785
1 INTRODUCTION
The Internet of Things (IoT) and data science
significantly impact the maritime industry. IoT
involves the interconnection of devices like sensors
and cameras that can gather and exchange data. In
contrast, data science utilises this data to make
informed decisions. IoT and data science are helping
the maritime industry improve safety, efficiency, and
sustainability [1], [2], [3].
One of the key areas where IoT and data science
are being used in the maritime industry is vessel
monitoring and tracking. With sensors and cameras
installed on ships, shipping companies can now
collect data on the ship's location, speed, fuel
consumption, and cargo. This data can then be
analysed using data science techniques to identify
patterns and trends, which can help to improve the
ship's overall performance. For example, shipping
companies can use this data to optimise their routes,
reduce fuel consumption, and avoid delays.
1.1 Role of domestic Ferry in Global and Indonesian
nationwide perspective
Ro-ro ferries are regarded as the world's most
successful maritime operation due to their service
reliability, carrying capacity, and operational
Domestic RoRo Ferry Safety Performance Level
Monitoring Based on Risk Assessment Model Using IoT:
A Literature Review and Application
A. Nurwahyudy, T. Pitana & S. Nugroho
Institut Teknologi Sepuluh Nopember (ITS) Surabaya, Indonesia
ABSTRACT: Domestic RoRo (roll-on/roll-off) ferry safety has been a growing concern for years due to its
continued casualty events, which have significant consequences. However, the development of the transport
mode from the perspective of safety performance is considered a slow process due to the nature of its operation
and less stakeholder concern. One of the significant issues, among others, lies in monitoring the safety level of
the service. This condition results in a lack of awareness in every aspect of operation and every mindset of
related parties. On the other hand, the Internet of Things (IoT) development has been significantly progressive,
covering nearly every aspect of the transport system. The progressive process has been followed by accessibility
and affordability of the technology so that every stakeholder can utilise it to the fullest. The paper explores the
possibility of IoT technology being included in improving the safety of domestic ferry operations by monitoring
the overall safety performance from the perspective of its risk status. The research maps the stakeholder's
position based on their function and current or future IoT system. As a risk assessment model, the F-N Curve is
used as the base concept for assessing the operation's safety performance and risk state condition. The research
identified the possibility of integration under the IoT scheme in dynamic risk assessment. The research also
recognises the significant strengths and challenges of integrating every available IoT system, which is
contributed by the system's openness.
http://www.transnav.eu
the International Journal
on Marine Navigation
and Safety of Sea Transportation
Volume 18
Number 4
December 2024
DOI: 10.12716/1001.18.04.04
786
flexibility. [4]. Stakeholders have considered ferry
transport a more affordable, timely service and
reliable mode of transporting passengers and goods
between islands. Its capability to provide cost-
effectiveness and support the operational efficiency of
other transport modes has also led to using ferry
transport to integrate islands and create efficient
routes to reduce distance and operation time [5].
Ferry operation has been utilised worldwide. For
developed countries, ferries are considered the safest
transportation form. Their safety record demonstrates
significant achievement. Domestic ferries have been a
crucial foundation for national economic activities in
developing countries. The most common ferry type
used in these regions is the RoPax ferry, which
typically offers space for passengers, vehicles, and
cargo simultaneously. [6].
For archipelagic nations, domestic ferries are
crucial for the efficient transport of many passengers.
The transportation modes bridge islands and road
transport networks and facilitate commercial
activities, which helps maintain national unity.
Technological advancements in RoRo passenger ferry
operations enable these ships to function as
connecting bridges. RoRo passenger ferries remain the
most cost-effective transportation option compared to
actual bridges. Consequently, their service must be
swift, dependable, structurally sound, and punctual
while ensuring adequate safety. [7].
As an archipelagic nation, Indonesia fully
recognises the crucial role of maritime transport in
fostering the country's development. Domestic RoPax
ferries are vital as a national asset to maintain
connectivity throughout the country. The current
system is designed to link significant islands, serving
as a transport hub for other modes of transportation.
Domestic RoRo ferries facilitate inter-island
connectivity and spur regional growth, aiding the
national development equality program. This
transport system bolsters logistics distribution and
promotes economic parity by offering affordable
transportation nationwide. [8].
1.2 Safety program for domestic ferry service
Despite its achievements, ferry operations carry
significant risks. The nature of these operations means
that ferry disasters can have catastrophic outcomes.
Incidents such as the Herald of Free Enterprise (UK,
1989), Estonia (Baltic Sea, 1992), Dona Paz
(Philippines, 1985), Al Salam Boccaccio (Red Sea,
2001), and Princess Ashika (Tonga, 2009) have
heightened public concerns about the safety of
domestic ferry operations in developing countries.
The international maritime community has also raised
alarms due to ongoing accidents involving domestic
ferries despite improvements in their operations. In
2006, the International Maritime Organisation (IMO)
and the international ferry operators' community,
Interferry, launched a pilot project to provide
technical assistance to enhance the safety of domestic
ferry operations in developing countries. This project
occurred in Bangladesh, a country known for its
severe ferry accidents.[4], [9].
Key findings from investigations into ferry-related
incidents have been shared with all stakeholders in
ferry operations to raise safety awareness and serve as
a reference for enhancing shipboard safety. Despite
these efforts, catastrophic accidents persist, as
demonstrated by the Sewol incident in South Korea in
early 2014. Even with safety improvements driven by
technological advancements, public interest, and
human involvement, there is always the potential for
errors that could result in a disastrous accident. In
other words, certain latent factors may be overlooked,
eventually accumulating and leading to a single
catastrophic event. [6], [10], [11].
