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101
Network Performance Optimization Using Odd and Even
Dual Interleaving Routing Algorithm For Oil and Gas
Pipeline Network
M. Y. Lee1, A. S. Azman2, S.K. Subramaniam3,*, F. S. Feroz4
1,2Fakulti Kejuruteraan Elektonik dan Kejuruteraan Komputer, Universiti Teknikal Malaysia Melaka (UTeM), Durian
Tunggal, Melaka, 76100, Malaysia.
3Advance Sensors & Embedded Controls System (ASECS), Centre for Telecommunication Research & Innovation
(CeTRI), Fakulti Kejuruteraan Elektonik dan Kejuruteraan Komputer, Universiti Teknikal Malaysia Melaka (UTeM),
Durian Tunggal, Melaka, Malaysia.
4Centre of Advanced Computing Technology (C-ACT), Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer,
Universiti Teknikal Malaysia Melaka (UTeM), Malaysia.
*Corresponding author: siva@utem.edu.my
INTRODUCTION
The oil and gas sector is divided into three main parts, namely upstream, midstream, and
downstream, which are required to obtain commercial products as shown in Figure. 1. The process often
begins with industry discovery and field development in the upstream sector, where all crude oil
exploration and extraction are carried out. Using trucks, tanker vessels, or pipelines, raw materials are
transported to the next station through the midstream, which plays an active role in the storage of crude
A R T I C L E I N F O
A B S T R A C T
Article history
Received: August 2020
Revised : September 2021
Accepted: September 2021
The oil and gas industry is one of the world’s largest conglomerates,
involving the production of complicated and critical methods for
refining. This indicates the high necessity for a secure and reliable
system, such as the Wireless Sensor Network (WSN), which provides
auspicious and flexible solutions for the industry. It is one of the most
excellent and trendy solutions to the crisis existing within the oil and
gas industry, especially in the midstream pipeline. In this application,
the nodes were arranged in a linear architecture, to cover a long
distance of the pipe. The factors causing the degradation of the overall
network performance with increasing density were also identified, due
to the increment of the load causing clogging and inhabiting the packet
queue. This subsequently led to packet loss, throughput unfairness,
higher power consumption, and passive nodes’ presence in the
network. The proposed routing protocol (AODVEO) was also reactive
based on the AODV reducing the instabilities by splitting the traffic into
even and odd paths. Additionally, the performances of AODV and
DSDV were used to benchmark the efficacy of the proposed routing
protocol.
This is an open-access article under the CC–BY-SA license.
Copyright © 2022 the Authors
Keywords
Wireless sensor network
Linear
Oil and gas
Pipeline
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Network performance optimization… (Lee, et.al.)
102
oil before transmission to the downstream. In this sector, the transported materials are then refined and
commercialized. Various methods are also observed for the transportation of raw materials, such as
truck or ship utilization. However, pipeline transportation is found to be a cost-effective and more
pragmatic mode of transportation (Abbas et al., 2018; Muller, 2017). Despite being a popular
transportation choice, issues such as leakage, corrosion, and sabotage still occurred, leading to the
unexpected disasters responsible for the destruction of the economy, workers, and nature.
On January 10, 2018, The Star reported that Petronas, the only administrator of Malaysia’s oil
reserves and third-largest exporter of global liquefied natural gas after Qatar and Australia, confirmed a
leakage at the Long Luping section of the Sabah-Sarawak pipeline in Lawas. This showed that the 600
km oil and gas facility connecting Kimanis (Sabah) to Bintulu (Northern Sarawak) had a leakage at 1.45
am, which led to a devastating explosion (“Gas leak at Petronas Sabah-Sarawak Pipeline in Lawas The
Star Online,” 2018). Another explosion was also reported on January 13, 2020, serving as the fourth
occurrence to be recorded since June 11, 2014, along the same pipeline (“Another explosion along
Sarawak-Sabah interstate gas pipeline _ The Star,” 2020).
Figure 1. Overview of Upstream, Midstream, and Downstream.
