CHAPTER 4 Simulation Results
4.2 Simulation Settings
4.3.3 Node Density
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4.3.3 Node Density
In the section, we analyze the results with other approaches, Locus and Epidemic routing schemes. And we want to see what the importance part of our approach is, so we make two changing in our approach. One is we don’t matter the Data replication strategic and we only use the Epidemic routing to spread the data messages, and the other is we don’t care the Query replication strategic, just use Epidemic to disseminate the query messages.
In Figure 23, we can see our approach and Locus have a similar performance in query-reply success ratio, because both of we are using the region concept to centralize the messages. But, as the number of nodes increase, Locus will perform well than ours. Because our region concept base on local area, if there are many nodes in this area, it will have many messages (data, query, reply) in every node. Although nodes have rich data source, the transmission rate is fixed, and nodes intermittent connected, they can’t send all the messages. So, some messages might be ignore, and the performance isn’t well. And we want to see the importance part of our approach, we modify it in two types. (1) We use epidemic routing to replace the data replication strategic, and (2) we use epidemic routing to replace the query replication. The result shows the (1) type got worse performance. Because the data messages couldn’t be centralized to the inside area, the messages would spread to whole network. It is difficult to query unique data message in the network.
Otherwise, in order to compare fairly, we present a scenario with only Offline-node in our approach. Although the success ratio is worse than Locus, but the overhead and latency are better than Locus. And the result will discuss below.
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nod hav
And we ca des success
e high prob
Figure 23:
an see the F searching d bability to qu
Figure 2
Query-Rep
Figure 24 an data in Insid uery succes
24: Node Di
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ply Success
nd Table 3, de Area. It ss.
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Ratio (Nod
in our appr means nod
of Success S
de Density)
roach, there des in the In
Searching
e are 71% o nside Area
f the will
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nside Areaorder Area utside Area
Figure 25 t the query m
the figure, sn’t just on query mess cess deliver Querier is t
Figure 26 cess ratio o
e 3: Detail n y 30
15 3 0
shows the q message, an , all the ap e copy spre sages, we ju ry. Therefor the most dif
Figure
6 is overhea of Locus is
number of N 40 50 15 22 7 7 5 3
query deliv nd it deliver pproaches h ead to the ne ust need one
re, it is easy fficult.
25: Query D
ad, and we s higher th
41
ery ratio, w ry to the Re have high d
etwork, and e query mes y to find the
Delivery Ra
can see the han LCS, b
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Figure 26: O
Latency. We pidemic. Be r Border Are
o. They wi but they ar
42
data in the to look up ore message the local ar ty to spend l
Overhead (N
e can see the ecause all th ea, it will qu ill centralize re not near
e same area data quickl e in delivery
rea, it sprea lower cost t
Node Density
e result of o he data wil uickly get th e the same
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a, if Querie ly. But, if Q ll be spread he data. Alt data in the t will spend
er closed to Querier far a verhead wi
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8 shows the S and Locu d it could i milar rule t dvantage of
Figure 28
Figure 27: L
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8: Query-Re
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Latency (No
ply success buffer size e query suc
messages,
ss Ratio (Bu y)
can see th , nodes cou ability. Our
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he result of uld store m r approach are similar.
f our more
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Figure 29: O
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44
the buffer s e node has more messag nd more as
Overhead (B
As the bu e nodes hav
ge to the R local region ould store m delay time o
size increas more space ges. But the
buffer size
Buffer Size
uffer size in ve more spa Replier. Our n concept
ncrease, all ace to store
approach L to determin rtant messa
ly activity.
overhead of messages, w ion rate and
the approa e messages,
LCS is the ne the mes ages, like r
f the
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me to Store
on we want query-reply h display the
t have muc us is worse area, but th query data.
Figure 30:
t to see the i success ra e bed perfor ch time to d than LCS, he live time
45
Latency (B
influence o atio results,
rmance, bec delivery, so it is becaus e of data isn
Buffer Size)
f messages we can see cause messa the success se they will n’t enough,
live time ch e the low T ages just hav s ratio does
try their be it drops qu
hanging. Fi TTS parame ave short tim
sn’t well. In est to centra uickly, so n
gure
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Figur
shows the s will incre ply message
to the Quer verhead.
e 31: Query
overhead re ease. Becau es success d rier still ex
Figure 3
46
y-Reply Suc
esults, as th use messag delivery to t xist in the
32: Overhea
ccess Ratio
he TTS grow ges have a Then, these
overhead o
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Figure 33 roaches wil ding queue, n, it could sp
shows the ll increase.
, the old me pend much
latency res Because the essage will b time to forw
Figure
47
sults. As th ere are man be sent first ward.
33: Latency
he TTS gro ny message t. So the new
y (TTS)
wth, the lat s in every n w one is dif
tency of all node, and in fficult to be
l the n the sent,
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CHAPTER 5
Conclusions and Future Work
In this thesis, we proposed a location-based content search approach. We use four strategies to achieve our objective. It is Data replication strategic, Query replication strategic, Data reply strategic and Data synchronize and update. Then we proposed a three-tier area concept, and every strategic in different area will have different rule to replicate messages. We divided all nodes into two parts, Online-node and Offline-node. If Offline-node wants to search some information, besides the other Offline-node, it can ask Online-node for searching. And in the sending message period, we proposed a message queue selection algorithm to decide which message will be sent first. Finally, we evaluate the results with Epidemic and Locus. And we use some parameters to verify our approach. Although LCS is worse than Locus in Query-Reply success ratio, in Overhead and Latency, LCS is much better than Locus.
In the future, we will consider the adaptive weighted value of messages.
According to the message forwarding times or the estimation numbers of unique messages in some area. The weighted value of messages could be revised, and the nodes can spread the most important message out. Finally, we will implement this work into Plastory [28] system which is a mobile storytelling platform we developed before.
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