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Strictly localized sensor selfdeployment for optimal focused coverage
 IEEE Transactions on Mobile Computing
"... Abstract—We consider sensor selfdeployment problem, constructing FOCUSED coverage (Fcoverage) around a Point of Interest (POI), with novel evaluation metric, coverage radius. We propose to deploy sensors in polygon layers over a locally computable equilateral triangle tessellation (TT) for optimal ..."
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Abstract—We consider sensor selfdeployment problem, constructing FOCUSED coverage (Fcoverage) around a Point of Interest (POI), with novel evaluation metric, coverage radius. We propose to deploy sensors in polygon layers over a locally computable equilateral triangle tessellation (TT) for optimal Fcoverage formation, and introduce two types of deployment polygon, Hpolygon and Cpolygon. We propose two strictly localized solution algorithms, Greedy Advance (GA), and GreedyRotationGreedy (GRG). The two algorithms drive sensors to move along the TT graph to surround POI. In GA, nodes greedily proceed as close to POI as they can; in GRG, when their greedy advance is blocked, nodes rotate around POI along locally computed H orCpolygon to a vertex where greedy advance can resume. We prove that they both yield a connected network with maximized holefree area coverage. To our knowledge, they are the first localized sensor selfdeployment algorithms that provide such coverage guarantee. We further analyze their coverage radius property. Our study shows that GRG guarantees optimal or near optimal coverage radius. Through extensive simulation we as well evaluate their performance on convergence time, energy consumption, and node collision. Index Terms—Coverage, selfdeployment, localized algorithms, mobile sensor networks. Ç 1
A Cellular Learning Automatabased Deployment Strategy for Mobile Wireless Sensor Networks
"... Abstract: One important problem which may arise in designing a deployment strategy for a wireless sensor network is how to deploy a specific number of sensor nodes throughout an unknown network area so that the covered section of the area is maximized. In a mobile sensor network, this problem can be ..."
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Cited by 5 (3 self)
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Abstract: One important problem which may arise in designing a deployment strategy for a wireless sensor network is how to deploy a specific number of sensor nodes throughout an unknown network area so that the covered section of the area is maximized. In a mobile sensor network, this problem can be addressed by first deploying sensor nodes randomly in some initial positions within the area of the network, and then letting sensor nodes to move around and find their best positions according to the positions of their neighboring nodes. The problem becomes more complicated if sensor nodes have no information about their positions or even their relative distances to each other. In this paper, we propose a cellular learning automatabased deployment strategy which guides the movements of sensor nodes within the area of the network without any sensor to know its position or its relative distance to other sensors. In the proposed algorithm, the learning automaton in each node in cooperation with the learning automata in the neighboring nodes controls the movements of the node in order to attain high coverage. Experimental results have shown that in noise free environments, the proposed algorithm can compete with the existing algorithms such as PF, DSSA, IDCA, and VEC in terms of network coverage. It has also been shown that in noisy environments, where utilized location estimation techniques such as GPSbased devices and localization algorithms experience inaccuracies in their measurements, or the movements of sensor nodes are not perfect and follow a probabilistic motion model, the proposed algorithm outperforms the existing algorithms in terms of network coverage.
Decreasing Impact of SLA Violations: A Proactive Resource Allocation Approach for Cloud Computing Environments
"... Abstract—User satisfaction as a significant antecedent to user loyalty has been highlighted by many researchers in market based literatures. SLA violation as an important factor can decrease users ’ satisfaction level. The amount of this decrease depends on user’s characteristics. Some of these char ..."
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Cited by 4 (4 self)
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Abstract—User satisfaction as a significant antecedent to user loyalty has been highlighted by many researchers in market based literatures. SLA violation as an important factor can decrease users ’ satisfaction level. The amount of this decrease depends on user’s characteristics. Some of these characteristics are related to QoS requirements and announced to service provider through SLAs. But some of them are unknown for service provider and selfish users are not interested to reveal them truly. Most the works in literature ignore considering such characteristics and treat users just based on SLA parameters. So, two users with different characteristics but similar SLAs have equal importance for the service provider. In this paper, we use two user’s hidden characteristics, named willingness to pay for service and willingness to pay for certainty, to present a new proactive resource allocation approach with aim of decreasing impact of SLA violations. New methods based on learning automaton for estimation of these characteristics are provided as well. To validate our approach we conducted some numerical simulations in critical situations. The results confirm that our approach has ability to improve users ’ satisfaction level that cause to gain in profitability. Index Terms—Users satisfaction level, cloud service, resource allocation, willingness to pay, learning automaton Ç 1
A mobilitybased 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.
