PIR-Based WSN for Outdoor Deployment
Arpan Chattopadhyay, Raviteja Upadrashta, Abhijit Bhattacharya, Tarun Choubisa,
Anu Krishna, V. S. Aswath, S. Vikas, Christo Thomas, Akhila Rao, Bharat Dwivedi
S. V. R. Anand, Anurag Kumar, P. Vijay Kumar
Dept. of Electrical Communication Engg., Indian Institute of Science, Bangalore, 560012, India
T. V. Prabhakar, Madhuri S. Iyer, B. S. Sudhangathan, Sripad Kowshik
Department of Electronic Systems Engineering, Indian Institute of Science, Bangalore, 560012, India
Abstract—The aim of this paper is to discuss the application of
passive infrared sensors to the problem of intrusion detection and
classification in the presence of sources of clutter. Intruders are
to be classified as being human or animal and sources of clutter
include swaying vegetation and hot-air currents. The paper
includes background on passive infrared sensing, an overview
of the past experience by the authors in building a PIR-based
intrusion detection demonstration system called SmartDetect, as
well as a description of a wildlife conservation project in which
the authors are currently engaged in. A description of some
approaches that are currently being pursued as well as current
progress is included. Several aspects of an intrusion detection
system are covered here, including signal processing algorithms
that make for reliable detection, networking algorithms that will
allow the processed signals from the sensors to be relayed back
to the base station in the presence of uncertainties in terrain and
node placement, as well as energy-efficient methods of operation
including energy harvesting.
Index Terms—Wireless sensor network, Passive Infrared sensor
review, Node placement, Intrusion detection and classification.
I. I NTRODUCTION
Passive Infra-Red (PIR) sensors have for long been used
for detecting human motion in various indoor applications
such as security [1], [2], smart homes [3], [4], health care
[5], [6], hallway monitoring [7], [8], fire detection [9], gesture
recognition [10], [11] etc. Not many attempts have been made
however, to use them for detecting human motion in outdoor
settings. The outdoor setting is known to be a challenging one
account of the fact that PIR sensors are prone to false alarms
triggered by blowing debris, birds, animals, hot-air currents
and wind blown vegetation collectively termed as clutter. Thus,
a principle challenge is of reliable detection with low falsealarm rate, of human motion in the presence of clutter in a
variety of environmental conditions. An intrusion detection
system, called SmartDetect [12], was recently developed by
some co-authors of present paper, at the Indian Institute of
Science (IISc), where reliable intrusion detection was demonstrated with low false-alarm rate in a local outdoor setting in a
variety of environments. There were environmental conditions
however, under which the false alarm rate did climb to higher,
unacceptable levels.
The authors are currently involved in a wildlife-conservation
project [13]1 with the aim of the project being the use
1 The work done on this project, was supported in part, by the Department
of Electronics and Information technology (DeitY).
of wireless sensor networks to help manage human-animal
conflicts in the vicinity of a protected area such as a national
park or wildlife sanctuary. The project team consists of an
international team with complimentary expertise in various
aspects of Wireless Sensor Networks (WSN). It is planned
to setup a WSN, consisting of a variety of several sensing
modalities, including PIR sensing. Apart from harsh environmental conditions, an additional challenge posed in the
wildlife setting is the need to distinguish human from animal
intrusion. There are also difficult challenges to be overcome
on the networking front. The network referred here is the
network which relays the information gathered and processed
by one or more sensors, back to a base station. A planned
deployment of communicating sensors with full knowledge
of propagation conditions will not be possible because of
unknown terrain, uncertainty with respect to locations of
sensors as well as communication relays as well as the need in
many situations for repaid deployment. Yet another challenge
is that of network lifetime, given that the motes used in a
sensor network are powered by cells which can supply only
limited amount of power if required cooperate for long periods
without cell replacement. Thus, there is a need to design
hardware, software and algorithms so as to minimize power
consumption. It is planned to compliment such an approach
in conjunction with energy-harvesting techniques.
In the present paper, we provide background on PIR sensing
as well as brief descriptions of the prior SmartDetect as well as
current wildlife conservation project. The challenges that need
to be overcome in setting up an effective PIR-based WSN,
both in terms of PIR sensing as well as the accompanying
networking are identified and a brief overview of our current
approaches and progress made thus far, provided.
Sections II and III discuss PIR sensing and the SmartDetect
system respectively. An overview of the wildlife conservation
project appears in Section IV. Current approaches and progress
made on the sensing front are discussed in Sections V and VI.
Progress made on the design of as-you-go network deployment
is presented in Sections VII and VIII. Energy considerations
including energy harvesting are discussed in Section IX.
II. PASSIVE I NFRARED S ENSING
PIR sensors have been used as human motion detectors over
the last 3-4 decades. An excellent account of the evolution of
the PIR sensor over the years is given in [14].
Fig. 2. The equivalent circuit of the pyroelectric crystal and the preamplifier
[16].
Fig. 1.
The physical phenomenon of pyroelectricity [15].
PIR sensors work on the principle of pyroelectricity [15].
Pyroelectricity can be described as the temperature dependence of the spontaneous polarization Ps (dipole moment
per unit volume) of a dielectric crystal. The spontaneous
polarization of the crystal can be alternately viewed as the
creation of a layer of bounded charge on opposite faces of
the crystal (Fig. 1, top). This net dipole moment exhibited
by the crystal occurs naturally even in the absence of any
applied electric field, hence, the term spontaneous polarization.
When the two faces of the crystal are connected using metal
electrodes, free ions are attracted towards the two faces of the
crystal. As long as Ps is constant (under thermal equilibrium)
there will not be any current flow in the circuit (Fig. 1,
middle). An increase in the temperature of the crystal results
in a decrease in Ps , which in turn, results in a decrease in
the amount of bounded charge. This creates free ions that
get redistributed resulting in a transient current flow (Fig. 1,
bottom). Similarly, cooling the crystal will result in a current
flow in the opposite direction.
The amount of free charge produced by a temperature
change depends on the pyroelectric coefficient p of the crystal,
where p is the ratio of change in Ps , dPs , to the change in
2
s
temperature, dT , i.e. p = dP
dT with units C/m K. Thus, the
PIR output current i(t) arising from a temperature change in
the crystal, can be calculated from
dT
,
(1)
dt
in which t denotes time and where A is the surface area of
the crystal face. The current is usually converted to a voltage
using a pre-amplifier.
Let the pyroelectric crystal have an equivalent resistance
RX and capacitance CX . Let the preamplifier have a load
resistance RL and capacitance CL . The pyroelectric crystal
can be alternately viewed as a current source I(t) in parallel
with a resistor RX and capacitor CX . The equivalent circuit
of the pyroelectric crystal is given in Fig. 2. Let w(t) be the
radiation power incident on the pyroelectric element. Let a
i(t) = pA
fraction of this power be absorbed by the crystal due to its
emissivity η. Let the crystal have a heat capacity of H and a
thermal conductance GT . From the heat balance equation we
get [17]:
dT (t)
ηw(t) = H
+ GT T (t).
