Target Tracking in Confined Environments with Uncertain Sensor Positions

To ensure safety in confined environments such as mines or subway tunnels, a (wireless) sensor network can be deployed to monitor various environmental conditions. One of its most important applications is to track personnel, mobile equipment and vehicles. However, the state-of-the-art algorithms assume that the positions of the sensors are perfectly known, which is not necessarily true due to imprecise placement and/or dropping of sensors. Therefore, we propose an automatic approach for simultaneous refinement of sensors' positions and target tracking. We divide the considered area in a finite number of cells, define dynamic and measurement models, and apply a discrete variant of belief propagation which can efficiently solve this high-dimensional problem, and handle all non-Gaussian uncertainties expected in this kind of environments. Finally, we use ray-tracing simulation to generate an artificial mine-like environment and generate synthetic measurement data. According to our extensive simulation study, the proposed approach performs significantly better than standard Bayesian target tracking and localization algorithms, and provides robustness against outliers.Comment: IEEE Transactions on Vehicular Technology, 201

Mobile sink wireless underground sensor communication monitor

Mine disasters claim thousands of human lives and cause millions of property loss every year. The safety of the mine worker is of paramount importance in any underground environment. Advances in the development of Wireless Sensor Networks (WSNs) for monitoring infrastructure health, and environmental conditions provide end users with the benefit of low-cost installation, maintenance and scalability. This paper will investigate the challenges around a development of a real-time mine monitoring system using wireless sensor nodes to prevent mine disasters such as gas explosions or mine collapses. We propose a mobile, real-time gateway that will be able to process data collected from static wireless sensor nodes monitoring underground infrastructure, to prevent underground disasters

Wearable Real Time Health and Security Monitoring Scheme for Coal mine Workers

This paper deals with implementing a supervision system for coal mine and underground workers, which is essential to avoid the workers illness and death. The proposed recovery system consists of all primary aspects of the coal mine and underground areas. This system incorporates a sensor array, GSM, RF and controller modules. ARM 7 (LPC2148) Microcontroller is fully automated measuring system. ARM7 processor is used for measuring the environment parameters with high reliability and accuracy and smooth control by using sensor networks. Consequently, advance detecting crucial conditions the microcontroller starts alerting the mine workers by the alarm system and sends the alert messages to fire and ambulance services by using GSM modem. In addition, the observed parameter's value will be displayed on a PC by using RF (CC2500) module, which is at the control station. At the hazardous situation, this system shows the shortest and available way out path for the workers to move away from the harmful environment. DOI: 10.17762/ijritcc2321-8169.15037

An IoT Based Worker Safety Helmet Using Cloud Computing Technology

Miner safety and security is a major challenge around the world due to the exposure to toxic gases that are frequently released in underground mines. Miners' health is adversely affected primarily by toxic gases, which endanger the workers' lives. Furthermore, human sensory abilities do not detect these dangerous gases. As a result, this paper proposes a safety monitoring system that includes a temperature sensor, humidity sensor, and gas sensors to detect harmful gases and alert miners to those harmful gases using the smart helmet they wear. These gases are transmitted to the control station via the cloud using Internet of Things devices. The station monitors parameters like temperature, humidity, and toxic gases like methane and carbon monoxide to detect any abnormalities and alert the miner via a buzzer on the helmet. The data is processed by the Thing Speak cloud, which enables users to communicate via internet-connected devices and displays a field graph of the transmitted data

Anchor-Free Localization Method for Mobile Targets in Coal Mine Wireless Sensor Networks

Severe natural conditions and complex terrain make it difficult to apply precise localization in underground mines. In this paper, an anchor-free localization method for mobile targets is proposed based on non-metric multi-dimensional scaling (Multi-dimensional Scaling: MDS) and rank sequence. Firstly, a coal mine wireless sensor network is constructed in underground mines based on the ZigBee technology. Then a non-metric MDS algorithm is imported to estimate the reference nodes’ location. Finally, an improved sequence-based localization algorithm is presented to complete precise localization for mobile targets. The proposed method is tested through simulations with 100 nodes, outdoor experiments with 15 ZigBee physical nodes, and the experiments in the mine gas explosion laboratory with 12 ZigBee nodes. Experimental results show that our method has better localization accuracy and is more robust in underground mines

SMART SENSOR AND TRACKING SYSTEM FOR UNDERGROUND MINING

The thesis predominantly discusses a smart sensor and tracking system for under- ground mining, as developed by the author. The tracking system is developed by two steps, the rst of which involves nding an e cient way to measure the distance, and the second of which involves localizing the positions of each miner in real-time. For the rst step, a Received Signal Strength Indicator (RSSI) is used to measure the distance between two points by indicating the amount of energy lost during the transmission. Due to environmental and human factors, errors exist when using RSSI to measure distance. Three methods are taken to reduce the error: Gaussian distribution, statistical average and preset points. It can be observed that the average error between actual distance and measured distance is only 0.1145 meters using the proposed model. In regards to the localization, the "3-point localization method" is considered rst. With the proposed method, the result of the localization is improved by 0.6 meters, as compared to the "2-point localization method". The transmission method for the project is then discussed. After comparing sev- eral transmission protocols in the market, ZigBee was chosen for the signal trans- mission. With the Zigbee protocol, up to 65000 nodes can be connected, which are suitable for many miners using the system at the same time. The power supply for the ZigBee protocol is only 1mW for each unit, thus potentially saving a great amount of energy during the transmission. To render the tracking system more powerful, two smart sensors are installed: an MQ-2 sensor and a temperature sensor. The MQ-2 sensor is used to detect the harmful gas and smoke. In the event that the sensor's detected value is beyond the threshold, it will provide a warning for the supervisor on the ground

Min Eng

The Industrial Internet of Things (IIoT), a concept that combines sensor networks and control systems, has been employed in several industries to improve productivity and safety. U.S. National Institute for Occupational Safety and Health (NIOSH) researchers are investigating IIoT applications to identify the challenges of and potential solutions for transferring IIoT from other industries to the mining industry. Specifically, NIOSH has reviewed existing sensors and communications network systems used in U.S. underground coal mines to determine whether they are capable of supporting IIoT systems. The results show that about 40 percent of the installed post-accident communication systems as of 2014 require minimal or no modification to support IIoT applications. NIOSH researchers also developed an IIoT monitoring and control prototype system using low-cost microcontroller Wi-Fi boards to detect a door opening on a refuge alternative, activate fans located inside the Pittsburgh Experimental Mine and actuate an alarm beacon on the surface. The results of this feasibility study can be used to explore IIoT applications in underground coal mines based on existing communication and tracking infrastructure.CC999999/Intramural CDC HHS/United States2018-01-16T00:00:00Z29348699PMC5769960vault:2590

A Wireless Sensor Network Based Personnel Positioning Scheme in Coal Mines with Blind Areas

This paper proposes a novel personnel positioning scheme for a tunnel network with blind areas, which compared with most existing schemes offers both low-cost and high-precision. Based on the data models of tunnel networks, measurement networks and mobile miners, the global positioning method is divided into four steps: (1) calculate the real time personnel location in local areas using a location engine, and send it to the upper computer through the gateway; (2) correct any localization errors resulting from the underground tunnel environmental interference; (3) determine the global three-dimensional position by coordinate transformation; (4) estimate the personnel locations in the blind areas. A prototype system constructed to verify the positioning performance shows that the proposed positioning system has good reliability, scalability, and positioning performance. In particular, the static localization error of the positioning system is less than 2.4 m in the underground tunnel environment and the moving estimation error is below 4.5 m in the corridor environment. The system was operated continuously over three months without any failures