Simulation of Gradient-Based Data Dissemination in Wireless Sensor Networks

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Nov 21, 2013 (3 years and 6 months ago)

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Simulation of Gradient
-
Based Data Dissemination in
Wireless Sensor Networks


Jun Li, Mani Srivastava, Peter Reiher

Computer Science Department Electrical Engineering Department

University of California, Los Angeles

lijun@cs.ucla.edu

mbs@janet.ucla.edu

reiher@cs.ucla.edu

1. Communication paradigm of sensor network


Sensor network has many characteristics different from conve
ntional computation
and communication models. More important, a user making queries of an environment
via sensor network mainly cares that his query is forwarded to the sensor that has the data
with specific attributes, not the identity or address of the s
ensor nodes at all. The address
based packet routing does not fit well of the data
-
centric communication paradigm of
sensor networks
[1]
.

Gradient
-
based data dissemination for sensor networks has been proposed as the
communicati
on model for sensor networks
[1]
. The sensor network is composed of very
large number of sensors distributed arbitrarily to collectively sense some information. It
is a two
-
phase strategy. The first phase is interest propagation

phase, in which the interest
on particular data is propagated to reach sensor nodes that have data satisfying the
interest. Meanwhile the gradients on some links are established. The interest initiator is
called sink node. The second phase is the data pro
pagation phase, in which the data
matching the interest propagates back to the sink node(s) according to the gradient value
of links from node to node.

2. Gradient establishment and utilization


The unsolved problem for the gradient
-
based data disseminatio
n in sensor network
is that how to establish the gradient value and how to utilize them for data dissemination.
We invented three gradient establishment strategies and found them interesting


direction
-
based, distance
-
based and gravity
-
based. The directio
n
-
based scheme aims to
forward data at each intermediate node with least deviation from the straight line toward
the sink. The distance
-
based scheme aims to make data flow to the neighbor nearest to
the sink. And in the gravity
-
based scheme the data flows
towards the sink as if the sink
node is exerting a gravitation pull. In each strategy the data flows along the link(s) with
greatest gradient(s).

3. Simulation of gradient
-
based dissemination


PARSEC
[2]

language is used to simu
late the data dissemination in wireless sensor
networks. Each different gradient
-
based scheme is modeled. Moreover, since each sensor
is energy
-
sensitive, each gradient establishment with energy as a factor is also simulated.

Sensors are divided into parti
tions and each partition is an entity. The interest or
data propagation from one sensor to another has to be done by message passing from
partition to partition. This is important for scalability


letting each sensor be an entity is
just too resource cons
uming. There are also other messages defined for simulation
purposes. Each sensor also maintains a structure including topology, transmission radius,
energy amount, interest information, statistical information, gradient table, and so on.

The evaluation cr
iteria for comparing each different scheme include data
availability (whether the data flow can hit sinks), latency and the energy consumption.
We made some assumptions on sensor networks. We assume constant power for
reception and transmission (ratio 1:3)
, constant size of interest and data (128 bytes and
1024 bytes), and constant transmission rate (1MB/s).

Different topologies are used for simulation. The result shows that the distance
-
based strategy consumes the least amount of energy, and the direction
-
based the most.
The reason is that the former usually goes through the least number of hops to reach the
sink. But in a mesh topology the result for different strategies are basically same.

Furthermore, with energy as an additional factor in calculation gr
adient, if a
neighbor has less energy, the possibility of data flowing to this neighbor is also lower, i.e.
lower gradient value, preventing that neighbor from burning out. The energy
consumption is fairer this way and the whole sensor network is expected
to have longer
lifetime.

4. Gradient aggregation and its simulation


At each sensor node, instead of maintaining a gradient value toward its neighbor
for each sink, the gradient values for multiple sinks can be aggregated and replaced by
just one gradient
value. The data dissemination based on the aggregated gradient is also
simulated. Whereas the data availability might be compromised and the energy
consumption for interest propagation phase is still the same, the energy consumption for
data propagation ph
ases can be greatly decreased.

5. Conclusion


Gradient
-
based data dissemination is a promising communication paradigm for
wireless sensor networks. Our simulation using PARSEC language shows many
interesting results. Three different strategies on establish
ing and utilizing gradients are
compared in simulation, with or without taking energy consumption as a factor.
Aggregation over multiple sinks when calculating gradients is also simulated to compare
the energy consumption.

References


[1]

Estrin, D., Govindan,

R., Heidemann, J., and Kumar, S. “
Next Century Challenges: Scalable Coordination in Sensor
Networks
”,
ACM MobiCom 99
, August 1999, Seattle, WA.

[2]

UCLA Parallel Computing Laboratory
. PARSEC



Parallel Simulation Environment for Complex Systems.

http://parsec.cs.ucla.edu
.

[3]

Broch, J., Maltz, D., Johnson, D., Hu, Y. Jetcheva, J., “
A performance comparison of multi
-
hop wireless ad hoc network
routing proto
cols

Proceedings of the Mobicom’98 Conference
, Dallas, Texas, pp. 85
-
97, October 1998.

[4]

Kulik, J., Rabiner, W., Balakrishnan, H., “
Adaptive Protocols for Information Dissemination in Wireless Sensor
Networks

.

Proc. 5th ACM/IEEE Mobicom Conference, Seattle, WA,
August 1999. Also MIT
-
LCS
-
TR
-
772 (Feb. 1999).

[5]

Bagrodia, R. and Takai M. “
Position Paper on Validation of Network Simulation Models
”, DARPA/NIST Network
Simulation Validation Workshop, May 1999.

[6]

Zeng, X., Bagrodia, R., Gerla M., “
GloMoSim: a Library for Parallel Simulation of Large
-
scale Wireless Networks
”,
Proceedings of the 12th Workshop on Parallel and Distributed Simulations
--

PADS '98
, May 26
-
29, 1998 in Banff, Alberta,
Canada.

[7]

Liu, W., Chia
ng, C., Wu, H., Gerla, M., Jha, V., Bagrodia, R., “
Parallel Simulation Environment for Mobile Wireless
Networks”,
Proceedings of the 1996 Winter Simulation Conference
-

WSC '96,
Coronado, CA, 1996, pp. 605
-
612.

[8]

Bagrodia, R., "
Parallel Languages for Discret
e
-
Event Simulation Models
", IEEE Computational Science and Engineering,
Apr
-
Jun 1998, pp. 27
-
38.

[9]

Bagrodia, R., Meyer, R., Takai, M., Chen, Y., Zeng, X., Martin, J., Park, B., Song, H.,
“Parsec: A Parallel Simulation
Environment for Complex Systems
",
Compu
ter
, Vol. 31(10), October 1998, pp. 77
-
85.