Information Technology Reference

In-Depth Information

schemes for mobile sensors which react to events such that the distribution of the

group of sensors tends toward the distribution of the sensed events. The algorithms

rely on an initial uniform distribution of sensors and can then generate good results

without requiring the sensors to communicate and exchange the information about

their positions to each other. One technique uses a very simple update rule to move

each sensor based only on the position of the event and the sensor itself (similar to

the potential field's approach described in the previous section). This movement is

easily computed and the amount of motion can be easily bounded. The second

technique uses more computation and keeps a compressed history of the events.

Here, to determine its correct position, each sensor maintains a discrete version of

the CDF (cumulative distribution) which is updated after each event. The CDF is

scaled based on the number of events and the length
l
of the interval of interest, so

that CDF(
l
) =
l.
Then each segment of the CDF is associated with a proportional

number of sensors, and thus the sensor density will truck the event density. Or in

other words, each sensor chooses its new position so that the CDF at this position

returns its initial position.

Another method for
stochastic event capturing
(SEC) is presented in [
32
]. The

authors have derived algorithms for motion planning based on defined quality of

coverage (QoC) metrics, such as the fraction of events captured and the probability

that an event is lost. The analytical results provide guidelines for choosing the

velocity and the number of sensors to be deployed for satisfying constraints on

fraction of captured events. Only mathematical analyses and proofs derived on

approximation factor are presented, while neither simulations nor emulations are

made to test the feasibility of the proposed solutions.

Another event-driven sensor self-deployment algorithm (EDSSA) is proposed in

[
33
], based on the virtual force algorithm, where the potential force of the detected

event is added to the calculation of the vector sum. This force pulls distant nodes

toward the event location and pushes nearby nodes away. By adjusting the event inten-

sity threshold, different node density can be achieved around the event location. The

approach considers large-scale relocation of nodes, compared with relocation toward

coverage holes in some of the previous approaches. Starting from the initial topology,

the nodes will tend to a regular triangular pattern position, achieving the optimal cover-

age when the length of the grid parts is

D
=
, where
R
s
is the sensing range.

These examples for sensor mobility are representative examples of the models

found in literature. They form the basics allowing for more comprehensive study

later on, since familiarity with the state-of-the-art approaches and their pros & cons

is helpful for a research in the considered area. Moreover, the following text pres-

ents the main mobility challenges and possible ways to handle them.

3

s

6.4

Mobility Challenges: Data Dissemination

Mobility in WSNs is a permanent and challenging issue. It is often followed by

network topology modification, necessity of new protocols, and consequently,

requirement of appropriate mobility models. Above all, challenges lie in designing

Search WWH ::

Custom Search