Power Optimization Strategies for Wireless Sensor Networks in Coal Mines

K. A. Unnikrishna Menon, Deepa Maria, Hemalatha Thirugnanam Amrita Centre for Wireless Networks and Application

Amrita Vishwa Vidyapeetham Amritapuri, Kollam, Kerala, India

Email: kaumenon@am.amrita.edu, deepamaria89@gmail.com, hemalatha@am.amrita.edu

Abstract— Coal continues to be mined in over 50 countries. Coal accounts for approximately 64% of India’s electricity. Spontaneous explosions in the coal mines are one of the most common accidents.

The main cause for mine explosions is the presence of toxic methane gas in an environment with insufficient oxygen. Gas detection is crucial for explosion prediction. Currently gas monitoring in coal mines is not so reliable. This paper introduces the use of a wireless sensor network (WSN) with gas sensors to predict methane outbursts in coal mines.

This research work has developed an Early Warning System (EWS) deployment in the complicated environment of mines as low cost and power efficient wireless sensor network nodes have been designed to substitute the traditional wired and fixed monitoring equipment. The system can thus reduce the impact of mining explosions by facilitating the timely evacuation the miners from the areas of highest risk by providing an early warning and alarm system. Special attention has been paid to the WSN power optimization design strategies that include: using low power components, only sending the minimum amount of data (with efficient data acquisition protocols and data aggregation protocols), appropriate configuration of nodes (with clustering protocols) and automatic timely variation of power consuming states (with state transitions of listening, sleeping and transmitting modes).

Keywords— Underground Sensor Network, Clustering, power optimization

I. INTRODUCTION

Mining is dangerous and various types of mining disasters lead to a heavy loss of human life, property and infrastructure [1]. The issue of safety in the mines has not been resolved in most of the world. Coal continues to be mined in over 50 countries. Coal accounts for approximately 64% of China and India’s electricity [9]. China is by far the largest producer of coal in the world. As many as 7,000 miners die in accidents each year in China alone [10]. Most Chinese mines are deep underground. There are a number of risk factors involved in the underground coal mines such as faulting, mine explosions, mine fires, suffocation by poisonous gases, mine inundations (flooding from water table), mine cave-ins or roof falls and seismic changes (due to natural earthquakes or due to mining operations). When surveyed it was found that mine explosions are the main cause of most underground mine accidents. The

main cause for mine explosions is the presence of toxic methane gas in an environment with insufficient oxygen.

The proposed system aims to develop an advanced power-efficient Early Warning System (EWS) to safeguard the miners from explosions by detecting concentrations of methane gas. A EWS must be established in order to send signals before a disaster happens. Along with the technology for a EWS, the proposed system helps to decrease the rate of accidents and to improve the emergency safety measures.

The main advantage of using a wireless system as oppose to the traditional existing wired safety systems is that wired systems are inefficient in this high risk environment. The mining process always aims to extend and thus the wiring must also continually be extended, significantly increasing the cost of extensions. The extremely long wires are prone to damage during disasters or power failure compromising the safety of the miners. There are many incidents such as explosions and disasters like earthquakes and flooding that hamper wired infrastructure, destroying the wired communication links underground.

The objective of this research is to develop a power optimized, efficient wireless sensor network to sense the presence of methane gas so as to warn the monitoring station about the increase in gas concentration and evacuate the miners who are in immediate danger. This research work introduces a faster, easier, larger scale EWS deployment in the complicated environment of mines as light, small, low cost and power efficient wireless sensor network nodes have been designed to substitute the traditional wired, heavy and fixed monitoring equipment. The system can thus reduce the impact of mining explosions by facilitating the timely evacuation the miners from the areas of highest risk by providing an early warning and alarm system.

Wireless sensor networks have attracted more and more research interest in coal mine applications for their advantages of self-organization, low cost and high reliability. WSNs consist of many tiny sensor nodes with wireless transmission ability and computational ability. The distances between the nodes of WSN are always very short (currently up to 30m). Nodes use multi-hop wireless communication when used for long distance applications. Combining WSN with appropriate communication technology is a perfect scheme for mine emergency communication. A monitoring system for mine safety needs to be designed to solve to the problems faced by

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miners and to monitor various environmental parameters in the coal mines.

