Performance evaluation of LoRa LPWAN technology for IoT-based blast-induced ground vibration system

The recent proliferation of wireless sensor networks (WSNs) evolution into the Internet of Things (IoT) vision enables a variety of low-cost monitoring applications which allows a seamless transfer of information via embedded computing and network devices. Ambiguous ground vibration can be induced by blasting demolition is a severe concern which grievously damages the nearby dwellings and plants. It is an indispensable prerequisite for measuring the blast-induced ground vibration (BIGV), accomplishing a topical and most active research area. Thus, proposed and developed an architecture which emphasizes the IoT realm and implements a low-power wide-area networks (LPWANs) based system. Especially, using the available Long-Range (LoRa) Correct as Radio Frequency (RF) module, construct a WSN configuration for acquisition and streaming of required data from and to an IoT gateway. The system can wirelessly deliver the information to mine management and surrounding rural peoples to aware of the intensity of BIGV level. In this article, an endeavor has been made to introduce a LoRa WAN connectivity and proved the potentiality of the integrated WSN paradigm by testing of data transmission-reception in a non-line of sight (NLOS) condition. The path loss metrics and other required parameters have been measured using the LoRa WAN technology at 2.4 GHz frequency

Monitoring of blast-induced ground vibration using WSN and prediction with an ANN approach of ACC dungri limestone mine, India

Blast-induced ground vibration (BIGV) is an undesirable environmental issue in and around mines. Usage of a high amount of explosive causes ground vibrations that are harmful to the nearby habitats and dwellings. In this paper, an attempt has been made to monitor the BIGV with low-cost wireless sensor network (WSN) and prediction of peak particle velocity (PPV) using an artificial neural network (ANN) technique at ACC Dungri limestone mine, Bargarh, Odisha, India. Eleven blasts PPV were recorded at different locations using wireless sensor network prototype system. The data has been transmitted by ZigBee (IEEE 802.15.4) protocol. The results are very promising and the recorded PPV varies from 0.191 mm/s to 8.60 mm/s. A three-layer, feed-forward back propagation neural network consists of 6 input parameters, 5 hidden neurons, and one output parameters were trained. Obtained results were compared based on correlation of determination (R2) and standard error between recorded and predicted values of PPV

Review of experimental methods to determine spontaneous combustion susceptibility of coal – Indian context

This paper presents a critical review of the different techniques developed to investigate the susceptibility of coal to spontaneous combustion and fire. These methods may be sub-classified into the two following areas: (1) Basic coal characterisation studies (chemical constituents) and their influence on spontaneous combustion susceptibility. (2) Test methods to assess the susceptibility of a coal sample to spontaneous combustion. This is followed by a critical literature review that summarises previous research with special emphasis given to Indian coals