Robust optimization based on ant colony optimization in the data transmission path selection of WSNs

2021 ◽  
Author(s):  
Zhaohui Zhang ◽  
Jing Li ◽  
Nan Xu
Keyword(s):  
Robust Optimization ◽  
Ant Colony Optimization ◽  
Data Transmission ◽  
Path Selection ◽  
Ant Colony ◽  
Transmission Path ◽  
Selection Of

scholarly journals A data transmission approach based on ant colony optimization and threshold proxy re-encryption in WSNs

2021 ◽  
Author(s):  
Jing Liu ◽  
Chenyu Sun ◽  
Yingxu Lai
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Ant Colony Optimization ◽  
Data Transmission ◽  
Data Encryption ◽  
Path Selection ◽  
Ant Colony ◽  
Communication Process ◽  
Wireless Sensor ◽  
Data Path

Wireless sensor networks have become increasingly popular due to the rapid growth of the Internet of Things. As open wireless transmission mediums are easy to attack, security is one of the primary design concerns for wireless sensor networks. Current solutions consider routing and data encryption as two isolated issues, providing incomplete security. Therefore, in this paper we divide the WSN communication process into a data path selection phase and a data encryption phase. By proposing an improved transmission method based on ant colony optimization and threshold proxy re-encryption for wireless sensor networks,named as ACOTPRE, it resists internal and external attacks and ensures safe and efficient data transmission. In the data path selection stage, the ant colony optimization algorithm is used for network routing. The improvement of the pheromone concentration is proposed. In order to resist attacks from external attackers, proxy re-encryption is extended to WSN in the data encryption stage. The threshold secret sharing algorithm is introduced to generate a set of re-encryption key fragments composed of random numbers at the source node. We confirm the performance of our model via simulation studies.

Implementasi Algoritma MAX-MIN Ant System pada Penjadwalan Mata Kuliah

2013 ◽  
Vol 5 (2) ◽  
pp. 48-53
Author(s):  
William Aprilius ◽  
Lorentzo Augustino ◽  
Ong Yeremia M. H.
Keyword(s):  
Ant Colony Optimization ◽  
Ant Colony ◽  
Ant System ◽  
University Course Timetabling ◽  
Alternative Approach ◽  
Ant System Algorithm ◽  
Index Terms ◽  
Course Timetabling Problem ◽  
University Course ◽  
Selection Of

University Course Timetabling Problem is a problem faced by every university, one of which is Universitas Multimedia Nusantara. Timetabling process is done by allocating time and space so that the whole associated class and course can be implemented. In this paper, the problem will be solved by using MAX-MIN Ant System Algorithm. This algorithm is an alternative approach to ant colony optimization. This algorithm uses two tables of pheromones as stigmergy, i.e. timeslot pheromone table and room pheromone table. In addition, the selection of timeslot and room is done by using the standard deviation of the value of pheromones. Testing is carried out by using 105 events, 45 timeslots, and 3 types of categories based on the number of rooms provided, i.e. large, medium, and small. In each category, testing is performed 5 times and for each testing, the data recorded is the unplace and Soft Constraint Penalty. In general, the greater the number of rooms, the smaller the unplace. Index Terms—ant colony optimization, max-min ant system, timetabling

scholarly journals An Energy Aware Multipath Routing Algorithm for Wireless Sensor Networks

2017 ◽  
Vol 13 (04) ◽  
pp. 45 ◽  
Author(s):  
Liping LV
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Sensor Network ◽  
Data Transmission ◽  
Routing Algorithm ◽  
Path Selection ◽  
Low Energy ◽  
Wireless Sensor ◽  
Energy Aware ◽  
Transmission Path

<p class="0abstract"><span lang="EN-US">Wireless sensor network is a new field of computer science and technology research. It has a very broad application prospects. In order to improve the network survival time, it is very important to design efficient energy-constrained routing protocols. In this paper, we studied the characteristics of wireless sensor networks, and analyzed the design criteria of sensor network routing algorithms. In view of the shortcomings of traditional algorithms, we proposed an energy-aware multi-path algorithm. When selecting a data transmission path, the energy-aware multi-path algorithm can avoid nodes with low energy levels. At the same time, it takes the remaining energy of the node and the number of hops as one of the measures of the path selection. The multi-path routing algorithm realized the low energy consumption of the data transmission path, thus effectively prolonging the network lifetime. Compared with the traditional algorithm, the results show that our method has high reliability and energy efficiency.</span></p>

Optimal Selection of Parameters for Nonuniform Embedding of Chaotic Time Series Using Ant Colony Optimization

2013 ◽  
Vol 43 (2) ◽  
pp. 790-802 ◽  
Author(s):  
Meie Shen ◽  
Wei-Neng Chen ◽  
Jun Zhang ◽  
Henry Shu-Hung Chung ◽  
O. Kaynak
Keyword(s):  
Time Series ◽  
Ant Colony Optimization ◽  
Chaotic Time Series ◽  
Ant Colony ◽  
Optimal Selection ◽  
Selection Of

scholarly journals Self-Adjusting Ant Colony Optimization Based on Information Entropy for Detecting Epistatic Interactions

Genes ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 114 ◽  
Author(s):  
Boxin Guan ◽  
Yuhai Zhao
Keyword(s):  
Ant Colony Optimization ◽  
Information Entropy ◽  
Large Scale ◽  
Association Studies ◽  
Detection Algorithm ◽  
Path Selection ◽  
Ant Colony ◽  
Selection Strategy ◽  
Nucleotide Polymorphisms ◽  
Epistatic Interactions

The epistatic interactions of single nucleotide polymorphisms (SNPs) are considered to be an important factor in determining the susceptibility of individuals to complex diseases. Although many methods have been proposed to detect such interactions, the development of detection algorithm is still ongoing due to the computational burden in large-scale association studies. In this paper, to deal with the intensive computing problem of detecting epistatic interactions in large-scale datasets, a self-adjusting ant colony optimization based on information entropy (IEACO) is proposed. The algorithm can automatically self-adjust the path selection strategy according to the real-time information entropy. The performance of IEACO is compared with that of ant colony optimization (ACO), AntEpiSeeker, AntMiner, and epiACO on a set of simulated datasets and a real genome-wide dataset. The results of extensive experiments show that the proposed method is superior to the other methods.

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