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International Journal of Software Engineering and Its Applications

Vol. 9, No. 12 (2015)

ISSN: 1738-9984 IJSEIA

Copyright ⓒ 2015 SERSC

Power Aware Ant Colony Routing Algorithm for Mobile Ad-hoc

Networks

Alaa E. Abdallah1, Emad E. Abdallah

1, Feras Hanandeh

1, Ashraf Aljammal

1, and

Essam Al-Daoud2

1 Faculty of Prince Al-Hussein Bin Abdallah II For Information Technology,

Hashemite University, P.O. Box 150459, Zarqa 13115, Jordan

[e-mail: aabdallah,bsoul,emad,[email protected]] 2 Department of Computer Science, Zarqa University

Zarqa- Jordan

[e-mail: aabdallah, emad, feras, ashrafj @hu.edu.jo, [email protected] ]

Abstract

Due to the limited lifetime of nodes in ad hoc and sensor network, energy efficiency

needs to be an important design consideration in any routing algorithm. Most of the

existing Ant colony based routing algorithms grantee the packet delivery. However, they

suffer from the high power consumption due to the huge number of control messages to

establish and maintain a route from a source to a destination. This paper introduces two

new power-aware ant colony routing algorithms for mobile ad hoc network under three

main concurrent constraints. (1) Localized algorithms where only information about

neighbors' nodes is needed. (2) Maximize the algorithms delivery rate. (3) Minimize the

energy consumption. Our new algorithms are based on the idea of extracting a sub-graph

of the original network topology and combine it with the advantage of ant based routing

algorithms. Extensive experiments are conducted to prove that the new algorithms have

significant improvement on the network lifetime (up to twice) without affecting the high

delivery rate.

Keywords: Ad hoc network, Local algorithm, Energy efficient, Ant colony routing,

Geometric sub-graph

1. Introduction A Mobile ad hoc network (MANET) is a network formed of a set of wireless nodes

which communicate with other nodes without any supporting network infrastructure. In

MANET, a node can directly communicate with another node if and only if the two nodes

are located within the transmission range of each other. However, if a node wishes to

communicate with another node from outside the transmission range, it uses the help from

other nodes which play as a router to receive and forward packets. Mobile nodes in ad hoc

networks can move, join and leave the network freely. Thus, routing in such networks is a

crucial problem. However for many networks, a more important problem is providing an

energy efficient routing protocol because of the limited battery life of the wireless nodes.

Routing algorithms in mobile ad hoc network can be classified into two basic types [1] (1)

Proactive [2, 3], where each node in the network keeps routing information about every

other node up-to-date all the time. In this kind of protocol the nodes periodically

broadcast their routing tables to their neighbors. Proactive routing has a short response

time but it consume huge overhead exchanging routing information especially when the

mobility rate is big. (2) Reactive [4, 5, 6], where a node maintains only the routes that are

currently in use. In this strategy, if the route is not available, the current node holding the

packet issues a destination search request which is performed by flooding a short control

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Vol. 9, No. 12 (2015)

2 Copyright ⓒ 2015 SERSC

message. Obviously, there will be no routing table broadcast, thus less overhead but

longer time to find a route.

Another family of routing algorithms called position-based routing [1, 7, 8, 9, 10, 11].

Position-based routing algorithms assume that a node is aware of its position using cheap

instruments like GPS (Global Positioning System), the position of its neighbors by using

periodic hello messages, and the position of the destination using location service

algorithms [12, 13]. Position-based routing is known to have much less overhead than

proactive and reactive protocols but they may fail to find a route or they may find a non-

optimum route in some situations.

Over the last few years, researchers proposed a new set of routing algorithms that are

inspired by real ants’ behavior and their way to find the shortest path between food and

their nest [14, 15, 16, 17, 18]. The protocols works as follows [19, 20], while searching

for food, ants deposit on the ground a substance called pheromone to give them a way

back to the nest. By time the shorter paths would have more ants going through them, thus

more pheromone consecration. Other ants can smell pheromone, and when searching for a

path, they tend to choose, with high probability, paths marked by strong pheromone

concentrations. Most of ant-colony routing algorithms suffer from the high power

consumption due to the huge number of control messages which are needed to establish a

route from the source to the destination.

