A Cognitive Data Gathering Scheme for Gas Monitoring
Employing Mobile Phone based Wireless Sensor Networks
Jin Qi, Shigeru Shimamoto
Graduate School of Global Information and Telecommunication Studies, Waseda University, Japan

1. Introduction

3. Data Gathering by Cognitive RadioWith the progressing of industrialization and urbanization, theleakage of gas or other chemicals has been a serious threat to thesafety of humans. In the recent years, with the development ofelectronic and communication technology, wireless sensor
network has been widely utilized for gas monitoring [1]-[2].
In the paper, mobile phone based wireless sensor networks areproposed for gas monitoring. In the network, each mobile
terminal is equipped with a sensor, which senses gas data fromsurrounding environment. Using such mobile devices as sensors,
significant advantages can be achieved over unattended wirelesssensor networks. First, mobile phones can provide coverage
where static sensors are hard to deploy and maintain. Second,
mobile phones and cellular networks have already existed
around the world, providing the physical sensing infrastructure.
Third, each mobile phone is associated with a human user,
whose assistance can sometimes be used to enhance application
functionality.
However, cellular networks have been overloaded in someparts of world. To avoid congestion of the cellular networks,
new data gathering schemes have to be proposed. In our past
research [3], a data gathering scheme based on location update
has been proposed. In the proposal, sensor employed mobilephones send data to base station within location update
information. This proposal could gather data without utilizing
data channel, but because location update just happens at theboundaries of the location registration areas, only the gas data ina large area can be monitored. In order to monitor gas data inevery area or every intended time, a cognitive data gathering
scheme is proposed in this paper.
The organization of the rest of this paper is as follows: insection 2, we present the network model; in section 3, we
propose the cognitive data gathering scheme; in section 4, wepresent computer simulation; in section 5, we conclude thispaper.

3.1 General Model for Spectrum Sensing

2. System ModelIn the proposal, energy detection is utilized for spectrum
sensing. Fig. 2 shows the frame structure designed for ourproposal with periodic spectrum sensing. Each frame consists ofone sensing slot and one data transmission slot. Suppose thesensing duration is ts and the frame duration is T. If N is thenumber of detection samples, then we assume N=ts・fs
Frame 1

Frame 2

Frame 3

Data Transmission

T-tstsFig.2. Frame Structure for Spectrum Sensing
Suppose the variance of noise is σn2, the signal to noise ratioof primary users is SNRp. If the primary users’ signal is complexPSK, and the noise is circularly symmetric complex valued, thefalse alarm probability is [4]:   4  n  Q   2  1 N pf  Q  4 / N 
 n

 n


The probability of detection is: Npd  Q   2  SNRP  1
 2 SNRP  1  nTherefore,



1

Pd  Q Q 1  Pf   N SNR 2SNR
1

3.2 Sensing Duration and Energy Consumption

Base StationMobile Phone with Sensor

Other Users

Fig.1. System ModelIn the network, each mobile terminal is equipped with asensor, which senses data from surrounding environment. Sensor
employed mobile phones send data to base stations, so that thebase stations not only support cellular network, but also perform
as sink nodes. Besides that, the sensor employed mobile phones
are supposed secondary users, and send data just when thespectrum is not utilized by primary users.

Error control protocol is supposed to be utilized, so that if thebase station can’t receive the data from a mobile phone correctly,
the mobile phone will send this data once more. For cognitive
radio, if the detection is missed, the signals of primary users andmobile phone will be mixed together, and the data can’t bereceived by base station. Therefore, the mobile phone willtransmit the data once more. In our proposed system, theprobability that the data is transmitted by a mobile phone is:
A  P  H 0  1  Pf   P  H1 1  Pd The probability that the data is not transmitted by a mobilephone can be expressed as:
B  P  H1  Pd  P  H 0  PfFor one frame, we define Ps as the power consumption ofspectrum sensing, then as the energy consumption of spectrum
sensing:

Es  Ps  ts
Suppose Et as the energy consumption of data transmission, theaverage energy consumption of one transmission can beexpressed as: B A  nE  E n 1sn 1t

However, if the detection is missed, the mobile phone has totransmit the data again, so that the average energy consumption
for the transmission of one data can be expressed as: B A  nE  E   P  H 1  P   B A  nE  E n 1n 1st

1dn 1n 1st

For a given frame duration T, the longer the sensing time ts, themore energy is consumed on spectrum sensing. On the otherhand, for a given false alarm probability, the longer the sensingtime ts, the lower the probability of miss detection, and the lessenergy is consumed on data retransmission. In the following
simulation, we will show the optimal sensing time ts so that theenergy consumption can be minimized.
4. Simulation
The simulation parameters:
Table 1. Simulation Parameters

T100msPf
0.9
fs1P(Η0)0.8
Ps1mW
Figure3 shows the relationship between sensing time andenergy consumption for different signal noise ratio of theprimary users when signals of primary users’ are complex PSK.

Fig.3. Relationship between Sensing Time with Energy
Consumption
From this result, we can see that there is an optimal sensingtime that the energy consumption can achieve minimum. For thesignal to noise ratio of primary users equals 0dB, the optimal
sensing time that energy consumption can achieve minimum is12ms. Besides that, the optimal sensing time increases as thesignal to noise ratio of the primary users decreases.
5. Conclusion
A cognitive data gathering scheme for gas monitoring
employing mobile phone based wireless sensor networks is
proposed in this paper. In the network, each mobile phone isequipped with a sensor, so that this sensor employed mobile
phone can sense data from surrounding environment. The sensor
employed mobile phone send gas data to base station bycognitive radio, and energy detection is utilized for spectrum
sensing. The relationship between sensing duration and energy
consumption is studied, and there exists an optimal sensing
duration that the minimum energy consumption can be achieved.
6. References[1] W. Tsujita, H. Ishida and T. Moriizumi, “Dynamic gas sensor

network for air pollution monitoring and its auto-calibration”, Proc.
of 2004 IEEE Conference on Sensors, October 2004.
[2] S. Nakano, Y. Goto, K. Yokosawa and K. Tsukada, “Hydrogen gas
detection system prototype with wireless sensor networks”, Proc. of2005 IEEE Conference on Sensors, October-November 2005.
[3] J. Qi, J. Wu and S. Shimamoto, “A Data Gathering Scheme for Gas
Monitoring in Mobile Phone Based Wireless Sensor Networks”,
Proc. of IEICE Society Conference, September 2011.
[4] H. Z. Zhang, Z. Y. Zhang and X. M. Chen, “Energy Efficient Joint
Source and Channel Sensing in Cognitive Radio Sensor Networks”,
Proc. of IEEE International Conference on Communications,
September 2011.