On April 12, 2019, The Straits Time reported an explosion at Petronas oil and gas complex, where
two local workers and more than ten houses in Kampung Lepau were badly injured and damaged,
respectively (“Explosion at Petronas oil and gas complex in Johor injures two, damages houses, SE Asia
News & Top Stories - The Straits Times,” 2019). These reports proved that the remote pipeline integrity
monitoring system was essential to avoid any unforeseen disaster.
Introduction to Wireless Sensor Network
Wireless Sensor Network(WSN) has recently prevailed in the mobile tracking of pipeline health, due
to its usability and cost-effectiveness (Ali, S., Qaisar, S., Saeed, H., Khan, M., Naeem, M., & Anpalagan,
2015; Raza et al., 2018). This is a collection of sensors with the ability to sense, process, and
communicate, subsequently forming a network for monitoring the physical world (W. Z. Khan, Aalsalem,
Gharibi, & Arshad, 2017). It has also been implemented in both sensor-integrated ground and
underwater pipelines, to detect any form of unwanted irregularities (Abbas, Bakar, Ayaz, Mohamed, &
Tariq, 2017; Aldosari, Elfouly, Ammar, & Alsulami, 2020; Felemban, Shaikh, Qureshi, Sheikh, & Qaisar,
2015; Watt, Phillips, Campbell, Wells, & Hole, 2019). Furthermore, the network topology is one of the
techniques used to differentiate several types of sensors, due to being a node placement architecture.
This is divided into two main structures, namely linear and spread-out topologies. Based on covering
hundreds/thousands of kilometres, the nodes of the midstream pipeline are often positioned in the linear
topology (“Malaysia Oil and Gas Midstream Market | Growth, Trends, and Forecasts (2020 - 2025),”
2016). Subsequently, the classification of the networks is separated into a one-tier flat and multi-tier
hierarchical topology, as shown in Figs. 2 and 3, respectively.
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Figure 2. Flat one-tier topology.
Hierarchical topology often involves the communication of a cluster head and the nodes, with the
generated information being forwarded to a higher level, as shown in Figure 3.
Figure 3. Hierarchical topology.
Wireless standards also significantly affect network efficiency or result, depending on the utilized
application. In a real linear topology scale, the two criteria with implementational capabilities are as follows, (1) the
IEEE 802.11, which contains the individual modules used to communicate with the wireless transmission
serving a stationary or mobile terminal collection (S. M. Khan, Nilavalan, & Sallama, 2015), and (2) the
IEEE 802.15.04, which is known as a low-rate private area wireless network. Based on this condition,
the IEEE 802.11 was selected due to the tremendous data rate, compared to the IEEE 802.15.04. A
brief comparison is subsequently displayed in Table 1.
Challenges and Limitations of Conventional Routing Protocols
Based on the preliminary stage of the study, several analyses were carried out on the existing
conventional protocols, to identify their challenges and limitations. In this condition, multi-hop was used
as the data transmission technique, where the sender node transferred the information to the receiver
(Yao, Cao, Vasilakos, & Member, 2014). However, some problems were observed as the number of
nodes increased, such as (1) energy consumption, (2) communication reliability, (3) network scalability,
(4) robustness, and (5) security. According to A. Khan et al. (2019), the lifetime of a network was a
crucial factor affecting restricted power supply in linear WSN topology, where a massive amount of
energy was needed for data transmission in a large-scale system. Although the idea of preparing backup
power was applicable, it was still unsuitable in underground or underwater nodes. In the oil and gas field,
a secure contact network is highly needed, as the nodes are expected to obtain and transmit the data
or signal to the destination when an anomaly is observed. This needs to be carried out within a specified
amount of time, as failure often leads to a catastrophic accident. Subsequently, these data were crucial
for monitoring the pipeline’s health, to avoid extra costs.
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Table 1. Comparison of IEEE 802.11 and IEEE802.15
Parameter Wifi ZigBee WirelessHart Z-Wave
IEEE standard 802.11a/b/g/n/ac 802.15.04 802.15.04 802.15.04
Operational
frequency
5GHz(A,ac)/2.4GHz(b,g)/2.4GHz-
5GHz(n)
2.4GHz 2.4GHz 2.4GHz
Nodes per
master
2007 >65000 500 232
Range 100meters(a,b,g)/250meters(n) 1600 meters 250 meters 100 meters
Date rate 11Mbps(b)/54Mbps(a,g)/450Mbps(a
,c)600Mbps(n)
250Kbps 250Kbps 250Kbps
Battery
Life/Cost
Days-weeks/High Months-
years/Low
Months-
years/Low
Months-
years/Low
The scalability of a network often leads to the achievement of a stable performance without being
affected by the nodes. This explains that when the network expands, the number of nodes being deployed
is increased. In this condition, more data and traffic are then generated and created, respectively.
