The mechanism of digitized landscape architecture design under edge computing

The purposes are to analyze the mechanism of digitized landscape architecture design and stablize the garden landscape image display in constructing garden landscape digitization platform. According to previous research and mobile edge computing, a scheme of digitized landscape architecture design is proposed based on edge computing. This scheme uses discrete elevation calculation to preserve the landscape design image’s frame. It adopts the Roberts edge detection and Laplacian operator for high-level stable preservation of landscape images. Simultaneously, the displayed image is stablized using edge computing algorithms. Simulation experiments are performed to verify the effectiveness of the proposed scheme of digitized landscape architecture design based on mobile edge computing. Results demonstrate that the discrete elevation calculation algorithm can avoid low visual rendering in the 3D image generation process, optimize the seed point matching of edge correlation, and ensure image clarity and stability. The edge computing algorithm can fundamentally avoid the problem of image shaking. The impact of different algorithm models on the classification and accuracy of landscape images is analyzed through parameter optimization. Compared with some latest models, the proposed landscape design scheme based on edge computing has better accuracy. The average accuracy can reach more than 90%, and the Kappa coefficient remains at 86.93%. The designed garden landscape digitization platform can stably display 3D garden landscape images while avoiding the shaking of 3D images, which can provide a theoretical basis and practical value for designing and planning landscape architecture.

Download Full-text

Research on Task-Offloading Decision Mechanism in Mobile Edge Computing based Internet of Vehicle

10.21203/rs.3.rs-225233/v1 ◽

2021 ◽

Author(s):

Jun Cheng ◽

Dejun Guan

Keyword(s):

Particle Swarm Optimization ◽

Particle Swarm ◽

Edge Computing ◽

Computation Offloading ◽

Mobile Edge Computing ◽

Cloud Infrastructure ◽

Swarm Optimization ◽

Decision Mechanism ◽

The Impact ◽

Task Offloading

Abstract As a technology integrated with Internet of Things (IoT), mobile edge computing (MEC) can provide real-time and low latency services to the underlying network, and improve the storage and computation ability of the networks instead of central cloud infrastructure. In Mobile Edge Computing based Internet of Vehicle(MEC-IoV), the vehicle users can deliver their tasks to the associated MEC servers Based on offloading policy, which improves the resource utilization and computation performance greatly. However, how to evaluate the impact of uncertain interconnection between the vehicle users and MEC servers on offloading decision-making and avoid serious degradation of the offloading efficiency are important problems to be solved. In this paper, a task-offloading decision mechanism with particle swarm optimization for IoV-based edge computing is proposed. First, a mathematical model to calculate the computation offloading cost for cloud-edge computing system is defined. Then, the particle swarm optimization (PSO) is applied to convert the offloading of task into the process and obtain the optimal offloading strategy. Furthermore, to avoid falling into local optimization, the inertia weight factor is designed to change adaptively with the value of the objective function. The experimental results show that the proposed offloading strategy can effectively reduce the energy consumption of terminal devices while guarantee the service quality of users.

Download Full-text

Interpretation of Contemporary Landscape Architecture Design in System View of Nature

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.243-249.6818 ◽

2011 ◽

Vol 243-249 ◽

pp. 6818-6821

Author(s):

Liu Cui ◽

Jin Zhang

Keyword(s):

Dynamic Process ◽

Landscape Architecture ◽

Landscape Design ◽

Transmission System ◽

Architecture Design ◽

System View ◽

Basic Model ◽

Global Nature ◽

The One

Systematic is a global nature cognition theory which is defined as a basic model of things existence. Contemporary landscape architecture is now confronting a designery view transmission. System view of nature landscape asks for a dynamic way to treat landscape site by organizing the natural elements. Therefore landscape design is no longer the one of scenery prospect, but a benign “intervention” during site natural developing process and it is a dynamic process. This article aims to explain how the view of nature influences the landscape architecture and what the landscape design do with the systematic natural cognition.

