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Mobile edge computing (MEC) technology integrates IT service environment and cloud computing technology at the edge of the network,improving the capacities of computing and storage of the edge of network,reducing network operation and service delivery delay and further enhancing the quality of experience (QoE) of users.Thus,MEC has attracted extensive attention and has been added in the 5G standard as a key technology.Firstly,the basic concept,reference architecture and technical advantages of MEC were introduced,and the current research state of MEC were summarized from three perspectives:academia,industry and standard setting.Then,the key enablers,application scenarios and specific deployment methods of MEC were introduced in detail.Finally,the existing problems of MEC,such as mobility,security and billing,as well as privacy protection,were discussed.
Based on the advantages of high-bandwidth and low-latency brought by mobile edge computing (MEC),which could provide IT service environment and cloud computing capability,combined with the long-term evolution unlicensed (LTE-U) technology,the task offloading decision and resource allocation issues in vehicle heterogeneous network were studied.Considering the link differentiation requirements,which were the high capacity of vehicle-to-roadside unit (V2I) links and the super reliability of vehicle-to-vehicle (V2V) links,quality of service (QoS) was modeled as the combination of capacity and latency.Firstly,the improved K-means algorithm was used to cluster the request vehicles according to different QoS to determine the communication mode.Secondly,the LTE-U technology based on non-competition period (CFP) which was combined with carrier aggregation (CA) technology,and the distribution Q-Learning algorithm were adopted to allocate the channel and power.The simulation results show that the proposed mechanism can maximize the V2I link traversal capacity while ensuring the reliability of the V2I link.
With the rapid development and extensive application of the Internet of things (IoT),big data and 5G network architecture,the massive data generated by the edge equipment of the network and the real-time service requirements are far beyond the capacity if the traditional cloud computing.To solve such dilemma,the edge computing which deploys the cloud services in the edge network has envisioned to be the dominant cloud service paradigm in the era of IoT.Meanwhile,the unique features of edge computing,such as content perception,real-time computing,parallel processing and etc.,has also introduced new security problems especially the data security and privacy issues.Firstly,the background and challenges of data security and privacy-preserving in edge computing were described,and then the research architecture of data security and privacy-preserving was presented.Secondly,the key technologies of data security,access control,identity authentication and privacy-preserving were summarized.Thirdly,the recent research advancements on the data security and privacy issues that may be applied to edge computing were described in detail.Finally,some potential research points of edge computing data security and privacy-preserving were given,and the direction of future research work was pointed out.
Computation offloading in mobile edge computing would transfer the resource intensive computational tasks to the edge network.It can not only solve the shortage of mobile user equipment in resource storage,computation performance and energy efficiency,but also deal with the problem of resource occupation,high latency and network load compared to cloud computing.Firstly the architecture of MEC was introduce and a comparative analysis was made according to various deployment schemes.Then the key technologies of computation offloading was studied from three aspects of decision on computation offloading,allocation of computing resource within MEC and system implement of MEC.Based on the analysis of MEC deployment scheme in 5G,two optimization schemes on computation offloading was proposed in 5G MEC.Finally,the current challenges in the mobility management was summarized,interference management and security of computation offloading in MEC.
Edge computing is a new type of computing models that performs computing tasks at the edge of the network.Compared with cloud computing,it can respond to user’s needs more quickly and reliably.Starting from the shortcomings of cloud computing,the concept and general architecture of edge computing were illustrated,and then two reference frameworks proposed by industry alliances were elaborated.Four challenges of edge computing and their latest research progress were summarized.With the development of theory and technology related to edge computing,it will become a key technology to promote the upgrade of Internet of things (IoT) services.For this reason,two applications of edge computing in manufacturing and security monitoring were introduced.
Given the advantages of low cost and easy deployment,large-scale Internet of things (IoT) has been deployed for environment monitoring pervasively.Within such systems,cloud platform is typically utilized as a remote data and control center.However,tremendous amount of data uploading and processing induce huge challenges on bandwidth load and real-time data gathering.In order to overcome these challenges,edge computing enabled IoT system architecture was proposed for environmental monitoring.As the intermediate layer,local processing could be supported for end devices with low latency and assist with preliminary analysis to offload computational tasks from cloud and the amount of data uploading could be reduced.Based on this system architecture,an autoencoder neural network-based abnormal data detection scheme was developed newly.Performance evaluation has been conducted based on the practical oceanic atmospheric data.Simulation results indicate that the proposed scheme can accurately detect the abnormal data by fully exploiting the spatial data correlation.
