In Simulation 11, we set the number of items to be
covered to 30. The number of reporters is 4 and each
reporter has 3 photos from which he needs to choose
one from. The probability of a item being included
in a photo varies from 0 to 1 by .05. The number of
runs for each probability is 15. LP converges to cov-
ering all items at a probabiliy of about .5. GREEDY
converges to covering all items at a probability of .6.
CLIQUE converges to covering all items at a probabil-
ity of 0.85. While its performance is on par with LP
and GREEDY when the probability is between 0 and
.4, it underperforms both from .4 to .85.
In Simulation 12, we set the number of items to be
covered to 30. The number of reporters is 3 and each
reporter has 3 photos from which he needs to choose
one from. The probability of a item being included
in a photo varies from 0 to 1 by .05. The number of
runs for each probability is 15. LP converges to cov-
ering all items at a probabiliy of about .5. GREEDY
converges to covering all items at a probability of .6.
CLIQUE converges to covering all items at a probabil-
ity of 0.85. While its performance is on par with LP
and GREEDY when the probability is between 0 and
.4, it underperforms both from .4 to .85.
In Simulation 13, we once again vary probability
that an item is included in a photo from 0 to 1 by
.05. This time there are 80 total items, the number of
reporters is 8 and the number of photos per reporter
is 10. All three algorithms converge to covering all
items rather quickly, with LP converging at a proba-
bility of about .15, GREEDY converging at a probabil-
ity of about .25 and CLIQUE converging at a probabil-
ity of about .4. A large gap of about 7 items on aver-
age can be seen in this simulation between GREEDY
and CLIQUE at a probability of about 0.2.
In Simulation 14, we vary the number of photos
per reporter. We ﬁx the number of reporters to 2 and
the number of items to be covered to 30. We set the
probability of an item appearing in a photo to be .6.
We run each of the three algorithms 30 times for a
given number of photos per reporter. We see the per-
formance of CLIQUE falls off signiﬁcantly in com-
parison to the other two algorithms as p increases, se-
lecting on average 23 items over all p, and remaining
relatively ﬂat on average.
From these simulations, we see that GREEDY
outperforms CLIQUE with GREEDY performing very
well compared to an LP-relaxation upper bound on
the optimal solution.
5 CONCLUSIONS AND FUTURE
WORK
We propose two novel models, a maximum cover-
age based model and a clique based model for mod-
eling the problem of maximizing the amount of rel-
evant diversity of social swarming data received by
a commander. We provide well-performing algo-
rithms/heuristics for both models with the maximum
coverage algorithm outperforming the clique heuris-
tic. In future work, we look to experimentally ana-
lyze the multiple-format models and develop a physi-
cal system based on our models.
ACKNOWLEDGEMENTS
Research was sponsored by the Army Research Lab-
oratory and was accomplished under Cooperative
Agreement Number W911NF-09-2-0053. The views
and conclusions contained in this document are those
of the authors and should not be interpreted as rep-
resenting the ofﬁcial policies, either expressed or im-
plied, of the Army Research Laboratory or the U.S.
Government. The U.S. Government is authorized
to reproduce and distribute reprints for Government
purposes notwithstanding any copyright notation here
on.
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