A
wireless sensor network (WSN)
essentially ad
hoc networks consists of spatially
distributed
autonomous
sensors
to
monitor
physical
or environmental conditions, such
as
temperature,
sound,
pressure, etc. and to
cooperatively pass their data through the
network to a main location
Sensor module collects the observations from
surrounding environmental analog information
such as light, sound, shocks,
etc
, converts it to
the digital signal via the analog to digital
converter (ADC), and then transfers to the
processor
unit
Processor
module manages the cooperation
between the units in the sensor, the
collaborations between other WSN nodes in the
network.
Wireless
communication
module communicate
between WSN nodes and Base Station
Wireless Sensor
Network Technology
Operating systems
Contiki ,ERIKA Enterprise, Nano
-
RK,
TinyOS ,LiteOS ,OpenTag, NanoQplus
Industry standards
ANT, 6LoWPAN, DASH7, ONE
-
NET,
ZigBee, Z
-
Wave, Wibree,
WirelessHART, 802.15.4, MiWi
Programming languages
C, LabVIEW,nesC
Hardware
Iris Mote, Sun SPOT, Xbee, Arduino
Software
TinyDB, TOSSIM, NS
-
2, OPNET,
NetSim, LinuxMCE
Applications
Key distribution, Location estimation,
Sensor Web, Telemetry
Protocols
AODV, DSR, TSMP
Used in Project
OS (
TinyOS
), platform(
Micaz
),
Programming language
C++,Protocol(AODV, DSR, TSMP)
Sensor Web/ Sensor Grid, Internet of
things,M2M
Ubiquitous Computing : Smart home/Cities,
smart meter, smart TV/appliances,.
Future with Graphene
Smart dust is hot.
Message Queue Telemetry Transport (MQTT
).
Used by Facebook,IBM smart planet
initiatives
Already in use Disaster management,
Alternative energy , health monitoring,
agriculture, defense.
WSN nodes are very small and design is
dominates by size of battery. WSN are using
various technology like energy harvesting
,piezoelectric material technology to reduce
size of battery.
Power consumption in sensor networks can
be divided into three domains: sensing,
communication and data
processing.
R
outer node consume more power than leaf
nodes. Due to unbalanced energy
consumption, WSN nodes on busy routing
paths may drain their batteries faster than
other nodes, Energy aware routing is
important.
data
-
centric: like directed diffusion, sensor
protocols for information via negotiation
(SPIN) and power aware many
-
to
-
many
routing fall into this category
cluster
-
based: Low
-
energy adaptive
clustering hierarchy (LEACH) is an example of
a cluster
-
based sensor network routing
algorithm.
location
-
based: minimum energy
communication network (MECN) and
geographic adaptive fidelity (GAF)
are
location
-
based routing algorithms.
Q
Overall
= Q
CPU
+ Q
RadioTrans
+
Q
RadioRcv
Q
CPU
= P
CPU
* T
CPU
=
P
CPU
* (B
Enc
* TB
Enc
+ B
Dec
* TB
Dec
+B
Mac
*
TB
Mac
+ T
RadioActive
).
P
X
in Eq. 2 represents the power of device X.
T
X
means the computation time of device X
TB
Y
denotes the per
-
byte time consumed for doing
operation Y.
B
Y
indicates the amount of bytes to be computed by
operation Y
Enc
, Dec and Mac denote the encryption, decryption, and
MAC digest generation operations respectively
T
RadioActive
represents the radio transceiver’s active time, as
the processor module remains active while the radio chip
is turned on
.
Q
RadioTrans
= P
RadioTrans
* (K
Trans
* B
Trans
+ T
Startup
)
Q
RadioRcv
= P
RadioRcv
* (K
Rcv
* B
Rcv
+
T
Idle
)
D
ecide
the price of adding security to
WSN
routing
protocols or further estimate the
network lifetime of their
WSNs
M
athematical
models
used to
estimate extra
energy consumption
of routing protocols due
to security features in
WSN.
Empirical values and physical actual values
are compared to validate model for different
routing protocols.
S.No.
Task
From date
To Date
1
Setting up MicaZ platform
March 01 2013
15 March 2013
2
Routing protocols setup in on
hardware and OS platform.
15 March 2013
31 March 2013
3
Vulnerability and security of
protocols
31 March 2013
15 April 2013
4
Writing code on TinyOS on
MicaZ
15 April 2013
15 May 2013
5
Testing codes for various
routing protocols
15 May 2015
30 May 2013
6
Taking measure for each
protocol and applying model
30 May 2015
30 June 2013
7
Comparing empirical values
with model values
30 June 2013
15 July 2013
8
Presentation of results
15 July 2013
25.
July
2013
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