However, the network and its performance become overcrowded and degraded. This confirms that the
scalability of the system is affected by the following, (1) network capacity, (2) queue threshold, and (3)
range of source node to the destination. Based on robustness and security, the flexibility level of the
network is determined by the management of massive data, intrusion, or malicious attack volumes,
respectively. Since the nodes are implemented to decrease human interference, unauthorized
personnel are likely to attack the system towards the obstruction of data collection, by triggering a false
alarm or manipulating the packets (W. Z. Khan, Hossain, Aalsalem, Saad, & Atiquzzaman, 2016).
Additionally, a routing protocol is deemed decent based on the adaptability and delivery of optimum
performance within the network.
Related Work
At the beginning of the studies, several performance issues were mostly observed due to the
increasing number of nodes. In this condition, the deprivation of delivery ratio, throughput, and the high
energy consumption are reflected in the network layer (routing layer) performances. This revealed that
many experts were attracted to routing, to improve the overall network performance. It. is also known
as a high-level decision-making mechanism, where information is transferred from the source to
destination nodes through an inter-network containing one or more transitional structures
(Radhakrishnan et al., 2016). Moreover, the efficiency of routing protocols is typically calculated from
the link reliability perspective among the nodes, disconnection, and restoration of connections, which is
an essential operation where approximately all data packets are likely to be missed. In this case, the three
most popular protocols are reactive, proactive, and hybrid systems. The reactive routing protocols use
an on-demand approach for discovering paths (Kaur & Singh Kahlon, 2014), indicating that the routes
are dynamically changing based on the present network conditions (Mohammed, 2019). When the
network’s status is not continuously monitored or updated, the flooded messages and route tables are
minimized (Goswami S, Joardar S, Das C B, Kar S, 2017). However, the path discovery process is found
to continuously occur, causing more time to establish the connection, which leads to increased end-to-
end delay. The Ad-hoc On-Demand Distance Vector (AODV) is also an example of a reactive routing
protocol, which uses RREQ (route request), RREP (route reply), and RRER (route error) for route
management (Govindasamy & Punniakody, 2018; Xin & Yang, 2015). In this condition, RREQ and
RREP are used as broadcast and acknowledgement packets, respectively. Meanwhile, RERR is
transferred to the source node during the link interruption. The verifies the systematic restart of the route
detection process when some data are still observed for transmission (Govindasamy & Punniakody,
2018).
According to proactive routing protocols (table-driven protocols), the update of the table was
regularly carried out, with information such as the hop, sequence numbers, hop figure, and destination,
being made permanent by occasionally transmitting control messages between all network nodes
(Hamid & El Mokhtar, 2016; Pandey, Raina, & Rao, 2015). These have a more rapid route establishment,
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subsequently verifying consistent path availability. Since the routes are continuously updated, the will delay is
outrightly minimized, with network traffic being more constant (Mohammed, 2019). On the downside,
the network is flooded with routing information (control packets and routing overhead), as congestion is
observed due to the frequent table updates consuming all the systematic resources. Moreover, the
DSDV (Destination Sequence Distance Vector) is an example of a proactive routing protocol, where
each node is needed to transfer a sequence number that is periodically increased and transmitted to all
neighbouring structures with other updates (Chavan, Kurule, & Dere, 2016; Singh & Verma, 2015). The
hybrid routing protocol is a combination of proactive and reactive systems, due to their utilization
advantages. This obtains correct path information to determine the optimum direction of the target node,
by updating routing information when needed. It is also generally known as the combination of DSDV and
Link State Routing (LSR), optimized for rapid integration with lower power and memory consumption.
Therefore, network traffic should be denser regarding the number of nodes, irrespective of the routing
protocol selected for implementation. This relationship occurred due to the increment of both control and
data packets congesting the traffic of the system. All the nodes were also considered as sources in real-
life deployment, with data being simultaneously transmitted. From each node, the sum of transmitted data
is shown in Equation. (1).
