Research on coverage control algorithm based on wireless sensor network

Zhou, Jie; Xu, Min; Yi, Lu; Yang, Rui

doi:10.1051/matecconf/202030903003

Cited by 3 publications

(6 citation statements)

References 7 publications

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“…The method used in [25] is able to bring down the number of active WSNs and energy consumption ensuring an adequate percentage of coverage. An improved immune fuzzy genetic algorithm (IIFGA) is suggested in [26] to remove redundancy among WSNs and to select a set of working WSNs without lowering the quality of the coverage much. Both [25,26] lower the number of active WSNs in the target area though they are unable to produce an optimal (minimum) value of the number of active WSNs.…”

Section: Related Workmentioning

confidence: 99%

“…An improved immune fuzzy genetic algorithm (IIFGA) is suggested in [26] to remove redundancy among WSNs and to select a set of working WSNs without lowering the quality of the coverage much. Both [25,26] lower the number of active WSNs in the target area though they are unable to produce an optimal (minimum) value of the number of active WSNs. Besides, the coverage model of 2D WSNs is used in [22][23][24][25][26][27].…”

Section: Related Workmentioning

confidence: 99%

“…Both [25,26] lower the number of active WSNs in the target area though they are unable to produce an optimal (minimum) value of the number of active WSNs. Besides, the coverage model of 2D WSNs is used in [22][23][24][25][26][27]. Being Omni-directional (circular), the coverage model of a 2D WSN is very simple, but it cannot be implemented in reality.…”

Section: Related Workmentioning

confidence: 99%

“…Both the approaches (PA_1 and PA_2) which utilize the 3D coverage model of VSN is a more realistic model of camera coverage than the 2D coverage model of VSN as considered in [1][2][3][4][5][6][7][8] and 2D Omni-directional coverage model of WSN as considered in [13,[22][23][24][25][26][27]. Besides, the proposed approaches (PA_1 and PA_2) provide optimal and near-optimal values for the number of active VSNs and energy consumption respectively unlike [1-9, 13, 14, 17].…”

Section: Related Workmentioning

confidence: 99%

“…Unlike [22][23][24]27], in the present work, the whole area needs to be monitored and the motivation of the present work is to minimize the number of 3D active VSNs without losing initial area coverage which is the percentage of coverage when all VSNs were active. The approach PA_1/(PA_2) of the work proposed in this paper is able to minimize the number of active VSNs under the coverage and connectivity constraints unlike [25,26], while PA_1/(PA_2) provides an optimal/(near-optimal) solution.…”

Section: Related Workmentioning

confidence: 99%

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Efficient approaches to optimize energy consumption in 3D wireless video sensor network under the coverage and connectivity constraints

2023

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Two advanced approaches (PA_1, and PA_2) based on a realistic 3D model of video sensor nodes (VSNs) deployed over a 2D target area are proposed to minimize energy consumption in the network maintaining area coverage and connectivity. Reducing the number of active VSNs decreases energy consumption, but lessens area coverage and connectivity too. These conflicting issues are resolved and an optimal solution is obtained by using an integer linear programming based approach PA_1. The problem being an NP-Hard one, PA_1 is not tractable for large instances, and a heuristic PA_2 based on an advanced genetic algorithm, is also developed to obtain the near-optimal solution. Simulation studies are carried out to compare the performance of PA_1, and PA_2 with the other three state-of-the-art approaches (APP_5, APP_6, and ET_3). Among three existing approaches, APP_6/(ET_3) is the best in energy consumption/(area coverage). It is observed that for the same simulation environment, both PA_1, and PA_2 guarantee higher network services, by reducing energy consumption by 40.85% and 33.34% respectively compared to the best existing approach APP_6; and as well as by increasing area coverage by 0.94% than the best existing approach ET_3 for the node density 150 on the target area of size 75x75 square meter. Between PA_1 and PA_2, PA_2 generates sub-optimal solution and PA_1 substantiates its superiority by reducing energy consumption by 11.26% than PA_2 without losing area coverage for the same simulation environment.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Efficient approaches to optimize energy consumption in 3D wireless video sensor network under the coverage and connectivity constraints

2023

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Efficient Approaches to Optimize Energy Consumption in 3D Wireless Video Sensor Network under the Coverage and Connectivity Constraints

Bairagi

Mitra

Bhattacharya

2022

Preprint

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Generative Deep Belief Differential Lotka Volterra-Based Data Transmission in Wsn for Real-Time Health Care Monitoring

Sachin,

Sarvagya

2024

Preprint

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Wireless Sensor Networks (WSNs) have considerable awareness owing to their extensive scope of applications, to name a few being, natural disaster prohibition, health care monitoring video surveillance and so on. Moreover, the energy consumed during communication is a substantial quantity of energy loss and the routing protocol is the most suitable means to address this issue by fine-tuning weights to conserve the energy. Therefore the main issue experienced by WSN is the routing and data transmission and hence distinct points of reference need to be addressed when designing the routing protocol for real time healthcare monitoring. Motivated by these issues, in this work a method called, Probabilistic Generative Deep Belief and Differential Game Lotka Volterra (PGDB-DGLV) based data transmission in WSN for real-time health care monitoring is proposed. The PGDB-DGLV method is split into two sections, namely, optimal route discovery and robust data transmission. To measure route stability Balanced Centrality and Probabilistic Generative Deep Belief Network algorithm is designed. In the proposed model, a fine-tuned adjacency matrix employing the Stable Balanced Centrality function is defined to determine the next hop based on certain parameters like, hop count, residual energy and link quality. These parameters are salient assessments in adjacent node selections with which routing can be performed. Also, the initialization of the adjacency matrix and its corresponding fine-tuning is performed via the Probabilistic Generative Deep Belief Network. Second, to minimize the transmission speed and therefore reduce packet loss significantly, two kinds of game models, i.e., cooperative and cooperative models are constructed. Finally, the Lotka Volterra function is applied to ensure robust data transmission. At last, the performance of the proposed PGDB-DGLV method is evaluated using different evaluation metrics they are energy consumption, throughput transmission speed and packet loss rate. Finally, the evaluated results are compared with the prevailing data transmission methods. Among these methods, the proposed PGDB-DGLV method has achieved a better transmission speed with minimum energy consumption and maximum throughput considerably.

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Research on coverage control algorithm based on wireless sensor network

Cited by 3 publications

References 7 publications

Efficient approaches to optimize energy consumption in 3D wireless video sensor network under the coverage and connectivity constraints

Efficient approaches to optimize energy consumption in 3D wireless video sensor network under the coverage and connectivity constraints

Efficient Approaches to Optimize Energy Consumption in 3D Wireless Video Sensor Network under the Coverage and Connectivity Constraints

Generative Deep Belief Differential Lotka Volterra-Based Data Transmission in Wsn for Real-Time Health Care Monitoring

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