A Concept for an Ultra-low Power Sensor Network

Detecting and Monitoring Disaster Events in Underground Metro Systems

Jonah Vincke, Scott Kempf, Niklas Schnelle, Clemens Horch and Frank Schäfer

Fraunhofer Institute for High-Speed Dynamics, Ernst-Mach-Institut, EMI, Eckerstrasse 4, Freiburg, Germany

Keywords: Ultra-low Power, Wireless Sensor Network, Energy Harvesting, Tunnel System.

Abstract: In this paper, the concept for an ultra-low power wireless sensor network (WSN) for underground tunnel

systems is presented highlighting the chosen sensors. Its objectives are the detection of emergency events

either from natural disasters, such as flooding, or from terrorist attacks. Earlier works have demonstrated that

the power consumption for the communication can be reduced such that the data acquisition (i.e. sensor sub-

system) becomes the most significant energy consumer. By using ultra-low power components for the smoke

detector, a hydrostatic pressure sensor for water ingress detection and a passive acoustic emission sensor for

explosion detection, all considered threats are covered while the energy consumption can be kept very low in

relation to the data acquisition. The total average consumption for operating the sensor sub-system is

calculated to be less than 35.9 µW.

1 INTRODUCTION

More than half of the planet’s population now lives in

urban areas. This creates the need for various mass

rapid transport systems including metro systems.

New vulnerabilities for society that arise due to

disaster events, such as terrorist attacks, flooding or

fire, are increased by a higher population density and

current political processes. To address these

challenges – in particular for underground metro

systems – as part of the bi-national research project

SenSE4Metro (Sensor-based Security and Emergen-

cy management system for underground Metro

systems during disaster events) (SenSE4Metro,

2016), a concept for a wireless sensor system is

introduced that can detect the most significant threats

and which provides rescue forces with the relevant

necessary information in case of an emergency. The

particular operation site leads to the requirement for

the WSN that each node must be energy autarkic,

which in turn necessitates the application of ultra-low

power (ULP) components. The focus in this work lies

on the needed sensors to achieve these goals and fulfil

the project requirements.

First, the state-of-the-art of wireless sensor

networks for different kind of tunnels is presented.

After that, an overview of the proposed wireless

sensor network is given, taking the special linear

topology for a tunnel system into account. Finally, a

concept for the needed sensors that can cover all

addressed threats is presented that focuses on the

power consumption and takes the applicability for a

metro tunnel system into account.

2 STATE-OF-THE-ART

The use of wireless sensor networks for tunnel

systems has been investigated in several works. Of

the systems described, most are designed primarily

for road tunnels (e.g. M. Ceriotti, 2011; L. Mottola,

2010; Z. Sun, 2008) or mine tunnels, (e.g. D. Wu,

2010; H. Jiang, 2009). Of the latter, some focus on the

radio transmission in tunnels (e.g. D. Wu and H.

Jiang) and others on special protocols designed to

increase robustness against underground collapses

(e.g. M. Li, 2007). Of the former, Ceriotti et al.

presents a WSN consisting of 40 nodes to monitor the

light conditions of a 260 m long tunnel. Mottola et al.

compared the data of a traffic tunnel (here, railroad

tunnels are proposed as analogous with the assessed

road tunnel) with a WSN for a vineyard to make

suggestions for the communication in road tunnels.

For underground rail tunnels, only a few works

exist. Wischke et al. (2011) discuss the generation

side of the energy autarkic nodes by proposing a

vibration energy harvesting solution for maintaining

the wireless sensor nodes in rail tunnels. Bennett et al.

150

Vincke J., Kempf S., Schnelle N., Horch C. and SchÃd’fer F.

A Concept for an Ultra-low Power Sensor Network - Detecting and Monitoring Disaster Events in Underground Metro Systems.

DOI: 10.5220/0006186901500155

In Proceedings of the 6th International Conference on Sensor Networks (SENSORNETS 2017), pages 150-155

ISBN: 421065/17

(2010) used MICAz boards as wireless sensor nodes

to monitor cracks in a 170 m long part of the Prague

Metro and in a 115 m long section of the London

Metro. Sivaram Cheekiralla (2005) used a WSN to

monitor the deformation of a train tunnel during

construction using 18 nodes using only a star

topology.

Raza et al. introduced a combination of an ULP

wake-up receiver with model-based sensing to reduce

the power consumption of the rail tunnel WSN by

Ceriotti et al. (2011). They simulated an increased

lifetime of the nodes by a factor of over 2000 due to

their method, which demonstrated the influence of a

data model to reduce necessary data transmission and

the use of a wake-up receiver.