Indonesia has developed an extensive safety
program to enhance the safety of the domestic ferry
service. The focus of development not only focuses on
vessel quality but also on considering upstream
factors such as implementing the safety management
system, reviewing, and developing regulations to
answer ongoing safety issues, and extending
regulatory oversight in even smaller populations.
Regarding vessel standards, the government had
appointed BKI as the recognised organisation to
conduct the statutory survey and certification of the
domestic ferry [12].
2 INTERNET OF THINGS TECHNOLOGY:
AFFORDABILITY AND ACCESSIBILITY ISSUE
2.1 Concept of IoT
While instances of interconnected electronic devices
can be traced back to the early 19th century, notably
with the invention of the telegraph that transmitted
coded signals over distances, the inception of the
Internet of Things (IoT) can be pinpointed to the late
1960s [13], [14], [15], [16]. During this period, a
notable group of researchers initiated efforts to
establish connections between computers and
systems. One significant example of their efforts was
ARPANET, a network created by the Advanced
Research Projects Agency (ARPA) of the US Defense
Department, which acted as a precursor to the
modern Internet. In the late 1970s, there was a
growing interest among businesses, governments, and
consumers in interconnecting personal computers
(PCs) and other machines. By the 1980s, local area
networks (LANs) emerged as an effective and widely
adopted means of real-time communication and
document sharing among groups of P.C.s. [17], [18],
[19].
In the mid-1990s, the Internet expanded its
capabilities on a global scale, prompting researchers
and technologists to investigate improved connections
between humans and machines. In 1997, Kevin
Ashton, a British technologist and co-founder of the
Auto-ID Center at MIT, initiated the exploration of a
technological framework known as radio-frequency
identification (RFID) [13]. In 1999, Kevin Ashton
introduced the "Internet of Things" concept,
envisioning a system where physical devices could
communicate through microchips and wireless
signals. Subsequently, technological advancements,
including smartphones, cloud computing, enhanced
processing power, and improved software algorithms,
laid the foundation for a more robust framework for
787
collecting, storing, processing, and sharing data.
Simultaneously, sophisticated sensors emerged,
capable of measuring various conditions such as
motion, temperature, moisture levels, wind direction,
sound, light, images, vibrations, and geolocation.
These developments enabled real-time
communication with both digital devices and physical
objects. The widespread adoption of mobile devices
and pervasive wireless connectivity further facilitated
the nearly ubiquitous connection of people and
things. Consequently, intelligent traffic networks,
connected storage tanks, and industrial robotics
systems became standard practices. [16].
In general, IoT is developed by adopting layers of
architecture. Some scholars mentioned three layers of
architecture: the application layer, network layer, and
perception/sensing layer. In comparison, five layers of
architecture were also introduced to provide more
details on filling the gaps between the application
layer and the perception layer. These include the
business layer and middleware layer [20].
The number of different aspects characterises the
IoT, so it would function perfectly and meet its needs
[20]. Srinadh (2021) described that IoT requires
interaction with other mediums or platforms
(interconnectivity). The IoT needs to be safe to use
and capable of handling the threat without
compromising the operation (safety), adapt to
different and dynamic conditions (heterogeneity),
capable of conducting large-scale computations
(enormous scale), be aware and adapt to dynamic
change, connect with a relevant network, and be
accessible by general users.
2.2 Relevant proven technology and ongoing IoT research
in the aspect of transportation system
The Internet of Things (IoT) is a system where
physical objects are linked together in a network that
allows them to gather and exchange data through the
Internet. These objects are typically embedded with
sensors, actuators, and other technology that enable
them to collect and transmit information, facilitating
their ability to interact with their surroundings and
execute tasks autonomously without human
involvement. [16], [21]. The main objective of the IoT
is to establish a smooth and intelligent network where
devices work together and exchange data to improve
efficiency, productivity, and convenience in different
areas of our everyday lives [16]. IoT applications can
be found in diverse fields, including home
automation, healthcare, agriculture, industrial
processes, transportation, and other aspects of life.
Refer to the critical components of the IoT include:
− Devices/Things: These are the physical objects or
devices embedded with sensors, actuators, and
other technologies to collect and transmit data.
− Connectivity: IoT relies on various communication
technologies such as Wi-Fi, Bluetooth, Zigbee,
cellular networks, and more to facilitate data
transfer between devices.
− Data Processing: Collected data is processed and
analysed to derive meaningful insights, often done
locally on the device or in the cloud.
− Cloud Computing: Cloud platforms are commonly
used to store and process large volumes of data
generated by IoT devices. Cloud computing allows
for scalable and efficient data storage and analysis.
− Applications: IoT applications leverage the data
generated by connected devices to provide
valuable services and functionalities, improving
efficiency and decision-making processes.
− Security: Given the interconnected nature of IoT
devices, security is a critical concern. Data privacy
and protection are essential to prevent
unauthorised access and potential misuse.
Numerous research studies on IoT utilisation have
been conducted and followed by promising
technological developments. The research explores
reputable journal index sources to acquire information
on the latest developments of IoT in transportation
systems. Following findings in the journal database,
IoT research has grown exponentially in the past 13
years.
Figure 1. IoT research subject development. Data extracted
and reconfigured from Scopus metadata source 2023.
Using bibliometric review, the keywords related to
IoT and its connection with other research focuses
found strong relationships between IoT and other
research subjects.
Figure 2. vos viewer results from SCOPUS metadata using
IoT, transportation, safety, and ferry keywords.
The research explored and reviewed over 30
journal papers on the IoT and related subject matters
with transportation safety. Additional journals have
also been reviewed to support IoT as a safety
performance monitoring model. The following tables
summarise and categorise the literature review into
several specific research topics.
