclone II. Results showed that only 21% of the total logic elements, 5.19% of dedicated logic registers, and 64% of the total eight-bits multipliers of the FPGA were used. On the other hand, the estimated power consumption required by the FPGA-embedded optimization algorithm was only 146 mW.

International audience; At time of crisis, relief teams must have assured connectivity, not only just within the team but also across different service agencies in the area. Since emergency agencies and essential services always send service cars to affected zones, advanced technologies and computing resources aboard these vehicles can be pooled together to boost network capacity temporarily, just where it is crucially needed. These vehicles become automotive virtual edge communicators (AVECs). They are managed by a vehicular inter-agency service orchestration and resourcing (ViSOR) system that creates transient proximity-based “trust circles” to manage novel cooperative hosting, opportunistic virtualization, and “car sourcing” of crisis zone data. This study evaluates the feasibility for this challenging but highly rewarding concept and identifies gaps in emerging technologies.

In this paper, a downlink device-to-device (D2D)-assisted cellular networks with mobile edge caching, where most popular video files are independently cached in D2D users and cellular base station (BS), are studied. In the considered system model, each user may obtain the requested video from the cache of BS or/and D2D users surrounding them. According to the different collaborative schemes of BS caching and D2D caching, it can be divided into two different resource allocation schemes. In the hybrid caching transmission scheme, users could adopt the BS caching mode or alternatively the D2D caching mode. In the joint caching transmission scheme, each user may obtain the requested files from the BS server and the adjacent D2D users, simultaneously. By taking the required data rate and the interference constraint into account, we formulate two joint resource allocation problems integrating link selection, channel allocation, and power control to maximize the system energy efficiency (EE). Leveraging on the Dinkelbach method, the EE optimization problems are transformed into mixed-integer nonlinear programming problems and can be decomposed into three subproblems: link selection, channel allocation, and power control. To solve these complicated problems, we propose two optimization algorithms that consist of a modified branch and bound method as well as Lagrange dual decomposition approach. The simulation results demonstrate the superiority of these two proposed algorithms in improving system throughput and EE compared with other algorithms.

One of the most promising use cases of 5G/IMT2020 is the unmanned aerial vehicle (UAV). Due to their small size, the UAVs are resource constraint devices. To this end, this paper proposes an offloading algorithm for UAVs to assist in the execution of computationally intensive tasks. The proposed algorithm provides two UAV offloading methods. The first offloading method is the air-offloading, where a UAV can offload its computing tasks to nearby UAVs that have available computing and energy resources. The second offloading method is the ground-offloading, which enables the offloading of tasks to an edge cloud server from the multi-level edge cloud units connected to ground stations. The proposed algorithm is energy- and latency-aware, i.e., it selects the execution device and the offloading method based on the latency and energy constraints. The intensive algorithm simulation over reliable conditions for various scenarios with different cases for each scenario is conducted and results are presented.

The fifth generation (5G) mobile telecommunication network is expected to support Multi- Access Edge Computing (MEC), which intends to distribute computation tasks and services from the central cloud to the edge clouds. Towards ultra-responsive, ultra-reliable and ultra-low-latency MEC services, the current mobile network security architecture should enable a more decentralized approach for authentication and authorization processes. This paper proposes a novel decentralized authentication architecture that supports flexible and low-cost local authentication with the awareness of context information of network elements such as user equipment and virtual network functions. Based on a Markov model for backhaul link quality, as well as a random walk mobility model with mixed mobility classes and traffic scenarios, numerical simulations have demonstrated that the proposed approach is able to achieve a flexible balance between the network operating cost and the MEC reliability., Comment: Accepted by IEEE Access on Feb. 02, 2019

© 2019 IEEE. The emerging intelligent applications in Industrial Cyber-Physical Systems (ICPS), such as product inspection by deep-learning-based image recognition technology, are highly computation-consuming. However, the smart devices without sufficient computing resources fail to handle this kind of applications. Moreover, the Internet has very high latency compared with the local network which fails to meet the requirements of time-sensitive tasks, therefore we can not offload these tasks over the cloud. Mobile Edge Computing (MEC) brings the opportunities to offload the tasks of ICPS to the MEC servers to satisfy strict latency requirements, as well as to meet the demand for security requirements. Considering MEC servers owned by the third parties, resource allocation in MEC should be solved jointly with network economics to maximize the utility of system. In this paper, we investigate the task offloading problem under the access capability, latency and security constraints. Specifically, we present a novel Adaptive Task Offloading (ATO) auction mechanism to determine which MEC server to offload with access capability and security constraints, and how to schedule tasks with various deadline constraints, which incentives the third party of MEC providers to share their computing resources with the maximum profit. According to our theoretical analysis, the proposed auction mechanism has the properties of individual rationality, computational efficiency and truthfulness. Extensive simulations have been conducted to evaluate the performance of ATO auction and the experimental results show our method provides better solutions with the classic greedy algorithms in terms of maximizing the utility of the MEC server.