(2)
dt
Taking the Laplace transform and rearranging the terms we
get
ηW (s)
.
(3)
T (s) =
(sH + GT )
Substituting this into the Laplace transform of Eqn. 1 we get
ηpAsW (s)
.
(sH + GT )
I(s) =
(4)
Continuing in the transform domain, the output voltage
V (s) can be written as
V (s)
=
=
=
I(s)
,
Y (s)
(5)
ηpAsW (s)
HCE (s + GHT )(s +
H(s)W (s),
GE
CE )
,
(6)
(7)
where the crystal has an admittance Y (s) = sCE + GE . Thus,
"
#
ηpA
s
H(s) =
.
(8)
E
HCE (s + GHT )(s + G
CE )
Taking the inverse Laplace transform the impulse response of
the PIR element can be written as
E
ηpA
GE − G
GT − GT t
CE t
H
h(t) =
e
−
e
. (9)
GT
E
CE
H
HCE ( G
C − H )
E
Thus, the output voltage v(t) of the PIR sensor to an
input radiation w(t) incident on it can be found by simply convolving w(t) with the impulse response h(t) i.e.
v(t) = w(t) ∗ h(t). A plot of the impulse response and the
corresponding frequency response of a typical PIR element is
shown in Figs. 3(a) and 3(b) respectively.
The voltage responsivity of the PIR element is defined as
Rv
= |H(jω)| ,
=
where τT =
H
GT
(10)
ηpAω
1
1
GT GE (1 + ω 2 τT2 ) 2 (1 + ω 2 τE2 ) 2
and τE =
CE
GE
(11)
are the thermal and electrical
(a)
Fig. 4.
[20].
(b)
Fig. 3. The impulse response and the associated frequency response of the
pyroelectric crystal.
time constants respectively. Observe in Eqn. 11 that Rv → 0 as
ω → 0. This indicates that the PIR element does not respond to
a constant radiation but rather to a change in radiation incident
on it. This ability to respond to changes in incident radiation
makes it an excellent choice for detecting moving objects.
All objects above absolute zero temperature emit radiation
at all wavelengths. The radiation profile of the object (the
bandwidth it occupies) is characteristic of its temperature T
[18]. Thus, as long as there is a temperature contrast between
the moving object and the background temperature, a moving
object will effect a change in the incident radiation on the
PIR which will trigger the sensor. An optical filter is used
to reduce the influence of moving objects with temperatures
much different from that of the source of interest. The filter
limits the radiation falling on it to the bandwidth of interest.
Assuming that one can model the object as a black body, the
maximum radiation of the object is emitted at a wavelength
λmax , given by Wien’s law [19] as λmax = Tb , where b is
Wien’s displacement constant and T is the temperature of the
black body in Kelvin. The hotter the body the shorter the
wavelength λmax .
Fig. 4 compares the radiation profiles of an electric bulb
and a human represented by black bodies at temperatures 3000
K and 300 K respectively. It can be observed that the peak
emission of the human is around 10 µm wavelength. The PIR
Comparison of radiation profiles of a human and an electric bulb
sensor’s sensitivity is limited to human motion (or motion
of other mammals of similar range of body temperature) by
employing optical filters that are transparent around 10 µm
wavelength. Typical optical filters used for human motion
detection are the 8-14 µm bandpass and 5 µm high pass filters.
The fraction of the total radiation emitted by a human within
these bands can be calculated to be approximately 50% and
98% respectively2 .
The PIR sensor typically consists of several elements arranged in a 1D or 2D fashion, the most common being the
2 × 1 and the 2 × 2 arrangements. Since, each single pixel is
capable of capturing temporal variations in temperature, the
PIR sensor is susceptible to being triggered by ambient temperature changes. Pairs of pixels are typically wired together
in a differential manner as shown in Fig. 5. The output of the
sensing unit is the sum of the currents produced by each pixel
pair. Thus, any common temperature change simultaneously
perceived by two pixels having opposite polarities will not be
detected by the sensor.
Fig. 5.
Differential wiring of the quad or 2 × 2 PIR sensor [21].
A lens system is typically used to provide an optical gain
by focusing incident radiation from different directions onto
the sensor die. The lens system can consist of a single lens
or multiple lenses (referred to as multi-lens). The lens system
expands the Field of View (FoV) of the sensor and it plays a
key role in defining the sensor output signal. The FoV of the
sensor can be viewed as a set of virtual beams cast out into
space along which radiation is received and focused onto the
sensor die.
Figs. 6 illustrates the virtual beams cast out by a single
2 PIR sensors have also been used in fire detection applications in which
the optical filter used is typically in the 3-5 µm band.
Fig. 6.
frequency content of intruder and clutter waveforms (see Fig.
7(a)).
A functional block diagram of the algorithm appears in Fig.
8. A block of 128 (N) consecutive samples is transformed by
the HT. The energy in each of these transformed components
are binned into 8 frequency bins. The resultant binned vector
is passed on to a Support Vector Machine (SVM) classifier
(obtained by off-line SVM training) which classifies it as either
intruder or clutter. This entire process is repeated on a slidingwindow basis, every 16 (L) samples.
Virtual Pixel Array.
Fresnel lens, when placed in front of a quad sensor die (pixels
arranged as a 2 × 2 grid). The sensor and its associated
electronics are housed in a metal package to shield it from
Electro-Magnetic Interference (EMI). It is hermetically sealed
to protect it from humidity and pressure waves [14].
III. S MART D ETECT: A N I NTRUSION D ETECTION S YSTEM
During the period 2006-2010, a demonstrable PIR-based
intrusion detection system called SmartDetect [12] system was
developed for a local outdoor application setting. This section
will describe the intrusion detection algorithm, the field testing
results and the limitations of the system.
A. The Intrusion Detection Algorithm
The team was initially faced with a choice between digital
and analog PIR sensors. The analog PIR sensor was chosen as
the spectral signatures of intruder and clutter generated by the
analog PIR sensor were better separated when compared to
the corresponding spectral signatures produced by the digital
PIR sensor (see Figs. 7(a) and 7(b)).
(a)
Fig. 8.
Functional block diagram of the detection algorithm.
The data used for training SVM was collected in a laboratory (i.e., clutter-free) environment (see Fig. 9(a)) in the case
of intruder and across 20 outdoor locations on the forested
campus of the IISc (see Fig. 9(b)) in the case of clutter.
The training data set for intruder included data collected after
making a human walk along different straight lines oriented in
a variety of ways with respect to the sensor. The clutter data
was accumulated over several months from October 2008 to
March 2009.