788
Table 1. Recaps the literature review from different focus groups and research topics
___________________________________________________________________________________________________
Topic of research Focus of research Reference
___________________________________________________________________________________________________
IoT with transport service Integration of IoT to improve the quality of transportation service. [1], [22], [18], [17], [23],
safety, effectiveness, Some research proposes that IoT should be used to overview system [24], [25], [14], [26], [27],
and efficiency health by allocating sensor modules to monitor the machinery system [28], [29], [30] , [2], [30],
and other installed equipment. [15], [31], [32], [33]
IoT security system The IoT security guard is the primary concern in preventing and [21], [13], [34], [35],
mitigating any threat to the system.
IoT with the transport Specific research focuses on utilising the IoT to monitor safety [36], [37], [38], [39], [40],
safety system systems such as tourism, vehicle health, and inland waterway [41],
transport.
IoT with machine learning The coming expansion of machine learning and artificial intelligence [42], [43], [44], [45], [46]
and artificial intelligence aims to enhance and expand the quality of service in transportation.
for future transport
development
___________________________________________________________________________________________________
The use of the Internet of Things (IoT) in ferry
transportation safety is a growing area of research.
Gahalyan (2021) and Maulidi (2022) both propose IoT-
based systems for monitoring various aspects of ferry
operations, such as boat conditions, passenger
numbers, and ship movements [36], [37]. These
systems aim to improve safety by providing real-time
data to ferry operators and crew. Aslam (2020)
provides a broader perspective on the potential of IoT
in the maritime industry, including safety
enhancements and route optimisation [1]. Thakur
(2017) discusses the application of IoT in road safety
and traffic management, which could be adapted to
ferry transportation [22]. Collectively, these studies
highlight the potential of IoT in enhancing the safety
of ferry transportation services. Shostak et al. (2020)
explore IoT technology to ensure cargo transportation
safety by applying an integrated camera module on
the vehicle to record the driver's facial expressions
and transmit them to the company server for further
analysis [38]. Hapsari et al. adopt IoT technology to
monitor real-time fuel consumption performance [47].
The fuel level is monitored by a series of installed
ultrasonic sensors connecting to the microprocessor,
which is transmitted to the server to identify fuel
discharged and transferred. Kamolov and Park (2019)
used ultrasonic sensors integrated with a
microprocessor to help berthing operations [48].
Relevant to using IoT for timely monitoring a
system, Shah et al. (2023) developed a dynamic
Bayesian network model with the medical Internet of
Things to assess resilience in healthcare engineering
[49].
3 IOT INTEGRATION IS USED TO MONITOR
DOMESTIC FERRY SAFETY PERFORMANCE.
Safety performance monitoring can be applied to
determine the safety performance level of the ferry
transport system in a dynamic and timely manner.
IoT will play a significant role in integrating
information from relevant stakeholders. The research
identified 15 relevant stakeholders that significantly
contribute to maintaining safety in domestic ferry
service.
Ship Owner/
Management
DYNAMIC MONITORING FOR
SAFETY PERFORMANCE OF
DOMESTIC FERRY TRANSPORT
Risk Based Analysis:
R = f(s,c,p)
Regulator
SAR
Transport Customer
Classification Society
Recognize
Organisation
Navigation and
Traffic Management
Port Management
Shipyards/
Maintenance facility
LSA Service Station
providers
Cargo Owner
Expedition/Trucking
Local Government
Weather
Information Service
Hydrographic office
Private User
Cargo/Passenger
service Area
Local Fuel Bunker
Ticketing System Class Matter
Local Transport
feeder
Courtesy service
Casualty
Investigation
Seafarer Training/
Education Institution
Register
Law Maker
Enforcement
Local Port Service
Aids to Navigation
Service
Figure 3. List of stakeholders responsible for maintaining
safety in domestic ferry service.
After reviewing each stakeholder's current
operation state, IoT has been developed and used to a
certain extent, providing resourceful information.
Each stakeholder mainly used IoT as a supporting tool
to implement their task, responsibility, and
accountability system.
Table 6 in the appendix presents each stakeholder's
primary responsibilities and current or proposed IoT.
Some stakeholders do not reasonably adopt IoT in
specific applications, but a database system is
developed for any purpose within their business
environment transfer database data using
conventional connections such as email and direct
message platforms.
3.1 Concept of Monitoring Domestic Ferry Transport
Safety Performance
Dynamic monitoring refers to the real-time
monitoring system integrating relevant resources to
create a comprehensive display presentation on
relevant aspects of safety performance. Safety
performance level presents a state of service that
meets specific requirements in different levels of
safety. The level should indeed be an absolute
terminology and condition. However, the safety level
fluctuates and is not absolute.
The research proposed that the concept of safety
performance level is viewed as lowering risk level by
adopting significant risk mitigation and control
system action.
Risk is viewed as the combination of scenario,
probability, and consequence [50].
789
Risk = f {s, c, p}
The logical effort to reduce risk level is to put
every element of risk formulation as low as possible
(ALARP). A scenario in risk conception is defined as
an event that could happen within the system or
entities. This event condition contributes to different
sub-events in the risk cluster. Higher risk conditions
could be considered critical [50], [51], [52].
3.2 Proposed modules in the decision-making network for
the safe Domestic ferry operation using big data
cluster
The research proposes dynamic safety performance
monitoring by adopting the risk assessment concept.
A dynamic risk assessment is an ongoing safety
procedure that enables workers to promptly recognise
and evaluate risks and hazards in real-time, mitigate
them, and proceed with work securely. These
evaluations involve regular observation and analysis
of work environments with elevated risks or frequent
changes, requiring workers to make swift yet well-
thought-out decisions [49], [53].