Recently, information-centric wireless networks (ICWNs) have become a promising Internet architecture of the next generation, which allows network nodes to have computing and caching capabilities and adapt to the growing mobile data traffic in 5G high-speed communication networks. However, the design of ICWN is still faced with various challenges with respect to capacity and traffic. Therefore, mobile edge computing (MEC) and device-to-device (D2D) communications can be employed to aid offloading the core networks. This paper investigates the optimal policy for resource allocation in ICWNs by maximizing the spectrum efficiency and system capacity of the overall network. Due to unknown and stochastic properties of the wireless channel environment, this problem was modeled as a Markov decision process. In continuousvalued state and action variables, the policy gradient approach was employed to learn the optimal policy through interactions with the environment. We first recognized the communication mode according to the location of the cached content, considering whether it is D2D mode or cellular mode. Then, we adopt the Gaussian distribution as the parameterization strategy to generate continuous stochastic actions to select power. In addition, we use softmax to output channel selection to maximize system capacity and spectrum efficiency while avoiding interference to cellular users. The numerical experiments show that our learning method performs well in a D2D-enabled MEC system. 2020 Association for Computing Machinery. All rights reserved. This work was supported in part by the National Natural Science Foundation of China under Grant 61772387, in part by the Fundamental Research Funds of Ministry of Education and China Mobile under Grant MCM20170202, in part by the National Natural Science Foundation of Shaanxi Province under Grant 2019ZDLGY03-03, in part by the Graduate Innovation Fund of Xidian University under Grant 5001-20109195456, and in part by the ISN State Key Laboratory. Scopus 2-s2.0-85077531009

Merging mobile edge computing (MEC) service with the wireless local area networks (WLANs) provides enormous benefits such as intensive computation capabilities. Access to high-speed WLANs has become an urgent challenge due to the fast rate of application development and user adoption. Providing increased wireless resources is one solution, but millimeter (mmWave) multi-user massive Multiple-Input Multiple-Output (MIMO) technology has also attracted attention due to a series of features such as high throughput, strong anti-interference ability, and small fading. Hence, exploring the potential of mmWave MIMO systems for WLAN could play a significant role in improving network performance, especially throughput. In this paper, we introduce mmWave and massive MIMO into traditional WLAN with MEC, which enables a large portion of spatial resources to be allocated among users, and mitigates inter-user interference. Moreover, we propose a novel media access control (MAC) protocol, named as LSMWN-MAC, to adapt to a series of unique characteristics of mmWave and massive MIMO, which adopts space division multiple access (SDMA) as one of the resource methods, taking full advantage of spatial resources. In addition, we modify the protocol and propose the MLSMWN-MAC protocol to allocate resources dynamically, a solution that is particularly suitable for scenes with 30-50 users. Simulation results show that compared to 802.11ad, the proposed two protocols can increase the saturated throughput by 4.7Gb/s and 5.4Gb/s respectively, which are close to the throughput upper limit of the mmWave MIMO system. Meanwhile, we observe that when the number of users exceeds the threshold, the performance of the LSMWN-MAC protocol using a static allocation method is superior to the MLSMWN-MAC protocol using a dynamic method. In other cases, the dynamic protocol has better performances.

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.

The fifth-generation (5G) wireless communication systems development has brought out a paradigm shift using advanced technologies, e.g., softwarization, virtualization, ultra-densification, distributed intelligence. This trend is expected to continue with sixth-generation (6G) systems. Over the past few decades, Geosynchronous Orbit (GSO) satellite systems have been deployed to support broadband services, backhauling, disaster recovery, and emergency services. Today, there is a considerable renewed interest in planning and developing Non-GSO satellite systems. Within the next few years, several thousands of Low Earth Orbit (LEO) satellite systems will form mega-constellations to provide global communication services. This paper addresses challenges and possible solutions towards satellite 5G networks and beyond as a part of the Roadmap study addressed by the Satellite Working Group of IEEE Future Networks Initiative.