Many routing strategies use a sub-graph of the unit disk graph such that only the edges in

the sub-graph are used for routing. Therefore, much research effort has gone into the

development of algorithms for ad hoc network topology control (see [21, 22, 23] for

surveys). In this paper, we propose a new set of routing algorithms for mobile ad hoc

network which utilize the network nodes positions to extract a sub-graph of the original

Unit Disk Graph (UDG). Then we apply ANTHOCNET [24] routing over the extracted

sub-graph. Our simulations show that the new algorithms have significant improvement

on the network lifetime.

The rest of this paper is organized as follows. Section 2 is devoted to background

material. In Section 3, we briefly review some related position-based and ant-based

routing algorithms. In Section 4 we give detailed descriptions of the new proposed

routing algorithms. In Section 5, we present experimental results to demonstrate the

much improved performance of the proposed methods in comparison with existing

techniques. Finally, we conclude and point out some future directions in Section 6.

2. Preliminaries

2.1. The Network Model

We assume that a set of n wireless hosts is represented as a point set V in the 2D

space. All nodes have the same communication range of r, which represented as a

circle of radius r. Let ( ) be the Euclidean distance between the nodes u and

v: ( ) √( ) ( )

. Two nodes are connected by an edge if the

Euclidean distance between them is at most r. The resulting graph is called a unit

disk graph UDG(V, r). For node u, we denote the set of its neighbors by N(u). A

path from s to d is a sequence of nodes s = v1, v2, ….., vk = d, such that (vi ,vi+1) are

neighbors.

2.2 The Routing Problem

Given a unit disk graph UDG(V) corresponding to a set of points V , and a pair (s,d)

where , the problem of routing is to discover a path in UDG(V) from s to d. It’s


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Copyright ⓒ 2015 SERSC 3

not an easy task to design a power efficient routing algorithm for many reasons (1) the

wireless links in the network are not known to any node in the network because nodes

may not have fixed locations. (2) Number of neighbors keeps changing because of the

interference and noise which affect the transmission range. (3) Nodes in the network can

only see neighbors and does not have a global look to the whole network.

We are interested in the following performance measures for routing algorithms: the

delivery rate [11] which is the percentage of times that the algorithm succeeds in

delivering its packet, the average packet delay, the overall overhead and the power

consumption [25, 26, 27, 28], it’s the amount of energy consumed by a message sent in

the network. If the transmission range is fixed for all the nodes, then the number of nodes

in the route path and the number of control messages are the main source of the energy

required for the routing task.

2.3 Related UDG sub-graphs

A distributed algorithm is called local if each node of the network only uses

information obtained uniquely from the nodes located no more than a constant

(independent of the size of the network) number of hops from it. Thus, during the

algorithm, no node is ever aware of the existence of other nodes in the network further

away than this constant number of hops. In the following we present some local

distributed algorithm to extract a sub-graph from UDG.

• Gabriel Graph (GG(G)) [29,30]: given any two adjacent nodes p and q in

UDG, the edge (p, q) belongs to GG if, and only if, the closed disc of which

line segment pq is a diameter contains no other nodes of V , see Figure 1. It

is known that if UDG is 2-dimensional then the Gabriel sub-graph is planar,

where the edges intersect only at their common end-vertices, and if the UDG

is connected, then its Gabriel sub-graph is also connected [31].

• YAO_K(G) Graph [32, 33]: with an integer parameter k ≥ 6 is defined as follows. Each node p divides the unit disk around it to k equal cones, see Figure 2. In each cone, only the edge (p,q) to the nearest neighbor q, if any, is part of the directed YAO_K(G). Ties are broken arbitrarily. The undirected graph obtained if the direction of each edge is ignored. If the UDG is connected, then its YAO_K(G) Graph is also connected [34, 35].

• Half Space Proximal Graph HSP(G) is defined as follows [36]. Each node p in UDG performs the following procedure: p draws an edge (p,q) to the nearest neighbor q, and draws a perpendicular line intersecting the edge (p, q) at its midway point, all nodes in the half plane contains q are discarded. The procedure then continues with the next non discarded node. See Figure

Figure 1. Edge x is counted in GG(G) graph if the disc of diameter x contains no other

nodes


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3. It has been proved in [36] that HSP(G) is connected, has an out-degree of at most six, and contains the minimum weight spanning tree as its sub_graph.

