IMPLEMENTATION OF BIOLOGICAL ROUTING PROTOCOL IN TUNNEL WIRELESS SENSOR NETWORK (TWSN)

International Journal of Wireless & Mobile Networks (IJWMN) Vol. 5, No. 4, August 2013
DOI : 10.5121/ijwmn.2013.5409 119
IMPLEMENTATION OF BIOLOGICAL ROUTING
PROTOCOL IN TUNNEL WIRELESS SENSOR
NETWORK (TWSN)
M.Muzaffar Zahar1
and Sharifah H. S. Ariffin, Liza A. Latiff, N. Fisal2
1
Industrial Training Institute of Marang, Terengganu, Malaysia
muzaffar@ilpmrg.gov.my
2
Universiti Teknologi Malaysia, Johor, Malaysia
sharifah@fke.utm.my, liza@ic.utm.my, sheila@fke.utm.my
ABSTRACT
A routing protocol is a core issue in Wireless Sensor Network (WSN) especially on undetermined situation
and crucial condition to guarantee the transmission of data. Therefore, any implementation of routing
protocol in a tunnel environment will suit with their application to minimize dropped data in its
communication. This paper presents a biological routing protocol named as Biological Tunnel Routing
Protocol (BioTROP) in Tunnel Wireless Sensor Network (TWSN). BioTROP has been tested with four
challenging situations for marking its standard soon. By setting it in low power transmission, all nodes
appear as source node and intermediate nodes concurrently, faster transmission rate and free-location
setup for each node; these conditions make BioTROP as an ad-hoc protocol with lightweight coding size in
tunnel environment. This protocol is tested only in real test bed experiment using 7 TelosB nodes at a
predetermined distance. The results have shown more than 70 percent of the transmitted data packets were
successfully delivered at the base station.
KEYWORDS
Routing Protocols, Tunnel Wireless Sensor Network, Biological Inspired Techniques, Ant Colony
Optimization
1. INTRODUCTION
As one of the areas under Wireless Underground Sensor Network (WUSN), Tunnel Wireless
Sensor Networks (TWSN) have different propagation characteristics of EM waves due to
confined space condition although the signal propagates through the air. The structures of the
mines and road or subway tunnels can contribute to refraction and reflection effect to create an
unbalanced communication which leads to early invalidation of nodes close to the base station
[1][2]. On the other hand, multi-path fading phenomenon and material absorption will contribute
to extreme path loss in TWSN communication.
Apart from the challenging environment, TWSN area also circles by the limitations of sensor
nodes manufactured setup such as low memory computational and limited battery usage.
Therefore, most of the researchers come with unique and special method in developing routing
protocol. A lightweight routing protocol is a major scheme chosen by certain researchers to
minimize the power consumption and total coding size in sensor node. Even though in reality, the
detail and various problem handler schemes in proposed routing protocol will contribute inversely
to tackle thousands of possibilities and unexpected risk situation.

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Otherwise, the researcher set the different active time of sensor node as well as limits the number
of the source node to control the signal traffic. However, it is quite tough in the installation
process and not maximizes the sensor nodes that distributed. Therefore, the ideal routing protocol
must have nature awareness, reliable in heavy source node, effective in ad hoc situations and
faster transmission rate with lower power consumption. Although many kinds of application
occur in a tunnel underground such as environmental monitoring, infrastructure monitoring,
location determination, and security monitoring, the core challenging problem in TWSN is how
the routes in the network maintain and reliable in multi-hop communication.
Based on the challenging issues that discussed, the proposed routing protocol must ensure the
secure communication and connection to send the data from source node to base station. In this
project, it is envisaged, the proposed routing protocol, Biological Tunnel Routing Protocol
(BioTROP) shall be an easier method than previous model done in various approaches. This is
considered an ant’s behavior concept that will try to find any way to reach the food area. Besides
that, the ant agent is guided also by optimal forwarding calculation to determine the best next hop
neighbour.
2. SYSTEM DESIGN OF BIOTROP
By the awareness of more refraction and reflection of signal occurred in tunnel condition, TWSN
needs the simple technique and practice to suit the limited memory and storage space in the
wireless sensor device. Therefore, BioTROP uses the cross-layer interaction [3] in its design
process to achieve high gains in the overall performance. The concept of cross-layer design for
sharing information among two or more layers for adaptation purposes is to increase the inter-
layer interactions [4, 5, 6]. BioTROP uses communication between physical layer and network
layer in order to select the next hop forwarding as shown in Figure 1. The process at the network
layer consists of the discovery of an optimal neighbouring node based on calculated optimal
forwarding progress value via physical parameters translated as metrics. The physical parameters
are the signal strength, link quality indicator (LQI) and remaining power. The forwarding metrics
are used in routing management module to determine the next hop communication.
Figure 1. Cross-layer concepts in BioTROP
Based on BioTROP’s role played in design approach, it consists of two functional modules that
include routing management and neighbourhood management as shown in Figure 2. The
neighbourhood management discovers a subset of forwarding candidate nodes and maintains a

