The Coverage Optimization for Wireless Sensor Networks Based on Quantum-Inspired Cultural Algorithm

Guo, Yinan; Liu, Dandan; Liu, Yun; Chen, Meirong

doi:10.1007/978-3-642-38524-7_9

Cited by 8 publications

(9 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To eliminate the experimental error caused by randomness, the simulation was run for 20 independent runs and the average of results is calculated. The performance of the proposed algorithm is evaluated based on probabilistic sensing model and compare its performance with CSAPO [5] and QCA [3] algorithms.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…To verify the validity of the proposed deployment algorithm, this experiment compares the performance of the proposed algorithm with QCA algorithm [3]. Simulation utilizes 25 mobile sensor nodes are randomly distributed within 20x20 m 2 sensor field with initial coverage is 84% as shown in Fig.…”

Section: Methodsmentioning

confidence: 99%

“…Both objectives are achieved in separate phases with coverage maximization in the first phase while energy conservation in the second phase. Quantum inspired Cultural Algorithm (QCA) based on a probabilistic sensing model has been adopted in[3] to maximize the coverage area. This algorithm uses dual evolution structure to improve the performance of WSN in term of coverage area and redundant area.…”

mentioning

confidence: 99%

See 2 more Smart Citations

C19. Immune node deployment algorithm for mobile wireless sensor networks with limited mobility based on probabilistic sensing model

Abo‐Zahhad

Ahmed

Sabor

et al. 2015

2015 32nd National Radio Science Conference (NRSC)

View full text Add to dashboard Cite

Coverage has direct effect on the network performance, thus it considered as the measure of quality of service in WSNs. The deployment strategy of sensor nodes in the sensor field is the most critical factor related to the network coverage. So, in this paper a centralized deployment algorithm based on immune optimization algorithm is proposed to improve the coverage of mobile sensor networks. The proposed algorithm redeploys the random deployed sensor nodes to maximize the coverage area based on a probabilistic sensing model. Moreover, the proposed algorithm limits the moving distance of mobile sensor nodes to reduce the dissipated energy in mobility and to ensure the connectivity among the sensor nodes. The performance of the proposed algorithm is compared with the CSAPO algorithm using MATLAB simulation. Simulation results show that the proposed algorithm outperforms the CSAPO algorithm in terms of the network coverage, mobility cost and convergence speed.

show abstract

Section: Simulation Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

C19. Immune node deployment algorithm for mobile wireless sensor networks with limited mobility based on probabilistic sensing model

Abo‐Zahhad

Ahmed

Sabor

et al. 2015

2015 32nd National Radio Science Conference (NRSC)

View full text Add to dashboard Cite

show abstract

“…Many coverage maximisation algorithms for WSNs have appeared in the literature [2][3][4][5][6][7][8][9]. In [2], authors utilise the mobility for all sensor nodes in the network to maximise the coverage rate based on a binary sensing model.…”

Section: Related Workmentioning

confidence: 99%

“…In the literature [1], the coverage problem in WSNs has been studied either as target coverage or area coverage. The area coverage algorithms are used to maximise the covered area of the sensing field [2][3][4][5][6][7][8][9]. On the other hand, the target coverage algorithms are adopted to maximise the number of targets that could be covered based on the assumption that the sensing field is divided into targets [10,11].…”

Section: Introductionmentioning

confidence: 99%

Utilisation of multi‐objective immune deployment algorithm for coverage area maximisation with limit mobility in wireless sensors networks

Abo‐Zahhad

Ahmed

Sabor

et al. 2015

IET wirel. sens. syst.

View full text Add to dashboard Cite

Coverage is one of the most important performance metrics for wireless sensor network (WSN) since it reflects how well a sensor field is monitored. The coverage issue in WSNs depends on many factors, such as the network topology, sensor sensing model and the most important one is the deployment strategy. Random deployment of the sensor nodes can cause coverage holes formulation. This problem is non-deterministic polynomial-time hard problem. So in this study, a new centralised deployment algorithm based on the immune optimisation algorithm is proposed to relocate the mobile nodes after the initial configuration to maximise the coverage area. Moreover, the proposed algorithm limits the moving distance of the mobile nodes to reduce the dissipation energy in mobility and to ensure the connectivity among the sensor nodes. The performance of the proposed algorithm is compared with the previous algorithms using Matlab simulation. Simulation results clear that the proposed algorithm based on binary and probabilistic sensing models improves the network coverage and the redundant covered area with minimum moving consumption energy. Furthermore, the simulation results show that the proposed algorithm also works when obstacles appear in the sensing field.

show abstract

Cultural Algorithms (CAs)

Jalili

2022

Engineering Optimization: Methods and Applications

View full text Add to dashboard Cite

The Coverage Optimization for Wireless Sensor Networks Based on Quantum-Inspired Cultural Algorithm

Cited by 8 publications

References 7 publications

C19. Immune node deployment algorithm for mobile wireless sensor networks with limited mobility based on probabilistic sensing model

C19. Immune node deployment algorithm for mobile wireless sensor networks with limited mobility based on probabilistic sensing model

Utilisation of multi‐objective immune deployment algorithm for coverage area maximisation with limit mobility in wireless sensors networks

Cultural Algorithms (CAs)

Contact Info

Product

Resources

About