Coverage Control Algorithm for DSNs Based on Improved Gravitational Search

Yao, Yindi; Liao, Huanmin; Xiong, Li; Zhao, Feng; Yang, Xuan; Hu, Shanshan

doi:10.1109/jsen.2022.3152792

Cited by 11 publications

(3 citation statements)

References 25 publications

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“…The SSA algorithm is an intelligent optimization method inspired by the behavior of sparrow population foraging and predator avoidance. It has been widely used in many fields [29][30][31][32]. This algorithm continuously conducts local searches, selecting local operations that can improve the solution each time, and updating the current solution to achieve position optimization [33].…”

Section: Sparrow Search Algorithmmentioning

confidence: 99%

Prediction Model of Nitrogen, Phosphorus, and Potassium Fertilizer Application Rate for Greenhouse Tomatoes under Different Soil Fertility Conditions

Yu,

Luo,

Bai

et al. 2024

Agronomy

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To reach the target yield of crops, nutrient management is essential. Selecting the appropriate prediction model and adjusting the nutrient supply based on the actual situation can effectively improve the nutrient utilization efficiency, crop yield, and product quality. Therefore, a prediction model of the NPK fertilizer application rate for greenhouse tomatoes under the target yield was studied in this study. Under low, medium, and high soil fertility conditions, a neural network prediction model based on the sparrow search algorithm (SSA-NN), a neural network prediction model based on the improved sparrow search algorithm (ISSA-NN), and a neural network prediction model based on the hybrid algorithm (HA-NN) were used to predict the NPK fertilizer application rate for greenhouse tomatoes. The experimental results indicated that the evaluation indexes (i.e., the mean square error (MSE), explained variance score (EVS), and coefficient of determination (R2)) of the HA-NN prediction model proposed in this study were superior than the SSA-NN and ISSA-NN prediction models under three different soil fertility conditions. Under high soil fertility, compared with the SSA-NN prediction model, the MSE of the ISSA-NN and HA-NN prediction models decreased to 0.007 and 0.005, respectively; the EVS increased to 0.871 and 0.908, respectively; and the R2 increased to 0.862 and 0.899, respectively. This study showed that the HA–NN prediction model was superior in predicting the NPK fertilizer application rate for greenhouse tomatoes under three different soil fertility conditions. Due to the significance of NPK fertilizer application rate prediction for greenhouse tomatoes, this technique is expected to bring benefits to agricultural production management and decision support.

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Section: Sparrow Search Algorithmmentioning

confidence: 99%

Prediction Model of Nitrogen, Phosphorus, and Potassium Fertilizer Application Rate for Greenhouse Tomatoes under Different Soil Fertility Conditions

Yu,

Luo,

Bai

et al. 2024

Agronomy

View full text Add to dashboard Cite

show abstract

“…To calculate the coverage rate (COVR) of WMSNs, the monitoring area is divided into M grids. A grid C j being sensed successfully by S i once (1) and ( 2) are both satisfied [29] dis…”

Section: A Multimedia Sensing Modelmentioning

confidence: 99%

“…In literature [28], a virtual angle boundary-aware PSO is designed to improve the coverage and convergence speed according to the relationship among the angles of different sensors. By introducing the global optimal position to update the particle's position and defining the virtual force of uncovered grids for poor particles, Yao et al [29] proposed a coverage control algorithm based on an improved gravitational search algorithm, which effectively improved the convergence speed. The work of [30] proposed a constrained artificial fish-swarm algorithm to optimize the sensor distribution by regarding the sensing centroid as artificial fish, which improved the coverage rate in the monitoring area.…”

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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Improved sand cat swarm optimization algorithm for enhancing coverage of wireless sensor networks

Li,

Zhao,

Wang

et al. 2024

Measurement

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Coverage Control Algorithm for DSNs Based on Improved Gravitational Search

Cited by 11 publications

References 25 publications

Prediction Model of Nitrogen, Phosphorus, and Potassium Fertilizer Application Rate for Greenhouse Tomatoes under Different Soil Fertility Conditions

Prediction Model of Nitrogen, Phosphorus, and Potassium Fertilizer Application Rate for Greenhouse Tomatoes under Different Soil Fertility Conditions

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

Improved sand cat swarm optimization algorithm for enhancing coverage of wireless sensor networks

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