The research suggests the safety performance level
is based on the risk rating assessment. Risk grading
can be presented in a 4x4 matrix defined in the IMO
formal safety assessment model [55].
Figure 4. Cluster of potential supporting analysis from
different stakeholders
The scenario of any potential mishap is extracted
from the IMO FSA model but is not limited to the
prescribed list. The FSA model identifies scenarios
stated in the accident type: collision, contact,
foundered, fire/explosion, hull damage, machinery
damage, war loss, grounding, other ship accidents,
other oil spills, and personal accidents.
The consequence factor is based on consequences
that affect human safety and ship or property.
The frequency factor is defined according to the
FSA formulation.
The FSA proposed a risk matrix, and the user
suggested using a log model to calculate risk levels.
Scoring is defined based on the log function as shown
in the matrix below:
Table 4. The risk matrix model as suggested by the FSA.
________________________________________________
FI Frequency Severity
1 2 3 4
Minor Significant Severe Catastrophic
________________________________________________
7 Frequent 8 9 10 11
6 7 8 9 10
5 Reasonably 6 7 8 9
probable
4 5 6 7 8
3 Remote 4 5 6 7
2 3 4 5 6
1 Extremely 2 3 4 5
Remote
________________________________________________
The matrix provides significant information on the
level of the risk. Higher log numbers in the matrix
present higher risk conditions, while smaller log
numbers present the opposites. From this point of
view, it is reasonable to propose risk criteria for
higher risk, acceptable risk, and lower risk.
The dynamic conception of safety level based on
the risk analysis model will continue to retrieve data
from different stakeholders based on each of their
functions. The data will be simultaneously retrieved,
analysed, and presented to indicate the risk level from
a time perspective. The scoring result will be dynamic
and varied according to the simultaneous data feed
from the relevant sources. The critical data would
mainly rely on the events' frequency and the major
event's consequences. Statistical approaches could be
used to determine the probability of the event as a
function of dynamic data feed from day-to-day
operations.
Table 2. Consequence index as suggested by FSA model of IMO
___________________________________________________________________________________________________
CI Consequence Effect on Human Safety Effect on Ship S (Equivalent fatalities)
___________________________________________________________________________________________________
1 Minor Single or Minor Injuries Local Equipment damage 0.01
2 Significant Multiple or severe injuries Non-severe ship damage 0.1
3 Severe Single fatality or multiple severe injuries Severe damage 1
4 Catastrophic Multiple fatalities Total Loss 10
___________________________________________________________________________________________________
Table 3. Frequency index as suggested by FSA model of IMO.
___________________________________________________________________________________________________
FI Frequency Definition F (per ship year)
___________________________________________________________________________________________________
7 Frequent It is possible to occur once per month on one ship 10
5 Reasonably probable It could occur once per year in a fleet of 10 ships. i.e. likely to occur a few times during 0.1
the ship's life
3 Remote It is likely to occur once per year in a fleet of 1000 ships. i.e. likely to occur in the total 10^-3
life of several similar ships
1 Extremely Remote It is likely to occur once in the lifetime (20 years) of a world fleet of 5000 ships 10^-5
___________________________________________________________________________________________________
790
4 DISCUSSION ON POSSIBLE APPLICATIONS
The IoT concept applies to overviewing safety level
performance based on the risk assessment model. The
risk level algorithm is developed to accommodate the
live data feed from different sources of IoT, as
presented in Figure 4. It is argued that the network
can hinder or obstruct system connection.
4.1 Dynamic risk evaluation model supported by IoT data
transfer network.
The research presents an example of integrating data
used as risk evaluation in domestic roro ferry
operations in Indonesia from 2007 to 2022. The
research examines 84 cases involving domestic roro
ferry, which resulted in a fatality. The example
focuses on evaluating the event's frequency to identify
the probability of causation. The societal risk analysis
model, as shown by the f-N Curve, model is used to
determine the dynamic level of risk. The curve is
developed based on the risk integral formula as
discussed by Pitblado et al. [56]:
=
a
ii
RI FN
where F is the outcome frequency of occurrence that
resulting in one or more fatalities, N is the number of
fatalities, and a is scale aversion factors. "a" is
considered one or no scale aversion for this research.
The directorate general of land transportation
provides the data on ship trips, as shown in Table 5
below. In contrast, the data on casualty is extracted
from the National Transportation Safety Committee,
the National Agency for Search and Rescue, and the
local harbour master offices.
Table 5. Domestic roro ferry casualty and production data
during 2007 – 2022 [57].
________________________________________________
Year Fatality No events Trip Frekuensi Risk
(N) per year Year per ship Rating
year (f)
________________________________________________
2007 50 2 388,964 5.14E-06 10-High Risk
2008 1 1 427,134 2.34E-06 3-Low Risk
2009 1 1 433,226 2.31E-06 3-Low Risk
2011 89 5 383,052 1.31E-05 10-High Risk
2012 8 4 482,041 8.30E-06 10-High Risk
2014 6 3 519,911 5.77E-06 10-High Risk
2016 7 3 405,050 7.41E-06 10-High Risk
2017 5 10 588,164 1.70E-05 10-High Risk
2018 42 15 558,896 2.69E-05 10-High Risk
2019 5 13 488,004 2.66E-05 10-High Risk
2021 25 20 465,542 4.30E-05 10-High Risk
2022 1 7 434,887 1.61E-05 10-High risk
________________________________________________
The risk rating is determined according to the
criteria set forth by the IMO FSA standard. The
number in the Risk Rating column suggests the risk
number that comes from the multiplication of severity
and frequency index in the respective year. Risk
Acceptance level (RAL) in this research adopts a
standard from the UK HSE, which is also considered
in the IMO FSA model. The UK HSE indicates RAL
for passengers is 10-3 and 10-4 for the crew [58].