The optimization of wireless communication in

tunnel systems has been widely discussed and

especially Raza et al. (2016) showed that only the

power consumption of the data acquisition is of

interest when using a wake up receiver. Therefore, the

focus of this paper lies on the conception of the sensor

system based on an initial layout and requirement

specification for the WSN.

3 WIRELESS SENSOR

NETWORK

For the purposes of defining the requirements of the

WSN, an assessment of past terrorist attacks on

underground, tunnel and rail infrastructure was

performed (S. Kempf, 2016). The assessment results

highlighted several distinct differences between

attacks on underground systems as compared to

above-ground networks, with respect to tactics and

effectiveness (in terms of casualties). Ultimately,

event scenarios were defined based on explosive and

arson attacks targeting the trains and tunnels

themselves. While the assessment also highlighted

the threat of biological/chemical attacks, due to

previous studies (A. Pflitsch, 2010), these were

explicitly omitted from the scope of the project.

Adding recent historical flooding in underground

networks (Prague 2002, New York 2012) and

accidents (Valencia 2006, Moscow 2014): the

following events should be detected and the

corresponding data acquired by the WSN:

• Train passage (positioning and movement),

• Fire (temperature and smoke presence),

• Explosion (impact peak pressure and

specific impulse)

• Flooding (water presence and depth).

The combined data will be applied to determine

danger levels and traversability of tunnel segments

and to coordinate paths of access (rescue forces) and

escape (passengers).

In order to acquire the necessary data above,

sensors must be positioned both at ground and ceiling

level and all along the tunnel segment. It was decided

that the implementation would be performed as a

parallel linear topology (see Figure 1) with cable

wired gateway nodes at each metro station. The

topology provides added robustness via path

redundancy, as both nodes can be used to forward

messages. Its relatively long path lengths however

require special tuning and adaptation of routing

algorithms. While classic tree based routing

protocols, such as Contiki Collect or CTP (O.

Gnawali, 2009), can in principle be applied directly

nodes at the end of long paths would have to forward

all the messages generated by preceding nodes in the

path thus creating significant load and using

disproportionally more energy. To counter this data

from different nodes may be combined or filtered to

only update for significant changes instead of simply

forwarding all generated data towards the nearest

gateway. This however remains an area of active

research and development.

The upper nodes can be used to measure smoke

and impact pressure and are powered using a wind

energy harvester. The lower nodes measure water

ingress and temperature and are powered using a

piezoelectric vibration energy harvester attached to a

rail. In standard (non-emergency) operation,

situational status messages are transmitted during

Figure 1: Applied WSN topology in underground tunnel.

A Concept for an Ultra-low Power Sensor Network - Detecting and Monitoring Disaster Events in Underground Metro Systems

151

train passage events, which means the energy

required for data acquisition and transmission is

provided directly via energy harvesting processes.

For emergency event detection, an energy storage

system is provided to ensure a constant power supply.

For all nodes, the CC2650 from Texas

Instruments is chosen as the MCU due to its very low

power consumption and integrated RF module.

4 SENSORS

To achieve low power consumption, a holistic

concept has been developed. This includes the

application of modern ultra-low power sensors,

enabled only when necessary, as well as the re-

application of the same sensors for various disaster

events if possible. The precision of the sensors is of

less importance in contrast to the power consumption.

A robust detection of a dangerous event is sufficient.

4.1 Water Ingress

There are several methods for the detection of water

ingress and determination of the resulting water level.

These vary from mechanical solutions using floats to

change a resistance, a capacitance or to close a contact

to pure capacitance or resistance measurements as

well as hydrostatic, ultrasonic and radar methods.

Many can be realized with an ultra-low power

consumption but vary with respect to their robustness,

dependence on the medium and the tunnel’s shape.

Mechanical solutions have the disadvantage that

their dimensions need to be in the same range as the

measurable water level and their shapes are limited.

On the other hand they are independent on the media

and can be realized as ultra-low power systems.

Optical or ultrasound distance sensors are not

dependent on such limitations nor on the media or the

shape of the tunnel. But they lack on the measurable

distance and power consumption. As an example the

infrared distance sensor GP2Y0A710 from Sharp

needs above 1 mW for one measurement every 5

seconds while only covering a distance of up to 5 m.