3. Related routing algorithms

Routing in Mobile ad hoc network depends on many factors including network

topology, the type of information available during routing, and the specific underlying

network characteristics that could be used to define a heuristic to find a path quickly and

efficiently. The following reviews some representative ant-based and position-based

routing algorithms that are closely related to our proposed approach. We briefly discuss

the algorithmic methodologies as well as their limitations.

Greedy routing [30]: a position-based routing, where the current node c forwards

the packet to the neighbor node p that minimizes the remaining distance to the

destination node d. the same procedure is repeated until the destination node is

reached or no such node p exists. This routing method suffers from the so called

local minimum phenomenon, in which a packet may get stuck at a node that does

not have a neighbor that makes a progress to the destination, even though the

source and the destination are connected by a path in the network.

DREAM [37]: in this algorithm, the current node c forwards the packet to all

neighbors in the direction of the destination d. A node is considered to be in the

direction of d if it is located in a 2D cone that starts at current node and ends with

a circle centered at d, the circle radius equal to vmax * (t1 − t0) where t1 is the

current time, t0 is the time stamp of the position information that c has about d

and vmax is the maximum speed of the node in the network.

Figure 2. The shortest edge in each cone is counted in YAO_ K(G)

Figure 3. HSP(G) graph, the shortest edge is chosen, all other nodes behind the

perpendicular line are discarded


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ANTHOCNET [24] is a reactive ant colony optimization algorithm. To find a

route from a source to a destination, two types of control packet are generated,

forward and backward ants. Over a specific time interval, the source node

generates periodic forward ants (control packet) for the destination. The ants

flood the network through all possible ways Figure 4. Each ant updates the weight

of every visited link by a fixed number (deposit on the ground a substance called

pheromone). Obviously ants that pick the shortest path from the source to the

destination will arrive first to the destination. At this point the destination will

generate backward ants to go through the exact same shortest path which will

change the status of the links by increasing the pheromone concentration on the

links along the path. The source node knows now the shortest path from the

source to the destination. To prevent infinite loops in the route discovery process,

each visited node u keeps the following about the forward ants: the identity of the

forward ants and the arrival time to u. When an ant visits node u, it checks the

ants arrival time and its identity. If the time are within a certain limit of those

produced by another ant of the same generation then the ant is forwarded.

Otherwise, the ant is discarded. The drawback of ANTHOCNET is the number of

forward and backward control messages that needs to be sent in the network for

establishing routes to the destination and the time needed before paths between

the nodes of the network is established.

ANTNET [38]: considered to be a proactive ant colony routing algorithm for

mobile switch networks. The algorithm starts by lunching a forward ant at regular

intervals from the source node. Depend on the information stored at the current

node routing table, the next node is selected as follows: suppose that a forward

ant is currently residing in node n and this node has k neighbors h1,h2, ...,hk, and

Φi is the amount of pheromone assigned to nhi. The forward ant will select hi as

the next node with a probability pi which is calculated using Equation 1. The

probability of selecting the next node is proportional to the pheromone

concentration. As in ANTHOCNET, when the forward ant reaches the

destination, it generates a backward ant that goes through the opposite direction

Figure 4. ANTHOCNET route discovery process, to find a route from S to D, S

generates periodic forward ants (control packet) for the destination D. The ants flood

the network through all possible ways.


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of the path chosen by the corresponding forward ant. The backward ant updates

pheromone values as it moves on its way to the source node. Because ANTNET

route discover process select one neighbor at each intermediate node, the end to

end delay is higher than many other ant colony routing algorithms. In General

most of the ant colony routing algorithms including ANTHOCNET have a very

high delivery rate and find routes whose lengths are very close to the length of the

shortest path [39, 38].