121
neighbour table of the forwarding candidate nodes. The routing management determines the
optimal route to the destination and forwards the packet based on the unicast forwarding
mechanism. If any problem occurs while routing the data packets, the routing management
implements routing problem handler. Unicast forwarding mechanism is used to forward the data
packet to best next node towards the destination.
Figure 2. Functional Components of BioTROP
2.1. Routing Management Module
As a main role in BioTROP, routing management supports three sub functional processes;
optimal forwarding calculation, unicast forwarding mechanism and routing problem handler as
shown in Figure 2. The selection of next hop node determined by optimal forwarding calculation
that considers three parameters or criteria which are packet reception rate (PRR), link quality
indicator (LQI) and remaining power level. The routing problem handler is used when the sender
node does not receive Backward Ant agent. Figure 3 shows the state machine diagram of the
routing management.
Routing Management
Routing Problem
Handler
Optimal Forwarding Calculation
Unicast
Forwarding
Neighbourhood Management

122
Figure 3. State Machine Diagram of Routing Management Module
The routing management module starts when the sensor node sends or receives a Request to
Reply (RTR) packets to or from its neighbour nodes. Each node was set with 1 packet per second
on their transmission rates and will broadcast the RTR packets twice at the earlier of 2 second. At
the same time, each node will be on the alert to receive any RTR packets and reply to the sender
immediately with three parameters. Therefore, there are different role played in searching the
optimal neighbour node at an earlier stage. Based on TelosB datasheet, the transmit data rate of
TelosB node is 250 kilobits per second (kbps). So, this project allocates 2 second so that each
source node has sufficient period in receiving, analyzing and re-sending process.
Refer to Figure 3, the optimal forwarding calculation task acts as the main role in routing
management module. Each receiver node will prepare the forwarding metrics as requested from
source node to get the optimal solution. For realizing it, the optimal forwarding calculation task
will request the neighbour information twice in 2 second to obtain physical parameters such as
packet reception rate, link quality indicator and remaining power. The calculation will follow as
equation (1). The value of λ1, λ2 and λ3 is determined by a table tabulated by Ahmed [7] using
heuristic methods. Therefore, this project chose λ1= 0.4, λ2 = 0.3 and λ3= 0.3. After receiving the
needed parameters, the routing management stores the replies in the neighbour table. The router
management will forward a data packet to the one-hop neighbor that has an optimal forwarding.
The optimal forwarding (OF) is computed as follows:
= max[( × ) + ( × ) + ( + )]
ℎ + + = 1
= 0.4, = 0.3 = 0.3 (1)
The forwarding mechanism will be invoked after routing management module selects the best
progress neighbour in the optimal forwarding calculation. Finally, the data packets will be sent to
the selected neighbour node by unicast forwarding mechanism. The complete finding of route
from source node to destination node will be guided by the ant agent. However, the routing
management module will invoke the neighbourhood management module again if does not
receive Backward Ant agent after launched the Forward Ant. Therefore, if the connection
between a source node and destination node is fine, the survey process only takes once and then
will minimize the delay time to 2 second.

