WSN node coverage optimization algorithm based on global and neighborhood difference DE

Wang, Yi; Peng, Yubo; Chen, Li; Duan, Yanzhong; Li, Jing

doi:10.23919/jcc.2022.03.016

Cited by 5 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wireless sensor networks (WSN [1] ) are an area of intense research that spans multiple disciplines and knowledge domains. A WSN is a distributed sensor network consisting of end sensors that can sense and monitor the external world.…”

Section: Introductionmentioning

confidence: 99%

Research on DV-Hop localization algorithm of sensor nodes optimized by cuckoo search algorithm based on quantum behavior

Chen,

Hu,

Yang

et al. 2023

Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023)

View full text Add to dashboard Cite

Wireless sensor networks are widely used in various fields, including scientific research, industry, and agriculture. However, due to the influence of geographical location and random placement, low localization accuracy and waste of resources may occur. To address these issues, this paper proposes a sensor node DV-Hop localization algorithm based on quantum behavior cuckoo search algorithm, called the QBCS-D algorithm, which is more effective and lower in cost. Firstly, the algorithm establishes a quantized potential well model using individual information in the cuckoo, which obtains the individual optimal solution and the global optimal solution based on the cuckoo update formula. The algorithm uses Monte Carlo random sampling to update the individual extreme value and searches in parallel angle near the individual and global extreme values to improve the local search performance of the algorithm. This reduces the probability of the cuckoo search algorithm falling into local optimal values in the search process. Then, the QBCS algorithm replaces the least square method in the DV-Hop localization algorithm to search for unknown nodes in the target area, transforming the problem of locating unknown nodes into an intelligent optimization problem. Simulation results demonstrate that the combination of the two algorithms can effectively reduce the location error of unknown nodes and improve the positioning accuracy of nodes. The improvement is particularly noticeable when the communication radius is between 25 and 40. Overall, the proposed QBCS-D algorithm offers promising results in improving localization accuracy and optimizing resource utilization in wireless sensor networks.

show abstract

Section: Introductionmentioning

confidence: 99%

Research on DV-Hop localization algorithm of sensor nodes optimized by cuckoo search algorithm based on quantum behavior

Chen,

Hu,

Yang

et al. 2023

Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023)

View full text Add to dashboard Cite

show abstract

“…However, high node deployment density causes energy waste in receiving, processing, and forwarding data, while low density tends to create blind spots and affect monitoring quality. Therefore, to avoid overlapping node coverage that causes monitoring and communication blind areas and reduces the waste of network resources transmission, thus extending the network life cycle, this paper uses the swarm intelligence algorithm to optimize sensor node deployment and improve sensor network coverage [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Coverage Maximization of WSNs in 3D Space Based on Hybrid Lion Swarm Optimization

Wu,

Huang,

Lin

et al. 2023

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

The wireless sensor network (WSN) collects all the physical information required by the user for the target area and transmits the data to the sink or base station via multihop communication. With a limited number of nodes, it is important to reduce the overlapping area between nodes to improve coverage. However, increasing the coverage rate tends to create isolated points, which reduces the connection efficiency. Therefore, considering coverage and connection efficiency simultaneously is a challenging optimization task. In order to solve the above problems, this paper proposes a spatial configuration optimization strategy based on the hybrid lion swarm optimization (HLSO) algorithm. The proposed algorithm is experimented on a large number of benchmark graphs. The experimental results prove very competitive with and even better than different algorithms. Finally, in order to avoid isolated points in the node deployment process, the optimal deployment scheme of the nodes is found by considering both conditions of node coverage and connection efficiency at the same time.

show abstract

A novel self-adaptive multi-strategy artificial bee colony algorithm for coverage optimization in wireless sensor networks

Wang,

Liu,

Rao

et al. 2023

Ad Hoc Networks

View full text Add to dashboard Cite

WSN node coverage optimization algorithm based on global and neighborhood difference DE

Cited by 5 publications

References 0 publications

Research on DV-Hop localization algorithm of sensor nodes optimized by cuckoo search algorithm based on quantum behavior

Research on DV-Hop localization algorithm of sensor nodes optimized by cuckoo search algorithm based on quantum behavior

Coverage Maximization of WSNs in 3D Space Based on Hybrid Lion Swarm Optimization

A novel self-adaptive multi-strategy artificial bee colony algorithm for coverage optimization in wireless sensor networks

Contact Info

Product

Resources

About