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

2019 ◽  
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 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.

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.

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.

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

2019 ◽  
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 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.

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.  

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