NP=(CPi+DPi )+ ∑ (CPj+DPj)≤ IfQlen (1)
j=i+1
Where,
x = n − (2)
Where NP = the total packets of the network constricted by the IfQ length limit, n = the number of
nodes, CPi and DPi = the sum of control and data packets for node i, respectively, with the condition 1 ≤ i
≤ x, as well as CPj and DPj = the control and data packets for the adjacent nodes j, respectively. Based
on Eq. (1), the generated total packets increased with higher overall node quantities, leading to several
network performance issues. This led to the proposition of the Ad hoc On-Demand Distance Vector
Even and Odd (AODVEO) routing protocol.
Ad Hoc On-Demand Distance Vector Even And Odd (AODVEO)
The AODVEO routing algorithm was mostly developed based on the AODV system. This was not in
line with the conventional AODV algorithm, which determines its path by selecting the shortest and
freshest route (van Glabbeek, Höfner, Portmann, & Tan, 2016). The AODVEO system also establishes
its path based on Even and Odd paths, as shown in Fig. 4. Furthermore, it is designed to deliver improved
results regarding the overall network performance for linear topology, compared to the conventional
routing algorithm. Different from the standard practice, AODVEO also separates the route into Even and
Odd traffic (Figure. 4), for the reduction of congestion. When an odd/even node transfers the RREQ to its
surroundings, only the compatible systems are eligible for acceptance and continuous transmission.
This process is then prolonged until the RREQ is dropped when arriving at its destination node. Once
dropped, the destination node transmits RREP in a reverse direction, where collection at the source
structure leads to the transmission of the data packet through the established route.
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Figure 4. AODVEO routing algorithm
Figure 5 shows the forward transmission of the RREQ packet to the neighbouring nodes, E2 and
O2, through the odd-numbered source (On). Since E2 is an even-numbered node, the transmitted
packet was rejected and dropped. Meanwhile, the packet was acknowledged in O2 and transmitted to
the subsequent hop, due to the oddness of the node. This process was continuously conducted until
RREQ reached ND, with the packet interchange procedure subsequently carried out by discarding
RREQ and forwarding RREP in the opposite direction to the source.
Figure 5. AODVEO Even and Odd path.
A queue limit is a simple mechanism for controlling the bidirectional packet movement within every
network node. This is observed in the AODVEO system, although the dual-path (Even and Odd)
approach lessens the routing overhead by half, subsequently ensuring better network traffic. In the
system, the total packets accumulated for Even and Odd nodes are shown in Eqs. (3) and (4),
respectively.
x
NPE=(CPEi+DPEi)+ ∑ (CPEj+DPEj)≤ IfQlen (3)
j=i+1
NPE = the even packet queues for the x nodes in the network, CPEi and DPEi = the sum of overall
control and data packets for node i, where 1 ≤ i ≤ x, as well as CPEj and DPEj = the control and data
packets for the neighbouring nodes j, where i ≤ j ≤ N.
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x
NPO=(CPOi+DPOi)+ ∑ (CPOj+DPOj)≤ IfQlen (4)
j=i+1
NPO = the odd packet queues for x nodes in the network, CPOi and DPOi = the sum of overall
control and data packets for node i, with the restriction of 1≤ i ≤ x, as well as CPOj and DPOj = the control
and data packets for the neighbouring nodes j, where i ≤ j ≤ x. Furthermore, the accepted data at the
destination node is shown in Eq. (5), where the packages (data and control packets) arriving with a
queue length more than IfQlen were oddly and evenly dropped from the network.
TNP = NPE + NPO (5)
TNP = the packets accumulated in the network, as well as NPE and NPO = the total numbers of
packets available in the even and odd traffics (Eqs. 3 and 4). In this condition, the limitation of IfQlen
was found to bound the equations, with simulations carried out using the proposed AODVEO, AODV,
and DSDV routing protocols. This was conducted in a specified environment and condition, to validate
the results of splitting the traffic into two paths.
