Sleep-based topology control in the Ad Hoc networks by using fitness aware learning automata

Authors:
Mina Shirali;Nasrin Shirali;Mohamad Reza Meybodi
Affiliations:
Islamic Azad University, Zarandieh Branch, Department of Computer, Markazi, Iran;Islamic Azad University, Tehran North Branch, Department of Computer, Tehran, Iran;Department of Computer, Amirkabir University of Technology, Tehran, Iran
Venue:
Computers & Mathematics with Applications
Year:
2012

Citing 21
Cited 0

Learning automata: an introduction

Learning automata: an introduction
The K-Neigh Protocol for Symmetric Topology Control in Ad Hoc Networks

Proceedings of the 4th ACM international symposium on Mobile ad hoc networking & computing
The Node Distribution of the Random Waypoint Mobility Model for Wireless Ad Hoc Networks

IEEE Transactions on Mobile Computing
A Nash game algorithm for SIR-based power control in 3G wireless CDMA networks

IEEE/ACM Transactions on Networking (TON)
Energy efficient distributed connected dominating sets construction in wireless sensor networks

Proceedings of the 2006 international conference on Wireless communications and mobile computing
Extended Dominating Set and Its Applications in Ad Hoc Networks Using Cooperative Communication

IEEE Transactions on Parallel and Distributed Systems
TAP: Traffic-aware topology control in on-demand ad hoc networks

Computer Communications
Energy-aware topology control for wireless sensor networks using memetic algorithms

Computer Communications
Mobility-aware topology control in mobile ad hoc networks

Computer Communications
Stochastic learning solution for distributed discrete power control game in wireless data networks

IEEE/ACM Transactions on Networking (TON)
Multi-objective optimization for coverage control in wireless sensor network with adjustable sensing radius

Computers & Mathematics with Applications
Pareto-efficient and goal-driven power control in wireless networks: a game-theoretic approach with a novel pricing scheme

IEEE/ACM Transactions on Networking (TON)
Effective page recommendation algorithms based on distributed learning automata and weighted association rules

Expert Systems with Applications: An International Journal
Data aggregation in sensor networks using learning automata

Wireless Networks
Mobility-based multicast routing algorithm for wireless mobile Ad-hoc networks: A learning automata approach

Computer Communications
An intelligent backbone formation algorithm for wireless ad hoc networks based on distributed learning automata

Computer Networks: The International Journal of Computer and Telecommunications Networking
An efficient cluster-based CDMA/TDMA scheme for wireless mobile ad-hoc networks: A learning automata approach

Journal of Network and Computer Applications
Poly: A reliable and energy efficient topology control protocol for wireless sensor networks

Computer Communications
Design and analysis of an MST-based topology control algorithm

IEEE Transactions on Wireless Communications
On cognitive radio networks with opportunistic power control strategies in fading channels

IEEE Transactions on Wireless Communications
The capacity of wireless networks

IEEE Transactions on Information Theory

Quantified Score

Hi-index	0.09

Visualization

Abstract

Learning automata have been found to be useful in the systems with incomplete knowledge. Therefore, it can be used as a tool to solve problems of Ad Hoc networks, where nodes are mobile and operate within a dynamic environment, which entails possibly unknown and time varying characteristics. In this paper, after a short review on the related works, learning automata and CEC algorithm, which is a sleep based topology control algorithm, a modified version (called MCEC) is proposed. In addition, a probabilistic algorithm is recommended to make decision about whether or not a node has to sleep. Furthermore, a distributed algorithm is recommended; in order to improve the proposed probabilistic algorithm, using learning automata. Finally, nominated algorithms have been simulated in both of the stationary and non-stationary networks. In conclusion, as the simulation results show, the proposed algorithms outperform corresponding topology control algorithms and reveal the effectiveness of using learning automata.