scholarly journals Application of Genetic Algorithm in Time-Based Wireless Sensor Network Schedule Optimization

2021 ◽  
Vol 31.2 (149) ◽  
pp. 29-34
Keyword(s):  
Genetic Algorithm ◽  
Sensor Network ◽  
Fitness Function ◽  
Optimal Solution ◽  
Complex Problem ◽  
Wireless Sensor ◽  
Network Scheduling ◽  
Schedule Optimization ◽  
Wide Range ◽  
Network Schedule

In recent years, wireless sensor networks (WSN) have been particularly interested, studied and applied very strongly. A sensor network is generally limited in resources and energy, which greatly restrict its applicability. Sensor network optimization in practice is a very diverse with a wide range of applications, whereas sensor network scheduling is important in lowering energy consumption and maximizing network lifetime. However, optimization of sensor network schedule a very complex problem with many constraints that is not trivial to solve by analytical methods. This article discusses a heuristical approach using a genetic algorithm to find an optimal solution for network scheduling. The evaluation of fitness function, as well as selection with crossover and mutation operations help to evolve individuals in the population through generations in an optimal direction.

Optimization of Clustering in Wireless Sensor Networks Using Genetic Algorithm

2020 ◽  
pp. 822-836
Author(s):  
Pritee Parwekar ◽  
Sireesha Rodda
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Network ◽  
Sensor Node ◽  
Fitness Function ◽  
Wireless Sensor ◽  
Sleep Mode ◽  
Communication Distance ◽  
Selection Of

The energy of a sensor node is a major factor for life of a network in wireless sensor network. The depletion of the sensor energy is dependent on the communication range from the sink. Clustering is mainly used to prolong the life of a network with energy consumption. This paper proposes optimization of clustering using genetic algorithm which will help to minimize the communication distance. The cluster overhead and the active and sleep mode of a sensor is also considered while calculating the fitness function to form the cluster. This approach helps to prolong the network life of sensor network. The proposed work is tested for different number of nodes and is helping to find the correct solution for the selection of cluster heads.

scholarly journals An Improved Ant Colony Algorithm in Wireless Sensor Network Routing

2017 ◽  
Vol 13 (05) ◽  
pp. 174 ◽  
Author(s):  
Liping LV
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Ant Colony Algorithm ◽  
Fitness Function ◽  
Optimal Solution ◽  
Network Routing ◽  
Ant Colony ◽  
Wireless Sensor ◽  
Destination Node ◽  
Ant Colony Optimization Algorithm

<p class="0abstract"><span lang="EN-US">In order to make the energy consumption of network nodes relatively balanced, we apply ant colony optimization algorithm to wireless sensor network routing and improve it.</span><span lang="EN-US"> In this paper, we propose a multi-path wireless sensor network routing algorithm based on energy equalization. The algorithm uses forward ants to find the path from the source node to the destination node, and uses backward ants to update the pheromone on the path. In the route selection, we use the energy of the neighboring nodes as the parameter of the heuristic function. At the same time, we construct the fitness function, and take the path length and the node residual energy as its parameters. The simulation results show that the algorithm can not only avoid the problem of local optimal solution, but also prolong the life cycle of the network effectively.</span></p>

Optimization of Clustering in Wireless Sensor Networks Using Genetic Algorithm

2017 ◽  
Vol 8 (4) ◽  
pp. 84-98 ◽  
Author(s):  
Pritee Parwekar ◽  
Sireesha Rodda
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Network ◽  
Sensor Node ◽  
Fitness Function ◽  
Wireless Sensor ◽  
Sleep Mode ◽  
Communication Distance ◽  
Selection Of

The energy of a sensor node is a major factor for life of a network in wireless sensor network. The depletion of the sensor energy is dependent on the communication range from the sink. Clustering is mainly used to prolong the life of a network with energy consumption. This paper proposes optimization of clustering using genetic algorithm which will help to minimize the communication distance. The cluster overhead and the active and sleep mode of a sensor is also considered while calculating the fitness function to form the cluster. This approach helps to prolong the network life of sensor network. The proposed work is tested for different number of nodes and is helping to find the correct solution for the selection of cluster heads.

