We evaluate the proposed solutions by two metrics, execution time and message cost, on our application prototype. The evaluation testbed is six commercial Android smartphones (Google Nexus 4) as shown in Fig. 5.6. The parameters in the experiments are as follows. We assume that z = 7 and use MD5 [25] to generate hash values. In the basic Bloom filter solution, the expected false positive rate f is set to 0.001%. In the iterative Bloom filter solution (2
(a) (b) (c) (d) (e) (f)
Figure 5.3: Screenshots of the prototyped Android app: (a) Touch the “Start” to participate in an MDSN. (b) Select the countries the user visited before. (c) Touch the “MATCH” to start the CPM procedure with nearby users. (d) The answer to the all -common problem. (e) The answer to the β-common problem. (f) The answer to the top-γ-popular problem.
iterations), we respectively set f = 10% and f = 0.01% for the first and the second iterations.
The total false positive rate is also 0.001% as that of the basic Bloom filter solution. In addition, we also use the exact strings of attribute items as the baseline solution. We set the average string length of attribute items to 10.
Message size is the major factor that affects the computation and communication perfor-mance. Recall that we determine the Bloom filter size m by considering the potential number of inserted items n , the expected false positive rate f , and the number of hash functions z.
Here, we study the performance issue of the all -common problem by varying n and f with different numbers of users.
1. Varying n: We vary n from 200 to 1000 in each user’s attribute profile. We observe the execution time, which contains computation and communication time during matching.
Fig. 5.4 shows that basic BF and the IBF solutions perform much better than the baseline solution when n is large. This is because Bloom filter reduces message size with its hashing mechanism. Also, the result shows that Bloom filter is computation-efficient since the execution time increases slightly when n increases. Fig. 5.4 shows that the gap between basic BF and IBF shrinks when the number of users increases. This is because the messages exchanged during the IBF procedure increase as the number of users increases.
Fig. 5.5 shows the message costs of the entire network incurred by these solutions. As expected, IBF significantly outperforms baseline and basic BF solutions in message costs.
This is because IBF uses smaller Bloom filter in each iteration.
2. Varying f : We vary f for IBF in its second iteration from 0.001% to 10%. Note that the value of f in the first iteration is fixed. (We omit the performance of basic BF because it
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Figure 5.4: Comparison of execution time by increasing the number of inserted items to a Bloom filter with (a) two parties, (b) three parties, (c) four parties, (d) five parties and (e) six parties.
is similar to that of IBF.) In this experiment, we set n = 1000. However, in the second iteration of IBF, the number of attribute items to be inserted is the intersection of two sets. We assume here that the expected size of the intersected set is 50. We then use these values and f to choose the Bloom filter sizes in the first and the second iterations of IBF. Fig. 5.7(a) shows that the execution time only decreases slightly when f increases.
The reason is that the intersected set size of the second iteration (50) is much smaller
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Figure 5.5: Comparison of message cost by increasing the number of inserted items to a Bloom filter with (a) two parties, (b) three parties, (c) four parties, (d) five parties and (e) six parties.
than that of the first iteration (1000). Because m is proportional to n if f is fixed, the reduced message size is small when varying f in IBF’s second iteration. Fig. 5.7(b) shows the message costs of the entire network in different cases. To sum up, varying f of IBF’s second iteration will not significantly affect the execution time.
Figure 5.6: Application prototyping on Android phones and evaluation testbed.
2Ͳparties 3Ͳparties 4Ͳparties 5Ͳparties 6Ͳparties
1748 1656 1562 1496
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Figure 5.7: Comparison of (a) execution time and (b) message cost by increasing the expected false positive rate of the IBF.
Chapter 6 Conclusions
In this work, we have presented CPM in an MDSN, which is to identify common attributes among physical neighbors via D2D communications. We consider three CPM problems: all -common, β--common, and top-γ-popular. We propose basic and iterative Bloom filter-based solutions to these problems. We have conducted some evaluations on commercial smartphones to prove that the proposed solutions are communication-efficient. The basic Bloom filter and IBF solutions perform well in execution time when the number of attribute items in each profile is increased. However, the IBF solution incurs lower message costs. We have also demonstrated a prototype on Android smartphones. The application verifies that the proposed solutions are feasible on off-the-shelf smartphones.
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