Hosein Mohamadi

A learning automata-based algorithm for solving coverage problem in directional sensor networks

Wireless sensor networks have been used in a wide variety of applications. Recently, networks consisting of directional sensors have gained prominence. An important challenge facing directional sensor networks (DSNs) is maximizing the network lifetime while covering all the targets in an area. One effective method for saving the sensors’ energy and extending the network lifetime is to partition the DSN into several covers, each of which can cover all targets, and then to activate these covers successively. This paper first proposes a fully distributed algorithm based on irregular cellular learning automata to find a near-optimal solution for selecting each sensor’s appropriate working direction. Then, to find a near-optimal solution that can cover all targets with the minimum number of active sensors, a centralized approximation algorithm is proposed based on distributed learning automata. This algorithm takes advantage of learning automata (LA) to determine the sensors that must be activated at each stage. As the presented algorithm proceeds, the activation process is focused on the sensor nodes that constitute the cover set with the minimum number of active sensors. Through simulations, we indicate that the scheduling algorithm based on LA has better performance than the greedy algorithm-based scheme in terms of maximizing network lifetime.

Solving target coverage problem using cover sets in wireless sensor networks based on learning automata

Recent years have witnessed a significant increase in employing wireless sensor networks (WSNs) for a variety of applications. Monitoring a set of discrete targets and, at the same time, extending the network lifetime is a critical issue in WSNs. One method to solve this problem is designing an efficient scheduling algorithm that is able to organize sensor nodes into several cover sets in such a way that each cover set could monitor all the targets. This study presents three learning automata-based scheduling algorithms to solve the problem. Moreover, several pruning rules are devised to avoid the selection of redundant sensors and manage critical sensors for extending the network lifetime. To evaluate the performance of proposed algorithms, we conducted several experiments, and the obtained results indicated that Algorithm 3 was more successful in terms of extending the network lifetime.

Learning Automata-Based Algorithms for Solving the Target Coverage Problem in Directional Sensor Networks

Recently, directional sensor networks have received a great deal of attention due to their wide range of applications in different fields. A unique characteristic of directional sensors is their limitation in both sensing angle and battery power, which highlights the significance of covering all the targets and, at the same time, extending the network lifetime. It is known as the target coverage problem that has been proved as an NP-complete problem. In this paper, we propose four learning automata-based algorithms to solve this problem. Additionally, several pruning rules are designed to improve the performance of these algorithms. To evaluate the performance of the proposed algorithms, several experiments were carried out. The theoretical maximum was used as a baseline to which the results of all the proposed algorithms are compared. The obtained results showed that the proposed algorithms could solve efficiently the target coverage problem.

Heuristic methods to maximize network lifetime in directional sensor networks with adjustable sensing ranges

During recent years, several efficient algorithms have been designed for solving the target coverage problem in directional sensor networks (DSNs). Conventionally, it is assumed that sensors have a single power level. Though, it is clear that, in real applications, sensors may have multiple power levels that determine different sensing ranges and power consumptions. One of the most significant challenges associated with the DSNs is monitoring all the targets in a given area and, at the same time, maximizing the network lifetime. In this paper, this issue is known as Maximum Network Lifetime with Adjustable Ranges (MNLAR) which has not been already studied in the DSNs. In this paper, we propose two heuristic algorithms (Algorithms 1 and 2) to solve the problem. In order to evaluate the performance of the proposed algorithms, extensive experiments were conducted. The obtained results were compared to a theoretical upper bound in order to measure the quality of the solutions provided by the proposed algorithms. The results demonstrated that Algorithm 2 was more successful than Algorithm 1 in terms of extending the network lifetime. HighlightsIntroducing the problem of maximum network lifetime with adjustable ranges in a DSN.Designing two efficient heuristics using greedy technique to solve the problem.Evaluating the performance of the algorithms through simulations.

Learning automata-based algorithms for finding cover sets in wireless sensor networks

Wireless sensor networks (WSNs) have been widely used in different applications. One of the most significant issues in WSNs is developing an efficient algorithm to monitor all the targets and, at the same time, extend the network lifetime. As sensors are often densely deployed, employing scheduling algorithms can be considered a promising approach that is able ultimately to result in extending total network lifetime. In this paper, we propose three learning automata-based scheduling algorithms for solving target coverage problem in WSNs. The proposed algorithms employ learning automata (LA) to determine the sensors that should be activated at each stage for monitoring all the targets. Additionally, we design a pruning rule and manage critical targets in order to maximize network lifetime. In order to evaluate the performance of the proposed algorithms, extensive simulation experiments were carried out, which demonstrated the effectiveness of the proposed algorithms in terms of extending the network lifetime. Simulation results also revealed that by a proper choice of the learning rate, a proper trade-off could be achieved between the network lifetime and running time.