Ultrasonic sensors that can measure distances of up to

8 m or more have commonly a power consumption of

over 1 Watt during operation and need several

hundreds of milliseconds until the first measurement

is possible. As an example the UC30-2 from SICK

needs up to 1.2 W for approximately 450 ms until a

measurement can take place. Sensors for smaller

distances such as the LV-MaxSonar-EZ have a power

consumption of about 10mW for half a second for a

measurable distance of 6.45 m.

Of the other solutions, measuring the hydrostatic

pressure seems most promising for achieving very

low power consumption. Here the pressure caused by

the water ingress at the bottom of the tunnel has to be

measured as well as that above the water level. The

disadvantage of this principle is that calculating the

water depth according to the induced pressure

difference is dependent on the mediums density by

design. Also the system’s robustness in a harsh

environment such as an underground tunnel has to be

investigated. Since passing trains induce pressure

disturbance, the measurement has to be adjusted

during train passage events. The simulations

(ThermoTun Online, 2016) have shown that a train

with a cross-sectional area of 8 m² in a 5 m high

tunnel with a speed of 200 km/h creates a pressure

difference of up to 1.36 kPa in the tunnel. This would

be equivalent to a water level of 138.7 mm. How the

pressure is disturbed over the cross section and over

the time in a real tunnel has to be investigated.

As an example for the advantages of pressure

sensors, two MS5806 can be used to measure the

pressure at the bottom and above the water level.

Using these values, water levels of up to 9 m with a

precision of 0.13 cm can be measured in theory while

consuming less than 3 µW for each sensor when

measuring once per second. A temperature sensor is

also included that can be used for the other sensors in

addition, reducing the overall power consumption.

Because of the independence on the tunnel’s shape,

the water ingress detection will be based on

measuring the pressure induced by the water. Since in

most cases the media will be ground water the density

of the media will be similar in most cases and

therefore the dependence of the system on the media

can be neglected. The sensor will be located at the

wall. The pressure at the bottom of the tunnel is

measured using a tube mounted to the wall that is

connected to the sensor and goes to the ground as

shown in Figure 2.

Figure 2: Schematic of the water level measurement system

using a tube to measure the pressure at the ground of the

tunnel.

SENSORNETS 2017 - 6th International Conference on Sensor Networks

152

The water ingress depth ℎ



can be determined

using the pressure at the base of the tube, assuming

that the media is water:

ℎ







−







∙

(1)

In order to determine the pressure p

, it is

necessary to consider the air compression induced by

the water ingress within the tube. Using the ideal gas

law (pV=nRT), the reference pressure (external

sensor) and assuming constant mass, temperature and

cross-sectional area within the tube, the height of the

air column can be determined:

ℎ



=ℎ



∙









(2)

The pressure at the tube base is a summation of

the air pressure due to compression and the water

ingress within the tube:





=

,

+



(3)

Reapplying the water depth equation, the total

pressure p

can be determined:





=



∙∙



ℎ



−ℎ





+



(4)

With (2):





=



∙∙ℎ



1−









+



(5)

Finally, inserting back into (1):

ℎ



=ℎ



1−









+





−







∙

(6)

4.2 Fire

To detect fires, heat and smoke are measured. Heat is

measured using the integrated sensor of the pressure

sensor. Three classical smoke detection systems

exists. While measuring the concentration of carbon

monoxide either consumes too much power or is

limited in life time, ionic sensors can reach a power

consumption as low as 25 µW (Z. Moktari, 2013) but

consist of a radioisotope. Common photoelectric

smoke detectors consume approximately 90 µW. This

power can be reduced down to less than 6 µW by

using ultra-low power microcontroller units (MCUs)

and operational amplifiers and by reducing the

sampling ratio down to one sample each 8 s (M.

Mitchell, 2012). Based on this and because of the

German regularities regarding ionic materials, the

smoke detectors are realized based on the

photoelectric effect. An infrared LED emits light in a

smoke chamber that has to be reflected by particles

such that the reflection can be measured by a

photodiode. To increase the robustness of the sensor

against disturbances such as ambient light, the sensor

measures the output of the photodiode when the

IRLED is turned off additionally. As in (M. Mitchell,

2012), a measurement is done every 8 seconds. If

smoke is detected, the interval is reduced down to 4

seconds. After three detections an alarm is triggered.

4.3 Explosion

Due to the physical sensor node layout and the

limitations of ultra-low power WSNs with regard to

time resolution and synchronization, an exact

determination of explosion position is not feasible.

Using empirically determined thresholds based on the

results of in-house explosion experiments at the EMI

(A. Stolz, 2010; O. Millon, 2013; F. Schäfer, 2014),

the sensor system can however determine the

remaining structural capacity of tunnel walls based on

the peak pressure and specific impulse observed at the

node. The expected variable impact distance of 0-50

meters (based on 100 m node spacing) can be taken

into account when establishing these thresholds.