RESEARCH METHOD
The simulation was conducted using AODV, DSDV, and AODVEO, through the Network Simulator
2.35. In this condition, only the best five of the seven runs (seven seeds) were selected and averaged,
with the spacing (d) and time (t) being 50 m and 500 s, respectively. Moreover, the transport agent and
traffic type applied were the Transmission Control Protocol (TCP) and Constant Bit Rate (CBR),
respectively. The size of the executed packet was also 512 bytes, with the transfer rate being two
packets/secs, as shown in Table 2.
Table 2. Simulation parameters.
Parameters Value
MAC IEEE 802.111
Routing protocols AODV, DSDV, AODVEO
Topology Linear
Number of nodes 20,40,60,80,100,120,140,160,180,200
Packet size 512 bytes
Seed 1-20
Interface queue type Drop tail
Packet queue length 50 packet
Propagation mode Two ray ground
Simulation time 500 seconds
RESULTS AND DISCUSSION
Delivery Ratio
This is the correlation between the successfully obtained and total transferred packets, due to being
an important performance measure for the reliability of a specific network. Since most implementations
were data-critical in the oil and gas sector, all the lost information values were found to be highly
enormous to the industry. In this condition, the lower delivery ratio indicated more network packet loss.
Based on Fig. 6, the packet distribution ratio decreases with an increase in the network size, clarifying
that the delivery ratios of AODV and AODVEO were almost identical at a smaller scale of 20-40 nodes,
with DSDV being slightly higher. In the deployment of 80 nodes, the AODVEO routing protocol
significantly surpassed AODV and DSDV by 15 and 11%, respectively. This revealed that the proposed
protocol (AODVEO) was more efficient in preserving the packet transferred to the target node, compared
to AODV and DSDV. Additionally, the packet queue was minimized by separating the traffic into two
distinct routes, leading to the elimination and accomplishment of congestion and more data flow,
respectively.
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Figure 6. Delivery ratio (%) vs the number of nodes.
Throughput
This is defined by the rate of the received data (from the packet) transferred from the source to the
destination nodes (kbps) within the network. Based on a consumer’s perspective, throughput is more
important when highly compared to the delivery ratio within the available resources, indicating a more
significant system capacity. However, the delivery ratio is more critical from the designer’s perspective,
due to determining the problems causing low network throughput. In this report, AODVEO outperformed
both AODV and DSDV from a small to large-scale network size of 20-200 nodes, regarding the analysis
of throughput (Fig. 7). This showed that AODVEO highly delivered 8.82 and 12.17 kpbs at 20 and 200
nodes, respectively, compared to DSDV. In Fig. 6, the throughput trend was also a reflection of the
packet delivery ratio (Fig. 5), where the source node attempted to re-transmit the data based on the loss
of information. This led to a lower distribution of delivery ratio and successfully received data, with the
throughput subsequently affected severely.
Figure 7. Throughput vs the number of nodes.
Ehnergy Consumption
Energy consumption is measured in Joule (J) and described as the overall network power utilized
over the total received packet. This is because energy management is an essential wireless parameter in
linear topology, with a communication connection discontinuity being created by a single node failure.
Since more packets were being delivered, the power consumption closer to destination nodes was often
higher, causing the congestion of the traffic line. However, the congestion is likely to drop when a
package is produced by or crosses through the nodal area. In this condition, issues such as energy
waste were generated due to packet regeneration and hopping. Based on Fig. 8, the energy expenditure
is also increased with the elevating value of the nodes. This proved that the DSDV and AODVEO
networks consumed the highest and lowest amount of available energy, respectively. Despite having
higher throughput values, the AODVEO routing protocol still outperformed AODV and DSDV by 0.00398
and 0.00536 J, respectively, at a small network size of 20 nodes. Meanwhile, it used less energy of
0.0377 and 0.0953 J for AODV and DSDV at a larger network size of 200 nodes, respectively.
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Figure 8. Energy consumption vs the number of nodes.
