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(IJACSA) International Journal of Advanced Computer Science and Applications,
Vol. 8, No. 8, 2017
361 | P a g e
www.ijacsa.thesai.org
AES-Route Server Model for Location based Services
in Road Networks
Mohamad Shady Alrahhal
Department of Computer Science
King Abdulaziz University (KAU)
Jeddah, Saudi Arabia
Muhammad Usman Ashraf
Department of Computer Science
King Abdulaziz University (KAU)
Jeddah, Saudi Arabia
Adnan Abesen
Department of Computer Science
King Abdulaziz University (KAU)
Jeddah, Saudi Arabia
Sabah Arif
Department of Computer Science
Superior university Lahore
Lahore, Pakistan
Abstract—The now ubiquitous use of location based services
(LBS), within the mobile computing domain, has enabled users to
receive accurate points of interest (POI) to their geo-tagged
queries. While location-based services provide rich content, they
are not without risks; specifically, the use of LBS poses many
serious challenges with respect to privacy protection.
Additionally, the efficiency of spatial query processing, and the
accuracy of said results, can be problematic when applied to road
networks. Existing approaches provide different online route
APIs to deliver the precise POI, but mobile user demand not only
Accurate, Efficient and Secure (AES) results, but results that do
not threaten their privacy. In this paper, we have addressed these
challenges by proposing an AES-Route Server (RS) approach for
LBS, which supports common spatial queries, including Range
Queries and k-Nearest Neighbor Queries. We can secure the user
location through the proposed AES-RS model because it provides
the query results accurate and efficiently. The proposed model
satisfies the primary goals including accuracy, efficiency and
privacy for a location base system.
Keywords—Mobile computing; location based services; location
based services (LBS) privacy; LBS accuracy; LBS efficiency;
ubiquitous computing
I. INTRODUCTION
Recent years have witnessed the emergence of mobile
computing technology as both a ubiquitous and extremely
popular paradigm [1], wherein mobile users are capable of
accessing information about nearby points-of-interest (POI).
The devices used (smart phones, tablets, etc.) are integrated
with a global positioning system (GPS), thereby facilitating the
usage of location-based services (LBS). In short, location-
based services are value-added services that leverage a user’s
geographic location when making queries. By geo-tagging a
query, users are able to receive more personal, and valuable,
results. While helpful, this service depends on many factors,
including Points-of-Interest, the precise information
surrounding the user and their current location, and the inherent
need for privacy protection [7].
A basic architecture for location-based services is depicted
in Fig. 1, where a mobile user connects to the LBS Server
through a communication network. The user then posts a query
to the LBS for some location by sending his current location.
The LBS then responds to mobile user with the geographically
appropriate set of results.
Fig. 1. A common LBS architecture.
In traditional mobile technologies, a mobile user posts a
spatial query, q, such as a k Nearest Neighbor (kNN) or Range
Query, to a server, requesting particular information; the server
will then process the spatial query and return results to the
mobile user with appropriate POI information [2], [3]. Without
doubt, this “Point-to-Point access model” (POP) is quite ideal
and easy to use. Unfortunately, several challenges arise for
spatial query processing, such as when there are multiple users
and issuing the same query, q, for their POI, or when all mobile
users belong to the same location. In these scenarios, the server
accrues additional overhead, and resources are wasted [3].
In a conventional mobile computing system, we find three
primary goals with respect to a mobile user and the issuance of
a spatial query:
(G1) Accurate results,
(G2) Efficient results and
(G3) Privacy protection
G1 and G2 always present challenges due to the inherent
realities of a mobile system. Accuracy and efficiency appear as
luxuries in a system where both the user and the query are
mobile. Additionally, LBS infrastructures and approaches have
known limitations with respect to G1 and G2. In terms of G1
for LBS systems, a very famous framework “SMashQ” was
proposed [5], which supports kNN query processing. The main
(IJACSA) International Journal of Advanced Computer Science and Applications,
Vol. 8, No. 8, 2017
362 | P a g e
www.ijacsa.thesai.org
purpose of SMashQ was to leverage online route APIs, such as
Google Maps, Yahoo Maps, Bing Maps, etc. to provide
accurate query results for live travel in real road networks.
