Building optimizied web service based on service discovery technique and artificial algorithms aco and HMM

onditions are met, the node switches state. Transferring this state will set up or 
break the network links. It is necessary to build the state file of each node, the state 
transition of the node. Then guess the probability of linking at each node. As with 
HMM for node prediction, the use of HMM to find the probability of link existence 
is also of concern in such networks as MANET. 
4.2. Describe the working principle of HMM on the basis of probability 
graphs 
HMM works on the basis of some state sets, among which there is a state 
transition probability: 
 There is a set of derived states: this is the state set whose existence exists at 
the beginning of a defined probability (S). 
 This is a set of states that the current process is going through (H). 
 The set of end states is the states that contain the result of the problem (F). 
Upon departure, each initial state has a probability of existence that the process 
may belong to. On the basis of the network parameters, the state transition will 
occur to give an end state which is the result of the problem. The thesis 
recommends using Fuzzy Logic to evaluate the probability of this existence. In 
addition, we can map the states of HMM to the vertices (V) of the graph (G), the 
ability to shift between states mapped to the probability of existence of the (E) 
linkages of G. Therefore, we can construct a directed state transition graph for 
HMM, based on this graph to improve the performance of the HMM. Based on the 
minimum tree search algorithms, colorize, to find the optimal search tree and 
transmission tree for anything in the real world. In addition, we can use ACO to 
update the probability of linking between peaks over time. 
4.3. Perform ACO problem using HMM 
From the definition of HMM, it is possible to map the optimization problem on 
the graph using ACO as the maximum probability problem on the HMM to move 
from start state () to end state (). Managed networks are structured as a 
directed graph with each vertex having links with the probability of existence 
Research 
Journal of Military Science and Technology, Special Issue, No.57A, 11 - 2018 67
defined by ACO/Termite mapped to the HMM model as analyzed in the preceding 
section. 
 Map the set of vertices of the network graph to the states of the HMM 
system under review. Map each vertex of the ACO as a subgraph in the 
HMM. These are the states in which the node belongs. 
The probability of the state transition between any two states I and J in HMM is 
the probability of linking from node I to node J in the ACO graph (corresponding 
to two nodes of two subgraphs in HMM). as determined by ACO/Termite. 
Routing tables defined by ACO/Termite are also mapped to the state transition 
matrix (A) of the HMM, but each node can belong to a set of states in the HMM. 
 So finding the optimal line between any two vertices ,  in ACO/Termite 
is mapped into the problem finding the maximum probability of moving the 
state from initial state I to target state J. 
Where: I, J are the states of the observed L states, the intermediate vertices in the 
line are the hidden states of the set N. One example, in the HMM graph, optimized 
from the source node to the set of target nodes, at this point the source node and 
target node set are the states of L. 
4.4. State the problem 
Design a hidden Markov model to predict the location of network nodes to 
reduce control costs by reducing the transmission of control packets when 
rebuilding routing table supports optimal route detection in mobile ad hoc 
networks. 
The meaning of the solution: 
In case there is no routing table or the remaining energy is small, we must 
predict the location of the node for transmission based on the neighboring table 
(NT). 
Input: A network graph consisting of nodes, links between nodes is 
determined based on the state of the nodes received from the NT table. 
Output: Find the multi-lane optimization route in a fast-moving network such 
as the MANET network. 
Algorithm: Use the hidden Markov model [4] to predict the position of the 
node based on the collected state of the nodes. Uses HMM to predict the mobility 
of each network node. This is done at a base station or cluster node in each cluster 
in a network of small nodes such as sensors or clustering networks. If the network 
is large and unconnected, this is done at each node. Information about the mobility 
of each node is predicted, then sent back to the nodes in the cluster and stored in 
the coordinate table (CT). When a node wants to transmit data to another node: i) 
The source node takes the coordinates of the nodes in the cluster of the CT table; 
ii) If the destination node belongs to a cluster, the node transmits data directly to 
that node in coordinates; iii) If the node does not belong to the cluster, then the 
data is transferred to the cluster head by coordinates. The cluster head will base on 
neighbor cluster information to propagate the message as well as the coordinates. 
