边缘计算
Aiming at the problem of edge computing data security, a ciphertext search and sharing solution was proposed, where the above-mentioned edge computing advantages were used to achieve user privacy data protection, edge nodes were used to construct encrypted inverted indexes, indexes and keys between edge nodes and cloud computing platforms were securely shared, and ciphertext search, secure data sharing, and dynamic index update were realized without changing the edge computing architecture and cloud computing architecture.Finally, compared to existing schemes, performance and security were analyzed and discussed, which proves that the proposed scheme has high security strength under ciphertext search attack model, and the ciphertext search efficiency and document dynamic update function are taken into account based on encrypted inverted index.
The rise of visual deep learning algorithms based on convolutional neural network (CNN) has promoted the rapid development of the artificial intelligence (AI) vision chip design research.The step of chip verification is a bottleneck in the development of AI vision chips.A software and hardware verification method for AI vision chip design based on hardware simulation system was introduced.Taking AI vision chip design for edge computing as an example, the chip was run on the hardware simulation system (ZeBu) and the simulation verification work of typical deep learning network MobileNet was completed.The results show that the network model implemented on the hardware chip architecture keeps accuracy while the detection time of a single frame is only 18.51 ms under a 200 MHz clock frequency.The spread of the hardware simulation is 7 times faster than than of the software simulation.
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.
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.
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.
Multi-access edge computing (MEC) can provide high-quality service capabilities for computing-intensive services and delay-sensitive services in urban rail traffic.However, many edge facilities in rail traffic edge computing network are exposed to an open environment, and their privacy protection and transmission security are facing great challenges.Blockchain has functional characteristics such as distributed ledger, consensus mechanism, smart contract, and decentralized application.Therefore, the use of blockchain can build a systematic security protection mechanism for the distributed rail traffic edge computing network to ensure network security and data security and realize high-quality urban rail traffic services.Firstly, the basic concept of the blockchain and the urban rail traffic edge computing network architecture were introduced.Then, the structure and application content of the rail traffic edge computing network security protection mechanism integrated with the blockchain was discussed in detail.Finally, the open research issues and challenges of the security protection mechanism were analyzed.
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 development of the Internet of things (IoT) and edge computing, the computation-intensive tasks of IoT devices can be offloaded to edge devices and processed at the edge of networks.Due to the variation of the distribution and computation requirements of IoT devices, the computation resources of edge networks need to be managed dynamically.The optimal transport theory was adopted to optimize the computation resources allocation in IoT networks.An optimized regional partition mechanism was proposed based on the distribution of IoT devices and locations of edge computing devices.Under constraints on the computing capabilities of edge computing devices, the energy consumption and delay of IoT devices were optimized.The simulation results show that, compared with the traditional Voronoi partition scheme, the proposed optimization mechanism shows better balance.The average transmitting power can be reduced by 21% and the average delay can be reduced by 45%.
Edge computing can provide users with low-latency and high-bandwidth services by deploying many edge servers at the network edge.However, a large number of deployments also bring problems of high energy consumption.When dispatching tasks from end devices to different edge servers, different energy consumption and delays will occur due to the edge servers’ heterogeneity.Therefore, it is a challenge to select an optimal server among many edge servers for task dispatching so that energy consumption and delay are relatively low.An energy-aware task dispatching method with quality of service (QoS) guarantee based on online learning was proposed.It can obtain real-time information by interacting with the environment to ensure energy consumption was minimal while the QoS was acceptable when dispatching tasks.Experiments show that the proposed method can dispatch tasks efficiently to the optimal server compared with other methods, thereby reducing the edge computing network’s overall energy consumption significantly.
Mobile edge computing (MEC) emerges as a new paradigm that pushes the computing infrastructure from the remote cloud data center to the edge equipments.It provides a new solution to meet the delay sensitive and computing intensive requirements of Internet of things (IoT).In this work, the problem of tasks offloading and scheduling in the multi-user and multi-server MEC system was considered.Specifically, each user had a task-dependent application and the tasks could be either executed locally or remotely according to the dependence.Thus, the network performance was improved by unloading and scheduling the sub tasks.Quality of experience (QoE) and fairness between users were used to characterize the network performance, and the optimization problem was modeled as a joint dependent task offloading and scheduling (J-DTOS) problem.The J-DTOS problem was a non-linear mixed integer programming, which was NP-hard in general.The original problem was reformulated by introducing intermediate variables and proposing a near-optimal solution.Simulation results show that the proposed offloading and scheduling design can significantly improve the performance of the system.
