Today, a directional sensor network is a popular environment for solving the target coverage problem. Monitoring all targets in a DSN is a crucial challenge to scholars working in this field of study. Adjusting the an...
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Today, a directional sensor network is a popular environment for solving the target coverage problem. Monitoring all targets in a DSN is a crucial challenge to scholars working in this field of study. Adjusting the angle and range of the sensors can be an efficient technique for improving the network performance. In this way, the network has the most extended lifespan and, at the same time, spends the least time to find the best cover set. In this method, each sensor dynamically adjusts its own sensing angle in order to find the targets by choosing the best range. The present study proposed a continuous learning automata-based method to choose the optimum sensing angle for the sensors in a DSN. Then, to evaluate the proposed algorithm performance, its results were compared to those of a conventional automata-based method whose algorithm worked based on continuous automata. The comparative analysis confirmed the superiority of the proposed method over the conventional automata-based method regarding the extension of the network lifespan.
Quality of coverage is one of the fundamental issues in wireless sensor networks, particularly for the deterministic placement of sensors. One of the methods to improve the quality of coverage is to place the minimum ...
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Quality of coverage is one of the fundamental issues in wireless sensor networks, particularly for the deterministic placement of sensors. One of the methods to improve the quality of coverage is to place the minimum number of sensors in the optimal position to cover the entire target. This paper proposes a discrete Haar wavelet transform for deterministic sensor placement in the target coverage problem. Dilation and translation of Haar wavelet transform are used for identifying the optimal position of sensors. Simulation results validate the performance of discrete Haar wavelet transform better than random placement in terms of optimal placement, quality of coverage and network traffic reduction.
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 ...
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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.
Network lifetime is a critical factor in determining the effectiveness of Wireless Sensor Networks (WSNs). The optimization of the battery lives of the sensor nodes following the targets in terms of continuity of WSNs...
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Network lifetime is a critical factor in determining the effectiveness of Wireless Sensor Networks (WSNs). The optimization of the battery lives of the sensor nodes following the targets in terms of continuity of WSNs coverage in military and civil applications plays an important role in extending the network's lifetime. Since the sensor nodes that constitute WSNs have limited battery life, the energy of the sensors gradually decreases as a result of communicating among themselves and perceiving field of interest. Ultimately, the node consumes its energy completely and causes WSN to fail to function. For this reason, the optimization of the lifetime of WSNs has been one of the most frequently studied topics in the literature. In this article, it was aimed to optimize the lifetime of the network by performing dynamic distributions of the nodes provided that the coverage requirements (1 <= k <= 4) of the maximum four sensor nodes are met to find solution to the target coverage problem in WSNs. It was aimed to determine the accessible lifetime of the network by calculating the remaining battery life of the nodes and the upper limit of the network lifetime when the coverage requirements of the targets are met. In addition, Electromagnetism-Like (EM) algorithm, which is meta-heuristic in performing the dynamic distributions of sensor nodes, was taken as a basis, and a new energy-efficient algorithm was developed. The accessible network lifetimes calculated with this algorithm were compared with the Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO) algorithms in the literature. According to the obtained simulation results, it was found that the algorithm developed in reaching the upper limit of the network lifetime gave more optimum results.
One of the main operations in directional sensor networks (DSNs) is the surveillance of a set of events (targets) that occur in a given area and, at the same time, maximization of the network lifetime;this is due to l...
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ISBN:
(纸本)9783319162928;9783319162911
One of the main operations in directional sensor networks (DSNs) is the surveillance of a set of events (targets) that occur in a given area and, at the same time, maximization of the network lifetime;this is due to limitation in sensing angle and battery power of the directional sensors. This problem gets more complicated by the possibility that targets may have different coverage requirements. In the present study, this problem is referred to as priority-based targetcoverage (PTC). As sensors are often densely deployed, organizing the sensors 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 could satisfy coverage requirements of all the targets. Several experiments are conducted to evaluate the performance of the proposed algorithm. The results demonstrated that the algorithms were able to contribute to solving the problem.
The Internet of Things (IoT) has become widely popular due to its rapid progress and broad range of practical uses, which have significantly impacted our daily lives. One such key utilization of IoT is making consumer...
