Download Bloom filter for fixed-size Beacon in VANET

Bloom filter for fixed-size Beacon in VANET
Project description
Introduction
VANETs or vehicular ad hoc networks are the spontaneous creation of a wireless network for
data exchange – to the domain of vehicles. They are a main component of intelligent
transportation systems.
The advantage of VANET that nodes can directly communicate with each other.
Consequently, most work in these networks concentrates on efficient data delivering.
Mechanism of delivering need a beacon to discover neighbors and share local data.
Beacons - packets that are periodically broadcast to maintain integrity of network. Such
packets need to transmit a lot of necessary information like source ID, neighbors’ ID,
packets’ ID, etc. This leads to some serious problems.
Description of the problem
One of the main problem is limited resources in wireless networks. Many approaches exist to
avoid this problem.
1) Reducing the frequency of beacon broadcasting.
2) Reducing the number of beacons.
3) Desynchronization methods to prevent the collision of beacon broadcasting.
These approaches are useful to solve contention problem, but all of them affects the accuracy
and volume of beacon information.
Size of beacon depend on information inside it. For instance, beacons in topology-based
protocol require an 1-hop neighbor identifier list and packet identifier list as
acknowledgments for reliable data dissemination. The beacon structure can be illustrated in
Fig. 1.
Source ID
Neighbor ID
#1
Neighbor ID
#2
...
Neighbor ID
#n
Packet ID #1 Packet ID #2 ...
List of 1-hop Neighbor IDs
Packet ID #n
List of packet IDs
Fig. 1. Beacon structure.
As result, the beacon size will increase when a node has more neighbors. Moreover, in many
cases beacon need to transmit 2-hop neighbor identifier list too. This problem often is not
considered, because it require additional space and can lead to deterioration of connection.
To solve these problems we use Bloom filter to store packet identifier list and 1-hop, 2-hop
neighbor identifier list.
Motivation
Today a lot of applications and services are used for a safe and convenient driving: a
navigation system, traffic management system, collision avoidance system, etc. All of these
applications require an efficient way to collect and exchange information, so the problem of
limited resources is actual. Our motivation to solve this problem by using Bloom filters and
creating fixed-size beacon. It will help the networks be more scalable and beacon will not
affect the data exchange.
Existing solutions and their limitations
A Bloom filter is simple structure that was designed for reducing memory usage in limited
resource systems. In initial state Bloom filter contain a bit array that all bits set to “0”. When
new element is added, it will be fed different hash functions. These functions compute
position in bit array that will be set to “1”. Bloom filter structure can be illustrated in Fig.2.
Fig. 2. Bloom filter structure.
Kulit Na Nakorn, Yusheng Ji and Kultida Rojviboonchai have already proposed efficient
beacon structure with Bloom Filter. This structure is shown in Fig.3.
Source ID
List of
elements
Bloom Filter
Source ID
Neighbor ID
#1
Neighbor ID
#2
... Neighbor ID Packet ID #1 Packet ID #2 ... Packet ID #n
#n
List of 1-hop Neighbor IDs
List of packet IDs
Fig. 3. Fixed-size beacon structure.
This solution has one important disadvantage. Bloom filter never gives false negative errors,
but the result can be a false positive. Suppose that m – number of bits in a Bloom Filter
array, n – number of elements in the set and k – number of hash functions.
The probability of false positive results can be calculated by (1).
𝑝 = (1 − (1 −
1 𝑘𝑛 𝑘
) )
𝑚
(1)
Since this solution uses one Bloom filter to store both list of neighbors’ ID and list of packets’
ID the probability of false positive errors increases.
To improve this solution and solve the problem with 2-hop neighbors we designed beacon
structure that can be illustrated in Fig.4.
List of
Neighbor
IDs
Source ID
List of
packet IDs
Bloom Filter Bloom Filter
Source ID
Neighbor ID
#1
Neighbor ID
#2
... Neighbor ID Packet ID #1 Packet ID #2 ... Packet ID #n
#n
List of 1-hop and 2-hop
Neighbor IDs
List of packet IDs
Fig. 4. New fixed-size beacon structure.
We use two separate Bloom filters in beacon structure. First filter store packets’ identifiers
which are used by protocol for missing packet detection. Second filter contains list of 1-hop
and 2-hop neighbors. 2-hop neighbors’ identifiers are represented as result of exclusive or
operation with the source ID and the ID of intermediate vehicle.
False positive error results
The optimal number of hash functions was calculated by (2).
𝑚
ln 2
(2)
𝑛
Since we solve problem with neighbor detection Bloom filter with nodes’ identifiers is tasted.