4. Proposed Routing Algorithms

In this section we explain our new proposed algorithms, ANTSUB(HSP(G)) and

ANTSUB(YAO_ 6(G)). They are able to find nearly optimum routes and save up to 50%

of the energy consumption compared with the famous ANTHOCNET routing. As stated

before, ANTSUB(HSP(G)) and ANTSUB(YAO_6(G)) are reactive algorithms, thus a

route is searched only when a node wants to send data to another node outside its

transmission range. The route discovery is done only through control ant messages. Data

packets to be sent after the route discover process is finished and the route is established.

In order to decrease the energy consumption at each node in the network, we have to keep

the number of control messages as small as possible.

ANTSUB(HSP(G)): consider a source node, s, has a set of data packets and wants to send

them to a destination node, say d. Since s does not know how to reach d, it starts a route

discover process as follows: using HSP(G) algorithm s finds the set of edges around it that

belongs to HSP(G), then s sends several forward ants with unique sequence numbers

through each edge at regular time intervals, see Figure 5. Each neighbor receives a

forward ants run the HSP(G). Based on the values of pheromone trails the current node

selects the next hop as follows: consider the forward ant is currently at node n and this

node has k neighbors h1,h2, ...,hk, and Φi is the amount of pheromone assigned to nhi. The

forward ant will select hi as the next node with a probability pi which is calculated using

Equation 1. As on any ant colony routing algorithm, when an ant visits a node through an

edge, it deposits a specific amount of pheromone on that edge. The information about the

values of pheromone on the neighbors edges is stored on a table at each node (which is

going to be used while sending the data packets).

∑

(1)

In addition to the pheromone trial table, each node keeps another table which we call back

routing table. The table saves the id of each ant enters the node, the ant source node, the

ant destination node and the ant time stamp. This is used to discard duplicate ants. With

the help of the same table, the backward ant traverses in the reverse direction. If a node

does not hear any packet going to a given destination for a specific amount of time (order

of seconds), it deletes the back routing table. The pheromone trails for that destination

will be deleted from the pheromone trail table as well. Algorithm 1 shows in details the

route discovery process.

We have tried to use YAO_6(G) instead HSP(G) graph during the routing process of the

Algorithm 1 to a get a new routing algorithm ANTSUB(YAO_6(G)). It did not show

better power saving than ANTSUB(HSP(G)) but still it’s better than other ant colony

routing algorithms.


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5. Simulation results

In this section we describe our simulation environment, evaluate the performance

of our new algorithms under several simulation scenarios and compare them with

other related routing algorithms.

5.1 Simulation Environment

The event-driven simulator can be described as follows:

1. We define 300mX300m square

2. A set V of n points (where n 100, 150, 200, 250, 300) is randomly

positioned in the square, this would lead to different node densities.

3. Each node has a transmission range equal to 15m.

4. If the network graph is not fully connected, all connected components in

the graph are calculated.

5. The largest connected component (LCC) among all the connected com-

ponents is selected to perform the routing algorithms.

6. For each network, one source-destination pair is selected randomly from

the LCC.

7. It is suggested in [24] to consider simulations with node density per unit

disk of around 5 in 2D environment. The proposed simulation environment

satisfies this density.

8. To estimate the performance, the algorithms are executed on 300 networks

and report the average results.

9. The algorithms are evaluated in terms of delivery rate, the average packet

delay, the overall overhead, and the power consumption of the whole

network.

10. The delivery rate is defined as the ratio of the number of packets received

by the destination node to the number of packets sent by the source node.

Figure 5. ANTSUB(HSP(G)) route discovery process, to find a route from S to D. Each

node receives a forward ant extracts the edges around C that belongs to HSP(G) and

broadcast the ant to all neighbors in HSP(G). This process is done in a periodic basses.


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11. To calculate the average power consumption, the model proposed by

Heinzelman et al. [40] is utilized, which assumes the radio dissipates

Eelec=50nJ/bit power to run the transmitter. Thus both transmitter and

receiver nodes consume Eelec to transmit one bit receiver circuitry.

12. The radio dissipates Eamp=100pJ/bit/m2 to run the transmit amplifier,

assuming d2 energy loss due to channel transmission, where d is the

distance between nodes. This implies the sender consumes (Eamp * d2)

power to transmit one bit.

