Coverage Optimization of Hybrid Wireless Sensor Networks Based on Modified Particle Swarm Algorithm

2013 ◽  
Vol 846-847 ◽  
pp. 914-917
Author(s):  
Su Fen Yao ◽  
Jian Qiang Zhao
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Pso Algorithm ◽  
Wireless Sensor ◽  
Particle Swarm Algorithm ◽  
Local Optimum ◽  
Network Coverage ◽  
Mobile Nodes ◽  
Coverage Optimization ◽  
Adaptive Neighborhood

A strategy for controlling mobile nodes based on PSO algorithm with neighborhood disturbance was proposed for improving the network coverage rate in wireless sensor networks. The non-dominated sorting strategy was led into basic PSO algorithm to seek best particle and adaptive neighborhood disturbance operation was used to conquer the drawback of PSO falling into local optimum. Therefore, the effect of network coverage had been improved and the network energy consumption can be reduced.

scholarly journals Improved whale optimization algorithm and its application in heterogeneous wireless sensor networks

2021 ◽  
Vol 17 (5) ◽  
pp. 155014772110181
Author(s):  
Yinggao Yue ◽  
Hairong You ◽  
Shuxin Wang ◽  
Li Cao
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Optimization Algorithm ◽  
Whale Optimization Algorithm ◽  
Wireless Sensor ◽  
Network Coverage ◽  
Coverage Optimization ◽  
Network Cost ◽  
Whale Optimization ◽  
Heterogeneous Wireless Sensor Networks

Aiming at the problems of node redundancy and network cost increase in heterogeneous wireless sensor networks, this article proposes an improved whale optimization algorithm coverage optimization method. First, establish a mathematical model that balances node utilization, coverage, and energy consumption. Second, use the sine–cosine algorithm to improve the whale optimization algorithm and change the convergence factor of the original algorithm. The linear decrease is changed to the nonlinear decrease of the cosine form, which balances the global search and local search capabilities, and adds the inertial weight of the synchronous cosine form to improve the optimization accuracy and speed up the search speed. The improved whale optimization algorithm solves the heterogeneous wireless sensor network coverage optimization model and obtains the optimal coverage scheme. Simulation experiments show that the proposed method can effectively improve the network coverage effect, as well as the utilization rate of nodes, and reduce network cost consumption.

scholarly journals Method for Patching Three-Dimensional Surface Coverage Loopholes of Hybrid Nodes in Wireless Sensor Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
Zhanjun Hao ◽  
Hongwen Xu ◽  
Xiaochao Dang ◽  
Nanjiang Qu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Three Dimensional ◽  
Wireless Sensor ◽  
Utilization Rate ◽  
Network Coverage ◽  
Mobile Nodes ◽  
Research Information ◽  
Information Monitoring ◽  
Dimensional Surface

Target sensing and information monitoring using wireless sensor networks have become an important research field. Based on two-dimensional plane research, information monitoring, and transmission for three-dimensional curved target events, due to the uneven deployment of nodes and failures in sensor networks, there are a lot of coverage loopholes in the network. In this paper, a method of detecting and repairing loopholes in monitoring the coverage of three-dimensional surface targets with hybrid nodes is proposed. In the target monitoring area where the hybrid nodes are randomly deployed, the three-dimensional surface cube is meshed, and the coverage loopholes are gradually detected according to the method of computational geometry, and then, the redundant mobile nodes around the coverage loopholes are selected. According to the calculated distance to cover the moving direction and distance of the loophole, the virtual force is used to adjust the mobile nodes to repair the coverage loopholes. Simulation results show that compared with other algorithms, this algorithm has a higher utilization rate of mobile nodes, uses fewer nodes to complete coverage, reduces network coverage costs, meets the overall network coverage requirements, and has lower mobile energy consumption and longer network life. The actual scene further verifies the good connectivity and high coverage of the whole network.

scholarly journals Node coverage optimization algorithm for wireless sensor networks based on improved grey wolf optimizer

2019 ◽  
Vol 13 ◽  
pp. 174830261988949 ◽  
Author(s):  
Zhendong Wang ◽  
Huamao Xie ◽  
Zhongdong Hu ◽  
Dahai Li ◽  
Junling Wang ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Wireless Sensor Network ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Grey Wolf Optimizer ◽  
Local Optimum ◽  
Grey Wolf ◽  
Coverage Optimization ◽  
Convergence Performance ◽  
Improved Grey Wolf Optimizer

Aiming at the problem of wireless sensor network node coverage optimization with obstacles in the monitoring area, based on the grey wolf optimizer algorithm, this paper proposes an improved grey wolf optimizer (IGWO) algorithm to improve the shortcomings of slow convergence, low search precision, and easy to fall into local optimum. Firstly, the nonlinear convergence factor is designed to balance the relationship between global search and local search. The elite strategy is introduced to protect the excellent individuals from being destroyed as the iteration proceeds. The original weighting strategy is improved, so that the leading wolf can guide the remaining grey wolves to prey in a more reasonable way. The design of the grey wolf’s boundary position strategy and the introduction of dynamic variation strategy enrich the population diversity and enhance the ability of the algorithm to jump out of local optimum. Then, the benchmark function is used to test the convergence performance of genetic algorithm, particle swarm optimization, grey wolf optimizer, and IGWO algorithm, which proves that the convergence performance of IGWO algorithm is better than the other three algorithms. Finally, the IGWO algorithm is applied to the deployment of wireless sensor networks with obstacles (rectangular obstacle, trapezoidal obstacle and triangular obstacles). Simulation results show that compared with GWO algorithm, IGWO algorithm can effectively improve the coverage of wireless sensor network nodes and obtain higher coverage rate with fewer nodes, thereby reducing the cost of deploying the network.