(b)
Fig. 7. Spectral signatures of intruder and clutter at the output of the analog
and digital PIR sensors respectively.
For the purpose of maximizing battery life, the Walsh
Hadamard Transform (WHT) and Haar Transform (HT) were
considered for computing the spectrum of the intruder and
clutter signals in preference to the computationally intensive
Discrete Fourier Transform as only additions and subtractions
suffice to compute them. An overlapping sliding-window
based detection algorithm was adopted. The HT was preferred
as possessed the additional advantage of being able to reuse
past computed HT coefficients for the next sliding window. A
sampling frequency (fs ) of 12.5 Hz was chosen based on the
(a)
Fig. 9.
(b)
The ECE lab and lawn from where training data was collected.
B. Field-Testing Results
When tested over a period of several hours across the
week, the experimental setup successfully detected intrusions
at various speeds ranging from a slow walk to sprint with
some false alarms being registered. A detailed description of
the setup is provided below.
1) Sensor Platform Employed: The particular PIR sensor chosen for SmartDetect was the analog motion sensor
AMN24111 [21] from Panasonic. The angular field view of
each sensor is approximately 110◦ and thus, 3 sensors were
mounted oriented at 120◦ relative to each other on a single
platform, so as to obtain an omni-directional sensing range
(see Fig. 10). The data from the three sensors were fed to the
3 ADC channels of a TelosB mote [22].
(a)
Fig. 10.
SmartDetect sensor platform.
2) Network Deployment and Self-Organization: Field testing was conducted on the lawns of Electrical Communication
Engineering (ECE) Department in the IISc campus. The sensor
nodes were deployed in the form of two parallel “wireless
trip wires” (see Fig. 11(a)) spaced apart by 5m (the maximum
sensing radius was approximately 6m). The communication
nodes were placed in a semi-planned manner in which the area
of deployment was tessellated into square cells, and the nodes
were placed within these cells randomly (see Fig. 11(b)). This
approach emulates a practical setting in which the presence of
obstructions such as trees and ditches, makes it impractical to
assume that communication nodes can be placed so as to form
a perfect grid. A Graphic User Interface (GUI) was developed
to display the information regarding the network at the BaseStation (BS).
Self-organization was done via a choice of transmit power
level at each node. Each node chooses the minimum power
level so as to have at least two downstream neighbor nodes that
are closer to BS. The self-organization process starts by the
BS broadcasting the coordinates of all nodes in the network
relative to it. Each node in the network broadcasts request
packets starting with a minimum power level. All neighboring
nodes receiving this packet send acknowledgement packets
choosing the same power level. After broadcasting a predefined number of request packets, the node checks for the
existence of two downstream neighbors. If a sufficient number
of nodes in the network are not discovered then the process is
repeated by choosing the next higher power level. Each node
in the network does this process in a time-ordered fashion. By
the end of the self-organization process, every communication
node will have multiple paths to the BS, while using minimum
power. Figure 12 shows a snapshot of the GUI indicating the
power levels chosen by a network consisting of 50 nodes after
the self-organization process.
(b)
Fig. 11.
Wireless trip wire placement of sensor nodes and network
deployment.
Fig. 12. Snapshot of the GUI indicating the result of the self-organization
of a network of 50 nodes.
3) Alarm Generation and Routing: Alarm generation by the
network was done as follows: If a node detected an intruder
in its vicinity using the HT-cum-SVM based algorithm, it
would broadcast its local detection (via the Zigbee protocol
available on TelosB motes) to all of its neighbours. A node
was permitted to declare a confirmed detection if, in addition
to making a local detection, it also received news of local
detection from any other node within a distance of twice the
sensing range of each sensor. The confirmed detection was
then relayed back to the BS using an appropriately designed
network routing algorithm.
Alarm forwarding in the network involves routing of the
alarm packets from the originating node to the sink. A
geographic routing protocol called Geographyaware MAC
(GeoMac) was proposed and used for packet delivery. Geographic routing exploits the geographic information instead of
topological connectivity information to route packets to the
destination. The key parameter used here is the distance of
the nodes from the BS. Here a node with a packet to forward
initiates handshakes by broadcasting probes at regular intervals
in order to determine greedy nodes. The probes contain the
forwarding metric (distances or hop counts) and the ID of the
node broadcasting it. Nodes closer to the destination respond
with a probe ACK which contains its ID and forwarding
metric. An optimal timer starts on the reception of the first
probe ACK. All the received probe ACKs within the optimal
time period are queued. The sender now unicasts the packet
to the relay node closest to the destination. The value of the
optimal timer determines the relay node to which the packet
will be forwarded.
C. Limitations of the SmartDetect System
One limitation of the SmartDetect system is its inability to
distinguish between human and animal. This can be attributed
to the lack of spatial resolution in the AMN24111 Panasonic
PIR sensor. In addition, hot-air currents, especially at background temperatures close to the human body in the peak of
summer, and swaying short grass in the immediate vicinity of
the sensors were observed to major sources of false alarms
and pose a significant challenge that is yet to be overcome.
As observed earlier, with regard to network setup, a planned
deployment as carried out in the SmartDetect system may not
be possible in settings likely to be encountered in the wildlife
protection project. Situations will likely arise, in which there
is uncertainty with respect to location of both sensor and relay
nodes, and where the network will have to be deployed in an
as-you-go fashion.
IV. A W ILDLIFE P ROTECTION P ROJECT
The objective of the wildlife project [13] is to carry out
research and development aimed at designing a WSN to
protect humans, animals and the environment. The specific
goal is to investigate the use of a WSN to set up virtual fences
as well as identifying and monitoring selected regions of the
forest where the inhabitants of the forest or the environment
are threatened. This project puts together a global, interdisciplinary team consisting of faculty drawn from Indian Institute
of Information Technology Allahabad, Wildlife Institute of
India, Ohio State University, Cornell University, and Indian
Institute of Science, Bangalore.
Of particular concern in this project is the rapid escalation in
human-animal conflict (illegal human intrusion into the forest
and animal excursion into surrounding villages), in the vicinity
of protected areas. Fig. 13 provides a general overview of the
proposed system to address intrusion and excursion problems.
This involves the setting up three types of WSN systems
(Virtual Fences (VF), Activity Region Monitoring (ARM) and
Forest Probes (FP)). The VF is deployed on the boundary
between forest and village. It is used to detect and identify a
set of mammals entering or exiting the forest. It is planned to
deploy virtual fences in Panna Tiger Reserve with the aid of
the Wildlife Institute of India. An ARM system spans a region
where there is relevant mammal activity that is ongoing and
expected to last for some length of time. Some of these regions
could be trail-like, i.e., narrow tracks over extended portions
of the forest. The objective here is to identify and track the
mammals and notify the authority of harmful activities such
as tree felling, poaching and forest burning. The FP is aimed
at collecting information from sources like humans, wildlife
experts and long-range sensors to identify the activity regions
and their evolution and accordingly deploy an ARM system
or a VF. Thus, the aim is that these WSNs will act as an early
warning system for forest personnel.
