For target tracking applications,
wireless sensor nodes provide accurate information since they can be deployed
and operated near the phenomenon. These sensing devices have the opportunity of
collaboration among themselves to improve the target localization and tracking
accuracies. An energy-efficient collaborative target tracking paradigm is
developed for wireless sensor networks (WSNs). In addition, a novel approach to
energy savings in
WSNs is devised in the
information-controlled transmission power (ICTP) adjustment, where nodes with
more information use higher transmission powers than those that are less
informative to share their target state information with the neighboring nod
MODULES
:1. Networking Module.
2.
Sensor selection module.
3.
ICTP Module.
4.
Target tracking Module.
5. Average energy consumed module
Hardware Requirment
REQUIREMENTS:
- System : Pentium IV 2.4 GHz.
- Hard Disk : 40 GB.
- Floppy Drive : 1.44 Mb.
- Monitor : 15 VGA Colour.
- Mouse : Logitech.
- Ram : 256 Mb
Software
requirements:
- Operating system : - Windows XP
Professional.
- Front End : - Asp .Net 2.0.
- Coding Language : - Visual C#
.Net.
Awirelesssensornetwork(WSN)
ofspatiallydistributed autonomous sensors to monitor physical o r environmental conditions,
such as temperature, sound, pressure, etc. and to cooperatively pass their
data through the network to a main location. The more modern networks are
bi-directional, also enabling control of sensor activity. The development of
wireless sensor networks was motivated by military applications such as
battlefield surveillance; today such networks are used in many industrial and
consumer applications, such as industrial process monitoring and control,
machine health monitoring, and so on.
·
The WSN is built of
"nodes" – from a few to several hundreds or even thousands, where
each node is connected to one (or sometimes several) sensors. Each such sensor
network node has typically several parts: a radio transceiver with
an internal antenna or
connection to an external antenna, a microcontroller, an electronic circuit for
interfacing with the sensors and an energy source, usually a battery or an embedded form of energy harvesting. A sensor node might
vary in size from that of a shoebox down to the size of a grain of dust,
although functioning "motes" of genuine microscopic dimensions have
yet to be created. The cost of sensor nodes is similarly variable, ranging from
a few to hundreds of dollars, depending on the complexity of the individual
sensor nodes. Size and cost constraints on sensor nodes result in corresponding
constraints on resources such as energy, memory, computational speed and
communications bandwidth. The topology of the WSNs can vary from a simple star network to
an advanced multi-hop wireless mesh network.
The propagation technique between the hops of the network can be routing or flooding.[1][2]
·
In computer science and telecommunications,
wireless sensor networks are an active research area with numerous workshops
and conferences arranged each year.[3]
·
Area
monitorin
A
·
Health
care monitoring
·
The medical applications
can be of two types: wearable and implanted. Wearable devices are used on the
body surface of a human or just at close proximity of the user. The implantable
medical devices are those that are inserted inside human body. There are many
other applications too e.g. body position measurement and location of the
person, overall monitoring of ill patients in hospitals and at homes. Body-area
networks can collect information about an individual's health, fitness, and
energy expenditure.[4]
·
Air
pollution monitoring
·
Wireless sensor networks
have been deployed in several cities (Stockholm, London and Brisbane) to
monitor the concentration of dangerous gases for citizens. These can take
advantage of the ad hoc wireless links rather than wired installations, which
also make them more mobile for testing readings in different areas.
·
Forest
fire detection
·
A network of Sensor
Nodes can be installed in a forest to detect when a fire has started. The nodes
can be equipped with sensors to measure temperature, humidity and gases which
are produced by fire in the trees or vegetation. The early detection is crucial
for a successful action of the firefighters; thanks to Wireless Sensor
Networks, the fire brigade will be able to know when a fire is started and how
it is spreading.
·
Landslide
detection
·
A landslide detection
system makes use of a wireless sensor network to detect the slight movements of
soil and changes in various parameters that may occur before or during a
landslide. Through the data gathered it may be possible to know the occurrence
of landslides long before it actually happens.
·
Water
quality monitoring
·
Water quality monitoring
involves analyzing water properties in dams, rivers, lakes & oceans, as
well as underground water reserves. The use of many wireless distributed
sensors enables the creation of a more accurate map of the water status, and
allows the permanent deployment of monitoring stations in locations of
difficult access, without the need of manual data retrieval.
·
Natural
disaster prevention
·
Wireless sensor networks
can effectively act to prevent the consequences of natural disasters, like
floods. Wireless nodes have successfully been deployed in rivers where changes
of the water levels have to be monitored in real time.
