Coverage Enhancement Strategy for WSNs Based on Virtual Force-Directed Ant Lion Optimization Algorithm

Yao, Yindi; Li, Ying; Xie, Dangyuan; Hu, Shanshan; Wang, Chen; Li, Yangli

doi:10.1109/jsen.2021.3091619

Cited by 31 publications

(7 citation statements)

References 34 publications

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“…Initialize the population of ants and antlions randomly Find the elite (the best antlion) while termination condition is not satisfied do for each ant do Select an antlion using Roulette wheel Decrease the radius of ants random walk to mimic the sliding process of an ant inside the trap Create a random walk and normalize it to keep it in the research space end for end while deployment [126]- [129]. In [129], the authors combined the ALO algorithm with the virtual force to optimize the coverage rate and the moving distance of mobile sensor nodes. The authors improved the ALO algorithm using three methods.…”

Section: Volume 4 2016mentioning

confidence: 99%

A Survey of Artificial Intelligence Based WSNs Deployment Techniques and Related Objectives Modeling

Zaimen¹,

Brahmia²,

Moalic

et al. 2022

IEEE Access

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Recent advances in hardware and communication technologies have accelerated the deployment of billions of wireless sensors. This transformation has created a wide range of applications adapted to the evolving trends of our daily life requirements. Wireless sensor networks (WSNs) could be deployed in several target areas including buildings, forests, oceans, and smart cities. Nevertheless, finding the optimal location for each sensor node is a challenging task, typically when the environment involves heterogeneous obstacles. Many approaches and methods have been proposed to deal with the problem of WSN deployment, each addressing one or more objectives and constraints, such as network coverage, lifetime, connectivity, and energy consumption. The purpose of this survey paper is to provide the needed background to understand and study the WSNs deployment problem with a focus on its two key aspects: the optimization model and the solving methods based on artificial intelligence (AI). Additionally, it covers recent works on WSNs deployment and identifies their advantages and limitations. Furthermore, simulation experiments were carried out to compare the performance of widely used algorithms in the context of WSNs deployment problem, primarily genetic algorithm, particle swarm optimization, flower pollination, and ant colony optimization. Finally, this paper discusses and highlights several open challenges and research issues that must be explored in the future.

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Section: Volume 4 2016mentioning

confidence: 99%

A Survey of Artificial Intelligence Based WSNs Deployment Techniques and Related Objectives Modeling

Zaimen¹,

Brahmia²,

Moalic

et al. 2022

IEEE Access

View full text Add to dashboard Cite

show abstract

“…To evaluate the performance of the final COVR in WMSNs, AASO is compared with IALO [25], IAPSO, and VFA, where VFA includes attraction force from uncovered grids and repulsion force between sensor nodes. The main parameters are shown in Table VI.…”

Section: B Aaso For Coverage Enhancement In Wmsnsmentioning

confidence: 99%

“…SI has been developed rapidly due to its simplicity, strong adaptability, and high stability [22], [23]. The application of SI in coverage enhancement has become the focus of scholars [24], [25], [26]. The coverage problem of DSNs is solved by researchers with particle swarm optimization (PSO), which is one of the most classical SI algorithms.…”

mentioning

confidence: 99%

Coverage Enhancement Strategy in WMSNs Based on a Novel Swarm Intelligence Algorithm: Army Ant Search Optimizer

et al. 2022

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As one of the most crucial scenarios of the Internet of Things (IoT), wireless multimedia sensor networks (WMSNs) pay more attention to the information-intensive data (e.g., audio, video, image) for remote environments. The area coverage reflects the perception of WMSNs to the surrounding environment, where a good coverage effect can ensure effective data collection. Given the harsh and complex physical environment of WMSNs, which easily form the sensing overlapping regions and coverage holes by random deployment. The intention of our research is to deal with the optimization problem of maximizing the coverage rate in WMSNs. By proving the NP-hard of the coverage enhancement of WMSNs, inspired by the predation behavior of army ants, this article proposes a novel swarm intelligence (SI) technology army ant search optimizer (AASO) to solve the above problem, which is implemented by five operators: army ant and prey initialization, recruited by prey, attack prey, update prey, and build ant bridge. The simulation results demonstrate that the optimizer shows good performance in terms of exploration and exploitation on benchmark suites when compared to other representative SI algorithms. More importantly, coverage enhancement AASO-based in WMSNs has better merits in terms of coverage effect when compared to existing approaches.

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“…Most existing methods use swarm-intelligence algorithms to enhance network coverage quality but often ignore the network’s life cycle. For the problem of the high aggregation and poor uniformity of random deployment nodes, a suitable secondary deployment strategy can be used to solve the problem [ 9 ]. According to the idea of clustering and computational geometry, the target area is partitioned into different clusters, and a method that calculates the optimal sensing radius of different structures is used to form HWSNs clusters in which nodes move to the center–of–mass position, which can reduce the network average movement distance.…”

Section: Introductionmentioning

confidence: 99%

VKECE-3D: Energy-Efficient Coverage Enhancement in Three-Dimensional Heterogeneous Wireless Sensor Networks Based on 3D-Voronoi and K-Means Algorithm

Gou

Guo

et al. 2023

Sensors

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During these years, the 3D node coverage of heterogeneous wireless sensor networks that are closer to the actual application environment has become a strong focus of research. However, the direct application of traditional two-dimensional planar coverage methods to three-dimensional space suffers from high application complexity, a low coverage rate, and a short life cycle. Most methods ignore the network life cycle when considering coverage. The network coverage and life cycle determine the quality of service (QoS) in heterogeneous wireless sensor networks. Thus, energy-efficient coverage enhancement is a significantly pivotal and challenging task. To solve the above task, an energy-efficient coverage enhancement method, VKECE-3D, based on 3D-Voronoi partitioning and the K-means algorithm is proposed. The quantity of active nodes is kept to a minimum while guaranteeing coverage. Firstly, based on node deployment at random, the nodes are deployed twice using a highly destructive polynomial mutation strategy to improve the uniformity of the nodes. Secondly, the optimal perceptual radius is calculated using the K-means algorithm and 3D-Voronoi partitioning to enhance the network coverage quality. Finally, a multi-hop communication and polling working mechanism are proposed to lower the nodes’ energy consumption and lengthen the network’s lifetime. Its simulation findings demonstrate that compared to other energy-efficient coverage enhancement solutions, VKECE-3D improves network coverage and greatly lengthens the network’s lifetime.

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Coverage Enhancement Strategy for WSNs Based on Virtual Force-Directed Ant Lion Optimization Algorithm

Cited by 31 publications

References 34 publications

A Survey of Artificial Intelligence Based WSNs Deployment Techniques and Related Objectives Modeling

A Survey of Artificial Intelligence Based WSNs Deployment Techniques and Related Objectives Modeling

Coverage Enhancement Strategy in WMSNs Based on a Novel Swarm Intelligence Algorithm: Army Ant Search Optimizer

VKECE-3D: Energy-Efficient Coverage Enhancement in Three-Dimensional Heterogeneous Wireless Sensor Networks Based on 3D-Voronoi and K-Means Algorithm

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