The chart below presents the f-N curve for
domestic roro ferry service in Indonesia based on the
data above.
Figure 5. The F-N Curve for Domestic RoRo Ferry in
Indonesia water
Figure 5 is a critical reference for understanding
the current safety situation of the domestic RoRo
ferry. As the graph indicates, the state of ferry
operation lies within the intolerable zone. From this
condition, relevant stakeholders should bring
attention to the relevant authorities to take the
initiative to reduce the risk status to the acceptable
zone. The above data shows that high-risk conditions,
such as fire and capsize events, come with distinct
conditions. From this, further exploration can be done
by evaluating the contributing factors. The condition
above can dynamically be changed when all the
inputs of risk equations contribute to it.
For the IoT, an algorithm can be developed by
identifying the contributors behind the numbers. This
effort might require extra work in sorting and
classifying data from different sources, such as
investigation reports, maritime court verdicts, audit
results, and near-miss report analysis, but it can be
quickly done by developing proper algorithms. The
Figure 5 graph can be presented in an accessible
platform where relevant authorities can view and
assess the contributing factors.
From this example, it can be presented that the
integration of the system is the key. All the necessary
data must be simultaneously available and adequate
in quality and quantity. The data transmitted must be
verified and finalised. In addition, the data is also
sufficiently available for risk dynamic analysis. This
condition requires a robust and reliable network.
791
4.2 Strength and opportunity
The IoT concept applies to overviewing safety level
performance based on the risk assessment model. The
risk level algorithm is developed to accommodate the
live data feed from different sources of IoT, as
presented in Figure 4. It is argued that the connection
among systems can be hindered or obstructed by the
network.
The IoT technology has proved beneficial and
applicable to every system. Sensory modules
integrated with affordable and small microprocessors
will present excellent capabilities. The technology of
sensors is accessible and affordable. The sensor unit's
accuracy is improving even within the most
affordable unit. The critical point is the reliability of
the sensor unit. The sensor unit is a frontline that
senses the condition daily. The sensor unit can
provide wrong data when operating outside its design
conditions. This condition becomes critical since the
vessel might operate in different environments.
4.3 Challenge and weakness
Common weaknesses in IoT are primarily focused on
security issues and information accessibility.
Implementing sufficient security measures is a
significant issue facing the Internet of Things (IoT).
Hackers have successfully infiltrated video systems,
Internet-connected vehicle monitors, vessel health
sensor devices, and even automobiles. Furthermore,
the infiltrator has gained unauthorised access to
corporate networks by exploiting vulnerabilities in
unprotected IoT devices.
Srinadh (2021) identified the common
vulnerabilities in the IoT, which comprise insecure
web interface, insufficient
authentication/authorisation, insecure network
services, privacy concerns, lack of data encryptions
and verification, insecure cloud interface, insecure
mobile interface, insecure security configurations,
insecure software/firmware, and poor physical
security. Srinadh also identified weaknesses in each of
the architecture layers. One significant weakness in
the perception layer is malicious code injection, false
data tampering, and jamming. This finding is
reasonable since perception is the main gate to the
system. Once the attacker gets access, this could
severely threaten the system.
5 CONCLUSION REMARKS AND FUTURE
STUDIES.
The utility of IoT technology has expanded in nearly
every sector of transportation. Technology is
becoming affordable and accessible, so the
possibilities for applications are limitless. The IoT
technology has been proven to improve the system's
effectiveness and make it more efficient. The research
has identified potential IoT integration to monitor
safety performance levels based on risk assessment.
Each stakeholder is currently developing system
information that can be integrated into the risk
assessment algorithm. The dynamic of data is a
resource reference to monitor the risk level in
domestic ferry transport, which can be viewed from a
general perspective or per fleet or company situation.
From this perspective, the monitoring model can be
used as a reference for every stakeholder to mitigate
the upscaling risk conditions and maintain awareness
of the safety of the transport system. The stakeholder
might consider actions to avoid, reduce, or transfer
the risk effectively.
Despite the possible application of the model, the
challenge lies mainly in the willingness of each
stakeholder to open their data. Before that, not every
stakeholder considered expanding their IoT system
into something more accessible to other systems. This
challenge needs to be overcome by the central
regulator since they have more authority to enforce
the policy. From this point of view, the regulator
needs to present a solid will to develop the IoT
environment so other institutions can follow and
develop their IoT accordingly based on the
characteristics provided.
This challenging situation will create additional
research focus that future researchers can consider. A
super IoT app that can seamlessly integrate every IoT
in domestic transport services. Extensive development
of the sensor unit to make it more affordable and
accurate has become a critical point in developing the
IoT environment.
REFERENCES
[1] S. Aslam, M. P. Michaelides, and H. Herodotou,
“Internet of Ships: A Survey on Architectures, Emerging
Applications, and Challenges,” IEEE Internet Things J.,
vol. 7, pp. 9714–9727, 2020.
[2] A. A. Brincat, F. Pacifici, S. Martinaglia, and F. Mazzola,
“The internet of things for intelligent transportation
systems in real smart cities scenarios,” presented at the
2019 IEEE 5th World Forum on Internet of Things (WF-
IoT), IEEE, 2019. doi: 10.1109/wf-iot.2019.8767247.
[3] I. Durlik, T. Miller, D. Cembrowska-Lech, A.
Krzemińska, E. Złoczowska, and A. Nowak,
“Navigating the sea of data: A comprehensive review on
data analysis in maritime IoT applications,” Appl Sci
Basel, vol. 13, no. 17, p. 9742, 2023, doi:
10.3390/app13179742.