Electronics and Automation 
N. T. Long, N. D. Thuy, P. H. Hoang, “Building optimized web service ... ACO and HMM.” 68 
4.5. Uses HMM to predict the position of the network node 
To find the route between the source and destination node, the source node 
must propagate the RREQ route packet. RREQ is only transmitted to the node with 
a probability of existence greater than a threshold value in the coverage of the 
source node under the HMM model. The process continues like this with multiple 
paths to the destination node. Initially the source node transmits the message 
requesting neighboring nodes to inform it of its future location [4]. Based on that 
node will determine the probability of each neighbor node in its coverage area to 
decide whether to send the incoming RREQ message. Repeat process until 
destination node, route is formed. Initially, each node can belong to one of the N 
states, with each state having a probability P_i. During operation, each node can 
move state to a set of unobserved states with probabilities that can change over 
time. These states are predictable based on the information received from the node, 
such as signal strength, trajectory, time elapsed from the beginning. For example, 
when we know the signal strength we can determine the relative distance to node 
(d), so the node will be located on a sphere of radius d. It is easy to see that the 
position of the node is the orbital intersection of the node with the sphere. In this 
problem the state of the node is indicated by the coordinates of the node. Each 
node can move to a set of observed states, here are some visible positions of the 
predefined node based on observation or node information. If you do not receive 
the information from the node, you can predict the current position based on the 
previous position of the node and the node parameters. The parameters received 
from the node are used to deduce where the node can belong to which state with 
the highest probability. The parameters received from the network node are the 
results in HMM (F). One example might be that the parameters received from the 
network node are the signal strength. 
The simulation showed that the routing results using HMM gave better results 
than DSR, with the number of hop lines decreasing. 
4.6. Use HMM to find the path in the clustered network 
For clustered networks we can look at the whole network as a hidden Markov 
model. Where each cluster is a state of the HMM, at which point each node can 
belong to a cluster with a probability. This probability can be determined by the 
relative position of the node relative to the cluster head in question. It is also 
possible to use the coordinates of a node as defined above to allow any node of a 
node. Initially, the node belongs to a particular cluster of the network with a 
probable probability. Based on the coordinates of the predicted node, when no 
cluster node information is received, we can predict based on the previous state of 
the node and the received node information such as the velocity of travel of the 
node compared to other cluster heads. It is easy to find paths in the probability-
based network, at each cluster if the destination node is not in cluster, select cluster 
with cluster head that has a total distance to the current node and the shortest 
destination to transmit the packet. 
Research 
Journal of 
and in the previous service orientation. Graphs are built on the basis of the 
structure of the functions and procedures of Web services that can be run 
independently before performing the tree search algorithm. Using the ACO 
algorithm to evaluate the optimal route selection probability that is performed over 
a given time period, or where the route meets certain criteria. In the future, the 
author will stu
the basis of adaptive routing in conjunction with artificial intelligence algorithms.
The simulation is written in C# programming language, the input of the system is a 
number of real Web s
production and business at the work unit of the author. The simulation of the Web 
service setup was conducted a number of times, measuring the time taken to graph 
the results. Finding the lines satisfy
the ACO algorithm to find the probability of each route being selected. From the 
find paths with probability, choose the best path that has the highest probability of 
creating a Web service. As a result of th
perform all t_All jobs and the optimal route search, the generated Web service was 
created with the request being passed through the t_Ex interface to the file. Logs 
and graphs show the time variability of the algori
Each time the implementation is reported results on the program interface:
Fig
search, service implementation) with time (find optimal
is quite stable over time and the ratio of time 
Use the graph to find the broadcast multiples mentioned in the previous section 
Looking at the graph above, we find the optimal route and service performance 
ure
Military 
 1. Comparison of Y
z 
dy additional optimization algorithms to find the optimal route on 
Science and Technology, Special I
5. SIMULATIO
y 
ervices, so simulation results can be used to recover. 
-axis execution time (graph generation, optimal route 
axis) self
ing the problem from the graph is formed, using 
N AND ASSESSMENT
-service motion.
e simulation program, the time taken to 
ssue, No.57


 varies in a relatively stable range.
thm in some implementations. 
A, 
 route, implementation) (X
11 
x 
- 201
8 
69
-
Electronics and Automation 
N. T. Long, N. D. Thuy, P. H. Hoang, “Building optimized web service ... ACO and HMM.” 70 
6. CONCLUSIONS 
Service discovery is an important function in the upper layer network, so that 
this function works optimally, especially in order to reduce energy consumption 
and network control costs. The thesis proposed a solution for intelligent network 
segmentation, multi-path routing, and multiplexing, to enable this function to be 
effective. Map the problem finds efficient, optimal optimization problem to find 
multi-line route, optimization on the graph, to ease the use of search trees, reduce 
costs. On the other hand, this method makes it easy to use the ACO algorithm to 
optimize and evaluate the probability of existence. 
REFERENCES 
[1]. Nguyen Thanh Long, Nguyen Duc Thuy, Pham Huy Hoang, “Building 
Multiple Multicast Trees with Guarranteed QOS for Service Based Routing 
Using Artificial Algorithms”. Springer, ISBN: 978-3-319-29235-9, ICCASA 
2015, LNICST 165, pp. 354–369, 2016. 