The role of the team at IISc is to design, develop and test
a PIR-based sensor platform for detection and classification
of intruders, design a PIR sensor that will trigger a camera
trap, design algorithm that will permit as-you-go network
deployment, and investigate, low-energy approaches to sensing, detection and communication that will include energyharvesting. While we hope to leverage our past experience
with SmartDetect, there are many challenges to overcome.
One major challenge is to distinguish between human and
animal in the presence of clutter. It is planned to design
and deploy PIR sensor platform that have spatial resolution
capability. It is planned to design camera traps that are
triggered by a PIR sensor. The camera trap will help provide
the “ground truth” for verification of an event sensed by the
PIR sensor. A second challenge is to deploy a network in
a forest environment that is filled with uncertainties such as
the lack of a priori knowledge of the location of sensors
and relay nodes as well as of the nature of the propagation
terrain. There could also arise situations that demand a rapid,
as-you-go deployment of a sensor network. With regard to
working in an energy-limited environment, it was noted that
24 mA is the quiescent current of the (2 × 2) sensor platform
employed in SmartDetect and hence there is need for making
energy harvesting an integral part of sensor platform design.
Following sections will describe our current approaches at
overcoming some of these challenges.
Fig. 13.
Overview of proposed WSN system.
V. S ENSING P LATFORMS WITH I NCREASED S PATIAL
R ESOLUTION
An essential requirement on a sensor node deployed inside a
forest is the ability to distinguish between human and animal.
Our approach is to design a PIR sensor platform that has
some spatial resolution capability. This calls for an array
of sensors. We draw a parallel here between signal design
for communicating across a communication channel and lens
design for PIR sensing. It is the lens (or multi lens) that will
determine the signal registered when human, animal or clutter,
cross the virtual beam of a sensor platform. The plane where
intruder or clutter moves is called intruder plane hereafter.
Cutting virtual beams at intruder plane is equivalent to cutting
the projection of virtual beam onto the intruder plane, known
as the Virtual Pixel Array (VPA) at the intruder plane. The aim
is thus to design the VPA in such a way that the signals that
will result when the virtual beams are cut by human, animal or
clutter are easily separable. As with SmartDetect, it is planned
to use machine learning techniques to make the classification.
with Nicera’s (2 × 2) pixel package to create two rows (one
above the other) of virtual beams. While the beam pattern
obtained will naturally not overlap in azimuth, intelligent
sensor placement is called for to avoid overlap in elevation. A
simple but effective step here to avoid vertical overlap, is to
vertically offset the sensors with respect to their optical axes
as shown in Fig. 15.
Fig. 15.
Vertical stacking of sensors with and without offset.
VI. S IMULATION OF A PIR S ENSOR P LATFORM
Fig. 14. Utilizing geometry of intruder through a 4 × 4 sensor array for
classification.
Imparting a spatial resolution capability calls for an array
of PIR sensors to be used. To our knowledge, there are only a
few PIR sensor arrays that are commercially available. These
include the (4 × 4), (3 × 3) and (128 × 1) sensor arrays from
Pyreos as well as the (2×2) array (SDA02-54-P) from Nippon
Ceramic (NiCera).
The 4 × 4 sensor array from Pyreos was observed to
have small range, (less than 5m), possibly on account the
use of smaller pixel size. To improve the range further a
costly lens system would be needed. For this reason, we
are currently looking to build in-house, a sensor array out
of commercially available individual components. The array
would be composed of individual (2 × 1) pixel arrangements.
The (2×2) pixel arrangement from NiCera is actually an array
of two (2 × 1) arrangements.
Several single lenses and multi-lenses that can be used in
conjunction with the above sensor to provide the desired array
of virtual beams in the intruder plane are available from Kube
Electronics Ltd. The TR426 multi-lens from Kube electronics
[23], that provides wide horizontal coverage (110◦ ) and small
vertical coverage of (4◦ ) seems well-suited for our current
application. A total of 10 Fresnel lenses, each having a focal
length of 25mm, are arranged consecutively alongside each
other in such a manner that they all have a common focus.
It is planned to use this single multi lens in conjunction
Given that the intruders of interest include wild animals, we
foresee an inherent difficulty in obtaining training data. As a
way around this, it was decided to use techniques borrowed
from the field of animation to simulate motion of human,
animal and clutter across the virtual beam.
In technical terms, the aim of the simulation is to calculate
the radiation incident on the pixels. This is taken to be
proportional to the intersection of the image of the subject with
the VPA at the intruder plane. This radiation is then convolved
with the impulse response of the PIR pixel, described in
Section II, to generate the output signal of the PIR.
A. Virtual Pixel Array of Sensor Platform
The first step in simulation is to determine the VPA in the
intruder plane. This is done by finding the image of the sensor
die on the specified plane3 .
Fig. 16 shows the VPA at a plane 2 m away from sensor
node, with a pair of vertically stacked TR426 lenses from Kube
electronics used to image a corresponding pair of SDA02-54-P
sensors from Nicera.
B. Radiation Incident on a Pixel
The radiation power w(t) incident on the PIR at time t can
be calculated as [24]:
F Ae τ Aproj (t)M
(12)
πR2
where F is the fraction of the total power radiated by the
intruder that falls within the sensor’s optical filter bandwidth,
Ae is the effective area of the lens aperture, R is the distance
of the intruder from the detector, τ is the attenuation due to
w(t) =
3 Although, Fourier optics describes the VPA more accurately compared
to geometrical optics. We found that for this problem, geometrical optics
describes the beams with sufficient accuracy.
to find the area of intersection of the subject with the VPA
using numerical computation.
D. A Damped-Oscillator Model for the Clutter
Fig. 16.
The vegetation considered in the simulation are shrubs. The
shrub figure is made up of a set of randomly oriented stems,
branches and leaves. Branches and leaves are placed randomly
along their associated stems and branches respectively (see
Fig 18). To simulate the motion of the shrub in a gust of
wind, we model each part of the shrub as a rigid rod pivoted
at an appropriate joint whose motion is described by a second
order oscillatory system. The motion can be represented by a
Virtual pixel array at 2m.
the atmosphere τ = e−Rb , where b is the attenuation factor,
Aproj (t) is the overlapping projected area of the source with
the virtual pixel at time instant t, and M is the excitance of
the intruder given by Stefan-Boltzmann’s law [25]:
4
4
M = σ(Tint
− Tback
)
(13)
where Tint and Tback are the intruder and background temperatures in Kelvin respectively. σ is Stefan-Boltzmann constant
and is emissivity.