The main characteristics of a WSN include:
·
Power consumption
constraints for nodes using batteries or energy harvesting
·
Ability to cope with
node failures
·
Mobility of nodes
·
Communication failures
·
Heterogeneity of nodes
·
Scalability to large
scale of deployment
·
Ability to withstand
harsh environmental conditions
·
Ease of use
Sensor nodes can be imagined as small computers,
extremely basic in terms of their interfaces and their components. They usually
consist of a processing unit with limited computational power
and limited memory, sensors or MEMS (including specific conditioning
circuitry), a communication device (usually radio transceivers
or alternatively optical), and a power source usually in the form of a battery.
Other possible inclusions are energy
harvesting modules,[7] secondary ASICs, and possibly
secondary communication interface (e.g. RS-232 or USB).
The base stations are one or more components of
the WSN with much more computational, energy and communication resources. They
act as a gateway between sensor nodes and the end user as they typically
forward data from the WSN on to a server. Other special components in routing
based networks are routers, designed to compute, calculate and distribute the
routing tables.
Hardware
One major challenge in a WSN is to produce low cost and tiny sensor nodes. There are an increasing
number of small companies producing WSN hardware and the commercial situation
can be compared to home computing in the 1970s. Many of the nodes are still in
the research and development stage, particularly their software. Also inherent
to sensor network adoption is the use of very low power methods for data
acquisition.
In many applications, a WSN communicates with a Local Area Network or Wide Area Network through a gateway. The Gateway acts as
a bridge between the WSN and the other network. This enables data to be stored
and processed by device with more resources, for example, in a remotely located server.
Energy is the scarcest resource of WSN nodes,
and it determines the lifetime of WSNs. WSNs are meant to be deployed in large
numbers in various environments, including remote and hostile regions, where ad
hoc communications are a key component. For this reason, algorithms and
protocols need to address the following issues:
·
Lifetime maximization
·
Robustness and fault
tolerance
·
Self-configuration
Lifetime maximization: Energy/Power Consumption
of the sensing device should be minimized and sensor nodes should be energy
efficient since their limited energy resource determines their lifetime. To
conserve power the node should shut off the radio power supply when not in use.
Some of the important topics in WSN(Wireless
Sensor Networks) software research are:
·
Operating systems
·
Security
·
Mobility
Operating systems
Operating systems for wireless sensor network nodes are
typically less complex than general-purpose operating systems. They more
strongly resemble embedded systems, for two reasons. First, wireless sensor
networks are typically deployed with a particular application in mind, rather
than as a general platform. Second, a need for low costs and low power leads most
wireless sensor nodes to have low-power microcontrollers ensuring that
mechanisms such as virtual memory are either unnecessary or too expensive to
implement.
It is therefore possible to use embedded operating systems
such as eCos or uC/OS for
sensor networks. However, such operating systems are often designed with
real-time properties.
TinyOS is
perhaps the first[8] operating
system specifically designed for wireless sensor networks. TinyOS is based on
an event-driven
programming model
instead of multithreading.
TinyOS programs are composed of event
handlers and tasks with run-to-completion semantics. When
an external event occurs, such as an incoming data packet or a sensor reading,
TinyOS signals the appropriate event handler to handle the event. Event
handlers can post tasks that are scheduled by the TinyOS kernel some time
later.
LiteOS is
a newly developed OS for wireless sensor networks, which provides UNIX-like
abstraction and support for the C programming language.
Contiki is an OS which uses a simpler
programming style in C while providing advances such as 6LoWPAN and Protothreads.
RIOT implements a microkernel architecture.
It provides multithreading with standard API and allows for
development in C/C++. RIOT supports common IoT protocols such as 6LoWPAN, IPv6,RPL, TCP,
and UDP.[9]
ERIKA Enterprise is an open-source and royalty-free OSEK/VDX Kernel
offering BCC1, BCC2, ECC1, ECC2, multicore, memory protection and kernel fixed
priority adopting C programming language.