123
2.2. Neighbourhood Management Module
Refer to Figure 4, this module starts after routing management module requests neighbour
information. First, it will request neighbour discovery by broadcast the RTR message. Any
neighbour information received will be recorded in the neighbour table. Otherwise, this initiative
was carried out 2 times to maximize the number of neighbour node that can be detected.
Therefore, the neighbourhood procedure will update the neighbour table such as PRR, LQI and
remaining power when receive the new ID of neighbour node and the neighbour table is less than
6 nodes. If the selected neighbour node at routing management module faces any problem, the
neighbourhood management module will invoke neighbour replacement task to replace neighbour
information. Finally, the neighbourhood management module will send the updated neighbour
information to routing management module.
Figure 4. State Machine Diagram of Neighbourhood Management Module
3. METHODS AND EXPERIMENTAL SETUP
The tests were run using a simple network topology in one of the culverts at the Industrial
Training Institute (ITI) Marang, Terengganu as shown in Figure 5. The BioTROP are embedded
into 7 TelosB nodes and the nodes are sending at rate 1 packet per second, which is double with
suggestion rate. According to TelosB’s manufacturer, the optimum rate should be 0.5 packets per
second, because higher rates can lead to congestion [8]. In this experiment, all of six nodes (Node
1 to 6) will send the data packets or acts as Full Function Device (FFD). Node 0 was set as the
base station and each node might run as the intermediate node between a source node and base
station node that created. Therefore, the role of each node will maximize to collect the data while
in tunnel condition.

124
Figure 5. The view of experimental setup
The TelosB RF transceiver uses CC2420 component that compliant with IEEE 802.15.4.
Therefore, there are 8 power settings transmission that are allowed as specified at Figure 6 [9].
For this project, the BioTROP is embedded with two different power transmissions which are -25
dBm and -15 dBm, where these are the two lowest settings in TelosB mote. Meanwhile, the
distance between the nodes, d, was varied from two meters to eight meters. The performance will
be in term of Packet Delivery Ratio (PDR), Packet Drop Ratio (PDropR) and Packet Reception
Rate (PRR). These performances will be observed at the base station in 5 minutes. The analyzing
of result between both power transmissions were done on the three performances.
Figure 6. Output Power Settings and Typical Current Consumption @ 2.45 GHz
4. RESULTS AND DISCUSSIONS
This section discusses the implementation’s result of BioTROP in TWSN testbed. The three sub-
section below has been designed to simplify the explanation and conclusion will be made.
4.1. Power Transmission of -25 dBm
In this subdivision, BIOTROP was tested on TelosB motes using power transmission -25 dBm or
3.16 µWatt. Refer to Figure 6, the transmission power was set to code 3 which is lower on the list.
The graph in Figure 7 shows the percentage of packet delivery at the base station from each node
in different distance. The detail value for each result is shown in Table 1.
Node 3Node 1
1.7 m
1.1 m
Base
Station
d
Node 5
d d
Node 4 Node 6dNode 2
32 meter
1.8 meter
1.8meter

125
Figure 7. Packet Delivery Ratio (PDR) at -25 dBm power transmission
Table 1. The value of PDR (%) at -25 dBm power transmission.
Node
Distance
1 2 3 4 5 6
2 meters 86 94 94 90 86 86
4 meters 87 97 68 77 61 67
6 meters 81 80 45 44 62 78
8 meters 75 62 22 10 1 5
Since node 1 and node 2 are near to the base station, so the data packets delivered are high which
exceed 80 percent at 2, 4 and 6 meters. Generally, all data packets from all nodes consistently
deliver to the base station at 2 meters. This shows the BioTROP algorithm able to construct the
entire available sender nodes at this distance. However, the PDR drops quite drastically at 6 meter
especially on node 3 and node 4. The decreasing is happening due to the symptom of coverage
loss that caused by interference and attenuation in the tunnel if compared to node 5 and 6
performances that farther located. Meanwhile at 8 meter distance, most of the nodes do not
deliver well to base station node. The lowest power transmission in this setting might contribute
to low PDR that obtained.
0
20
40
60
80
100
120
2 4 6 8
Distance (m)
Numberofpacketdelivered(%)
PDR at Power Transmission of -
25dBm
Node 1
Node 2
Node 3
Node 4
Node 5
Node 6