1 Strictly Localized Sensor SelfDeployment for Optimal Focused Coverage
"... Abstract—We consider sensor selfdeployment problem, constructing FOCUSED coverage (Fcoverage) around a Point of Interest (POI), with novel evaluation metric, coverage radius. We propose to deploy sensors in polygon layers over a locallycomputable equilateral triangle tessellation (TT) for optimal ..."
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Abstract—We consider sensor selfdeployment problem, constructing FOCUSED coverage (Fcoverage) around a Point of Interest (POI), with novel evaluation metric, coverage radius. We propose to deploy sensors in polygon layers over a locallycomputable equilateral triangle tessellation (TT) for optimal Fcoverage formation, and introduce two types of deployment polygon, Hpolygon and Cpolygon. We propose two strictly localized solution algorithms, Greedy Advance (GA) and GreedyRotationGreedy (GRG). The two algorithms drive sensors to move along the TT graph to surround POI. In GA, nodes greedily proceed as close to POI as they can; in GRG, when their greedy advance is blocked, nodes rotate around POI along locally computed H or C polygon to a vertex where greedy advance can resume. We prove that they both yield a connected network with maximized holefree area coverage. To our knowledge they are the first localized sensor selfdeployment algorithms that provide such coverage guarantee. We further analyze their coverage radius property. Our study shows that GRG guarantees optimal or near optimal coverage radius. Through extensive simulation we as well evaluate their performance on convergence time, energy consumption, and node collision.
An Adaptive Scheduling Algorithm for Set Cover Problem in Wireless Sensor Networks: A Cellular Learning Automata Approach
"... Abstract—Redundant node deployment is a common strategy in wireless sensor networks. This redundancy can be due to various reasons such as high probability of failures, long lifetime expectation, etc. One major problem in wireless sensor networks is to use this redundancy in order to extend the netw ..."
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Abstract—Redundant node deployment is a common strategy in wireless sensor networks. This redundancy can be due to various reasons such as high probability of failures, long lifetime expectation, etc. One major problem in wireless sensor networks is to use this redundancy in order to extend the network lifetime while keeping the entire area under the coverage of the network. In this problem, which is known as set cover problem, the main objective is to select a subset of sensor nodes as active nodes so that the set of active nodes covers the entire area of the network. In this paper, an scheduling algorithm is presented for solving the set cover problem using cellular learning automata. In this algorithm, each node is equipped with a learning automaton which decides for the node to be active or not locally and based on the situations of its neighbors. Simulation results in JSim simulator environment specify the efficiency of the proposed scheduling algorithm over existing algorithms such as PEAS and PECAS.
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"... Abstract—Redundant node deployment is a common strategy in wireless sensor networks. This redundancy can be due to various reasons such as high probability of failures, long lifetime expectation, etc. One major problem in wireless sensor networks is to use this redundancy in order to extend the netw ..."
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Abstract—Redundant node deployment is a common strategy in wireless sensor networks. This redundancy can be due to various reasons such as high probability of failures, long lifetime expectation, etc. One major problem in wireless sensor networks is to use this redundancy in order to extend the network lifetime while keeping the entire area under the coverage of the network. In this problem, which is known as set cover problem, the main objective is to select a subset of sensor nodes as active nodes so that the set of active nodes covers the entire area of the network. In this paper, an scheduling algorithm is presented for solving the set cover problem using cellular learning automata. In this algorithm, each node is equipped with a learning automaton which locally decides for the node to be active or not based on the situations of its neighbors. Simulation results in Jsim simulator environment specify the efficiency of the proposed scheduling algorithm over existing algorithms such as PEAS and PECAS. Index Terms—Area coverage, cellular learning automata, learning automata, scheduling algorithm, wireless sensor networks. 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, steadystate analysis. I.
Wireless Pers Commun (2014) 77:1923–1933 DOI 10.1007/s1127701416163 Learning Automata Based FaceAware 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 forwardzone constrained and FAR in terms of energy consumption, number of active nodes, number of exchanged packets and slack time.