scholarly journals Wireless sensor network coverage optimization based on whale group algorithm

2018 ◽  
Vol 15 (3) ◽  
pp. 569-583 ◽  
Author(s):  
Lei Wang ◽  
Weihua Wu ◽  
Junyan Qi ◽  
Zongpu Jia
Keyword(s):  
Wireless Sensor Networks ◽  
Wireless Sensor Network ◽  
Sensor Networks ◽  
Sensor Network ◽  
Network Performance ◽  
Mathematic Model ◽  
Initial Distribution ◽  
Wireless Sensor ◽  
Network Coverage ◽  
Coverage Optimization

For all of types of applications in wireless sensor networks (WSNs), coverage is a fundamental and hot topic research issue. To monitor the interest field and obtain the valid data, the paper proposes a wireless sensor network coverage optimization model based on improved whale algorithm. The mathematic model of node coverage in wireless sensor networks is developed to achieve full coverage for the interest area. For the model, the idea of reverse learning is introduced into the original whale swarm optimization algorithm to optimize the initial distribution of the population. This method enhances the node search capability and speeds up the global search. The experiment shows that this algorithm can effectively improve the coverage of nodes in wireless sensor networks and optimize the network performance.

scholarly journals Enhanced Particle Swarm Optimization for Effective Relay Nodes Deployment in Wireless Sensor Networks

2021 ◽  
Vol 13 (1) ◽  
pp. 53-73
Author(s):  
Bader Alshaqqawi ◽  
Sardar Anisul Haque ◽  
Mohammed Alreshoodi ◽  
Ibrahim Alsukayti
Keyword(s):  
Wireless Sensor Networks ◽  
Particle Swarm Optimization ◽  
Sensor Networks ◽  
Greedy Algorithm ◽  
Pso Algorithm ◽  
Optimization Process ◽  
Wireless Sensor ◽  
Optimization Approach ◽  
Swarm Optimization ◽  
The Greedy Algorithm

One of the critical design problems in Wireless Sensor Networks (WSNs) is the Relay Node Placement (RNP) problem. Inefficient deployment of RNs would have adverse effects on the overall performance and energy efficiency of WSNs. The RNP problem is a typical example of an NP-hard optimization problem which can be addressed using metaheuristics with multi-objective formulation. In this paper, we aimed to provide an efficient optimization approach considering the unconstrained deployment of energy-harvesting RNs into a pre-established stationary WSN. The optimization was carried out for three different objectives: energy consumption, network coverage, and deployment cost. This was approached using a novel optimization approach based on the integration of the Particle Swarm Optimization (PSO) algorithm and a greedy technique. In the optimization process, the greedy algorithm is an essential component to provide effective guidance during PSO convergence. It supports the PSO algorithm with the required information to efficiently alleviate the complexity of the PSO search space and locate RNs in the spots of critical significance. The evaluation of the proposed greedy-based PSO algorithm was carried out with different WSN scenarios of varying complexity levels. A comparison was established with two PSO variants: the classical PSO and a PSO hybridized with the pattern search optimizer. The experimental results demonstrated the significance of the greedy algorithm in enhancing the optimization process for all the considered PSO variants. The results also showed how the solution quality and time efficiency were considerably improved by the proposed optimization approach. Such improvements were achieved using a simple integration technique without adding to the complexity of the system and introducing additional optimization stages. This was more evident in the RNP scenarios of considerably large search spaces, even with highly complex and challenging setups.

scholarly journals Using Mobile Elements as Dynamic Bridges in Sparse Wireless Sensor Networks

2014 ◽  
pp. 107
Author(s):  
Hoang Dang Hai ◽  
Thorsten Strufe ◽  
Pham Thieu Nga ◽  
Hoang Hong Ngoc ◽  
Nguyen Anh Son ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Exchange ◽  
Data Transfer ◽  
Mobile Elements ◽  
Sensor Nodes ◽  
Pollution Monitoring ◽  
Wireless Sensor ◽  
Mobile Nodes ◽  
Road Distance

Sparse  Wireless  Sensor  Networks  using several  mobile  nodes  and  a  small  number  of  static sensor  nodes  have  been  widely  used  for  many applications,  especially  for  traffic-generated  pollution monitoring.  This  paper  proposes  a  method  for  data collection and forwarding using Mobile Elements (MEs), which are moving on predefined trajectories in contrast to previous works that use a mixture of MEsand static nodes. In our method, MEscan be used as data collector as well as dynamic bridges for data transfer. We design the  trajectories  in  such  a  way,  that  they  completely cover  the  deployed  area  and  data  will  be  gradually forwarded  from  outermost  trajectories  to  the  center whenever  a  pair  of MEs contacts  each  other  on  an overlapping road distance of respective trajectories. The method  is based  on  direction-oriented  level  and  weight assignment.  We  analyze  the  contact  opportunity  for data  exchange  while MEs move.  The  method  has  been successfully tested for traffic pollution monitoring in an urban area.

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