126
Figure 8. Packet Dropped Ratio (PDropR) at -25 dBm power transmission
The Figure 8 shows the percentage graph of the Packet Dropped Ratio (PDropR) meanwhile
Table 2 shows the value for each result obtained. Refer to Figure 8 and Table 2, the data packets
averagely dropped are 4 percent, 12 percent, 24 percent, 26 percent, 29 percent, and 34 percent
respectively for node 1, node 2, node 3, node 4, node 5 and node 6 as the distance changes.
Although most of packets sent dropped exceeds than 20 percent at 8 meters, it is not enough to
evaluate the BioTROP algorithm totally. In lowest power transmission, the probability to detect
the available neighbour nodes is low. Thus, this situation pushes the neighbourhood management
module frequently to initiate new neighbour node discovery. Then, it will use the time located for
searching process without sending any data packets as well as no neighbour node detected.
Table 2. The value of PDropR (%) at -25 dBm power transmission.
Node
Distance
1 2 3 4 5 6
2 meters 14 6 6 10 14 14
4 meters 13 3 32 23 39 33
6 meters 19 20 55 56 38 22
8 meters 25 38 78 90 99 95
Figure 9 shows the packet reception rate at the base station for each node with detail value in
Table 3. As discussed earlier, the experiment sets the packets sending rate of 1 packet per second.
Thus, the best performance of PRR at the base station for each node is 1 packet per second
supposedly. As shown in Figure 9 and read in Table 3, all nodes achieve packet delivery more
than 0.85 PRR (85%) at the closest distance. Meanwhile at 4 meters, only node 1 and node 2
perpetual achieve exceed 0.85 PRR. Meanwhile node 3, 4, 5 and 6 had started to decline to 0.68
PRR, 0.77 PRR, 0.61 PRR and 0.67 PRR respectively. From a distance of 6 meters, three
different phenomena were observed. Node 6 shows increments of packet reception rate
meanwhile node 5 maintains at 0.6 PRR. However, node 1, 2, 3 and 4 decreased to 0.81 PRR,
0
20
40
60
80
100
120
2 4 6 8
Distance (m)
Numberofpacketdropped(%)
PDropR at Power Transmission of -
25dBm
Node 1
Node 2
Node 3
Node 4
Node 5
Node 6

127
0.80 PRR, 0.45 PRR and 0.44 PRR respectively. At 8 meter distance, only node 1 and 2 still
exceed than 0.5.
Figure 9. Packets Received Rate (PRR) at -25 dBm power transmission
Table 3. The value of PRR at -25 dBm power transmission.
Node
Distance
1 2 3 4 5 6
2 meters 0.863 0.940 0.943 0.903 0.863 0.863
4 meters 0.873 0.973 0.680 0.773 0.607 0.673
6 meters 0.813 0.797 0.450 0.437 0.617 0.776
8 meters 0.750 0.617 0.220 0.100 0.010 0.047
4.2. Power Transmission of -15 dBm
This experiment sets the power transmission at -15dBm or 31.6 µWatt for all nodes with the same
distance. As inferred in Section 4.2, the low power transmission setup will contribute to the
decreasing pattern when the distance between source nodes is increased in the first experiment.
Hence, this experiment is done to compare and analyze the performance with initial experiment.
Otherwise, the more sources node can be detected by the base station and more precise conclusion
can be made for both experiments.
0
0.2
0.4
0.6
0.8
1
1.2
2 4 6 8
Distance (m)
PacketReceptionRate
PRR at Power Transmission of -
25dBm
Node 1
Node 2
Node 3
Node 4
Node 5
Node 6