C. Simulating Intruder Motion through Animation
The intruder is simulated using a professional animation
technique, known as motion capture. In motion capture, key
points of the intruder body (eg. joints, pivot points) that
best represent the gait are identified and it’s 3-D position
is recorded over all frames of motion. Bio-Vision Hierarchy
(BVH)[26] is one of the popular formats used to represent
motion capture data. Free BVH files representing different
kinds of motion [27] and programs that help decode them [28]
are available. Although BVH files describing various human
motion are available in plenty, BVH files for animal subjects
are very rare.
(a)
(b)
Fig. 17. (a)Stick model obtained from motion capture data. (b) Muscles are
approximated using cylinders.
Each BVH motion capture file describes a stick figure as
shown in Fig. 17(a). The lines joining the joints represent
bones. A cylinder is erected about each bone to approximate
muscles as shown in Fig. 17(b). From here it is straightforward
Fig. 18.
Shrub figure.
differential equation given by
d2 θ(t)
dθ(t)
+ 2ζωn
+ ωn2 θ(t) = y(t)
(14)
dt
dt
where y(t) is the excitation due to the wind, ωn is the
natural oscillation frequency and ζ is the damping factor.
The differential equation was solved using Laplace-transform
techniques.
E. SVM-Based Classification Results
Intruder and clutter data for training and testing were generated. The various intruder and clutter planes were considered
at various distances and angles from the sensor, with varying
speeds.
The clutter and intruder waveforms were again found to
be separable in the frequency domain (as in the case of
SmartDetect), hence a similar Haar-SVM based algorithm was
used. Support vectors were learned using LibSVM library[29].
The training was carried out using 92 animal, 70 clutter and
100 human intrusion samples.
A two-level classification was employed. At the first level,
classification between intruder and clutter was carried out.
A second-level classifier, triggered whenever the first-level
classifier detected an intruder, was used to further classify the
intruder as human or animal.
A high classification accuracy was obtained with this classifier. Thirty clutter instances were used for testing and all of
them were successfully rejected. At the second level, 2 out of
190 human test examples were misclassified as animal and 1
out of 246 animal test examples was misclassified as human.
Further improvements are in the planning, by using a more
Potential relay locations
Known locations of
sinks, sensors, and
potential relay
placement points
Relay
Sensor
Sensor
Relay
Relay
?
Relay
Relay
?
Known locations of
sinks
Sensor
?
?
Deployment
Person
Statistical models for
trails and wireless
propagation
Relay
Relay
Relay
Relay
Base Station
(Sink)
Relay
Sensor
SmartConnect v1 can be used to
solve this relay placement
problem
?
Relay
Fig. 19. The network design problem with complete knowledge of all sensor
locations, the trails, and all potential relay placement locations.
?
?
Sensor
Sensor
Requires a statistical model for
wireless propagation in the forest:
Study under way
?
Base Station
(Sink)
Sensor
Sensor
We know there are sensor placement points along each trail, but
we do not know their coordinates; we discover them only as we
walk along the trails
Fig. 20. The network design problem with partial information. In the extreme
case (shown here) the sensor placement locations, and the trails, and the relay
placement locations have to be discovered as the terrain is explored by the
deployment personnel.
sophisticated VPA pattern using additional sensors and more
advanced feature extraction and classification.
VII. D ESIGN AND D EPLOYMENT OF W IRELESS
N ETWORKS
Wireless interconnection of sensors to the wireline communication network or to the computing infrastructure is an
important requirement. These are battery operated, resource
constrained devices. Hence, due to the physical placement
of these devices, or due to the channel conditions, a direct
one-hop link to the infrastructure “base-station” might not be
feasible. In such situations, other nodes could serve as relays
in order to realize a multi-hop path between the source device
and the infrastructure. In the wireless sensor network context,
the relays could be other wireless sensors or battery operated
radio routers deployed specifically as relays. In either case,
the relays are also resource constrained and a cost might be
involved placing them. Hence, there arises the problem of
optimal relay placement. The deployment region might be
known apriori, or the apriori knowledge about the placement
locations and the terrain might only be statistical.
In the following discussion, we assume that we use
IEEE 802.15.4 radios, operating in the 2.4 GHz band. Thus,
the channel bandwidth is 2 MHz, and the bit rate is 250 Kbps.
We also assume that the multihop networks work in the
beaconless mode.
A. Known Sensor Locations and Potential Relay Placement
Points
Figure 19 depicts the problem of deployment of a wireless
sensor network in a forest, where the locations where sensors
have to be placed are precisely known, the layout of the trails
along which relays have to be placed is known, and also the
potential locations, on the trails, where the relays can be placed
are also precisely known. In addition, we have measurements
that provide a statistical model for RF propagation in the
forested terrain.
The problem is then to place as small a number of relays as
possible at some of the potential locations on the trails, and
defining a routing topology, so that the resulting beaconless
multi-hop IEEE 802.15.4 network meets certain QoS objectives.
This problem is essentially the same as the one that is
addressed by SmartConnect our system for design and deployment of wireless sensor networks, and reported in [30].
We, henceforth, call this system SmartConnect v1 (version 1).
The algorithms reported in [30] apply only to the light traffic
situation, which we, formally, call the “lone packet” traffic
model, as the traffic is assumed to be so light that at any
instant no more than one packet traverses the network. One of
our aims is to extend the algorithms in SmartConnect v1 so
as to be able to handle positive traffic.
B. Relay Placement with Partial Information
We also propose to extend the SmartConnect system to
address the problem of of optimal sequential (“as-you-go”)
deployment of relay nodes (and perhape even sensor nodes if
their locations are not apriori given). Such problems have earlier been motivated by the need for impromptu deployment of
wireless networks by “first responders,” for situation monitoring in an emergency such as a building fire or a terrorist siege
(for reference, see [31], [32]). In the forest sensor networking
context, as-you-go deployment might become necessary when
deploying wireless sensor networks in an area where it is
difficult to plan a deployment due to the unavailability of a
precise map of the terrain, or when such networks need to
be deployed and redeployed quickly and there is little time in
between to plan, or in situations where the deployment needs
to be stealthy (for example, when deploying sensor networks
for detecting poachers or fugitives in a forest). The following
are some of the issues that will need to be addressed in order
to develop a comprehensive system for as-you-go deployment
of wireless relay networks.
1) How do we model trails, and potential relay placement
points on the trails.
Sink Node Relay
(Node 0)
1
Relay
Relay
2
Soure Node
(Node 4)
3
L
Fig. 21. Deployment of relays on a straight line path of apriori unknown
length, so as to connect a sink at the beginning of the line to a sensor at the
end of the line.
γ (4)
γ (3)
Fig. 23. A sketch of the layout ground floor of the building of the ECE
Department, IISc, Bangalore.
