Multi-feature fusion energy-saving routing in internet of things based on hybrid ant colony algorithm

2020 ◽  
Vol 11 (2/3) ◽  
pp. 103
Author(s):  
Xiao Li Ren ◽  
Jian Wei Yang ◽  
Nai Qian Li
Keyword(s):  
Internet Of Things ◽  
Energy Saving ◽  
Ant Colony Algorithm ◽  
Feature Fusion ◽  
Fusion Energy ◽  
Ant Colony

scholarly journals Energy-Saving Virtual Machine Placement Method for User Experience in Cloud Environment

2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Shanchen Pang ◽  
Kexiang Xu ◽  
Shudong Wang ◽  
Min Wang ◽  
Shuyu Wang
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Virtual Machine ◽  
User Experience ◽  
Ant Colony Algorithm ◽  
Data Centers ◽  
Virtual Machine Placement ◽  
Ant Colony ◽  
Local Optimum ◽  
Placement Method

Green computing focuses on the energy consumption to minimize costs and adverse environmental impacts in data centers. Improving the utilization of host computers is one of the main green cloud computing strategies to reduce energy consumption, but the high utilization of the host CPU can affect user experience, reduce the quality of service, and even lead to service-level agreement (SLA) violations. In addition, the ant colony algorithm performs well in finding suitable computing resources in unknown networks. In this paper, an energy-saving virtual machine placement method (UE-ACO) is proposed based on the improved ant colony algorithm to reduce the energy consumption and satisfy users’ experience, which achieves the balance between energy consumption and user experience in data centers. We improve the pheromone and heuristic factors of the traditional ant colony algorithm, which can guarantee that the improved algorithm can jump out of the local optimum and enter the global optimal, avoiding the premature maturity of the algorithm. Experimental results show that compared to the traditional ant colony algorithm, min-min algorithm, and round-robin algorithm, the proposed algorithm UE-ACO can save up to 20%, 24%, and 30% of energy consumption while satisfying user experience.

Improved Ant Colony Algorithm Using in Design of the New Internet of Things Storage Mailbox System

2013 ◽  
Vol 753-755 ◽  
pp. 2845-2848
Author(s):  
Ke Wang Huang
Keyword(s):  
Internet Of Things ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
The Internet ◽  
User Selection ◽  
The Internet Of Things ◽  
Improved Ant Colony Algorithm ◽  
Selection Of

The paper focuses on improved ant colony algorithm using in design of the internet of things storage and mailbox system. The improved ant colony algorithm solves the problems of the location of the storage mailbox mounting and user selection of optimal delivery.

Intelligent traffic cloud computing system based on ant colony algorithm

2020 ◽  
Vol 39 (4) ◽  
pp. 4947-4958
Author(s):  
Xiaobo Guo ◽  
Yongping Liu
Keyword(s):  
Cloud Computing ◽  
Information Search ◽  
Distribution System ◽  
Ant Colony Algorithm ◽  
Planning Process ◽  
Feature Fusion ◽  
Intelligent Vehicles ◽  
Ant Colony ◽  
Steering Wheel ◽  
Cloud Computing System

With the growth of data volume in transportation system, requirements of big data technologies are rapidly increasing. This paper presented an improved ant colony algorithm by using data analysis technologies of cloud computing and data mining. And the influence of different spatio-temporal feature fusion methods on the steering wheel angle value of intelligent vehicles is explored by feature fusion method. Furthermore, time-constrained and space-constrained networks are utilized to extract the key features that affect the steering wheel angle value. Experimental results show that the proposed algorithm improves the efficiency of data processing and information search by 35%, comparing to traditional ant colony algorithm. It is very effective in the shortest path analysis of ITS. Our research shows that the application of real-time information in the logistics distribution system can make the planning process more dynamic and the prediction results closer to reality.

scholarly journals Cross-Modal Travel Route Recommendation Algorithm Based on Internet of Things Awareness

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yuran Zhang ◽  
Ziyan Tang
Keyword(s):  
Genetic Algorithm ◽  
Internet Of Things ◽  
Ant Colony Algorithm ◽  
Daily Life ◽  
Ant Colony ◽  
The Internet ◽  
Evaluation Score ◽  
Route Recommendation ◽  
The Internet Of Things ◽  
Greedy Solution

In recent years, the Internet of Things has developed rapidly in people’s lives. This brand-new technology is flooding people’s lives and widely used in many fields, such as medical field, science and technology field, and industry and agriculture field. As a modern technology, the Internet of Things has many characteristics of low power consumption and multifunction, and it also has the characteristics of data-aware computing. This is the characteristic of this new product. In people’s daily life, the Internet of Things is also closely related to people’s daily life. In the tourism industry, the Internet of Things can make the best use of everything and give full play to its various advantages as much as possible. The Internet of Things can perceive cross-modal tourism routes. So here, this paper summarizes various algorithms recommended by the Internet of Things for this tourist route and works out the experimental data methods of these algorithms for cross-modal tourism route recommendation. The proposed algorithm is verified by data simulation, compared with related algorithms. We analyze and summarize the simulation results. At present, there is no comparative analysis of the performance of ant colony algorithm, genetic algorithm, and its optimization algorithm in tourism route recommendation. On the basis of crawling the tourism data in the Internet, this paper applies ant colony algorithm, genetic algorithm, max–min optimization ant colony algorithm, and hybrid ant colony algorithm based on greedy solution to tourism route recommendation and evaluates and compares the algorithms from three aspects: average evaluation score, optimal evaluation score, and algorithm time. Experimental results show that the max–min optimization ant colony algorithm and the hybrid ant colony algorithm based on greedy solution can be effectively applied to automated tourist route recommendation.

Double ant colony algorithm based on dynamic feedback for energy-saving route planning for ships

2021 ◽  
Vol 25 (7) ◽  
pp. 5021-5035
Author(s):  
Liangxiong Dong ◽  
Jun Li ◽  
Wei Xia ◽  
Qiang Yuan
Keyword(s):  
Energy Saving ◽  
Ant Colony Algorithm ◽  
Route Planning ◽  
Ant Colony ◽  
Dynamic Feedback
Sign in / Sign up

Export Citation Format

Share Document