While novel and an advancement in this research domain,
SMashQ suffered with efficiency. Each time a user posts a
query, q, to any LBS server, the LBS in turn would call the
online route API for the most recent results and then return the
results back mobile user. In short, the query response times
were tragically slow. As expected, the proposed system was
very accurate; the overhead of repeated queries on the server,
followed by the server repeated calling route API, decreased
the entire system efficiency. To overcome this problem, a more
efficient approach was proposed, “Route Server (RS)” [6]. The
primary goal of Route Server was to enhance the system
efficiency with respect to query response time by reducing the
number of route query requests. Furthermore, they used upper
and lower limit calculation approach for this purpose. They
also introduced a new mechanism such as “Query
Parallelism” by parallelizing the query with different
scenarios. RS was able to maintain accuracy while avoiding the
repeated calls to the server and the online route API. The
proposed approach seems to have addressed G1 and G2,
leaving only G3.
The rapid growth and ever-increasing number of mobile
users brings a variety of new challenges to LBS providers.
Privacy protection, G3, is inherently challenging, as users, who
want answers to their queries, must, in fact, reveal their
locations and potentially sensitive personal data in order to
receive answers to said queries.
What if the mobile user’s location is revealed?
What kind of risks could be faced when mobile user’s
precise information becomes exposed?
How can one protect mobile user’s location privacy
from bad actors?
What factors should be involved under privacy
protection?
These questions have formed the framework for a plethora
of research within privacy and security of mobile data systems.
A variety of approaches have been proposed to overcome
privacy protection related challenges. Many depend on specific
scenarios and basic privacy attributes such as the mobile user’s
identity, his current location, and time information [9]. For
instance, a mobile user, who is at an unknown and unimportant
location, may have no issue in sharing his personal data. But if
the same mobile user is inside a residence or within its
proximity, this location data may inadvertently reveal addition
information that an adversary could misuse. Accordingly,
many privacy attacks were identified, effectively creating a
taxonomy of attacks, and respective solutions were proposed,
each with its advantages and disadvantages.
A. Location Privacy Attacks
LBS location privacy attacks depend on protection
attributes, described previously. Therefore, based on these
protection attributes, we have classified Location Privacy
Attacks into two major categories as follows (Fig. 2):
Fig. 2. Classification of location privacy attacks.
1) Location Homogenity Attacks
Location Homogeneity attacks are one the most common
attacks seen within LBS systems. They take advantage of the
rare case in k-anonymity, where a sensitive value is
indistinguishable and posted along a set of k-cluster values.
Despite the dataset being k-anonymized, the sensitive value is
revealed by any adversary [8], [9]. Additional homogeneity
attacks include map utilization by reducing the area. In this
case, the adversary reveals the diversity of the position
information by analysing some location related information.
2) Background Knowledge Attack
In this attack, the attacker exploits the mobile user’s
contextual information and is able to accurately predict precise
data. The contextual information of the user provides the
background knowledge to the malicious attacker. In short, the
attacker is able to leverage the background knowledge to prune
the set of possible answers.
Maximum Movement Boundary Attack is another
background knowledge based attack approach used by
adversaries to reveal mobile user’s actual information.
The adversary discovers the mobile user’s region by
identifying the maximum movement between two
successful POI against posted queries in that specific
region [10].
Multiple Query Attacks The attacker follows the
query log and identifies the query posted or updated
frequently within a specific interval. The attacker
effectively shrinks the specific region based on where
he got consecutive query updates of a particular k-
anonymity set and corresponding actual query [9], [11].
Context Linking Attacks are categorized into three
groups: personal context linking attacks, probability
distribution attacks, and map matching attacks. Personal
context linking attacks are related to the personal
contextual information of a mobile user, which might
be belong to his preferences or POI. Whereas
probability distribution attacks are based on the high
probability function of mobile user’s location position.
An adversary discovers the user’s most frequent visited
location position, along with a particular time span, and
then applies a probability function to identify his
precise information. Finally, Map matching is the third
context linking attack, wherein a mobile user can be
traced for a certain location by removing all irrelevant
regions from the Map. Moreover, in order to leak the
actual location information, an adversary could use the
semantic information gained from the Map [12].
