A Metaheuristic Algorithm for Coverage Enhancement of Wireless Sensor Networks

Wang, Zhigang; Tian, Li; Wu, Wenxing; Lin, Lianhai; Li, Zongjin; Tong, Yinghua

doi:10.1155/2022/7732989

Cited by 6 publications

(4 citation statements)

References 40 publications

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“…Some classical swarm-intelligence algorithms are commonly employed to decide coverage enhancement tasks in WSNs. For example, Wang et al [ 14 ] came up with a heuristic algorithm, the Enhanced Sparrow Search Algorithm according to Firefly (EFSSA), to improve the sparrow search algorithm by using an elite inverse solution strategy and the firefly algorithm to avoid the local optimum problem during population search to enhance the network’s coverage quality. The literature [ 15 ] proposed the virtual force-embedded Lévy flight grey wolf optimization algorithm (VFLGWO).…”

Section: Related Workmentioning

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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Section: Related Workmentioning

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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“…Equation (13) shows that the data output deviation index of the sensor set, γ i→j , is decided by the measurement deviation of the sensor itself, and it considers the influence of the sensor failure rate. d i,j = 1 means that the physical parameter F j can be observed by sensor S i .…”

Section: Sensor Allocation Model and Algorithmmentioning

confidence: 99%

“…Furthermore, another application area includes WSNs, which have helped improve smart environments in various fields, such as intelligent manufacturing, structural health monitoring, smart transport and cities, and energy consumption [11]. Their review focuses on where to locate the sensors in every physical area to increase the sensing coverage [12,13], lifetime, and energy savings [14,15] at minimum costs [16].…”

Section: Introductionmentioning

confidence: 99%

Optimal Sensor Deployment for Parameter Estimation Precision by Integrating Bayesian Networks in Wet-Grinding Systems

He¹,

Wu²,

Sun³

et al. 2023

Applied Sciences

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Accurately and efficiently determining a system’s physical variables is crucial for precise product-quality control. This study proposes a novel method for optimal sensor deployment to increase the accuracy of sensing data for physical variables and ensure the timely detection of the product’s particle size in a wet-grinding system. This approach involves three steps. First, a Bayesian network (BN) is designed to model the cause–effect relationship between the physical variables by applying the path model. The detectability is determined to confirm that the mean shifts of all the physical variables are identifiable using sensor sets in the wet-grinding system. Second, the sensing location of accelerometers mounted on the chamber shell is determined according to the coupled computational fluid dynamics–discrete element method simulations. Third, the shuffled frog leaping algorithm is developed by combining the BN to minimize the maximum data output deviation index among all sensor sets and sensory costs; this is achieved under the constraints of the mean shift detectability, achieving optimum sensor allocation. Subsequently, a case study is performed on a zirconia powder production process to demonstrate that the proposed approach minimizes the requirements of the data output deviation index, sensory costs, and detectability. The proposed approach is systematic and universal; it can be integrated into monitor architecture for parameter estimation in other complex production systems.

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“…Simulation results show that compared with previous work, IALO provides higher coverage, makes the sensor distribution more uniform, and efectively reduces the deployment cost. Wang et al proposed a coverage algorithm of wireless sensor networks based on an improved meta-heuristic algorithm [21]. By establishing the sensor deployment coverage model, they transformed the sensor network coverage problem into a high-dimensional multimodal function optimization problem.…”

Section: Introductionmentioning

confidence: 99%

Optimal Deployment for Hybrid Sensor Networks Based on Efficient Node Configuration

Sun,

Peng,

Wang

et al. 2023

International Journal of Intelligent Systems

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Hybrid sensor networks, which contain mobile nodes and stationary nodes, are being used more and more widely. The second deployment of mobile nodes is a key problem to be solved, and the deployment performance of the network directly affects the monitoring effect of the network. Optimizing the configuration ratio of the two nodes can effectively reduce the network cost. In this paper, under the premise of knowing the coverage of the required monitoring area, the impact of sensor devices on node configuration is studied through parameter analysis, and the number and types of sensors that should be deployed in the hybrid sensor network are deduced, which can be conveniently and accurately used to design the actual hybrid sensor network. At the same time, for the secondary deployment of mobile nodes, this paper proposes a new mobile coverage method BS-CCP (box search and concentric circle positioning) to improve the coverage of the hybrid sensor network and maximize the coverage of the target area with the specified sensor types and numbers. Compared with existing work, the method in this paper reduces the number of iterations and reduces the number of required nodes. Comparing BS-CCP with the existing network mobile coverage algorithm, the experimental results show that the coverage obtained by this method is larger and more efficient.

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A Metaheuristic Algorithm for Coverage Enhancement of Wireless Sensor Networks

Cited by 6 publications

References 40 publications

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

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

Optimal Sensor Deployment for Parameter Estimation Precision by Integrating Bayesian Networks in Wet-Grinding Systems

Optimal Deployment for Hybrid Sensor Networks Based on Efficient Node Configuration

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