scholarly journals A Variable-Length Chromosome Genetic Algorithm for Time-Based Sensor Network Schedule Optimization

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3990
Author(s):  
Van-Phuong Ha ◽  
Trung-Kien Dao ◽  
Ngoc-Yen Pham ◽  
Minh-Hoang Le
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Sensor Network ◽  
Optimal Solution ◽  
Stochastic Approach ◽  
Variable Length ◽  
Sensor Nodes ◽  
Fixed Length ◽  
Network Schedule ◽  
Application Requirements

Scheduling sensor nodes has an important role in real monitoring applications using sensor networks, lowering the power consumption and maximizing the network lifetime, while maintaining the satisfaction to application requirements. Nevertheless, this problem is usually very complex and not easily resolved by analytical methods. In a different manner, genetic algorithms (GAs) are heuristic search strategies that help to find the exact or approximate global optimal solution efficiently with a stochastic approach. Genetic algorithms are advantageous for their robustness to discrete and noisy objective functions, as they are only evaluated at independent points without requirements of continuity or differentiability. However, as explained in this paper, a time-based sensor network schedule cannot be represented by a chromosome with fixed length that is required in traditional genetic algorithms. Therefore, an extended genetic algorithm is introduced with variable-length chromosome (VLC) along with mutation and crossover operations in order to address this problem. Simulation results show that, with help of carefully defined fitness functions, the proposed scheme is able to evolve the individuals in the population effectively and consistently from generation to generation towards optimal ones, and the obtained network schedules are better optimized in comparison with the result of algorithms employing a fixed-length chromosome.

scholarly journals Abnormal Vehicle Load Identification Method Based on Genetic Algorithm and Wireless Sensor Network

2020 ◽  
Author(s):  
Sorush Niknamian
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Network ◽  
False Alarm ◽  
False Alarm Rate ◽  
Sensor Network ◽  
Fitness Function ◽  
Recognition Rate ◽  
Accurate Method ◽  
Wireless Sensor ◽  
Vehicle Load

The current abnormal wireless sensor network vehicle load data recognition <br>method is more complex, which leads to low recognition rate, false alarm rate and slow <br>recognition speed. Based on the genetic algorithm, the accurate method for abnormal <br>wireless sensor network vehicle load data recognition is proposed. The effective feature <br>set of abnormal vehicle load data in the wireless sensor network is constructed, to <br>remove irrelevant features and redundant features from existing abnormal wireless <br>sensor network vehicle load data. The abnormal wireless sensor network vehicle load <br>data in the effective feature set are coded, to reduce the recognition time of abnormal <br>wireless sensor network vehicle load data. The adaptive fitness function, crossover <br>operator and mutation operator are applied to genetic algorithm, which can improve the <br>recognition rate, reduce the false alarm rate, and realize the recognition of abnormal <br>vehicle load data wireless sensor network. The experimental results show that the <br>recognition rate of this method is high, the false alarm rate is low, and the time of <br>recognition is less.

scholarly journals Abnormal Vehicle Load Identification Method Based on Genetic Algorithm and Wireless Sensor Network

2020 ◽  
Author(s):  
Sorush Niknamian
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Network ◽  
False Alarm ◽  
False Alarm Rate ◽  
Sensor Network ◽  
Fitness Function ◽  
Recognition Rate ◽  
Accurate Method ◽  
Wireless Sensor ◽  
Vehicle Load

The current abnormal wireless sensor network vehicle load data recognition <br>method is more complex, which leads to low recognition rate, false alarm rate and slow <br>recognition speed. Based on the genetic algorithm, the accurate method for abnormal <br>wireless sensor network vehicle load data recognition is proposed. The effective feature <br>set of abnormal vehicle load data in the wireless sensor network is constructed, to <br>remove irrelevant features and redundant features from existing abnormal wireless <br>sensor network vehicle load data. The abnormal wireless sensor network vehicle load <br>data in the effective feature set are coded, to reduce the recognition time of abnormal <br>wireless sensor network vehicle load data. The adaptive fitness function, crossover <br>operator and mutation operator are applied to genetic algorithm, which can improve the <br>recognition rate, reduce the false alarm rate, and realize the recognition of abnormal <br>vehicle load data wireless sensor network. The experimental results show that the <br>recognition rate of this method is high, the false alarm rate is low, and the time of <br>recognition is less.