Location Privacy Attacks
Location Homogeneity
Attack
Background Knowledge
Attack
(IJACSA) International Journal of Advanced Computer Science and Applications,
Vol. 8, No. 8, 2017
363 | P a g e
www.ijacsa.thesai.org
B. Location Privacy Approaches
A variety of approaches have been proposed to solve the
aforementioned privacy attacks.
1) K-anonymity
One of the most commonly used approaches for location
privacy preserving in LBS system is “K-Anonymity”, which
insures that the precise information of targeted mobile user is
indistinguishable from the value of set K-1 posts to LBS
server. We can find out the probability [13] to trace the actual
user’s data as follows:
Let’s have K a set of position of all anonymity users K
= {k1, k2, k3, ... ,kn-1}. Therefore Probability of target user
could be discovered as: 1/ K (1)
The basic idea of k-anonymity to protect location privacy
was demonstrated by Gruteser and Grunwald [31]. The theme
of k-anonymity was that a mobile user can post a query, q, to
the LBS server with an obfuscation area, along with k-1
anonymity positions of other users, rather than sending his
precise location position. Certainty, k-anonymity approach is
better in order to achieve the location privacy in LBS system;
but in some cases, there are serious challenges when using this
approach as follows:
Homogenous Attack.
Background Knowledge Attack.
2) Cryptography Based Approaches
Cryptography is another powerful approach to preserve a
user’s location privacy from malicious attackers in a LBS
system. The core idea behind cryptography based approach is
utilization of encryption and decryption schemes for precise
data that need to be sent over a network. A mobile user posts a
query over the network; this query includes his secret data,
which is encrypted by apply some particular algorithms at
mobile user’s end. The same algorithm is available at server
side to decrypt the data sent by user and utilized for further
processing. The use of encryption and decryption schemes is
dependent on the required level and kind of privacy.
Cryptography approaches are classified into two main phases
and then sub types as shown below in Fig. 3.
Fig. 3. Cryptography classification.
Certainly cryptography approach is very secured and
implementable for LBS system but In contrast, a big challenge
for cryptography based approach is the requirement of a
massive level of computation during encryption and decryption
takes more time than the system required. In LBS system, time
is very significant attribute in order to provide the results
efficiently. However, implementation of cryptography might
be costly regarding to this factor [4].
3) Mix Zones
Beresford introduced a new approach as “Mix Zone” for
privacy location protection in mobile computing system [14].
Main theme of Mix zone was to conceal the precise location
position of mobile user in his current locating region just like
showing that “No existing in this area”. Once a mobile user
enters in a mix zone area, his ID is shuffled by all other users
belonging to that particular zone and the user’s precise location
is protected. The major challenge for this approach is that an
eavesdropper can easily find out the sensitive data of multiple
mobile users through limited mix zone area [15].
4) Position Dummies
Leading to privacy location protection in LBS system, a
new approach was introduced as “Position Dummies”. The
fundamental principle of position dummies approach is that,
user sends his actual position along with number of dummy
location where mobile user’ precise information is
indistinguishable [16]. Once user change his position from A to
B with (x, y) coordinates, he posts a new query by sending his
current position along with new dummies according to new
place as shown in Fig. 4.
Fig. 4. Dummies on changing position.
In past, it has been remained a major challenge for dummy
position that how to generate the number of dummies that have
to post along user query to find any route path or POI [17],
[26]. Later on, this challenge was overtaken by introducing
different tools to generate these dummies [18]. In this paper,
we also have proposed an efficient algorithm to generate the
dummies at user end and then post to LBS along actual data.
Position dummies have been considers the most approachable
technique to secure the user’s precise location information. In
our proposed AES-RS approach, we have implemented
“position dummies technique” and made a secured route server
approach for location based services in road network as
discussed in other section.
The remainder of the paper is organized as follows. Section
II describes related work, while the proposed AES-RS
approach is discussed in Section III. In Section IV, we have
discussed the implementation and results. Finally, Section V
discusses the conclusion and future work directions.