[2]. Sami Zhioua (2013): “Tor traffic analysis using Hidden Markov Models”, 
Security Comm. Networks 2013; 6:1075–1086. 
[3]. B.G.Obulla Reddy, Maligela Ussenaiah, Rayalaseema (2012): “An Adaptive 
Fuzzy Clustering and Location Management inMobile Ad Hoc Networks”. 
International Journal of Computer Applications & Information Technology 
Vol. I, Issue III (ISSN: 2278-7720). 
[4]. R. Nagwani, D. S. Tomar (2012): “Mobility Prediction based Routing in 
Mobile Adhoc Network using Hidden Markov Model”. International Journal of 
Computer Applications (0975 – 8887), Vol. 59– No.1. 
[5]. Seok K. Hwang, Dongsoo S. Kim (2007): “Markov model of link connectivity 
in mobile ad hoc networks”. Telecommun Syst (2007) 34:51–58. 
[6]. Amardeep Sathyanarayana, Pınar Boyraz, John H.L. Hansen (2008): “Driver 
Behavior Analysis and Route Recognition by Hidden Markov Models”. 
Proceedings of the 2008 IEEE International Conference on Vehicular 
Electronics and Safety, Columbus, OH, USA. 
[7]. R.Sharma, D. K. Lobiyal (2015): “Proficiency Analysis of AODV, DSR and 
TORA Ad-hoc Routing Protocols for Energy Holes Problem in Wireless 
Sensor Networks”. 3rd Inter. Conference on Recent Trends in Computing. 
[8]. Jaspreet Kaur, Dinesh Kumar, Giani Zial Singh (2017): “Distributed Hash 
Table based Routing for P2P Data sharing in MANETs”, e-ISSN: 2454-6615, 
WWJMRD 2017; 3(7): 82-85. 
[9]. Franc¸oise Sailhan, Val´ erie Issarny (2009): “Scalable Service Discovery for 
MANET”. HAL Id: inria-00414946. 
[10]. Gianni Di Caro (2004): “Ant Colony Optimization and its Application to 
Adaptive Routing in Telecommunication Networks”. PROMOTEUR: PROF. 
MARCO DORIGO, ANNEE ACAD´ EMIQUE´ 2003-2004. 
Research 
Journal of Military Science and Technology, Special Issue, No.57A, 11 - 2018 71
TÓM TẮT 
XÂY DỰNG MÔ HÌNH DỊCH VỤ WEB TỐI ƯU TRÊN CƠ SỞ KỸ THUẬT 
KHÁM PHÁ DỊCH VỤ VÀ CÁC THUẬT TOÁN TRÍ TUỆ NHÂN TẠO 
Khám phá dịch vụ là một chức năng mạng mới phát triển từ chức năng khám phá 
thiết bị trong hệ điều hành đã có trước đây. Nhưng việc áp dụng hiệu quả kỹ thuật 
này trong định tuyến hướng dịch vụ là một việc không tầm thường. Hơn thế nữa 
ngày nay, công nghệ điện toán phát triển, mạng máy tính đã phát triển đến một giai 
đoạn mới. Thế hệ mạng này gọi là mạng lớp trên, các nút mạng liên kết với nhau 
trên cơ sở kết nối theo dịch vụ. Trong bài báo này, chúng tôi đề xuất một kỹ thuật sử 
dụng lý thuyết đồ thị kết hợp với thuật toán tối ưu ACO, để tạo ra tuyến tối ưu hỗ trợ 
bài toán khám phá dịch vụ. Trên cơ sở nghiên cứu các thuật toán tối ưu, chúng tôi 
đã kế thừa nghiên cứu này, mô phỏng nghiên cứu này bằng bài toán hình thành dịch 
vụ tối ưu trên cơ sở các dịch vụ thành viên, bổ sung thêm thuật toán tối ưu Gen để 
tạo ra các tuyến tối ưu nhanh và tránh được tối ưu cục bộ. 
Từ khóa: Service discovery service, Web service, Multicast routing, ACO, HMM. 
Received 27th June 2018 
Revised 10th October2018 
Accepted 27th October 2018 
Author affiliations: 
1Trung tâm Công nghệ thông tin, Viễn Thông Hà Nội; 
2Viện kỹ thuật Bưu điện, Học Viện Bưu chính Viễn thông; 
3Viện Công nghệ thông tin, Đại học Bách khoa Hà Nội. 
*Email: ntlptpm1@yahoo.com, hoangph@soict.hut.edu.vn.