Aiming at the needs of edge computing for data privacy, the correctness of calculation results and the auditability of data processing, a privacy protection method for edge computing based on blockchain and federated learning was proposed, which can realize collaborative training with multiple devices at the edge of the network without a trusted environment and special hardware facilities.The blockchain was used to endow the edge computing with features such as tamper-proof and resistance to single-point-of-failure attacks, and the gradient verification and incentive mechanism were incorporated into the consensus protocol to encourage more local devices to honestly contribute computing power and data to the federated learning.For the potential privacy leakage problems caused by sharing model parameters, an adaptive differential privacy mechanism was designed to protect parameter privacy while reducing the impact of noise on the model accuracy, and moments accountant was used to accurately track the privacy loss during the training process.Experimental results show that the proposed method can resist 30% of poisoning attacks, and can achieve privacy protection with high model accuracy, and is suitable for edge computing scenarios that require high level of security and accuracy.
Edge computing has become an important innovative business model in the 5G era,especially its low latency characteristics,which are considered to be unavailable in traditional solutions.Therefore,edge computing can provide more service capabilities and more application scenarios.However,the synergy of computing resources between edge computing and cloud computing has become a new technical problem,so it is necessary to realize cloud network collaboration,cloud edge collaboration and even edge collaboration between edge computing,cloud computing and network,so as to achieve the optimization of resource utilization.A computing network solution based on cloud,network and edge depth fusion was introduced,and a typical system for AI application was proposed,which could effectively cope with the future.
Artificial intelligence (AI) and edge computing (EC) represent two of today’s most popular technologies.There is a great potential to coordinate these two emerging techniques to facilitate the further advent of both sides.Through three research cases,the profound benefits were demonstrated when AI and EC synergize.Specifically,from the perspective of EC for AI,to efficiently run deep learning at the network edge,a collaborative and on-demand deep neural network (DNN) co-inference framework with device-edge synergy was proposed.By applying DNN partitioning and right-sizing,it minimizes the inference latency under target accuracy.On the other hand,from the perspective of AI for EC,for the dynamical placement of edge computing services,two methods were proposed:an online-learning based adaptive service migration strategy and a factor graph model driven predictive service migration technique.
In order to solve the problem that traditional mobile edge computing network can’t be straightforwardly applied to the Internet of vehicles (IoV) due to high speed mobility and dynamic network topology,a vehicular edge multi-access computing network (VE-MACN) was introduced to realize collaborative computing offloading between roadside units and smart vehicles.In this context,the collaborative computation offloading was formulated as a joint multi-access model selection and task assignment problem to realize the good balance between long-term system utility,diverse needs of IoV applications and energy consumption.Considering the complex joint optimization problem,a deep reinforcement learning-based collaborative computing offloading scheme was designed to overcome the curse of dimensionality for Q-learning algorithm.The simulation results demonstrate that the feasibility and effectiveness of proposed offloading scheme.
In order to meet the requirements of higher computing speed,lower communication delay and higher access density in the future Internet of things,a computational framework of EdgeFlow for mobile edge computing and a task unloading algorithm considering blocking and non-blocking state were proposed.The simulation experiments on the universal software radio platform show that the computational efficiency can be improved significantly and the communication delay can be reduced by EdgeFlow.It points out a new development direction,lays a theoretical and practical foundation for solving the problem of higher computational requirements of the Internet of things.And it will be widely used in the Internet of things scenarios.
Edge computing plays an extremely important role in the environment perception and data processing of autonomous driving.Autonomous driving vehicles can expand their perception scope by obtaining environmental information from edge nodes, and can also deal with the problem of insufficient computing resources by offloading tasks to edge nodes.Compared with cloud computing, edge computing avoids high latency caused by long-distance data transmission, and provides autonomous driving vehicles with faster responses, and relieves the traffic load of the backbone network.Firstly, the edge computing-based cooperative perception and task offloading technologies for autonomous vehicles were introduced firstly, and related challenging issues were also proposed.Then the state-of-the-art of cooperative perception and task offloading technologies were analyzed and summarized.Finally, the problems need to be further studied in this field were discussed.