II. RELATED WORKS Although there are a number of mine monitoring systems

[3] [4] [6], only a few of them make use of wireless communication technology.

A crucial element to our explosion EWS is to find an appropriate methane gas sensor. We chose a semiconductor gas sensor for use in our EWS. Previously, a single semiconductor gas sensor device was used in a hierarchical identification strategy for use in underground coal mines early fire detection systems [2].

When envisioning the design of a WSN for mines we need to consider the layout and environment. The underground WSN system to be created will be extensive (spreading out over many kilometers) and elaborate (with many obstructions). An appropriate topology must be sort. Three topology structures (basic star, mesh and mixed network) were compared [1]. A mixed network topology (which is a combination of star and mesh networks) was found to be the most appropriate topology for large scale sensor networks. This mixed topology was chosen for our system for its simplicity (star attribute), and its multi-hop transmission and self-healing nature (mesh attribute).

III. PROPOSED SYSTEM Power efficiency is a major challenge for WSN design.

WSN power consumption is mainly due to the use of high power consuming components, processing, data transmission and continual power consuming states. Processing power cannot be reduced since it is a must and essential activity. A WSN can reduce its power consumption by the configuration of its nodes, by using low power components, by only sending the minimum amount of data and by automatic timely variation of power consuming states for each Sensor Node (SN).

Power efficient considerations have been integrated into our WSN design using low power components, only sending the minimum amount of data (with efficient data acquisition protocols and data aggregation protocols), appropriate configuration of nodes (with clustering protocols) and automatic timely variation of power consuming states for each SN (with state transitions of listening, sleeping and transmitting modes).

The proposed System Architecture is shown in the figure 1. At each SN, there will be different modules such as: a sensor unit, a processing unit to perform several computations, a memory to store the values temporarily, a power unit to supply external power and a communication module to transmit the sensed values wirelessly.

In the proposed system we make use of many SNs deployed in the mines. As methane has a low density than other gases it raises above, therefore concentrations of methane gas will be found on the roofs and upper walls of mines. This being the case, our semiconductor methane gas sensors will be placed on the roofs and upper walls of the mines. WSN power consumption can be reduced by using low power components. Semiconductor type methane sensors were chosen for their low

Fig 1: Underground Mine Wireless Sensor Network

power consumption and high sensitivity.

WSN power consumption can be reduced by the automatic timely variation of power consuming states for each SN, this is achieved by introducing state transitions for each SN of listening, sleeping and transmitting modes (ON, LISTEN, SLEEP, TRANSMIT and OFF).

A mixed topology was chosen for our system for its simplicity (star attribute), and its multi-hop transmission and self-healing nature (mesh attributes) [1]. WSN power consumption can be reduced by the configuration of its nodes, this is achieved by clustering. Our clustering routing protocol (also called multi-hop chain type clustering of nodes) provides an effective method to have long range communication, obtain energy efficiency and prolong the lifetime of a WSN. All individual SNs have the ability to temporarily become Cluster Heads (CHs). SNs cannot remain CHs for long periods of time as each individual SN has a limited amount of power. This being the case, all SNs share the responsibility and become CHs at different times. SNs simply sense the methane gas concentration and periodically transmit their sensed data to a CH (Transmitter Module). Like SNs, CHs also sense and transmit, yet have the added ability to aggregate the sensed data (Transmitter and Reception Module). Each CH connects to neighboring CHs in order to transmit the gathered data to central monitoring station which is a long distance away.

WSN power consumption can be reduced by only sending the minimum amount of data; this is achieved by efficient data

Fig 2: Architecture of a single Sensor Node

acquisition protocols and data aggregation protocols. SNs simply sense the methane gas concentration and periodically transmit their sensed data to a CH (Transmitter Module). SNs have a Transmitter Module. Like SNs, CHs also sense and transmit, yet have the added ability to aggregate the sensed data (Transmitter and Reception Module). CHs have Transmitter and Reception Module. A Transmitter Module has a sensing unit, processing unit, communication unit. Architecture of a single node is as shown in figure 2. Sensing unit consists of a methane sensor. The processing unit contains a PIC microcontroller which processes the sensed data to digital data (analog to digital conversion). The digital data obtained will be compared with a threshold value and the data above the threshold will be wirelessly transmitted. The communication unit uses a ZigBee module for wireless communication. ZigBee has been chosen for its low cost and reliable short range transmission. Reception Modules also have a ZigBee module to receive the data from the Transmitter Module. Once the data is received, a warning alarm or message will be automatically sent to the miners underground and to the central monitoring station.