Download Full-text

Research on task offloading based on deep reinforcement learning in mobile edge environment

MATEC Web of Conferences ◽

10.1051/matecconf/202030903026 ◽

2020 ◽

Vol 309 ◽

pp. 03026

Author(s):

Xia Gao ◽

Fangqin Xu

Keyword(s):

Reinforcement Learning ◽

Learning Algorithm ◽

Edge Computing ◽

Internet Technology ◽

Good Effect ◽

Mobile Edge Computing ◽

Data Set ◽

The Impact ◽

Task Offloading ◽

Reinforcement Learning Algorithm

With the rapid development of Internet technology and mobile terminals, users’ demand for high-speed networks is increasing. Mobile edge computing proposes a distributed caching approach to deal with the impact of massive data traffic on communication networks, in order to reduce network latency and improve user service quality. In this paper, a deep reinforcement learning algorithm is proposed to solve the task unloading problem of multi-service nodes. The simulation platform iFogSim and data set Google Cluster Trace are used to carry out experiments. The final results show that the task offloading strategy based on DDQN algorithm has a good effect on energy consumption and cost, it has verified the application prospect of deep reinforcement learning algorithm in mobile edge computing.

Download Full-text

A Robust Optimization Based Energy-Aware Virtual Network Function Placement Proposal for Small Cell 5G Networks with Mobile Edge Computing Capabilities

Mobile Information Systems ◽

10.1155/2017/2603410 ◽

2017 ◽

Vol 2017 ◽

pp. 1-14 ◽

Cited By ~ 4

Author(s):

Bego Blanco ◽

Ianire Taboada ◽

Jose Oscar Fajardo ◽

Fidel Liberal

Keyword(s):

Power Consumption ◽

Robust Optimization ◽

Edge Computing ◽

Virtual Network ◽

Small Cells ◽

Mobile Edge Computing ◽

Energy Aware ◽

Network Function ◽

The Impact ◽

Virtual Network Function

In the context of cloud-enabled 5G radio access networks with network function virtualization capabilities, we focus on the virtual network function placement problem for a multitenant cluster of small cells that provide mobile edge computing services. Under an emerging distributed network architecture and hardware infrastructure, we employ cloud-enabled small cells that integrate microservers for virtualization execution, equipped with additional hardware appliances. We develop an energy-aware placement solution using a robust optimization approach based on service demand uncertainty in order to minimize the power consumption in the system constrained by network service latency requirements and infrastructure terms. Then, we discuss the results of the proposed placement mechanism in 5G scenarios that combine several service flavours and robust protection values. Once the impact of the service flavour and robust protection on the global power consumption of the system is analyzed, numerical results indicate that our proposal succeeds in efficiently placing the virtual network functions that compose the network services in the available hardware infrastructure while fulfilling service constraints.

Download Full-text

Enhancing Mobile Edge Computing with Efficient Load Balancing Using Load Estimation in Ultra-Dense Network

Sensors ◽

10.3390/s21093135 ◽

2021 ◽

Vol 21 (9) ◽

pp. 3135

Author(s):

Wen Chen ◽

Yongqi Zhu ◽

Jiawei Liu ◽

Yuhu Chen

Keyword(s):