128
Figure 10. Packet Delivery Ratio (PDR) at -15 dBm power transmission
Figure 10 shows the consistent result of Packet Delivery Ratio (PDR) for each node at the base
station. As can be observed in Table 4, the average of percentage packets delivered fluctuates
slightly from 2 meters to 8 meter distance. In closest position, node 1, 2 and 3 shows that 81
percent, 93 percent and 87 percent of data packets delivered at base station respectively. Despite
the distance of node 4 same with node 3, only 78 percent of data packets are delivered.
Meanwhile, 77 percent and 71 percent of data packets delivered respectively for node 5 and node
6. This unbalance observation may be due to the physical condition of the tunnel that prevent
certain signals cannot be achieved by most nodes. On the other hand, the result of each node for
scenario 2 displays the excellent accomplishment. The entire node delivers the packet data
successfully to base station more than 90 percent. Although the result for scenario 3 and scenario
4 decreased slightly for average node, the data packets delivered still exceed 70 percent.
However, only node 6 given less than 60 percent at 8 meters. This might due to the distances and
interference in the tunnel during an experiment.
Table 4. The value of PDR (%) at -15 dBm power transmission.
Node
Distance
1 2 3 4 5 6
2 meters 81 93 87 78 77 71
4 meters 92 96 90 95 96 91
6 meters 82 76 81 73 73 86
8 meters 79 79 81 73 72 56
0
20
40
60
80
100
120
2 4 6 8
Distance (m)
Numberofpacketdelivered(%)
PDR at Power Transmission of -
15dBm
Node 1
Node 2
Node 3
Node 4
Node 5
Node 6

129
Figure 11. Packet Dropped Ratio (PDropR) at -15 dBm power transmission
Table 5. The value of PDropR (%) at -15 dBm power transmission.
Node
Distance
1 2 3 4 5 6
2 meters 19 7 13 22 23 29
4 meters 8 4 10 5 4 9
6 meters 18 24 19 27 27 14
8 meters 21 21 19 27 28 44
Figure 11 focuses on the Packet Dropped Ratio (PDropR) of each node at the base station. Similar
to the initial experiment result discussed previously, the total of packets data supposedly sent
minus to the packets delivered will produce the value of packets dropped. Based on the graph, all
nodes only lost its data packets not more than 30 percent at 2 meters. However, excellent result
showed at 4 meters where only minimum of 10 percent data packets dropped. Although there is
slightly increasing of data packets dropped at 6 meters, it still lower than the maximum data
packet drop at 2 meters. The result showed consistent at 8 meters where the range of data packets
lost similar to 6 meters. Only node 6 shows an unpredictable result even though the distances
setup same with opposite neighbour, node 5. Generally, most of nodes receive the other routes to
forward and same time sends its data packets to the base station fluently.
0
10
20
30
40
50
2 4 6 8
Distance (m)
Numberofpacketdropped(%)
PDropR at Power Transmission of -
15dBm
Node 1
Node 2
Node 3
Node 4
Node 5
Node 6

130
Figure 12. Packets Received Rate (PRR) at -15dBm power transmission
Figure 12 shows the PRR result of this experiment and Table 6 interprets the detail value.
Referring to the result at 2 meters, the rate of packets received at the base station for each source
node is more than 0.7. Even at the shortest distance setup, only node 1 and 2 can deliver almost
all its data packet to base station. Differ at 4 meters, all nodes can interact between each other
very well by producing more than 0.89 or 89 percent data delivered. However, the data packets of
all nodes gradually decrease at 6 meters. Only node 1, 3 and 6 accomplish the data packets
delivered more than 0.8. For farthest distance, the PRR of node 1, 2, 3, 4 and 5 is similar which is
above 0.7 PRR. However node 6 was given less than 0.6 PRR.
Table 6. The value of PRR at -15 dBm power transmission.
Node
Distance
1 2 3 4 5 6
2 meters 0.810 0.933 0.873 0.783 0.770 0.713
4 meters 0.917 0.957 0.897 0.947 0.960 0.907
6 meters 0.817 0.757 0.817 0.730 0.733 0.863
8 meters 0.793 0.793 0.807 0.730 0.720 0.557
4.3. Comparison for Both Experiments
This section will discuss the comparison result between first experiment and second experiments
by referring to the distances setup as follows:
Refer to the result of 2 meter setup in the first experiment, all nodes successfully deliver its data
packet despite of the lower power transmission setup. This observation differs to the same
distance in the second experiment as discussed previously where there are disorders in receiving
data especially on node 6. Theoretically, when the power transmission increase, more neighbour
node can be detected by source nodes at the same distance. Therefore, more signal of source node
will be detected by the base station node. This phenomenon will contribute the lower result of
PRR for node 5 and node 6 because of signal interference or high multi-path effect.
0
0.2
0.4
0.6
0.8
1
1.2
2 4 6 8
Distance (m)
PacketReceptionRate
PRR at Power Transmission of -
15dBm
Node 1
Node 2
Node 3
Node 4
Node 5
Node 6