Cryptograp
hy
Symmetr
ic
Asymmetr
ic
CB
C
DE
S
AE
S
RS
A
Digital
Signature
(IJACSA) International Journal of Advanced Computer Science and Applications,
Vol. 8, No. 8, 2017
364 | P a g e
www.ijacsa.thesai.org
II. RELATED WORK
In this section, we have illustrated the existing approaches
utilized by others, advantages, limitations and future
perspective directions to provide privacy for location
protection of these approaches.
W. Sun, C. Chen and B. Zheng [19] emphasized road
networks query processing approach. They proposed Network
Partition Indexing (NPI) an Air Indexing that was supportive
for spatial queries such as Range Query, CNN Query and kNN
query. The basic idea of NPI was the processing of these
spatial queries on road network by splitting the whole road
network into small number of regions. They consider the road
network concerning area as a grid G, and make its partition
into number of cells where some information like upper and
lower limit of each cell, border point and data segment was
pre-computed to utilized in future query processing. Once
mobile user posted any spatial query for a POI or route path,
using these precomputed parameters, server broadcast the
results in response through wireless network. They
implemented NPI approach in real application and evaluated
valued results. The one major challenge using this NPI
approach was lost of information in case of link error over the
network. They considered error-resilient and efficiency as
future related challenges.
Z. Shao, D. Taniar and K. Adhinugraha presented Range-
kNN queries supportive approach for privacy protection in
[20]. The proposed algorithm was basically consisting of two
major parts. In first part, they presented a new approach as
Landmark Tree (LT) that was used to discover an appropriate
landmark area by concealing the actual user’s actual position.
For LT, only a radius as parameter was required from mobile
user for Range-kNN query implementation. After discovering
the query range, another part as search algorithm was
implemented to find out the most nearest neighbor from LT. In
shortly, first part is responsible to find position inside the query
range whereas second part is responsible to discover the
location position from outside the range such as iNN (i > 1).
The proposed algorithm was implementable limited to static
objects but not for complex moving objects in real time
applications.
B. Niu et al introduced Cashing-Based approach for
location privacy protection of user’s position in Location
Based Service system [21]. Cashing based approach leads
basically two algorithms such as CaDSA that was related to k-
anonymization to improve privacy through utilizing cashing
dummy selections. Leading to CaDSA the author discovers
some other performance effecting attributes such as how to
normalize distance and how we can make sure the data
freshness. Leading to privacy enhancement, the second
algorithm called “enhanced CADSA” was proposed.
Admittedly, the proposed algorithms provide privacy in
location but the overhead of frequent queries to LBS makes the
system performance down.
In [22], [23], the authors emphasized on location
monitoring challenge for real time distributed system in mobile
environment. According to author, the mobile objects should
itself be responsive rather than increasing load on central
server for objects related computation. In order to develop such
a responsive system, they make a set of assumptions such
follows:
The Moving Objects (MOs) have ability to locate its
position.
MOs have ability to determine their velocity vector.
All MOs existing in mobile environment have ability of
computation for assigning tasks.
There is a synchronized clock among MOs.
They considered that in mobile computing system a
distributed approach should be discovered that support
continues moving queries along moving objects and proposed
“MobiEyes”. Furthermore, they brought in some optimization
approaches constrict self-computation power at MOs end.
Admittedly, the proposed approach is valuable but assumptions
for such system are still challenges and future work for LBS
system.
More on privacy protection, as discussed above Route-
Server approach is one of the most efficient and accurate query
results providing approach in LBS road networks. But the
major challenge for RS was privacy protection of mobile user’s
precise information from adversary who can infer the faulty
information in real data when a mobile user wants to post a
spatial query for any route path or POI. We grouped privacy
goal as G3 in above section.
Leading to G3, Privacy protection is another major
challenge for LBS in road networks as it is very common
practice to send some personal information when user issues
any query for some POI information such as cinemas, bars,
friend’s location or any route path on a road network. For
instance, Let’s have a set Q of queries {q1, q2, q3 … qn} where
each q Q belongs to Q set and posted as a route query, it will
allow to an adversary to infer some false information by
revealing mobile user’s precise information [4] which is a big
challenge for “Route Server” approach. In order to improve the
privacy factor in Route Server algorithm we have proposed
AES-RS a new secure approach presented in next section.