Mobile edge computing（MEC）makes it possible to deploy and provide service locally,which is close to the users,by enabling the IT and cloud computation capacity at the radio access network (RAN).Thus,MEC can improve the quality of experience (QoE) by reducing the latency,and decrease the network operation cost through reducing backhaul capacity requirement.Meanwhile,based on the network context information (location,network traffic load,radio information,etc) exposure to applications and services,MEC can further improve the QoE of user and provide the platform to third-party partners for application and service innovation.Besides the introduction of MEC,the detailed MEC platform was presented.Furthermore,the traffic offloading solution based on MEC was proposed and compared with the solution of local IP access and selected IP traffic offload (LIPA/SIPTO).In addition,the problems and challenges of the MEC were also discussed in detail.
Given the limited resources at early stages for recovery, a failure recovery mechanism of the edge computing network considering both computational demands and repair costs was proposed, which intends to tackle the problem of the high probability of large-scale cascading failure caused by the interdependence between the edge computing network and other subnetworks in industrial Internet of things (IIoT).Considering the network structure (topology and link capacity) and network dynamics (computational demands), a joint link recovery selection and computation migration optimization problem was formulated under the conservation of node computing requirements.By leveraging the Benders decomposition algorithm, the NP-hard problem was transformed into a main problem and a sub-problem, which were interdependent and could be solved in polynomial time through the approximation of cutting planes.A local branching method was further introduced to guarantee the non-increasing nature of the Benders upper bound, thus accelerating the convergence of Benders decomposition.Simulation results demonstrate that the proposed algorithm outperforms the conventional topology-based recovery algorithm in system utility, and can perform well in multiple scenarios.
For the large-scale network mobile edge computing and caching technology of future 6G mobile communications, firstly, the architectures and principles of mobile edge computing and caching in large-scale wireless networks were introduced, and the necessity and universality were clarified.Then, from the perspective of the five key issues in the mobile edge computing and caching enabled large-scale wireless network, including computing offloading, edge caching, multi-dimensional resource allocation, user association and privacy protection, the recent researches and further pointed out the future development trends and research directions were reviewed and analyzed.Finally, for the privacy preservation issue, a federated learning based privacy-preserving scheme was proposed.Simulation results show that the proposed scheme can simultaneously preserve user privacy and improve the quality of service.
Edge computing is a method of processing data physically close to where data is generated,and it is one of the key technologies of 5G.The development of network technology and the demand of business applications have jointly promoted the maturity of 5G edge computing.The 5G edge computing technology system was elaborated through the perspective of edge infrastructure,edge computing network and edge platform,and the 5G edge computing application was prospected.
Mobile edge computing (MEC) is an innovative computing paradigm.Offloading computing tasks to MEC server can effectively solve the contradiction between resource-constrained of mobile user and computing-intensive applications.Moreover,it can meet the delay of computing task and reduce energy consumption of mobile user.The current research results and achievements of computation offloading scheme in MEC system were summarized and the existing problems and challenges were analyzed.Furthermore,the development directions of MEC in the future were explored.
As a cost-efficient way to meet the wide range of use cases that the fifth generation wireless network will provide,network slicing and edge computing have been advocated by both academia and industry.In the concept of network slicing,the network entities are sliced into several logical networks to provide the requested services for different use cases.The edge computing pushes the frontier of computing applications,data,and services away from centralized nodes to the logical extremes of a network and even the user equipments,which can improve the traditional mobile broadband service capability,and handle with the emerging machine type service.The edge computing based radio access network slicing was proposed as a combination of edge computing and network slicing.The radio access network slicing can meet the diverse use cases and business models in 5G,and enable operators to flexibly provide personalized network services based on third-party need and network situation in a cost-efficient way.
In mobile edge computing system,the quality of computing experience can be improved greatly by offloading computing tasks from mobile devices to mobile edge computing servers.Consider incorporating renewable energy into a multi-user mobile edge system.Moreover,a battery as an energy harvesting device was added to the model to harvest energy and storage.The task allocation strategy in mobile edge computing system was formulated through the resource management algorithm based on reinforcement learning,which achieved the cost minimization of mobile devices (including delay cost and computing cost).The simulation results show that the proposed algorithm significantly minimizes the cost of mobile devices compared with other algorithms.
