Trust model for wireless network security based on the edge computing

Feng-lian, Jiang; Tseng, Hsien‐Wei

doi:10.1007/s00542-019-04459-3

Cited by 10 publications

(9 citation statements)

References 13 publications

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“…With the advancement of technical means, the network education management service model is relatively limited and does not meet the development requirements of the times. It is unavoidable that network education management will be digitalized and informationized [17][18][19]. The rapid development of scientific and technical revolutions has resulted in the online education platform, which includes online education statistics and management capabilities.…”

Section: Related Workmentioning

confidence: 99%

The Construction of University Network Education System Based on Mobile Edge Computing in the Era of Big Data

Zhu

2022

Wireless Communications and Mobile Computing

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This article first established a university network education system model based on physical failure repair behavior at the big data infrastructure layer and then examined in depth the complex common causes of multiple data failures in the big data environment caused by a single physical machine failure, all based on the principle of mobile edge computing. At the application service layer, a performance model based on queuing theory is first established, with the amount of available resources as a conditional parameter. The model examines important events in mobile edge computing, such as queue overflow and timeout failure. The impact of failure repair behavior on the random change of system dynamic energy consumption is thoroughly investigated, and a system energy consumption model is developed as a result. The network education system in colleges and universities includes a user login module, teaching resource management module, student and teacher management module, online teaching management module, student achievement management module, student homework management module, system data management module, and other business functions. Later, the theory of mobile edge computing proposed a set of comprehensive evaluation indicators that characterize the relevance, such as expected performance and expected energy consumption. Based on these evaluation indicators, a new indicator was proposed to quantify the complex constraint relationship. Finally, a functional use case test was conducted, focusing on testing the query function of online education information; a performance test was conducted in the software operating environment, following the development of the test scenario, and the server’s CPU utilization rate was tested while the software was running. The results show that the designed network education platform is relatively stable and can withstand user access pressure. The performance ratio indicator can effectively assist the cloud computing system in selecting a more appropriate option for the migrated traditional service system.

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Section: Related Workmentioning

confidence: 99%

The Construction of University Network Education System Based on Mobile Edge Computing in the Era of Big Data

Zhu

2022

Wireless Communications and Mobile Computing

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“…e current research on application partitioning and scheduling is mainly based on the cloud computing scenario, when different microservices are scheduled by the partitioning algorithm and assigned to the same cloud center, by default there is no communication cost between them, and all communication time cost comes from the cross-end communication cost when scheduling to different clouds [15]. e coupling between different microservices, such as function calls, or the transfer of data, is then represented by the edges of two nodes on the graph, and the input/output data of the microservices transferred between computing servers include data and code related to the microservices (e.g., application data such as image/video data, mobile system settings, parameters, program code, intermediate state, and return values of method calls) [16]. For applications or services to reduce latency, the entire model has two types of time costs, one is the computational time cost of running the microservice locally or scheduling to an offsite server, represented by the weights of the nodes, and the other is the time cost of communication and data transfer between microservices, represented by the weights of the edges.…”

Section: Modeling Edge Computingmentioning

confidence: 99%

Research on the Construction Model of 5G Special Town Based on Edge Computing

Pengchao

2022

Scientific Programming

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This study conducts a study on edge computing technology, focusing on the advantages of edge computing technology and its applications, and delves into the edge computing architecture, which leads to an in-depth study on the construction of a 5G special town model. Based on the existing 5G characteristic town construction model, this study proposes a new model of edge computing and proposes an application partitioning algorithm based on this model by analyzing the edge computing scheduling scenarios to minimize the total time cost of the system. By analyzing the edge computing scheduling scenarios, an application partitioning algorithm based on this model is proposed, so that the total time cost of the system is minimized. This study analyzes and studies the current situation of infrastructure construction, and investment in the special town analyzes the significance and purpose of its construction and develops the research method and technical route of this study as a result. Then, the connotation of characteristic town and infrastructure is elaborated, and the value, characteristics, and role of infrastructure construction of characteristic town are analyzed; the influencing factors of infrastructure investment in characteristic town are analyzed; the development of the characteristic town has endless potential, and the development of characteristic town must meet the demand of social economy as well as town population for infrastructure support. However, there are more risk factors for infrastructure investment in characteristic towns, and the investment risk is larger than general projects, so it is necessary to strengthen risk management and do a good job of investment protection.

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“…The core concept of edge computing is a distributed computing form that places computing resources and data storage at the edge nodes of the network [16] and supports new human-centric services by providing users with low-latency service awareness and controllable network transmission costs [17]. It gives full play to the advantages of large broadband, low latency, and wide link in 5G network [18].…”

Section: Relevant Workmentioning

confidence: 99%

An Efficient and Credible Multi-Source Trust Fusion Mechanism Based on Time Decay for Edge Computing

Kong

Hou

et al. 2020

Electronics

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With the development of 5G, user terminal computing moves up and cloud computing sinks, thus forming a computing fusion at the edge. Edge computing with high-efficiency, real-time, and fast features will become part of 5G construction. Utilizing distributed computing and storage resources at the edge of the network to perform distributed data processing tasks can alleviate the load on the cloud computing center, which is also the development trend of edge computing. When a malicious node exists, the error information feedback by the node will affect the result of local perception decision. To solve the problem of malicious behavior of the node, a node trust evaluation mechanism of interactive behavior is introduced. The trust mechanism for edge computing network environments is introduced as a novel security solution. First, the key thought of the trust mechanism proposed in this paper is to establish a trust relationship between edge nodes in open edge computing environment. Then, a multi-source trust fusion algorithm based on time decay aggregates direct interaction trust and different third-party recommendation trust to calculate the global trust of the evaluated nodes. Finally, simulation experiments show that the algorithm has a certain degree of improvement in computational efficiency and interaction success rate over other existing models, which reduces the situation of malicious node deception.

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Trust model for wireless network security based on the edge computing

Cited by 10 publications

References 13 publications

The Construction of University Network Education System Based on Mobile Edge Computing in the Era of Big Data

The Construction of University Network Education System Based on Mobile Edge Computing in the Era of Big Data

Research on the Construction Model of 5G Special Town Based on Edge Computing

An Efficient and Credible Multi-Source Trust Fusion Mechanism Based on Time Decay for Edge Computing

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