A new learning automata-based approach for maximizing network lifetime in wireless sensor networks with adjustable sensing ranges

Abstract Recently, several algorithms have been proposed to solve the problem of target coverage in wireless sensor networks (WSNs). A conventional assumption is that sensors have a single power level (i.e., fixed sensing range); however, in real applications, sensors might have multiple power levels, which determines different sensing ranges and, consequently, different power consumptions. Accordingly, one of the most important problems in WSNs is to monitor all the targets in a specific area and, at the same time, maximize the network lifetime in a network in which sensors have multiple power levels. To solve the problem, this paper proposes a learning-automata based algorithm equipped with a pruning rule. The proposed algorithm attempts to select a number of sensor nodes with minimum energy consumption to monitor all the targets in the network. To investigate the efficiency of the proposed algorithm, several simulations were conducted, and the obtained results were compared with those of two greedy-based algorithms. The results showed that, compared to the greedy-based algorithms, the proposed learning automata-based algorithm was more successful in prolonging the network lifetime and constructing higher number of cover sets.

A Learning Automata-Based Solution to the Priority-Based Target Coverage Problem in Directional Sensor Networks

In recent years, directional sensor networks composed of directional sensors have attracted a great deal of attention due to their extensive applications. The main difficulties associated with directional sensors are their limited battery power and restricted sensing angle. Moreover, each target may have a different coverage quality requirement that can make the problem even more complicated. Therefore, satisfying the coverage quality requirement of all the targets in a specific area and maximizing the network lifetime, known as priority-based target coverage problem, has remained a challenge. As sensors are often densely deployed, organizing the sensor directions into several cover sets and then activating these cover sets successively is a promising solution to this problem. In this paper, we propose a learning automata-based algorithm to organize the directional sensors into several cover sets in such a way that each cover set can satisfy coverage quality requirement of all the targets. In order to verify the performance of the proposed algorithm, several simulations were conducted. The obtained results showed that the proposed algorithm was successful in extending the network lifetime.

Scheduling algorithms for extending directional sensor network lifetime

Recently, directional sensor networks that are composed of a large number of directional sensors have attracted a great deal of attention. The main issues associated with the directional sensors are limited battery power and restricted sensing angle. Therefore, monitoring all the targets in a given area and, at the same time, maximizing the network lifetime has remained a challenge. As sensors are often densely deployed, a promising approach to conserve the energy of directional sensors is developing efficient scheduling algorithms. These algorithms partition the sensor directions into multiple cover sets each of which is able to monitor all the targets. The problem of constructing the maximum number of cover sets has been modeled as the multiple directional cover sets (MDCS), which has been proved to be an NP-complete problem. In this study, we design two new scheduling algorithms, a greedy-based algorithm and a learning automata (LA)-based algorithm, in order to solve the MDCS problem. In order to evaluate the performance of the proposed algorithms, several experiments were conducted. The obtained results demonstrated the efficiency of both algorithms in terms of extending the network lifetime. Simulation results also revealed that the LA-based algorithm was more successful compared to the greedy-based one in terms of prolonging network lifetime.

Solving Priority-Based Target Coverage Problem in Directional Sensor Networks with Adjustable Sensing Ranges

The extensive applications of directional sensor networks (DSNs) in a wide range of situations have recently attracted a great deal of attention. DSNs primarily operate based on simultaneously observing a group of events (targets) occurring in a set area and maximizing network lifetime, as there are limitations to the directional sensors’ sensing angle and battery power. The higher the number of sensing ranges of the sensors and the more different the coverage requirements for the targets, the more complex this issue will be. Also known as priority-based target coverage with adjustable sensing ranges (PTCASR), this issue, which has not yet been investigated in the field of study, is the highlight of this research. A potential solution to this problem, based on the fact that sensors are frequently densely deployed, would be to organize the sensors into a few cover sets. After that the cover sets needs to be successively activated—this process is referred to as the scheduling technique. This paper aims to resolve the issue of PTCASR with the proposal of two scheduling algorithms i.e. greedy-based and learning automata-based algorithms. These proposed algorithms were assessed for their performance via a number of experiments. Additionally, the effect of each algorithm on maximizing network lifetime was also investigated via a comparative study. Both algorithms were successful in solving the problem; however, the learning automata-based scheduling algorithm proved relatively superior to the greedy-based algorithm when it came to extending network lifetime.

Cover set formation for target coverage using genetic algorithm in directional sensor networks

Maximizing the network lifetime is one important factor in covering a set of targets in directional sensor networks (DSNs). The targets may take time to be located, and a good tracking management system is necessary to locate them. Target coverage problem arises due to limitation in the sensing angle and battery power of directional sensors. The problem becomes more challenging when the targets have different coverage quality requirements. In the present study, this problem is referred to as Priority-based Target Coverage (PTC) that has been proven to be an NP-complete problem. As sensors are often deployed densely, a promising solution to this problem is the use of scheduling technique through which the sensors are partitioned into several cover sets, then the cover sets are activated successively. In this paper, we propose a genetic-based scheduling algorithm to solve the problem. In order to examine the impact of different factors on the size of the resulting subset, three different experiments were performed to test the effectiveness of our algorithm. The results demonstrated that the proposed algorithm was able to contribute to solving the problem significantly.