[4] I. IMO, “Philippines domestic ferry conference urges
action to improve safety record,”
https://www.imo.org/en/MediaCentre/PressBriefings/pa
ges/16-ferry-conf-
manila.aspx#:~:text=The%20Manila%20Statement%20hig
hlights%20that,societies%20and%20organizations%20w
hich%20Governments. [Online]. Available:
https://www.imo.org/en/MediaCentre/PressBriefings/pa
ges/16-ferry-conf-
manila.aspx#:~:text=The%20Manila%20Statement%20hig
hlights%20that,societies%20and%20organizations%20w
hich%20Governments
[5] D. L. Ditjen Hubdat, Cetak Biru Transportasi
Penyeberangan. Jakarta: Direktorat Lalu Lintas
Angkutan Sungai Danau dan Penyeberangan, 2010.
[6] R. Weisbrod and N. Rahman, “Improving Ferry Safety
for Lake Toba and for the Republic of Indonesia as a
whole,” WorldBank, Jakarta, 2019.
[7] CSIS, maritime safety in Indonesia: mapping the
challenges and opportunities. Jakarta: Centre for
Strategic International Studies, 2020. [Online]. Available:
https://mail.csis.or.id/publications/maritime-safety-in-
indonesia-mapping-the-challenges-and-opportunities
792
[8] G. Gapasdap, “Operasional kapal angkutan
penyeberangan yang berkeselamatan,” presented at the
Acciden review Forum, Merak, Banten, Mar. 2023.
[9] F. Lloyds Register, “Transforming ferry safety in the
Philippines,”
https://www.lrfoundation.org.uk/en/impact-
stories/ferrysafe-project/. [Online]. Available:
https://www.lrfoundation.org.uk/en/impact-
stories/ferrysafe-project/
[10] A. Nurwahyudy, “Contemporary issues in domestic ro-
ro passenger ferry operation in developing countries :
identification of safety issues in domestic ferry operation
based on accident investigation reports on ferry
involved accidents in Indonesian water,” World
Maritime University, Malmo, 2014. [Online]. Available:
https://commons.wmu.se/all_dissertations/463/?utm_sou
rce=commons.wmu.se%2Fall_dissertations%2F463&utm
_medium=PDF&utm_campaign=PDFCoverPages
[11] I. Komang Hedi Pramana Adiputra, “Analysis domestic
ferry safety in Indonesia,” 2020. [Online]. Available:
https://commons.wmu.se/all_dissertations
[12] D. Hubdat, “Rencana Strategis Direktorat Jenderal
Perhubungan Darat 2020 - 2024,” Direktorat Jenderal
Perhubungan Darat, Jakarta, Government report, Dec.
2020.
[13] Y. Sabri, A. Siham, and A. Maizate, “Internet of things
(IoT) based smart vehicle security and safety system,”
Int J Adv Comput Sci Appl, vol. 12, no. 4, 2021, doi:
10.14569/ijacsa.2021.0120487.
[14] A. Chatzimichail et al., “Internet of things infrastructure
for security and safety in public places,” Inf. Basel, vol.
10, no. 11, p. 333, 2019, doi: 10.3390/info10110333.
[15] S. Yang, L. Shi, D. Chen, Y. Dong, and Z. Hu,
“Development of ship structure health monitoring
system based on IOT technology,” IOP Conf Ser Earth
Env. Sci, vol. 69, p. 012178, 2017, doi: 10.1088/1755-
1315/69/1/012178.
[16] S. Greengard, “Internet of Things,”
https://www.britannica.com/science/Internet-of-Things.
[17] N. Anwar, A. K. Among Praja, H. Akbar, M. F. A.
Adhikara, R. Rasjidin, and D. R. Adhy, “Review
literature performance: Quality of service from internet
of things for transportation system,” presented at the
2021 1st International Conference on Computer Science
and Artificial Intelligence (ICCSAI), IEEE, 2021. doi:
10.1109/iccsai53272.2021.9609761.
[18] Y. Zhuang and S. Song, “Use of internet of things for
ship management of inland rivers,” presented at the
ICTIS 2013, Reston, VA: American Society of Civil
Engineers, 2013. doi: 10.1061/9780784413036.327.
[19] N. Rezaee, S. M. Zanjirchi, N. Jalilian, and S. M. H.
Bamakan, “Internet of things empowering operations
management; A systematic review based on bibliometric
and content analysis,” Telemat. Inform. Rep., vol. 11, p.
100096, 2023, doi:
https://doi.org/10.1016/j.teler.2023.100096.
[20] V. Srinadh, M. S. Rao, M. R. Sahoo, and K.
Rameshchandra, “WITHDRAWN: An analytical study
on security and future research of Internet of Things,”
Mater. Today Proc., 2021, doi:
https://doi.org/10.1016/j.matpr.2020.12.342.
[21] V. Mishra and M. K. Yadav, “IoT based smart
transportation system: Implementation and security
challenges,” Int J Veh Struct Syst, vol. 14, no. 5, 2022, doi:
10.4273/ijvss.14.5.03.
[22] A. Thakur, R. Malekian, and D. C. Bogatinoska,
“Internet of things based solutions for road safety and
traffic management in intelligent transportation
systems,” in ICT Innovations 2017, Cham: Springer
International Publishing, 2017, pp. 47–56. doi:
10.1007/978-3-319-67597-8_5.
[23] L. Shengguang, T. Lin, Z. Yuanshuo, and Z. Rucai,
“Internet of things for special materials transportation
vehicles,” presented at the 2013 IEEE International
Conference on Green Computing and Communications
and IEEE Internet of Things and IEEE Cyber, Physical
and Social Computing, IEEE, 2013. doi:
10.1109/greencom-ithings-cpscom.2013.351.
