Abstract. Automatic fire detection is important for early detection and promptlyextinguishing fire. There are ample studies investigating the best sensor
combinations and appropriate techniques for early fire detection. In the previous
studies fire detection has either been considered as an application of a certain field
(e.g., event detection for wireless sensor networks) or the main concern for which
techniques have been specifically designed (e.g., fire detection using remote
sensing techniques). These different approaches stem from different backgrounds
of researchers dealing with fire, such as computer science, geography and earth
observation, and fire safety. In this report we survey previous studies from three
perspectives: (1) fire detection techniques for residential areas, (2) fire detection
techniques for forests, and (3) contributions of sensor networks to early fire
detection.
1 Introduction
There are many concerns in automatic fire detection, of which the most
important ones are about different sensor combinations and appropriate
techniques for quick and noise-tolerant fire detection. Researchers have been
studying fires taking place in various places such as residential area (Milke and
McAvoy 1995), forest (Yu, Wang et al. 2005; Bagheri 2007) and mines (Tan,
Wang et al. 2007) to find some solutions for fire monitoring.
An important issue in automatic fire detection is separation of fire sources
from noise sources. For the residential fires, being flaming or non-flaming
(smouldering smoke fires), the general trend is to focus either on the sensor and
sensor combinations or detection techniques. In another word, researchers have
focused either on identifying the best set of sensors which collaboratively can
detect fire using simple techniques (Milke and McAvoy 1995; Milke 1999;
Cestari, Worrell et al. 2005) or on designing complex detection techniques that
use single or at best very small set of simple sensors (Okayama 1991; Thuillard
2000).
Several decades of forestry research have resulted in many advances in field
of forest fire monitoring. The Fire Weather Index (FWI) system being developed
by the Canadian Forest Service (CFS; Bagheri 2007) and the National Fire
Danger Rating System (NFDRS) introduced by the National Oceanic and
Atmospheric Administration (NOAA; Yu, Wang et al. 2005) are two examples
of such advances.
Studying the state-of-the-art techniques reveals two main trends in fire
detection, i.e., existing techniques have either considered fire detection as an
application of a certain field (e.g., event detection for wireless sensor networks)
or the main concern for which techniques have been specifically designed (e.g.,
fire detection using remote sensing techniques).
The rest of this paper is organised as follows. Section 2 presents related work
on residential fire detection. Section 3 introduces some indices for forest
monitoring. Section 4 reviews contribution of wireless sensor networks (WSN)
for fire detection that may occur in any places. In Section 5 some conclusions
are drawn.
2 Automatic Residential Fire Detection
Human nose is a terrific fire detector. It can smell odours by using millions of
neurons (sensors) and then process the signals in the brain, where patterns are
classified, decisions are taken, and the best reaction is generated. Human nose is
sensitive enough to smell even light concentrated gases. Then, brain seeks its
database to find out what is the source of such a smell. If the odour is not
familiar and does not match with the previous experiences, it is labelled as
‘strange odour’ that should be learnt as a new pattern signature (Bryan 1988;
Shurmer and Gardner 1992).
Many commercial products can only detect airborne smoke by using either
ionization sensors or photoelectric sensors (Brain 2000). An alarm is generated
upon increase of the airborne smoke. The problem with such detection is
nuisance sources such as a cigarette or a toasting bread (Milke 1999; Gottuk,
Peatross et al. 2002). Therefore, many researchers agree on the fact reducing
false alarm rates in fire detection necessitates using more than one sensor along
with an appropriate detecting algorithm (Milke and McAvoy 1995; Milke 1999;
Gottuk, Peatross et al. 2002).
Some standards such as the European EN 54 standard and the Dutch NEN
2575 standard have been compiled for fire detection. EN 54 is a suit of many
standards for fire detection and alarm systems. Each part relates to a different
part of an equipment, e.g., part 3 relates to alarm devices, part 11 to call points
and part 4 to power supplies (EU; Wikipedia; Cooper 2008). NEN 2575, on the
other hand, is a Dutch national standard for all evacuation alarm systems that are
meant for emergency situations such as fire. It does not only specify
requirements and standards that products used in case of emergency situation
(e.g. smoke detector and fire alarms) should conform to but also the guidelines
covering installation and cabling process (Ooperon).
Okayama presented a residential fire detection technique by incorporating
neural networks (Okayama 1991). Milke et al (Milke and McAvoy 1995)
extended Okayama’s work in two facets: (1) introducing more sensors for
reducing false alarms by using the same feed-forward neural network as used in
(Okayama 1991), and (2) presenting an expert system working with three
sensors, i.e., CO, CO2 and Taguchi sensor.


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