Wireless sensor network minimum beacon set selection algorithm based on tree model

Wu, Bin; Luo, Jian; Yang, Chaoyu

doi:10.1007/s00521-016-2734-5

Cited by 5 publications

(5 citation statements)

References 16 publications

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“…Suppose the coordinates of the four vertices of R 3 are A 1 , A 2 , A 3 , and A 4 , which can be solved according to formula (3).…”

Section: Mobile Positioning Based On Improved MCLmentioning

confidence: 99%

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Algorithm of Underground Personnel Positioning Based on Improved Monte Carlo

2021

Wireless Communications and Mobile Computing

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In order to improve the positioning accuracy of underground targets, especially the positioning accuracy of moving targets, an improved weighted Monte Carlo positioning algorithm is proposed. In the sampling initialization stage, the beacon node gradually constructs the sampling area according to the RSSI size and combines the Monte Carlo method to further narrow the range and improve the sampling success rate. In the filtering stage, refer to the sampling area at time and further improve the sample quality at t − 1 after two filterings. In the recollection stage, cooperate with invalid sample sets to reduce the number of recollections and weigh the final samples to improve the positioning accuracy of the nodes to be tested.

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“…Suppose the coordinates of the four vertices of R 3 are A 1 , A 2 , A 3 , and A 4 , which can be solved according to formula (3).…”

Section: Mobile Positioning Based On Improved MCLmentioning

confidence: 99%

“…The positioning of underground personnel in coal mines is essential to safe production. Therefore, the research of underground personnel positioning is also one of the research hotspots of the mine safety monitoring network [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Algorithm of Underground Personnel Positioning Based on Improved Monte Carlo

2021

Wireless Communications and Mobile Computing

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“…In addition to ITDA for the node uniform deployment, RNDA and Linear Bipartite Deployment Algorithm(LBDA) are also applicable [37], [38]. The following part will compare the forwarding optimization algorithm based on energy balance proposed in this paper with the traditional continuous forwarding algorithm.…”

Section: B Comparative Analysis Of Different Algorithmsmentioning

confidence: 99%

Optimal Data Routing Algorithm for Mine WSNs Based on Maximum Life Cycle

Zhou

Huang

2020

IEEE Access

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Coal mine shafts are narrow, the environment is complex and changeable, the wired monitoring system has poor scalability, complicated wiring, and high costs and maintenance costs. The use of Wireless Sensor Networks (WSNs) can effectively solve the defects of the wired system and improve the monitoring level. Node energy is the most valuable resource for WSNs deployed in the complex environment of coal mines. Due to the limited energy of sensor nodes, how to balance the energy consumption, extend the network lifetime and pursue the maximum resource utilization are the hot issues in WSNs research. Underground coal mines have narrow and long lanes, and WSNs are distributed in strips. Therefore, based on the existing research, this paper proposes that when sensor nodes are evenly deployed in narrow and narrow lanes, the node's forwarded data volume and forwarded routing are dynamically adjusted to make nodes Energy consumption is balanced and network life cycle is maximized. The experimental simulation proves that the algorithm proposed in this paper is superior to other algorithms. INDEX TERMS Energy consumption balance, WSNs, data forwarding, best route.

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“…The decisions should be made to obtain good network performance and optimal coverage. These problems can be solved by applying mathematical models [4,5,6] or heuristic models [7,8,9,10,11,12]. The related studies presented above all aim at finding the AP coverage, the optimal locations of the APs and identifying path losses in indoor settings, which is a difficult problem.…”

Section: Introductionmentioning

confidence: 99%

“…-0 log N j (7) Equation 7, in order to determine , that is, the distance between the AP and the cell, the Euclidean distance formula is used [22].…”

mentioning

confidence: 99%

Optimizasyon Teknikleri Kullanılarak Kapalı Alan 802.11ac Kablosuz WLAN Modelleme için Bir Karar Destek Aracı

Ersoy¹,

Yi̇ği̇t²,

Yüksel³

2020

El-Cezeri Fen Ve Mühendislik Dergisi

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Wireless Local Area Network (WLAN) is a technology that provides wireless broadband Internet access in indoor or outdoor environments. It is low cost, easy to use and widely deployed. However, there are many factors that affect its coverage due to the complex structure of indoor settings. For this reason, it is necessary to locate the Access Points (APs) in such a way as to provide the wireless network with the strongest and most consistent coverage. Since these factors directly affect WLAN performance, network specialists often encounter unexpected problems and tend to optimize network coverage by trial and error, taking measurements from access points after they're located. Several metrics such as antenna type, channels, wall structures, throughput can be used for WLAN optimization. Our decision support tool helps network specialists generate a network model by employing these metrics. Determining the coverage of APs in indoor settings is a nonlinear problem. Therefore, our tool is based on the heuristic approaches of the simulated annealing and genetic algorithm. Via this tool, the optimum number of APs to be placed in an indoor setting can be decided and the best AP placement for optimal network coverage can be easily obtained.

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Wireless sensor network minimum beacon set selection algorithm based on tree model

Cited by 5 publications

References 16 publications

Algorithm of Underground Personnel Positioning Based on Improved Monte Carlo

Algorithm of Underground Personnel Positioning Based on Improved Monte Carlo

Optimal Data Routing Algorithm for Mine WSNs Based on Maximum Life Cycle

Optimizasyon Teknikleri Kullanılarak Kapalı Alan 802.11ac Kablosuz WLAN Modelleme için Bir Karar Destek Aracı

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