13. According to the above wireless model, transmitting k−bits message at

distance d the transmitter expends ETx(k, d) = Eelec * k + Eamp * k * d2. To

receive the k−bits, the receiver node expends ERx(k) = Eelec * k.

5.2 Observed Result

In Figure 6, we studied the effect of the running time on the average delivery rate of

the new proposed algorithms and compared them with other well-known routing

algorithms. Clearly, all studied routing algorithms reach 100% delivery rate by time,

except of greedy algorithm due to the local minimum problem. Our algorithms

ANTSUB(HSP(G)) and ANTSUB(YAO_6(G)) reaches 100% delivery rate way faster

than ANTNET. The reason is that the new algorithms generate discovery ants though all

possible ways at the source node and employ a connected sub-set of the original set of

edges used by ANTNET, which means less chance to go through wrong paths, thus the

shortest and the accurate path will be reached faster. ANTNET forwards ants to only one

neighbor depend on the information stored at the node itself, which may lead to a longer

path. Hence, more time to reach the destination.

Figure 6. The average delivery rate of (HSP(G)), ANTSUB(YAO_6(G)), and other routing

algorithms at different time clock. The number of nodes equal to 150


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In Figure 7, we studied the effect of the node density on the delivery rate. The running

time is fixed in this study (80ms). The new proposed algorithms ANTSUB(HSP(G))

ANTSUB(YAO_6(G)) share highest delivery rate with ANTHOCNET. By increasing the

node density, greedy algorithm delivery rate increases. This can be explained by the

number of links, more links means more paths to the destination and less chances to face a

local minimum. The delivery rate of all examined ant colony routing algorithms decreases

by increasing the number of nodes because they all need more time to find an accurate

route to the destination. Since, ANTSUB(HSP(G)) uses less number of edges than

ANTSUB(YAO_6(G)) it has higher delivery rate.

Figure 7. The average delivery rate of (HSP(G)), ANTSUB(YAO_6(G)), and other routing

algorithms at different network density. The results are taken after 80ms.

Figure 8. The average packet delay of (HSP(G)), ANTSUB(YAO_6(G)), and other routing

algorithms at different time clock. The number of nodes equal to 150


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In Figure 8 and Figure 9, we studied the delay of our proposed routing algorithms and

compare them with ANTNET and ANTHOCNET. In both figures we calculated end to

end delay based on the number of hops visited by each packet. Figure 8 shows the average

packet delay vs. time, in the beginning, the average packet delay of ANTHOCNET is

smaller than all other routing techniques. The average delay of ANTSUB(HSP(G)) and

ANTSUB(YAO_6(G)) reach to their minimum faster than the ANTNET. As time passes

on, all algorithms converge to the paths with almost the same lengths, where packets

experience almost the same delay. Figure 9 studied the effect of increasing number of

nodes against the delay; as expected increasing number of nodes will increase the delay

for all studied algorithms because the length of the path to the destination will increase.

Figure 9. The average packet delay of (HSP(G)), ANTSUB(YAO_6(G)), and other routing

algorithms at different network density. The results are taken after 80ms.

Figure 10. The average power consumption of (HSP(G)), ANTSUB(YAO_6(G)), and other

routing algorithms at different time clock. The number of nodes equal to 150


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The main contribution of the paper can be found in Figure 10, where we show the average

power consumption from the whole network after running the algorithm for a specific

amount of time; clearly the ANTHOCNET has consumed huge power to discover and

maintain routes. It’s almost double the power consumed by our proposed algorithms.

ANTNET has the lowest power consumptions compared with other studied algorithms;

this due to the nature of the algorithm, each node receives an ant, instead of re- broadcast

it, the ant is forwarded to one of the neighbors. ANTSUB(HSP(G)) and

ANTSUB(YAO_6(G)) send ants to all source neighbors in the sub-graph and then uses

similar forwarding schema to ANTNET. The power consumption is close to ANTNET

since they use a sub-graph of UDG which lead to reduce the number of ants, because the

pheromone concentration will increase on the right path much faster. After the

millisecond 160, in all algorithms the power consumption stops increasing dramatically

because most of the algorithms usually finds route by this time, thus they stop generating

discovery ants. Figure 11, depict how the number of nodes affect the overall power

consumption of all algorithms. As predicted, ANTSUB(HSP(G)), ANTSUB(YAO_6(G)),

and ANTNET have much less power consumption than ANTHOCNET.