1. Jump up^ Dargie,
W. and Poellabauer, C., "Fundamentals of wireless sensor networks: theory
and practice", John Wiley and Sons, 2010 ISBN 978-0-470-99765-9, pp. 168–183, 191–192
2. Jump up^ Sohraby,
K., Minoli, D., Znati, T. "Wireless sensor networks: technology,
protocols, and applications, John Wiley and Sons", 2007 ISBN 978-0-471-74300-2, pp. 203–209
3. Jump up^ http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6550437
4. Jump up^ Peiris, V. (2013).
"Highly integrated wireless sensing for body area network
applications". SPIE Newsroom. doi:10.1117/2.1201312.005120. edit
5. Jump up^ Spie (2013).
"Vassili Karanassios: Energy scavenging to power remote sensors". SPIE
Newsroom. doi:10.1117/2.3201305.05. edit
6. Jump up^ Tiwari,
Ankit et. al, Energy-efficient wireless sensor network design and
implementation for condition-based maintenance, ACM Transactions on Sensor
Networks (TOSN),http://portal.acm.org/citation.cfm?id=1210670
7. Jump up^ Magno, M.; Boyle, D.;
Brunelli, D.; O'Flynn, B.; Popovici, E.; Benini, L. (2014). "Extended
Wireless Monitoring Through Intelligent Hybrid Energy Supply". IEEE
Transactions on Industrial Electronics 61(4): 1871. doi:10.1109/TIE.2013.2267694. edit
8. Jump up^ TinyOS Programming, Philip Levis, Cambridge University
Press, 2009
9. Jump up^ Oliver
Hahm, Emmanuel Baccelli, Mesut Günes, Matthias Wählisch, Thomas C. Schmidt, RIOT
OS: Towards an OS for the Internet of Things, In: Proc. of the 32nd IEEE INFOCOM. Poster Session, Piscataway,
NJ, USA:IEEE Press, 2013.
10. Jump up^ Silva, D.; Ghanem,
M.; Guo, Y. (2012). "WikiSensing: An Online Collaborative Approach for
Sensor Data Management". Sensors 12 (12): 13295. doi:10.3390/s121013295. edit
11. Jump up^ Muaz
Niazi, Amir Hussain (2011). A Novel Agent-Based Simulation Framework for
Sensing in Complex Adaptive Environments. IEEE Sensors Journal, Vol.11 No. 2,
404–412. Pap
Student projects
We are always looking for highly motivated students that
are interested in working with us on our research projects. We have a number
of Bachelor (B) and Master's (M) thesis available. The topics of available
theses are related to the fields of wireless sensor networks, mobile
sensing, cyber-physical systems, and similar areas.
If you are interested in learning about available topics,
please contact
us.
Templates for project reports and presentations are
available here.
Ongoing
projects
Type
|
Title
|
Student
|
Supervisor
|
Submission
|
M
|
Breaking
Down the Demographics: How Our Demographic Data Influences Our Mobility
Behavior
|
Herrero
Domínguez, Christian
|
May,
2014
|
Completed
projects
Type
|
Title
|
Student
|
Supervisor
|
Submission
|
B
|
Monitoring
Mechanisms for Wireless Sensor Networks
|
Yan,
Qingli
|
Sep,
2012
|
|
B
|
Evaluation
of Event-management Systems based on Actual Data Platforms at the Railway
Operating Field Darmstadt
|
Stroeher,
Niels
|
Jun,
2012
|
|
B
|
A
Survey of State-of-the-art Prediction Algorithms and Models for Human
Mobility
|
Kruk,
Sabina
|
May,
2013
|
|
B
|
Proton:
Protocol-independent Monitoring of Mobile Ad Hoc Networks
|
Richerzhagen,
Nils
|
Jan,
2013
|
|
S
|
Efficient
and Standard-compliant Integration of Sensor Networks and the ROS Robot
Operating System
|
El
Mahjoub, Brahim
|
Silvia Santini
Philipp Scholl |
|
M
|
Automatic
Classification of Private Households Using Electricity Consumption Data
|
Sadamori,
Leyna
|
Nov,
2012
|
|
M
|
MONET:
A Component-based Framework for Efficient Active Monitoring in Wireless
Sensor Networks
|
Ullrich,
Thomas
|
May,
2013
|
|
M
|
Design
and Development of a Forest of Wireless Sensor Network Testbeds
|
Gurov,
Iliya
|
Silvia Santini
Pablo Guerrero |
Jan,
2013
|
M
|
Lissome
Development of Variable Software Features for Mobile Business Applications
|
Kolokolov,
Viktor
|
Paul Baumann
Stefan Ruehl |
Jun,
2013
|
Above data is collected and shared from various sources
available on websites and in general article sections like newspaper, magazines
etc .data might not be 100% correct. Request all the users to re verify if
again. Web world group India
Data taken on dated
21/03/2014
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