Load Balancing ◽

Mobile Devices ◽

Edge Computing ◽

Load Estimation ◽

Mobile Edge Computing ◽

Dense Network ◽

Ping Pong ◽

Load Balancing Algorithm ◽

The Impact ◽

Ultra Dense Network

With the exponential growth of mobile devices and the emergence of computationally intensive and delay-sensitive tasks, the enormous demand for data and computing resources has become a big challenge. Fortunately, the combination of mobile edge computing (MEC) and ultra-dense network (UDN) is considered to be an effective way to solve these challenges. Due to the highly dynamic mobility of mobile devices and the randomness of the work requests, the load imbalance between MEC servers will affect the performance of the entire network. In this paper, the software defined network (SDN) is applied to the task allocation in the MEC scenario of UDN, which is based on routing of corresponding information between MEC servers. Secondly, a new load balancing algorithm based on load estimation by user load prediction is proposed to solve the NP-hard problem in task offloading. Furthermore, a genetic algorithm (GA) is used to prove the effectiveness and rapidity of the algorithm. At present, if the load balancing algorithm only depends on the actual load of each MEC, it usually leads to ping-pong effect. It is worth mentioning that our method can effectively reduce the impact of ping-pong effect. In addition, this paper also discusses the subtask offloading problem of divisible tasks and the corresponding solutions. At last, simulation results demonstrate the efficiency of our method in balancing load among MEC servers and its ability to optimize systematic stability.

Download Full-text

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

Wireless Communications and Mobile Computing ◽

10.1155/2022/1368841 ◽

2022 ◽

Vol 2022 ◽

pp. 1-11

Author(s):

Min Zhu

Keyword(s):

Big Data ◽

Energy Consumption ◽

Education System ◽

Edge Computing ◽

Performance Ratio ◽

Mobile Edge Computing ◽

Network Education ◽

Model Based ◽

The Impact ◽

A Performance

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.

Download Full-text

Discussion on Teaching Reform of “Traditional Landscape Architecture Design” Course in Applied Universities

E3S Web of Conferences ◽

10.1051/e3sconf/20197901012 ◽

2019 ◽

Vol 79 ◽

pp. 01012

Author(s):

Zhiwei Zhang ◽

Yi Que ◽

Haiyan Tang ◽

Zhiwei Zhang

Keyword(s):

Landscape Architecture ◽

Teaching Method ◽

Design Principles ◽

Teaching Quality ◽

Landscape Design ◽

Architecture Design ◽

Garden Design ◽

Teaching Reform ◽

Practical Teaching

Traditional garden design is considered as the main course of landscape architecture. Through the teaching reform of this course, students can learn the design principles and methods of landscape design and combine classroom theoretical teaching with practical teaching. In view of the characteristics of application-oriented universities, this paper improves the teaching quality of traditional landscape architecture design course through the discussion-based teaching method.

Download Full-text

Research on task-offloading decision mechanism in mobile edge computing-based Internet of Vehicle

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-021-01984-6 ◽

2021 ◽

Vol 2021 (1) ◽

Author(s):

Jun Cheng ◽

Dejun Guan

Keyword(s):

Particle Swarm Optimization ◽

Particle Swarm ◽

Edge Computing ◽

Computation Offloading ◽

Mobile Edge Computing ◽

Cloud Infrastructure ◽

Swarm Optimization ◽

Decision Mechanism ◽

The Impact ◽

Task Offloading

AbstractAs a technology integrated with Internet of things, mobile edge computing (MEC) can provide real-time and low-latency services to the underlying network and improve the storage and computation ability of the networks instead of central cloud infrastructure. In mobile edge computing-based Internet of Vehicle (MEC-IoV), the vehicle users can deliver their tasks to the associated MEC servers based on offloading policy, which improves the resource utilization and computation performance greatly. However, how to evaluate the impact of uncertain interconnection between the vehicle users and MEC servers on offloading decision-making and avoid serious degradation of the offloading efficiency are important problems to be solved. In this paper, a task-offloading decision mechanism with particle swarm optimization for MEC-IoV is proposed. First, a mathematical model to calculate the computation offloading cost for cloud-edge computing system is defined. Then, the particle swarm optimization is applied to convert the offloading of task into the process and obtain the optimal offloading strategy. Furthermore, to avoid falling into local optimization, the inertia weight factor is designed to change adaptively with the value of the objective function. The experimental results show that the proposed offloading strategy can effectively reduce the energy consumption of terminal devices while guarantee the service quality of users.

Download Full-text