[24] K. M. Sankar and B. Booba, “The Usage of Internet of
Things in Transportation and Logistic Industry,” 2020.
[Online]. Available:
https://api.semanticscholar.org/CorpusID:219496380
[25] O. Sokulskyi, K. Hilevska, V. Chumakevych, V.
Ptashnyk, A. Tryhuba, and A. Sachenko, “The internet of
things solutions in the investigation of urban passenger
traffic and passenger service quality,” presented at the
2020 IEEE European Technology and Engineering
Management Summit (E-TEMS), IEEE, 2020. doi:
10.1109/e-tems46250.2020.9111658.
[26] Y. Xu, R. Jiang, S. Yan, and D. Xiong, “The research of
safety monitoring system applied in school bus based on
the internet of things,” Procedia Eng, vol. 15, pp. 2464–
2468, 2011, doi: 10.1016/j.proeng.2011.08.463.
[27] F. Zheng, S. Chen, J. Zhang, and F. Qiu, “Internet of
things technologies for urban public transport systems:
A case application in Chengdu, China,” presented at the
ICTE 2015, Reston, VA: American Society of Civil
Engineers, 2015. doi: 10.1061/9780784479384.066.
[28] L. Cai, W. Xia, P. Li, L. Zhang, and J. Liu, “An
intelligent transportation system for hazardous materials
based on the Internet of Things,” presented at the
Proceedings of the 4th International Conference on
Information Technology and Management Innovation,
Paris, France: Atlantis Press, 2015. doi: 10.2991/icitmi-
15.2015.100.
[29] C. Chen and S. Helal, “A device-centric approach to a
safer internet of things,” presented at the Proceedings of
the 2011 international workshop on Networking and
object memories for the internet of things, New York,
NY, USA: ACM, 2011. doi: 10.1145/2029932.2029934.
[30] A. Garcia-Dominguez, “Mobile applications, cloud and
bigdata on ships and shore stations for increased safety
on marine traffic; a smart ship project,” presented at the
2015 IEEE International Conference on Industrial
Technology (ICIT), IEEE, 2015. doi:
10.1109/icit.2015.7125314.
[31] P. Vaya, R. Simon, and S. K. Khatri, “Motorcycle Safety
Solution using the Internet of Things,” presented at the
2019 4th International Conference on Information
Systems and Computer Networks (ISCON), IEEE, 2019.
doi: 10.1109/iscon47742.2019.9036155.
[32] H. D. Zhao, H. Z. Wang, G. N. Liu, C. Li, and M. H.
Zhao, “The application of internet of Things (IOT)
technology in the safety monitoring system for hoisting
machines,” Appl Mech Mater, vol. 209–211, pp. 2142–
2145, 2012, doi: 10.4028/www.scientific.net/amm.209-
211.2142.
[33] K. Nybom et al., “IoT at Sea,” presented at the 2018
IEEE International Symposium on Broadband
Multimedia Systems and Broadcasting (BMSB), IEEE,
2018. doi: 10.1109/bmsb.2018.8436741.
[34] B. Gassmann, F. Oboril, I. Alvarez, and K.-U. Scholl,
“An online safety guard for intelligent transportation
systems,” presented at the 2021 IEEE International
Intelligent Transportation Systems Conference (ITSC),
IEEE, 2021. doi: 10.1109/itsc48978.2021.9565101.
[35] M. Jovic, E. Tijan, S. Aksentijevic, and D. Cisic, “An
overview of security challenges of seaport IoT systems,”
presented at the 2019 42nd International Convention on
Information and Communication Technology,
Electronics and Microelectronics (MIPRO), IEEE, 2019.
doi: 10.23919/mipro.2019.8757206.
[36] S. Gahalyan and P. Sriramalakshmi, “IoT based smart
ferry system,” J Phys Conf Ser, vol. 2115, no. 1, p. 012015,
2021, doi: 10.1088/1742-6596/2115/1/012015.
[37] A. Maulidi, T. Irmiyana, A. F. Ilman, Annafiyah, and
Kuzzairi, “The development of internet-of-things (IoT)-
based safety monitoring system in north sea madura,”
IOP Conf Ser Earth Env. Sci, vol. 972, no. 1, p. 012013,
2022, doi: 10.1088/1755-1315/972/1/012013.
793
[38] I. Shostak, M. Danova, O. Morozova, and T. Kliuieva,
“Using internet of things technologies to ensure cargo
transportation safety,” presented at the 2020 IEEE 11th
International Conference on Dependable Systems,
Services and Technologies (DESSERT), IEEE, 2020. doi:
10.1109/dessert50317.2020.9125069.
[39] M. Derawi, Y. Dalveren, and F. A. Cheikh, “Internet-of-
things-based smart transportation systems for safer
roads,” presented at the 2020 IEEE 6th World Forum on
Internet of Things (WF-IoT), IEEE, 2020. doi: 10.1109/wf-
iot48130.2020.9221208.
[40] L. Peng and Y. Chen, “Tourism safety monitoring
information service system based on internet of things
and block-chain,” J Intell Fuzzy Syst, pp. 1–7, 2021, doi:
10.3233/jifs-219101.
[41] M. Mohaimenuzzaman, S. M. Monzurur Rahman, M.
Alhussein, G. Muhammad, and K. Abdullah Al Mamun,
“Enhancing safety in water transport system based on
Internet of Things for developing countries,” Int J Distrib
Sens Netw, vol. 12, no. 2, p. 2834616, 2016, doi:
10.1155/2016/2834616.