With the time-delay sensitive compute-intensive tasks growing exponentially, 5G and future 6G mobile cellular communication networks will form differentiated network slices with edge computing resource to service the multiple communication services.To achieve this, the wireless frequency and edge computing resources will be shared among the network slices, thus the efficient management of the resources is of great importance.The demands of resources management in the life duration of the network slice was analyzed.Then the research progress of the corresponding technologies for slices, i.e., access control, resources allocation and stimulating cooperation among terminals, were summarized.And the open research issue was given.Thus, the resources are managed efficiently for the slices with edge computing resource.
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.
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.
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.
With the coming of 5G, Internet of things (IoT) has gradually become a reality.However, the function of many terminals is limited to insufficient computing resources in IoT.Mobile edge computing (MEC) is a new network paradigm, where terminals can offload the resource sensitive applications to nearby edge services with rich resources, and thus reduced the operational cost/delay and increased the quality of service (QoS).The resource deployment of MEC was studied, a scenario was considered that the Internet service provider (ISP) paid for deploying the MEC resources, and meanwhile gained revenue from leasing resources to terminals.Note that the deployment of MEC resource is a long-term strategy, while the demands from terminals are time varying.Therefore, it is critical to deploy the MEC resource properly.A hierarchical architecture built upon the 5G radio access network for MEC resource deployment and sharing was proposed, where the MEC resource could be deployed at different network levels.Based on the hierarchical network architecture, the optimal MEC resource deployment problem was formulated and solved as a mixed integer programming problem, aiming at maximizing the ISP’s revenue.And the CVX toolbox in MATLAB was used to solve the problem.Simulation results demonstrate that the proposed solution outperforms the flat resource deployment solution in terms of both the ISP’s revenue and the deployment cost.
Nowadays, vehicular network is confronting the challenges to support ubiquitous connections and vast computation-intensive and delay-sensitive smart service for numerous vehicles.To address these issues, non-orthogonal multiple access (NOMA) and mobile edge computing (MEC) are considered as two promising technologies by letting multiple vehicles to share the same wireless resources, and the powerful edge computing resources were adopted at the edge of vehicular wireless access network respectively.A NOMA-based vehicular edge computing network was studied.Under the condition of guaranteeing task processing delay, the joint optimization problem of task offloading, user clustering, computing resource allocation and transmission power control was proposed to minimize the task processing cost.Since the proposed problem was difficult to solve, it was divided into sub-problems, and a low-complexity and easy-to-implement method was proposed to solve it.The simulation results show that compared with other benchmark algorithms, the proposed algorithm performs well in minimizing costs.
An unmanned aerial vehicle (UAV)-assisted mobile edge computing system was proposed in which multiple UAVs equipped with computing resources were employed to provide computation offloading opportunities for mobile users with limited local resources.The computing tasks of each user can be divided into two parts.One portion was offloaded to its associated UAV for computing and the remaining portion was processed locally.It was aimed at minimizing the sum of the maximum delay among all user devices by jointly optimizing the user scheduling and the UAV trajectory in a finite period.The proposed problem was a mixed-integer non-convex optimization problem.To facilitate solving this problem, it was equivalently converted into a more tractable problem by introducing some auxiliary variables, and then a penalty concave-convex procedure algorithm was proposed to solve the converted problem.Simulation results show that the proposed joint optimization scheme achieves significantly better performance than other benchmark schemes.
Multi-access edge computing can effectively guarantee the low-latency, high-reliability data transmission of ocean monitoring sensor networks and various related maritime applications.In the offshore scenario, two offloading models of multi-user single-hop unicast and multi-user multi-hop unicast were established in combination with the distribution of edge computing resources.The mixed integer nonlinear programming was used to separate optimization targets and effectively allocate transmission power.The unloading decisions were made by improving the traditional artificial fish swarms algorithm.The results show that the proposed optimization algorithm can reduce the network delay by nearly 19% compared with the traditional scheme.In the far-sea scenario, a multi-user single-hop unicast offloading model was established, and a reasonable channel allocation algorithm was proposed based on the network connection probability.The results show that when the network connection time is sufficient, the number of allowable sub-channels can be increased to reduce the network delay.When the network connection time is limited, the number of unloaded marine user equipment can be controlled to ensure the network transmission delay.
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.
