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An Intelligent and Energy Efficient Area Coverage Protocol for Wireless Sensor Networks
"... One major problem in the area of wireless sensor networks is the coverage problem. The coverage problem deals with the ability of the network to cover a certain area or some certain events. In this paper, we focus on the area coverage problem. We propose SRAHS, a sensing radius adjusting protocol, t ..."
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One major problem in the area of wireless sensor networks is the coverage problem. The coverage problem deals with the ability of the network to cover a certain area or some certain events. In this paper, we focus on the area coverage problem. We propose SRAHS, a sensing radius adjusting protocol, to deal with the problem of area coverage. In this protocol, proper sensing radius can be determined using the harmony search algorithm. Due to the proposed protocol accuracy in adjusting the nodes sensing radius, it is able to provide the full coverage in less densities. Moreover, as the result of increasing nodes density, the proposed protocol decreases both the nodes sensing radius and the energy consumption. We have simulated our protocol and simulation results show high efficiency of the proposed protocol
A mobility-based cluster formation algorithm for wireless mobile ad hoc networks
- Journal of Cluster Computing
"... In the last decade, numerous efforts have been devoted to design efficient algorithms for clustering the wireless mobile ad‐hoc networks (MANET) considering the network mobility characteristics. However, in existing algorithms, it is assumed that the mobility parameters of the networks are fixed, wh ..."
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In the last decade, numerous efforts have been devoted to design efficient algorithms for clustering the wireless mobile ad‐hoc networks (MANET) considering the network mobility characteristics. However, in existing algorithms, it is assumed that the mobility parameters of the networks are fixed, while they are stochastic and vary with time indeed. Therefore, the proposed clustering algorithms do not scale well in realistic MANETs, where the mobility parameters of the hosts freely and randomly change at any time. Finding the optimal solution to the cluster formation problem is incredibly difficult, if we assume that the movement direction and mobility speed of the hosts are random variables. This becomes harder when the probability distribution function of these random variables is assumed to be unknown. In this paper, we propose a learning automata‐based weighted cluster formation algorithm called MCFA in which the mobility parameters of the hosts are assumed to be random variables with unknown distributions. In the proposed clustering algorithm, the expected relative mobility of each host with respect to all its neighbors is estimated by sampling its mobility parameters in various epochs. MCFA is a fully distributed algorithm in which each mobile independently chooses the neighboring host with the minimum expected relative mobility as its cluster‐head. This is done based solely on the local information each host receives from its neighbors and the hosts need not to be synchronized. The experimental results show the superiority of MCFA over the best existing mobility‐ based clustering algorithms in terms of the number of clusters, cluster lifetime, reaffiliation rate, and control message overhead.
Irregular Cellular Learning Automata and Its Application to Clustering in Sensor Networks
"... In the first part of this paper, we propose a generalization of cellular learning automata (CLA) called irregular cellular learning automata (ICLA) which removes the restriction of rectangular grid structure in traditional CLA. This generalization is expected because there are a number of applicatio ..."
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In the first part of this paper, we propose a generalization of cellular learning automata (CLA) called irregular cellular learning automata (ICLA) which removes the restriction of rectangular grid structure in traditional CLA. This generalization is expected because there are a number of applications which cannot be adequately modeled with rectangular grids. One category of such applications is in the area of wireless sensor networks. In these networks, nodes are usually scattered randomly throughout the environment, so no regular structure can be assumed for modeling their behavior. In the second part of the paper, based on the proposed model we design a clustering algorithm for sensor networks. Simulation results show that the proposed clustering algorithm is very efficient and outperforms similar existing methods.
Maximizing Lifetime of Target Coverage in Wireless Sensor Networks Using Learning Automata
"... 1 23Your article is protected by copyright and all rights are held exclusively by Springer Science +Business Media New York. This e-offprint is for personal use only and shall not be selfarchived in electronic repositories. If you wish to self-archive your work, please use the accepted author’s vers ..."
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1 23Your article is protected by copyright and all rights are held exclusively by Springer Science +Business Media New York. This e-offprint is for personal use only and shall not be selfarchived in electronic repositories. If you wish to self-archive your work, please use the accepted author’s version for posting to your own website or your institution’s repository. You may further deposit the accepted author’s version on a funder’s repository at a funder’s request, provided it is not made publicly available until 12 months after publication.
Learning Automata-Based Adaptive Petri Net and Its Application to Priority Assignment in Queuing Systems With Unknown Parameters
"... Abstract—In this paper, an adaptive Petri net (PN), capable of adaptation to environmental changes, is introduced by the fusion of learning automata and PN. In this new model, called learning automata-based adaptive PN (APN-LA), learning automata are used to resolve the conflicts among the transitio ..."