[42] A. Rawson and M. Brito, “A survey of the opportunities
and challenges of supervised machine learning in
maritime risk analysis,” Transp. Rev., 2022, doi:
10.1080/01441647.2022.2036864.
[43] N. Suthanthira Vanitha, J. Karthikeyan, G. Kavitha, and
K. Radhika, “Modelling of intelligent transportation
system for human safety using IoT,” Mater Today, vol.
33, pp. 4026–4029, 2020, doi: 10.1016/j.matpr.2020.06.421.
[44] A. I. Levina, A. S. Dubgorn, and O. Y. Iliashenko,
“Internet of things within the service architecture of
intelligent transport systems,” presented at the 2017
European Conference on Electrical Engineering and
Computer Science (EECS), IEEE, 2017. doi:
10.1109/eecs.2017.72.
[45] S. Popov, M. Kurochkin, L. Kurochkin, and V.
Glazunov, “The implementing of the Internet of things
concepts for the continuous provision of informational
services for vehicle drivers and passengers,” presented
at the 2015 1st International Conference on Telematics
and Future Generation Networks (TAFGEN), IEEE,
2015. doi: 10.1109/tafgen.2015.7289565.
[46] C. Wang and G. Peng, “Application of Internet of
Things in Development of e-Navigation Architecture,”
Inf. Sci., pp. 579–586, 2015.
[47] A. W. Hapsari, H. Prastowo, and T. Pitana, “Real-time
fuel consumption monitoring system integrated with
internet of Things (IOT),” Kapal, vol. 18, no. 2, pp. 88–
100, 2021, doi: 10.14710/kapal.v18i2.37180.
[48] A. Kamolov and S. Park, “An IoT-Based Ship Berthing
Method Using a Set of Ultrasonic Sensors,” Sensors, vol.
19, no. 23, 2019, doi: 10.3390/s19235181.
[49] C. Shah, N. U. I. Hossain, M. M. Khan, and S. T. Alam,
“A dynamic Bayesian network model for resilience
assessment in blockchain-based internet of medical
things with time variation,” Healthc. Anal., vol. 4, p.
100280, 2023, doi:
https://doi.org/10.1016/j.health.2023.100280.
[50] S. Kaplan, “The Words of Risk Analysis,” 1997.
[51] T. Aven and O. Renn, Risk Management and
Governance: Concepts, Guidelines and Applications,
vol. 16. Heidelberg Dordrecht London New York:
Springer, 2010. doi: DOI 10.1007/978-3-642-13926-0.
[52] S. Center, Guidelines for developing quantitative safety
risk criteria. New York: John Wiley & Sons, 2009.
[53] L. Tao et al., “An integrated probabilistic risk
assessment methodology for maritime transportation of
spent nuclear fuel based on event tree and
hydrodynamic model,” Reliab. Eng. Syst. Saf., vol. 227,
Nov. 2022, doi: 10.1016/j.ress.2022.108726.
[54] T. Neumann, “Fuzzy routing algorithm in telematics
transportation systems”, Communications in Computer
and Information Science, vol. 715, pp 494-505, 2017. doi:
10.1007/978-3-319-66251-0_40
[55] I. IMO, “MSC-MEPC.2/Circ.12/Rev.2 on revised
guidelines for formal safety assessment (FSA) for use in
the IMO Rule-making process.” International Maritime
Organisation, Sep. 04, 2018. Accessed: Dec. 27, 2023.
[Online]. Available:
https://wwwcdn.imo.org/localresources/en/OurWork/Sa
fety/Documents/MSC-MEPC%202-Circ%2012-
Rev%202.pdf
[56] R. Pitblado et al., “International Comparison on the
Application of Societal Risk Criteria,” Process Saf. Prog.,
p. 31, Dec. 2012, doi: DOI:10.1002/prs.11525.
[57] D. Hubdat, “Perhubungan Darat Dalam Angka (PDDA)
- 2022,” Kementerian Perhubungan, Jakarta, 2023.
[58] R. Skjong, “Risk Acceptance Criteria: current proposals
and IMO position,” presented at the Surface transport
technologies for sustainable development, Valencia,
Spain, Jun. 2002.
[59] D. MoT, “Laporan Kinerja Instansi Pemerintah (LKIP)
Tahun 2021 - Direktorat Transportasi Sungai, Danau dan
Penyeberangan,” Kementerian Perhubungan, Jakarta,
2022.
[60] ASDP, “https://www.asdp.id/.” Accessed: Dec. 27, 2023.
[Online]. Available: https://www.asdp.id/
[61] BNPP, “https://bnpp.go.id/,” Badan Nasional Pencarian
dan Pertolongan.
[62] Pushidrosal, “https://www.pushidrosal.id/,” Pusat
Hidro Oceanografi TNI Angkatan Laut.
[63] P. BMKG, “https://www.bmkg.go.id/tag/?tag=pusat-
meteorologi-maritim&lang=ID,” Pusat Meteorologi
Maritim. Accessed: Dec. 27, 2023. [Online]. Available:
https://www.bmkg.go.id/tag/?tag=pusat-meteorologi-
maritim&lang=ID
[64] BKI, “https://bki.co.id/home,” Biro Klasifikasi
Indonesia.
[65] K. BPSDM, “https://bpsdm.dephub.go.id/,” Badan
Pengembangan SDM Perhubungan. Accessed: Dec. 27,
2023. [Online]. Available: https://bpsdm.dephub.go.id/
[66] K. KNKT, “KNKT,” knkt.go.id.
[67] K. Ditnav, “https://hubla.dephub.go.id/home/unit-
kerja/direktorat-kenavigasian.” Accessed: Dec. 27, 2023.
[Online]. Available:
https://hubla.dephub.go.id/home/unit-kerja/direktorat-
kenavigasian