Edge computing integrate the computing,storage and other resources on the edge of the network into a unified user service platform.According to the principle of nearest service,the network edge node task request is timely and correspondingly processed.Computational migration is a key issue in edge computing due to limited edge node capabilities,resources,bandwidth,and energy.The causes,evolution and development trends of mobile edge computing were reviewed from the distributed computing,pervasive computing,cloud computing to edge computing as the main line.The characteristics of computing migration at each stage were compared and analyzed,and the classical models were reviewed.The latest research progress and application fields of edge computing were focused on,mobile edge computing models based on energy optimization management were researched and compared.Finally,a time division multiple access based multi-user edge mobile terminal computing migration strategy system for LTE applications was proposed.
Cloud native computing, featured by low-cost container technology, well fits edge computing.It was proposed to apply cloud native computing to make edge computing resource management and control transparent to application development and operation.Compared with cloud computing, edge computing resources are widely distributed, highly heterogeneous, and fragmented, which called for collaborative resource management and control.According to the development status of cloud native related technologies, through the integration of future networking technologies such as soft were defined network and network function virtualization, a full-stack cloud native based edge computing architecture was proposed.Then, considering the hierarchical characteristics of containers, a low-overhead container deployment optimization problem for resource-limited edge computing was studied.Finally, the development challenges faced by cloud native based edge computing were discussed.
With the rise of edge computing,more and more attentions have been paid to security issues of edge computing.The basic concepts,system architecture and the relationship between edge computing and other computing paradigms were introduced.Then the security threats to edge computing were analyzed,and the security technology of edge computing was discussed for these security threats.Finally,the technologies of intrusion detection,access control,defense strategy and key management in edge computing were summarized and the further research directions were pointed out.
With the explosion of global data,centralized cloud computing cannot provide low-latency,high-efficiency video surveillance services.A distributed edge computing model was proposed,which directly processed video data at the edge node to reduce the transmission pressure of the network,eased the computational burden of the central cloud server,and reduced the processing delay of the video surveillance system.Combined with the federated learning algorithm,a lightweight neural network was used,which trained in different scenarios and deployed on edge devices with limited computing power.Experimental results show that,compared with the general neural network model,the detection accuracy of the proposed method is improved by 18%,and the model training time is reduced.
How to mitigate such pressure has become a critical challenge for global grid enterprises.Empowered by the development of IoT techniques,high-speed communications,and AI-chips,traditional grid enterprises are trying to adopt these new techniques to solve the challenge,especially in heterogeneous data collection,energy scheduling,and AI-enabled monitoring.Firstly,the background of smart grid and the history of edge computing were introduced.Then,edge computing enabled solutions were illustrated in three typical grid scenarios.Finally,two edge computing platforms at home and abroad were introduced.The main technologies and application scenarios of them were also analyzed.
Given the contradiction between limited network resources and massive user demands in Internet of vehicles, an intelligent vehicular edge computing network architecture was proposed to achieve the comprehensive cooperation and intelligent management of network resources.Based on this architecture, a joint optimization scheme of task offloading and service caching was furtherly devised, which formulated an optimization problem about how to offload tasks and allocate computation and cache resources.In view of the dynamics, randomness and time variation of vehicular networks, an asynchronous distributed reinforcement learning algorithm was employed to obtain the optimal task offloading and resource management policy.Simulation results demonstrate that the proposed algorithm achieves significant performance improvement in comparison with the other schemes.
With the development of the Internet of things,multiple real-time applications will emerge in future 6G communication network.Driven by demands on low-latency services of multiple real-time applications,mobile edge computing (MEC) will become an important technology to improve the user experience and reduce the network cost.However,the computing capacity of a single MEC server is limited,which induces that it is difficult to meet the low-latency requirement of data processing for the computation-intensive applications.A heterogeneous multi-layer mobile edge computing (HetMEC) network architecture was proposed to jointly utilize the computing and transmission resources of both the cloud computing center and multi-layer MEC servers.By reasonably dividing and offloading computing tasks,reliable and efficient computing services were provided for edge applications.Simulation results show that the proposed algorithm could effectively reduce the processing latency,improve the network processing rate and robustness.
