Mobile Sensor Networks

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29 Οκτ 2013 (πριν από 3 χρόνια και 10 μήνες)

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Distributed Algorithms for
Mobile Sensor Networks

Chelsea Sanders

Ben
Tullis

What is a Mobile Sensor Network?


Sensors are set up in a field and they pass the data
collected through a network to a main location.


They are used
to monitor temperature, sound,
vibration, pressure, motion, pollutants, and other
things.


Motivated by military applications such as battlefield
surveillance.


Today they are used in the military, industrial process
monitoring and control, machine health monitoring,
environment and habitat monitoring, healthcare
applications, home automation, and traffic control.


Problems with

Mobile Sensor Networks


Where
to deploy
mobile sensor networks


Affects
the performance and the lifetime of the
network.


In unknown or hostile environments,
is
it
is hard to
determine a good way to
optimally deploy
the
sensors.


How
to gather the data
efficiently


Lots
of communication to one node, the master node
.


Since the sensors are either continuously gathering data or
gathering often, how much data should be sent back?


Loss in communication or failure in sensor


Can cause break in the coverage of the area


Can also cause communication to stop all together if it is
set up in communication relay.


How Can Parallel Programming Help?


The
most
efficient algorithms are the
ones
that
are distributed algorithms.


Distributed algorithms are designed to run on
computers that are constructed from
interconnected processors
.


Typically
executed concurrently with the other
processors having limited information about what
the other parts of the algorithm are doing
.


It helps to cut down are communication between
the processors and helps speed up the calculation
time.

Using Parallel Programming to Solve
Problems


Where
to deploy the
sensors


A distributed algorithm can help figure out where
to deploy sensors efficiently and quickly.


Distributed Self
-
Spreading Algorithm (DSSA)
developed by Yong Zhang and Li Wang (and then
later studied by
Heo

and
Varshney
).



Distributed Self
-
Spreading Algorithm


First, a specified
number of nodes are pre
-
deployed randomly in an area.


Each
node has a sensing range,
communication range, and its initial
location.


Each node has to be able to communicate to:


Find what nodes are around it and the locations of
the nodes around it.


Transmit and forward sensed data.

Distributed Self
-
Spreading Algorithm


Next
each node finds its expected density which is the average number of
nodes required to cover the entire area and the initial local density which
is the number of nodes within is communication range.


Formula:




Where N is the number of nodes and
cR

is the communication range of each node,
and A is the range of interest.


It
then uses a force function which takes into consideration how much
‘force’ it would take to move to a location and then
adds
all the partial
forces together.


Formula:


Distributed Self
-
Spreading Algorithm


With that information, each node can decide its
next movement.


It then recollects the information to find the local
density, uses the force function again, and
decides its next movement.


It keeps repeating that process until the
nodes
move less than a specified amount for a certain
amount of moves or the node is just moving back
and forth between two places, the movement is
stopped and it is considered in a stable place.

Distributed Self
-
Spreading Algorithm

Using Parallel Programming to Solve
Problems


Communication
of
data


A distributed
algorithm
can
help figure out how
much data to send to the master node and the
best way to route it to the master node
.


Zhao and Yang created a distributed
algorithm,
the Distributed Self
-
Spreading Algorithm, that
focuses primarily on a sensor network that has
master node that actually travels to specific
anchor points to collect the
data.



Distributed Self
-
Spreading Algorithm


The algorithm allows the sensor nodes to be
collecting data constantly and then
summarizing it using algorithms.


Then
it sends its data to an anchor point every
so often (as determined by the algorithm),
and the master mobile node goes around and
collects the data from the anchor points.

Using Parallel Programming to Solve
Problems


Loss in communication or failure in sensor


Lee and Lin suggest several situations.


The first is dynamic coverage with migration of
redundant nodes.


The second is loss of a node is handled by
increasing the radii of the nodes nearest to the
lost area until the area is completely covered
again.

Parallel Solution




In both cases parallel programming quickly and
efficiently regains the lost coverage by dynamically
maintaining the worst case coverage distance

Worst case coverage distance

Maximum breach path: a path where the minimum distance from points on the path to




the sensor network is maximized.

Dynamic Recovery

Advantages


Low communication complexity




No need for a tight bound on message propagation
delay



BlueCube


Construction
of a hypercube that incorporates
communication environment over Bluetooth
radio
systems.


The algorithms that are used focus on the
already existing algorithms for
hypercube
and
then shows how they can be applied to
Bluetooth
communication.

Hyper cube backbone


Hypercube Backbone optimizes communication path

While Bluetooth communication provides higher communication speed

Conclusion


Mobile
sensor networks are very widely used
and are all around
us.


They are very important in our everyday life,
but they do have some problems
.


Distributed algorithms have helped improve
upon those problems and helped make mobile
sensor networks more efficient and effective.

Works Cited


Chao
-
Tsun

Chang;
Chih
-
Yung Chang; Jang
-
Ping
Sheu
; , "
BlueCube
:
constructing a hypercube parallel computing and communication
environment over Bluetooth radio system,"
Parallel Processing, 2003.
Proceedings. 2003 International Conference on

, vol., no., pp.447
-
454, 9
-
9
Oct. 2003


Heo
, N.;
Varshney
, P.K.; , "A distributed self spreading algorithm for mobile
wireless sensor networks,"
Wireless Communications and Networking,
2003. WCNC 2003. 2003 IEEE

, vol.3, no., pp.1597
-
1602 vol.3, 20
-
20 March
2003


Miao Zhao;
Yuanyuan

Yang; , "An Optimization Based Distributed
Algorithm for Mobile Data Gathering in Wireless Sensor Networks,"
INFOCOM, 2010 Proceedings IEEE

, vol., no., pp.1
-
5, 14
-
19 March 2010


Yong Zhang; Li Wang; , "A distributed sensor deployment algorithm of
mobile sensor network,"
Intelligent Control and Automation (WCICA),
2010 8th World Congress on

, vol., no., pp.6963
-
6968, 7
-
9 July 2010