131
Furthermore, almost all node start decreases gradually at 4 meter setup for the first experiment.
Only the closest node to a base station which node 1 and node 2 have shown very slightly
increased. It happens due to lack of power transmission for others source nodes. Thus, signal
interference for base station node in minimizes condition at this situation. For signal condition
between node 3 and 6, more than one device transmits at the same time which can create co-
channel interference [1]. As a result, signal attenuates and data packet cannot be sent. However,
the result at second experiment gives the confident performance for BioTROP. All nodes sent
successfully more than 89 percent PDR. These outcomes prove that the power transmission of -15
dBm is able to overcome the tunnel environment problem that occurred in the first experiment.
At 6 meter distance in both experiments, no nodes increase its packet reception rate at the base
station besides on node 5 in the first experiment. However, the slight increasing of PRR on node 5
in first experiment still does not exceed the packet reception rate observed at each node in the
second experiment. Other problems that have been discussed previously are environmental
interference. Certain location node has unexpected surface such as small tree that grows in
between the structure connector and also water hole. Thus, in a weak signal condition and more
types of interference occurred, no more data packet can be sent at 6 meter distance.
The furthest distance, 8 meters imposes more challenges to BioTROP algorithm. Totally source
nodes decreased drastically especially on node 3, 4, 5 and 6 in the first experiment. At this
distance with lowest power transmission, the adjacent neighbours cannot be detected by source
nodes to make guarantee connection. Thus, the complete route from source to destination node
cannot be developed. On the other hands, it will be interesting to discuss the result at the same
distance in the second experiment. As shown in Figure 4.8, most of nodes managed well to reduce
the reduction of data packet dropped beside of node 6. Refer to the result of node 6 in previous
distance setup, the increase or reduction of the graph not drastically as shown in 8 meter distance.
Thus, it may cause of the software problem that happen in reading process.
As an overall, the results obtained at four different distance setup as discussed above attributed
more to external factor only. By the way, there are neighbourhood discovery times in each node
when the neighbour discovery module invoked. The finding of routes from source to destination
by Ant Agent for each node was set to push the neigbour discovery module in 2 minutes at the
starting point. Thus, it will maintain the neighbour table as long as the source node receives the
Backward Ant before sending the data packets. If the source node does not receive the Backward
Ant agent, the request to reply (RTR) message will broadcast again to find the new neighbour.
Thus, it will take the time allocated for sending the data. If the more frequent unstable signal
occurs and then the source node does not receive Backward Ant agent, it will cause the neighbor
discovery module invoked much time. Nevertheless, BioTROP’s performance showed can be
improved by restructuring or minimize the timer of finding neighbor nodes.
5. CONCLUSIONS
The routing protocol developed in WSN area using two types of function nodes which are Full
Function Device (FFD) and Reduce Function Device (RFD) [1] to create multi hop
communication. FFD node will play two roles at the same time which are sourced node and
intermediate node. Meanwhile RFD node just receives and forwards the data packet from source
to destination. Both functions are implemented to reduce the congestion of the signal in order to
guarantee the data packet delivered. Therefore, BioTROP challenged this limitation by setting all
nodes as FFD and run in the confine region which is tunneled underground. Hence, the usage of
sensor node is in fully functional condition to improve the effectiveness of sensing in any
application. In addition, the proposed algorithm in BioTROP has been developed in simple
programming codes in TinyOS 1.x. So, the further improvement and study will be easier and