It is essential to keep the overhead of any ad-hoc routing algorithm as small as possible.

Large overhead makes a routing algorithm not scalable and sometimes useless. One of the

necessary parameters that should be kept small is the amount of control traffic exchanged

in the network when a routing protocol is running. Figure 12 displays the number of

generated ants by the new proposed algorithms along with the other studied algorithms in

different clock times. Clearly, the number of generated ant in ANTHOCNET grows very

quickly specially before the millisecond 160. For ANNET the number of ants grows in a

linear function of time, because ANTNET generates an ant at every time clock. The total

number of ants generated in ANTSUB(HSP(G)) and ANTSUB(YAO_6(G)) is relatively

higher than ANTNET but it still raises linearly, because each node has a bounded degree

after extracting the sub-graph.

Figure 11. The average power consumption of (HSP(G)), ANTSUB(YAO_6(G)), and other

routing algorithms at different network density. The results are taken after 80ms.


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6. Conclusions

In this paper, we propose two new ant colony routing algorithms for mobile ad hoc

network (ANTSUB(YAO_6(G)), and ANTSUB(HSP(G))). We utilize the network

nodes positions to extract a sub-graph of the original UDG graph. The route discovery

process of the new algorithms is done only over the extracted sub-graph. Simulation

results demonstrate that the new proposed algorithms have improved the network

lifetime dramatically, up to twice the lifetime of other ant colony routing algorithms,

without affecting the high delivery rate advantage. For future work, we plan to analyze

the relationship between number of nodes, size of sub-graph, transmission range,

delivery rate, and power consumption for further improvement.

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Authors

A.E. Abdallah, is currently an Assistant Professor in the Department of

Computer Science at the Hashemite University (HU), Jordan. He received his

PhD in Computer Science from Concordia University in 2008, where he

worked on routing algorithms for mobile ad hoc networks. He received his

BS from Yarmouk University, Jordan, and MS in from the University of

Jordan in 2000 and 2004, respectively. Prior to joining HU, he was a network

researcher at consulting private company in Montreal (2008 - 2011). His

current research interests include Routing Protocols for Ad Hoc Networks,

Parallel and Distributed Systems, and Multimedia Security.

E.E. Abdallah, is currently an Associate Professor in the Department of

Computer Information Systems at the Hashemite University (HU), Jordan.

He received his PhD in Computer Science from Concordia University in

2008, where he worked on multimedia security, pattern recognition and 3D

object recognition. He received his BS in Computer Science from Yarmouk

University, Jordan, and MS in Computer Science from the University of

Jordan in 2000 and 2004, respectively. Prior to joining HU, he was a

Software Developer at SAP Labs Montreal.

Feras Hanandeh, is currently an Associate professor at the Prince Al

Hussein Bin Abdullah II Faculty of Information Technology, Hashemite

University, Jordan. He obtained his PhD in computer science from

University Putra Malaysia, Malaysia in 2006. His current research in terests

include distributed databases, parallel databases focusing on issues related to

integrity maintenance, transaction processing, query processing and

optimization; grid computing, artificial intelligence and geographical

information systems


Vol. 9, No. 12 (2015)


Ashraf Aljammal, received the BSc degree in computer science from

Albalqa' Applied University, Al-Salt, Jordan, in 2006, the master's degree

from Science University of Malaysia, USM, Malaysia, in 2007, and the PhD

degree from Science University of Malaysia, USM, Malaysian, in 2011. He is

currently an assistant professor in the department of Computer Science of the

Hashemite University, Zarqa, Jordan, since 2012. His research interests

include network monitoring, network security, cloud computing, parallel and

distributed system security.

Essam Al-Daoud, is currently a Professor in the Department of

Computer Science at Zarka University. He received his BSc from Mu’tah

University, MSc from Al al-Bayt University and his PhD in Computer Science

from University Putra Malaysia in 2002. His research interests include

machine learning, soft-computing, cryptography, bioinformatics and quantum

cryptography.

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