Energy efficient wireless infrastructure solution for open pit mine

Vellingiri, Shanthi; Tandur, Deepaknath; Kande, Mallikarjun

doi:10.1109/icacci.2013.6637395

Cited by 4 publications

(7 citation statements)

References 2 publications

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“…The results are shown for macro and small cells separately. However, although the deployment of macro and small cells is a reality in open-pit mines [1], there is no clear definition of what a small cell and a macro cell are in this scenario. Macro and small cell scenarios are normally defined considering heterogeneous networks deployed in urban environments.…”

Section: Resultsmentioning

confidence: 99%

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Radio propagation in open-pit mines: A first look at measurements in the 2.6 GHz band

Almeida

Caldwell²,

Rodríguez

et al. 2017

2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)

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In this paper we present the results of an extensive measurement campaign performed at two large iron ore mining centers in Brazil at the 2.6 GHz band. Although several studies focusing on radio propagation in underground mines have been published, measurement data and careful analyses for open-pit mines are still scarce. Our results aim at filling this gap in the literature. The research is motivated by the ongoing mine automation initiatives, where connectivity becomes critical. This paper presents the first set of results comprising measurements under a gamut of propagation conditions. A second paper detailing sub-GHz propagation is also in preparation. The results indicate that conventional wisdom is wrong, in other words, radio-frequency (RF) propagation in surface mines can be far more elaborate than plain free-space line-of-sight conditions. Additionally, the old mining adage "no two mines alike" seems to remain true in the RF domain.

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Section: Resultsmentioning

confidence: 99%

“…Consequently, coverage holes and data service outages are common. When bespoke Wi-Fi solutions become an insurmountable bottleneck, self-healing IEEE 802.11 mesh networks augmented by proprietary algorithms are the go-to solutions for the vast majority of miners [1]. In short, radio propagation was little more than a nuisance.…”

Section: Introductionmentioning

confidence: 99%

Radio propagation in open-pit mines: A first look at measurements in the 2.6 GHz band

Almeida

Caldwell²,

Rodríguez

et al. 2017

2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)

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“…Second, the usage of part of the 700 MHz spectrum band is under discussion in Brazil to support infrastructure and mission critical communications. Third, the 2.6 GHz band is also close to the 2.4 GHz ISM band, which is widely used in wireless networks deployments in open-pit mines in Brazil, and around the world [1]. Besides these three reasons, sub-6 GHz frequencies are already envisioned as a fundamental part of upcoming 5G technologies, specially in earlier implementation phases [7].…”

Section: A Measurement Scenario and Setupmentioning

confidence: 99%

“…Gradually, new applications such as fleet management, real-time telemetry and GPS-augmentation systems required those voice networks to be complemented by narrowband solutions, such as TETRA and TEDS. Lately, broadband technologies such as WiMAX, Wi-Fi and even LTE coexist with narrowband technologies, supporting applications such as video surveillance, real-time data acquisition and analytics [1], [2].…”

Section: Introductionmentioning

confidence: 99%

5G in Open-Pit Mines: Considerations on Large-Scale Propagation in Sub-6 GHz Bands

Almeida

Caldwell²,

Rodríguez

et al. 2017

2017 IEEE Globecom Workshops (GC Wkshps)

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Abstract-5G will play a pivotal role in the digitization of the industrial sector and is expected to make the best use of every bit of spectrum available. In this light, this paper presents the results of an extensive measurement campaign in two iron-ore open-pit mining complexes, at the 700 MHz and 2.6 GHz bands, considering macro and small cell deployments. The study is further motivated by the rise of unmanned machinery in the mining industry. We present values of path loss exponents, shadow fading standard deviations, autocorrelation distances and inter-frequency crosscorrelation, which are all useful for the future wireless network design, simulation and performance evaluation. The results show that, in order to comply with ultra-reliable communications (URC) availability requirements, larger shadowing margins will have to be considered in the network planning in open-pit mines, when compared to traditional industrial environments. Furthermore, large cross-correlation between the shadowing in both frequency bands limits the gains when using multi-connectivity in order to enhance overall network availability.

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“…Information communication technologies (ICTs), such as wireless communications, sensor networks, global positioning system (GPS), and cloud computing, have been implemented at open-pit-mining sites to facilitate real-time monitoring of the operating status and haulage information of equipment, as well as collection of equipment-tracking data on web servers [30][31][32][33][34][35][36][37][38]. Further, several In this study, an open-pit limestone mine installed with an ICT-based mine-safety-management system was used as the investigation location.…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Network for Predicting Ore Production by Truck-Haulage Systems in Open-Pit Mines

Baek

Choi

2020

Applied Sciences

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This paper proposes a deep neural network (DNN)-based method for predicting ore production by truck-haulage systems in open-pit mines. The proposed method utilizes two DNN models that are designed to predict ore production during the morning and afternoon haulage sessions, respectively. The configuration of the input nodes of the DNN models is based on truck-haulage conditions and corresponding operation times. To verify the efficacy of the proposed method, training data for the DNN models were generated by processing packet data collected over the two-month period December 2018 to January 2019. Subsequently, following training under different hidden-layer conditions, it was observed that the prediction accuracy of morning ore production was highest when the number of hidden layers and number of corresponding nodes were four and 50, respectively. The corresponding values of the determination coefficient and mean absolute percentage error (MAPE) were 0.99% and 4.78%, respectively. Further, the prediction accuracy of afternoon ore production was highest when the number of hidden layers was four and the corresponding number of nodes was 50. This yielded determination coefficient and MAPE values of 0.99% and 5.26%, respectively. truck-dispatch techniques have been proposed to simulate truck systems using real-time data collected from mining sites. For example, Tan et al. [39] identified discrete haulage-operation events by analyzing historical GPS truck-tracking data and implemented a truck-dispatch simulation algorithm using the Arena ® software. Chaowasakoo et al. [40] developed a real-time truck-dispatch system that plans truck-haulage strategies using truck-shovel activity-time data obtained from GPS.However, providing a comprehensive and effective truck-haulage systems simulation algorithm is difficult. First, open-pit mines are characterized by frequent changes in loading points during operations along with the use of multiple equipment-movement paths, unlike underground mines [1]. Additionally, several environmental factors, such as the weather, rock characteristics, and road conditions, affect haulage operations [12,41]. Thus, it is difficult to design a simulation algorithm for truck-haulage systems in large open-pit mines, where ore loading and haulage operations are simultaneously performed at multiple locations. Moreover, several types of simulation algorithms can be developed for a haulage plan that changes in real time, and it is difficult to alter these algorithms depending on variations in the haulage plan. Therefore, it is necessary to develop a simulation strategy that understands not only the characteristics of truck-haulage systems but also the sequence of discrete haulage operations. Additionally, the simulation technique must accurately predict ore production and equipment utilization using equipment-tracking data previously accumulated without use of prior-knowledge-based algorithms.Deep learning methods have recently attracted increased research interest because they facilitate easy analysis ...

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Energy efficient wireless infrastructure solution for open pit mine

Cited by 4 publications

References 2 publications

Radio propagation in open-pit mines: A first look at measurements in the 2.6 GHz band

Radio propagation in open-pit mines: A first look at measurements in the 2.6 GHz band

5G in Open-Pit Mines: Considerations on Large-Scale Propagation in Sub-6 GHz Bands

Deep Neural Network for Predicting Ore Production by Truck-Haulage Systems in Open-Pit Mines

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