A Power-Efficient Clustering Protocol for Coal Mine Face Monitoring with Wireless Sensor Networks Under Channel Fading Conditions

Ren, Pinyi; Qian, Jiansheng

doi:10.3390/s16060835

Cited by 13 publications

(5 citation statements)

References 30 publications

(27 reference statements)

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“…A network model of multiple transmitters and one receiver is included in the current wireless sensor networks [ 26 , 27 , 28 ]. Provided that data can be sent to the fusion center directly with N nodes and L links, each source node will put packets into the transmission queue in accordance with FIFO principle, as shown in Figure 1 .…”

Section: Problem Definitionmentioning

confidence: 99%

Power Allocation Based on Data Classification in Wireless Sensor Networks

Wang

Zhou

2017

Sensors

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Limited node energy in wireless sensor networks is a crucial factor which affects the monitoring of equipment operation and working conditions in coal mines. In addition, due to heterogeneous nodes and different data acquisition rates, the number of arriving packets in a queue network can differ, which may lead to some queue lengths reaching the maximum value earlier compared with others. In order to tackle these two problems, an optimal power allocation strategy based on classified data is proposed in this paper. Arriving data is classified into dissimilar classes depending on the number of arriving packets. The problem is formulated as a Lyapunov drift optimization with the objective of minimizing the weight sum of average power consumption and average data class. As a result, a suboptimal distributed algorithm without any knowledge of system statistics is presented. The simulations, conducted in the perfect channel state information (CSI) case and the imperfect CSI case, reveal that the utility can be pushed arbitrarily close to optimal by increasing the parameter V, but with a corresponding growth in the average delay, and that other tunable parameters W and the classification method in the interior of utility function can trade power optimality for increased average data class. The above results show that data in a high class has priorities to be processed than data in a low class, and energy consumption can be minimized in this resource allocation strategy.

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Section: Problem Definitionmentioning

confidence: 99%

Power Allocation Based on Data Classification in Wireless Sensor Networks

Wang

Zhou

2017

Sensors

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“…4 For this kind of WSNs, the wireless communication environments are supposed to be closed or semi-closed, and the communication channels are supposed to be the fading ones. [5][6][7] Taking the underground coal mine supervision system as an example, the communication environment in coal mine is becoming more and more complex since the underground pressure, the high-level water pressure, and the coal mine gases are the main threats for the coal mine safety mining. 8 Topology control is the basis of WSNs' network construction and communication, which directly affects the performance of network's routing protocol, the time synchronization protocol, the localization protocol, and so on.…”

Section: Introductionmentioning

confidence: 99%

One energy-efficient random-walk topology evolution method for underground wireless sensor networks

Huang

Chen

Han

et al. 2018

International Journal of Distributed Sensor Networks

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Aimed at the limited energy supply and imperfect topological tolerance for underground wireless sensor networks, one energy-efficient random-walk scale-free topology model is proposed in this article, and a power network topology structure with adjustable rate index gets generated. At first, the network is divided into several clusters, and the cluster heads are selected with the use of random-walk strategy. During the growth of the network, with the introduction of preferred connection for scale-free network, together with considering both the node's residual energy and the distance among nodes, nodes with larger residual energy present higher connectivity probability, so that the energy balance of the network gets realized. Simulation results show that the communication among the cluster heads selected by the proposed random-walk scale-free topology model presents not only the power-law characteristics of scale-free networks but also has better stability, higher fault tolerance, and it can still balance the energy consumption for nodes and the network and therefore can prolong the lifetime of the network.

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“…Richards et al introduced a weighted method of clustering the individual units of a segmented image to analyze geologic maps generated from experts' analysis of remote sensing images [5]. Ren and Qian proposed a novel power-efficient and anti-fading clustering based on a cross-layer that is specific to the time-varying fading characteristics of channels in the monitoring of coal mine faces with wireless sensor networks [6]. Weintraub et al proposed cluster analysis approach based on K-mean algorithm which approximates reasonably well the original large scale, detailed models.…”

Section: Introductionmentioning

confidence: 99%

Multi-Attribute Technological Modeling of Coal Deposits Based on the Fuzzy TOPSIS and C-Mean Clustering Algorithms

Gligorić

Beljić

et al. 2016

Energies

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Abstract:The main aim of a coal deposit model is to provide an effective basis for mine production planning. The most applied approach is related to block modeling as a reasonable global representation of the coal deposit. By selection of adequate block size, deposits can be well represented. A block has a location in XYZ space and is characterized by adequate attributes obtained from drill holes data. From a technological point of view, i.e., a thermal power plant's requirements, heating value, sulfur and ash content are the most important attributes of coal. Distribution of attributes' values within a coal deposit can vary significantly over space and within each block as well. To decrease the uncertainty of attributes' values within blocks the concept of fuzzy triangular numbers is applied. Production planning in such an environment is a very hard task, especially in the presence of requirements. Such requirements are considered as target values while the values of block attributes are the actual values. To make production planning easier we have developed a coal deposit model based on clustering the relative closeness of actual values to the target values. The relative closeness is obtained by the TOPSIS method while technological clusters are formed by fuzzy C-mean clustering. Coal deposits are thus represented by multi-attribute technological mining cuts.

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A Power-Efficient Clustering Protocol for Coal Mine Face Monitoring with Wireless Sensor Networks Under Channel Fading Conditions

Cited by 13 publications

References 30 publications

Power Allocation Based on Data Classification in Wireless Sensor Networks

Power Allocation Based on Data Classification in Wireless Sensor Networks

One energy-efficient random-walk topology evolution method for underground wireless sensor networks

Multi-Attribute Technological Modeling of Coal Deposits Based on the Fuzzy TOPSIS and C-Mean Clustering Algorithms

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