Passive Nodes
These nodes are unable to transmit data to the target network area, due to the unnecessary or
unevenly allocated bandwidths within the system. They also occur mostly in high traffic networks with a
constrained energy source. Moreover, the passive nodes cause a breakdown of communication, which
affects the network’s lifetime. Based on Fig. 9, the occurrence of these elements in AODVEO and DSDV is
found at the deployment of 80 nodes, with subsequent observation confirmed at 60 for AODV. In a large-scale
network of 200 nodes, the total number of these elements in AODVEO, AODV, and DSDV was 59.8, 72.6,
and 68.9%, respectively. Although AODVEO had a higher value of throughput, its passive nodes were
still lower than AODV and DSDV.
Figure 9. Passive nodes vs the number of nodes.
Fairness Index
This is the network-wide measure of resource equality allocation, where the closeness to 1 leads to
a better outcome over the network. In linear WSN, network imbalances are an important factor with any
protocol, as AODVEO outperformed AODV and DSDV by 0.06 and 0.05 at the deployment of 20 nodes,
respectively (Fig. 10). Despite this, the fairness index for AODVEO was still below 0.5 at a 40 node
deployment. This subsequently became worse with the continuous elevation of the nodes being
deployed. Based on Fig. 10, the graphical numbers indicated that the issues surrounding network
fairness were yet to be completely resolved since the resources allocated were far from being equally
distributed.
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Figure 10. Fairness index vs the number of nodesCONCLUSION
The rice planting and harvesting process is at risk of falling into the level 4 category in terms of
ergonomics. According to the RULA (Rapid Upper Limb Assessment) method analysis, the high
category with recommendations for an investigation and change immediately. Gender, BMI, length of
employment, and tenure have no bearing on MSD levels. The waist and the neck have a percentage of
MSDs of 98 % and 95%, respectively. These were the body parts subjected to a high level of ergonomic
risk. Traditional agriculture workers were advised to improve work procedures and tools before the
situation worsened to reduce long-term risks.Several factors, including job demands, socio-cultural
factors, workplace characteristics, and environmental factors, cause or exacerbate work-related
disorders, according to WHO (1985). Otherwise, musculoskeletal problems such as awkward posture,
prolonged standing, kneeling, slouching, and repetitive muscle activity occur in most cases of
agricultural work due to the physical demands on the body. Fatigue, illness, and accidents will inevitably
result from this posture. Workers' lack of knowledge of agricultural health and safety puts them in the
most dangerous situations. This study included agricultural activities in the occupational group with the
highest risk of musculoskeletal disorders (MSDs). When combined with tool design and related
educational interventions, these ergonomic considerations effectively prevent MSD problems. The
study's conclusion emphasizes the importance of ergonomic hand tool design as a form of intervention.
CONCLUSIONS
Many interrelated factors were found to affect the overall network performance in a pipeline network,
as reactive and provocative routing protocols (AODV and DSDV) were simulated in the early analytical
stages, with the system efficiency subsequently reviewed. This proved that numerous network
performance issues were identified with a continuous increase in system size. Based on the results, the
proposed reactive routing protocol, AODVEO, was found to be very reliable and efficient. This improved
the overall network performance of a wireless sensor network with linear topology. In the most extensive
configuration (200 nodes), AODVEO routed the network to produce more throughput and delivery ratio,
as well as less energy and passive nodes at 8.19kbps, 7.546%, 0.03772J, and 12.8%, respectively.
However, a negligible development of the fairness index was observed, where the IP (index point) was
found to be below par (0.5). In Figure. 10, the reflected values also indicated that the resource was not
yet distributed equally through the network. Therefore, more studies need to be conducted in refining
the issues of fairness, especially for the large-scale network.
ACKNOWLEDGMENTS
This study is part of the project analysis and development of a static multi-hop linear routing algorithm
for the oil and gas pipeline, using an 802.11 wireless sensor network. The authors are grateful to the
Ministry of Higher Education, Malaysia and the University Teknikal Malaysia Melaka, for their support,
laboratory facilities, and sincere encouragement.