132
more flexible. Otherwise, BioTROP uses low power transmission to enhance the lifetime of
sensor node in real implementations.
ACKNOWLEDGEMENTS
The authors would like to thank the Ministry of Higher Education Malaysia for their full support
and Research Management Center, Universiti Teknologi Malaysia (UTM) for their partial and the
researchers in Telematic Research Group, UTM.
REFERENCES
[1] Ian A.F. Akyildiz and Mehmet Can Vuren. Wireless Sensor Network. John Wiley & Sons Ltd, UK.
A John Wiley and Sons, Ltd, Publication. 2010.
[2] Haifeng Jiang and Jiansheng Qian Wei Peng. Nonuniform Clustering Routing Protocol for Tunnel
Wireless Sensor Network in Underground Mine. 2009 International Conference on Wireless
Communications & Signal Processing. November 13-15, 2009. Nanjing, China: IEEE. 2009. 1-5.
[3] Adel Ali Ahmed, L. A. Latiff and Norsheila Fisal. Simulation-Based Realtime Routing Protocol
With Load Distribution In Wireless Sensor Networks. Wireless Communications and Mobile
Computing. 10 (7). 1002-1016.
[4] Yu-Pin Hsu and Kai-Ten Feng. Cross-Layer Routing For Congestion Control in Wireless Sensor
Networks. 2008 IEEE Radio and Wireless Symposium. January 22-24, 2008. Orlando, Florida.
IEEE Xplore. 2008. 783-786.
[5] Jheng-Da Jhong, Chi-Yuang Chang and Han-Chieh Chao. Cross-Layer Ant Based Algorithm
Routing for MANETs. International Conference On Mobile Technology, Applications, and
Systems. September 10-12, 2008. Yilan, Taiwan. 2008.
[6] N. Chilamkurti, S. Zeadally, A. Vasilakos and V. Sharma1. Cross-Layer Support for Energy
Efficient Routing in Wireless Sensor Networks. Journal of Sensors, Hindawi Publishing
Corporation, 2009. 9.
[7] Adel Ali Ahmed, Liza A. Latiff and Norsheila Fisal. Real-Time Routing Protocol With Load
Distribution In Wireless Sensor Network Based On IEEE 802.11 and IEEE 802.15.4. Universiti
Teknologi Malaysia. Jurnal Teknologi. December 2007. 47 (D): 71-90.
[8] Crossbow Tech. Avoiding RF interference between WiFi and Zigbee. Crossbow Technical Report.
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[9] Chipcon. CC2420 low power radio transceiver. http://www.chipcon.com. 2010.
Authors
Mohd Muzaffar Zahar received his B.Sc. in Electrical Engineering (Mechatronics) from
Universiti Teknologi Malaysia, Skudai, Malaysia in 2008 and graduated in same university
with a Master in Electrical Engineering in 2013 under the supervision of Dr. Sharifah
Hafizah Syed Ariffin. He is now a lecturer at the Industrial Training Institute of Marang.
Sharifah Hafizah Syed Ariffin received her BEng in Electronic and Communication
Engineering from London Metropolitan University, London, UK and her MEE by research
(mobility management in wireless telecommunication) from University Technology
Malaysia (UTM), Malaysia, in 1997 and 2001, respectively. She obtained her PhD in
Telecommunication (accelerated simulation) from Queen Mary University, London, UK, in
2006. She is a Associate Professor at UTM and her research interests include network modelling and
performance, accelerated simulation, self similar traffic and priority scheduling.

133
Liza Abdul Latiff graduated from South Dakota State University, USA with a BSc in
Electrical Engineering in 1985 and from UTM with a MEE in Data Communication by
research (Enhancement to Menu Selection Facility in Videotext) in 1988. She obtained her
PhD in Electrical Engineering (Wireless Communication) from UTM in 2007. She is an
Associate Professor in UTM since 1995 and her research interest include routing in
wireless network, resource management and scheduling and also quality of Service and security in
MANET and WSN.
Norsheila Fisal received her BSc in Electronic Communication from University of Salford,
Manchester, UK in 1984, and her MSc in Telecommunication Technology and her PhD in
Data Communication from University of Aston, Birmingham, UK in 1986 and 1993,
respectively. Currently, she is a Professor in the Faculty of Electrical Engineering,
University Technology Malaysia and the Director of Telematic Research Group.
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MANET and WSN.
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MANET and WSN.

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