III. AES-RS SYSTEM MODEL
This section consists of proposed AES-RS system
architecture which is essentially enhanced Route Server
Architecture. One of the major components of AES-RS model
is middleware Location Server that must be considered
carefully. However before moving toward AES-RS model, we
must introduce briefly the common models of location servers
(LS) that are being used in LBS system [27], [28], 30]. These
models are assorted into three basic categories including
Untrusted Location Server (ULS), Trusted Location Server
(TLS) and Peer to Peer based network (P2P) [29] where each
model consist of three components as Mobile User Devices,
Location Server and clients. In basic scenario each client
interact with location server for desired POI or location
finding, Location server further contact with clients to get the
requested position. From Fig. 5(a) that elaborates the untrusted
location server model, Fig. 5(b) shows the trusted location
server using anonymizer that ensure trustworthy to deal with
dummy position based request model or k-anonymity model
(IJACSA) International Journal of Advanced Computer Science and Applications,
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and Fig. 5(c) describes the third option as peer to peer network
where each mobile user could interact with other mobile users
or devices to find out the desired location or POI [8].
(a) (b) (c)
(a) Untrusted Location Server. (b) Trusted Location Server using anonymizer.
(c) Peer to peer network.
Fig. 5. Common LBS models.
Subsequently AES-RS is dummy position based model
where a request is made along with number of dummy
positions, however based on model features, we have selected
second option as Trusted Location Server to ensure the
provision of locations to mobile devices or users with privacy.
A. AES-RS System Arcitecture
AES-RS (Secured Route Server) architecture is
enhancement by location privacy perspectives in Route Server
Architecture proposed by [6]. AES-RS system architecture
consists of three major entities such as mobile user, LBS and
Route API. In AES-RS, mobile user part is now differ as in RS
architecture as shown in Fig. 6. We implemented dummy
position approach to protect user’s location privacy where a
mobile user locating to grid area G post a query q along
multiple dummies to AES-RS for any route path or POI. AES-
RS executes that query, find out the required results from local
Log “L” if find then return the required query results to user
otherwise call Route API for the latest results.
In order to approach the goals G3 discussed in previous
section, we have modified the definition 2 as “query results”
for range and KNN query. Let’s a query q a set of dummy
positions along actual location locating to Grid G and having
time limited T, the results for Range query is:
Q = {k1, k2, k3 ….. kn} then the query resulting
definition should be modified q by Q, considering the multiple
positions instead of single actual position. However,
And for KNN with K size
According to our AES-RS approach, before posting query
to LBS, measure the minimum (L lower limit) and maximum
(U upper limit) width and height of the specific area called grid
“G”. The purpose to determine (L, U) coordinates is to make
partition of “G” into equal number of cells “Ci”. Each cell (E,
V) C representing that cells are connected through set of V
Vertices and E Edges where (v V) and (e E) as shown in
Fig. 7. Further to generate dummy positions, vertices are
calculated beyond each cell and one cell position is attached to
mobile user’s actual position. Finally, an array is generated that
contained all dummy K positions and index of actual user’s
position by following the proposed algorithm DDA (Dummy
Data Array.
Input: User location (X, Y), Anonymous_Area A,
Anonymity_Number K;
Output: array[K(x,y) + (X,Y)]
Procedure:
1: G(L, U) \\ Calculate Both Height and Width, U,L limit.
2: C ← \\ Calculate Number of cells in G
3: (V,E) C \\ Determine vertices and edges of each cell.
4: Px ← Random (0, v(C-1)) , Py= ← Random (0, v(C-1))
5: array[0 to C][ 0 to C] \\ Initialize 2-D array
6: i = 0, j =0 , x,y=0 \\ Initialize values upto x-axis, y-axis
7: While (i < (C-1)) \\ Fill array with dummy positions
8: While (j < (C-1))
9: if ( Ci.posX != X and Cj.posY != Y)
10: x ← C.posX , y ← C.posY
11: array[i][j] ← x , y
12: j ++; // Repeat step 8
13: end if
14: end loop
15: i ++; // Repeat step 7
16: end loop
17: add Px,Py in array
18: Return array
Fig. 6. RES-RS system arcitecture.