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Abstract—In this paper, an adaptive Petri net (PN), capable of adaptation to environmental changes, is introduced by the fusion of learning automata and PN. In this new model, called learning automata-based adaptive PN (APN-LA), learning automata are used to resolve the conflicts among the transitions. In the pro-posed APN-LA model, transitions are portioned into several sets of conflicting transitions and each set of conflicting transitions is equipped with a learning automaton which is responsible for controlling the conflicts among transitions in the correspond-ing transition set. We also generalize the proposed APN-LA to ASPN-LA which is a fusion between LA and stochastic PN (SPN). An application of the proposed ASPN-LA to priority assign-ment in queuing systems with unknown parameters is also presented. Index Terms—Adaptive Petri net (APN), conflict resolution, learning automata, Petri nets (PNs). I.
This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. IEEE TRANSACTIONS ON CYBERNETICS 1 Irregular Cellular Learning Automata
"... Abstract—Cellular learning automaton (CLA) is a recently introduced model that combines cellular automaton (CA) and learning automaton (LA). The basic idea of CLA is to use LA to adjust the state transition probability of stochastic CA. This model has been used to solve problems in areas such as cha ..."
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Abstract—Cellular learning automaton (CLA) is a recently introduced model that combines cellular automaton (CA) and learning automaton (LA). The basic idea of CLA is to use LA to adjust the state transition probability of stochastic CA. This model has been used to solve problems in areas such as channel assignment in cellular networks, call admission control, image processing, and very large scale integration placement. In this paper, an extension of CLA called irregular CLA (ICLA) is introduced. This extension is obtained by removing the structure regularity assumption in CLA. Irregularity in the structure of ICLA is needed in some applications, such as computer networks, web mining, and grid computing. The concept of expediency has been introduced for ICLA and then, conditions under which an ICLA becomes expedient are analytically found. Index Terms—Expediency, irregular cellular learning automata (ICLA), Markov process, steady-state analysis. I.
A Coverage Monitoring Algorithm based on Learning Automata for Wireless Sensor Networks
, 2015
"... Abstract: To cover a set of targets with known locations within an area with limited or prohibited ground access using a wireless sensor network, one approach is to deploy the sensors remotely, from an aircraft. In this approach, the lack of precise sensor placement is compensated by redundant de-pl ..."
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Abstract: To cover a set of targets with known locations within an area with limited or prohibited ground access using a wireless sensor network, one approach is to deploy the sensors remotely, from an aircraft. In this approach, the lack of precise sensor placement is compensated by redundant de-ployment of sensor nodes. This redundancy can also be used for extending the lifetime of the network, if a proper scheduling mechanism is available for scheduling the active and sleep times of sensor nodes in such a way that each node is in active mode only if it is required to. In this paper, we propose an efficient scheduling method based on learning automata and we called it LAML, in which each node is equipped with a learning automaton, which helps the node to select its proper state (active or sleep), at any given time. To study the performance of the proposed method, computer simulations are conducted. Results of these simulations show that the proposed scheduling method can better prolong the lifetime of the network in comparison to similar existing method.
Wireless Pers Commun (2014) 77:1923–1933 DOI 10.1007/s11277-014-1616-3 Learning Automata Based Face-Aware Mobicast
, 2014
"... © The Author(s) 2014. This article is published with open access at Springerlink.com Abstract Target tracking is one of the most popular applications of the wireless sensor networks. It can be accomplished using different approaches and algorithms, one of which is the spatiotemporal multicast protoc ..."
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© The Author(s) 2014. This article is published with open access at Springerlink.com Abstract Target tracking is one of the most popular applications of the wireless sensor networks. It can be accomplished using different approaches and algorithms, one of which is the spatiotemporal multicast protocol, called “mobicast”. In this protocol, it is assumed that the area around the moving target, called the delivery zone, is known at any given time during the operation of the network. The aim of the protocol is to awake sensor nodes, which will be within the delivery zone in the near future, to be prepared for tracking the approaching moving target. In this paper, we propose a novel mobicast algorithm, aiming at reducing the number of awakened sensor nodes. To this end, we equipped every sensor node with a learning automaton, which helps the node in determining the sensor nodes it must awaken. To evaluate the performance of the proposed algorithm, several experiments have been conducted. The results have shown that the proposed algorithm can significantly outperform other existing algorithms such as forward-zone constrained and FAR in terms of energy consumption, number of active nodes, number of exchanged packets and slack time.
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"... A mobility-based cluster formation algorithm for wireless mobile ad-hoc networks ..."
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A mobility-based cluster formation algorithm for wireless mobile ad-hoc networks