To meet the growing quest for enhanced network capacity, mobile network operators (MNOs) are deploying dense infrastructures of small cells. This, in turn, increases the power consumption of mobile networks, thus impacting the environment. As a result, we have seen a recent trend of powering mobile networks with harvested ambient energy to achieve both environmental and cost benefits. In this paper, we consider a network of virtualized small cells (vSCs) powered by energy harvesters and equipped with rechargeable batteries, which can opportunistically offload baseband (BB) functions to a grid-connected edge server depending on their energy availability. We formulate the corresponding grid energy and traffic drop rate minimization problem, and propose a distributed deep reinforcement learning (DDRL) solution. Coordination among vSCs is enabled via the exchange of battery state information. The evaluation of the network performance in terms of grid energy consumption and traffic drop rate confirms that enabling coordination among the vSCs via knowledge exchange achieves a performance close to the optimal. Numerical results also confirm that the proposed DDRL solution provides higher network performance, better adaptation to the changing environment, and higher cost savings with respect to a tabular multi-agent reinforcement learning (MRL) solution used as a benchmark., Submitted to IEEE transaction on sustainable computing. arXiv admin note: text overlap with arXiv:1906.05735

Digital Twins (DTs) are becoming a reference design abstraction for many Internet of Things (IoT) application scenarios. Also, data processing is shifting to a decentralised setting leveraging the edge computing paradigm to move computation closer to the physical devices. In this context, Multi-access Edge Computing (MEC) technologies on 5G cellular networks are redefining the IoT networking infrastructure by enabling ultra low latency, and reliable and responsive connectivity. However, evaluation of the MEC architecture from the application developer standpoint is currently missing from literature, as well as an assessment of performance while adopting DT on top of MEC. Therefore, this paper reports on a MEC implementation based on OpenNESS toolkit, in the context of DT-based mobility, and an evaluation of its service-level performance.

MEC is considered as a promising candidate to support the deployment of the 6G communication networks, enabling scalability, adaptability and resilience. We propose a modeling approach based on complex system to design a novel organizational aspect of mobile nodes acting as MECs in a 6G scenario, through the introduction of the temporal multiplex social network, which allows us to analyze proximity contacts, by embedding also the social aspects, and the evolutionary game theory. Our findings demonstrate how the proposed model gives a boost to MECs’ cooperative behavior, finding out an overall reduction of the blocking probability.

Named-Data Networking is a prominent solution for offering flexible communications in Metropolitan Area Networks. Its features were recently extended with SDN and MEC capabilities for better supporting consumer mobility and improving packet forwarding functionalities. However, the scientific literature lacks a thorough investigation on the impact on the energy consumption of these protocols. Focusing the attention on the novel approach presented by the same authors of this paper, this contribution analyzes the energy saving in Information-Centric Metropolitan Area Networks. Results demonstrate the efficacy of the investigated approach by evaluating the different components of energy consumption in the considered scenario.

The advent of multi access edge computing (MEC) will enable latency-critical applications such as cooperative adaptive cruise control (also known as platooning) to be hosted at the edge of the network. MEC-based platooning will leverage the coverage of the cellular infrastructure to enable inter-vehicular communications, potentially overcoming crucial problems of vehicular ad-hoc networks (VANETs) such as non-trivial packet loss rates. However, MEC-based platooning will require the controller to be migrated to the most suitable positions at the network edge, in order to maintain low-latency connections as the platoon moves. In this paper, we propose a context-aware 𝑄-learning algorithm that carries out such migrations only as often as is necessary, and thereby reduces the additional delays implicit in application migration across MEC hosts. When compared to the state-of-the-art approach named FollowME, our scheme exhibits better compliance of vehicle speed and spacing values to preset targets, as well as a reduced statistical dispersion. Italian Ministry for University and Research (MIUR) under the initiative “Departments of Excellence” (Law 232/2016) Free University of Bolzano–Bozen under the SECEDA project, RTD Call 2021 Spanish State Research Agency (AEI) PID2019-109805RB-I00/AEI/10.13039/501100011033 TRUE inpress

Medical data exchange between diverse e-health entities can lead to a better healthcare quality, improving the response time in emergency conditions, and a more accurate control of critical medical events (e.g., national health threats or epidemics). However, exchanging large amount of information between different e-health entities is challenging in terms of security, privacy, and network loads, especially for large-scale healthcare systems. Indeed, recent solutions suffer from poor scalability, computational cost, and slow response. Thus, this article proposes medical-edge-blockchain (MEdge-Chain), a holistic framework that exploits the integration of edge computing and blockchain-based technologies to process large amounts of medical data. Specifically, the proposed framework describes a healthcare system that aims to aggregate diverse health entities in a unique national healthcare system by enabling swift, secure exchange, and storage of medical data. Moreover, we design an automated patients monitoring scheme, at the edge, which enables the remote monitoring and efficient discovery of critical medical events. Then, we integrate this scheme with a blockchain architecture to optimize medical data exchanging between diverse entities. Furthermore, we develop a blockchain-based optimization model that aims to optimize the latency and computational cost of medical data exchange between different health entities, hence providing effective and secure healthcare services. Finally, we show the effectiveness of our system in adapting to different critical events, while highlighting the benefits of the proposed intelligent health system. 2014 IEEE. Scopus