The traditional centralized architecture,known as cloud computing,cannot accommodate such user demands in an efficient and timely manner.To cope with this problem,edge computing architectures have been proposed with the core concept of that“data processing should be close to the data source”.Firstly,paradigms of edge computing was introduced,including micro data center,cloudlet,fog computing,and mobile edge computing,and the advantages of edge computing from the perspective of resource integration was discussed.Then,related works of resource optimization in edge computing was reviewed and summarized,and these works was discussed via three directions,i.e.,computation offloading,distributed caching and high performance transmission,corresponding to core resources as computing,storage and communication.Finally,trends of development and future directions were presented as well.
The modern communication network technology represented by 5G and the accompanying mobile edge computing technology play a crucial role in the connection of the industrial internet, closely integrating production factors and exerting the value of collaborative operations.By describing the basic development status and technical characteristics of 5G edge computing, the application method for the industrial internet field and the comparison with the traditional model were explained, a reference implementation solution for industry services was proposed, and the expected application scenarios were summarized.
Aiming at maximizing the cache revenue,a joint user association and power allocation algorithm was proposed for the matching of base stations with users and power allocation in a NOMA heterogeneous network with caches,a NOMA joint optimization algorithm which was achieved by combing with message passing and DC programming was proposed.First,the constraints were incorporated into the objective function,and the user association result was obtained by calculating the marginal of the message passing between the function node and the variable node in the new optimization problem.Then,the original optimization problem was transformed into the form of the difference between two convex functions,and allocate power resources through DC programming.Finally,the final user association and power allocation results are obtained through iterative calculations.Simulation results prove that the proposed algorithm effectively improves network performance.
The popularity of smart devices has driven the development of the application of Internet of things (IoT) technology,and the resulting massive amount of IoT data has brought challenges to traditional centralized data management methods,such as performance,privacy,and security.Therefore,a data management framework of IoT based on blockchain and edge computing was proposed to support the distributed IoT data management.The distributed storage and access control could be provided by the framework for the IoT data.At the same time,a set of the built-in encryption scheme was designed to protect the data security and privacy and safeguard the data ownership.By introducing edge computing,the scalability bottleneck of the blockchain the system was relieved.The processes of data storage and data access based on this framework were given,and the algorithm of the system implementation based on the smart contract technology was explained in detail.Experiments show that the IoT data management system based on this framework outperforms the traditional cloud-based data management systems.
Firstly, the basic concept of edge computing (EC) and its current state of research were introduced.Moreover, the design requirements of edge computing platforms from multiple perspectives were discussed.Then, four typical open source platforms were presented in detail, and their similarities and differences in terms of application areas, deployment methods were analyzed.Later, with regard to two typical use cases, their installations and advantages were summarized and analyzed.Finally, key challenges such as the cooperation among edge computing platforms, security and standardization were discussed.
With the increasing development of Internet of things, traditional data processing methods based on cloud computing have shown many problems, such as high bandwidth occupation and time delay.Edge computing can be a supplement for cloud computing with the characteristics of low latency and high reliability to process data.The KubeEdge edge computing system and its application were mainly discussed and analyzed.Firstly, the system architecture, functions, and key technologies of KubeEdge were introduced.Secondly, the KubeEdge was applied to the parts assembly scenario with dual-arm cooperative robot, and the functions and performances of a cloud-edge collaboration system based on KubeEdge for the robot assembly application were analyzed and tested.The experimental results show that the system can satisfy the functional and application requirements of the scenario, which also provides basic reference and guidance for practical applications of KubeEdge.
With the rapid development of artificial intelligence,the global robot market continues to grow quickly,and the capabilities of robots have evolved from performing fixed operations to the ability to autonomously sense,understand and make decisions.However,to achieve large-scale application of robots,robots need to have powerful computing capabilities and low deployment costs under the constraints of limited power consumption.Using edge computing to provide more cost-effective services,enhance the computing power of the robot body,and achieve large-scale deployment is the key to achieving this goal.The challenges faced by robot systems with the edge enhancement were analyzed,the concept of cloud-native robot systems based on edge computing was proposed,and four feasible technical solutions for implementing the system were discusses.The cloud-native robot system is the inevitable direction for the development of robot systems from intelligent systems based on robot ontology to cloud-edge-end fusion multi-robot collaborative intelligent systems and the key technology for promoting the large-scale application of robots.