For monitoring the safety of the miners working underground there is a central monitoring station, above the ground. Whenever an alarm or warning of some explosion is received at the station, rescue operators can be contacted using a cellular network and quick rescue or evacuation operations can begin.

IV. HARDWARE MODULE The hop transmission and self-healing nature (mesh

attributes) sensor node (SN) hardware module has a sensing unit, processing unit, and communication unit, all powered by an external battery. The sensing unit consists of sensors which will sense the gas concentration. In this system, we make use of a [7] semiconductor type MQ4 sensor which can sense the presence of methane. MQ4 methane sensor uses the sensitive material SnO2 (Tin Oxide) which will increase its conductivity when it comes into contact with methane. The sensor converts the conductivity to a voltage value corresponding to the gas concentration. The sensor can measure the methane gas concentration ranging from 200 to 10000 ppm. It is very important that the sensitivity of an MQ4 sensor is adjusted, depending on the particular application, in this case underground mines. Hence, the sensor has to be calibrated for

Fig 3: Methane Sensor Module connected to a voltage regulator

Fig 4: Heater circuit to MQ4 Methane Sensor

measuring the methane concentration in air and for this a load resistor of about 20KΩ (10KΩ to 47KΩ) has been used. After the analog output from the sensor is obtained, it is converted to a digital value. The digital outputs are compared with a predefined threshold value and only those values above the threshold are transmitted to ZigBee module. In this system, we make use of the PIC16f877A microcontroller to convert the analog to digital values and to compare the values above the threshold. This minimization of the data transmitted will reduce the power consumed to a large extent. Along with the values above the threshold, a particular message will be transmitted to indicate the level of danger. Inorder to transmit those values wirelessly, ZigBee technology is used where it makes use of XBee Series2 module. Inorder to transmit to XBee module we make use of UART and code the PIC accordingly to transmit value. Figure 3 shows the sensor module which was designed on a PCB board. The PCB board is connected to a voltage regulator circuit. The load resistor of 24KΩ was used for biasing across the Vout and GND terminals. The resistance across the H-H pins of the sensor was found to be 27KΩ (31KΩ ± 3KΩ in datasheet). The figure 4 shows the circuit connected for testing the MQ4 Methane sensor. Current drawn by the sensor was found to be 185mA (180mA in datasheet). Power consumption was found to be 925mW (900mW in datasheet).The above readings were taken by providing a heater voltage supply of 5V to the sensor for half an hour. When the sensor was exposed to gas, the output voltage which was almost stable rose abruptly to 4.8V. Thus, the sensor was found to be sensitive to the gas, and our WSN system for providing an appropriate methane gas warning was validated.

V. ALGORITHMS USED IN THE SYSTEM A. Efficient Clustering and Data Aggregation Protocol

Power optimization is brought to the system using chain type clustering protocol which also performs a data aggregation

technique at the cluster heads (CHs). There are 3 phases for the algorithm:

1) Cluster Formation: Sensor Nodes (SNs) are largely deployed in the underground region. All SNs have their own unique individual personal id. The location of the SNs is known to their neighboring SNs. SNs will form clusters and will designate their particular cluster with a unique region-id. After a CH is elected, the elected CH informs the other SNs (in its cluster) by broadcasting the CH-Message. The CH-Message will contain the elected CHs own unique individual personal id and the unique region cluster id.

2) Synchronization Phase: The Central Monitoring Station (CMS) and CHs and SNs are synchronized in time. Initially, all nodes (CMS, CHs and SNs) send the Synch-Message to all their neighboring nodes (CHs and SNs). The Synch-Message will contain a timestamp and state information (ON, LISTEN, SLEEP, TRANSMIT, OFF). This Synch-Message informs the remaining nodes to adjust, changing their transition state and time synchronizing all the nodes. With the reception of Synch-Message, CMS and CHs can also change their transition according to the state of the SNs with the.