Algorithm: DDA (Dummy Data Array)
(IJACSA) International Journal of Advanced Computer Science and Applications,
Vol. 8, No. 8, 2017
366 | P a g e
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Fig. 7. Grid partition into cells.
According to DDA algorithm, it takes three input
parameters as (X,Y) coordinates of user locating at current
position, anonymous area A which is required to generate
anonymity data and K number of dummies which are required
to generate. It consider the anonymous area A as grid G and
first calculate the upper and lower limits of whole anonymous
area with respect to height and width denoted as ˂Min_X/
Min_Y, Max_X / Max_Y>. By using computed LU limits,
anonymous area A partitioned into equal number of cells (Ci
G) according to given input number of K as in equation 2 that
was discovered by equation 3.
|C1|C2|C3|C4|- - - - - |Cn| = 1 (2)
Number of Cells = (3)
Once, number of cells are defined, it calculates the vertices
and edges beyond each cell mentioned in step 3. Now, assign
the mobile user’s current location (Px, Py) to one random cell
from G. Next, declare an array that will contain all the dummy
positions and fill it according to number of cells because each
cell is located with one dummy position. Once the array with
dummy positions is filled, it adds the index of user’s actual
position in array and return.
B. AES-RS for Spatial Queries
As AES-RS is supportive for spatial queries such as Range
query and KNN query as well. In this section, we present the
consequences of Secured Route Server approach for spatial
queries for a given query point q, along with a value d and data
set P that reduce the number of requests. As described above,
for AES-RS approach, we have used a Trusted Location Server
(TLS) which ensure that only actual query request posted from
mobile device to TLS along with set of dummy locations array
will be computed to determine the POI or desired location.
However, there will not be any change in spatial queries.
For range query in AES-RS, it first comport the distance
range search for data set P on G road graph from q query point,
denoted as range (q, d, P) = {o | o ∈ P ˄ || o, q || ≤ d} and
then store the retrieved results from range in a set R. Similarly
for KNN query with given query point q with data set P on G
road network, a K Nearest Neighbor (KNN) query determine
the k objects in P whole network distance which is represented
as follows:
Unlike range query, KNN query doesn’t have the fixed area
for searching and contingent upon the current location of query
point q and k value it find out the candidate point by defining
upper and lower bounds.
C. AES-RS Effects on Accuracy
The one objective of RS algorithm was to provide accurate
query results. As accuracy assurance in RS algorithm was
achieved by calling route API frequently to get most updated
query results and generate log L for Ѱt routes that is validate
till expiry time otherwise expire routes Ѱt. In case of
dummies along actual position, certainly it requires larger
space to manage log L but no effect on accuracy in query
results. However we can manage L by adding more memory
space in the system.
D. AES-RS Effects on Effecincy
Efficiency was another essence factor in AES-RS and
achieved by maintaining Ѱt routes log L. Definitely, it will
affect on query response time because of requiring number of
locations, doesn’t matter it is dummy or actual location, LBS
processing is required. But powerful approach as log L, POI
and Road Network G at LBS maintain route path and minimize
the overhead of frequent route API calling.
IV. EXPEREMENTAL AND RESULTS
In this section we demonstrated our AES-RS approach and
simulated to evaluate performance after enhancing RS
approach by privacy factor. We used Riverbed Modeler
academic edition 17.5 simulator tools that can be used to drive
accuracy and performance in real network applications. Its old
name was OPNet Modeler [24]. In our experiments, we used
france_highway road network map provided in riverbed
modeler. Further we selected multiple nodes as actual user
location where he wants a route path to find out the nearest
ATM from his current location using over the road network. In
order to protect his precise information as current location, we
draw multiple dummy positions (k-1) then posted a query
containing actual location along generated dummy positions to
LBS server through a wireless network. The tenure in which
multiple queries were posted to LBS and it respond back with
query results was evaluated by setting 1 week duration. By
following a basic wireless network routing approach, we used
two Ethernet routers and sixteen dummy nodes from different
locations were connected to each, which is further linked to an
Ethernet switch and it post user’s query to LBS for query
results. Fig. 9 illustrates the rate at which data packets are
being received by LBS server sending from Ethernet switch.