γ (2)
Sink Node Relay 1
γ (1)
Relay 2
Relay 3
y3
y2
y1
Current loation
y4
Fig. 22.
The network design problem with partial information. Power
measurement at a location in order to decide whether to place a relay or
not.
2) How many passes are allowed to achieve the deployment? A single pass is the most challenging, whereas if
the number of passes is unbounded, we can essentially
employ the SmartConnect v1 approach. If a few passes
are permitted, then the initial passes can be used to
make measurements for partial deployment, while the
subsequent passes can complete the deployment.
3) If several persons are together deploying the network,
how should their actions be coordinated?
4) What should be done if a sensor is required to be placed
at a particular point and the signal quality to the already
deployed relays is poor?
5) How do we incorporate positive traffic with QoS?
6) What sort of constraints should be used for the number
of relays: a bound on the average number, or a bound
on the absolute number.
[33] and [34] has made commendable attempts to address
the modeling issues (1) and (6), but still a lot more needs to
be done.
1) A Simple Model Illustrating As-You-Go Deployment:
In order to explore the issues via a simple model, we have
considered the problem of “as-you-go” deployment of relay
nodes between a sink node and a source node (see Figure 21),
along a line of random length L with known distribution.
There is a cost for placing a relay, and the communication
cost of a deployment is measured as some function of the
powers required to communicate over the links in the network.
The power required to establish a link (with a given link
quality) between any two nodes in the network is modeled
by a random variable which captures the effect of path-loss,
multipath fading and shadowing. As the deployment person
walks along the line from the sink towards the source, at
every step he measures the channel quality (required power,
to be specific) to one (or more) previously placed relays, and
places the relay nodes based on these measurements so as to
minimize the expectation of the sum of the relay cost and the
sum power/ maximum power from the source to the sink node
in the resultant network. For each of these two objectives, two
different relay selection strategies are considered: (i) each relay
communicates with the sink via its immediate previous relay,
(ii) the communication path can skip some of the deployed
relays. The first relay selection strategy allows communication
only via the solid links as shown in Figure 21, whereas for the
second strategy some of the dotted links can be used. Under
certain assumptions on the distribution of the length L and
on the statistical model of powers required at the relays, we
formulate each of these sequential placement problems as a
Markov decision process (MDP). Note that if we do not allow
the possibility of skipping any relay after deployment, then at
each step the decision will be whether to place a relay there
or not, based on the power γ (1) required to establish a link
to the previous relay and the distance y1 to the previous relay
(see Figure 22). Instead, if we allow the possibility of skipping
some relays after the deployment is over, then the decision will
be made based on the measurements to more than one previous
relays. The optimal policies for various MDPs formulated in
our work turn out to be threshold policies, indicating that a
relay can be placed at a given location if and only if either
the power required at that location is sufficiently low or the
location is already far from the previous relay. Our analysis
and numerical work also suggest that allowing the possibility
of skipping some of the deployed relays requiring high power
may result in significant reduction in the power and relay cost.
VIII. P ROPAGATION M ODELING AND A V IRTUAL T ESTBED
Sequential relay placement models, such as the one described earlier, require a statistical model for wireless propagation. Further, we need a realistic setting to experiment with
our algorithms. To both these ends, we have been exploring
the use of REMCOM’s Wireless Insite, an RF propagation
modeling tool. This tool permits a modeler to describe a
terrain, and to place transmitters and receivers in the terrain.
The tool then uses numerical electromagnetic computations to
1
ρ for InSite
ρ for actual experiment
0.8
0.6
ρ
0.4
0.2
0
−0.2
−0.4
0
Fig. 24. The layout of the ground floor of the ECE Department defined
using the Insite GUI. The dimensions and materials have matched to those in
the building.
2
4
6
8
Distance in (m)
10
12
Fig. 26. Correlation vs. distance plots: comparison between actual experimental results and InSite.
−20
RSSI from InSite
Power−law best fit for InSite (exponent=3.71)
Mean RSSI for t=150s
Power−law best fit
for measurements (exponent=3.64)
RSSI in (dBm)
−30
−40
−50
−60
−70
−80
0
5
10
distance in m
15
20
Fig. 25. Comparison between Insite and measurements using TelosB motes:
Average received power vs. distance for a receiver mote placed outside the
door of Room E (in Figure 23) and transmitters placed along the corridor
leading away from Room E, proceeding along Rooms G and H, going towards
the Foyer J. The distance from the door of Room E and the corner of Room H,
where we emerge into Foyer J, is 20 m. The curves of best-fit are obtained
by assuming a power-law path-loss model.
estimate the power received at the receivers. Outdoor terrains
can be defined using DEM (Digital Elevation Models) files,
and ground cover can be modeled using GLCC (Global Land
Cover Characterisation) files. We aim to use Insite in two
ways:
1) For propagation modeling in the specific environment of
the Panna Tiger Reserve
2) For creating a virtual testbed on the computer, in which
we can experiment with our deployment algorithms
We have been experimenting with Insite to understand how
well it can model the propagation environment in our department building. Figure 25 shows the results of measurements
using TelosB motes, and numerical computations from Insite.
The variation of the received signal strength (RSSI) in dBm
for 0 dBm (1 mW) transmit power has been plotted from
actual experiments as well as numerical computation from
InSite, for several distances between the transmitter-receiver
pair. The measured RSSI for the actual experimentation for
each distance was averaged over packets transmitted over 150
seconds, in order to average out the effect of fading. This
was not needed for InSite, since it does not model any time
variation in the channel. Clearly, the variation of the RSSI
values are due to path-loss and shadowing in both cases.
We have fitted minimum mean square-error power-law pathloss curves to the RSSI vs. distance curves, for the actual
experimentation curve as well as the curve obtained from
InSite. We observe that the average path-loss curves from
Insite and from measurements are nearly identical; the pathloss exponent from Insite was found to be 3.71 and that from
measurements was 3.64. This indicates that InSite is able to
model the average variation in RF propagation accurately at
least in the indoor environment.
The experimental data and the measurements obtained from
InSite were used to find the correlation ρ between the received
signal strengths corresponding to two different transmitters
placed at a fixed distance apart from each other. The correlation as a function of the distance between two transmitters
has been shown in Figure 26. It shows that the correlation coefficients are small for the distance more than 1 m, indicating
that the link strengths of two different links can be assumed
to be independent in analysis if either the distance between
the transmitters or that between the receivers is more that
1 m. This observation is useful in justifying the assumption
of independence between the measured required power values
in the simple formulation presented in Section VII-B1.