The identifiers are representing like 32-bit numbers. Table 1 shows the false positive error
rates and Bloom filter configurations.
𝑘=
Table 1. Bloom filter configuration
False positive error rate
5%
10%
15%
20%
25%
No. of hash functions
No. of bits
4
886
3
681
3
561
2
476
2
410
Number of elements, number of bits in array and number of hash functions initializes Bloom
filter. The given number of elements is generated like list of neighbors. Then each neighbor
from list is added to the Bloom filter. After this filter is randomly queried with elements that
are and are not in the set.
Testing was carried out on a machine with the characteristics shown in table 2.
Table 2. Characteristics of machine
CPU
RAM
System type
Intel(R) Core(TM) i5-5200U 2.20 GHz
6.00 GB
64-bit operating system, processor x64
The false positive error results are shown in table 3.
Table 3. False positive error results
Number Number
Number of
Theoretical
Averege
of bits
of hashes elements
error
Worst case case
Best case
886
4
250
20%
24%
20,9%
18%
200
12%
17%
12,4%
10%
150
5%
10%
5,3%
2%
681
3
561
3
476
2
410
2
100
50
10
250
200
150
100
50
10
250
200
150
100
50
10
250
200
150
100
50
10
250
200
150
100
50
10
1%
0%
0%
29%
20%
11%
4%
0%
0%
40%
28%
16%
7%
1%
0%
42%
32%
21%
11%
3%
0%
49%
38%
26%
14%
4%
0%
3%
0%
0%
33%
24%
18%
9%
4%
0%
50%
36%
20%
11%
4%
0%
47%
39%
28%
18%
6%
0%
52%
43%
29%
18%
8%
0%
1,6%
0,0%
0,0%
29,5%
20,6%
11,7%
4,7%
1,0%
0,0%
40,4%
28,9%
16,2%
7,6%
1,3%
0,0%
41,4%
32,3%
20,2%
10,0%
3,6%
0,0%
48,6%
38,2%
26,2%
14,4%
3,8%
0,0%
0%
0%
0%
27%
18%
7%
3%
0%
0%
36%
27%
12%
4%
0%
0%
36%
28%
14%
5%
2%
0%
41%
35%
24%
11%
0%
0%
Comparison analysis
Theoretical, the worst, average and the best false positive error results are shown in Fig. 5-8.
50%
45%
40%
Error rate (%)
35%
30%
5%
25%
10%
20%
15%
20%
15%
25%
10%
5%
0%
0
50
100
150
200
250
300
Number of elements
Fig. 5. Theoretical false positive error.
50%
45%
40%
Error rate (%)
35%
30%
5%
25%
10%
20%
15%
20%
15%
25%
10%
5%
0%
0
50
100
150
200
250
Number of elements
Fig. 6. The worst-case false positive error.
300
50%
45%
40%
Error rate (%)
35%
30%
5%
25%
10%
20%
15%
20%
15%
25%
10%
5%
0%
0
50
100
150
200
250
300
Number of elements
Fig. 7. Average case false positive error.
50%
45%
40%
Error rate (%)
35%
30%
5%
25%
10%
20%
15%
20%
15%
25%
10%
5%
0%
0
50
100
150
200
250
300
Number of elements
Fig. 8. The best-case false positive error.
The false positive error results for beacon structure with one Bloom filter are shown in Fig. 9.
Obviously, results also depend on types of hash functions, machine settings, etc. Comparison
analysis shows that in average case proposed Bloom filter gives better or worse results
depending on different filter settings. In additional our Bloom filter contain information
about 2-hop neighbors. This property can be useful for many applications.
Fig. 9. The false positive error results for beacon structure with one Bloom filter.
Conclusions
New structure of fixed-size beacon was designed and tasted in this project. Proposed beacon
allows to transmit necessary data and additional information about 2-hop neighbors in
efficient way. Advantage of this structure that volume of data do not effect on beacon size. It
can solve problem with limited resources, which was described in the beginning.
References
[1]
[2]
K. N. Nakorn, Y. Ji and K. Rojoviboonchai, “Bloom filter for fixed-size beacon in
VANET”, Proc. IEEE Vehicular Technology Conference (VTC Spring), 2014
G. Samara, A. O Abu Salem and T. Alhmiedat, “Pover control protocols in VANET”,
Proc. European Journal of Scientific Research, ISSN 1450-216X / 1450-202X Vol. 111
No 4 September, 2013, pp.571 – 576.
http://www.europeanjournalofscientificresearch.com