Aiming at the problem of high-latency,high-energy-consumption,and low-reliability mobile caused by computing-intensive and delay-sensitive emerging mobile applications in the explosive growth of IoT smart mobile terminals in the mobile edge computing environment,an offload decision-making model where delay and energy consumption were comprehensively included,and a computing resource game allocation model based on reputation that took into account was proposed,then improved particle swarm algorithm and the method of Lagrange multipliers were used respectively to solve models.Simulation results show that the proposed method can meet the service requirements of emerging intelligent applications for low latency,low energy consumption and high reliability,and effectively implement the overall optimized allocation of computing offload resources.
As a new architecture,mobile edge computing gives edge users stronger capabilities of computing,storage and communication,but it needs reasonable incentives mechanism to motivate edge users to provide resources.In terms of the three typical scenarios of mobile intelligent edge computing:computation offloading,edge caching and data collection,the incentive mechanism in the above scenarios was studied at first,then the core scientific problems were proposed that need to be solved in the incentive mechanism design of mobile intelligent edge computing from three perspectives of service quality,network quality and data quality.Finally,the technical challenges in the process of solving the above problems were analysed deeply and the corresponding feasible solutions were given.
For the unmanned aerial vehicle (UAV)-assisted edge computing system,a two-stage alternative algorithm was proposed to solve the formulated complex non-convex problem.Firstly,the formulated non-linear fractional programming problem was reformulated to the equivalent parametric problem by using Dinkelbach method.Secondly,two sub-problems were further considered based on it.By employing the Lagrange duality method,the closed-form solutions for the central processing unit frequencies and the number of data bits were derived.Finally,based on the solutions obtained,the conditions that the source node prefers to offload/share its data and the relay chooses to forward the computation results,as well as the approaches to achieve high energy efficiency were revealed.Numerical results demonstrate that the proposed design can achieve a performance improvement of up to 20 times over the conventional schemes.
With the continuous development of Internet of things applications,a large number of mobile terminal devices participate in service computing.Traditional cloud computing models cannot adapt to the rapid growth of data generated by network edge devices,and edge computing models emerge and become a research hotspot in recent years.The concept of edge computing and the reference model of edge computing in the Internet of things were introduced,the vulnerabilities of edge devices and the main research results of cryptographic security technology in edge computing were summarized,and that symmetric cryptography technology is not suitable for communication between edge devices,and identity-based cryptography technology is more suitable for communication between edge devices and edge devices was pointed out.Paired-based cryptography is more suitable for the communication between edge devices and base stations.The application of two post-quantum cryptography technologies in edge devices was discussed.Finally,some suggestions on the research of edge computing security technology were put forward.
Aiming at the fact that the existing encrypted image retrieval schemes do not consider different keys to encrypt images,a multi-key encrypted image retrieval system based on edge computing (including basic scheme and enhanced scheme) based on local sensitive hashing,secure nearest neighbor and proxy re-encryption technologies was proposed.The retrieval efficiency and accuracy were improved and the extra computational cost of query users was reduced.Security analysis shows that the basic scheme can only resist the known ciphertext attack,while the enhanced scheme can resist the known background attack.The experimental performance test based on the real-world dataset shows that the proposed schemes are feasible in practical application scenarios.
Mobile Edge Computing can solve the problem of heavy backhaul link overloaded and long delay effectively by further extending the telecommunication cellular network to other wireless access networks.However， the MEC nodes are exposed to the edge of the network whose computing resource, storage capacity and energy resource are limited, they are more vulnerable to the illegal attacks by attackers.Based on the briefly analysis of the security threats faced by mobile edge computing, some key problems and challenges of mobile edge computing for four different security subjects, device security, node security, network resources and tasks, and migration security are summed up and expounded, and the existing security solutions were summarized.Finally, the open research and future development trend of mobile edge computing security defense on three aspects that limited resource defense model in the dynamic scene, resource deployment based on comprehensive trust and user-centered service reliability assurance are discussed.
Mobile edge computing (MEC) has ability of reducing network load and transmission delay, and improving user service experience by offloading computing tasks to edge servers.Therefore, MEC has attracted extensive attention and become a key technology of 5G.As one of the main issues of MEC, resource allocation has great significance in improving energy efficiency, shortening task delay and reducing cost.Firstly, the basic concept, reference architecture and technical advantages of MEC were introduced.Then, the up-to-date achievements of resources allocation and pricing strategies were summarized from both technical and economic aspects.Finally, some possible problems and challenges associated with resource allocation and pricing strategies in MEC were discussed, and the enabling methods to address these challenges were also proposed to provide reference for subsequent research and development.
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