IX. I DEAS FOR R EDUCED P OWER C ONSUMPTION , FALSE
A LARM P ROBABILITY, E NERGY H ARVESTING AND
C AMERA -T RAP T RIGGERS
It is proposed here to investigate several hardware and
software schemes for an existing intrusion detection system
with the following goals: (a) reducing the overall power
consumption of the system, (b) proposing a triggering mechanism for a decoupled PIR - Camera Trap sensor system, (c)
reducing the false-alarm triggers and at the same time, identify
signatures of a human intrusion or otherwise, (d) investigating
energy-harvesting technologies for the system. Also in the
planning is the building of a thermal imaging camera that
would improve detection range as well as significantly reduce
false-alarm probability.
A. Motivation
PIR sensors are widely used in security applications to
detect trip wire intrusions and break-ins into homes and office
buildings. Such sensors provide an analog signal during an
intrusion event which is then supped to an electronic circuits
that converts it into a digital value. A series of such values
are processed and compared with pre-calibrated thresholds to
ascertain intrusion. An intrusion is detected through changes
in incident infrared radiation which differs from that supplied
by the surroundings. These sensors are energy efficient and are
typically, low power consuming devices. Typically 1 milliwatt
of continuous power is consumed by such devices. Usually,
the intrusion is communicated to a gateway or an aggregation
node over a low power communication device. However, the
low power radios of such devices consume typically 15 mA of
current at 3 V; resulting in a power consumption of about 45
milliwatts. Some radios such as the TI’s CC2420 support only
two degrees of freedom for the power state, namely the power
“on” and “off” states. This forces embedded programmers to
configure the radio in the “always on” state to ensure the
system’s readiness to instantly communicate intrusions. The
detection section itself has two associated problems. On the
one hand, clutter can cause false probability of detection due to
random changes in IR radiation caused by swaying vegetation
in the surroundings. Clutter can be caused by a swinging
branch of a tree or some other random movement. We will
in this section, refer to such sources of clutter as “flutter”.
Secondly, with single PIR-pixel systems, separating human
from animal is quite a challenging task.
In this work, we revisit the PIR detection mechanism to
explore the possibility of reducing energy consumption of
the system. The core idea is to ensure that the embedded
communication electronics is configured to a near power down
state. To achieve this goal, we explored an interrupt driven
system as against an “always on” system. We show by time
budget analysis that the probability of missing a detection is
near 0. Thus, this method is highly energy efficient. We also
outline an approach which can help PIRs classify the type of
intrusion as being human or otherwise.
B. Observations
Fig 27 shows the continuous current (CCl ) of 24mA consumed by the “always on” system. Given that these nodes are
powered using a 2 AA batteries connected in series, each with
a service capacity (SC) of 700mAH, a rough calculation of the
battery life using equation (15) shows that the batteries would
last for 30 hours. In practice, when the 2 batteries were used
to power these nodes we found that the batteries lasted for
a period of 42 hours possibly due to higher service capacity.
We also found that the amount of current consumed in the idle
state and during detection state is the same.
Battery Life =
SC
CCl
(15)
Fig. 27.
Current Consumption in idle and detection mode
C. Proposed changes
This section focuses on the modifications to the system
configuration and hardware changes to reduce power consumption, keeping in mind the challenges that we may face when
making the modifications.
1) Configuration related changes: The nominal operating
frequency of the microcontroller is 4.5MHz. The first proposed
change is to lower the operating frequency of the microcontroller to 1 or 2MHz during an idle state. Additionally, the
radio can be in shutdown mode. Alternately, the configuration
change can include setting the microcontroller to any of
the supported low power modes (LPMs). Changes in the
clock frequency and low power mode might help lower the
power consumption significantly. The microcontroller can be
programmed to increase its operating frequency and turn on
the radio only when there is an intrusion.
2) Hardware Changes: Presently, the PIR sensor’s analog
signal is wired to the ADC of the microcontroller. In addition
to this, we propose to wire the connection to a General Purpose
Input Output (GPIO) port, as this would facilitate interrupt
generation. Hence, we can now place the microcontroller and
radio to low power mode. In this mode, an interrupt wakes
the microcontroller and subsequently the radio to ensure the
intrusion detection and transmission is completed without
missing events and completing the tasks in real-time. We
expect a time budget of 6µs to complete this operation, which
is negligible given that an intrusion might last a few 10’s of
ms.
3) Pulse width estimation for event classification: Thus far,
we have discussed possible hardware and software changes
that could be made to lower the power consumption and
increase the lifetime of the battery. We however did not
consider the possible challenges in back-to-back interrupts that
might occur due to false detection. This would get the node to
frequently turn on and end up consuming power unnecessarily.
Furthermore, classification of humans or otherwise and thus
perhaps differentiate between a two legged and four legged
intrusions is a challenge. During our detailed signal analysis,
we found that the pulse width of the detected signal changes
is proportional to the time taken by the intruder to cross the
FOV. Figures 28 and 29 show the difference in pulse width
of a quick and slow movement in front of one PIR sensor,
respectively. We propose to carry out data analytics and signature analysis from the pulse widths. The idea is flutter, human
intrusion or otherwise should be clearly distinguishable.
Fig. 30.
Fig. 28.
sensor
Pulse width when motion of intruder is quick in front of the PIR
Current Consumption to take one picture
components of the system, starting from intrusion detection,
communication and camera traps are expected to be powered
from harvested energies. Since it is required to decide the
size of harvester including energy storage requirements, a
detailed energy budgeting will be carried out. This includes
energy for sensing, communication, camera trap and Wi-Fi
communication. For the present, we conducted an experiment
to measure the current consumption to shoot a picture with
the flash enabled. Figure 30 is an example of one energy
consuming operation of the camera trap, where a picture is
shot using the flash. The figure shows that approximately
900 mA is required; although for a short burst interval. The
complete operation seems to last at least 2 seconds. For a WiFi communication, the system power requirement is about 1.4
W at 5 V operation. It takes 0.7J to transfer each picture from
the camera to the communication system’s memory and about
2.1J to mail each picture with a good Wi-Fi connectivity.
F. Future work - Towards Thermal Imaging
Fig. 29.
sensor
Pulse width when motion of intruder is slow in front of the PIR
D. Camera Trap
Camera traps are usually accompanied with their own PIR
sensors that trigger the taking of pictures upon the detection
of an event. In our setting, the PIR sensor in the camera trap
is redundant; as the main sensor and camera are regarded
as two separate entities. The challenge now is to wirelessly
communicate the intrusion event and trigger the camera trap.
We propose to use “energy detection” between the communication module and the camera trap’s communication module.
Specifically, we propose to use IEEE 802.15.4 and Bluetooth
technologies to facilitate the communication.
E. Energy Harvesting
In this work, our overall goal is to use energy harvesting technologies to power the complete system. Thus, all
In the near future, thermal imaging cameras will start
gaining popularity both due to increased detection range as
well as lower cost of thermal sensors. A low cost open source
thermal imaging camera attachment is already available for
iPhone and Andriod based mobile phones. We propose to build
the camera for our specific requirement and thus reduce false
probabilities. Such cameras can easily detect forest fires as
well.