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The Internet of Things (IoT) has become widely popular due to its rapid progress and broad range of practical uses, which have significantly impacted our daily lives. One such key utilization of IoT is making consumer electronics interactive, interconnected and self-responsible by means of sensors and Internet. Sensors play a crucial role as the primary technology that enables the Internet of Things (IoT) and its various applications. The inherent difficulty hinders the further growth of IoT. One such problem is utilizing minimum number of sensors to achieve maximum coverage and connectivity in the network. The most accurate method to encounter this issue is optimal Sensor Placement (OSP). OSP predetermines the sensor's position for its deployment to attain maximum network coverage and connectivity. Hence, this paper proposes a coverage and connectivity-preserving hierarchical algorithm based on discrete Haar wavelet transform for optimal sensor placement algorithm. The proposed algorithm utilizes discrete Haar wavelet transform, for identifying sensor's position and to ensure connectivity Breadth first search algorithm is used. Further, to enhance the coverage, proposed algorithm determines redundant sensors and redeploy it in determined spots. The results obtained from the proposed algorithm effectively address the research problem and attain better results compared with the existing efficient methods.
In this paper, a novel improved memetic algorithm is proposed for maximizing the sensor covers from the randomly deployed sensors in a hostile environment. This is achieved by optimizing the sensing range of the senso...
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In this paper, a novel improved memetic algorithm is proposed for maximizing the sensor covers from the randomly deployed sensors in a hostile environment. This is achieved by optimizing the sensing range of the sensors by reducing the redundant targetcoverage and partitioning the set of all sensors into several subsets or sensor covers in such a way that each sensor cover monitors the entire targets. Further, sensor covers are activated one after another for maximizing the lifetime of a sensor network. The proposed algorithm identifies the maximum number of sensor covers by selecting the best sensors and adjusts the required sensing range. Simulation results of various problem instances proves that network lifetime of improved memetic algorithm is 1.1662 times higher than the existing memetic algorithm and 1.6848 times higher than the existing genetic algorithm.
Wireless sensor networks have become one of the prominent and persuasive methods for surveillance of inaccessible physical areas and are employed in innumerable applications in various fields. Some applications may re...
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Wireless sensor networks have become one of the prominent and persuasive methods for surveillance of inaccessible physical areas and are employed in innumerable applications in various fields. Some applications may require directional sensor nodes in contrast to the conventional omnidirectional ones. Many challenges emerge during deployment of such sensor networks especially in terms of energy constraints. These sensor nodes are furnished with non-rechargeable energy sources which may lead to inefficiency in the network within a very short span of time. Conserving energy of the sensor nodes by organizing the sensor nodes into cover sets and actuating them one after the other, while ensuring maintenance of the complete coverage of all targets, is one of the common approaches to extend the network lifetime. This paper addresses Q-coverageproblem of directional sensor networks in which each target is required to be covered by different number of sensor nodes for effective surveillance and Q-coverage constraints are considered while segregating the sensor nodes operating in different sensing directions to a different cover sets. An approach based on genetic algorithm is proposed to find an ideal solution to the directional Q-coverageproblem and the simulation results confirm that the network lifetime is prolonged.
The wireless sensor network technology of Internet of Things (IoT) senses, collects and processes the data from its interconnected intelligent sensors to the base station. These sensors help the IoT to understand the ...
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The wireless sensor network technology of Internet of Things (IoT) senses, collects and processes the data from its interconnected intelligent sensors to the base station. These sensors help the IoT to understand the environmental change and respond towards it. Thus sensor placement is a crucial device of IoT for efficient coverage and connectivity in the network. Many existing works focus on optimal sensor placement for two dimensional terrain but in various real-time applications sensors are often deployed over three-dimensional ambience. Therefore, this paper proposes a vertex coloring based sensor deployment algorithm for 3D terrain to determine the sensor requirement and its optimal spot and to obtain 100% targetcoverage. Further, the quality of the connectivity of sensors in the network is determined using Breadth first search algorithm. The results obtained from the proposed algorithm reveal that it provides efficient coverage and connectivity when compared with the existing methods.
WSN has been playing a significant role in various applications in recent days. The main issue in wireless sensor network, particularly for target coverage problem is to achieve maximum quality of coverage with limite...
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ISBN:
(纸本)9781538647073
WSN has been playing a significant role in various applications in recent days. The main issue in wireless sensor network, particularly for target coverage problem is to achieve maximum quality of coverage with limited sensors. To short out these issues, identify the optimal position for each sensor to satisfy the coverage requirement. This paper proposes a particle swarm optimization for Q-coverageproblem, where each target can be covered by one or more sensors. The main objective of the proposed algorithm is to optimize the position of sensors for satisfying Q-coverage constraints. Simulation results show that the proposed algorithm achieves a good Q-coverage compared with random deployment of sensors.
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