Aiming at the privacy protection problem in the multi-application scenarios of edge computing, two policy-based key distribution protocols were proposed.The proposed protocols are based on the concept of constrained pseudo-random functions to achieve efficient and flexible policy selection.Specifically, based on the GGM pseudo-random number generator, the key distribution protocol with a prefix-predicate is constructed, which can effectively support lightweight and efficient key distribution.Moreover, based on the multilinear pairing, the key distribution protocol with a bit fixing predicate is constructed.This protocol can support flexible and fine-grained strategy selection and is suitable for dynamic and flexible multi-device scenarios in heterogeneous networks.Finally, the security proof of the proposed protocols is presented.
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.
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.
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.
Considering a physical-layer-security-aided confidential document transmission problem,a secure transmission strategy was proposed where the base station equipped with a mobile edge computing (MEC) server served as a relay to help compress,forward,and decompress.First,the Poisson point process was used to calculate the secure transmission probability for the scenario with multiple potential eavesdroppers.Then,a delay and energy minimization problem was formulated under a constraint on the two-hop secure transmission probability.The optimal compression and decompression scheme was obtained using the one-dimensional search combined with linear programming.Simulation results show that the document compression is necessary for the link with small achievable secrecy rate under a given secure transmission probability; otherwise,the document should be directly transmitted without compression.
In order to meet the needs of the intelligent management of modern commercial buildings,with the Internet of things (IoT) and edge computing technology,the intelligent building management system (IBMS) was developed based on ubiquitous building IoT by using the self-developed IoT gateway.The ubiquitous IoT eliminated the boundaries between the traditional building automation and information automation systems.The edge computing technology was used in the integrated management system to enhance the on-site decision-making ability and response speed of IBMS.The architecture of the integrated management system and the implementation of edge computing were discussed.The application of the system in the Shenzhen Dashi intelligent building shows the feasibility and effectiveness of the intelligent integrated management system based on ubiquitous building IoT.
With an aim of maximizing the efficiency of edge offloading and the resource utilization of edge computing server simultaneously, a new flow-of-traffic prediction based edge computing offloading solution was proposed for Internet of vehicles (IoV).Firstly, both the efficiency utility function of vehicle and the resource utilization of mobile edge computing (MEC) server were established by considering task priority.Next, the formulated dual-objective optimization problem was transformed into a double auction problem between vehicles and MEC servers.Finally, based on the designed flow-of-traffic based pricing function of vehicle and MEC server, a McAfee auction algorithm was adopted to complete the edge computing process.Simulation results show that benefiting from the flow-of-traffic prediction information, the proposed solution can significantly improve both the efficiency of computation offloading and the utilization of computation resource.
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.
To resolve the excessive system overhead and serious traffic congestion in user-oriented service function chain (SFC) embedding in mobile edge computing (MEC) networks,a content-oriented joint wireless multicast and SFC embedding algorithm was proposed for the multi-base station and multi-user edge networks with MEC servers.By involving four kinds of system overhead,including service flow,server function sustaining power,server function service power and wireless transmission power,an optimization model was proposed to jointly design SFC embedding with multicast beamforming.Firstly,with Lagrangian dual decomposition,the problem was decoupled into two independent subproblems,namely,SFC embedding and multicast beamforming.Secondly,with the L<sub>p</sub> norm penalty term-based successive convex approximation algorithm,the integer programming-based SFC embedding problem was relaxed to an equivalent linear programming one.Finally,the non-convex beamforming optimization problem was transformed into a series of convex ones via the path following technique.Simulation results revealed that the proposed algorithm has good convergence,and is superior to both the optimal SFC embedding with unicasting and random SFC embedding with multicasting in terms of system overhead.
For wireless powered mobile edge computing (MEC) network,a system computation energy efficiency (CEE) maximization scheme by considering the limited computation capacity at the MEC server side was proposed.Specifically,a CEE maximization optimization problem was formulated by jointly optimizing the computing frequencies and execution time of the MEC server and the edge user(EU),the transmit power and offloading time of each EU,the energy harvesting time and the transmit power of the power beacon.Since the formulated optimization problem was a non-convex fractional optimization problem and hard to solve,the formulated problem was firstly transformed into a non-convex subtraction problem by means of the generalized fractional programming theory and then transform the subtraction problem into an equivalent convex problem by introducing a series of auxiliary variables.On this basis,an iterative algorithm to obtain the optimal solutions was proposed.Simulation results verify the fast convergence of the proposed algorithm and show that the proposed resource allocation scheme can achieve a higher CEE by comparing with other schemes.
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.
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.