Based on Genetic Algorithm for Wireless Sensor Network Node Self-Localization

2012 ◽  
Vol 482-484 ◽  
pp. 1225-1228
Author(s):  
Hua Zhang
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Continuous Process ◽  
Optimal Solution ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Network Node ◽  
Positioning Accuracy ◽  
Optimization Simulation

Wireless sensor network node positioning technology is one of the key technologies. Due to self-localization of sensor nodes in the process of positioning accuracy is not high, In this paper， the genetic algorithm approach to take, through the evolution of control, making the location of the nodes for continuous progress toward the optimal solution, in order to achieve continuous process of node positioning optimization. Simulation results show that the evolution of the genetic algorithm control, can reduce errors, improve positioning accuracy.

scholarly journals Reinforce Coverage Area of Sensor Mote in Wireless Ad-Hoc Networks Using Genetic Algorithm

2018 ◽  
Vol 7 (3.27) ◽  
pp. 508
Author(s):  
B Hemalatha ◽  
S Srinivasan
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Network ◽  
Energy Dissipation ◽  
Sensor Network ◽  
Network Lifetime ◽  
Clustering Algorithm ◽  
Fitness Function ◽  
Cluster Head ◽  
Wireless Sensor ◽  
Coverage Area

Wireless sensor based communication is everlasting growing sector within the industry of communication. In WSN improving the life expectancy of the network depends on the energy dissipation of senor devices. Diminishing the energy dissipation of sensor device will enhance the lifetime and device failure which helps in better availability and coverage area of sensor network.  One of the dynamic research fields in wireless sensor network is that of coverage. Coverage can be defined as how well each point of interest is monitored by sensor network. In this paper, we investigate the cluster head selection issue, particularly focusing on applications where the upkeep of full network coverage is the fundamental prerequisite. Coverage maintenance for extended period is a pivotal issue in wireless sensor network because of the constrained inbuilt battery in sensors. Coverage maintenance may be prolonged by utilizing the network energy efficiently, by keeping an adequate number of sensors in sensor covers. The clustering algorithm is a solution to reduce energy consumption which can be helpful to the scalability and network lifetime. Assuming serious energy rebalancing with additional clustering algorithm, a Genetic algorithm (GA) based clustering algorithm which evaluates the fitness function by considering the two major parameters distance and energy has been proposed in this paper. Simulation result shows that the proposed solution finds the optimal cluster heads and has prolonged network lifetime and maximum coverage.  

scholarly journals Abnormal Vehicle Load Identification Method Based on Genetic Algorithm and Wireless Sensor Network

2020 ◽  
Author(s):  
Sorush Niknamian
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Network ◽  
False Alarm ◽  
False Alarm Rate ◽  
Sensor Network ◽  
Fitness Function ◽  
Recognition Rate ◽  
Accurate Method ◽  
Wireless Sensor ◽  
Vehicle Load

Abstract: The current abnormal wireless sensor network vehicle load data recognition method is more complex, which leads to low recognition rate, false alarm rate and slow recognition speed. Based on the genetic algorithm, the accurate method for abnormal wireless sensor network vehicle load data recognition is proposed. The effective feature set of abnormal vehicle load data in the wireless sensor network is constructed, to remove irrelevant features and redundant features from existing abnormal wireless sensor network vehicle load data. The abnormal wireless sensor network vehicle load data in the effective feature set are coded, to reduce the recognition time of abnormal wireless sensor network vehicle load data. The adaptive fitness function, crossover operator and mutation operator are applied to genetic algorithm, which can improve the recognition rate, reduce the false alarm rate, and realize the recognition of abnormal vehicle load data wireless sensor network. The experimental results show that the recognition rate of this method is high, the false alarm rate is low, and the time of recognition is less.

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