R EFERENCES
[1] R. P. Smurlo and H. R. Everett, “Intelligent security assessment for a
mobile robot,” Naval Command, Control and Ocean Surveillance Center,
San Diego, CA, Tech. Rep., 1993.
[2] T. Sugimoto, M. Shibata, and Y. Imuro, “Electronic security system,”
1997.
[3] B. Ivanov, H. Ruser, and M. Kellner, “Presence detection and person
identification in smart homes,” in Int. Conf. Sensors and Systems, St.
Petersburg, 2002.
[4] S. Lee, K. N. Ha, and K. C. Lee, “A pyroelectric infrared sensorbased indoor location-aware system for the smart home,” Consumer
Electronics, 2006.
[5] B. U. Toreyin, E. B. Soyer, I. Onaran, and A. E. Cetin, “Falling person
detection using multisensor signal processing,” EURASIP Journal on
Advances in Signal Processing, 2008.
[6] A. R. Kaushik, N. H. Lovell, and B. G. Celler, “Evaluation of pir detector
characteristics for monitoring occupancy patterns of elderly people living
alone at home,” in Engineering in Medicine and Biology Society, 2007.
EMBS 2007. 29th Annual International Conference, Aug. 2007, pp.
3802–3805.
[7] Q. Hao, D. J. Brady, B. D. Guenther, J. B. Burchett, M. Shankar,
and S. Feller, “Human tracking with wireless distributed pyroelectric
sensors,” Sensors Journal, IEEE, 2006.
[8] P. Zappi, E. Farella, and L. Benini, “Tracking motion direction and
distance with pyroelectric ir sensors,” Sensors Journal, IEEE, vol. 10,
no. 9, pp. 1486–1494, 2010.
[9] O. U. B. Ugur Toreyin, E. Birey Soyer and A. E. Cetin, “Flame detection
system based on wavelet analysis of pir sensor signals with an hmm
decision mechanism,” 2008.
[10] E. H. R. Fuksis, M.Greitans, “Motion analysis and remote control
system using pyroelectric infrared sensors,” Electronics and Electrical
Engineering.–Kaunas: Technologija.
[11] P. Wojtczuk, A. Armitage, D. Binnie, and T. Chamberlain, “Recognition
of simple gestures using a pir sensor array.” Sensors & Transducers
Journal, vol. 14, no. 1, pp. 83–94, 2012.
[12] T. S. P. Team, “Wireless sensor networks for human intruder detection,”
Journal of the Indian Institute of Science, Special issue on Advances in
Electrical Science, vol. 90, pp. 347–380, 2010, (invited).
[13] “Wireless sensor networks for protecting wildlife and humans,” project
funded by NSF, USA, and Department of Electronics and Information
Technology (DeitY), India, under the Pervasive Communications and
Computing Collaboration (PC3) initiative.
[14] H. Keller, “30 years of passive infrared motion detectors-a technology
review,” 2000.
[15] S. B. Lang, “Pyroelectricity: From ancient curiosity to modern imaging
tool,” Phys. Today, vol. 58, pp. 31–35, 2005.
[16] J.Cooper, “Minimum detectable power of a pyroelectric thermal receiver,” Rev.Sci.Instrum., vol. 33, pp. 92–95, 1962.
[17] A. Hossain and M. H. Rashid, “Pyroelectric detectors and their applications,” IEEE Trans. Ind. Applicat., pp. 824–829, 1991.
[18] Wikipedia, “Planck’s law.” [Online]. Available: http://en.wikipedia.org/
wiki/Planck’s law
[19] ——, “Wien’s displacement law.” [Online]. Available: http://en.
wikipedia.org/wiki/Wien’s displacement law
[20] E. I. Inc., “Introduction to infrared pyroelectric detectors,” EltecData
No. 100, 2006.
[21] Panasonic,
“AMN24111
datasheet.”
available
online
at
http://www.panasonic-electric-works.com/peweu/en/downloads/ds
61802 0002 en napion.pdf.
[22] R. S. Joseph Polastre and D. Culler, “Telos: Enabling ultra-low power
wireless research,” In Proceedings of IPSN/SPOTS, 2005.
[23] K. E. Ltd., “TR426 datasheet.” available online at http://www.kube.ch/
Lenses/TR426.pdf.
[24] “Infrared propagation and detection,” Tech. Rep., available online at
www.fas.org/man/dod-101/navy/docs/es310/IR prop/IR prop.htm.
[25] Wikipedia, “Stefan-boltzmann law.” [Online]. Available: http://en.
wikipedia.org/wiki/Stefan-Boltzmann law
[26] M. Meredith and S. Maddock, “Motion capture file formats explained,” Tech. Rep., available at http://www.dcs.shef.ac.uk/intranet/
research/public/resmes/CS0111.pdf.
[27] “CMU graphics lab motion capture database,” available at http://mocap.
cs.cmu.edu/.
[28] N. Lawrance, “Matlab motion capture toolbox version 0.136,” available
at http://www.cs.man.ac.uk/∼neill/mocap/.
[29] C.-C. Chang and C.-J. Lin, “LIBSVM: A library for support vector
machines,” ACM Transactions on Intelligent Systems and Technology,
vol. 2, pp. 27:1–27:27, 2011, software available at http://www.csie.ntu.
edu.tw/∼cjlin/libsvm.
[30] A. Bhattacharya, S. Ladwa, R. Srivastava, A. Mallya, D. R. Sahib,
A. Rao, S. Anand, and A. Kumar, “Smartconnect: A system for the
design and deployment of wireless sensor networks,” in Proc. the Fifth
International Conference on Communications Systems and Networks
(COMSNETS), January 2013.
[31] S. Souryal, J. Geissbuehler, L. Miller, and N. Moayeri, “Real-time
deployment of multihop relays for range extension,” in Proc. of the ACM
International Conference on Mobile Systems, Applications and Services
(MobiSys), San Juan, Puerto Rico, June 2007.
[32] M. Howard, M. Matarić, and S. Sukhat Gaurav, “An incremental selfdeployment algorithm for mobile sensor networks,” Kluwer Autonomous
Robots, vol. 13, no. 2, pp. 113–126, 2002.
[33] P. Mondal, K. Naveen, and A. Kumar, “Optimal Deployment of Impromptu Wireless Sensor Networks,” in Proc. of the IEEE National
Conference on Communications (NCC),2012.
[34] A. Sinha, A. Chattopadhyay, K. Naveen, M. Coupechoux, and A. Kumar,
“Optimal sequential wireless relay placement on a random lattice path,”
http://arxiv.org/abs/1207.6318, 2012.