An Anti-Maloperation System by Improved Chaos Immune Genetic Algorithm

2014 ◽  
Vol 543-547 ◽  
pp. 1926-1929
Author(s):  
Shao Song Wan ◽  
Jian Cao ◽  
Cong Yan
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Chaos Theory ◽  
Circuit Diagram ◽  
Wireless Sensor ◽  
Single Chip ◽  
Initial Value ◽  
Immune Genetic Algorithm ◽  
Chaos Sequence

The over-spread character and randomness of chaos can be used to initialize population and improve the searching speed, and the initial value sensitivity of chaos can be used to enlarge the searching space. In order to resolve these problems, we put forward a new design of the intelligent lock which is mainly based on the technology of wireless sensor network. To avoid the local optimization, the algorithm renews population and enhances the diversity of population by using density calculation of immune theory and adjusting new chaos sequence. The paper gives the circuit diagram of the hardware components based on single chip and describe how to design the software. The experimental results show that the immune genetic algorithm based on chaos theory can search the result of the optimization and evidently improve the convergent speed and astringency.

Research on Image Vectorization Based on Improved Chaos Immune Genetic Algorithm

2014 ◽  
Vol 543-547 ◽  
pp. 2619-2622
Author(s):  
Shao Song Wan ◽  
Jian Cao ◽  
Cong Yan
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Chaos Theory ◽  
Initial Value ◽  
Immune Genetic Algorithm ◽  
Processing Data ◽  
Image Vectorization ◽  
Chaos Sequence

Image vectorization plays an important role in the digital image processing. Because the traditional linear vectorization methods have some shortcomings including processing data slowly, being sensitive to noises and being easy to be distorted, this paper proposes an image vectorization method based on mathematical morphology. In the paper we present an improving immune genetic algorithm based on chaos theory. The over-spread character and randomness of chaos can be used to initialize population and improve the searching speed, and the initial value sensitivity of chaos can be used to enlarge the searching space. To avoid the local optimization, the algorithm renews population and enhances the diversity of population by using density calculation of immune theory and adjusting new chaos sequence.

scholarly journals Hybrid Niche Immune Genetic Algorithm for Fault Detection Coverage in Industry Wireless Sensor Network

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jie Zhou ◽  
Hu Qin ◽  
Yang Liu ◽  
Chaoqun Li ◽  
Mengying Xu
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Network ◽  
Fault Detection ◽  
Sensor Network ◽  
Fault Detection And Diagnosis ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Industrial Equipment ◽  
Mathematical Methods ◽  
Immune Genetic Algorithm

The industry wireless sensor network (IWSN) technology, which is used to monitor industrial equipment, has attracted more and more attention in recent years. Sensor nodes in IWSN can spontaneously complete distributed networking and carry out monitoring tasks under random deployment conditions. Therefore, a self-organized IWSN is particularly suitable for the fault detection and diagnosis of industrial equipment in complex environments. However, due to the detection, ability of a single sensor node is limited, and the monitoring distribution problem is a typical multidimensional discrete NP-hard combinatorial stochastic optimization problem, which is challenging to solve for the traditional mathematical methods. With the purpose of improving the target monitoring capability and prolonging lifetime of IWSN, a novel hybrid niche immune genetic algorithm (HNIGA) for optimizing the target coverage model of fault detection is proposed. It uses the genetic operation to evolve antibody groups and applies niche technology to maintain the diversity of antibody groups. As a result, HNIGA can effectively reduce the failure rate of detection targets. To verify the performance of HNIGA, a series of simulations under different simulation conditions are carried out. Specifically, HNIGA is compared with genetic algorithm (GA) and simulated annealing (SA). Simulation results show that HNIGA has a faster convergence speed and more robust global search capability than the other two algorithms.

Routing Using Genetic Algorithm in a Wireless Sensor Network

2020 ◽  
Vol 111 (4) ◽  
pp. 2703-2732 ◽  
Author(s):  
Naveen Muruganantham ◽  
Hosam El-Ocla
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Wireless Sensor

scholarly journals Node optimization coverage method under link model in passive monitoring system of three-dimensional wireless sensor network

2019 ◽  
Vol 15 (8) ◽  
pp. 155014771986987 ◽  
Author(s):  
Zhanjun Hao ◽  
Nanjiang Qu ◽  
Xiaochao Dang ◽  
Jiaojiao Hou
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Particle Swarm Optimization Algorithm ◽  
Three Dimensional ◽  
Wireless Sensor ◽  
Network Coverage ◽  
Swarm Optimization ◽  
Passive Monitoring ◽  
Link Model

3D coverage is not only closer to the actual application environment, but also a research hotspot of sensor networks in recent years. For this reason, a node optimization coverage method under link model in passive monitoring system of three-dimensional wireless sensor network is proposed in this article. According to wireless link-aware area, the link coverage model in three-dimensional wireless sensor network is constructed, and the cube-based network coverage is used to represent the quality of service of the network. This model takes advantage of the principle that the presence of human beings can change the transmission channel of the link. On this basis, the intruder is detected by the data packets transmitted between the wireless links, and then the coverage area is monitored by monitoring the received signal strength of the wireless signal. Based on this new link awareness model, the problem of optimal coverage deployment of the receiving node is solved, that is, how to deploy the receiving node to achieve the optimal coverage of the monitoring area when the location of the sending node is given. In the process of optimal coverage, the traditional genetic algorithm and particle swarm optimization algorithm are introduced and improved. Based on the genetic algorithm, the particle swarm optimization algorithm which integrates the idea of simulated annealing is regarded as an important operator of the genetic algorithm, which can converge to the optimal solution quickly. The simulation results show that the proposed method can improve the network coverage, converge quickly, and reduce the network energy consumption. In addition, we set up a real experimental environment for coverage verification, and the experimental results verify the feasibility of the proposed method.

scholarly journals Exploration of Wireless Sensor Network Based on Cloud Computing

2018 ◽  
Vol 14 (11) ◽  
pp. 16
Author(s):  
Qing Wan ◽  
Ying Wang
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Energy State ◽  
Wireless Sensor ◽  
Cloud Environment ◽  
Application Service ◽  
Operation And Maintenance ◽  
Starting Point

To realize the exploration of wireless sensor network (WSN) based on cloud computing, the application service of WSN is taken as the starting point, the resource advantage of the cloud platform is used, and a WSN service framework based on cloud environment is proposed. Based on this framework, the problems of data management and reconstruction, network coverage optimization and monitoring, and edge recognition of holes are solved. In view of the node deployment of WSN and coverage problem of operation and maintenance optimization, the genetic algorithm is used to adjust the dormancy and energy state of nodes, and a parallel genetic algorithm for covering optimization in the cloud environment is proposed. For the operation and maintenance requirements of WSN, a parallel data statistics method for network monitoring is proposed. The experimental results show that the parallel algorithm is greatly improved in terms of the accuracy and time efficiency.

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