Edge computing is an innovation of network architecture and business model for operators.5G edge computing node is composed of 5G UPF and edge computing platform.It can be deployed at different levels of the network based on the demand for bandwidth,delay and local streaming.In the perspective of 5G application scenarios,depending on edge computing can meet the customer needs of differentiated vertical industries and seize the market opportunities,which is a sharp tool for operators to enter the vertical industry.The business model,billing scheme,landing deployment strategy and scheme of edge computing deployment were mainly studied,including business scenario,infrastructure,hardware system,site selection methodology,construction scheme,peripheral networking scheme,etc.
Firstly,the current status of electrical topology discovery in low voltage distribution network was introduced,and then the high frequency synchronous acquisition and edge computing based electrical topology discovery in low voltage distribution network was proposed,including high-frequency synchronous data acquisition,Pearson correlation coefficient method for station area identification,analysis of mutual information between electric meters,generation of physical topology through spanning tree algorithm,and so on.Compared with other methods using pure software algorithm to realize topology discovery,the proposed method can not only ensure the simultaneity of data,but also reflect the changes of topology in time,which makes the judgment of electrical topology of low-voltage stations more accurate.
Aiming at the efficiency of cloud computing ciphertext retrieval scheme,a method of ciphertext retrieval in mobile edge computing based on block segmentations was proposed.Firstly,the edge server was introduced to calculate the document similarity score,thereby the computational cost of cloud server was reduced and the processing efficiency of cloud server was improved.Secondly,most keywords that are not related to the query were filtered out by a method of block segmentations based on the MRSE scheme,thereby the efficiency of calculating the document similarity score was improved.Theoretical analysis and experimental results show that the solution is safe under the known background threat model.Compared with the existing scheme,the proposed scheme has the same security and higher retrieval efficiency.
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.
The three application scenarios of 5G make forward higher requirements for network delay,bandwidth,connection and security,which drive 5G to sink the core capabilities of network,computing,storage and application to the edge of the network.The 5G base station which is closer to the terminal/user and has complete resources such as base station power,operation and maintenance will be the best choice for 5G edge computing deployment.Based on the multi-point 5G base station site resource and the architecture of edge computing deployment,the feasibility of multi-operator 5G edge computing shared deployment was studied,three feasible co-building and sharing schemes,such as base station infrastructure sharing,IaaS facility sharing and PaaS platform sharing were proposed.By coordinating the development needs of 5G edge computing and realizing the co-construction and sharing of edge computing,the construction cost and operation cost of edge computing can be greatly reduced,and the rapid scale development of edge computing can be realized,to build an intensive and efficient,economic and applicable,intelligent green,safe and reliable edge computing system.
With the rapid development of internet of things technology,in order to better develop modern medicine,avoid the “isolated” crisis of information,and meet the requirements of timeliness and computing performance of massive data generated by edge devices,it is a new trend of the times to combine edge computing with smart healthcare treatment.However,edge computing has a certain degree of openness,and it is prone to potential safety hazards.Therefore,people pay more and more attention to the security and privacy protection of edge computing system for smart healthcare treatment.The edge computing system and its architecture for smart healthcare were introduced,and the security risks encountered by smart healthcare in edge computing was explained,and some scholars’ solutions to the security risks were introduced.Finally,a security framework,and specific solutions for security and privacy protection under the framework were put forward,which providing some help for the trusted research of smart healthcare edge computing.
With the popularization of video surveillance systems and the diversification of their applications,traditional data processing methods using cloud computing to handle all the computations are more and more unsustainable.Edge computing pre-processes video data nearby,and performs well in terms of bandwidth,storage,and latency,but edge-cloud collaboration is still required to improve overall performance.The architecture of edge-cloud collaborative video surveillance system was proposed,then an open edge node design scheme and algorithm training and inferencing ideas were proposed.Finally,taking forest fire prevention and smart poles ＆amp; towers as examples,the application realization and value of edge computing in video surveillance services were illustrated.
Information security monitoring system is widely used in railway construction projects,but the existing safety supervision model has problems such as high server load,heavy network bandwidth pressure,monitoring data can’t be effectively shared,key monitoring data may be tampered,and security accident responsibility is difficult to determine.A credible sharing model of safety supervision data for railway construction projects based on edge computing was proposed.On one hand,the construction of a safety monitoring edge computing framework for construction projects was constructed,and the data processing flow of edge nodes was described; on the other hand,a trusted data sharing framework based on blockchain was established,and a safety supervision data sharing process was designed and trusted sharing data was implemented through smart contracts.The proposed solution combined with edge computing and blockchain technology,which not only could strengthen the safety supervision of railway construction projects by industry authorities,but also provide new opportunities for stakeholders to share safety monitoring data in real time.
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.
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边缘计算