In this work we propose a Disaster Management System on 5G ultra Reliable Low Latency Networks that targets unprecedented reliability levels as well as low latency. In fact, referring to the 5G vision a Structural Health Monitoring system can be considered depending on the operational scenario: in the case of data collection and processing from sensors in monitored buildings, considering the high number of sensors installed, it can refer to the massive Machine Type Communications context. Vice versa, during a seismic event or just after it, the use case requires high reliability connectivity and, sometimes, low latency. Those features refer to the ultra Reliable Low Latency context. It seems interesting to evaluate and experiment the ability of 5G network to dynamically adapt to the changing scenario that this use case can provide. Moreover this work presents an innovative 5G architecture for Earthquake Early Warning that uses Structural Health Monitoring systems to detect a seismic event and to propagate a message reporting the event detection to all the buildings that may be damaged by the event.

Technological advancement is currently focused on the miniaturization of devices, and integrated circuits allow us to observe the increase in the number of Internet of Things (IoT) devices. Most IoT services and devices require an Internet connection, which needs to provide the minimum processing, storage and networking requirements to best serve a requested service. One of the main goals of 5G networks is to comply with the user&rsquo, s various Quality of Service (QoS) requirements in different application scenarios. Fifth-generation networks use Network Function Virtualization (NFV) and Mobile Edge Computing (MEC) concepts to achieve these QoS requirements. However, the computational resource allocation mechanisms required by the services are considered very complex. Thus, in this paper, we propose an allocation and management resources mechanism for 5G networks that uses MEC and simple mathematical methods to reduce the model complexity. The mechanism decides to allocate the resource in MEC to meet the requirements requested by the user. The simulation results show that the proposed mechanism provides a larger amount of services, leading to a reduction in the service lock number and as a reduction in the blocking ratio of services due to the accuracy of the approach and its load balancing in the process of resource allocation.

Nowadays, we are ready to have precommercial Cooperative Intelligent Transport Systems (C-ITS), nevertheless there exist challenging functional and security aspects that need to be addressed. One of them is the fact that, in every era, there will be several radio technologies which will be used by vehicles that need to be connected between them, therefore, the systems needs to provide interoperability services. The other critical issue is to reinforce security against attacks on localization receivers or in vehicles equipment. Most of these functions are based in a large amount of computation power, to this end, this paper presents the approach taken by H2020 CARAMEL project, using a Multi-access Edge Computing (MEC) that could provide the necessary performance assets. This work was supported by the European Union’s H2020 research and innovation programme under CARAMEL project (Grant agreement No. 833611). The work of J. Casademont and P. Sayyad Khodashenas was also supported by FEDER and Secretaria d’Universitats i Recerca del Departament d’Empresa i Coneixement de la Generalitat de Catalunya through project Fem IoT. The work of C. Vitale and C. Laoudias was also supported by the European Union’s Horizon 2020 Research and Innovation Programme under Grant 739551 (KIOS CoE) and from the Republic of Cyprus through the Directorate General for European Programmes, Coordination and Development.

Recently, unmanned aerial vehicle (UAV) acts as the aerial mobile edge computing (MEC) node to help the battery-limited Internet of Things (IoT) devices relieve burdens from computation and data collection, and prolong the lifetime of operating. However, IoT devices can ONLY ask UAV for either computing or caching help, and collaborative offloading services of UAV are rarely mentioned in the literature. Moreover, IoT device has multiple mutually independent tasks, which make collaborative offloading policy design even more challenging. Therefore, we investigate a UAV-enabled MEC networks with the consideration of multiple tasks either for computing or caching. Taking the quality of experience (QoE) requirement of time-sensitive tasks into consideration, we aim to minimize the total energy consumption of IoT devices by jointly optimizing trajectory, communication and computing resource allocation at UAV, and task offloading decision at IoT devices. Since this problem has highly non-convex objective function and constraints, we first decompose the original problem into three subproblems named as trajectory optimization ($$\mathbf {P}_{\mathbf {T}}$$ P T ), resource allocation at UAV ($$\mathbf {P}_{\mathbf {R}}$$ P R ) and offloading decisions at IoT devices ($$\mathbf {P}_{\mathbf {O}}$$ P O ) and then propose an iterative algorithm based on block coordinate descent method to cope with them in a sequence. Numerical results demonstrate that collaborative offloading can effectively reduce IoT devices’ energy consumption while meeting different kinds of offloading services, and satisfy the QoE requirement of time-sensitive tasks at IoT devices.