3) Data Aggregation and Transmission Phase: At the CH, data aggregation procedures are performed. The data obtained with the maximum value of gas concentration is the highest priority data. This high priority data is transmitted to the next neighboring CH by the aggregation process, and eventually to the CMS. Along with the highest priority data (maximum gas concentration value) the specific CH’s unique region-id, the timestamp value and CH’s state information also transmitted to the CMS. At the SNs, the sensed data is transmitted along with the region-id, timestamp and state information to their CH. At this stage, SNs sense data and transmit to their CH nodes. Each CH aggregates the data received from all its SNs and then sends to its neighboring CH nodes as a continuing chain until the data reaches the CMS.

B. State Transition

Power optimization can be achieved by different methods like introducing state transitions for each SN and by prioritizing the data send. In our proposed system in order to manage the power of the system, we take in to consideration the following state: ON, LISTEN, SLEEP, TRANSMIT and OFF. In the initial state all the SNs which are in ON state will achieve LISTEN state after getting the enough power to sense. In LISTEN state, it senses the parameters (methane gas concentrations) and analyzes whether the data within the threshold value (1.7V). In this state the transmitter module is switched “on” since it might need to transmit. If a node doesn't sense data greater than threshold for a fixed time (30 minutes) it will move to SLEEP state. Here, it will only perform sensing operations and no receiving. In this state the transmitter module is switched off. If the values sensed exceed the threshold value the node will go to TRANSMIT state. In this state the transmitter module will be switched on and the data is send to

Fig 5: State transition Diagram

the CH node. In addition to the transmitter module, the sensing module is also working. Due to power failure, OFF state can be entered into from any other state. A State Transition Diagram is shown in figure 5. In the case of data transmission, at each CH it will perform a data aggregation procedure where only the maximum of all the received data values will be transmitted.

C. Processing Algorithm at the Node's Transmitter Module

The methane gas concentration sensed by the MQ4 sensor varies from 200-10000 ppm. Only analog voltage is obtained from the sensor. Therefore, to convert the analog voltage to digital values, an ADC (Analog to digital converter) is present in the pic16f877A microcontroller. In order to reduce the amount of transmissions the digital value is compared with a threshold which is set just below the explosive limit of methane gas. Only when the output value exceeds the threshold, will the value be transmitted. Along with the gas concentration values, a warning message is also transmitted according to the extent of danger.

VI. CONCLUSION AND FUTURE WORK This paper presents the design of a developed, efficient,

reliable, low cost EWS using a WSN with increased network lifetime. The proposed system was designed to enhance the safety of the miners working in the harsh environments in the underground coal mines by reporting high amounts of methane gas concentrations wirelessly to a monitoring station. Deployment of numerous sensor nodes in underground mines will be a cost effective technique as each SN is designed in a low cost manner. Another dominant feature is the energy optimization protocol implemented for the whole system. Since each SN is build using low power consuming components, the low power consumption is achieved by the whole system. The power consumption are reduced by introducing state transitions to the SNs in the system. Power consumption is reduced by data aggregation protocols, by only wirelessly transmitting data exceeding a certain threshold (information of high concentrations of combustible gas). Clustering protocols also reduce power consumption. Remote and real time monitoring of the underground mines is guaranteed with the help of

proposed architecture. Hence the proposed system is a cost effective, fast and power optimized EWS designed using WSN to warn the chances of mine explosions and to improve the safety of the miners.

In the proposed system only the methane gas was sensed, while a system which can monitor various other parameters like humidity, temperature, oxygen, carbon monoxide can be implemented. All these parameters can help predict mine explosions with even more accuracy. Development of a EWS which can detect other problems like underground roof falls will be taken up as future work. Moreover the research will be extended to find a more efficient wireless technique which can reduce the multi-hop delays. Thus, in future a system which could deal with delay tolerant wireless communications for emergency situations in mines could be implemented.

ACKNOWLEDGMENT Our thanks to the Chancellor of Amrita Vishwa

Vidyapeetham, Shri. Mata Amritanandamayi Devi for her grace, giving us strength and ideas for completing this work. We thank Dr. Maneesha V Ramesh, Director, Amrita Center for Wireless Networks and Applications for her tireless mentorship and support. We also thank Ms (Thapasya) Erica S. Fernandes for improving our writing skills.

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