scholarly journals Parallel genetic approach for routing optimization in large ad hoc networks

2022 ◽  
Vol 12 (1) ◽  
pp. 748
Author(s):  
Hala Khankhour ◽  
Otman Abdoun ◽  
Jâafar Abouchabaka
Keyword(s):  
Shortest Path ◽  
Ad Hoc Network ◽  
Ad Hoc ◽  
Processing Unit ◽  
Parallel Methods ◽  
Shortest Path Routing ◽  
Central Processing ◽  
Routing Optimization ◽  
The Difference ◽  
Hoc Networks

<span>This article presents a new approach of integrating parallelism into the genetic algorithm (GA), to solve the problem of routing in a large ad hoc network, the goal is to find the shortest path routing. Firstly, we fix the source and destination, and we use the variable-length chromosomes (routes) and their genes (nodes), in our work we have answered the following question: what is the better solution to find the shortest path: the sequential or parallel method?. All modern systems support simultaneous processes and threads, processes are instances of programs that generally run independently, for example, if you start a program, the operating system spawns a new process that runs parallel elements to other programs, within these processes, we can use threads to execute code simultaneously. Therefore, we can make the most of the available central processing unit (CPU) cores. Furthermore, the obtained results showed that our algorithm gives a much better quality of solutions. Thereafter, we propose an example of a network with 40 nodes, to study the difference between the sequential and parallel methods, then we increased the number of sensors to 100 nodes, to solve the problem of the shortest path in a large ad hoc network.</span>

Analysis of Reinforcement Based Adaptive Routing in MANET

2016 ◽  
Vol 2 (3) ◽  
pp. 648 ◽  
Author(s):  
Rahul Desai ◽  
B P Patil
Keyword(s):  
Reinforcement Learning ◽  
Shortest Path ◽  
Ad Hoc Network ◽  
Ad Hoc ◽  
Learning Algorithms ◽  
High Mobility ◽  
Delivery Ratio ◽  
Shortest Path Routing ◽  
Minimum Number ◽  
Shortest Path Algorithms

<p class="Abstract">This paper describes and evaluates the performance of various reinforcement learning algorithms with shortest path algorithms that are widely used for routing packets through the network. Shortest path routing is the simplest policy used for routing the packets along the path having minimum number of hops. In high traffic or high mobility conditions, the shortest path get flooded with huge number of packets and congestions occurs, So such shortest path does not provides the shortest path and increases delay for reaching the packets to the destination. Reinforcement learning algorithms are adaptive algorithms where the path is selected based on the traffic present on the network at real time. Thus they guarantee the least delivery time to reach the packets to the destination. Analysis done on a 6 by 6 irregular grid and sample ad hoc network shows that performance parameters used for judging the network - packet delivery ratio and delay provides optimum results using reinforcement learning algorithms. </p>

Adaptive Routing for an Ad Hoc Network Based on Reinforcement Learning

2015 ◽  
Vol 11 (2) ◽  
pp. 40-52
Author(s):  
Rahul Desai ◽  
B.P. Patil
Keyword(s):  
Reinforcement Learning ◽  
Shortest Path ◽  
Ad Hoc Network ◽  
Ad Hoc ◽  
Learning Algorithms ◽  
High Mobility ◽  
Delivery Ratio ◽  
Shortest Path Routing ◽  
Minimum Number ◽  
Shortest Path Algorithms

This paper describes and evaluates the performance of various reinforcement learning algorithms with shortest path algorithms that are widely used for routing packets throughout the network. Shortest path routing is simplest policy used for routing the packets along the path having minimum number of hops. In high traffic or high mobility conditions, the shortest path gets flooded with huge number of packets and congestions occurs, so such shortest path does not provide the shortest path and increases delay for reaching the packets to the destination. Reinforcement learning algorithms are adaptive algorithms where the path is selected based on the traffic present on the network at real time. Thus they guarantee the least delivery time to reach the packets to the destination. Analysis is done on a 6-by-6 irregular grid and sample ad hoc network shows that performance parameters used for judging the network such as packet delivery ratio and delay provide optimum results using reinforcement learning algorithms.

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