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REFERENCES
Abbas, M. Z., Abu Baker, K., Ayaz, M., Mohamed, H., Tariq, M., Ahmed, A., & Faheem, M. (2018). Key
factors involved in pipeline monitoring techniques using robots and WSNs: Comprehensive
survey. Journal of Pipeline Systems Engineering and Practice, 9(2), 1–15.
https://doi.org/10.1061/(ASCE)PS.1949-1204.0000305
Abbas, M. Z., Bakar, K. A., Ayaz, M., Mohamed, M. H., & Tariq, M. (2017). Hop-by-Hop dynamic
addressing based routing protocol for monitoring of long range underwater pipeline. KSII
Transactions on Internet and Information Systems, 11(2), 731–763.
https://doi.org/10.3837/tiis.2017.02.007
Aldosari, H., Elfouly, R., Ammar, R., & Alsulami, M. (2020). New Monitoring Architectures for underwater
oil/Gas Pipeline using Hyper sensors (Vol. 69, pp. 307–296). https://doi.org/10.29007/c84d
Ali, S., Qaisar, S., Saeed, H., Khan, M., Naeem, M., & Anpalagan, A. (2015). Network Challenges for
Cyber Physical Systems with Tiny Wireless Devices: A Case Study on Reliable Pipeline
Condition Monitoring. https://doi.org/10.3390/s150407172
Another explosion occurs along Sarawak-Sabah interstate gas pipeline _ The Star. (2020, January).
Retrieved from https://www.thestar.com.my/news/nation/2020/01/13/another- explosion-occurs-
along-sarawak-sabah-interstate-gas-pipeline
Chavan, A. A., Kurule, D. S., & Dere, P. U. (2016). Performance Analysis of AODV and DSDV Routing
Protocol in MANET and Modifications in AODV against Black Hole Attack. Procedia Computer
Science, 79, 835–844. https://doi.org/10.1016/j.procs.2016.03.108
Explosion at Petronas oil and gas complex in Johor injures two, damages houses, SE Asia News & Top
Stories - The Straits Times. (2019, April 12). Retrieved from
https://www.straitstimes.com/asia/se-asia/explosion-at-petronas-oil-and-gas-facility-in-
pengerang
Felemban, E., Shaikh, F. K., Qureshi, U. M., Sheikh, A. A., & Qaisar, S. Bin. (2015). Underwater Sensor
Network Applications: A Comprehensive Survey. International Journal of Distributed Sensor
Networks, 2015. https://doi.org/10.1155/2015/896832
Gas leak at Petronas Sabah-Sarawak Pipeline in Lawas The Star Online. (2018, January).
Retrieved from https://www.thestar.com.my/news/nation/2018/01/10/gas-leak-at-petronas-sabah-
sarawak-pipeline-in-lawas
Goswami S, Joardar S, Das C B, Kar S, P. K. D. (2017). Performance Analysis of Three Routing
Protocols in MANET Using NS-2 and ANOVA test with Varying Speed of Nodes. Intech,
i(tourism), 13. https://doi.org/10.5772/66521
Govindasamy, J., & Punniakody, S. (2018). A comparative study of reactive, proactive and hybrid
routing protocol in wireless sensor network under wormhole attack. Journal of Electrical Systems
and Information Technology, 5(3), 735–744. https://doi.org/10.1016/j.jesit.2017.02.002
Hamid, B., & El Mokhtar, E. N. (2016). Performance analysis of the Vehicular Ad hoc Networks (VANET)
routing protocols AODV, DSDV and OLSR. 2015 5th International Conference on Information
and Communication Technology and Accessibility, ICTA 2015.
https://doi.org/10.1109/ICTA.2015.7426885
Kaur, N., & Singh Kahlon, M. (2014). A Review on Reactive and Proactive Wireless Sensor Networks
Protocols. International Journal of Computer Applications, 95(11), 22–29.
https://doi.org/10.5120/16639-6603
Khan, A., Imran, M., Noreen, M., Tariq, M., Shoaib, M., & Subhan, F. (2019). Impact of Node
Deployment and Routing for Protection of Critical Infrastructures. IEEE Access, 7, 1–1.
https://doi.org/10.1109/access.2019.2891667
Khan, S. M., Nilavalan, R., & Sallama, A. F. (2015). A Novel Approach for Reliable Route Discovery in
Mobile Ad-Hoc Network. Wireless Personal Communications, 83(2), 1519– 1529.
https://doi.org/10.1007/s11277-015-2461-8
Khan, W. Z., Aalsalem, M. Y., Gharibi, W., & Arshad, Q. (2017). Oil and Gas monitoring using Wireless
Sensor Networks: Requirements, issues and challenges. Proceeding - 2016 International
https://doi.org/10.3837/tiis.2017.02.007
http://www.thestar.com.my/news/nation/2020/01/13/another-
http://www.straitstimes.com/asia/se-asia/explosion-at-petronas-oil-and-gas-facility-in-
http://www.thestar.com.my/news/nation/2018/01/10/gas-leak-at-petronas-
SPEKTRUM INDUSTRI Vol. 20 No. 1 April 2022 pp. 101-112
Network performance optimization… (Lee, et.al.)
