A new routing method based on ant colony algorithm for LEO satellite communication networks

2007 ◽  
Author(s):  
Ying Wang ◽  
Xiulin Hu
Keyword(s):  
Communication Networks ◽  
Ant Colony Algorithm ◽  
Satellite Communication ◽  
Ant Colony ◽  
Routing Method ◽  
Leo Satellite

scholarly journals LEO satellite communication networks?a routing approach

2003 ◽  
Vol 3 (3) ◽  
pp. 385-395 ◽  
Author(s):  
S. A. M. Makki ◽  
Niki Pissinou ◽  
Philippe Daroux
Keyword(s):  
Communication Networks ◽  
Satellite Communication ◽  
Leo Satellite

scholarly journals A Novel Algorithm to Optimize the Energy Consumption Using IoT and Based on Ant Colony Algorithm

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1709
Author(s):  
Baohui Shi ◽  
Yuexia Zhang
Keyword(s):  
Energy Consumption ◽  
Communication Networks ◽  
Ant Colony Algorithm ◽  
Cluster Head ◽  
Residual Energy ◽  
Initial Energy ◽  
Base Station ◽  
Ant Colony ◽  
Destination Node ◽  
Information And Communication

Internet of Things (IoT) is a new concept in the information and communication technology studies which indicates that any creature (human, animal, or object) can send and receive data through communication networks, such as the internet or intranet platform. Wireless sensors have limited energy resources due to the use of batteries to supply energy, and since it is usually not possible to replace the batteries of these sensors. In addition, the lifespan of the wireless sensor network is limited and short. Therefore, reducing the energy consumption of sensors in IoT networks for increasing network lifespan is one of the fundamental challenges and issues in these networks. In this paper, a routing protocol is proposed and simulated based on an ant colony optimization algorithm’s performance. The clustering is performed with a routing method based on energy level criteria, collision reduction, distance from the cluster-head to the destination, and neighborhood energy in the proposed method. The cluster head is selected based on the maximum residual energy, minimum distance with other clusters, and consumed energy. This energy is minimized to reach the base station. The node with more energy than the threshold is selected as the new cluster head. Then, four conditions are applied for routing: the shortest path, the leading path, the shortest distance to the source node and the destination node, and routing. Results show that after about 50 cycles of transferring information, only the average of 19.4% of the initial energy is consumed in the network nodes. Therefore, obtained results illustrate that the proposed method helps to retain the energy more than 40% comparing the available methods.

scholarly journals Simulation of Sports Venue Based on Ant Colony Algorithm and Artificial Intelligence

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Rui Zhang ◽  
Weibo Sun ◽  
Sang-Bing Tsai
Keyword(s):  
Partial Order ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Order Relation ◽  
Ant Colony ◽  
Route Optimization ◽  
Sports Facilities ◽  
Routing Method ◽  
Evacuation Plan ◽  
Evacuation Route

In order to improve the congestion of the evacuation plan and further improve the evacuation efficiency, this paper proposes the priority Pareto partial order relation and the vector pheromone routing method based on the priority Pareto partial order relation. Numerical experiments show that compared with the hierarchical multiobjective evacuation path optimization algorithm based on the hierarchical network, the fragmented multiobjective evacuation path optimization algorithm proposed in this paper effectively improves the evacuation efficiency of the evacuation plan and the convergence of the noninferior plan set. However, the congestion condition of the noninferior evacuation plan obtained by the fragmented multiobjective evacuation route optimization algorithm is worse than the congestion condition of the noninferior evacuation plan obtained by the hierarchical multiobjective evacuation route optimization algorithm. The multiple factors that affect the routing process considered in the probability transfer function used in the traditional ant colony algorithm routing method must be independent of each other. However, in actual route selection, multiple factors that affect route selection are not necessarily independent of each other. In order to fully consider the various factors that affect the routing, this paper adopts the vector pheromone routing method based on the traditional Pareto partial order relationship instead of the traditional ant colony algorithm. The model mainly improves the original pheromone distribution and volatilization coefficient of the ant colony, speeds up the convergence speed and accuracy of the algorithm, and obtains ideal candidate solutions. The method is applied to the location of sports facilities and has achieved good results. The experimental results show that the improved ant colony algorithm model designed in this paper is suitable for solving the problem of urban sports facilities location in large-scale space.

A TWO-STATE ANT COLONY ALGORITHM FOR SOLVING THE MINIMUM GRAPH BISECTION PROBLEM

2009 ◽  
Vol 08 (04) ◽  
pp. 487-498 ◽  
Author(s):  
RONG-LONG WANG ◽  
KOZO OKAZAKI
Keyword(s):  
Genetic Algorithm ◽  
Approximation Algorithm ◽  
Communication Networks ◽  
Ant Colony Algorithm ◽  
Printed Circuit Board ◽  
Ant Colony ◽  
Circuit Board ◽  
Printed Circuit ◽  
Graph Bisection ◽  
Np Complete

The graph bisection problem is an important problem in printed circuit board layout and communication networks. Since it is known to be NP-complete, approximation algorithm have been considered. In this paper, we propose a so-called two-state ant colony algorithm for efficiently solving the problem. In the proposed algorithm two kinds of pheromone and two kinds of heuristic information are introduced to reinforce the search ability. The proposed algorithm is tested on a large number of instances and is compared with a heuristic algorithm and a genetic algorithm. The experimental results show that the proposed approach is superior to its competitors.

scholarly journals Dynamic Network Formation for FSO Satellite Communication

2022 ◽  
Vol 12 (2) ◽  
pp. 738
Author(s):  
Revital Marbel ◽  
Roi Yozevitch ◽  
Tal Grinshpoun ◽  
Boaz Ben-Moshe
Keyword(s):  
Spanning Tree ◽  
Ant Colony Algorithm ◽  
Satellite Communication ◽  
Network Formation ◽  
Ant Colony ◽  
Acquisition Time ◽  
Large Set ◽  
Bounded Degree ◽  
Classical Communication ◽  
Leo Satellites

Satellite network optimization is essential, particularly since the cost of manufacturing, launching and maintaining each satellite is significant. Moreover, classical communication optimization methods, such as Minimal Spanning Tree, cannot be applied directly in dynamic scenarios where the satellite constellation is constantly changing. Motivated by the rapid growth of the Star-Link constellation that, as of Q4 2021, consists of over 1600 operational LEO satellites with thousands more expected in the coming years, this paper focuses on the problem of constructing an optimal inter-satellite (laser) communication network. More formally, given a large set of LEO satellites, each equipped with a fixed number of laser links, we direct each laser module on each satellite such that the underlying laser network will be optimal with respect to a given objective function and communication demand. In this work, we present a novel heuristic to create an optimal dynamic optical network communication using an Ant Colony algorithm. This method takes into account both the time it takes to establish an optical link (acquisition time) and the bounded number of communication links, as each satellite has a fixed amount of optical communication modules installed. Based on a large number of simulations, we conclude that, although the underlying problem of bounded-degree-spanning-tree is NP-hard (even for static cases), the suggested ant-colony heuristic is able to compute cost-efficient solutions in semi-real-time.

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