The delay in transferring data packets to LBS server were
calculated by using “Little’s theorem” [25].
N (t) = A (t) + B (t) and t 0 (4)
Where A (t) is the number of data packets which are arrived
at in time (0, t) and B (t) is the number of data packets that are
depart from source location in time (0, t).
(IJACSA) International Journal of Advanced Computer Science and Applications,
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Fig. 8. Data transferring rate to LBS.
We observed that there were some other constituents like
data transferring rate as shown in Fig. 8, the delay at Ethernet
or wireless communication which could be cause of decreasing
AES-RS system performance. In our case as shown in Fig. 9,
during query transmitting over the network, delay size is very
small in Ethernet and wireless which couldn’t be reason to
decrease system performance. In Ethernet, it becomes constant
at a certain level by assuming that loss ratio in data packet is
consistently zero. In contrast, delay variation increase and
decrease after a certain time period which was overhead of
using LBS as single server. It could be maintained by utilizing
multiple LBS servers applying distributed approach.
The most significant part of AES-RS was to maintain LBS
performance in order to provide user’s query response
accurately and efficiently by protecting mobile user’s precise
location. We evaluated LBS server performance when multiple
query requests posted to it for any route path or POI and query
processing at server side to return query results. Graph in
Fig. 10 shows the number of requests posted to LBS server and
its response quick by using log L, POI and Road Network G
inside LBS.
Fig. 9. Delay in ethernet and wireless LAN.
Fig. 10. LBS server performance.
We also evaluated the route API data access rate depicted
in Fig. 10(a). The gradually decrease in graph 9 (a),(b) the
clearly shows the advantage of log L, POI and Road Network
G usage at server side that minimize route API hit rate due to
availability of data at LBS server side. At initial stage, due to
empty data in log it required to call route API for updated
query results that increased route API retransmission attempts
rate Fig. 10(b). But after a certain time t, when log L contained
number of query results it decrease route API attempt rate.
Furthermore, we assessed parallel route path approach
proposed in RS algorithm and implemented in our experiments.
Fig. 11(a), shows the results of data access delay through route
API where we implemented parallel route path approach at
LBS server side, it recognize firstly the required path against
any mobile query, then it evaluate the relevant queries which
are required route Path or POI from the same route. In this
way, it minimizes the data access delay along query hits to
LBS server.
Fig. 11. Route API rerensmission attempts and data access rate.
(IJACSA) International Journal of Advanced Computer Science and Applications,
Vol. 8, No. 8, 2017
368 | P a g e
www.ijacsa.thesai.org
V. CONCLUSION
In mobile computing environment, every LBS system
requires three primary goals such as accuracy, efficiency and
privacy. A significant research has been attempted and
delivered different LBS approaches to attain these goals. Route
Server (RS) is one of the approaches that provide LBS system
with accurate and efficient results for spatial queries. But RS
algorithm didn’t consider G3 as privacy goal to protect mobile
user’s precise information. However, by location privacy
perspectives, we proposed AES-RS architecture which is an
enhancement of RS algorithm and protect mobile user’s precise
location information from any adversary. On behalf of
adversary attacks for LBS system, we discussed different kind
attacks and various approaches to overcome these attacks. We
also highlighted the advantages and limitations in existing
approaches. After a critical analysis, we selected dummy
position approach that ensure mobile user’s privacy protection
in RS algorithm and proposed a new approach AES-RS as
Secure Route Server Architecture. As generating number of
dummies for Dummy Position approach was a major
challenge, we proposed an algorithm where dummy positions
are generated at user end. Further in term of evaluation (G1,
G2, G3) goals we simulated our approach using Riverbed
modeler and generated different results. We discussed Ethernet
and wireless WLAN as the factors that could be effective in
efficiency in LBS wireless network system. From experiment
results evaluation we can say AES-RS is an appropriate
approach for LBS system which secure the user privacy for
location protection by providing accurate and efficiently query
results. By future perspectives, it required to examine the
proposed solutions at large scale.
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