112
Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications, ICRAMET
2016, 31–35. https://doi.org/10.1109/ICRAMET.2016.7849577
Khan, W. Z., Hossain, M. S., Aalsalem, M. Y., Saad, N. M., & Atiquzzaman, M. (2016). A cost analysis
framework for claimer reporter witness based clone detection schemes in WSNs. Journal of
Network and Computer Applications, 63, 68–85. https://doi.org/10.1016/j.jnca.2016.01.014
Malaysia Oil and Gas Midstream Market| Growth, Trends, and Forecasts (2020 - 2025). (2016).
Retrieved May 25, 2020, from https://www.mordorintelligence.com/industry-reports/malaysia-oil-
and-gas-midstream-market
Mohammed, I. Y. (2019). Comparative analysis of proactive & reactive protocols for cluster based
routing algorithms in WSNs, 124(March), 131–142.
Muller, N. (2017). Petroleum Products By Pipelines and Rail :Evidence From Shipment of Crude Oil
From North Dakota.
Pandey, K., Raina, S. K., & Rao, R. S. (2015). Performance analysis of routing protocols for vehicular
adhoc networks using NS2/SUMO. Souvenir of the 2015 IEEE International Advance Computing
Conference, IACC 2015, 844–848. https://doi.org/10.1109/IADCC.2015.7154825
Radhakrishnan, R., Edmonson, W. W., Afghah, F., Rodriguez-Osorio, R. M., Pinto, F., & Burleigh, S.
C. (2016). Survey of Inter-Satellite Communication for Small Satellite Systems: Physical Layer
to Network Layer View. IEEE Communications Surveys and Tutorials, 18(4), 2442–2473.
https://doi.org/10.1109/COMST.2016.2564990
Raza, M., Aslam, N., Le-Minh, H., Hussain, S., Cao, Y., & Khan, N. M. (2018). A Critical Analysis of
Research Potential, Challenges, and Future Directives in Industrial Wireless Sensor Networks.
IEEE Communications Surveys and Tutorials, 20(1), 39–95.
https://doi.org/10.1109/COMST.2017.2759725
Singh, K., & Verma, A. K. (2015). Experimental analysis of AODV, DSDV and OLSR routing protocol
for flying adhoc networks (FANETs). Proceedings of 2015 IEEE International Conference on
Electrical, Computer and Communication Technologies, ICECCT 2015, 1– 4.
https://doi.org/10.1109/ICECCT.2015.7226085
van Glabbeek, R., Höfner, P., Portmann, M., & Tan, W. L. (2016). Modelling and verifying the AODV
routing protocol. Distributed Computing, 29(4), 279–315. https://doi.org/10.1007/s00446-015-
0262-7
Watt, A. J., Phillips, M. R., Campbell, C. E. A., Wells, I., & Hole, S. (2019). Wireless Sensor Networks
for monitoring underwater sediment transport. Science of the Total Environment, 667, 160–165.
https://doi.org/10.1016/j.scitotenv.2019.02.369
Xin, H. M., & Yang, K. (2015). Routing protocols analysis for internet of things. Proceedings - 2015 2nd
International Conference on Information Science and Control Engineering, ICISCE 2015, (i),
447–450. https://doi.org/10.1109/ICISCE.2015.104
Yao, Y., Cao, Q., Vasilakos, A. V, & Member, S. (2014). Lifetime-Balancing Data Collection Protocol for
Heterogeneous Wireless Sensor Networks. IEEE Transaction on Networking, 23(3), 1–14.
https://doi.org/10.1109/TNET.2014.2306592
https://doi.org/10.1016/j.jnca.2016.01.014
http://www.mordorintelligence.com/industry-