To measure the blast wave, an acoustic emission

sensor, the VS150-M, has been chosen which has

been tested successfully in previous projects

performed by researchers at the EMI (O. Millon,

2013; M. Erd, 2016). It generates an electrical signal

from the deformation of the sensor using a

piezoelectric element and therefore needs no supply

power. Nevertheless, to measure its signal, an electric

subsystem is needed. As in previous work, the most

power saving system is to measure the exceedance of

several thresholds. For this purpose three MOSFETs

are used that will consume up to 9 µW. As opposed

to the water ingress and fire detection sensors, the

signal of the VS150-M can be used to wake up the

MCU in case of an explosion. This allows measuring

the start time of the event, the exceedance of different

thresholds and the duration of the blast. Using several

nodes this is enough to estimate the severity of the

blast as well as the energy released.

5 EXPERIMENTS

The validation of the system is ongoing. A single

pressure sensor increases the power consumption by

14.79 µW when no measurement is done and

A Concept for an Ultra-low Power Sensor Network - Detecting and Monitoring Disaster Events in Underground Metro Systems

153

40.08 µW if the pressure and temperature is measured

once per second. In Figure 3 a depth measurement in

a water pipe filled with tap water is shown. The

hydrostatic pressure at the bottom is measured using

a hose connected to a MS5806 pressure sensor

located above the pipe.

Figure 3: Calculated depth from pressure readings.

The results of the system are shown in Figure 3.

The water depth is calculated according to equation

(6) using the measured pressure. The compression of

the air is compensated for the 645 mm long hose.

Here the mean error between the nominal and

measured depth increases from 1.2 mm for a real

depth of 0 mm up to -14.6 mm for a real depth of 500

mm. This corresponds to a relative error of up to

4.32 % as shown in Figure 4.

Figure 4: relative difference between the nominal and

measured depth.

The power consumption of the smoke sensor is

4.06 µW in the case of one measurement every 8

seconds. The response of the sensor to the application

of a test spray is shown in Figure 5. Here a

measurement is done every 0.25 seconds in contrast

to normal operation. The response of the smoke

detector when the IRLED is turned on is shown in red.

This can be compared with the response when the

IRLED is turned off as shown in orange. The induced

difference between both measurements is increased

from an average of 203.77 mV up to 758 mV.

Figure 5: Response of the smoke sensor when using a test

spray as a function of voltage (mV) over time (s). Red: IR

LED turned on, orange: IR LED turned off.

6 CONCLUSION

A concept for a wireless sensor network for

monitoring underground metro systems, specifically

focusing on the requirements and design of the

sensors themselves, has been presented. As energy

autonomy is desired, low energy consumption of the

components, while maintaining the minimum sensing

integrity and resolution, is of highest priority.

Using a highly integrated MEMS pressure sensor,

ULP components for a smoke detector with a reduced

sampling rate and an acoustic sensor for the explosion

detection, the expected average power consumption

for the sensor system in normal operation can be

reduced to 13.06 µW for upper nodes and 35.9 µW

for lower nodes. In this case, smoke and water depth

are measured every eight seconds. The accuracy of

the depth measurement is capable for giving the

rescue forces a situational awareness. The smoke

detector is very sensitive as its output is increased by

a factor of more than three. Therefore, the

combination of both nodes provides the capability of

detecting explosion, fire and water ingress, while only

consuming very low power.

In further work, the sensor system will be

integrated and validated. For the pressure sensor, a

hose ending has to be developed that ensures the

robustness of the system in such a harsh environment

like a metro. In addition, the pressure disturbances in

the tunnel systems induced by passing trains also has

to be analyzed.

Using the presented sensor system, a larger

security management and emergency response

system will be developed, whereby all interested

parties, such as metro network operators and rescue

forces, will be informed in real-time of critical

developments, for instance degradation of tunnel

structural integrity and impairment of traversability

of tunnel segments due to emergency events in order

SENSORNETS 2017 - 6th International Conference on Sensor Networks

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to minimize the secondary damage (e.g. in terms of

human life) of these events.

ACKNOWLEDGEMENTS

This work is part of the research project “Sensor-

based Security and Emergency management system

for underground Metro system during disaster

events” (SenSE4Metro) and is funded by BMBF

(German Federal Ministry of Education and

Research) through the joint program “International

cooperation in civil security research: Cooperation

between Germany and India” under Grant No.

13N13039.

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